Brainstem Functional Connectivity Disturbances in Epilepsy may Recover After Successful Surgery

Abstract BACKGROUND Focal seizures in temporal lobe epilepsy (TLE) are associated with widespread brain network perturbations and neurocognitive problems. OBJECTIVE To determine whether brainstem connectivity disturbances improve with successful epilepsy surgery, as recent work has demonstrated decreased brainstem connectivity in TLE that is related to disease severity and neurocognitive profile. METHODS We evaluated 15 adult TLE patients before and after (>1 yr; mean, 3.4 yr) surgery, and 15 matched control subjects using magnetic resonance imaging to measure functional and structural connectivity of ascending reticular activating system (ARAS) structures, including cuneiform/subcuneiform nuclei (CSC), pedunculopontine nucleus (PPN), and ventral tegmental area (VTA). RESULTS TLE patients who achieved long-term postoperative seizure freedom (10 of 15) demonstrated increases in functional connectivity between ARAS structures and fronto-parietal-insular neocortex compared to preoperative baseline (P = .01, Kruskal–Wallis), with postoperative connectivity patterns resembling controls’ connectivity. No functional connectivity changes were detected in 5 patients with persistent seizures after surgery (P = .9, Kruskal–Wallis). Among seizure-free postoperative patients, larger increases in CSC, PPN, and VTA functional connectivity were observed in individuals with more frequent seizures before surgery (P < .05 for each, Spearman's rho). Larger postoperative increases in PPN functional connectivity were seen in patients with lower baseline verbal IQ (P = .03, Spearman's rho) or verbal memory (P = .04, Mann–Whitney U). No changes in ARAS structural connectivity were detected after successful surgery. CONCLUSION ARAS functional connectivity disturbances are present in TLE but may recover after successful epilepsy surgery. Larger increases in postoperative connectivity may be seen in individuals with more severe disease at baseline.

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Thalamic Arousal Network Disturbances in Temporal Lobe Epilepsy and Improvement After Surgery

Neurosurgery ◽

10.1093/neuros/nyz310_150 ◽

2019 ◽

Vol 66 (Supplement_1) ◽

Author(s):

Hernán F J González ◽

Srijata Chakravorti ◽

Sarah E Goodale ◽

Kanupriya Gupta ◽

Daniel O Claassen ◽

...

Keyword(s):

Functional Connectivity ◽

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Epilepsy Surgery ◽

Occipital Lobe ◽

Visuospatial Attention ◽

Partial Recovery ◽

Thalamic Nuclei ◽

Spearman's Rho ◽

Spearman’S Rho

Abstract INTRODUCTION The effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery. METHODS We examined 26 adult TLE patients and 26 matched control participants and used resting-state functional magnetic resonance imaging (fMRI) to measure functional connectivity between the thalamus (entire thalamus and 19 bilateral thalamic nuclei) and both neocortex and brainstem ascending reticular activating system (ARAS) nuclei. Postoperative imaging was completed for 19 patients > 1 yr after surgery and compared to preoperative baseline. RESULTS Before surgery, TLE patients demonstrated abnormal thalamo-occipital functional connectivity, losing the normal negative fMRI correlation between the intralaminar central lateral (CL) nucleus and medial occipital lobe seen in controls (P < .001, paired t-test). Patients also had abnormal connectivity between ARAS and CL, lower ipsilateral intrathalamic connectivity, and smaller ipsilateral thalamic volume compared to controls (P < .05 for each, paired t-tests). Abnormal brainstem-thalamic connectivity was associated with impaired visuospatial attention (? = −0.50, P = .02, Spearman's rho), while lower intrathalamic connectivity and volume were related to higher frequency of consciousness-sparing seizures (P < .02, Spearman's rho). After epilepsy surgery, patients with improved seizures showed partial recovery of thalamo-occipital and brainstem-thalamic connectivity, with values more closely resembling controls (P < .01 for each, ANOVA). CONCLUSION Overall, TLE patients demonstrate impaired connectivity in thalamic arousal networks that may be involved in visuospatial attention, but these disturbances may partially recover after successful epilepsy surgery. Thalamic arousal network dysfunction may contribute to morbidity in TLE.

