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Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy
... Towards this goal, the concept of "hubness" and "hubs" in various contexts is the first step. For instance, different forms of connectivity of the brain regions, such as structural connectivity and functional connectivity are described in [2]. Using this modeling, many authors have defined the notion of hubs as being "brain regions with high connectivity to other parts of the brain" and "situated along the brain's most efficient communication pathways." ...
... In this paper, we highlighted the importance of Markov theory, but it is becoming increasingly clear that other deeper chapters of mathematics can be injected into the study of the brain, most notably, the theory of expander graphs. By all descriptions, the wiring of the brain connectome is not random nor regular but "rather characteristic of a small world" [2]. The network exhibits properties of a subclass of graphs called expander graphs which have few edges and yet have high connectivity, "an architecture that enables both the specialization and the integration of information transfer at relatively low wiring costs" [2]. ...
... By all descriptions, the wiring of the brain connectome is not random nor regular but "rather characteristic of a small world" [2]. The network exhibits properties of a subclass of graphs called expander graphs which have few edges and yet have high connectivity, "an architecture that enables both the specialization and the integration of information transfer at relatively low wiring costs" [2]. On the other hand, our Theorem 7.2 suggests that brain network theory gives rise to new questions in graph theory that have not been studied before. ...
Current concepts of neural networks have emerged over two centuries of progress beginning with the neural doctrine to the idea of neural cell assemblies. Presently the model of neural networks involves distributed neural circuits of nodes, hubs, and connections that are dynamic in different states of brain function. Advances in neurophysiology, neuroimaging and the field of connectomics have given impetus to the application of mathematical concepts of graph theory. Current approaches do carry limitations and inconsistency in results achieved. We model the neural network of the brain as a directed graph and attach a matrix (called the Markov matrix) of transition probabilities (determined by the synaptic strengths) to every pair of distinct nodes giving rise to a (continuous) Markov process. We postulate that the network hubs are the nodes with the highest probabilities given by the stationary distribution of Markov theory. We also derive a new upper bound for the diameter of a graph in terms of the eigenvalues of the Markov matrix.
... Furthermore, the cerebellum can have a profound impact on intra-neocortical oscillations and synchrony which can be dependent on behavioral context (Lindeman et al., 2021;Popa et al., 2013). Therefore, cortical changes in SCA8 could manifest as changes in functional connectivity (FC), as observed in other neurological disorders (Bernhardt et al., 2015;Harrington et al., 2015;Paldino et al., 2017;Vasilkovska et al., 2023). While FC has not been well investigated in SCA8, one study of SCA6 and 8 patients found smaller regional activations in the cerebellum and striatum when performing a motor task (Bares et al., 2011). ...
... Network efficiency and nodal centrality were increased in SCA8+ mice relative to NTCs and the network was partitioned into more, smaller communities with more clustered connectivity. Other disease states, like temporal lobe epilepsy, show similarly altered cortical networks (Bernhardt et al., 2015;Paldino et al., 2017). Epileptic children display hyper-clustered, highly efficient network configurations, whereas adults display hyper-clustered but less efficient networks compared to non-epileptic individuals. ...
Spinocerebellar Ataxia Type 8 (SCA8) is an inherited neurodegenerative disease caused by a bidirectionally expressed CTG•CAG expansion mutation in the ATXN8 and ATXN8OS genes. While SCA8 patients have motor abnormalities, patients may also exhibit psychiatric symptoms and cognitive dysfunction. It is difficult to elucidate how the disease alters brain function in areas with little or no degeneration producing both motor and cognitive symptoms. Using transparent polymer skulls and CNS-wide GCaMP6f expression, we studied neocortical networks throughout SCA8 progression using wide-field Ca²⁺ imaging in a transgenic mouse model of SCA8. Compared to wild-type controls, neocortical networks in SCA8+ mice were hyperconnected globally, which leads to network configurations with increased global efficiency and centrality. At the regional level, significant network changes occurred in nearly all cortical regions, however mainly involved sensory and association cortices. Changes in functional connectivity in anterior motor regions worsened later in the disease. Near perfect decoding of animal genotype was obtained using a generalized linear model based on canonical correlation strengths between activity in cortical regions. The major contributors to decoding were concentrated in the somatosensory, higher visual and retrosplenial cortices and occasionally extended into the motor regions, demonstrating that the areas with the largest network changes are predictive of disease state.
... Boerwinkle et al. [17] demonstrated that incorporating rs-fMRI connectivity into the presurgical evaluation resulted in a 50% increase in positive decisions for surgery. In this regard, the incorporation of graph-theory metrics can increase our comprehension of how distant brain regions contribute to information processing, introducing new biomarkers associated with brain network disorders such as epilepsy [19]. ...
... This aspect has sprung an increase in research attention towards the topological features that distinguish the epileptic brain network from the healthy one. Specifically, since epilepsy often involves abnormal connectivity patterns in the brain, network metrics, such as global efficiency, clustering coefficients, and path lengths, can highlight changes in brain connectivity over time, providing insights into the neural disruptions, aiding in diagnosis, treatment or surgical planning, and understanding the underlying mechanisms of epilepsy [19,20]. The use of graph-theory fMRI biomarkers is especially important in clinical applications [21]. ...
Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.
... Noninvasive techniques, including electroencephalography (EEG), magnetoencephalography (MEG) and resting state-functional magnetic resonance imaging (rs-fMRI) are well-established diagnostic tools for individual assessment and systematic clinical research. Graph theory is an optional analysis strategy that is increasingly utilized for exploring brain network characteristics (2,3). ...
... The majority of clinical studies focus on temporal lobe epilepsy (TLE), which is the most common type of epilepsy (3,4). TLE is characterized by convulsive seizures that arise from brain lesions caused by stroke, trauma, or tumor growth. ...
Introduction
Genetic Absence Epilepsy Rats from Strasbourg (GAERS) represent a model of genetic generalized epilepsy. The present longitudinal study in GAERS and age-matched non-epileptic controls (NEC) aimed to characterize the epileptic brain network using two functional measures, resting state-functional magnetic resonance imaging (rs-fMRI) and manganese-enhanced MRI (MEMRI) combined with morphometry, and to investigate potential brain network alterations, following long-term seizure activity.
Methods
Repeated rs-fMRI measurements at 9.4 T between 3 and 8 months of age were combined with MEMRI at the final time point of the study. We used graph theory analysis to infer community structure and global and local network parameters from rs-fMRI data and compared them to brain region-wise manganese accumulation patterns and deformation-based morphometry (DBM).
Results
Functional connectivity (FC) was generally higher in GAERS when compared to NEC. Global network parameters and community structure were similar in NEC and GAERS, suggesting efficiently functioning networks in both strains. No progressive FC changes were observed in epileptic animals. Network-based statistics (NBS) revealed stronger FC within the cortical community, including regions of association and sensorimotor cortex, and with basal ganglia and limbic regions in GAERS, irrespective of age. Higher manganese accumulation in GAERS than in NEC was observed at 8 months of age, consistent with higher overall rs-FC, particularly in sensorimotor cortex and association cortex regions. Functional measures showed less similarity in subcortical regions. Whole brain volumes of 8 months-old GAERS were higher when compared to age-matched NEC, and DBM revealed increased volumes of several association and sensorimotor cortex regions and of the thalamus.
Discussion
rs-fMRI, MEMRI, and volumetric data collectively suggest the significance of cortical networks in GAERS, which correlates with an increased fronto-central connectivity in childhood absence epilepsy (CAE). Our findings also verify involvement of basal ganglia and limbic regions. Epilepsy-related network alterations are already present in juvenile animals. Consequently, this early condition seems to play a greater role in dynamic brain function than chronic absence seizures.
... 2 While traditionally considered a prototypical "focal" epilepsy, increasing evidence suggests that atrophy and white matter alterations are not limited to the mesiotemporal region, but affect widely distributed grey and white matter systems. 3,4 Single and multi-site morphometric studies have reported significantly reduced grey matter volume in temporal, frontal, and centroparietal cortices as well as subcortical structures such as the thalamus and amygdala. [5][6][7] Moreover, there is a compromise of white matter microstructure and architecture of numerous fiber tracts, with pronounced effect sizes in limbic and subcortico-cortical systems. ...
... 9,10 As such, TLE is now increasingly recognized as a "network" disorder accompanied by profound changes in cortico-subcortical grey matter morphology and changes in related white matter compartments. 3,4,11,12 There is evidence that TLE is not a static condition, but one that shows progression over time. Advanced age is a critical risk factor for cognitive decline, particularly in TLE where the majority of patients older than 55 years have present with memory and language deficits that may meet the criteria for mild cognitive impairment. ...
Objectives: Temporal lobe epilepsy (TLE) is commonly associated with mesiotemporal pathology and widespread alterations of grey and white matter structures. Evidence supports a progressive condition although the temporal evolution of TLE is poorly defined. This ENIGMA-Epilepsy study utilized multimodal magnetic resonance imaging (MRI) data to investigate structural alterations in TLE patients across the adult lifespan. We charted both grey and white matter changes and explored the covariance of age-related alterations in both compartments.
Methods: We studied 769 TLE patients and 885 healthy controls across an age range of 17-73 years, from multiple international sites. To assess potentially non-linear lifespan changes in TLE, we harmonized data and combined median split assessments with cross-sectional sliding window analyses of grey and white matter age-related changes. Covariance analyses examined the coupling of grey and white matter lifespan curves.
Results: In TLE, age was associated with a robust grey matter thickness/volume decline across a broad cortico-subcortical territory, extending beyond the mesiotemporal disease epicentre. White matter changes were also widespread across multiple tracts with peak effects in temporo-limbic fibers. While changes spanned the adult time window, changes accelerated in cortical thickness, subcortical volume, and fractional anisotropy (all decreased), and mean diffusivity (increased) after age 55 years. Covariance analyses revealed strong limbic associations between white matter tracts and subcortical structures with cortical regions.
Conclusions: This study highlights the profound impact of TLE on lifespan changes in grey and white matter structures, with an acceleration of aging-related processes in later decades of life. Our findings motivate future longitudinal studies across the lifespan and emphasize the importance of prompt diagnosis as well as intervention in patients.
... En la literatura neurocientífica actual existe consenso acerca de que en las epilepsias focales la afectación no solo se da a nivel de la zona de la lesión (e.g. el hipocampo en el caso de la ELT-EH), sino que existen alteraciones en la conectividad y el funcionamiento de redes cerebrales más amplias, volviendo necesaria la adopción de una perspectiva de redes para abordar estas entidades (Bernhardt et al., 2015). Al respecto, se ha hallado que las alteraciones globales de las redes guardan relación con déficits de función cognitiva, y el estudio de esta relación contribuye a definir el target quirúrgico, así como a predecir resultados clínicos y neuropsicológicos luego de la cirugía de la epilepsia, lo que vuelve relevantes los trabajos sobre conectividad en estos tipos de epilepsia (Larivière et al., 2021). ...
... uso de estadística espacial basada en tractos vs NBS), y/o a la falta de consideración del efecto de la duración de la epilepsia. Es importante tener presente, respecto a esta última variable, que se ha reportado una relación significativa entre ésta y una afectación diferencial de la ELT (Bernhardt et al., 2015), así como una influencia de aquella en numerosos tractos cuya anisotropía cuantitativa (AQ) fue evaluada en cortezas temporales, tálamo, cuerpo calloso, cápsula interna y tallo cerebral (Ashraf-Ganjouei et al., 2019). En términos generales, este hallazgo concuerda con el presente trabajo, donde se encontró una afectación diferencial según el tiempo de evolución de la epilepsia, particularmente en el grupo con ELT-EHD. ...
La epilepsia es una enfermedad crónica y frecuente. Un tercio de los pacientes tienen
resistencia a los fármacos, más comúnmente en forma de epilepsia del lóbulo temporal
con esclerosis del hipocampo (ELT-EH) o displasia cortical focal (DCF). Estudios previos
reportaron alteraciones de la red asociadas, pero pocos han comparado el perfil de
conectividad en ambos tipos. Esta tesis tuvo como objetivo caracterizar la topología de
la red global y la conectividad estructural en ELT-EH, DCF y controles sanos utilizando
imágenes con tensor de difusión (DTI). 115 adultos de ambos sexos (DCF= 20, ELT-EH=
48 y controles= 47) fueron escaneados en un resonador 3T. Las matrices de conectividad
ponderadas se obtuvieron en DSI studio. El efecto de la epilepsia en la topología de la
red se evaluó mediante modelos lineales generales. El sexo, la edad, la lateralización y la
duración de la enfermedad se incluyeron como covariables en los modelos. Los
resultados de la evaluación de la topología global ponderada por anisotropía fraccional
(AF) revelaron que el coeficiente de agrupamiento, la medida de pequeño mundo, la
transitividad y la eficiencia global se encontraban disminuidos en DCF. Por otro lado, los
modelos predictivos generados demostraron la pérdida de AF en los pacientes mediante
el análisis discriminante lineal en NBS predict. La disminución de la conectividad en el
grupo ELT-EH derecha se asoció con la duración de la epilepsia, particularmente en
enlaces donde participaron nodos subcorticales. En tanto que el modelo de ELT-EH
izquierda reveló un rendimiento significativo como clasificador, utilizando enlaces que
unían las regiones temporales y orbitofrontales en el hemisferio ipsilateral. El modelo
DCF tuvo una capacidad predictiva sugestiva, sugiriendo una tendencia a la pérdida de
conectividad. En general, estos resultados apuntaron a la existencia de una
reorganización específica de la conectividad en relación con el tipo de epilepsia asociado.
Estos modelos predictivos podrían tener una utilidad clínica como clasificadores en
epilepsia.
