Latent cognitive phenotypes in juvenile myoclonic epilepsy: Clinical, sociodemographic, and neuroimaging associations.

Objective: Application of cluster analytic procedures has advanced understanding of the cognitive heterogeneity inherent in diverse epilepsy syndromes and the associated clinical and neuroimaging features. Application of this unsupervised machine learning approach to the neuropsychological performance of persons with juvenile myoclonic epilepsy (JME) has yet to be attempted, which is the intent of this investigation.

Methods: A total of 77 JME participants, 19 unaffected siblings, and 44 unrelated controls, 12 to 25 years of age, were administered a comprehensive neuropsychological battery (intelligence, language, memory, executive function, and processing speed), which was subjected to factor analysis followed by K-means clustering of the resultant factor scores.

Characterizing white matter connectome abnormalities in patients with temporal lobe epilepsy using threshold-free network-based statistics.

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.

The relevance of Spearman's for epilepsy.

Whilst the concept of a general mental factor known as '' has been of longstanding interest, for unknown reasons, it has never been interrogated in epilepsy despite the 100+ year empirical history of the neuropsychology of epilepsy. This investigation seeks to identify within a comprehensive neuropsychological data set and compare participants with temporal lobe epilepsy to controls, characterize the discriminatory power of compared with domain-specific cognitive metrics, explore the association of with clinical epilepsy and sociodemographic variables and identify the structural and network properties associated with in epilepsy. Participants included 110 temporal lobe epilepsy patients and 79 healthy controls between the ages of 19 and 60.

Topological Embedding of Human Brain Networks with Applications to Dynamics of Temporal Lobe Epilepsy.

We introduce a novel, data-driven topological data analysis (TDA) approach for embedding brain networks into a lower-dimensional space in quantifying the dynamics of temporal lobe epilepsy (TLE) obtained from resting-state functional magnetic resonance imaging (rs-fMRI). This embedding facilitates the orthogonal projection of 0D and 1D topological features, allowing for the visualization and modeling of the dynamics of functional human brain networks in a resting state. We then quantify the topological disparities between networks to determine the coordinates for embedding.

Unified topological inference for brain networks in temporal lobe epilepsy using the Wasserstein distance.

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance.

Association of neighborhood deprivation with white matter connectome abnormalities in temporal lobe epilepsy.

Objective: Social determinants of health, including the effects of neighborhood disadvantage, impact epilepsy prevalence, treatment, and outcomes. This study characterized the association between aberrant white matter connectivity in temporal lobe epilepsy (TLE) and disadvantage using a US census-based neighborhood disadvantage metric, the Area Deprivation Index (ADI), derived from measures of income, education, employment, and housing quality.

Methods: Participants including 74 TLE patients (47 male, mean age = 39.

Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition.

Temporal lobe epilepsy (TLE) is the most common epilepsy syndrome that empirically represents a network disorder, which makes graph theory (GT) a practical approach to understand it. Multi-shell diffusion-weighted imaging (DWI) was obtained from 89 TLE and 50 controls. GT measures extracted from harmonized DWI matrices were used as factors in a support vector machine (SVM) analysis to discriminate between groups, and in a k-means algorithm to find intrinsic structural phenotypes within TLE.

The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy.

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups.

Unified Topological Inference for Brain Networks in Temporal Lobe Epilepsy Using the Wasserstein Distance.

Persistent homology offers a powerful tool for extracting hidden topological signals from brain networks. It captures the evolution of topological structures across multiple scales, known as filtrations, thereby revealing topological features that persist over these scales. These features are summarized in persistence diagrams, and their dissimilarity is quantified using the Wasserstein distance.

Network topology of the cognitive phenotypes of temporal lobe epilepsy.

Purpose: The neuropsychological complications of temporal lobe epilepsy are characterized by a spectrum of reproducible cognitive phenotypes that vary in the presence, type and degree of impairment. The nature of the disruptions to the neuropsychological networks that underlie these phenotypes remain to be characterized and represent the subject of this investigation.

Methods: Participants included 30 healthy controls and 104 patients with temporal lobe epilepsy who fell into three cognitive phenotypes (intact, focal impairment, generalized impairment).

Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy.

This study explored the taxonomy of cognitive impairment within temporal lobe epilepsy and characterized the sociodemographic, clinical and neurobiological correlates of identified cognitive phenotypes. 111 temporal lobe epilepsy patients and 83 controls (mean ages 33 and 39, 57% and 61% female, respectively) from the Epilepsy Connectome Project underwent neuropsychological assessment, clinical interview, and high resolution 3T structural and resting-state functional MRI. A comprehensive neuropsychological test battery was reduced to core cognitive domains (language, memory, executive, visuospatial, motor speed) which were then subjected to cluster analysis.

Graph Theory Analysis of Functional Connectivity Combined with Machine Learning Approaches Demonstrates Widespread Network Differences and Predicts Clinical Variables in Temporal Lobe Epilepsy.

Understanding how global brain networks are affected in epilepsy may elucidate the pathogenesis of seizures and its accompanying neurobehavioral comorbidities. We investigated functional changes within neural networks in temporal lobe epilepsy (TLE) using graph theory analysis of resting-state connectivity. Twenty-seven TLE presurgical patients (age 41.

Network analysis of prospective brain development in youth with benign epilepsy with centrotemporal spikes and its relationship to cognition.

Objective: Benign epilepsy with centrotemporal spikes (BECTS) is the most common childhood idiopathic localization-related epilepsy syndrome. BECTS presents normal routine magnetic resonance imaging (MRI); however, quantitative analytic techniques have captured subtle cortical and subcortical magnetic resonance anomalies. Network science, including graph theory (GT) analyses, facilitates understanding of brain covariance patterns, potentially informing in important ways how this common self-limiting epilepsy syndrome may impact normal patterns of brain and cognitive development.

