Pamela Guevara

• PhD
• Professor (Full) at University of Concepción

Multiple sclerosis (MS) is a prevalent neurological disorder marked by inflammation and demyelination, with fatigue being one of the most reported and debilitating symptoms. While fatigue occurs across various neurological conditions and even in healthy individuals, the specific mechanisms contributing to fatigue in each context remain unclear. In...

Background Neuromodulation with electric, electromagnetic, or sensory stimulation presents a complex challenge in optimizing stimulation parameters to achieve specific physiological, cognitive, or behavioral outcomes. The effectiveness of stimulation depends on factors such as stimulation frequency, target location, and individual brain network arc...

Diffusion Magnetic Resonance Imaging tractography is a non-invasive technique that produces a collection of streamlines representing the main white matter bundle trajectories. Methods, such as fiber clustering algorithms, are important in computational neuroscience and have been the basis of several white matter analysis methods and studies. Nevert...

In recent years, there has been a growing interest in studying the Superficial White Matter (SWM). The SWM consists of short association fibers connecting near giry of the cortex, with a complex organization due to their close relationship with the cortical folding patterns. Therefore, their segmentation from dMRI tractography datasets requires ded...

We present a Python library (Phybers) for analyzing brain tractography data. Tractography datasets contain streamlines (also called fibers) composed of 3D points representing the main white matter pathways. Several algorithms have been proposed to analyze this data, including clustering, segmentation, and visualization methods. The manipulation of...

Gaussian processes (GPs) are a powerful machine learning tool to reveal hidden patterns in data. GPs hyperparam-eters are estimated from data, providing a framework for regression and classification tasks. We capitalize on the power of GPs to drive insights about the biophysical mechanisms underpinning metastable brain oscillations from observable...

There is ongoing interest in the dynamics of resting state brain networks (RSNs) as potential predictors of cognitive and behavioural states. Multivariate Autoregressors (MAR) are used to model regional brain activity as a linear combination of past activity in other regions. The coefficients of the MAR are taken as estimates of effective brain con...

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by onl...

Background Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationships, using the Symbol Digit Modalitie...

Multivariate autoregressive models [MAR] allows estimating effective brain connectivity by considering both power and phase fluctuations of the signals involved. A MAR models brain activity in one region as a linear combination of past activations in all other regions. A Hidden Markov model, HMM, whose states’ emisions are drawn from state-specific...

The study of short association fibers is still an incomplete task due to their higher inter-subject variability and the smaller size of this kind of fibers in comparison to known long association bundles. However, their description is essential to understand human brain dysfunction and better characterize the human brain connectome. In this work, w...

Background Prior studies in multiple sclerosis (MS) support reliability of telehealth-delivered cognitive batteries, although, to date, none have reported predictive external validity, i.e., relationships of cognitive test performance to neural correlates across administration modalities. In this study we aimed to compare brain-behavior relationshi...

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Fede...

Each variation of the cortical folding pattern implies a particular rearrangement of the geometry of the fibers of the underlying white matter. While this rearrangement only impacts the ends of the long pathways, it may affect most of the trajectory of the short bundles. Therefore, mapping the short fibers of the human brain using diffusion-based t...

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same in...

This paper presents an enhanced algorithm for automatic segmentation of superficial white matter (SWM) bundles from probabilistic dMRI tractography datasets, based on a multi-subject bundle atlas. Previous segmentation methods use the maximum Euclidean distance between corresponding points of the subject fibers and the atlas centroids. However, thi...

We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation wa...

Background and Purpose: To determine sensitivity of superficial white matter (SWM) integrity as a metric to distinguish early multiple sclerosis (MS) patients from healthy controls (HC). Methods: Fractional anisotropy and mean diffusivity (MD) values from SWM bundles across the cortex and major deep white matter (DWM) tracts were extracted from 29...

Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applicati...

Background The visualization and analysis of brain data such as white matter diffusion tractography and magnetic resonance imaging (MRI) volumes is commonly used by neuro-specialist and researchers to help the understanding of brain structure, functionality and connectivity. As mobile devices are widely used among users and their technology shows a...

We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the b...

This book constitutes the proceedings of the International Workshop on Computational Diffusion MRI, CDMRI 2021, which was held on October 1, 2021, in conjunction with MICCAI 2021. The conference was planned to take place in Strasbourg, France, but was held virtually due to the COVID-19 pandemic. The 13 full papers included were carefully reviewed a...