... Notably, the exact notions of SLF/AF connectivity and cortical termination are controversial and sometimes conflicting (Giampiccolo and Duffau 2022), highlighting the need for careful interpretation of our findings concerning the localization of FA differences, particularly in the context of their potential variability across methodologies and individual differences. Epilepsy is increasingly recognized as a network disease (Bernhardt et al. 2015;Galovic 2023;Kramer and Cash 2012), and language processing engages a distributed system of white matter pathways beyond the AF, including the IFOF and UF, both involved in semantic processing (Catani et al. 2013;Martino et al. 2010), as well as the SLF-III, implicated in phonological encoding and word perception (Giampiccolo and Duffau 2022). In this study, isolating the AF allows precise mapping of localized structural adaptations and their association with behavioral outcomes. ...
The neuropsychological crowding effect denotes the reallocation of cognitive functions within the contralesional hemisphere following unilateral brain damage, prioritizing language at the expense of nonverbal abilities. This study investigates structural white matter correlates of crowding in the arcuate fasciculus (AF), a key language tract, using hemispherotomy as a unique setting to explore structural reorganization supporting language preservation. We explore two main hypotheses. First, the contralesional right AF undergoes white matter reorganization correlated with preserved language function at the expense of nonverbal abilities following left‐hemispheric damage. Second, this reorganization varies with epilepsy etiology, influencing different stages of developmental language lateralization. This retrospective study included individuals post‐hemispherotomy and healthy controls. Inclusion criteria were; (1) being a native German speaker, (2) having no MRI contraindication, (3) the ability to undergo approximately 2 h of MRI scans, and (4) the ability to participate in neuropsychological assessments over two consecutive days. Neuroimaging included T1‐, T2‐, and diffusion‐weighted imaging, alongside postoperative neuropsychological assessments, where it was taken as evidence for crowding if verbal IQ exceeded performance IQ by at least 10 points. The AF was reconstructed using advanced tractography, and CoBundleMAP was used to compare morphologically corresponding AF subsections. Statistical significance was set at p<0.05, with correction for multiple comparisons applied across contiguous tract sections using Threshold‐Free Cluster Enhancement. The final cohort comprised 22 individuals post‐hemispherotomy (median age: 20.4 20.4 years, range: 12.3−43.9; 55% female; 55% with left‐sided surgeries) and 20 healthy controls (median age: 23.8 23.8 years, range: 15.5−54.0; 55% female). Crowding was associated with significantly higher fractional anisotropy (FA) in the AF (p=0.015 p=0.015 , Cohen's d=1.69 d=1.69 ), but only observed in individuals with left‐sided hemispherotomy, localized to a subsection between Geschwind's territory and Wernicke's area (pcorrected=0.02). This region also displayed significantly higher normalized FA in AF of individuals with congenital etiology and crowding compared to acquired etiology and no crowding (pcorrected=0.0189). This study identifies previously unreported neural correlates of crowding in right contralesional AF of individuals post‐hemispherotomy and highlights specific AF subsections involved in preserving language functions at the cost of nonverbal abilities. The findings suggest a link between crowding and epilepsy etiology, particularly in the region spanning Geschwind's territory and Wernicke's area.
... We analyzed GM and SWM abnormalities from a regional perspective, but it is crucial to acknowledge that epilepsy is a network disorder. 23,[53][54][55] Surgical resection can significantly affect the epileptogenic network, potentially preventing further seizures even if regions deemed abnormal are not removed. Prior research demonstrated the impact of surgery on the structural connectome, 23,55 with certain regions being more critical due to their higher connectivity. ...
Objective
Drug‐resistant focal epilepsy is associated with abnormalities in the brain in both gray matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post‐surgical seizure outcome in people with temporal lobe epilepsy (TLE).
Methods
We analyzed structural imaging data from 143 patients with TLE (pre‐op diffusion magnetic resonance imaging and pre‐op T1‐weighted MRI) and 97 healthy controls. We calculated GM volume abnormalities and SWM mean diffusivity abnormalities and evaluated if their surgical removal distinguished seizure outcome groups post‐surgically.
Results
At a group level, GM and SWM abnormalities were most common in the ipsilateral temporal lobe and hippocampus in people with TLE. Analyzing both modalities together, compared to in isolation, improved surgical outcome discrimination (GM area under the curve [AUC] = 0.68, p < 0.01; WM AUC = 0.65, p < 0.01; Union AUC = 0.72, p < 0.01; Concordance AUC = 0.64, p = 0.04). In addition, 100% of people who had all concordant abnormal regions resected had International League Against Epilepsy (ILAE)1,2 outcomes.
Significance
Resecting abnormalities in GM or SWM individually affects surgical outcomes but combining both provides clearer patient group distinctions. This approach improves outcome differentiation, showing higher rates of patients living without disabling seizures when all concordant abnormal regions are resected. These findings suggest that regions identified as abnormal from both diffusion‐weighted and T1‐weighted MRI are involved in the epileptogenic network and that resection of both types of abnormalities may enhance the chances of living without disabling seizures.
... There were people with abnormal clusters in multiple lobes that were not all resected ( Figure 4B) and who became seizure-free. In addition, epilepsy is a network disorder, 36 and epilepsy surgery has a significant impact on the wider structural F I G U R E 5 Resection of other substantial clusters may improve seizure freedom. (A) A decision tree was used to investigate whether, if the largest cluster was spared, the resection of other substantial clusters was related to an improved chance of seizure freedom. ...
Objective
Successful epilepsy surgery requires accurate localization and removal of the epileptogenic zone. Neuroimaging helps detect structural brain abnormalities to guide surgery, but current clinical practice does not use diffusion‐weighted magnetic resonance imaging (dwMRI). However, previous work has shown that diffusion abnormalities are present in epilepsy and may relate to the epileptogenic zone. Here, we investigate whether surgical resection of diffusion abnormalities relates to postoperative seizure freedom.
Methods
We investigated the association between surgical resection of diffusion abnormalities and postoperative seizure freedom in 200 individuals with drug‐resistant focal epilepsy using dwMRI. A cohort of 97 healthy controls provided a normative baseline for dwMRI metrics, allowing calculation of voxelwise z‐scores to identify abnormal clusters in both gray and white matter.
Results
Surgical resections overlapping with the largest abnormal cluster significantly correlated with sustained seizure freedom at 12 months (83% vs. 55%; p<.0001) and over 5 years (p<.0001). Notably, resecting only a small proportion of the largest cluster was associated with better seizure outcomes than cases with no resection of this cluster (p=.008). Furthermore, sparing the largest cluster but resecting other large clusters still improved seizure freedom rates compared to no overlap (p=.03).
Significance
Our results suggest that abnormal clusters, identified using dwMRI, are integral to the epileptogenic network, and even a partial removal of such an abnormal cluster is sufficient to achieve seizure freedom. This study highlights the potential of incorporating dwMRI into presurgical planning to improve outcomes in focal epilepsy by reliably identifying and targeting diffusion abnormalities.
... This count can be adjusted for factors such as the distance between ROIs and their respective volumes. Additionally, setting a fiber count threshold can simplify the connectivity matrix, focusing on significant links [6][7][8][9]. While diffusion MRI focuses on white matter pathways, covariance analysis of morphological markers compares gray matter distributions among individuals. ...
Epilepsy is widely known as a network disease. Ictal and interictal activities are generated and spread within the existing networks involving different regions of the brain. Network alterations affect both grey and white matter, deep brain nuclei, including those of the ascending reticular formation. These structures may be involved in a disorganized connectome associated with epilepsy. A growing body of neuroimaging and neuropsychological findings suggests that global and focal network aberrations are closely linked to cognitive deficits in epilepsy patients. This evidence relates equally to focal epilepsies, such as temporal lobe epilepsy or extra-temporal lobe epilepsy, as well as generalized epilepsies, such as juvenile myoclonic epilepsy. Network abnormalities have been associated with a broad range of cognitive impairments, including language, memory, and executive functions, as well as sensory and motor functions. Whole-brain structural connectome models help in the understanding of seizure generation and spread. Identifying key nodes of seizure propagation may help in planning surgical procedures in individual patients by simulating epilepsy surgery on virtual models. Functional connectomic profiles may predict seizure outcomes in patients who undergo deep brain stimulation due to intractable seizures. Therefore, individualized interventional strategies could be developed based on connectome characteristics.
... The renowned Königsberg Seven Bridges problem, along with Leonhard Euler's groundbreaking negative solution in 1736, marked the birth of graph theory as a distinct field of study [2,3]. Graph theory is fundamental to solving optimal path-finding problems, as it provides the mathematical foundation for representing and analyzing interconnected networks [4,5]. In the context of pathfinding, a graph models locations as nodes (or vertices) and the possible routes or connections between them as edges. ...
This paper introduces and explores q-rung orthopair fuzzy graphs with directed edges represented by q-rung orthopair fuzzy numbers and crisp vertices. The novelty, significance, and practical relevance of this concept are demonstrated through theoretical results, illustrative examples, and graphical representations. Key theoretical aspects such as subgraphs, complete graphs, in-degree, out-degree, and other properties are examined in detail. These graphs are particularly suited for scenarios where the vertices are deterministic, but the edges involve uncertainty. Practical applications include social influence networks, supply chains with variable trust levels in communication, online recommendation systems with uncertain influence, and healthcare referral networks with ambiguous impact. The primary contribution of this paper is the development of an optimal path-finding algorithm tailored for such graphs. The algorithm is based on Hamacher operators and the latest score function that is more reliable than the traditional one. The algorithm identifies a path from a defined source node to a target node by optimizing a specific parameter. To validate its utility, the algorithm is applied to a real-world example, demonstrating its effectiveness and potential in handling uncertainty in decision-making scenarios.
... Rather, emphasis is placed on characterizing dynamic patterns of network connectivity that enable seizure onset and propagation. Within this framework, brain regions are represented as nodes, connections between nodes (structural, functional, and effective) are represented as edges, and the topology of the network (i.e., the distribution of edges between nodes) can be summarized using mathematical concepts from network science [88]. ...
Cortical stimulation is the process of delivering brief pulses of electrical current and visualizing the distributed pattern of evoked responses across the brain. Compared to high-frequency stimulation, which has long been used for seizure provocation and functional mapping, low-frequency stimulation (<1–2 Hz) is rarely incorporated into the epilepsy surgery evaluation. Increasingly, researchers have demonstrated that various cortico-cortical evoked potential (CCEP) features, including early and delayed responses, evoked high-frequency oscillations, and derived network metrics, may be useful biomarkers of tissue excitability and abnormal connectivity. Emerging evidence also highlights a potential role of CCEPs in guiding neuromodulatory therapies like responsive neurostimulation. In this review, we examine the past two decades of innovation in low-frequency stimulation as it pertains to pre-surgical evaluation. We begin with a basic overview of single-pulse electrical stimulation and CCEPs, including definitions, methodology, physiology, and traditional interpretation. We then explore the literature examining CCEPs as markers of cortical excitability, seizure onset, and network-level dysfunction. Finally, the relationship between stimulation-induced and spontaneous seizures is considered. By examining these questions, we identify both opportunities and pitfalls along the path towards integrating low-frequency stimulation into clinical practice.
... Temporal lobe epilepsy (TLE) stands as the most common medically intractable epilepsy in adults. 1 Recent studies have characterized TLE as a network disorder typically involving widespread structural alterations beyond the epileptic focus. 2,3 Apart from hippocampus sclerosis (HS), a hallmark of TLE, structural magnetic resonance imaging (MRI) also found spanning and distributed gray matter reductions in neocortex and subcortical regions. 4 However, the specific mechanism underlying the spatial distribution of gray matter atrophy remains a critical question. ...
Objective
Temporal lobe epilepsy (TLE) has been recognized as a network disorder with widespread gray matter atrophy. However, the role of connectome architecture in shaping morphological alterations and identifying atrophy epicenters remains unclear. Furthermore, individualized modeling of atrophy epicenters and their potential clinical applications have not been well established. This study aims to explore how gray matter atrophy correlates with normal connectome architecture, identify potential atrophy epicenters, and employ individualized modeling approach to evaluate the impact of different epicenter patterns on surgical outcomes in patients with TLE.
Methods
This study utilized anatomic MRI data from 126 refractory TLE patients who underwent anterior temporal lobectomy and 60 healthy controls (HCs), along with normative functional and structural connectome data, to investigate the relationship between gray matter volume (GMV) changes and functional or structural connectivity. Two models were employed to identify atrophy epicenters: a data‐driven approach evaluating nodal and neighbor atrophy rankings, and a network diffusion model (NDM) simulating the spread of pathology from different seed regions. K‐means clustering was applied in patient‐tailored modeling to uncover distinct epicenter subtypes.
Results
Our findings indicate that the pattern of gray matter atrophy in TLE is constrained primarily by structural connectivity rather than by functional connectivity. Using the structural connectome, we pinpointed the hippocampus and adjacent temporo‐limbic regions as key atrophy epicenters. The patient‐tailored modeling revealed significant variability in epicenter distribution, allowing us to categorize them into two distinct subtypes. Notably, patients in subtype 2, with epicenters localized to the ipsilateral temporal pole and medial temporal lobe, exhibited significantly higher seizure‐free rates compared to patients in subtype 1, whose epicenters situated in frontocentral regions.
Significance
These findings highlight the central role of structural connectivity in shaping TLE‐related morphological changes. Individualized epicenter modeling may enhance surgical decisions and improve prognostic stratification in TLE management.
... The human brain is one of the world's most complex networks, and studies on it have increased dramatically in recent years [52], [73], [74]. This growth is fueled by graph theory and network neuroscience advancements, offering insights into brain structure and function [75]. ...
It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer’s disease (AD), Parkinson’s disease (PD), and cortical spreading depression (CSD). We investigate how the brain’s biological processes and associated multiphysics interact and how this influences the structure and functionality of the brain. We review the literature on brain network models and dynamic processes, highlighting the need for sophisticated mathematical and statistical modelling techniques. Specifically, we go through large-scale brain network models relevant to AD and PD, highlighting the pathological mechanisms and potential therapeutic strategies investigated in the literature. Additionally, we investigate the propagation of CSD in the brain and its implications for neurological disorders. Furthermore, we discuss how data-driven approaches and artificial neural networks refine and validate models related to NDDs. Overall, this review underscores the significance of coupled multiscale models in deciphering disease mechanisms, offering potential avenues for therapeutic development and advancing our understanding of pathological brain dynamics.