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Thalamic arousal network disturbances in temporal lobe epilepsy and improvement after surgery

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2019-320748 ◽

2019 ◽

Vol 90 (10) ◽

pp. 1109-1116 ◽

Cited By ~ 9

Author(s):

Hernán F J González ◽

Srijata Chakravorti ◽

Sarah E Goodale ◽

Kanupriya Gupta ◽

Daniel O Claassen ◽

...

Keyword(s):

Functional Connectivity ◽

Temporal Lobe Epilepsy ◽

Temporal Lobe ◽

Epilepsy Surgery ◽

Occipital Lobe ◽

Visuospatial Attention ◽

Partial Recovery ◽

Thalamic Nuclei ◽

Spearman's Rho ◽

Spearman’S Rho

ObjectiveThe effects of temporal lobe epilepsy (TLE) on subcortical arousal structures remain incompletely understood. Here, we evaluate thalamic arousal network functional connectivity in TLE and examine changes after epilepsy surgery.MethodsWe examined 26 adult patients with TLE and 26 matched control participants and used resting-state functional MRI (fMRI) to measure functional connectivity between the thalamus (entire thalamus and 19 bilateral thalamic nuclei) and both neocortex and brainstem ascending reticular activating system (ARAS) nuclei. Postoperative imaging was completed for 19 patients >1 year after surgery and compared with preoperative baseline.ResultsBefore surgery, patients with TLE demonstrated abnormal thalamo-occipital functional connectivity, losing the normal negative fMRI correlation between the intralaminar central lateral (CL) nucleus and medial occipital lobe seen in controls (p < 0.001, paired t-test). Patients also had abnormal connectivity between ARAS and CL, lower ipsilateral intrathalamic connectivity, and smaller ipsilateral thalamic volume compared with controls (p < 0.05 for each, paired t-tests). Abnormal brainstem–thalamic connectivity was associated with impaired visuospatial attention (ρ = −0.50, p = 0.02, Spearman’s rho) while lower intrathalamic connectivity and volume were related to higher frequency of consciousness-sparing seizures (p < 0.02, Spearman’s rho). After epilepsy surgery, patients with improved seizures showed partial recovery of thalamo-occipital and brainstem–thalamic connectivity, with values more closely resembling controls (p < 0.01 for each, analysis of variance).ConclusionsOverall, patients with TLE demonstrate impaired connectivity in thalamic arousal networks that may be involved in visuospatial attention, but these disturbances may partially recover after successful epilepsy surgery. Thalamic arousal network dysfunction may contribute to morbidity in TLE.

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Relating structural and functional brainstem connectivity to disease measures in epilepsy

Neurology ◽

10.1212/wnl.0000000000005733 ◽

2018 ◽

Vol 91 (1) ◽

pp. e67-e77 ◽

Cited By ~ 17

Author(s):

Dario J. Englot ◽

Hernan F.J. Gonzalez ◽

Bryson B. Reynolds ◽

Peter E. Konrad ◽

Monica L. Jacobs ◽

...

Keyword(s):

