Publications by authors named "Neda Bernasconi"

85 Publications

Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy.

Brain 2021 Mar 17. Epub 2021 Mar 17.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Episodic memory is the ability to accurately remember events from our past. The process of pattern separation is hypothesized to underpin this ability and is defined as the ability to orthogonalize memory traces, to maximize the features that make them unique. Contemporary cognitive neuroscience suggests that pattern separation entails complex interactions between the hippocampus and the neocortex, where specific hippocampal subregions shape neural reinstatement in the neocortex. To test this hypothesis, the current work studied both healthy controls and patients with temporal lobe epilepsy (TLE) who present with hippocampal structural anomalies. In all participants, we measured neural activity using functional magnetic resonance imaging (fMRI) while they retrieved memorized items compared to lure items which share features with the target. Behaviorally, TLE patients were less able to exclude lures than controls, and showed a reduction in pattern separation. To assess the hypothesized relationship between neural patterns in the hippocampus and the neocortex, we identified topographic gradients of intrinsic connectivity along neocortical and hippocampal subfield surfaces and identified the topographic profile of the neural activity accompanying pattern separation. In healthy controls, pattern separation followed a graded pattern of neural activity, both along the hippocampal long axis (and peaked in anterior segments that are more heavily engaged in transmodal processing) and along the neocortical hierarchy running from unimodal to transmodal regions (peaking in transmodal default mode regions). In TLE patients, however, this concordance between task-based functional activations and topographic gradients was markedly reduced. Furthermore, person specific measures of concordance between task-related activity and connectivity gradients in patients and controls related to inter-individual differences in behavioral measures of pattern separation and episodic memory, highlighting the functional relevance of the observed topographic motifs. Our work is consistent with an emerging understanding that successful discrimination between memories with similar features entails a shift in the locus of neural activity away from sensory systems, a pattern that is mirrored along the hippocampal long axis and with respect to neocortical hierarchies. More broadly, our study establishes topographic profiling using intrinsic connectivity gradients captures the functional underpinnings of episodic memory processes in manner that is sensitive to their reorganization in pathology.
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http://dx.doi.org/10.1093/brain/awab121DOI Listing
March 2021

Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy.

Epilepsia 2021 Apr 11;62(4):1022-1033. Epub 2021 Mar 11.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.

Objective: Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition.

Methods: We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters.

Results: We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities.

Significance: Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.
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http://dx.doi.org/10.1111/epi.16864DOI Listing
April 2021

A Structure-Function Substrate of Memory for Spatial Configurations in Medial and Lateral Temporal Cortices.

Cereb Cortex 2021 Feb 27. Epub 2021 Feb 27.

McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec H3A 2B4, Canada.

Prior research has shown a role of the medial temporal lobe, particularly the hippocampal-parahippocampal complex, in spatial cognition. Here, we developed a new paradigm, the conformational shift spatial task (CSST), which examines the ability to encode and retrieve spatial relations between unrelated items. This task is short, uses symbolic cues, incorporates two difficulty levels, and can be administered inside the scanner. A cohort of 48 healthy young adults underwent the CSST, together with a set of behavioral measures and multimodal magnetic resonance imaging (MRI). Inter-individual differences in CSST performance correlated with scores on an established spatial memory paradigm, but neither with episodic memory nor mnemonic discrimination, supporting specificity. Analyzing high-resolution structural MRI data, individuals with better spatial memory showed thicker medial and lateral temporal cortices. Functional relevance of these findings was supported by task-based functional MRI analysis in the same participants and ad hoc meta-analysis. Exploratory resting-state functional MRI analyses centered on clusters of morphological effects revealed additional modulation of intrinsic network integration, particularly between lateral and medial temporal structures. Our work presents a novel spatial memory paradigm and supports an integrated structure-function substrate in the human temporal lobe. Task paradigms are programmed in python and made open access.
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http://dx.doi.org/10.1093/cercor/bhab001DOI Listing
February 2021

Emerging Trends in Neuroimaging of Epilepsy.

Epilepsy Curr 2021 Mar 9;21(2):79-82. Epub 2021 Feb 9.

Epilepsy Center, Neurological Institute, 2569Cleveland Clinic, Cleveland, OH, USA.

Neuroimaging techniques, particularly magnetic resonance imaging, yield increasingly sophisticated markers of brain structure and function. Combined with ongoing developments in machine learning, these methods refine our abilities to detect subtle epileptogenic lesions and develop reliable prognostics.
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http://dx.doi.org/10.1177/1535759721991161DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010873PMC
March 2021

7T Epilepsy Task Force Consensus Recommendations on the Use of 7T MRI in Clinical Practice.

Neurology 2021 02 22;96(7):327-341. Epub 2020 Dec 22.

