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A simplified schematic of the hierarchical predictive coding in the cortex. This schematic is based on Figure 1 in (Edwards et al., 2012), published under the terms of Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0). Each yellow box represents a cortical column as a predictive coding unit. In this scheme, pyramidal cells are divided into two classes of prediction (black triangles) and prediction-error (red triangles). Predictive coding is then implemented according to a hierarchical scheme: 'top-down', 'backward' or 'descending' neuronal connections (black arrows) transfer predictions from higher processing levels to lower ones, whereas 'bottom-up', 'forward' or 'ascending' neuronal connections (red arrows) convey prediction-errors in the opposite direction. The term 'prediction-error' here refers to the (precision-weighted) difference between expectations and predictions at each unit. The precision-weights (green arrows) are controlled by postsynaptic neuromodulation (e.g. conferred by D1-dopamine receptors). The internal feedback loop within each unit constitutes 'intrinsic' connectivity, whereas between-unit interactions lead to 'extrinsic' connectivity.
Source publication
In this opinion paper, we describe a combined view of functional and effective brain connectivity along with the free-energy principle for investigating persistent disruptions in brain networks of patients with focal epilepsy. These changes are likely reflected in effective connectivity along the cortical hierarchy and construct the basis of increa...
Citations
... This approach to cognition has a strong theoretical basis, because it can be shown, under very general conditions (Worden, 1995), that Bayesian cognition affords the greatest the fitness-and so is the target towards which the evolution of brains converges. Furthermore, the Bayesian brain can explain many different aspects of cognition (Knill and Pouget, 2004;Doya, 2007;Seth, 2015;Omidvarnia et al., 2017). ...
It has been suggested that the thalamus acts as a blackboard, on which the computations of different cortical modules are composed, coordinated, and integrated. This article asks what blackboard role the thalamus might play, and whether that role is consistent with the neuroanatomy of the thalamus. It does so in a context of Bayesian belief updating, expressed as a Free Energy Principle. We suggest that the thalamus-as-a-blackboard offers important questions for research in spatial cognition. Several prominent features of the thalamus—including its lack of olfactory relay function, its lack of internal excitatory connections, its regular and conserved shape, its inhibitory interneurons, triadic synapses, and diffuse cortical connectivity—are consistent with a blackboard role.Different thalamic nuclei may play different blackboard roles: (1) the Pulvinar, through its reciprocal connections to posterior cortical regions, coordinates perceptual inference about “what is where” from multi-sense-data. (2) The Mediodorsal (MD) nucleus, through its connections to the prefrontal cortex, and the other thalamic nuclei linked to the motor cortex, uses the same generative model for planning and learning novel spatial movements. (3) The paraventricular nucleus may compute risk-reward trade-offs. We also propose that as any new movement is practiced a few times, cortico-thalamocortical (CTC) links entrain the corresponding cortico-cortical links, through a process akin to supervised learning. Subsequently, the movement becomes a fast unconscious habit, not requiring the MD nucleus or other thalamic nuclei, and bypassing the thalamic bottleneck.
... A particularly instructive example is the rich history of seizure disorder. Excitability is an intrinsic and essential aspect of neuronal electrical function (103,150,(177)(178)(179), but it also constitutes a natural problem because that excitability must be controlled (180)-and this control has been considered in relation to precision (181). The brainmind is particularly vulnerable to escape from excitability control (71). ...
Objective
This article offers a philosophical thesis for psychiatric disorders that rests upon some simple truths about the mind and brain. Specifically, it asks whether the dual aspect monism—that emerges from sleep research and theoretical neurobiology—can be applied to pathophysiology and psychopathology in psychiatry.
Methods
Our starting point is that the mind and brain are emergent aspects of the same (neuronal) dynamics; namely, the brain–mind. Our endpoint is that synaptic dysconnection syndromes inherit the same dual aspect; namely, aberrant inference or belief updating on the one hand, and a failure of neuromodulatory synaptic gain control on the other. We start with some basic considerations from sleep research that integrate the phenomenology of dreaming with the neurophysiology of sleep.