Functional Connectivity ◽

Network Effects ◽

Structural Connectivity ◽

Pedunculopontine Nucleus ◽

Brain Network ◽

Language Abilities ◽

Impaired Performance ◽

Generalized Seizures ◽

Ascending Reticular Activating System ◽

Reticular Activating System

ObjectiveWhile epilepsy studies rarely examine brainstem, we sought to examine the hypothesis that temporal lobe epilepsy (TLE) leads to subcortical arousal center dysfunction, contributing to neocortical connectivity and neurocognitive disturbances.MethodsIn this case-control study of 26 adult patients with TLE and 26 controls, we used MRI to measure structural and functional connectivity of the cuneiform/subcuneiform nuclei (CSC), pedunculopontine nucleus, and ventral tegmental area. Ascending reticular activating system connectivity patterns were related to neuropsychological and disease measures.ResultsCompared to controls, patients with TLE demonstrated reductions in ascending reticular activating system structural and functional connectivity, most prominently to neocortical regions (p < 0.05, unpaired t tests, corrected). While reduced CSC structural connectivity was related to impaired performance IQ and visuospatial memory, diminished CSC functional connectivity was associated with impaired verbal IQ and language abilities (p < 0.05, Spearman ρ, t tests). Finally, CSC structural connectivity decreases were quantitatively associated with consciousness-impairing seizure frequency (p < 0.05, Spearman ρ) and the presence of generalized seizures (p < 0.05, unpaired t test), suggesting a relationship to disease severity.ConclusionsConnectivity perturbations in brainstem arousal centers are present in TLE and may contribute to neurocognitive problems. These studies demonstrate the underappreciated role of brainstem networks in epilepsy and may lead to novel neuromodulation targets to treat or prevent deleterious brain network effects of seizures in TLE.

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Disrupted functional connectivity in PD with probable RBD and its cognitive correlates

Scientific Reports ◽

10.1038/s41598-021-03751-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Javier Oltra ◽

Anna Campabadal ◽

Barbara Segura ◽

Carme Uribe ◽

Maria Jose Marti ◽

...

Keyword(s):

Functional Connectivity ◽

Verbal Memory ◽

Path Length ◽

Behavior Disorder ◽

Brain Network ◽

Characteristic Path Length ◽

Whole Brain ◽

Sleep Behavior ◽

Cognitive Correlates ◽

Theoretical Approaches

AbstractRecent studies associated rapid eye movement sleep behavior disorder (RBD) in Parkinson’s disease (PD) with severe cognitive impairment and brain atrophy. However, whole-brain functional connectivity has never been explored in this group of PD patients. In this study, whole-brain network-based statistics and graph-theoretical approaches were used to characterize resting-state interregional functional connectivity in PD with probable RBD (PD-pRBD) and its relationship with cognition. Our sample consisted of 30 healthy controls, 32 PD without probable RBD (PD-non pRBD), and 27 PD-pRBD. The PD-pRBD group showed reduced functional connectivity compared with controls mainly involving cingulate areas with temporal, frontal, insular, and thalamic regions (p < 0.001). Also, the PD-pRBD group showed reduced functional connectivity between right ventral posterior cingulate and left medial precuneus compared with PD-non pRBD (p < 0.05). We found increased normalized characteristic path length in PD-pRBD compared with PD-non pRBD. In the PD-pRBD group, mean connectivity strength from reduced connections correlated with visuoperceptual task and normalized characteristic path length correlated with processing speed and verbal memory tasks. This work demonstrates the existence of disrupted functional connectivity in PD-pRBD, together with abnormal network integrity, that supports its consideration as a severe PD subtype.

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Post-stroke deficit prediction from lesion and indirect structural and functional disconnection

Brain ◽

10.1093/brain/awaa156 ◽

2020 ◽

Vol 143 (7) ◽

pp. 2173-2188 ◽

Cited By ~ 10

Author(s):

Alessandro Salvalaggio ◽

Michele De Filippo De Grazia ◽

Marco Zorzi ◽

Michel Thiebaut de Schotten ◽

Maurizio Corbetta

Keyword(s):

Functional Connectivity ◽

Functional Mri ◽

Verbal Memory ◽

Structural Connectivity ◽

Indirect Estimation ◽

Post Stroke ◽

Brain Connectome ◽

Left And Right ◽

Direct Measures ◽

Behavioural Deficits

Abstract Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient’s lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22–77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders.

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Dynamic functional connectivity and its anatomical substrate reveal treatment outcome in first-episode drug-naïve schizophrenia

Translational Psychiatry ◽

10.1038/s41398-021-01398-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhe Zhang ◽

Kaiming Zhuo ◽

Qiang Xiang ◽

Yi Sun ◽

John Suckling ◽

...

