Network topology of the cognitive phenotypes of temporal lobe epilepsy

Unveiling the cognitive network organization through cognitive performance

The evaluation of cognitive functions interactions has become increasingly implemented in the cognition exploration. In the present study, we propose to examine the organization of the cognitive network in healthy participants through the analysis of behavioral performances in several cognitive domains. Specifically, we aim to explore cognitive interactions profiles, in terms of cognitive network, and as a function of participants' handedness. To this end, we proposed several behavioral tasks evaluating language, memory, executive functions, and social cognition performances in 175 young healthy right-handed and left-handed participants and we analyzed cognitive scores, from a network perspective, using graph theory. Our results highlight the existence of intricate interactions between cognitive functions both within and beyond the same cognitive domain. Language functions are interrelated with executive functions and memory in healthy cognitive functioning and assume a central role in the cognitive network. Interestingly, for similar high performance, our findings unveiled differential organizations within the cognitive network between right-handed and left-handed participants, with variations observed both at a global and nodal level. This original integrative network approach to the study of cognition provides new insights into cognitive interactions and modulations. It allows a more global understanding and consideration of cognitive functioning, from which complex behaviors emerge.

Insights on cognitive reorganization after hemispherectomy in Rasmussen's encephalitis. A narrative review

Rasmussen's encephalitis is a rare neurological pathology affecting one cerebral hemisphere, therefore, posing unique challenges. Patients may undergo hemispherectomy, a surgical procedure after which cognitive development occurs in the isolated contralateral hemisphere. This rare situation provides an excellent opportunity to evaluate brain plasticity and cognitive recovery at a hemispheric level. This literature review synthesizes the existing body of research on cognitive recovery following hemispherectomy in Rasmussen patients, considering cognitive domains and modulatory factors that influence cognitive outcomes. While language function has traditionally been the focus of postoperative assessments, there is a growing acknowledgment of the need to broaden the scope of language investigation in interaction with other cognitive domains and to consider cognitive scaffolding in development and recovery. By synthesizing findings reported in the literature, we delineate how language functions may find support from the right hemisphere after left hemispherectomy, but also how, beyond language, global cognitive functioning is affected. We highlight the critical influence of several factors on postoperative cognitive outcomes, including the timing of hemispherectomy and the baseline preoperative cognitive status, pointing to early surgical intervention as predictive of better cognitive outcomes. However, further specific studies are needed to confirm this correlation. This review aims to emphasize a better understanding of mechanisms underlying hemispheric specialization and plasticity in humans, which are particularly important for both clinical and research advancements. This narrative review underscores the need for an integrative approach based on cognitive scaffolding to provide a comprehensive understanding of mechanisms underlying the reorganization in Rasmussen patients after hemispherectomy.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

A network approach to subjective cognitive decline: Exploring multivariate relationships in neuropsychological test performance across Alzheimer's disease risk states

Subjective cognitive decline (SCD) is characterized by subjective concerns of cognitive change despite test performance within normal range. Although those with SCD are at higher risk for developing further cognitive decline, we still lack methods using objective cognitive measures that reliably distinguish SCD from cognitively normal aging at the group level. Network analysis may help to address this by modeling cognitive performance as a web of intertwined cognitive abilities, providing insight into the multivariate associations determining cognitive status. Following previous network studies of mild cognitive impairment (MCI) and Alzheimer's dementia (AD), the current study centered upon the novel visualization and analysis of the SCD cognitive network compared to cognitively normal (CN) older adult, MCI, and AD group networks. Cross-sectional neuropsychological data from CIMA-Q and COMPASS-ND cohorts were used to construct Gaussian graphical models for CN (n = 122), SCD (n = 207), MCI (n = 210), and AD (n = 79) groups. Group networks were explored in terms of global network structure, prominent edge weights, and strength centrality indices. CN and SCD group networks were contrasted using the Network Comparison Test. Results indicate that CN and SCD groups did not differ in univariate cognitive performance or global network structure. However, measures of strength centrality, principally in executive functioning and processing speed, showed a CN-SCD-MCI gradient where subtle differences within the SCD network suggest that SCD is an intermediary between CN and MCI stages. Additional results may indicate a distinctiveness of network structure in AD, a reversal in network influence between age and general cognitive status as clinical impairment increases, and potential evidence for cognitive reserve. Together, these results provide evidence that network-specific metrics are sensitive to cognitive performance changes across the dementia risk spectrum and can help to objectively distinguish SCD group cognitive performance from that of the CN group.