Results
We then leverage this treatment by treating the brain as an organ of inference. Our particular focus is on the role of precision (i.e., the representation of uncertainty) in belief updating and the accompanying synaptic mechanisms.
Conclusions
Finally, we suggest a dual aspect approach—based upon belief updating (i.e., mind processes) and its neurophysiological implementation (i.e., brain processes)—has a wide explanatory compass for psychiatry and various movement disorders. This approach identifies the kind of pathophysiology that underwrites psychopathology—and points to certain psychotherapeutic and psychopharmacological targets, which may stand in mechanistic relation to each other.
HIGHLIGHTS
• The ‘mind’ emerges from Bayesian belief updating in the ‘brain’
• Psychopathology can be read as aberrant belief updating.
• Aberrant belief updating follows from any neuromodulatory synaptopathy
... PTZ is a well-characterised chemoconvulsant and acts as a GABA antagonist, thus disrupting inhibitory synaptic transmission. Acute seizures are believed to be associated with changes in (i) local microcircuit dynamics that allow for a (phase) transition between resting and seizure activity [19,30], and (ii) changes in whole-brain connectivity [31][32][33]. DCM allows concurrent testing of the following emerging hypotheses across these different spatial scales: (1) seizures lead to a measurable reorganisation of effective connectivity between regions [34], (2) local excitation-inhibition imbalance explains associated regional spectral changes [35], (3) in addition to changes in connection strengths, seizures are also associated with changes in synaptic transmission dynamics [29]. ...
... However, the asymmetric involvement of a single brain region-where only effective connectivity to (and not from) the optic tectum is reduced-suggests that local microcircuitry changes may underlie the macroscale changes. The relationship between local and macroscale network changes in epilepsy in the context of hierarchically coupled brain areas is discussed elsewhere [31]. This phenomenon has been formally described in other modelling work through a slow local permittivity variable that governs synchronisation between different brain regions and represents different slowly unfolding changes in local energy and metabolic milieu [54]. ...
Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of both synaptic connection strength and their temporal dynamics are required to explain observed seizure patterns. These findings highlight distinct changes in local (intrinsic) and long-range (extrinsic) synaptic transmission dynamics as a possible seizure pathomechanism and illustrate how our Bayesian model inversion approach can be used to link existing neural mass models of seizure activity and novel experimental methods.
... Some studies have found that intracranial recording during ictal periods revealed a network shift towards a more regular topology than that observed in partial seizures 7 , and that an increased clustering coefficient and path length can mark seizure onset. Thus, functional neural network analysis is a promising technique for more accurate identification of the target areas for epilepsy [8][9][10] . ...
Corpus callosotomy (CC) is an effective palliative surgical treatment for patients with Lennox-Gastaut Syndrome (LGS). However, research on the long-term functional effects of CC is sparse. We aimed to investigate these effects and their associated clinical conditions over the two years after CC. Long-term clinical EEG recordings of 30 patients with LGS who had good and bad seizure outcome after CC were collected and retrospectively studied. It was found that CC caused brain network 'hubs' to shift from paramedian to lateral regions in the good-recovery group, which reorganized the brain network into a more homogeneous state. We also found increased local clustering coefficients in patients with bad outcomes and decreases, implying enhanced network integration, in patients with good outcomes. The small worldness of brain networks in patients with good outcomes increased in the two years after CC, whereas it decreased in patients with bad outcomes. The covariation of small-worldness with the rate of reduction in seizure frequency suggests that this can be used as an indicator of CC outcome. Local and global network changes during the long-term state might be associated with the postoperative recovery process and could serve as indicators for CC outcome and long-term LGS recovery.