From the Neurobiology Research Unit (G.O., L.H.P.), and Epilepsy Clinic (L.H.P.), Department of Neurology, Rigshospitalet Copenhagen University Hospital; Faculty of Health and Medical Sciences (G.O.), UCPH, Denmark; Departments of Neurology and Neurosurgery (T.J.V.), UMC Utrecht Brain Center, and Department of Radiology (A.v.d.K.), University Medical Center Utrecht, Utrecht University; Department of Radiology (A.v.d.K.), Netherlands Cancer Institute Antoni van Leeuwenhoek Hospital, Amsterdam; Lund University Bioimaging Center (K.M.B.), Lund University, Sweden; Department of Neurology (A.J.C.), Neurophysiology and Neurosurgery, ACE Kempenhaeghe/MUMC, Heeze/Maastricht, the Netherlands; Department of Radiology (J.M.S.) and Penn Epilepsy Center (K.D.), Hospital of the University of Pennsylvania, Philadelphia; Department of Neurology (T.R.H.) and Center for Magnetic Resonance Research (P.-F.V.d.M., R.E.M.), University of Minnesota, Minneapolis; Department of Radiology and Nuclear Medicine (J.F.A.J.), Maastricht University Medical Center; School for Mental Health and Neuroscience (J.F.A.J.), Maastricht University; Department of Electrical Engineering (J.F.A.J.), Eindhoven University of Technology, the Netherlands; Imaging Institute (S.E.J.) and Epilepsy Center (I.W.), Cleveland Clinic, OH; Department of Neurology and Radiology (J.W.P.), University of Pittsburg, PA; Department of Neurosurgery (K.R.), Medical University of Vienna, Austria; Departments of Neurology and Clinical Sciences (M.C.S.), Lund University Hospital, Sweden; Department of Biomedical Imaging and Image Guided Therapy (S.T.), High Field MR Center, Medical University of Vienna, Austria; Neuroradiology Division, Diagnostic Unit (M.I.V.), University Hospitals and Faculty of Medicine of Geneva, Switzerland; Epileptology Department - INS (F.B.) and CRMBM - CEMEREM (J.-P.R., M.G.), Timone Hospital APHM, Aix Marseille Univ, INSERM, CNRS, France; Neuroimaging of Epilepsy Laboratory (NOEL) (N.B., A.B.), Montreal Neurological Institute (B.B.), and McConnell Brain Imaging Centre (N.B., A.B.), McGill University, Montreal, Canada; Department of Radiology (I.B.-B.), Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sweden; Department of Translational Research and New Technologies in Medicine and Surgery (M.C.), University of Pisa, Italy; Department of Neurology (S.R.D.), University of Pennsylvania, Philadelphia; NeuroSpin (L.H.-P., A.V.), Paris-Saclay University, CEA, CNRS, BAOBAB, Gif-sur-Yvette, France; UMR 1141 (L.H.-P), University of Paris, France; EEG Section (S.I.), NINDS, NIH, Bethesda, MD; Department of Medical Imaging (M.T.J.), Children's Hospital at London Health Sciences Centre; Department of Medical Biophysics (M.T.J., A.R.K.), Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada; Imaging Research Laboratories (A.R.K.), Robarts Research Institute, London, ON, Canada; Functional Neurosurgery Department (S.L.), Beijing Children's Hospital of Capital Medical University, Beijing, China; Department of Radiology (S.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; NYU Grossman School of Medicine (H.P.), New York; Harvard MIT Division of Health Sciences and Technology (J.R.P., S.S.), Massachusetts Institute of Technology, Cambridge; Athinoula A. Martinos Center for Biomedical Imaging (J.R.P., S.S.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA; Scannexus Ultrahigh Field MRI Research Center (E.S., C.J.W.), Maastricht; Department of Radiology and Nuclear Medicine (T.J.V.), Meander Medical Center, Amersfoort, the Netherlands; Wellcome Centre for Integrative Neuroimaging (N.V.), FMRIB Division, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom; EEG and Epilepsy Unit (S.V.), Neurology, Department of Clinical Neurosciences, University Hospitals and Faculty of Medicine of Geneva, Switzerland; State Key Lab of Brain and Cognitive Science (R.X.), Beijing MRI Center for Brain Research, Institute of Biophysics, Chinese Academy of Sciences, China; Neuroscience Department (R.G.), Children's Hospital A. Meyer-University of Florence; and IMAGO 7 Foundation (R.G.), Florence, Italy.

Identifying a structural brain lesion on MRI has important implications in epilepsy and is the most important factor that correlates with seizure freedom after surgery in patients with drug-resistant focal onset epilepsy. However, at conventional magnetic field strengths (1.5 and 3T), only approximately 60%-85% of MRI examinations reveal such lesions. Over the last decade, studies have demonstrated the added value of 7T MRI in patients with and without known epileptogenic lesions from 1.5 and/or 3T. However, translation of 7T MRI to clinical practice is still challenging, particularly in centers new to 7T, and there is a need for practical recommendations on targeted use of 7T MRI in the clinical management of patients with epilepsy. The 7T Epilepsy Task Force-an international group representing 21 7T MRI centers with experience from scanning over 2,000 patients with epilepsy-would hereby like to share its experience with the neurology community regarding the appropriate clinical indications, patient selection and preparation, acquisition protocols and setup, technical challenges, and radiologic guidelines for 7T MRI in patients with epilepsy. This article mainly addresses structural imaging; in addition, it presents multiple nonstructural MRI techniques that benefit from 7T and hold promise as future directions in epilepsy. Answering to the increased availability of 7T MRI as an approved tool for diagnostic purposes, this article aims to provide guidance on clinical 7T MRI epilepsy management by giving recommendations on referral, suitable 7T MRI protocols, and image interpretation.
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http://dx.doi.org/10.1212/WNL.0000000000011413DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8055334PMC
February 2021

Connectome biomarkers of drug-resistant epilepsy.