Keyword(s):

Treatment Outcome ◽

Functional Connectivity ◽

Large Scale ◽

Mental Health Center ◽

Structural Connectivity ◽

Brain Network ◽

First Episode ◽

Antipsychotic Treatment ◽

Dynamic Functional Connectivity ◽

Drug Naïve

AbstractConvergent evidence has suggested a significant effect of antipsychotic exposure on brain structure and function in patients with schizophrenia, yet the characteristics of favorable treatment outcome remains largely unknown. In this work, we aimed to examine how large-scale brain networks are modulated by antipsychotic treatment, and whether the longitudinal changes could track the improvements of psychopathologic scores. Thirty-four patients with first-episode drug-naïve schizophrenia and 28 matched healthy controls were recruited at baseline from Shanghai Mental Health Center. After 8 weeks of antipsychotic treatment, 24 patients were re-scanned. Through a systematical dynamic functional connectivity (dFC) analysis, we investigated the schizophrenia-related intrinsic alterations of dFC at baseline, followed by a longitudinal study to examine the influence of antipsychotic treatment on these abnormalities by comparing patients at baseline and follow-up. A structural connectivity (SC) association analysis was further carried out to investigate longitudinal anatomical changes that underpin the alterations of dFC. We found a significant symptomatic improvement-related increase in the occurrence of a dFC state characterized by stronger inter-network integration. Furthermore, symptom reduction was correlated with increased FC variability in a unique connectomic signature, particularly in the connections within the default mode network and between the auditory, cognitive control, and cerebellar network to other networks. Additionally, we observed that the SC between the superior frontal gyrus and medial prefrontal cortex was decreased after treatment, suggesting a relaxation of normal constraints on dFC. Taken together, these findings provide new evidence to extend the dysconnectivity hypothesis in schizophrenia from static to dynamic brain network. Moreover, our identified neuroimaging markers tied to the neurobiology of schizophrenia could be used as potential indicators in predicting the treatment outcome of antipsychotics.

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Assessing HD-EEG functional connectivity states using a human brain computational model

10.1101/2021.10.14.464481 ◽

2021 ◽

Author(s):

Judie Tabbal ◽

Aya Kabbara ◽

Maxime Yochum ◽

Mohamad Khalil ◽

Mahmoud HASSAN ◽

...

Keyword(s):

Functional Connectivity ◽

Human Brain ◽

Computational Model ◽

Diffusion Tensor ◽

Clustering Algorithms ◽

Structural Connectivity ◽

Brain Network ◽

Systematic Evaluation ◽

Fast Time ◽

Network States

Electro/Magnetoencephalography (EEG/MEG) source-space network analysis is increasingly recognized as a powerful tool to track fast electrophysiological brain dynamics. However, an objective and quantitative evaluation of the various steps, from source localization and functional connectivity to clustering algorithms, is challenging, due to the lack of realistic controlled data. Here, we used a human brain computational model containing both physiologically-based cellular GABAergic and Glutamatergic circuits coupled through Diffusion Tensor Imaging -based structural connectivity, to generate realistic High Density-EEG (256 channels) recordings. We designed a scenario of successive gamma-band oscillations in distinct cortical areas in order to emulate a virtual picture naming task. We identified the fast time-varying network states and quantified the performance of the key steps involved in the pipeline: (1) inverse models to reconstruct cortical-level sources, (2) functional connectivity measures to compute statistical interdependency between regional time series, and (3) dimensionality reduction methods to derive dominant brain network states (BNS). Using a systematic evaluation of the different independent/principal/non-negative decomposition techniques along with a clustering approach, results show significant variability among the tested algorithms in terms of spatial and temporal accuracy. We outlined the spatial precision, the temporal sensitivity, as well as the global accuracy of the extracted BNS relative to each method. Our findings suggest a good performance of wMNE/PLV combination to elucidate the appropriate functional networks and ICA techniques to derive relevant dynamic brain network states. Our aim here is twofold: 1) to provide quantitative assessment on the advantages and the limitations of each of the analyzed techniques and 2) to introduce (and share) a complete framework that can be used to optimize the entire pipeline of EEG/MEG source connectivity. With such framework, other tasks can be generated and used for validation and other methodological points can be also addressed.