... In this acute chemoconvulsant model, seizures were induced with pentylenetetrazole (PTZ) in healthy larval zebrafish and seizures recorded using brain wide expression of the genetically encoded reporter of neural activity (GCaMP6F) and light sheet imaging across a single slice capturing five main bilateral brain regions. The DCM analysis rests on the notion that these acute seizures are associated both with changes in localised microcircuit dynamics that result in a phase transition between resting activity and the pathological seizure state (Breakspear, 2005;Jirsa et al., 2014), and with measureable changes in whole-brain connectivity (Nehlig, 1998;Omidvarnia et al., 2017;Sinha et al., 2016). DCM allows concurrent testing of the following emerging hypotheses across these different spatial scales: (1) seizures lead to a measureable reorganisation of effective connectivity between regions (Burns et al., 2014), (2) local excitation-inhibition imbalance explains associated regional spectral changes (Netoff et al., 2004), (3) in addition to changes in connection strengths, seizures are also associated with changes in synaptic transmission dynamics (Papadopoulou et al., 2016). ...
... This phenomenon has been formally described in other modelling work through a slow local permittivity variable that governs synchronisation between different brain regions and represents different slowly unfolding changes in local energy and metabolic milieu (Proix et al., 2014). The relationship between local and macroscale network changes in epilepsy in the context of hierarchically coupled brain areas is discussed elsewhere (Omidvarnia et al., 2017). In our approach here, slow local changes may appear as the sort of changes in directed effective connectivity estimated for the optic tectum, linking local microcircuitry abnormality with pathological brain-wide reorganisation during the epileptic seizure. ...
Pathophysiological explanations of epilepsy typically focus on either the micro/mesoscale (e.g. excitation-inhibition imbalance), or on the macroscale (e.g. network architecture). Linking abnormalities across spatial scales remains difficult, partly because of technical limitations in measuring neuronal signatures concurrently at the scales involved. Here we use light sheet imaging of the larval zebrafish brain during acute epileptic seizure induced with pentylenetetrazole. Empirically measured spectral changes of spontaneous neuronal activity during the seizure are then modelled using neural mass models, allowing Bayesian inference on changes in effective network connectivity and their underlying synaptic dynamics. This dynamic causal modelling of seizures in the zebrafish brain reveals concurrent changes in synaptic coupling at macro- and mesoscale. Fluctuations of synaptic connection strength and their temporal dynamics are both required to explain observed seizure patterns. These findings challenge a simple excitation-inhibition account of seizures, and highlight changes in synaptic transmission dynamics as a possible seizure generation pathomechanism.
Abbreviations
LFP local field potential
PTZ pentylenetetrazole
DCM dynamic causal modelling
CSD cross spectral densities
PEB Parametric Empirical Bayes
Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used to extract knowledge from a vast amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals.
The editors connect four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. They present state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery.
This book is intended for data scientists, academicians, and industry professionals in the healthcare sector.
Structural neuroimaging of athletes who have sustained a sports-related concussion (SRC) can be viewed as either standard clinical imaging or with advanced neuroimaging methods that quantitatively assess brain structure. Negative findings from conventional computed tomography (CT) or magnetic resonance imaging (MRI) are the norm in SRC. Nonetheless, these conventional measures remain the first line of neuroimaging of the athlete as they do detect clinically significant pathologies, when present, such as hemorrhagic abnormalities in the form of hematomas, contusions and mircobleeds along with regions of focal encephalomalacia or other signal abnormalities, with CT best capable of detecting skull fractures. However, advanced neuroimaging techniques hold particular promise in detecting subtle neuropathology in the athlete which standard clinical neuroimaging cannot. To best understand what conventional as well as quantitative neuroimaging methods are detecting in SRC, this review begins by covering basic neuroanatomical principles associated with mild traumatic brain injury (mTBI) and the brain regions most vulnerable to injury from SRC, as these regions define where advanced neuroimaging methods most likely detect abnormalities. Advanced MRI techniques incorporate quantitative metrics that include volume, shape, thickness along with diffusion parameters that provide a more fine-grained analysis of brain structure. With advancements in image analysis, multiple quantitative neuroimaging metrics now can be utilized in assessing SRC. Such multimodality approaches are particularly relevant and important for assessing white matter and network integrity of the brain following injury, including SRC. This review focuses just on the structural side of neuroimaging in SRC, but these techniques also are being integrated with functional neuroimaging, where the combination of the two approaches may provide superior methods in assessing the pathological effects of SRC.