Epilepsia 2021 01 25;62(1):6-24. Epub 2020 Nov 25.

Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Drug-resistant epilepsy (DRE) considerably affects patient health, cognition, and well-being, and disproportionally contributes to the overall burden of epilepsy. The most common DRE syndromes are temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal epilepsy related to cortical malformations. Both syndromes have been traditionally considered as "focal," and most patients benefit from brain surgery for long-term seizure control. However, increasing evidence indicates that many DRE patients also present with widespread structural and functional network disruptions. These anomalies have been suggested to relate to cognitive impairment and prognosis, highlighting their importance for patient management. The advent of multimodal neuroimaging and formal methods to quantify complex systems has offered unprecedented ability to profile structural and functional brain networks in DRE patients. Here, we performed a systematic review on existing DRE network biomarker candidates and their contribution to three key application areas: (1) modeling of cognitive impairments, (2) localization of the surgical target, and (3) prediction of clinical and cognitive outcomes after surgery. Although network biomarkers hold promise for a range of clinical applications, translation of neuroimaging biomarkers to the patient's bedside has been challenged by a lack of clinical and prospective studies. We therefore close by highlighting conceptual and methodological strategies to improve the evaluation and accessibility of network biomarkers, and ultimately guide clinically actionable decisions.
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http://dx.doi.org/10.1111/epi.16753DOI Listing
January 2021

Network-based atrophy modeling in the common epilepsies: A worldwide ENIGMA study.

Sci Adv 2020 Nov 18;6(47). Epub 2020 Nov 18.

Florey Institute of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3010, Australia.

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1021 adults with epilepsy compared to 1564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy colocalized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Negative effects of age on atrophy further revealed a strong influence of connectome architecture in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided deeper insights into the macroscale features that shape the pathophysiology of common epilepsies.
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http://dx.doi.org/10.1126/sciadv.abc6457DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673818PMC
November 2020

Convergence of cortical types and functional motifs in the human mesiotemporal lobe.

Elife 2020 11 4;9. Epub 2020 Nov 4.

Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

The mesiotemporal lobe (MTL) is implicated in many cognitive processes, is compromised in numerous brain disorders, and exhibits a gradual cytoarchitectural transition from six-layered parahippocampal isocortex to three-layered hippocampal allocortex. Leveraging an ultra-high-resolution histological reconstruction of a human brain, our study showed that the dominant axis of MTL cytoarchitectural differentiation follows the iso-to-allocortical transition and depth-specific variations in neuronal density. Projecting the histology-derived MTL model to in-vivo functional MRI, we furthermore determined how its cytoarchitecture underpins its intrinsic effective connectivity and association to large-scale networks. Here, the cytoarchitectural gradient was found to underpin intrinsic effective connectivity of the MTL, but patterns differed along the anterior-posterior axis. Moreover, while the iso-to-allocortical gradient parametrically represented the multiple-demand relative to task-negative networks, anterior-posterior gradients represented transmodal versus unimodal networks. Our findings establish that the combination of micro- and macrostructural features allow the MTL to represent dominant motifs of whole-brain functional organisation.
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http://dx.doi.org/10.7554/eLife.60673DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671688PMC
November 2020

Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale.

Neuroimage Clin 2020 18;28:102438. Epub 2020 Sep 18.

Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada. Electronic address:

Objective: Focal cortical dysplasia (FCD) is the most common epileptogenic developmental malformation and a prevalent cause of surgically amenable epilepsy. While cellular and molecular biology data suggest that FCD lesional characteristics lie along a spectrum, this notion remains to be verified in vivo. We tested the hypothesis that machine learning applied to MRI captures FCD lesional variability at a mesoscopic scale.

Methods: We studied 46 patients with histologically verified FCD Type II and 35 age- and sex-matched healthy controls. We applied consensus clustering, an unsupervised learning technique that identifies stable clusters based on bootstrap-aggregation, to 3 T multicontrast MRI (T1-weighted MRI and FLAIR) features of FCD normalized with respect to distributions in controls.

Results: Lesions were parcellated into four classes with distinct structural profiles variably expressed within and across patients: Class-1 with isolated white matter (WM) damage; Class-2 combining grey matter (GM) and WM alterations; Class-3 with isolated GM damage; Class-4 with GM-WM interface anomalies. Class membership was replicated in two independent datasets. Classes with GM anomalies impacted local function (resting-state fMRI derived ALFF), while those with abnormal WM affected large-scale connectivity (assessed by degree centrality). Overall, MRI classes reflected typical histopathological FCD characteristics: Class-1 was associated with severe WM gliosis and interface blurring, Class-2 with severe GM dyslamination and moderate WM gliosis, Class-3 with moderate GM gliosis, Class-4 with mild interface blurring. A detection algorithm trained on class-informed data outperformed a class-naïve paradigm.