... Functional network analysis is a high-level FC computation based on graph theory [11,12]. In recent years, the International League Against Epilepsy (ILAE) emphasizes the concept of epileptic brain network to formulate the classification criteria of epilepsy [13,14]. For example, the classification predicted the response of children with epilepsy to the anti-epileptic drugs at a single network index, such as degree centrality [15]. ...
Purpose
Without any visible indicator on structure magnetic resonance imaging (MRI), the diagnosis of MRI-negative temporal lobe epilepsy (NTLE) gets harder. By considering healthy control (HC), a specific functional connectivity (FC) model was constructed in a network topology to improve FC computation to a high-level.
Methods
MRI data of 20 NTLE patients and 60 HC were pre-processed. Relative to HC, a network-level specific FC model of each network index was built to score the network functions for each NTLE patient. The specific brain areas (regarded as ROIs) were extracted for NTLE by sensitivity analysis of scores. By considering scores of specific ROIs as feature vectors to input into a SVM respectively, a specific NTLE classifier was constructed. Both 10-fold cross validation and hold-out method were utilized to validate the classification and to evaluate the effectiveness of our specific FC models. Simultaneously, the specific FC model was compared to the conventional FC model of Pearson correlation.
Results
By the constructed model for specific FC at a network-level, 11 specific ROIs, such as, frontal lobe, temporal lobe, parietal lobe, hippocampus, and occipital lobe, were extracted for NTLE. Accuracy of our specific NTLE classifier could reach up nearly 93 %, over 6 % greater than conventional FC model of Pearson correlation.
Conclusions
The network-level specific FC model might provide a new methodology for machine-aiding detection of functional abnormal lesions of NTLE by resting-state functional MRI.
... This suggests that the underlying mechanisms involved in PNES exert a lesser extent of destabilization on functional brain networks in comparison to neurological disturbances typically observed in many people with epilepsy [49]. This may be attributed to epileptic dysfunction itself or, in some cases, to structural abnormalities [50][51][52]. ...
Background: Psychogenic non-epileptic seizures (PNES) are seizure-like episodes that resemble behavioral aspects observed for epileptic seizures but are without the abnormal electrical activity typically seen in epilepsy. The lack of an etiologic model for PNES as well as limitations of available diagnostic methods largely hinders a clear-cut distinction from epilepsy and from a normal functioning brain. Methods: In this study, we investigate the brain dynamics of people with PNES and people with epilepsy during phases far-off seizures and seizure-like events as well as the brain dynamics of a control group. Probing for differences between these groups, we utilise the network ansatz and explore local and global characteristics of time-evolving functional brain networks. We observe subject-specific differences in local network characteristics across the groups, highlighting the physiological functioning of specific brain regions. Furthermore, we observe significant differences in global network characteristics—relating to communication, robustness, and stability aspects of the brain. Conclusions: Our findings may provide new insights into the mechanisms underlying PNES and offer a promising diagnostic approach to differentiate them from epilepsy.
... The alterations associated with epilepsy are not restricted to the medial temporal lobe. In fact, epilepsy is currently recognized as a brain network disorder [57]. Moreover, the network neuroscience concept can also be applied to psychiatric disorders [58], so brain network analysis of POE may also yield novel findings. ...
Epilepsy is a prevalent chronic neurological disorder that can significantly impact patients’ lives. The incidence and risk of psychosis in individuals with epilepsy are notably higher than in the general population, adversely affecting both the management and rehabilitation of epilepsy and further diminishing patients’ quality of life. This review provides an overview of the classification and clinical features of psychosis of epilepsy, with the aim of offering insights and references for the clinical diagnosis and treatment of various types of psychosis of epilepsy. Additionally, we examine the potential pathophysiological mechanisms underlying the psychosis of epilepsy from three perspectives: neuroimaging, neurobiology, and genetics. The alterations in brain structure and function, neurotransmitters, neuroinflammatory mediators, and genetic factors discussed in this review may offer insights into the onset and progression of psychotic symptoms in epilepsy patients and are anticipated to inform the identification of novel therapeutic targets in the future.
... Epilepsy is increasingly understood as a network disorder, 38 with seizure-generating "foci" embedded in webs of structural and functional connections. 39 Many researches have shown that EPN may be involved in the propagation of seizures under the condition of altered excitability during the epileptic process. ...
Objective
Neuromodulatory anterior thalamic deep brain stimulation (DBS) is an effective therapy for intractable epilepsy, but few patients achieve complete seizure control with thalamic DBS. Other stimulation sites may be considered for anti‐seizure DBS. We investigated bilateral low‐frequency stimulation of the endopiriform nuclei (LFS‐EPN) to control seizures induced by intracortically implanted cobalt wire in rats.
Methods
Chronic epilepsy was induced by cobalt wire implantation in the motor cortex unilaterally. Bipolar‐stimulating electrodes were implanted into the EPN bilaterally. Continuous electroencephalography (EEG) was recorded using electrodes placed into bilateral motor cortex and hippocampus CA1 areas. Spontaneous seizures were monitored by long‐term video‐EEG, and behavioral seizures were classified based on the Racine scale. Continuous 1‐Hz LFS‐EPN began on the third day after electrode implantation and was controlled by a multi‐channel stimulator. Stimulation continued until the rats had no seizures for three consecutive days.
Results
Compared with the control and sham stimulation groups, the LFS‐EPN group experienced significantly fewer seizures per day and the mean Racine score of seizures was lower due to fewer generalized seizures. Ictal discharges at the epileptogenic site had significantly reduced theta band power in the LFS‐EPN group compared to the other groups.
Interpretation
Bilateral LFS‐EPN attenuates cobalt wire‐induced seizures in rats by modulating epileptic networks. Reduced ictal theta power of the EEG broadband spectrum at the lesion site may be associated with the anti‐epileptogenic mechanism of LFS‐EPN. Bilateral EPN DBS may have therapeutic applications in human partial epilepsies.
... The human brain is one of the world's most complex networks, and studies on it have increased dramatically in recent years [52], [73], [74]. This growth is fueled by graph theory and network neuroscience advancements, offering insights into brain structure and function [75]. ...
It is essential to understand the complex structure of the human brain to develop new treatment approaches for neurodegenerative disorders (NDDs). This review paper comprehensively discusses the challenges associated with modelling the complex brain networks and dynamic processes involved in NDDs, particularly Alzheimer's disease (AD), Parkinson's disease (PD), and cortical spreading depression (CSD). We investigate how the brain's biological processes and associated multiphysics interact and how this influences the structure and functionality of the brain. We review the literature on brain network models and dynamic processes, highlighting the need for sophisticated mathematical and statistical modelling techniques. Specifically, we go through large-scale brain network models relevant to AD and PD, highlighting the pathological mechanisms and potential therapeutic strategies investigated in the literature. Additionally, we investigate the propagation of CSD in the brain and its implications for neurological disorders. Furthermore, we discuss how data-driven approaches and artificial neural networks refine and validate models related to NDDs. Overall, this review underscores the significance of coupled multiscale models in deciphering disease mechanisms, offering potential avenues for therapeutic development and advancing our understanding of pathological brain dynamics.
... extending beyond temporal cortices leads to poor surgical outcome (Bernhardt et al., 2015a;Garcia et al., 2017). Regarding the amygdala, while ipsilateral atrophy related to seizure freedom, hypertrophy had a negative impact. ...
Objective
In drug-resistant temporal lobe epilepsy (TLE), it is not well-established in how far surgery should target morphological anomalies to achieve seizure freedom. Here, we assessed interactions between structural brain compromise and surgery to identify region-specific predictors of seizure outcome.
Methods
We obtained pre- and post-operative 3D T1-weighted MRI in 55 TLE patients who underwent selective amygdalo-hippocampectomy (SAH) or anterior temporal lobectomy (ATL) and 40 age and sex-matched healthy subjects. We measured surface-based morphological alterations of the mesiotemporal lobe structures (hippocampus, amygdala, entorhinal and piriform cortices), the neocortex and the thalamus on both pre- and post-operative MRI. Using precise co-registration, in each patient we mapped the surgical cavity onto the MRI acquired before surgery, thereby quantifying the amount of pathological tissue resected; these features, together with the preoperative morphometric data, served as input to a supervised classification algorithm for postsurgical outcome prediction.
Results
On pre-operative MRI, patients who became seizure-free (TLE-SF) presented with severe ipsilateral amygdalar and hippocampal atrophy, while not seizure-free patients (TLE-NSF) displayed amygdalar hypertrophy. Stratifying patients based on the surgical approach, post-operative MRI showed similar patterns of mesiotemporal and thalamic changes, but divergent neocortical thinning affecting the parieto-temporo-occipital regions following ATL and the frontal lobes after SAH. Irrespective of the surgical approach, hippocampal atrophy on pre-operative MRI and its extent of resection were the most predictive features of seizure-freedom in 89% of patients (selected 100% across validations).
Significance
Our study indicates a critical role of the extent of resection of MRI-derived hippocampal morphological anomalies on seizure outcome. Precise pre-operative quantification of the mesiotemporal lobe provides non-invasive prognostics for individualized surgery.
... Additionally, using advanced techniques, such as graph theory, might unveil particular nodes of significance within the network (Piper et al., 2022). Graph theory utilizes mathematical techniques to characterize structural and functional connections of brain regions at multiple topographical levels (Bernhardt et al., 2015). While few studies have explored the clinical applicability of graph theory analyses based on ECoG recordings to characterize the seizure network, preliminary studies have found evidence of the power of connectivity techniques in predicting post-surgical outcomes for patients with focal epilepsy (Wilke et al., 2011). ...
Epilepsy is one of the most common neurological disorders, affecting over 65 million people worldwide. Despite medical management with anti-seizure medications (ASMs), many patients fail to achieve seizure freedom, with over one-third of patients having drug-resistant epilepsy (DRE). Even with surgical management through resective surgery and/or neuromodulatory interventions, over 50 % of patients continue to experience refractory seizures within a year of surgery. Over the past 2 decades, studies have increasingly suggested that treatment failure is likely driven by untreated components of a pathological seizure network, a shift in the classical understanding of epilepsy as a focal disorder. However, this shift in thinking has yet to translate to improved treatments and seizure outcomes in patients. Here, we present a narrative review discussing the process of surgical epilepsy management. We explore current surgical interventions and hypothesized mechanisms behind treatment failure, highlighting evidence of pathologic seizure networks. Finally, we conclude by discussing how the network theory may inform surgical management, guiding the identification and targeting of more appropriate surgical regions. Ultimately, we believe that adapting current surgical practices and neuromodulatory interventions towards targeting seizure networks offers new therapeutic strategies that may improve seizure outcomes in patients suffering from DRE.
... Recent studies have emphasized the importance of using whole-brain structural connectomes for understanding TLE and predicting seizure outcomes (Bernhardt et al., 2015;Bonilha et al., 2015;Taylor et al., 2018). In turn, our study investigated graph theoretic measures of ms-dMRI. ...
Introduction
Emerging evidence illustrates that temporal lobe epilepsy (TLE) involves network disruptions represented by hyperexcitability and other seizure‐related neural plasticity. However, these associations are not well‐characterized. Our study characterizes the whole brain white matter connectome abnormalities in TLE patients compared to healthy controls (HCs) from the prospective Epilepsy Connectome Project study. Furthermore, we assessed whether aberrant white matter connections are differentially related to cognitive impairment and a history of focal‐to‐bilateral tonic–clonic (FBTC) seizures.
Methods
Multi‐shell connectome MRI data were preprocessed using the DESIGNER guidelines. The IIT Destrieux gray matter atlas was used to derive the 162 × 162 structural connectivity matrices (SCMs) using MRTrix3. ComBat data harmonization was applied to harmonize the SCMs from pre‐ and post‐scanner upgrade acquisitions. Threshold‐free network‐based statistics were used for statistical analysis of the harmonized SCMs. Cognitive impairment status and FBTC seizure status were then correlated with these findings.
Results
We employed connectome measurements from 142 subjects, including 92 patients with TLE (36 males, mean age = 40.1 ± 11.7 years) and 50 HCs (25 males, mean age = 32.6 ± 10.2 years). Our analysis revealed overall significant decreases in cross‐sectional area (CSA) of the white matter tract in TLE group compared to controls, indicating decreased white matter tract integrity and connectivity abnormalities in addition to apparent differences in graph theoretic measures of connectivity and network‐based statistics. Focal and generalized cognitive impaired TLE patients showcased higher trend‐level abnormalities in the white matter connectome via decreased CSA than those with no cognitive impairment. Patients with a positive FBTC seizure history also showed trend‐level findings of association via decreased CSA.
Conclusions
Widespread global aberrant white matter connectome changes were observed in TLE patients and characterized by seizure history and cognitive impairment, laying a foundation for future studies to expand on and validate the novel biomarkers and further elucidate TLE's impact on brain plasticity.
... The resultant graphical network representations allow quantitative analysis. Compared to simplistic focal models, whole-brain graphs better recapitulate distributed processing intricacies in epilepsy [20]. Connectivity graph modeling enables mathematical examination of topological architecture and embedded dynamics to characterize normal or pathological states [19]. ...
Objective. To demonstrate the capability of utilizing graph feature-based supervised machine learning (ML) algorithm on intracranial electroencephalogram recordings for the identification of seizure onset zones (SOZs) in individuals with drug-resistant epilepsy. Approach. Utilizing three model-free measures of effective connectivity (EC)-directed information, mutual information-guided Granger causality index (MI-GCI), and frequency-domain convergent cross-mapping (FD-CCM) - directed graphs are generated. Graph centrality measures at different sparsity are used as the classifier’s features. Main results. The centrality features achieve high accuracies exceeding 90% in distinguishing SOZ electrodes from non-SOZ electrodes. Notably, a sparse graph representation with just ten features and simple ML models effectively achieves such performance. The study identifies FD-CCM centrality measures as particularly significant, with a mean AUC of 0.93, outperforming prior literature. The FD-CCM-based graph modeling also highlights elevated centrality measures among SOZ electrodes, emphasizing heightened activity relative to non-SOZ electrodes during ictogenesis. Significance. This research not only underscores the efficacy of automated SOZ identification but also illuminates the potential of specific EC measures in enhancing discriminative power within the context of epilepsy research.