The influence of working memory and processing speed on other aspects of cognitive functioning in de novo Parkinson's disease: Initial findings from network modelling and graph theory

Deficits in working memory (WM) and processing speed (PS) are thought to undermine other cognitive functions in de novo Parkinson's disease (dnPD). However, these interrelationships are only partially understood. This study investigated whether there are stronger relationships between verbal WM and verbal episodic memory encoding and retrieval, whether verbal WM and PS have a greater influence on other aspects of cognitive functioning, and whether the overall strength of interrelationships among several cognitive functions differs in dnPD compared to health. Data for 198 healthy controls (HCs) and 293 dnPD patients were analysed. Participants completed a neuropsychological battery probing verbal WM, PS, verbal episodic memory, semantic memory, language and visuospatial functioning. Deficit analysis, network modelling and graph theory were combined to compare the groups. Results suggested that verbal WM performance, while slightly impaired, was more strongly associated with measures of verbal episodic memory encoding and retrieval, as well as other measured cognitive functions in the dnPD network model compared to the HC network model. PS task performance was impaired and more strongly associated with other neuropsychological task scores in the dnPD model. Associations among task scores were stronger overall in the dnPD model. Together, these results provide further evidence that WM and PS are important influences on the other aspects of cognitive functioning measured in this study in dnPD. Moreover, they provide novel evidence that verbal WM and PS might bear greater influence on the other measured cognitive functions and that these functions are more strongly intertwined in dnPD compared to health.

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. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

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. However, the Wasserstein distance does not follow a known distribution, posing challenges for the application of existing parametric statistical models. To tackle this issue, we introduce a unified topological inference framework centered on the Wasserstein distance. Our approach has no explicit model and distributional assumptions. The inference is performed in a completely data driven fashion. We apply this method to resting-state functional magnetic resonance images (rs-fMRI) of temporal lobe epilepsy patients collected from two different sites: the University of Wisconsin-Madison and the Medical College of Wisconsin. Importantly, our topological method is robust to variations due to sex and image acquisition, obviating the need to account for these variables as nuisance covariates. We successfully localize the brain regions that contribute the most to topological differences. A MATLAB package used for all analyses in this study is available at https://github.com/laplcebeltrami/PH-STAT.

Cognitive phenotypes in late-onset epilepsy: results from the atherosclerosis risk in communities study

Introduction

Cognitive phenotyping is a widely used approach to characterize the heterogeneity of deficits in patients with a range of neurological disorders but has only recently been applied to patients with epilepsy. In this study, we identify cognitive phenotypes in older adults with late-onset epilepsy (LOE) and examine their demographic, clinical, and vascular profiles. Further, we examine whether specific phenotypes pose an increased risk for progressive cognitive decline.

Methods

Participants were part of the Atherosclerosis Risk in Communities Study (ARIC), a prospective longitudinal community-based cohort study of 15,792 individuals initially enrolled in 1987-1989. LOE was identified from linked Centers for Medicare and Medicaid Services claims data. Ninety-one participants with LOE completed comprehensive testing either prior to or after seizure onset as part of a larger cohort in the ARIC Neurocognitive Study in either 2011-2013 or 2016-2017 (follow-up mean = 4.9 years). Cognitive phenotypes in individuals with LOE were derived by calculating test-level impairments for each participant (i.e., ≤1 SD below cognitively normal participants on measures of language, memory, and executive function/processing speed); and then assigning participants to phenotypes if they were impaired on at least two tests within a domain. The total number of impaired domains was used to determine the cognitive phenotypes (i.e., Minimal/No Impairment, Single Domain, or Multidomain).

Results

At our baseline (Visit 5), 36.3% met criteria for Minimal/No Impairment, 35% for Single Domain Impairment (with executive functioning/ processing speed impaired in 53.6%), and 28.7% for Multidomain Impairment. The Minimal/No Impairment group had higher education and occupational complexity. There were no differences in clinical or vascular risk factors across phenotypes. Of those participants with longitudinal data (Visit 6; n = 24), 62.5% declined (i.e., progressed to a more impaired phenotype) and 37.5% remained stable. Those who remained stable were more highly educated compared to those that declined.