Significance: Machine learning applied to widely available MRI contrasts uncovers FCD Type II variability at a mesoscopic scale and identifies tissue classes with distinct structural dimensions, functional and histopathological profiles. Integrating in vivo staging of FCD traits with automated lesion detection is likely to inform the development of novel personalized treatments.
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http://dx.doi.org/10.1016/j.nicl.2020.102438DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520429PMC
September 2020

Whole-brain multimodal MRI phenotyping of periventricular nodular heterotopia.

Neurology 2020 10 14;95(17):e2418-e2426. Epub 2020 Aug 14.

From the Neuroimaging of Epilepsy Laboratory (F.D., S.-J.H., F.F., B.C., S.K., N.B., A.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; and Epilepsy Unit (F.D.), Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.

Objective: To test the hypothesis that in periventricular nodular heterotopia (PVNH) structure and function of cortical areas overlying the heterotopic gray matter are preferentially affected.

Methods: We studied a group of 40 patients with PVNH and normal-appearing cortex and compared their quantitative MRI markers of brain development, structure, and function to those of 43 age- and sex-matched healthy controls. Inspired by models of neocortical development suggesting that neuronal migration follows a curvilinear path to preserve topologic correspondence between the outer ventricular zone and the cortical surface, we computationally defined the overlying cortex using the Laplace equation and generated synthetic streamlines that link the ventricles, where nodules are located, and the neocortex.

Results: We found multilobar cortical thickening encompassing prefrontal, latero-basal temporal, and temporoparietal cortices largely corresponding with the PVNH group-averaged map of the overlying cortex, the latter colocalized with areas of abnormal function, as defined by resting-state fMRI. Patients also presented hippocampal functional hyperconnectivity and malrotation, the latter positively correlating with neocortical maldevelopment indexed by increased folding complexity of the parahippocampus. In clusters of thickness and curvature findings, there were no significant differences between unilateral and bilateral PVNH; contrasting brain-wide metrics between cohorts was also unrevealing. There was no relationship between imaging markers and disease duration except for positive correlation with functional anomalies.

Conclusion: Our quantitative image analysis demonstrates widespread structural and functional alterations in PVNH with differential interaction with the overlying cortex and the hippocampus. Right hemispheric predominance may be explained by an early insult, likely genetically determined, on brain morphogenesis.
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http://dx.doi.org/10.1212/WNL.0000000000010648DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682914PMC
October 2020

White matter abnormalities across different epilepsy syndromes in adults: an ENIGMA-Epilepsy study.

Brain 2020 08;143(8):2454-2473

Department of Neurology, Medical University of South Carolina, Charleston 29425 SC, USA.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P < 0.001). Across 'all epilepsies' lower fractional anisotropy was observed in most fibre tracts with small to medium effect sizes, especially in the corpus callosum, cingulum and external capsule. There were also less robust increases in mean diffusivity. Syndrome-specific fractional anisotropy and mean diffusivity differences were most pronounced in patients with hippocampal sclerosis in the ipsilateral parahippocampal cingulum and external capsule, with smaller effects across most other tracts. Individuals with temporal lobe epilepsy and normal MRI showed a similar pattern of greater ipsilateral than contralateral abnormalities, but less marked than those in patients with hippocampal sclerosis. Patients with generalized and extratemporal epilepsies had pronounced reductions in fractional anisotropy in the corpus callosum, corona radiata and external capsule, and increased mean diffusivity of the anterior corona radiata. Earlier age of seizure onset and longer disease duration were associated with a greater extent of diffusion abnormalities in patients with hippocampal sclerosis. We demonstrate microstructural abnormalities across major association, commissural, and projection fibres in a large multicentre study of epilepsy. Overall, patients with epilepsy showed white matter abnormalities in the corpus callosum, cingulum and external capsule, with differing severity across epilepsy syndromes. These data further define the spectrum of white matter abnormalities in common epilepsy syndromes, yielding more detailed insights into pathological substrates that may explain cognitive and psychiatric co-morbidities and be used to guide biomarker studies of treatment outcomes and/or genetic research.
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http://dx.doi.org/10.1093/brain/awaa200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7567169PMC
August 2020

Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.

Epilepsia 2020 09 7;61(9):1869-1883. Epub 2020 Aug 7.

Department of Clinical Neurosciences, University of Calgary, Calgary, Canada.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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http://dx.doi.org/10.1111/epi.16633DOI Listing
September 2020

Functional Networks in Epilepsy Presurgical Evaluation.