... 3 Epilepsies are classified by onset into 2 main groups: focal epilepsies and genetic generalized epilepsies (GGEs), 4 both of which are associated with widespread structural brain abnormalities beyond the epileptogenic focus. [5][6][7] While lower cortical thickness (TH) has been associated with both focal epilepsies and GGEs, there are also regional differences across epilepsy subtypes. 7 Differences in cortical surface area (SA) may also vary across epilepsy subtypes and cortical regions. ...
Background and Objectives
Epilepsies are associated with differences in cortical thickness (TH) and surface area (SA). However, the mechanisms underlying these relationships remain elusive. We investigated the extent to which these phenotypes share genetic influences.
Methods
We analyzed genome-wide association study data on common epilepsies (n = 69,995) and TH and SA (n = 32,877) using Gaussian mixture modeling MiXeR and conjunctional false discovery rate (conjFDR) analysis to quantify their shared genetic architecture and identify overlapping loci. We biologically interrogated the loci using a variety of resources and validated in independent samples.
Results
The epilepsies (2.4 k–2.9 k variants) were more polygenic than both SA (1.8 k variants) and TH (1.3 k variants). Despite absent genome-wide genetic correlations, there was a substantial genetic overlap between SA and genetic generalized epilepsy (GGE) (1.1 k), all epilepsies (1.1 k), and juvenile myoclonic epilepsy (JME) (0.7 k), as well as between TH and GGE (0.8 k), all epilepsies (0.7 k), and JME (0.8 k), estimated with MiXeR. Furthermore, conjFDR analysis identified 15 GGE loci jointly associated with SA and 15 with TH, 3 loci shared between SA and childhood absence epilepsy, and 6 loci overlapping between SA and JME. 23 loci were novel for epilepsies and 11 for cortical morphology. We observed a high degree of sign concordance in the independent samples.
Discussion
Our findings show extensive genetic overlap between generalized epilepsies and cortical morphology, indicating a complex genetic relationship with mixed-effect directions. The results suggest that shared genetic influences may contribute to cortical abnormalities in epilepsies.
... TLE is also defined as a category of focal epilepsy which is associated with hippocampal sclerosis [2]. Research using magnetic resonance imaging (MRI) and functional MRI demonstrated that TLE is a network disorder [3], which enables us to employ theories of network topology and network dynamics to quantify properties for such a complex system. Evidence from MRI illustrated in TLE patients that brain networks have the characteristics of a small-world [4], which is originally proposed by Watts and Strogatz [5], and simulations of seizure-like activity based on hippocampal slice electrophysiology also illustrated small-world property [6,7]. ...
The mechanisms of network and transition dynamics of epileptiform activity remain unclear. In general, the transitions of epileptiform discharges comprise slow interictal discharges, ictal discharges and postictal depression. Studies have indicated that network properties and the inherent parameters of neuronal models have great impacts on the transitions. Recently, a novel neuromodulation technique, transcranial magneto-acoustical stimulation (TMAS), has been tested for its efficiency experimentally and computationally. In this paper, we establish a biophysical computational network model of an ictogenic hippocampus area to investigate the underlying transitions mechanisms and reveal neuromodulation mechanisms combined with TMAS. Results demonstrate that long distance connections caused by increased connection probability and the number of nearest-neighbour edges make the network more random and focused. The cooperation of network topological structure and neuronal parameters including ion concentration and inherent external input of neurons could induce epileptic transitions. Moreover, the focused ultrasound transducer has the ability to launch and focus the transcranial ultrasound wave to the hippocampal area in the depth of the three-layer tissue. By coupling with a static magnetic field, the proposed modulated induced TMAS currents can terminate epileptiform activity but consumes more energy by regulating magnetic strength. However, changing modulation frequency was unable to fully suppress seizures. These computational results offer an explanation of the mechanisms of neurodynamics of epileptiform discharges and its neuromodulation by TMAS.
... Most of the prior studies have shown an increased local brain network, while a decreased global brain network in patients with epilepsy compared to healthy controls. (Bernhardt et al. 2015) Thus, we expected that the patients with PSE would reveal similar results to those of the previous studies compared to those without PSE. ...
We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.
... dysregulation of key neural circuits, including frontoparietal brain regions, plays an important role in the etiology of each. 9,19,20 Thus, neuroimaging can be utilized to investigate potential brain network abnormalities that may be involved in the relationship between QOL and depression symptoms in people with TBI and/or seizure disorders. One recent study examined global and network brain entropy and found that resting-state fronto-parietal network (FPN) complexity, as measured by SampEn of fMRI time series, mediated the negative relationship between depression severity and QOL in depressed elderly. ...
Objective
Traumatic brain injury (TBI) often precedes the onset of epileptic (ES) or psychogenic nonepileptic seizures (PNES) with depression being a common comorbidity. The relationship between depression severity and quality of life (QOL) may be related to resting‐state network complexity. We investigated these relationships in adults with TBI‐only, TBI + ES, or TBI + PNES using Sample Entropy (SampEn), a measure of physiologic signals complexity.
Methods
Adults with TBI‐only (n = 60), TBI + ES (n = 21), or TBI + PNES (n = 56) completed the Beck Depression Inventory‐II (BDI‐II; depression symptom severity) and QOL in Epilepsy (QOLIE‐31) assessments and underwent resting‐state functional magnetic resonance imaging (rs‐fMRI). SampEn values derived from six resting state functional networks were calculated per participant. Effects of group, network, and group‐by‐network‐interactions for SampEn were investigated with a mixed‐effects model. We examined relationships between BDI‐II, QOL, and SampEn of each of the networks.
Results
Groups did not differ in age, but there was a higher proportion of women with TBI + PNES (p = 0.040). TBI + ES and TBI‐only groups did not differ in BDI‐II or QOLIE‐31 scores, while the TBI + PNES group scored worse on both measures. The fixed effects of the model revealed significant differences in SampEn values across networks (lower SampEn for the frontoparietal network compared to other networks). The likelihood ratio test for group‐by‐network‐interactions was significant (p = 0.033). BDI‐II was significantly negatively associated with Overall QOL scale scores in all groups, and significantly negatively associated with network SampEn values only in the TBI + PNES group.
Significance
Only TBI + PNES had significant relationships between depression symptom severity and network SampEn values indicating that the resting state network complexity is related to depression severity in this group but not in TBI + ES or TBI‐only.
Plain Language Summary
The brain has a complex network of internal connections. How well these connections work may be affected by TBI and seizures and may underlie mental health symptoms including depression; the worse the depression, the worse the quality of life. Our study compared brain organization in people with TBI, people with epilepsy after TBI, and people with nonepileptic seizures after TBI. Only people with nonepileptic seizures after TBI showed a relationship between how organized their brain connections were and how bad was their depression. We need to better understand these relationships to develop more impactful, effective treatments.
A persistent, low-level neuroinflammatory state is a pathophysiological characteristic of multiple sclerosis, Alzheimer’s disease, and epilepsy, among other neurological disorders. Elucidating the mechanisms underlying neuroinflammation is crucial for understanding the pathophysiology of these disorders, monitoring their progression, and developing effective treatments. It is equally important to find a way to visualize neuroinflammation in vivo, particularly given the scarcity of reliable techniques. Focal, sustained brain temperature elevations are directly related to various peripheral cellular and molecular biomarkers of inflammation, and serve as an indirect proxy of increased metabolic activity associated with neuroinflammation. Brain temperature elevations can be noninvasively visualized using volumetric magnetic resonance spectroscopic imaging and thermometry (MRSI-t) based on the chemical shift of water. In addition to reviewing the MRSI-t analytical framework and methodology, this chapter addresses applications of MRSI-t, including how it can be used to localize brain temperature elevations indicative of central nervous system damage and track treatment response.
This study aimed to investigate the alterations in the intrinsic thalamic network in patients with poststroke epilepsy (PSE) based on electroencephalography (EEG) source-level analysis. This retrospective observational study followed the STROBE guidelines. Thirty-nine patients with stroke and PSE and 34 patients with stroke without PSE were enrolled. These patients underwent EEG in a resting state. Source localization based on scalp electrical potentials was computed using the minimum norm imaging method and the standardized low-resolution brain electromagnetic tomography approach. To construct a functional connectivity matrix, the Talairach atlas was used to define the nodes belonging to the thalamus, and the coherence method was applied to measure brain synchronization as edges. The intrinsic thalamic network was analyzed using graph theory and compared between patients with and without PSE. EEG source-level analysis revealed notable differences in the intrinsic thalamic network between patients with and without PSE. From the undirected weighted connectivity matrix, the measure of modularity was lower in patients with PSE than in those without PSE (0.038 vs 0.106, P = .024). Additionally, modularity measures showed significant differences between the groups, as demonstrated by graph theoretical analysis using binary undirected graphs with a fixed density range of connections. This study is the first to demonstrate the alterations in the intrinsic thalamic network in patients with stroke with PSE compared to those without PSE based on EEG source-level analysis. These intrinsic thalamic network changes may be related to PSE development.
Background: Central nervous system complications are common in sickle cell disease (SCD), and the defining associated biomarkers are becoming increasingly relevant for physicians in diagnostic and prognostic contexts. Recent studies have reported altered brain connectivity in pain processing, highlighting a new avenue for developing sensitive measures of SCD severity. Method: This cross-sectional study used graph theory concepts to analyze effective connectivity in individuals with SCD and healthy controls during rest and motor imagery tasks. The SCD group was further divided into two subgroups based on pain intensity (less pain or more pain) during the evaluation. Results: Individuals with SCD and chronic pain exhibited a distinct brain connectivity signature compared to healthy individuals and within pain sublevels. Conclusion: Chronic pain in SCD shows a unique brain connectivity pattern when compared to healthy subjects and across different pain levels. The results support the hypothesis that chronic pain condition is associated with decreased interhub connections and increased intrahub connections for specific brain rhythms. Furthermore, the small-world parameter can distinguish SCD individuals from controls and differentiate pain levels within SCD individuals, offering a promising biomarker for clinical assessment.
Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post-surgical seizure outcome in people with temporal lobe epilepsy (TLE). We analyzed structural imaging data from 143 TLE patients (pre-op dMRI and pre-op T1-weighted MRI) and 97 healthy controls. We calculated GM volume abnormalities and SWM mean diffusivity abnormalities and evaluated if their surgical removal distinguished seizure outcome groups post-surgically. At a group level, GM and SWM abnormalities were most common in the ipsilateral temporal lobe and hippocampus in people with TLE. Analyzing both modalities together, compared to in isolation, improved surgical outcome discrimination (GM AUC = 0.68, p < 0.01, WM AUC = 0.65, p < 0.01; Union AUC = 0.72, p < 0.01, Concordance AUC = 0.64, p = 0.04). Additionally, 100% of people who had all concordant abnormal regions resected had ILAE outcomes. These findings suggest that regions identified as abnormal from both diffusion-weighted and T1-weighted MRIs are involved in the epileptogenic network and that resection of both types of abnormalities may enhance the chances of living without disabling seizures.
Over the last two decades, it has become increasingly clear that epilepsy is a network disorder. However, it is unclear whether these networks are established only during seizures or persist interictally. The goal of this study was to identify whether functional seizure networks exist interictally and evaluate if there is a structural basis to these networks. We identified four patients with mesial temporal lobe epilepsy who underwent resective epilepsy surgery. We estimated functional and structural connectivity across intracranial electrode contacts involved in seizure onset, early spread, and uninvolved controls. Across all interictal epochs considered, we found higher functional and white matter connectivity across cortical regions involved in seizure spread. Additionally, we observed that the patient in our cohort with the best seizure outcome had the highest functional connectivity across seizure contacts. Functional connectivity findings suggest the presence of an interictal seizure network that parallels underlying structural connectivity. Furthermore, our findings suggest that disruption or ablation of highly connected seizure regions may be necessary to achieve improved post-operative seizure freedom.
The graph-based analysis of complex networks has emerged as a powerful mathematical tool to explore natural, social, and biological systems. The potential of graph features in reflecting the interconnected elements in a complex system makes it suitable for analyzing various biological phenomena and thereby enabling it as a diagnostic tool. Today, graph theory finds extensive application in biomedical signal and image processing. The present review deciphers the potential biomedical applications of graph theory, starting from the fundamentals. Special emphasis has been given to the application of graph theory to cancer, brain, cardiovascular, and protein–protein networks. The systematic approach of evaluating the literature through the PRISMA 2020 standards is followed to map the research done in this domain and identify the gaps in the field. The clinical relevance of graph-based disease detection with machine learning makes the identifying, predicting, and prognostic treatments of various illnesses easier. Novel developments in the examination and characterization of the network topologies of several biological networks indicate that complex networks may play a role in the early detection and treatment of a range of diseases. Amid challenges like large dataset handling capability, precise gene detection, and targeted drug delivery, future directions in this field would involve exploring graph-based deep learning and transfer learning techniques to analyze complex biological information, providing predictive treatment for various maladies.
Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain abnormalities is essential for successful neurosurgical intervention. Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormalities are present in epilepsy and may relate to the epileptogenic zone. Objective: To investigate whether surgical resection of diffusion abnormalities relates to post-operative seizure freedom. Design: This retrospective case-control study was conducted between 2009 and 2022. Data were acquired at the National Hospital for Neurology and Neurosurgery, UK. Study participants included 200 individuals with drug-resistant focal epilepsy, who underwent resective surgery, and 97 healthy controls used as a normative baseline. Main Outcomes: Spatial overlap between diffusion abnormality clusters and surgical resection masks, and relation to post-surgical outcome. Results: Surgical resections overlapping with the largest abnormal cluster significantly correlated with sustained seizure freedom at 12 months (83% vs 55%; p<0.0001) and over five years (p<0.0001). Notably, resecting only a small proportion of the largest cluster was associated with better seizure outcomes than cases with no resection of this cluster (p=0.008). Furthermore, sparing the largest cluster but resecting other large clusters still improved seizure freedom rates compared to no overlap (p=0.03). Conclusions: Our results suggest that abnormal clusters, identified using dMRI, are integral to the epileptogenic network, and even a partial removal of such an abnormal cluster is sufficient to achieve seizure freedom. The study highlights the potential of incorporating dMRI into pre-surgical planning to improve outcomes in focal epilepsy.