Discussion

Our results demonstrate the presence of identifiable cognitive phenotypes in older adults with LOE. These results also highlight the high prevalence of cognitive impairments across domains, with deficits in executive function/processing speed the most common isolated impairment. We also demonstrate that higher education was associated with a Minimal/No Impairment phenotype and lower risk for cognitive decline over time.

Establishing the cross-cultural applicability of a harmonized approach to cognitive diagnostics in epilepsy: Initial results of the International Classification of Cognitive Disorders in Epilepsy in a Spanish-speaking sample

OBJECTIVE

This study was undertaken to evaluate the cross-cultural application of the International Classification of Cognitive Disorders in Epilepsy (IC-CoDE) to a cohort of Spanish-speaking patients with temporal lobe epilepsy (TLE) living in the United States.

METHODS

Eighty-four Spanish-speaking patients with TLE completed neuropsychological measures of memory, language, executive function, visuospatial functioning, and attention/processing speed as part of the Neuropsychological Screening Battery for Hispanics. The contribution of demographic and clinical variables to cognitive performance was evaluated. A sensitivity analysis was conducted by examining the base rates of impairment across several impairment thresholds. The IC-CoDE taxonomy was then applied, and the base rate of cognitive phenotypes for each cutoff was calculated. The distribution of phenotypes was compared to the published IC-CoDE taxonomy data, which utilized a large, multicenter cohort of English-speaking patients with TLE.

RESULTS

Across the different impairment cutoffs, memory was the most impaired cognitive domain, with impairments in list learning ranging from 50% to 78%. Application of the IC-CoDE taxonomy utilizing a -1.5-SD cutoff revealed an intact cognitive profile in 47.6% of patients, single-domain impairment in 23.8% of patients, bidomain impairment in 14.3% of patients, and generalized impairment in 14.3% of the sample. This distribution was comparable to the phenotype distribution observed in the IC-CoDE validation sample.

SIGNIFICANCE

We demonstrate a similar pattern and distribution of cognitive phenotypes in a Spanish-speaking epilepsy cohort compared to an English-speaking sample. This suggests stability in the underlying phenotypes associated with TLE and applicability of the IC-CoDE for guiding cognitive diagnostics in epilepsy research that can be applied to culturally and linguistically diverse samples.

Moving towards a taxonomy of cognitive impairments in epilepsy: application of latent profile analysis to 1178 patients with temporal lobe epilepsy

In efforts to understand the cognitive heterogeneity within and across epilepsy syndromes, cognitive phenotyping has been proposed as a new taxonomy aimed at developing a harmonized approach to cognitive classification in epilepsy. Data- and clinically driven approaches have been previously used with variability in the phenotypes derived across studies. In our study, we utilize latent profile analysis to test several models of phenotypes in a large multicentre sample of patients with temporal lobe epilepsy and evaluate their demographic and clinical profiles. For the first time, we examine the added value of replacing missing data and examine factors that may be contributing to missingness. A sample of 1178 participants met the inclusion criteria for the study, which included a diagnosis of temporal lobe epilepsy and the availability of comprehensive neuropsychological data. Models with two to five classes were examined using latent profile analysis and the optimal model was selected based on fit indices, posterior probabilities and proportion of sample sizes. The models were also examined with imputed data to investigate the impact of missing data on model selection. Based on the fit indices, posterior probability and distinctiveness of the latent classes, a three-class solution was the optimal solution. This three-class solution comprised a group of patients with multidomain impairments, a group with impairments predominantly in language and a group with no impairments. Overall, the multidomain group demonstrated a worse clinical profile and comprised a greater proportion of patients with mesial temporal sclerosis, a longer disease duration and a higher number of anti-seizure medications. The four-class and five-class solutions demonstrated the lowest probabilities of a group membership. Analyses with imputed data demonstrated that the four-class solution was the optimal solution; however, there was a weak agreement between the missing and imputed data sets for the four-Class solutions (κ = 0.288, P < 0.001). This study represents the first to use latent profile analysis to test and compare multiple models of cognitive phenotypes in temporal lobe epilepsy and to determine the impact of missing data on model fit. We found that the three-phenotype model was the most meaningful based on several fit indices and produced phenotypes with unique demographic and clinical profiles. Our findings demonstrate that latent profile analysis is a rigorous method to identify phenotypes in large, heterogeneous epilepsy samples. Furthermore, this study highlights the importance of examining the impact of missing data in phenotyping methods. Our latent profile analysis-derived phenotypes can inform future studies aimed at identifying cognitive phenotypes in other neurological disorders.