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same in...

According to global neuronal workspace (GNW) theory, conscious access relies on long-distance cerebral connectivity to allow a global neuronal ignition coding for conscious content. In patients with schizophrenia and bipolar disorder, both alterations in cerebral connectivity and an increased threshold for conscious perception have been reported. T...

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same in...

In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead...

This work presents an effective multiple subject clustering method using whole-brain tractography datasets. The method is able to obtain fiber clusters that are representative of the population. The proposed approach first applies a fast intra-subject clustering algorithm on each subject obtaining the cluster centroids for all subjects. Second, it...

We present GeoSP, a parallel method that creates a parcellation of the cortical mesh based on a geodesic distance, in order to consider gyri and sulci topology. The method represents the mesh with a graph and performs a K-means clustering in parallel. It has two modes of use, by default, it performs the geodesic cortical parcellation based on the b...

Background: Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically group similar fibers into clusters. However, due to inter-s...

Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies...

Background Schizophrenia (SZ) and bipolar disorder (BD) have been increasingly viewed as psychotic mood disorders along a shared spectrum. Long-range and short-range structural connectivity have been implicated in both disorders, conceptualising them as “disconnection syndromes”. There has been a rise in neuroimaging tools to understand the overlap...

We present a hybrid method that performs the complete parcellation of the cerebral cortex of an individual, based on the connectivity information of the white matter fibers from a whole-brain tractography dataset. The method consists of five steps, first intra-subject clustering is performed on the brain tractography. The fibers that make up each c...

The mapping of human brain connections is still an on going task. Unlike deep white matter (DWM), which has been extensively studied and well documented, superficial white matter (SWM) has been often left aside. Improving our understanding of the SWM is an important goal for a better understanding of the brain network and its relation to several pa...

Background: Diffusion MRI is the preferred non-invasive in vivo modality for the study of brain white matter connections. Tractography datasets contain 3D streamlines that can be analyzed to study the main brain white matter tracts. Fiber clustering methods have been used to automatically regroup similar fibers into clusters. However, due to inter-...

We present an optimized algorithm that performs automatic classification of white matter fibers based on a multi-subject bundle atlas. We implemented a parallel algorithm that improves upon its previous version in both execution time and memory usage. Our new version uses the local memory of each processor, which leads to a reduction in execution t...

The study of white matter (WM) through diffusion Magnetic Resonance Imaging (dMRI) is crucial to obtain a better understanding of human brain connections and functions, at a macroscopic level. A large number of works have focused on long range brain connections, while recently, several studies have also analyzed superficial WM connectivity. In rece...

We present a new framework for the creation of an extended atlas of short fiber bundles between 20 and 80 mm length. This method uses a Diffeomorphic inter-subject alignment procedure including information of cortical foldings and forces the accurate match of the sulci that have to be circumvented by the U-bundles. Then, a clustering is performed t...

Schizophrenia (SZ) and bipolar disorder (BD) are often conceptualized as “disconnection syndromes,” with substantial evidence of abnormalities in deep white matter tracts, forming the substrates of long-range connectivity, seen in both disorders. However, the study of superficial white matter (SWM) U-shaped short-range tracts remained challenging u...

It is a fact that the brain cortical folding pattern morphology is specific to each human being. Neuroanatomists think that the folding pattern is strongly related to brain connectivity [1]. As each folding variation implies a specific rearrangement of the different white matter bundles, it also impacts the position of functional regions. This part...

Schizophrenia (SZ) and bipolar disorder (BD) are often conceptualized as 'disconnection syndromes', with substantial evidence of abnormalities in deep white matter tracts, forming the substrates of long-range connectivity, seen in both disorders. However, the study of superficial white matter (SWM) U-shaped short-range tracts remained challenging u...

The current theory implying local, short-range overconnectivity in autism spectrum disorder, contrasting with long-range underconnectivity, is based on heterogeneous results, on limited data involving functional connectivity studies, on heterogeneous paediatric populations and non-specific methodologies. In this work, we studied short-distance stru...

Most analysis and segmentation methods for diffusion MRI tractography datasets require a fiber distance measure able to determine the similarity between a pair of fibers. We present a stringent fiber distance measure able to perform a good discrimination between fiber shapes and lengths. It uses three terms: (i) a fiber maximum Euclidean distance,...