This scientific commentary refers to ‘Brain network changes after the first seizure: an insight into medication response?’, by Pedersen et al. (https://doi.org/10.1093/braincomms/fcae328)
Refractory temporal lobe epilepsy (TLE) is one of the most frequently observed subtypes of epilepsy and endangers more than 50 million people world-wide. Although electroencephalogram (EEG) had been widely recognized as a classic tool to screen and diagnose epilepsy, for many years it heavily relied on identifying epileptic discharges and epileptogenic zone localization, which however, limits the understanding of refractory epilepsy due to the network nature of this disease. This work hypothesizes that the microstate dynamics based on resting-state scalp EEG can offer an additional network depiction of the disease and provide potential complementary evaluation tool for the TLE even without detectable epileptic discharges on EEG. We propose a novel framework for EEG microstate spatial-temporal dynamics (EEG-MiSTD) analysis based on machine learning to comprehensively model millisecond-changing whole-brain network dynamics. With only 100 seconds of resting-state EEG even without epileptic discharges, this approach successfully distinguishes TLE patients from healthy controls and is related to the lateralization of epileptic focus. Besides, microstate temporal and spatial features are found to be widely related to clinical parameters, which further demonstrate that TLE is a network disease. A preliminary exploration suggests that the spatial topography is sensitive to the following surgical outcomes. From such a new perspective, our results suggest that spatiotemporal microstate dynamics is potentially a biomarker of the disease. The developed EEG-MiSTD framework can probably be considered as a general tool to examine dynamical brain network disruption in a user-friendly way for other types of epilepsy.
Despite the wide choice of antiepileptic drugs (AEDs), a third of patients remain resistant to the effects of modern AEDs. Drug-resistant epilepsy (DRE) is characterized by the inability to control seizures in a patient when using at least two adequate AED regimens at an effective daily dose as monotherapy or in combination. In this case, the mechanisms responsible for drug resistance are mainly either increased excretion of AEDs by transporters from epileptogenic tissue (the multidrug transporter hypothesis) or a decrease in the sensitivity of drug receptors in epileptogenic brain tissue. It is assumed that there are other mechanisms, but they remain understudied. A number of factors are associated with the risk of DRE developing in patients with diagnosed epilepsy, including genetic, iatrogenic, brain malformations, and others. Patients with DRE have a higher probability of developing psychopathological disorders (depression, anxiety, psychosis), the proportion of which is significantly higher than in the general population. They have a 10-fold increased risk of death due to injury, cognitive decline, and sudden unexpected death in epilepsy (SUDEP). The priority treatment method for DRE is surgery. Early identification of DRE is critical for identifying potential treatment alternatives and determining whether a patient is a surgical candidate. Analysis of data from clinical and instrumental research of operated patients with DRE in the early and late postoperative period will allow us to identify factors of unfavorable outcome and to increase the effectiveness of treatment for this category of patients.The aim was to study and to summarize literature data on the pathogenesis and risk factors of drug resistance to antiepileptic drugs in patients with epilepsy, justifying the need for timely identification of drug resistance and referral of patients with drugresistant epilepsy to specialized centers for possible surgical treatment.
Spinocerebellar Ataxia Type 8 (SCA8) is an inherited neurodegenerative disease caused by a bidirectionally expressed CTG●CAG expansion mutation in the ATXN-8 and ATXN8-OS genes. While primarily a motor disorder, psychiatric and cognitive symptoms have been reported. It is difficult to elucidate how the disease alters brain function in areas with little or no degeneration producing both motor and cognitive symptoms. Using transparent polymer skulls and CNS-wide GCaMP6f expression, we studied neocortical networks throughout SCA8 progression using wide-field Ca2+ imaging in a transgenic mouse model of SCA8. We observed that neocortical networks in SCA8+ mice were hyperconnected globally which led to network configurations with increased global efficiency and centrality. At the regional level, significant network changes occurred in nearly all cortical regions, however mainly involved sensory and association cortices. Changes in functional connectivity in anterior motor regions worsened later in the disease. Near perfect decoding of animal genotype was obtained using a generalized linear model based on canonical correlation strengths between activity in cortical regions. The major contributors to decoding were concentrated in the somatosensory, higher visual and retrosplenial cortices and occasionally extended into the motor regions, demonstrating that the areas with the largest network changes are predictive of disease state.
Introduction
Epilepsy is a common neurological disorder characterised by recurrent seizures. Almost half of patients who have an unprovoked first seizure (UFS) have additional seizures and develop epilepsy. No current predictive models exist to determine who has a higher risk of recurrence to guide treatment. Emerging evidence suggests alterations in cognition, mood and brain connectivity exist in the population with UFS. Baseline evaluations of these factors following a UFS will enable the development of the first multimodal biomarker-based predictive model of seizure recurrence in adults with UFS.
Methods and analysis
200 patients and 75 matched healthy controls (aged 18–65) from the Kingston and Halifax First Seizure Clinics will undergo neuropsychological assessments, structural and functional MRI, and electroencephalography. Seizure recurrence will be assessed prospectively. Regular follow-ups will occur at 3, 6, 9 and 12 months to monitor recurrence. Comparisons will be made between patients with UFS and healthy control groups, as well as between patients with and without seizure recurrence at follow-up. A multimodal machine-learning model will be trained to predict seizure recurrence at 12 months.
Ethics and dissemination
This study was approved by the Health Sciences and Affiliated Teaching Hospitals Research Ethics Board at Queen’s University (DMED-2681-22) and the Nova Scotia Research Ethics Board (1028519). It is supported by the Canadian Institutes of Health Research (PJT-183906). Findings will be presented at national and international conferences, published in peer-reviewed journals and presented to the public via patient support organisation newsletters and talks.
Trial registration number
NCT05724719.
Both the imbalance of neuronal excitation and inhibition, and the network disorganization may lead to hyperactivity in epilepsy. However, the insufficiency of seizure data poses the challenge of elucidating the network mechanisms behind the frequent and recurrent abnormal discharges. Our study of two extensive intracranial EEG datasets revealed that the seizure onset zone exhibits recurrent synchronous activation of interictal events. These synchronized discharges formed repetitive sequential patterns, indicative of a stable and intricate network structure within the seizure onset zone (SOZ). We hypothesized that the frequent replay of interictal sequential activity shapes the structure of the epileptic network, which in turn supports the occurrence of these discharges. The Hopfield-Kuramoto oscillator network model was employed to characterize the formation and evolution of the epileptic network, encoding the interictal sequential patterns into the network structure using the Hebbian rule. This model successfully replicated patient-specific interictal sequential activity. Dynamic change of the network connections was further introduced to build an adaptive Kuramoto model to simulate the interictal to ictal transition. The Kuramoto oscillator network with adaptive connections (KONWAC) model we proposed essentially combines two scales of Hebbian plasticity, shaping both the stereotyped propagation and the ictal transition in epileptic networks through the interplay of regularity and uncertainty in interictal discharges.
Background: This study investigated alterations in the intrinsic thalamic network of patients with juvenile myoclonic epilepsy (JME) based on an electroencephalography (EEG) source-level analysis. Materials and Methods: We enrolled patients newly diagnosed with JME as well as healthy controls. The assessments were conducted in the resting state. We computed sources based on the scalp electrical potentials using a minimum-norm imaging method and a standardized, low-resolution, brain electromagnetic tomography approach. To create a functional connectivity matrix, we used the Talairach atlas to define thalamic nodes and applied the coherence method to measure brain synchronization as edges. We then calculated the intrinsic thalamic network using graph theory. We compared the intrinsic thalamic network of patients with JME with those of healthy controls. Results: This study included 67 patients with JME and 66 healthy controls. EEG source-level analysis revealed significant differences in the intrinsic thalamic networks between patients with JME and healthy controls. The measures of functional connectivity (radius, diameter, and characteristic path length) were significantly lower in patients with JME than in healthy controls (radius: 2.769 vs. 3.544, p = 0.015; diameter: 4.464 vs. 5.443, p = 0.024; and characteristic path length: 2.248 vs. 2.616, p = 0.046). Conclusions: We demonstrated alterations in the intrinsic thalamic network in patients with JME compared with those in healthy controls based on the EEG source-level analysis. These findings indicated increased thalamic connectivity in the JME group. These intrinsic thalamic network changes may be related to the pathophysiology of JME.
This brief review summarizes presentations at the Temporal Lobe Club Special Interest Group session held in December 2022 at the American Epilepsy Society meeting. The session addressed newer methods to treat temporal epilepsy, including methods currently in clinical use and techniques under investigation. Brief summaries are provided for each of 4 lectures. Dr Chengyuan Wu discussed ablative techniques such as laser interstitial thermal ablation, radiofrequency ablation, focused ultrasound; Dr Joon Kang reviewed neuromodulation techniques including electrical stimulation and focused ultrasound; Dr Julia Makhalova discussed network effects of the aforementioned techniques; and Dr Derek Southwell reviewed inhibitory interneuron transplantation. These summaries are intended to provide a brief overview and references are provided for the reader to learn more about each topic.
The spatial coherence of spontaneous slow fluctuations in the blood-oxygen-level dependent (BOLD) signal at rest is routinely used to characterize the underlying resting-state networks (RSNs). Studies have demonstrated that these patterns are organized in space and highly reproducible from subject to subject. Moreover, RSNs reorganizations have been suggested in pathological conditions. Comparisons of RSNs organization have been performed between groups of subjects but have rarely been applied at the individual level, a step required for clinical application. Defining the notion of modularity as the organization of brain activity in stable networks, we propose Detection of Abnormal Networks in Individuals (DANI) to identify modularity changes at the individual level. The stability of each RSN was estimated using a spatial clustering method: Bootstrap Analysis of Stable Clusters (BASC) (Bellec et al., 2010). Our contributions consisted in (i) providing functional maps of the most stable cores of each networks and (ii) in detecting “abnormal” individual changes in networks organization when compared to a population of healthy controls. DANI was first evaluated using realistic simulated data, showing that focussing on a conservative core size (50% most stable regions) improved the sensitivity to detect modularity changes. DANI was then applied to resting state fMRI data of six patients with focal epilepsy who underwent multimodal assessment using simultaneous EEG/fMRI acquisition followed by surgery. Only patient with a seizure free outcome were selected and the resected area was identified using a post-operative MRI. DANI automatically detected abnormal changes in 5 out of 6 patients, with excellent sensitivity, showing for each of them at least one “abnormal” lateralized network closely related to the epileptic focus. For each patient, we also detected some distant networks as abnormal, suggesting some remote reorganization in the epileptic brain.
The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction.
Normal brain functioning is presumed to depend upon interacting regions within large-scale neuronal networks. Increasing evidence exists that interictal network alterations in focal epilepsy are associated with cognitive and behavioral deficits. Nevertheless, the reported network alterations are inconclusive and prone to low statistical power due to small sample sizes as well as modest effect sizes. We therefore systematically reviewed the existing literature and conducted a meta-analysis to characterize the changes in whole-brain interictal focal epilepsy networks at sufficient power levels. We focused on the two most commonly used metrics in whole-brain networks: average path length and average clustering coefficient. Twelve studies were included that reported whole-brain network average path length and average clustering coefficient characteristics in patients and controls. The overall group difference, quantified as the standardized mean average path length difference between epilepsy and control groups, corresponded to a significantly increased average path length of 0.29 (95% confidence interval (CI): 0.12 to 0.45, p = 0.0007) in the epilepsy group. This suggests a less integrated interictal whole-brain network. Similarly, a significantly increased standardized mean average clustering coefficient of 0.35 (CI: 0.05 to 0.65, p = 0.02) was found in the epilepsy group in comparison with controls, pointing towards a more segregated interictal network. Sub-analyses revealed similar results for functional and structural networks in terms of effect size and directionality for both metrics. In addition, we found individual network studies to be prone to low power due to the relatively small group differences in average path length and average clustering coefficient in combination with small sample sizes. The pooled network characteristics support the hypothesis that focal epilepsy has widespread detrimental effects, that is, reduced integration and increased segregation, on whole brain interictal network organization, which may relate to the co-morbid cognitive and behavioral impairments often reported in patients with focal epilepsy.
Aims:
In temporal lobe epilepsy (TLE) due to hippocampal sclerosis reorganisation in the memory encoding network has been consistently described. Distinct areas of reorganisation have been shown to be efficient when associated with successful subsequent memory formation or inefficient when not associated with successful subsequent memory. We investigated the effect of clinical parameters that modulate memory functions: age at onset of epilepsy, epilepsy duration and seizure frequency in a large cohort of patients.
Methods:
We studied 53 patients with unilateral TLE and hippocampal sclerosis (29 left). All participants performed a functional magnetic resonance imaging memory encoding paradigm of faces and words. A continuous regression analysis was used to investigate the effects of age at onset of epilepsy, epilepsy duration and seizure frequency on the activation patterns in the memory encoding network.
Results:
Earlier age at onset of epilepsy was associated with left posterior hippocampus activations that were involved in successful subsequent memory formation in left hippocampal sclerosis patients. No association of age at onset of epilepsy was seen with face encoding in right hippocampal sclerosis patients. In both left hippocampal sclerosis patients during word encoding and right hippocampal sclerosis patients during face encoding, shorter duration of epilepsy and lower seizure frequency were associated with medial temporal lobe activations that were involved in successful memory formation. Longer epilepsy duration and higher seizure frequency were associated with contralateral extra-temporal activations that were not associated with successful memory formation.