Neurosurg Clin N Am 2020 Jul 23;31(3):395-405. Epub 2020 Apr 23.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 Rue Université, Montreal, Quebec H3A 2B4, Canada. Electronic address:

Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.
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http://dx.doi.org/10.1016/j.nec.2020.03.004DOI Listing
July 2020

The ENIGMA-Epilepsy working group: Mapping disease from large data sets.

Hum Brain Mapp 2020 May 29. Epub 2020 May 29.

Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, Mexico.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.
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http://dx.doi.org/10.1002/hbm.25037DOI Listing
May 2020

Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome.

Epilepsia 2020 06 26;61(6):1221-1233. Epub 2020 May 26.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Objective: Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood.

Methods: We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques.

Results: Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1 year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76 ± 4% accuracy, outperforming classifiers operating on clinical and structural imaging features.

Significance: Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.
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http://dx.doi.org/10.1111/epi.16540DOI Listing
June 2020

Macroscale and microcircuit dissociation of focal and generalized human epilepsies.

Commun Biol 2020 05 18;3(1):244. Epub 2020 May 18.

McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, H3A2B4, Canada.

Thalamo-cortical pathology plays key roles in both generalized and focal epilepsies, but there is little work directly comparing these syndromes at the level of whole-brain mechanisms. Using multimodal imaging, connectomics, and computational simulations, we examined thalamo-cortical and cortico-cortical signatures and underlying microcircuits in 96 genetic generalized (GE) and 107 temporal lobe epilepsy (TLE) patients, along with 65 healthy controls. Structural and functional network profiling highlighted extensive atrophy, microstructural disruptions and decreased thalamo-cortical connectivity in TLE, while GE showed only subtle structural anomalies paralleled by enhanced thalamo-cortical connectivity. Connectome-informed biophysical simulations indicated modest increases in subcortical drive contributing to cortical dynamics in GE, while TLE presented with reduced subcortical drive and imbalanced excitation-inhibition within limbic and somatomotor microcircuits. Multiple sensitivity analyses supported robustness. Our multiscale analyses differentiate human focal and generalized epilepsy at the systems-level, showing paradoxically more severe microcircuit and macroscale imbalances in the former.
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http://dx.doi.org/10.1038/s42003-020-0958-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7234993PMC
May 2020

Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density.

Neuroimage Clin 2020 28;26:102231. Epub 2020 Feb 28.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, UK.

Purpose: Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics.

Methods: 20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured.

Results: In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF.

Conclusions: Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.
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http://dx.doi.org/10.1016/j.nicl.2020.102231DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063236PMC
February 2021

Response to commentary on recommendations for the use of structural MRI in the care of patients with epilepsy: A consensus report from the ILAE Neuroimaging Task Force.

Epilepsia 2019 10 29;60(10):2143-2144. Epub 2019 Aug 29.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

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http://dx.doi.org/10.1111/epi.16324DOI Listing
October 2019

Developmental MRI markers cosegregate juvenile patients with myoclonic epilepsy and their healthy siblings.

Neurology 2019 09 29;93(13):e1272-e1280. Epub 2019 Aug 29.

From the Neuroimaging of Epilepsy Laboratory (B.W., S.-J.H., B.C.B., F.F., N.B., A.B.), McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal; Department of Clinical and Experimental Epilepsy (B.W., C.V., M.J.K.), UCL Institute of Neurology, London, UK; Epilepsy Center, Department of Neurology (C.V.), Klinikum Großhadern, University of Munich, Germany; and Multimodal Imaging and Connectome Analysis Lab (B.C.B.), Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Objective: MRI studies of genetic generalized epilepsies have mainly described group-level changes between patients and healthy controls. To determine the endophenotypic potential of structural MRI in juvenile myoclonic epilepsy (JME), we examined MRI-based cortical morphologic markers in patients and their healthy siblings.

Methods: In this prospective, cross-sectional study, we obtained 3T MRI in patients with JME, siblings, and controls. We mapped sulco-gyral complexity and surface area, morphologic markers of brain development, and cortical thickness. Furthermore, we calculated mean geodesic distance, a surrogate marker of cortico-cortical connectivity.

Results: Compared to controls, patients and siblings showed increased folding complexity and surface area in prefrontal and cingulate cortices. In these regions, they also displayed abnormally increased geodesic distance, suggesting network isolation and decreased efficiency, with strongest effects for limbic, fronto-parietal, and dorsal-attention networks. In areas of findings overlap, we observed strong patient-sibling correlations. Conversely, neocortical thinning was present in patients only and related to disease duration. Patients showed subtle impairment in mental flexibility, a frontal lobe function test, as well as deficits in naming and design learning. Siblings' performance fell between patients and controls.

Conclusion: MRI markers of brain development and connectivity are likely heritable and may thus serve as endophenotypes. The topography of morphologic anomalies and their abnormal structural network integration likely explains cognitive impairments in patients with JME and their siblings. By contrast, cortical atrophy likely represents a marker of disease.
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http://dx.doi.org/10.1212/WNL.0000000000008173DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7011863PMC
September 2019

Targeting age-related differences in brain and cognition with multimodal imaging and connectome topography profiling.