Preoperative FMRI Associated with Decreased Mortality and Morbidity in Brain Tumor Patients.

Background: Functional Magnetic Resonance Imaging (fMRI) is a presurgical planning technique used to localize functional cortex so as to maximize resection of diseased tissue and avoid viable tissue. In this retrospective study, we examined differences in morbidity and mortality of brain tumor patients who received preoperative fMRI in comparison to those who did not.

Methods: Brain tumor patients (=206) were selected from a retrospective review of neurosurgical case logs from 2001-2009 at the University of Wisconsin-Madison.

Progressive dissociation of cortical and subcortical network development in children with new-onset juvenile myoclonic epilepsy.

Objective: Structural and functional magnetic resonance imaging (MRI) studies have consistently documented cortical and subcortical abnormalities in patients with juvenile myoclonic epilepsy (JME). However, little is known about how these structural abnormalities emerge from the time of epilepsy onset and how network interactions between and within cortical and subcortical regions may diverge in youth with JME compared to typically developing children.

Methods: We examined prospective covariations of volumetric differences derived from high-resolution structural MRI during the first 2 years of epilepsy diagnosis in a group of youth with JME (n = 21) compared to healthy controls (n = 22).

Predicting primary outcomes of brain tumor patients with advanced neuroimaging MRI measures.

Background: Advanced neuroimaging measures along with clinical variables acquired during standard imaging protocols provide a rich source of information for brain tumor patient treatment and management. Machine learning analysis has had much recent success in neuroimaging applications for normal and patient populations and has potential, specifically for brain tumor patient outcome prediction. The purpose of this work was to construct, using the current patient population distribution, a high accuracy predictor for brain tumor patient outcomes of mortality and morbidity (i.

Psychomotor slowing is associated with anomalies in baseline and prospective large scale neural networks in youth with epilepsy.

Purpose: Psychomotor slowing is a common but understudied cognitive impairment in epilepsy. Here we test the hypothesis that psychomotor slowing is associated with alterations in brain status reflected through analysis of large scale structural networks. We test the hypothesis that children with epilepsy with cognitive slowing at diagnosis will exhibit a cross-sectional and prospective pattern of altered brain development.

Brain structure and organization five decades after childhood onset epilepsy.

The purpose of this project was to characterize brain structure and organization in persons with active and remitted childhood onset epilepsy 50 years after diagnosis compared with healthy controls. Participants from a population-based investigation of uncomplicated childhood onset epilepsy were followed up 5 decades later. Forty-one participants had a history of childhood onset epilepsy (mean age of onset = 5.

Disruptions in cortico-subcortical covariance networks associated with anxiety in new-onset childhood epilepsy.

Anxiety disorders represent a prevalent psychiatric comorbidity in both adults and children with epilepsy for which the etiology remains controversial. Neurobiological contributions have been suggested, but only limited evidence suggests abnormal brain volumes particularly in children with epilepsy and anxiety. Since the brain develops in an organized fashion, covariance analyses between different brain regions can be investigated as a network and analyzed using graph theory methods.

Mapping the neuropsychological profile of temporal lobe epilepsy using cognitive network topology and graph theory.

Objective: Normal cognitive function is defined by harmonious interaction among multiple neuropsychological domains. Epilepsy has a disruptive effect on cognition, but how diverse cognitive abilities differentially interact with one another compared with healthy controls (HC) is unclear. This study used graph theory to analyze the community structure of cognitive networks in adults with temporal lobe epilepsy (TLE) compared with that in HC.

Age-Related Changes in BOLD Activation Pattern in Phonemic Fluency Paradigm: An Investigation of Activation, Functional Connectivity and Psychophysiological Interactions.

Healthy aging is associated with decline of cognitive functions. However, even before those declines become noticeable, the neural architecture underlying those mechanisms has undergone considerable restructuring and reorganization. During performance of a cognitive task, not only have the task-relevant networks demonstrated reorganization with aging, which occurs primarily by recruitment of additional areas to preserve performance, but the task-irrelevant network of the "default-mode" network (DMN), which is normally deactivated during task performance, has also consistently shown reduction of this deactivation with aging.

Developmental Reorganization of the Cognitive Network in Pediatric Epilepsy.

Traditional approaches to understanding cognition in children with epilepsy (CWE) involve cross-sectional or prospective examination of diverse test measures, an approach that does not inform the interrelationship between different abilities or how interrelationships evolve prospectively. Here we utilize graph theory techniques to interrogate the development of cognitive landmarks in CWE and healthy controls (HC) using the two-year percentage change across 20 tests. Additionally, we characterize the development of cognition using traditional analyses, showing that CWE perform worse at baseline, develop in parallel with HC, statically maintaining cognitive differences two years later.

Regional homogeneity (ReHo) changes in new onset versus chronic benign epilepsy of childhood with centrotemporal spikes (BECTS): A resting state fMRI study.

Objective: The purpose of this study was to investigate regional homogeneity (ReHo) in children with new-onset drug-naïve Benign Epilepsy with Centrotemporal Spikes (BECTS), chronic BECTS and healthy controls (HC) using the Regional Homogeneity (ReHo) method applied to resting state fMRI data.

Methods: Resting state fMRI data was collected from three groups of children aged 6-13, including new onset drug naïve BECTS, chronic BECTS with medication, and HC; the data analyzed by ReHo method. Mandarin school exams scores were acquired and compared across groups.