Conclusion:
Age at onset of epilepsy influenced verbal memory encoding in patients with TLE due to hippocampal sclerosis in the speech-dominant hemisphere. Shorter duration of epilepsy and lower seizure frequency were associated with less disruption of the efficient memory encoding network whilst longer duration and higher seizure frequency were associated with greater, inefficient, extra-temporal reorganisation.
Adolescence is a time when the ability to engage cognitive control is linked to crucial life outcomes. Despite a historical focus on prefrontal cortex functioning, recent evidence suggests that differences between individuals may relate to interactions between distributed brain regions that collectively form a cognitive control network (CCN). Other research points to a spatially distinct and functionally antagonistic system-the default-mode network (DMN)-which typically deactivates during performance of control tasks. This literature implies that individual differences in cognitive control are determined either by activation or functional connectivity of CCN regions, deactivation or functional connectivity of DMN regions, or some combination of both. We tested between these possibilities using a multilevel fMRI characterization of CCN and DMN dynamics, measured during performance of a cognitive control task and during a task-free resting state, in 73 human adolescents. Better cognitive control performance was associated with (1) reduced activation of CCN regions, but not deactivation of the DMN; (2) variations in task-related, but not resting-state, functional connectivity within a distributed network involving both the CCN and DMN; (3) functional segregation of core elements of these two systems; and (4) task-dependent functional integration of a set of peripheral nodes into either one network or the other in response to prevailing stimulus conditions. These results indicate that individual differences in adolescent cognitive control are not solely attributable to the functioning of any single region or network, but are instead dependent on a dynamic and context-dependent interplay between the CCN and DMN.
Brain networks or 'connectomes' include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly. Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P < 10(-4), permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P < 0.05, permutation test), including schizophrenia and Alzheimer's disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer's disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.
Working memory is a critical building block for almost all cognitive tasks, and impairment can cause significant disruption to daily life routines. We investigated the functional connectivity (FC) of the visuo-spatial working memory network in temporal lobe epilepsy and its relationship to the underlying white matter tracts emanating from the hippocampus. Fifty-two patients with unilateral hippocampal sclerosis (HS) (30 left) and 30 healthy controls underwent working memory functional MRI (fMRI) and Diffusion Tensor Imaging (DTI). Six seed regions were identified for FC analysis; 4 within a task-positive network (left and right middle frontal gyri and superior parietal lobes), and 2 within a task-negative network (left and right hippocampi). FC maps were created by extracting the time-series of the fMRI signal in each region in each subject and were used as regressors of interest for additional GLM fMRI analyses. Structural connectivity (SC) corresponding to areas to which the left and right hippocampi were connected was determined using tractography, and a mean FA for each hippocampal SC map was calculated. Both left and right HS groups showed atypical FC between task-positive and task-negative networks compared to controls. This was characterised by co-activation of the task-positive superior parietal lobe ipsilateral to the typically task-negative sclerosed hippocampus. Correlational analysis revealed stronger FC between superior parietal lobe and ipsilateral hippocampus, was associated with worse performance in each patient group. The SC of the hippocampus was associated with the intra-hemispheric FC of the superior parietal lobe, in that greater SC was associated with weaker parieto-frontal FC. The findings suggest that the segregation of the task-positive and task-negative FC networks supporting working memory in TLE is disrupted, and is associated with abnormal structural connectivity of the sclerosed hippocampus. Co-activation of parieto-temporal regions was associated with poorer working memory and this may be associated with working memory dysfunction in TLE.
Purpose:
Clinical, experimental, and neuroimaging data all indicate that the thalamus is involved in the network of changes associated with temporal lobe epilepsy (TLE), particularly in association with hippocampal sclerosis (HS), with potential roles in seizure initiation and propagation. Pathologic changes in the thalamus may be a result of an initial insult, ongoing seizures, or retrograde degeneration through reciprocal connections between thalamic and limbic regions. Our aim was to carry out a neuropathologic analysis of the thalamus in a postmortem (PM) epilepsy series, to assess the distribution, severity, and nature of pathologic changes and its association with HS.
Methods:
Twenty-four epilepsy PM cases (age range 25-87 years) and eight controls (age range 38-85 years) were studied. HS was classified as unilateral (UHS, 11 cases), bilateral (BHS, 4 cases) or absent (No-HS, 9 cases). Samples from the left and right sides of the thalamus were stained with cresyl violet (CV), and for glial firbillary acidic protein (GFAP) and synaptophysin. Using image analysis, neuronal densities (NDs) or field fraction staining values (GFAP, synaptophysin) were measured in four thalamic nuclei: anteroventral nucleus (AV), lateral dorsal nucleus (LD), mediodorsal nucleus (MD), and ventrolateral nucleus (VL). The results were compared within and between cases.
Key findings:
The severity, nature, and distribution of thalamic pathology varied between cases. A pattern that emerged was a preferential involvement of the MD in UHS cases with a reduction in mean ND ipsilateral to the side of HS (p = 0.05). In UHS cases, greater field fraction values for GFAP and lower values for synaptophysin and ND were seen in the majority of cases in the MD ipsilateral to the side of sclerosis compared to other thalamic nuclei. In addition, differences in the mean ND between classical HS, atypical HS, and No-HS cases were noted in the ipsilateral MD (p < 0.05), with lower values observed in HS.
Significance:
Our study demonstrates that stereotypical pathologic changes, as seen in HS, are not clearly defined in the thalamus. This may be partly explained by the heterogeneity of our PM study group. With quantitation, there is some evidence for preferential involvement of the MD, suggesting a potential role in TLE, which requires further investigation.
The objective of this study was to evaluate whether patients with surgically refractory medial temporal lobe epilepsy (MTLE) exhibit a distinct pattern of structural network organization involving the temporal lobes and extratemporal regions.
We retrospectively studied 18 healthy controls and 20 patients with medication refractory unilateral MTLE who underwent anterior temporal lobectomy for treatment of seizures. Patients were classified as seizure-free or not seizure-free at least 1 year after surgery. The presurgical brain connectome was calculated through probabilistic connectivity from MRI-diffusion tensor imaging from 83 anatomically defined regions of interest encompassing the whole brain. The connectivity patterns were analyzed regarding group differences in regional connectivity and network graph properties.
Compared with controls, patients exhibited a decrease in connectivity involving ipsilateral thalamocortical regions, with a pathologic increase in ipsilateral medial temporal lobe, insular, and frontal connectivity. Among patients, those not seizure-free exhibited a higher connectivity between structures in 1) the ipsilateral medial and lateral temporal lobe, 2) the ipsilateral medial temporal and parietal lobe, and 3) the contralateral temporal pole and parietal lobe. Patients not seizure-free also exhibited lower small-worldness in the subnetwork within the ipsilateral temporal lobe, with higher subnetwork integration at the expense of segregation.
MTLE is associated with network rearrangement within, but not restricted to, the temporal lobe ipsilateral to the onset of seizures. Networks involving key components of the medial temporal lobe and structures traditionally not removed during surgery may be associated with seizure control after surgical treatment of MTLE.
How epilepsy affects brain functional networks remains poorly understood. Here we investigated resting state functional connectivity of the temporal region in temporal lobe epilepsy. Thirty-two patients with unilateral temporal lobe epilepsy underwent resting state blood-oxygenation level dependent functional magnetic resonance imaging. We defined regions of interest a priori focusing on structures involved, either structurally or metabolically, in temporal lobe epilepsy. These structures were identified in each patient based on their individual anatomy. Our principal findings are decreased local and inter-hemispheric functional connectivity and increased intra-hemispheric functional connectivity ipsilateral to the seizure focus compared to normal controls. Specifically, several regions in the affected temporal lobe showed increased functional coupling with the ipsilateral insula and immediately neighboring subcortical regions. Additionally there was significantly decreased functional connectivity between regions in the affected temporal lobe and their contralateral homologous counterparts. Intriguingly, decreased local and inter-hemispheric connectivity was not limited or even maximal for the hippocampus or medial temporal region, which is the typical seizure onset region. Rather it also involved several regions in temporal neo-cortex, while also retaining specificity, with neighboring regions such as the amygdala remaining unaffected. These findings support a view of temporal lobe epilepsy as a disease of a complex functional network, with alterations that extend well beyond the seizure onset area, and the specificity of the observed connectivity changes suggests the possibility of a functional imaging biomarker for temporal lobe epilepsy.
Hubs within the neocortical structural network determined by graph theoretical analysis play a crucial role in brain function. We mapped neocortical hubs topographically, using a sample population of 63 young adults. Subjects were imaged with high resolution structural and diffusion weighted magnetic resonance imaging techniques. Multiple network configurations were then constructed per subject, using random parcellations to define the nodes and using fibre tractography to determine the connectivity between the nodes. The networks were analysed with graph theoretical measures. Our results give reference maps of hub distribution measured with betweenness centrality and node degree. The loci of the hubs correspond with key areas from known overlapping cognitive networks. Several hubs were asymmetrically organized across hemispheres. Furthermore, females have hubs with higher betweenness centrality and males have hubs with higher node degree. Female networks have higher small-world indices.
Autism spectrum conditions (ASC) are neurodevelopmental disorders characterized by abnormal social cognition. A core feature of ASC is disrupted Theory of Mind (ToM), our ability to take the mental perspective of others. ASC is also associated with alexithymia, a trait characterized by altered emotional interoception and empathy. Here, we applied structural MRI covariance analysis to assess whether ASC and alexithymia differentially affect structural brain networks associated with sociocognitive and socioaffective functions. Based on previous functional MRI findings, we expected disrupted ToM networks (centered on dorsomedial prefontal cortex [dmPFC], and temporo-parietal junction [TPJ]) in ASC, while alexithymia would affect networks centered on fronto-insular cortex (FI), regions associated with interoception of emotion and empathy. Relative to controls, ASC indeed showed reduced covariance in networks centered on dmPFC and TPJ, but not within FI networks. Irrespective of ASC, covariance was negatively modulated by alexithymia in networks extending from FI to posterior regions. Network findings were complemented by self-reports, indicating decreased perspective taking but normal empathic concern in ASC. Our results show divergent effects of ASC and alexithymia on inter-regional structural networks, suggesting that networks mediating socioaffective processes may be separable from networks mediating sociocognitive processing.
Object:
Functional neuroimaging has shown that the brain organizes into several independent networks of spontaneously coactivated regions during wakeful rest (resting state). Previous research has suggested that 1 such network, the default mode network (DMN), shows diminished recruitment of the hippocampus with temporal lobe epilepsy (TLE). This work seeks to elucidate how hippocampal recruitment into the DMN varies by hemisphere of epileptogenic focus.
Methods:
The authors addressed this issue using functional MRI to assess resting-state DMN connectivity in 38 participants (23 control participants, 7 patients with TLE and left-sided epileptogenic foci, and 8 patients with TLE and right-sided foci). Independent component analysis was conducted to identify resting-state brain networks from control participants' data. The DMN was identified and deconstructed into its individual regions of interest (ROIs). The functional connectivity of these ROIs was analyzed both by hemisphere (left vs right) and by laterality to the epileptogenic focus (ipsilateral vs contralateral).
Results:
This attempt to replicate previously published methods with this data set showed that patients with left-sided TLE had reduced connectivity between the posterior cingulate (PCC) and both the left (p = 0.012) and right (p < 0.002) hippocampus, while patients with right-sided TLE showed reduced connectivity between the PCC and right hippocampus (p < 0.004). After recoding ROIs by laterality, significantly diminished functional connectivity was observed between the PCC and hippocampus of both hemispheres (ipsilateral hippocampus, p < 0.001; contralateral hippocampus, p = 0.017) in patients with TLE compared with control participants. Regression analyses showed the reduced DMN recruitment of the ipsilateral hippocampus and parahippocampal gyrus (PHG) to be independent of clinical variables including hippocampal sclerosis, seizure frequency, and duration of illness. The graph theory metric of strength (or mean absolute correlation) showed significantly reduced connectivity of the ipsilateral hippocampus and ipsilateral PHG in patients with TLE compared with controls (hippocampus: p = 0.028; PHG: p = 0.021, after correction for false discovery rate). Finally, these hemispheric asymmetries in strength were observed in patients with TLE that corresponded to hemisphere of epileptogenic focus; 87% of patients with TLE had weaker ipsilateral hippocampus strength (compared with the contralateral hippocampus), and 80% of patients had weaker ipsilateral PHG strength.
Conclusions:
This study demonstrated that recoding brain regions by the laterality to their epileptogenic focus increases the power of statistical approaches for finding interhemispheric differences in brain function. Using this approach, the authors showed TLE to selectively diminish connectivity of the hippocampus and parahippocampus in the hemisphere of the epileptogenic focus. This approach may prove to be a useful method for determining the seizure onset zone with TLE, and could be broadly applied to other neurological disorders with a lateralized onset.
Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9-22 years at enrollment), comprising 3-6 longitudinal scans on each participant over 6-12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.
The majority of patients with temporal lobe epilepsy (TLE) experience disturbances of episodic memory from structural damage or dysfunction of the hippocampus. The objective of this study was to use functional Magnetic Resonance Imaging (fMRI) to identify regions where resting state connectivity to the left hippocampus (LH) is correlated with neuropsychological measures of verbal memory retention in TLE patients. Eleven left TLE (LTLE) patients and 15 control subjects participated in resting state fMRI scans. All LTLE patients underwent neuropsychological testing. Resting state functional connectivity maps to the LH were calculated for each patient, and subsequently used in a multiple regression analysis with verbal memory retention scores as a covariate. The analysis identified brain regions whose connectivity to the LH was linearly related to memory retention scores across the group of patients. In LTLE patients, right sided (contralateral) clusters in the precuneus and inferior parietal lobule (IPL) exhibited increased connectivity to the LH with increased memory retention score; left sided (ipsilateral) regions in the precuneus and IPL showed increased connectivity to the LH with decreased retention score. Patients with high memory retention scores had greater connectivity between the LH-right parietal clusters than between the LH-left parietal clusters; in contrast, control subjects had significantly and consistently greater LH-left hemisphere than LH-right hemisphere connectivity. Our results suggest that increased connectivity in contralateral hippocampal functional pathways within the episodic verbal memory network represents a strengthening of alternative pathways in LTLE patients with strong verbal memory retention abilities. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.