Hum Brain Mapp 2019 12 24;40(18):5213-5230. Epub 2019 Aug 24.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Aging is characterized by accumulation of structural and metabolic changes in the brain. Recent studies suggest transmodal brain networks are especially sensitive to aging, which, we hypothesize, may be due to their apical position in the cortical hierarchy. Studying an open-access healthy cohort (n = 102, age range = 30-89 years) with MRI and Aβ PET data, we estimated age-related cortical thinning, hippocampal atrophy and Aβ deposition. In addition to carrying out surface-based morphological and metabolic mapping experiments, we stratified effects along neocortical and hippocampal resting-state functional connectome gradients derived from independent datasets. The cortical gradient depicts an axis of functional differentiation from sensory-motor regions to transmodal regions, whereas the hippocampal gradient recapitulates its long-axis. While age-related thinning and increased Aβ deposition occurred across the entire cortical topography, increased Aβ deposition was especially pronounced toward higher-order transmodal regions. Age-related atrophy was greater toward the posterior end of the hippocampal long-axis. No significant effect of age on Aβ deposition in the hippocampus was observed. Imaging markers correlated with behavioral measures of fluid intelligence and episodic memory in a topography-specific manner, confirmed using both univariate as well as multivariate analyses. Our results strengthen existing evidence of structural and metabolic change in the aging brain and support the use of connectivity gradients as a compact framework to analyze and conceptualize brain-based biomarkers of aging.
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http://dx.doi.org/10.1002/hbm.24767DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864903PMC
December 2019

Community-informed connectomics of the thalamocortical system in generalized epilepsy.

Neurology 2019 09 12;93(11):e1112-e1122. Epub 2019 Aug 12.

From the Departments of Radiology (Z.W., B.Z., B.Z.) and Neurology (Z.W., Y.X.), Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, China; Multimodal Imaging and Connectome Analysis Laboratory (Z.W., S.L., R.V.d.W., S.-J.H., B.C.B.) and Neuroimaging of Epilepsy Laboratory (S.-J.H., N.B., A.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Quebec, Canada; and Department of Medical Imaging (Q.X., Z.Z.), Jinling Hospital, Nanjing University School of Medicine, China.

Objective: To study the intrinsic organization of the thalamocortical circuitry in patients with generalized epilepsy with tonic-clonic seizures (GTCS) via resting-state fMRI (rs-fMRI) connectome analysis and to evaluate its relation to drug response.

Methods: In a prospectively followed-up sample of 41 patients and 27 healthy controls, we obtained rs-fMRI and structural MRI. After 1 year of follow-up, 27 patients were classified as seizure-free and 14 as drug-resistant. We examined connectivity within and between resting-state communities in cortical and thalamic subregions. In addition to comparing patients to controls, we examined associations with seizure control. We assessed reproducibility in an independent cohort of 21 patients.

Results: Compared to controls, patients showed a more constrained network embedding of the thalamus, while frontocentral neocortical regions expressed increased functional diversity. Findings remained significant after regressing out thalamic volume and cortical thickness, suggesting independence from structural alterations. We observed more marked network imbalances in drug-resistant compared to seizure-free patients. Findings were similar in the reproducibility dataset.

Conclusions: Our findings suggest a pathoconnectomic mechanism of generalized epilepsy centered on diverging changes in cortical and thalamic connectivity. More restricted thalamic connectivity could reflect the tendency to engage in recursive thalamocortical loops, which may contribute to hyperexcitability. Conversely, increased connectional diversity of frontocentral networks may relay abnormal activity to an extended bilateral territory. Network imbalances were observed shortly after diagnosis and related to future drug response, suggesting clinical utility.
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http://dx.doi.org/10.1212/WNL.0000000000008096DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746209PMC
September 2019

WONOEP appraisal: Network concept from an imaging perspective.

Epilepsia 2019 07 9;60(7):1293-1305. Epub 2019 Jun 9.

Neuroimaging of Epilepsy Laboratory, Department of Neurosciences and McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Neuroimaging techniques applied to a variety of organisms-from zebrafish, to rodents to humans-can offer valuable insights into neuronal network properties and their dysfunction in epilepsy. A wide range of imaging methods used to monitor neuronal circuits and networks during evoked seizures in animal models and advances in functional magnetic resonance imaging (fMRI) applied to patients with epilepsy were discussed during the XIV Workshop on Neurobiology of Epilepsy (XIV WONOEP) organized in 2017 by the Neurobiology Commission of the International League Against Epilepsy (ILAE). We review the growing number of technological approaches developed, as well as the current state of knowledge gained from studies applying these advanced imaging approaches to epilepsy research.
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http://dx.doi.org/10.1111/epi.16067DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6667743PMC
July 2019

Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the International League Against Epilepsy Neuroimaging Task Force.