Global network alterations of the cognitive phenotypes in pediatric temporal lobe epilepsy

OBJECTIVE

An emerging literature suggests that the neuropsychological sequelae of pediatric temporal lobe epilepsy (TLE) are characterized by a continuum of cognitive phenotypes that range in type and severity. The goal of the present investigation was to better characterize the neuropsychological networks that underlie these phenotypes.

METHODS

The study included 59 patients with TLE who were empirically categorized into three cognitive phenotypes (normal, focal, and generalized impairment). Nine neuropsychological measures representing multiple cognitive domains (i.e., reasoning, language, visouperception, memory, and executive function) were examined by graph theory to characterize the global network properties of the cognitive phenotypes.

RESULTS

Across the cognitive phenotype groups (i.e., normal, focal, generalized impaired) the following findings emerged: (1) the adjacency matrices demonstrated different patterns of association between cognitive measures within the neuropsychological network; (2) global measures including global efficiency (GE) and average clustering coefficient (aCC) showed a stepwise increase across the range of impaired pediatric TLE phenotypes; however, modularity (M) demonstrated the opposite pattern.

IMPRESSIONS

Cognitive networks in pediatric TLE demonstrate stepwise perturbation in underlying neuropsychological networks. Graph theory offers a novel approach to examine cognitive abnormalities in pediatric TLE that may be applied to other pediatric epilepsies.

Cognitive phenotypes in frontal lobe epilepsy

OBJECTIVE

Neuropsychological profiles are heterogeneous both across and within epilepsy syndromes, but especially in frontal lobe epilepsy (FLE), which has complex semiology and epileptogenicity. This study aimed to characterize the cognitive heterogeneity within FLE by identifying cognitive phenotypes and determining their demographic and clinical characteristics.

METHOD

One hundred and six patients (age 16-66; 44% female) with FLE completed comprehensive neuropsychological testing, including measures within five cognitive domains: language, attention, executive function, processing speed, and verbal/visual learning. Patients were categorized into one of four phenotypes based on the number of impaired domains. Patterns of domain impairment and clinical and demographic characteristics were examined across phenotypes.

RESULTS

Twenty-five percent of patients met criteria for the Generalized Phenotype (impairment in at least four domains), 20% met criteria for the Tri-Domain Phenotype (impairment in three domains), 36% met criteria for the Domain-Specific Phenotype (impairment in one or two domains), and 19% met criteria for the Intact Phenotype (no impairment). Language was the most common domain-specific impairment, followed by attention, executive function, and processing speed. In contrast, learning was the least impacted cognitive domain. The Generalized Phenotype had fewer years of education compared to the Intact Phenotype, but otherwise, there was no differentiation between phenotypes in demographic and clinical variables. However, qualitative analysis suggested that the Generalized and Tri-Domain Phenotypes had a more widespread area of epileptogenicity, whereas the Intact Phenotype most frequently had seizures limited to the lateral frontal region.

SIGNIFICANCE

This study identified four cognitive phenotypes in FLE that were largely indistinguishable in clinical and demographic features, aside from education and extent of epileptogenic zone. These findings enhance our appreciation of the cognitive heterogeneity within FLE and provide additional support for the development and use of cognitive taxonomies in epilepsy.

Network neuropsychology: The map and the territory

In "network neuropsychology", network modelling and graph theory is applied to the neuropsychological test scores of patients with neurological disorders to investigate cognitive functioning. This review identifies the emerging literature on several disorders before focusing on the assumptions about cognition underlying the studies; specifically, that cognition can be thought of as a network of interrelated variables and that changes in these interrelationships, or cognitive rearrangement, can occur in neurological disorders. Next the review appraises how well network models can provide a "map" of this cognitive "territory". In particular, the review considers the lack of correspondence between the variables and properties of network models and cognitive functioning. The challenges of explicitly accounting for latent cognitive constructs and making inferences about cognition based on associative, as opposed to dissociative, methods are also discussed. It is concluded that the validity of network neuropsychological models is yet to be established and that cognitive theory and experiments, as well as network models, are needed to develop and interpret better maps.