Temporal lobe epilepsy (TLE) is the most frequent drug-resistant epilepsy in adults and commonly associated with variable degrees of mesiotemporal atrophy on MRI. Analyses of inter-regional connectivity have unveiled disruptions in large-scale cortico-cortical networks; little is known about the topological organization of the mesiotemporal lobe, the limbic subnetwork central to the disorder. We generated covariance networks based on high-resolution MRI surface-shape descriptors of the hippocampus, entorhinal cortex, and amygdala in 134 TLE patients and 45 age- and sex-matched controls. Graph-theoretical analysis revealed increased path length and clustering in patients, suggesting a shift towards a more regularized arrangement; findings were reproducible after split-half assessment and across two parcellation schemes. Analysis of inter-regional correlations and module participation showed increased within-structure covariance, but decreases between structures, particularly with regards to the hippocampus and amygdala. While higher clustering possibly reflects topological consequences of axonal sprouting, decreases in inter-structure covariance may be a consequence of disconnection within limbic circuitry. Preoperative network parameters, specifically the segregation of the ipsilateral hippocampus, predicted long-term seizure freedom after surgery.
Summary Despite neuropathological and electrophysiological evidence for the involvement of parahippocampal structures in temporal lobe epilepsy (TLE), little attention has been paid to morphometric measurements of these structures in patients with TLE. Using high resolution MRI, we previously showed that the volume of the entorhinal cortex was decreased in patients with TLE. The purpose of this study was: (i) to determine whether changes in the volume of the perirhinal cortex and posterior parahippocampal cortex were detectable by MRI; and (ii) to study the distribution and degree of atrophy in mesial temporal structures including the hippocampal head, body and tail, amygdala, entorhinal cortex, perirhinal cortex and posterior parahippocampal cortex. MRI volumetric analysis was performed using a T1-weighted three-dimensional gradient echo sequence in 20 healthy subjects and 25 TLE patients with intractable TLE. In patients with either left or right TLE, the hippocampal head, body and tail and the entorhinal and perirhinal cortices ipsilateral to the seizure focus were significantly smaller than in normal controls. The mean volume of the posterior parahippocampal cortex was not different from that of normal controls. Within the hippocampus, the hippocampal head was more atrophic than the hippocampal body and hippocampal tail. Within the parahippocampal region, the entorhinal cortex was more severely affected than the perirhinal cortex. Our MRI results confirm pathological findings of damage in the mesial temporal lobe, involving not only the hippocampus and the amygdala, but also the entorhinal and perirhinal cortices. The pattern of atrophy may be explained by cell loss secondary to a disruption of entorhinal‐hippocampal connections as a result of privileged electrical dialogue between these two structures.
Electrophysiologic hubs within the large-scale functional networks in mesial temporal lobe epilepsy (mTLE) with hippocampal sclerosis (HS) have not been investigated. We hypothesized that mTLE with HS has different resting-state network hubs in their large-scale functional networks compared to the hubs in healthy controls (HC). We also hypothesized that the hippocampus would be a functional hub in mTLE patients with HS.
Resting-state functional networks, identified by using magnetoencephalography (MEG) signals in the theta, alpha, beta, and gamma frequency bands, were evaluated. Networks in 44 mTLE patients with HS (left mTLE = 22; right mTLE = 22) were compared with those in 46 age-matched HC. We investigated betweenness centrality at the source-level MEG network.
The main network hubs were at the pole of the left superior temporal gyrus in the beta band, the pole of the left middle temporal gyrus in the beta and gamma bands, left hippocampus in the theta and alpha bands, and right posterior cingulate gyrus in all four frequency bands in mTLE patients; all of which were different from the main network hubs in HC. Only patients with left mTLE showed profound differences from HC at the left hippocampus in the alpha band.
Our analysis of resting-state MEG signals shows that altered electrophysiologic functional hubs in mTLE patients reflect pathophysiologic brain network reorganization. Because we detected network hubs in both hippocampal and extrahippocampal areas, it is probable that mTLE is a large-scale network disorder rather than a focal disorder. The hippocampus was a network hub in left mTLE but not in right mTLE patients, which may be due to intrinsic functional and structural asymmetries between left and right mTLE patients. The evaluation of cortical hubs, even in the spike-free resting-state, could be a clinical diagnostic marker of mTLE with HS.
Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.
Objective:
This study determined the ability of resting-state functional connectivity (rsFC) graph-theory measures to predict neurocognitive status postsurgery in patients with temporal lobe epilepsy (TLE) who underwent anterior temporal lobectomy (ATL).
Methods:
A presurgical resting-state functional magnetic resonance imaging (fMRI) condition was collected in 16 left and 16 right TLE patients who underwent ATL. In addition, patients received neuropsychological testing pre- and postsurgery in verbal and nonverbal episodic memory, language, working memory, and attention domains. Regarding the functional data, we investigated three graph-theory properties (local efficiency, distance, and participation), measuring segregation, integration and centrality, respectively. These measures were only computed in regions of functional relevance to the ictal pathology, or the cognitive domain. Linear regression analyses were computed to predict the change in each neurocognitive domain.
Results:
Our analyses revealed that cognitive outcome was successfully predicted with at least 68% of the variance explained in each model, for both TLE groups. The only model not significantly predictive involved nonverbal episodic memory outcome in right TLE. Measures involving the healthy hippocampus were the most common among the predictors, suggesting that enhanced integration of this structure with the rest of the brain may improve cognitive outcomes. Regardless of TLE group, left inferior frontal regions were the best predictors of language outcome. Working memory outcome was predicted mostly by right-sided regions, in both groups. Overall, the results indicated our integration measure was the most predictive of neurocognitive outcome. In contrast, our segregation measure was the least predictive.
Significance:
This study provides evidence that presurgery rsFC measures may help determine neurocognitive outcomes following ATL. The results have implications for refining our understanding of compensatory reorganization and predicting cognitive outcome after ATL. The results are encouraging with regard to the clinical relevance of using graph-theory measures in presurgical algorithms in the setting of TLE.
Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions primarily characterized by abnormalities in social cognition. Abundant previous functional MRI studies have shown atypical activity in networks encompassing medial prefrontal cortex (mPFC) and medial parietal regions corresponding to posterior cingulate cortex and precuneus (PCC/PCU). Conversely, studies assessing structural brain anomalies in ASD have been rather inconsistent. The current work evaluated whether structural changes in ASD can be reliability detected in a large multicenter dataset. Our comprehensive structural MRI framework encompassed cortical thickness mapping and structural covariance analysis based on three independent samples comprising individuals with ASD and controls (n = 220), selected from the Autism Brain Imaging Data Exchange open-access database. Surface-based analysis revealed increased cortical thickness in ASD relative to controls in mPFC and lateral prefrontal cortex. Clusters encompassing mPFC were embedded in altered inter-regional covariance networks, showing decreased covariance in ASD relative to controls primarily to PCC/PCU and inferior parietal regions. Cortical thickness increases and covariance reductions in ASD were consistent, yet of variable effect size, across the different sites evaluated and measurable both in children and adults. Our multisite study shows regional and network-level structural alterations in mPFC in ASD that, possibly, relate to atypical socio-cognitive functions in this condition. Hum Brain Mapp, 2015. © 2015 Wiley Periodicals, Inc.
© 2015 Wiley Periodicals, Inc.
Transcranial magnetic stimulation (TMS) has been used to elucidate the altered balance between excitatory and inhibitory circuits in the motor cortex in epilepsy; however, TMS could not well assess excitability changes beyond the motor cortex. This study aimed to address the spatial profile of cortical excitability changes in patients with temporal lobe epilepsy (TLE) by using TMS and magnetoencephalography (MEG).
Eighteen patients with TLE and 18 healthy control subjects were recruited. Resting motor threshold (RMT) and intracortical inhibition (ICI) were measured to reflect motor cortical excitability by using TMS. A whole-head MEG was applied to record auditory and somatosensory evoked responses to paired-pulse stimuli. A paired-pulse inhibition (PPI) ratio, defined as the amplitude ratio between responses to the second and the first stimuli, was used to assess the auditory and somatosensory cortical excitability. A high PPI ratio suggests an increase in cortical excitability, while a low ratio indicates a decrease in excitability.
Compared to control subjects, TLE patients exhibited increased RMT in motor cortex and higher PPI ratios for auditory P50m and somatosensory P35m responses. Notably, patients with a lower seizure frequency tended to exhibit a higher RMT or a lower P35m PPI ratio.
Present data suggest that the cortical excitability alteration in focal epilepsy is widely distributed beyond the epileptic focus and the profiles of excitability change correlate with clinical severity in terms of seizure frequency. Combined MEG and TMS studies provide new insight into the inter-ictal cortical excitability profiles in patients with epilepsy.
Copyright © 2015 Elsevier B.V. All rights reserved.
Objective:
In temporal lobe epilepsy (TLE), although hippocampal atrophy lateralizes the focus, the value of magnetic resonance imaging (MRI) to predict postsurgical outcome is rather modest. Prediction solely based on the hippocampus may be hampered by widespread mesiotemporal structural damage shown by advanced imaging. Increasingly complex and high-dimensional representation of MRI metrics motivates a shift to machine learning to establish objective, data-driven criteria for pathogenic processes and prognosis.
Methods:
We applied clustering to 114 consecutive unilateral TLE patients using 1.5T MRI profiles derived from surface morphology of hippocampus, amygdala, and entorhinal cortex. To evaluate the diagnostic validity of the classification, we assessed its yield to predict outcome in 79 surgically treated patients. Reproducibility of outcome prediction was assessed in an independent cohort of 27 patients evaluated on 3.0T MRI.
Results:
Four similarly sized classes partitioned our cohort; in all, alterations spanned over the 3 mesiotemporal structures. Compared to 46 controls, TLE-I showed marked bilateral atrophy; in TLE-II atrophy was ipsilateral; TLE-III showed mild bilateral atrophy; whereas TLE-IV showed hypertrophy. Classes differed with regard to histopathology and freedom from seizures. Classwise surface-based classifiers accurately predicted outcome in 92 ± 1% of patients, outperforming conventional volumetry. Predictors of relapse were distributed bilaterally across structures. Prediction accuracy was similarly high in the independent cohort (96%), supporting generalizability.
Interpretation:
We provide a novel description of individual variability across the TLE spectrum. Class membership was associated with distinct patterns of damage and outcome predictors that did not spatially overlap, emphasizing the ability of machine learning to disentangle the differential contribution of morphology to patient phenotypes, ultimately refining the prognosis of epilepsy surgery.
Modern network science has revealed fundamental aspects of normal brain-network organization, such as small-world and scale-free patterns, hierarchical modularity, hubs and rich clubs. The next challenge is to use this knowledge to gain a better understanding of brain disease. Recent developments in the application of network science to conditions such as Alzheimer's disease, multiple sclerosis, traumatic brain injury and epilepsy have challenged the classical concept of neurological disorders being either 'local' or 'global', and have pointed to the overload and failure of hubs as a possible final common pathway in neurological disorders.
Purpose
Temporal lobe epilepsy (TLE) is considered a brain network disorder, additionally representing the most common form of pharmaco-resistant epilepsy in adults. There is increasing evidence that seizures in TLE arise from abnormal epileptogenic networks, which extend beyond the clinico-radiologically determined epileptogenic zone and may contribute to the failure rate of 30-50% following epilepsy surgery. Graph theory allows for a network-based representation of TLE brain networks using several neuroimaging and electrophysiologic modalities, and has potential to provide clinicians with clinically useful biomarkers for diagnostic and prognostic purposes.
Methods
We performed a review of the current state of graph theory findings in TLE as they pertain to localization of the epileptogenic zone, prediction of pre- and post-surgical seizure frequency and cognitive performance, and monitoring cognitive decline in TLE.
Results
Although different neuroimaging and electrophysiologic modalities have yielded occasionally conflicting results, several potential biomarkers have been characterized for identifying the epileptogenic zone, pre-/post-surgical seizure prediction, and assessing cognitive performance. For localization, graph theory measures of centrality have shown the most potential, including betweenness centrality, outdegree, and graph index complexity, whereas for prediction of seizure frequency, measures of synchronizability have shown the most potential. The utility of clustering coefficient and characteristic path length for assessing cognitive performance in TLE is also discussed.
Conclusions
Future studies integrating data from multiple modalities and testing predictive models are needed to clarify findings and develop graph theory for its clinical utility.
Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.
The Human Connectome Project consortium led by Washington University, University of Minnesota, and Oxford University is undertaking a systematic effort to map macroscopic human brain circuits and their relationship to behavior in a large population of healthy adults. This overview article focuses on progress made during the first half of the 5-year project in refining the methods for data acquisition and analysis. Preliminary analyses based on a finalized set of acquisition and preprocessing protocols demonstrate the exceptionally high quality of the data from each modality. The first quarterly release of imaging and behavioral data via the ConnectomeDB database demonstrates the commitment to making HCP datasets freely accessible. Altogether, the progress to date provides grounds for optimism that the HCP datasets and associated methods and software will become increasingly valuable resources for characterizing human brain connectivity and function, their relationship to behavior, and their heritability and genetic underpinnings.
Objective
Brain imaging studies have shown widespread structural abnormalities in patients with temporal lobe epilepsy (TLE) within and beyond the affected temporal lobe, suggesting an altered network. Graph theoretical analysis based on white matter tractography has provided a new perspective to evaluate the connectivity of the brain. The alterations in the topologic properties of a whole brain white matter network in patients with TLE remain unknown. The purpose of this study was to examine the white matter network in a cohort of patients with left TLE and mesial temporal sclerosis (mTLE) compared to healthy controls.Methods
Anatomic brain networks of 16 patients with left mTLE were compared to those of 21 healthy controls. A white matter structural network was constructed from diffusion tensor tractography for each participant, and network parameters were compared between the patient and control groups.ResultsPatients with left mTLE exhibited concurrent decreases of global and local efficiencies and widespread reduction of regional efficiency in ipsilateral temporal, bilateral frontal, and bilateral parietal areas. Communication hubs, such as the left precuneus, were also altered in patients with mTLE compared to controls.SignificanceOur results demonstrate white matter network disruption in patients with left mTLE, supporting the notion that mTLE is a systemic brain disorder.A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.