Epilepsia 2019 06 28;60(6):1054-1068. Epub 2019 May 28.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Structural magnetic resonance imaging (MRI) is of fundamental importance to the diagnosis and treatment of epilepsy, particularly when surgery is being considered. Despite previous recommendations and guidelines, practices for the use of MRI are variable worldwide and may not harness the full potential of recent technological advances for the benefit of people with epilepsy. The International League Against Epilepsy Diagnostic Methods Commission has thus charged the 2013-2017 Neuroimaging Task Force to develop a set of recommendations addressing the following questions: (1) Who should have an MRI? (2) What are the minimum requirements for an MRI epilepsy protocol? (3) How should magnetic resonance (MR) images be evaluated? (4) How to optimize lesion detection? These recommendations target clinicians in established epilepsy centers and neurologists in general/district hospitals. They endorse routine structural imaging in new onset generalized and focal epilepsy alike and describe the range of situations when detailed assessment is indicated. The Neuroimaging Task Force identified a set of sequences, with three-dimensional acquisitions at its core, the harmonized neuroimaging of epilepsy structural sequences-HARNESS-MRI protocol. As these sequences are available on most MR scanners, the HARNESS-MRI protocol is generalizable, regardless of the clinical setting and country. The Neuroimaging Task Force also endorses the use of computer-aided image postprocessing methods to provide an objective account of an individual's brain anatomy and pathology. By discussing the breadth and depth of scope of MRI, this report emphasizes the unique role of this noninvasive investigation in the care of people with epilepsy.
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http://dx.doi.org/10.1111/epi.15612DOI Listing
June 2019

Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability.

Neurology 2019 05 19;92(19):e2209-e2220. Epub 2019 Apr 19.

From the Neuroimaging of Epilepsy Laboratory (B.C.B., F.F., M.L., B.C., A.B., N.B.) and Multimodal Imaging and Connectome Analysis Laboratory (B.C.B.), McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Canada; Department of Bioengineering and Electrical and Systems Engineering (S.G., D.S.B.), University of Pennsylvania, Philadelphia; and York Neuroimaging Center (E.J., J.S.), University of York, UK.

Objective: To assess whether hippocampal sclerosis (HS) severity is mirrored at the level of large-scale networks.

Methods: We studied preoperative high-resolution anatomical and diffusion-weighted MRI of 44 temporal lobe epilepsy (TLE) patients with histopathologic diagnosis of HS (n = 25; TLE-HS) and isolated gliosis (n = 19; TLE-G) and 25 healthy controls. Hippocampal measurements included surface-based subfield mapping of atrophy and T2 hyperintensity indexing cell loss and gliosis, respectively. Whole-brain connectomes were generated via diffusion tractography and examined using graph theory along with a novel network control theory paradigm that simulates functional dynamics from structural network data.

Results: Compared to controls, we observed markedly increased path length and decreased clustering in TLE-HS compared to controls, indicating lower global and local network efficiency, while TLE-G showed only subtle alterations. Similarly, network controllability was lower in TLE-HS only, suggesting limited range of functional dynamics. Hippocampal imaging markers were positively associated with macroscale network alterations, particularly in ipsilateral CA1-3. Systematic assessment across several networks revealed maximal changes in the hippocampal circuity. Findings were consistent when correcting for cortical thickness, suggesting independence from gray matter atrophy.

Conclusions: Severe HS is associated with marked remodeling of connectome topology and structurally governed functional dynamics in TLE, as opposed to isolated gliosis, which has negligible effects. Cell loss, particularly in CA1-3, may exert a cascading effect on brain-wide connectomes, underlining coupled disease processes across multiple scales.
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http://dx.doi.org/10.1212/WNL.0000000000007447DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6537128PMC
May 2019

Neuroimaging and connectomics of drug-resistant epilepsy at multiple scales: From focal lesions to macroscale networks.

Epilepsia 2019 04 19;60(4):593-604. Epub 2019 Mar 19.

Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.

Epilepsy is among the most common chronic neurologic disorders, with 30%-40% of patients having seizures despite antiepileptic drug treatment. The advent of brain imaging and network analyses has greatly improved the understanding of this condition. In particular, developments in magnetic resonance imaging (MRI) have provided measures for the noninvasive characterization and detection of lesions causing epilepsy. MRI techniques can probe structural and functional connectivity, and network analyses have shaped our understanding of whole-brain anomalies associated with focal epilepsies. This review considers the progress made by neuroimaging and connectomics in the study of drug-resistant epilepsies due to focal substrates, particularly temporal lobe epilepsy related to mesiotemporal sclerosis and extratemporal lobe epilepsies associated with malformations of cortical development. In these disorders, there is evidence of widespread disturbances of structural and functional connectivity that may contribute to the clinical and cognitive prognosis of individual patients. It is hoped that studying the interplay between macroscale network anomalies and lesional profiles will improve our understanding of focal epilepsies and assist treatment choices.
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http://dx.doi.org/10.1111/epi.14688DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447443PMC
April 2019

A connectome-based mechanistic model of focal cortical dysplasia.