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An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data

Frontiers in Integrative Neuroscience ◽

10.3389/fnint.2020.491403 ◽

2021 ◽

Vol 14 ◽

Author(s):

Sarah J. A. Carr ◽

Arthur Gershon ◽

Nassim Shafiabadi ◽

Samden D. Lhatoo ◽

Curtis Tatsuoka ◽

...

Keyword(s):

Functional Connectivity ◽

Hierarchical Clustering ◽

Structural Connectivity ◽

Network Connectivity ◽

Brain Network ◽

Reference Points ◽

Functional Network ◽

Patient Specific ◽

Integrative Approach ◽

Imaging Data

A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.

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Preoperative Epilepsy Network Distribution Correlates With Seizure Recurrence Following Epilepsy Surgery

Neurosurgery ◽

10.1093/neuros/nyz310_690 ◽

2019 ◽

Vol 66 (Supplement_1) ◽

Author(s):

Long Di ◽

Elliot G Neal ◽

Stephanie Maciver ◽

Fernando L Vale

Keyword(s):

Temporal Lobe ◽

Epilepsy Surgery ◽

Surgical Outcome ◽

Demographic Data ◽

Brain Network ◽

Patient Specific ◽

Seizure Freedom ◽

Seizure Recurrence ◽

Postoperative Seizure ◽

Epileptic Networks

Abstract INTRODUCTION Surgery remains an essential option for the treatment of medically intractable temporal lobe epilepsy. However, only 66% of patients achieve postoperative seizure freedom, perhaps attributable to an incomplete understanding of brain network alterations in surgical candidates. Here, we present a novel network modeling algorithm that may be used to identify key characteristics of epileptic networks correlated with improved surgical outcome. METHODS Twenty-nine patients were prospectively included, and relevant demographic information was attained. Resting-state functional magnetic resonance imaging (MRI) and electroencephalography (EEG) data were recorded and preprocessed. Using our novel algorithm, patient-specific epileptic networks were mapped preoperatively and geographic spread was quantified. Global functional connectivity was also determined using a volumetric functional atlas. Key demographic data and features of epileptic networks were then correlated with surgical outcome using Pearson's product-moment correlation. RESULTS At an average follow-up of 19 mo, 20/29 (69%) patients were seizure-free. Higher rates of seizure recurrence correlated with the localization of the epilepsy network to either temporal lobe (R = –0.415, P = .039), with the stronger correlation found with the localization to the contralateral temporal lobe (R = –0.566, P = .003). When the volumetric functional atlas connectivity was measured, increased connectivity globally was correlated with seizure recurrence (R = –0.541, P = .006). Seizure recurrence also correlated with greater atlas-based connectivity within the contralateral hemisphere (R = –0.390, P = .049). CONCLUSION Network localization to the temporal lobes, in particular the contralateral temporal lobe, and increased atlas-defined connectivity contralateral to the surgery side are associated with seizure recurrence. These findings may reflect network-level disruption that has infiltrated the contralateral temporal lobe contributing to relatively worse surgical outcomes. Further identification of network parameters that predict patient outcomes may aid in patient selection, resection planning, and ultimately the efficacy of epilepsy surgery.

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Mapping individual differences across brain network structure to function and behavior with connectome embedding

10.1101/2021.01.13.426513 ◽

2021 ◽

Author(s):

Gidon Levakov ◽

Joshua Faskowitz ◽

Galia Avidan ◽

Olaf Sporns

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Predictive Accuracy ◽

Structural Connectivity ◽

Brain Regions ◽

Brain Network ◽

Global Network ◽

Age Related ◽

And Behavior ◽

The Brain

AbstractThe connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

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