Brain network topology provides valuable information on healthy and pathological brain functioning. Novel approaches for brain network analysis have shown an association between topological properties and cognitive functioning. Under the assumption that "stronger is better", the exploration of brain properties has generally focused on the connectivity patterns of the most strongly correlated regions, whereas the role of weaker brain connections has remained obscure for years. Here, we assessed whether the different strength of connections between brain regions may explain individual differences in intelligence. We analyzed-functional connectivity at rest in ninety-eight healthy individuals of different age, and correlated several connectivity measures with full scale, verbal, and performance Intelligent Quotients (IQs). Our results showed that the variance in IQ levels was mostly explained by the distributed communication efficiency of brain networks built using moderately weak, long-distance connections, with only a smaller contribution of stronger connections. The variability in individual IQs was associated with the global efficiency of a pool of regions in the prefrontal lobes, hippocampus, temporal pole, and postcentral gyrus. These findings challenge the traditional view of a prominent role of strong functional brain connections in brain topology, and highlight the importance of both strong and weak connections in determining the functional architecture responsible for human intelligence variability. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
Objective
Temporal lobe epilepsy (TLE) is one of the most common forms of drug-resistant epilepsy. Previous studies have indicated that the TLE-related impairments existed in extensive local functional networks. However, little is known about the alterations in the topological properties of whole brain functional networks.
Method
In this study, we acquired resting state BOLD-fMRI (rsfMRI) data from 26 TLE patients and 25 healthy controls, constructed their whole brain functional networks, compared the differences in topological parameters between the TLE patients and the controls, and analyzed the correlation between the altered topological properties and the epilepsy duration.
Results
The TLE patients showed significant increases in clustering coefficient and characteristic path length, but significant decrease in global efficiency compared to the controls. We also found altered nodal parameters in several regions in the TLE patients, such as the bilateral angular gyri, left middle temporal gyrus, right hippocampus, triangular part of left inferior frontal gyrus, left inferior parietal but supramarginal and angular gyri, and left parahippocampus gyrus. Further correlation analysis showed that the local efficiency of the TLE patients correlated positively with the epilepsy duration.
Conclusion
Our results indicated the disrupted topological properties of whole brain functional networks in TLE patients.
Significance
Our findings indicated the TLE-related impairments in the whole brain functional networks, which may help us to understand their clinical symptoms of TLE patients and offer a clue for the diagnosis and treatment to the TLE patients.
Temporal lobe epilepsy (TLE) affects brain areas beyond the temporal lobes due to connections of the hippocampi and other temporal lobe structures. Using functional connectivity magnetic resonance imaging (MRI), we determined the changes of hippocampal networks in TLE to assess for a more complete distribution of abnormality.
Regions of interest (ROIs) were defined in the right and left hippocampi in three groups of participants: left TLE (n = 13), right TLE (n = 11), and healthy controls (n = 16). Brain regions functionally connected to these ROIs were identified by correlating resting-state low-frequency functional MRI (fMRI) blood oxygenation level-dependent (BOLD) signal fluctuations. The grouped results were compared using independent sample t-test.
TLE was associated with increased hippocampal connectivity involving several key areas of the limbic network (temporal lobe, insula, thalamus), frontal lobes, angular gyrus, basal ganglia, brainstem, and cerebellum, along with reduced connectivity involving areas of the sensorimotor cortex (visual, somatosensory, auditory, primary motor) and the default mode network (precuneus). Left TLE had more marked connectivity changes than right TLE.
The observed connectivity changes in TLE indicate dysfunctional networks that underlie widespread brain involvement in TLE. There are identifiable differences in the connectivity of the hippocampi between left and right TLE.
Depression is a common comorbidity in temporal lobe epilepsy (TLE) that is thought to have a neurobiological basis. This study investigated the functional connectivity (FC) of medial temporal networks in depression symptomatology of TLE and the relative contribution of structural versus FC measures. Volumetric MRI and functional connectivity MRI (fcMRI) were performed on nineteen patients with TLE and 20 controls. The hippocampi and amygdalae were selected as seeds, and five prefrontal and five cingulate regions of interest (ROIs) were selected as targets. Low-frequency blood-oxygen-level-dependent signals were isolated from fcMRI data, and ROIs with synchronous signal fluctuations with the seeds were identified. Depressive symptoms were measured by the Beck Depression Inventory-II. The patients with TLE showed greater ipsilateral hippocampal atrophy (HA) and reduced FC between the ipsilateral hippocampus and the ventral posterior cingulate cortex (vPCC). Neither HA nor hippocampal-vPCC FC asymmetry was a robust contributor to depressive symptoms. Rather, hippocampal-anterior prefrontal FC was a stronger contributor to depressive symptoms in left TLE (LTLE). Conversely, right amygdala FC was correlated with depressive symptoms in both patient groups, with a positive and negative correlation in LTLE and right TLE (RTLE), respectively. Frontolimbic network dysfunction is a strong contributor to levels of depressive symptoms in TLE and a better contributor than HA in LTLE. In addition, the right amygdala may play a role in depression symptomatology regardless of the side of the epileptogenic focus. These findings may inform the treatment of depressive symptoms in TLE and inspire future research to help guide surgical planning.
Evidence for disease progression in the mesiotemporal lobe is mainly derived from global volumetry of the hippocampus. In this study, we tracked progressive structural changes in the hippocampus, amygdala, and entorhinal cortex in drug-resistant temporal lobe epilepsy at a subregional level. Furthermore, we evaluated the relation between disease progression and surgical outcome.
We combined cross-sectional modeling of disease duration in a large cohort of patients (n = 134) and longitudinal analysis in a subset that delayed surgery (n = 31). To track subregional pathology, we applied surface-shape analysis techniques on manual mesiotemporal labels.
Longitudinal and cross-sectional designs showed consistent patterns of progressive atrophy in hippocampal CA1, anterolateral entorhinal, and the amygdalar laterobasal group bilaterally. These regions also exhibited more marked age-related volume loss in patients compared with controls. We found a faster progression of hippocampal atrophy in patients with a seizure frequency ≥6 per month. High rates of contralateral entorhinal cortex atrophy predicted postsurgical seizure relapse.
We observed progressive atrophy in hippocampal, amygdalar, and entorhinal subregions that frequently display neuronal loss on histology. The bilateral character of cumulative atrophy highlights the importance of early surgery. In patients who nevertheless delay this procedure, serial scanning may provide markers of surgical outcome.
Brain functioning is increasingly seen as a complex interplay of dynamic neural systems that rely on the integrity of structural and functional networks. Recent studies that have investigated functional and structural networks in epilepsy have revealed specific disruptions in connectivity and network topology and, consequently, have led to a shift from "focus" to "networks" in modern epilepsy research. Disruptions in these networks may be associated with cognitive and behavioral impairments often seen in patients with chronic epilepsy. In this review, we aim to provide an overview that would introduce the clinical neurologist and epileptologist to this new theoretical paradigm. We focus on the application of a theory, called "network analysis," to characterize resting-state functional and structural networks and discuss current and future clinical applications of network analysis in patients with epilepsy.
The amygdala has been described as a structure affected by mesial temporal lobe epilepsy (MTLE). Indeed, it is suggested that amygdala abnormalities are related to the co-morbid depression and anxiety reported in MTLE. In this context, we investigated the relation between functional connectivity (FC) emerging from this structure in fMRI and depression and anxiety levels reported in MTLE patients. We focused on resting-state BOLD activity and evaluated whether FC differences emerge from each of three amygdala subdivisions (laterobasal, centromedial and superficial) in left and right MTLE groups, compared with healthy controls. Results revealed significant differences between patient groups and controls. Specifically, the left MTLE group showed abnormal FC for the left-sided seeds only. Furthermore, regardless of the seed, we observed more reliable differences between the right MTLE group and controls. Further analysis of these results revealed correlations between these impaired connectivities and psychiatric symptoms in both MTLE groups. Opposite relations, however, were highlighted: the more depressed or anxious the right MTLE patients, the closer their FC values approached controls; whereas the less anxious the left MTLE patients, the closer their FC values were normative. These results highlight how MTLE alter FC emerging from the limbic system. Overall, our data demonstrate that right TLE has a more maladaptive impact on emotion-related networks, in ways specific to the amygdala region, and the emotion symptom involved, than left TLE.
Graph theoretical analysis of functional connectivity data has demonstrated a small-world topology of brain networks. There is increasing evidence that the topology of brain networks is changed in epilepsy. Here we investigated the basal properties of epileptogenic networks by applying graph analysis to intracerebral EEG recordings of patients presenting with drug-resistant partial epilepsies during the interictal period.
Interictal EEG activity was recorded in mesial temporal lobe of 11 patients with mesial temporal lobe epilepsy (MTLE group) and compared with a "control" group of 8 patients having neocortical epilepsies (non MTLE group) in whom depth-EEG recordings eventually showed an ictal onset outside the MTL structures. Synchronization likelihood (SL) was calculated between selected intracerebral electrodes contacts to obtain SL-weighted graphs. Mean normalized clustering index, average path length and small world index S were calculated to characterize network organization.
Broadband SL values were higher in the MTLE group. Although a small-world pattern was found in the two groups, normalized clustering index and to a lesser extend average path length were higher in the MTLE group.
We demonstrated a trend toward a more regular (less random) configuration of interictal epileptogenic networks. In addition S index was found to correlate with epilepsy duration.
These topological alterations might be a surrogate marker of human focal epilepsy and disclose some changes over time.
Functional imaging or diffusion-weighted imaging techniques are widely used to understand brain connectivity at the systems level and its relation to normal neurodevelopment, cognition or brain disorders. It is also possible to extract information about brain connectivity from the covariance of morphological metrics derived from anatomical MRI. These covariance patterns may arise from genetic influences on normal development and aging, from mutual trophic reinforcement as well as from experience-related plasticity. This review describes the basic methodological strategies, the biological basis of the observed covariance as well as applications in normal brain and brain disease before a final review of future prospects for the technique.
Temporal lobe epilepsy (TLE), affecting the medial temporal lobe, is a disorder affecting not just episodic memory but also working memory (WM). However, the exact nature of hippocampal-related network activity in visuospatial WM remains unclear. To clarify this, we utilized a functional connectivity (FC) methodology to investigate hippocampal network involvement during the encoding phase of a fMRI visuospatial WM task in right and left TLE patients. Specifically, we assessed the relation between FC within right and left hippocampus-seeded networks, and patient performance (rate of correct responses) during the encoding phase of a block span WM task. Results revealed that both TLE groups displayed a negative relation between WM performance and FC between the left hippocampus and ipsilateral parahippocampal gyrus. We also found a positive relationship between performance and FC between the left hippocampus seed and the precuneus, in the right TLE group. Lastly, the left TLE specifically demonstrated a negative relationship between performance and FC between both hippocampi and ipsilateral cerebellar clusters. Our findings indicate that right and left TLE groups may develop different patterns of FC to implement visuospatial WM. Indeed, the present result suggest that FC provides a unique means of identifying abnormalities in brain networks, which cannot be discerned at the level of behavioral output through neuropsychological testing. More broadly, our findings demonstrate that FC methods applied to task-based fMRI provide the opportunity to define specific task-related networks.
Hippocampal sclerosis (HS) is the most frequent histopathology encountered in patients with drug-resistant temporal lobe epilepsy (TLE). Over the past decades, various attempts have been made to classify specific patterns of hippocampal neuronal cell loss and correlate subtypes with postsurgical outcome. However, no international consensus about definitions and terminology has been achieved. A task force reviewed previous classification schemes and proposes a system based on semiquantitative hippocampal cell loss patterns that can be applied in any histopathology laboratory. Interobserver and intraobserver agreement studies reached consensus to classify three types in anatomically well-preserved hippocampal specimens: HS International League Against Epilepsy (ILAE) type 1 refers always to severe neuronal cell loss and gliosis predominantly in CA1 and CA4 regions, compared to CA1 predominant neuronal cell loss and gliosis (HS ILAE type 2), or CA4 predominant neuronal cell loss and gliosis (HS ILAE type 3). Surgical hippocampus specimens obtained from patients with TLE may also show normal content of neurons with reactive gliosis only (no-HS). HS ILAE type 1 is more often associated with a history of initial precipitating injuries before age 5 years, with early seizure onset, and favorable postsurgical seizure control. CA1 predominant HS ILAE type 2 and CA4 predominant HS ILAE type 3 have been studied less systematically so far, but some reports point to less favorable outcome, and to differences regarding epilepsy history, including age of seizure onset. The proposed international consensus classification will aid in the characterization of specific clinicopathologic syndromes, and explore variability in imaging and electrophysiology findings, and in postsurgical seizure control.
The default mode network (DMN) is composed of cerebral regions involved in conscious, resting state cognition. The hippocampus is an essential component of this network. Here, the DMN in TLE is compared to control subjects to better understand its involvement in TLE. We performed resting state connectivity analysis using regions of interest (ROIs) in the retrosplenium/precuneus (Rsp/PCUN) and the ventro-medial pre-frontal cortex (vmPFC) in 36 subjects (11 with right TLE, 12 with left TLE, 13 controls) to delineate the posterior and anterior DMN regions respectively. We found reduced connectivity of the posterior to the anterior DMN in patients with both right and left TLE. However, the posterior and anterior networks were found to be individually preserved. Lateralization of TLE affects the DMN with left TLE demonstrating more extensive networks. These DMN changes may be relevant to altered cognition and memory in TLE and may be relevant to right vs. left TLE differences in cognitive involvement.