Background Alterations in brain connectivity are strongly implicated in the pathophysiology of schizophrenia (SZ). Very recently, evidence is mounting to suggest that changes in superficial white matter (SWM) U-shaped short range fibers are integral components of disease neuropathology, a theory that is supported by findings from postmortem studies...

Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles....

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the...

Background: Abnormal maturation of brain connectivity is supposed to underlie the dysfunctional emotion regulation in patients with bipolar disorder (BD). To test this hypothesis, white matter integrity is usually investigated using measures of water diffusivity provided by MRI. Here we consider a more intuitive aspect of the morphometry of the wh...

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM di...

This paper is focused on the study of short brain association fibers. We present an automatic method to identify short bundles of the superficial white matter based on inter-subject hierarchical clustering. Our method finds clusters of similar fibers, belonging to the different subjects, according to a distance measure between fibers. First, the al...

The Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we expanded a previously developed method for the au...

Objective: High-functioning autism (HFA) and schizophrenia (SZ) are two of the main neurodevelopmental disorders, sharing several clinical dimensions and risk factors. Their exact relationship is poorly understood, and few studies have directly compared both disorders. Our aim was thus to directly compare neuroanatomy of HFA and SZ using a multimo...

In this work, we investigated the link between the blood-oxygen-level dependant (BOLD) effect observed using functional magnetic resonance imaging (fMRI) and the neurite density inferred from the Neurite Orientation Dispersion and Density Imaging (NODDI) model in some well-known lateralized cortical areas. We found a strong colocalization between t...

The advance of device technology has allowed the existence of many accessible powerful devices which can be taken anywhere. Large datasets, for example those generated from neuroimaging analyses, can now be displayed in portable devices, facilitating the access to the information for a quick and easy exchange of views for the scientific community....

We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of...

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of the pathogenesis associated to it. In this work we developed a method for the automatic creation of a SWM bundle multi...

Gilles de la Tourette syndrome is a childhood-onset syndrome characterized by the presence and persistence of motor and vocal tics. A dysfunction of cortico-striato-pallido-thalamo-cortical networks in this syndrome has been supported by convergent data from neuro-pathological, electrophysiological as well as structural and functional neuroimaging...

We present a fast algorithm for automatic segmentation of white matter fibers from tractography datasets based on a multi-subject bundle atlas. We describe a sequential version of the algorithm that runs on a desktop computer CPU, as well as a highly parallel version that uses a Graphics Processing Unit (GPU) as an accelerator. Our sequential imple...

The striatum including the caudate and putamen nuclei is involved in numerous functional tasks. It ensures senso-rimotor, associative and limbic functions supported by sensorimo-tor, associative and limbic sub-territories directly connected to the cortex and that may overlap. Damage to the striatum results in movement disorders like in Parkinson's...

Importance Tractography studies investigating white matter (WM) abnormalities in patients with bipolar disorder have yielded heterogeneous results owing to small sample sizes. The small size limits their generalizability, a critical issue for neuroimaging studies of biomarkers of bipolar I disorder (BPI).Objectives To study WM abnormalities using...

Human brain connection map is far from being complete. In part, because the study of the superficial white matter (SWM) is a complex and unachieved task. Its description is essential for the understanding of human brain function and the study of the pathogenesis associated to it. In this work we applied an automatic white matter bundle segmentation...

In this paper we performed an analysis of short brain association fibers based on 20 subjects of a high quality HARDI database. Fibers from all the subjects were clustered together in Talairach space. Generic fascicles presents in most of the subjects. For the left hemisphere, we obtained 87 representative fascicles, present in at least 17 of the 2...

Introduction A number of studies have investigated white matter abnormalities in patients with bipolar disorder (BD) using diffusion tensor imaging. However, tractography studies yielded heterogeneous results partly due to small sample sizes. Aims In this work we aimed to study white matter abnormalities using whole-brain tractography in a large m...

The construction of an atlas of the human brain connectome, in particular, the cartography of fiber bundles of superficial white matter (SWM) is a complex and unachieved task. Its description is essential for the understanding of human brain function and the study of several pathologies. In this work we applied an automatic white matter bundle segm...

This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the...

Within inter-individual comparison, brain image registration should align images as well as internal structures such as fibers. While using image-based registration[1,6], neural fibers appear uniformly white giving no information. Tensor-based registration improves white-matter alignment[2,3], but misregistration persist in regions where the tensor...