Brain 2019 03;142(3):688-699

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Neuroimaging studies have consistently shown distributed brain anomalies in epilepsy syndromes associated with a focal structural lesion, particularly mesiotemporal sclerosis. Conversely, a system-level approach to focal cortical dysplasia has been rarely considered, likely due to methodological difficulties in addressing variable location and topography. Given the known heterogeneity in focal cortical dysplasia histopathology, we hypothesized that lesional connectivity consists of subtypes with distinct structural signatures. Furthermore, in light of mounting evidence for focal anomalies impacting whole-brain systems, we postulated that patterns of focal cortical dysplasia connectivity may exert differential downstream effects on global network topology. We studied a cohort of patients with histologically verified focal cortical dysplasia type II (n = 27), and age- and sex-matched healthy controls (n = 34). We subdivided each lesion into similarly sized parcels and computed their connectivity to large-scale canonical functional networks (or communities). We then dichotomized connectivity profiles of lesional parcels into those belonging to the same functional community as the focal cortical dysplasia (intra-community) and those adhering to other communities (inter-community). Applying hierarchical clustering to community-reconfigured connectome profiles identified three lesional classes with distinct patterns of functional connectivity: decreased intra- and inter-community connectivity, a selective decrease in intra-community connectivity, and increased intra- as well as inter-community connectivity. Hypo-connectivity classes were mainly composed of focal cortical dysplasia type IIB, while the hyperconnected lesions were type IIA. With respect to whole-brain networks, patients with hypoconnected focal cortical dysplasia and marked structural damage showed only mild imbalances, while those with hyperconnected subtle lesions had more pronounced topological alterations. Correcting for interictal epileptic discharges did not impact connectivity patterns. Multivariate structural equation analysis provided a mechanistic model of such complex, diverging interactions, whereby the focal cortical dysplasia structural makeup shapes its functional connectivity, which in turn modulates whole-brain network topology.
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http://dx.doi.org/10.1093/brain/awz009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391612PMC
March 2019

Multimodal computational neocortical anatomy in pediatric hippocampal sclerosis.

Ann Clin Transl Neurol 2018 Oct 27;5(10):1200-1210. Epub 2018 Sep 27.

Developmental Neurosciences UCL Great Ormond Street Institute of Child Health University College London London United Kingdom.

Objective: In contrast to adult cohorts, neocortical changes in epileptic children with hippocampal damage are not well characterized. Here, we mapped multimodal neocortical markers of epilepsy-related structural compromise in a pediatric cohort of temporal lobe epilepsy and explored how they relate to clinical factors.

Methods: We measured cortical thickness, gray-white matter intensity contrast and intracortical FLAIR intensity in 22 patients with hippocampal sclerosis (HS) and 30 controls. Surface-based linear models assessed between-group differences in morphological and MR signal intensity markers. Structural integrity of the hippocampus was measured by quantifying atrophy and FLAIR patterns. Linear models were used to evaluate the relationships between hippocampal and neocortical MRI markers and clinical factors.

Results: In the hippocampus, patients demonstrated ipsilateral atrophy and bilateral FLAIR hyperintensity. In the neocortex, patients showed FLAIR signal hyperintensities and gray-white matter boundary blurring in the ipsilesional mesial and lateral temporal neocortex. In contrast, cortical thinning was minimal and restricted to a small area of the ipsilesional temporal pole. Furthermore, patients with a history of febrile convulsions demonstrated more pronounced FLAIR hyperintensity in the ipsilesional temporal neocortex.

Interpretation: Pediatric HS patients do not yet demonstrate the widespread cortical thinning present in adult cohorts, which may reflect consequences of a protracted disease process. However, pronounced temporal neocortical FLAIR hyperintensity and blurring of the gray-white matter boundary are already detectable, suggesting that alterations in MR signal intensities may reflect a different underlying pathophysiology that is detectable earlier in the disease and more pervasive in patients with a history of febrile convulsions.
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http://dx.doi.org/10.1002/acn3.634DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186946PMC
October 2018

Anatomical and microstructural determinants of hippocampal subfield functional connectome embedding.

Proc Natl Acad Sci U S A 2018 10 24;115(40):10154-10159. Epub 2018 Sep 24.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada;

The hippocampus plays key roles in cognition and affect and serves as a model system for structure/function studies in animals. So far, its complex anatomy has challenged investigations targeting its substructural organization in humans. State-of-the-art MRI offers the resolution and versatility to identify hippocampal subfields, assess its microstructure, and study topographical principles of its connectivity in vivo. We developed an approach to unfold the human hippocampus and examine spatial variations of intrinsic functional connectivity in a large cohort of healthy adults. In addition to mapping common and unique connections across subfields, we identified two main axes of subregional connectivity transitions. An anterior/posterior gradient followed long-axis landmarks and metaanalytical findings from task-based functional MRI, while a medial/lateral gradient followed hippocampal infolding and correlated with proxies of cortical myelin. Findings were consistent in an independent sample and highly stable across resting-state scans. Our results provide robust evidence for long-axis specialization in the resting human hippocampus and suggest an intriguing interplay between connectivity and microstructure.
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http://dx.doi.org/10.1073/pnas.1803667115DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6176604PMC
October 2018

Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains.

Brain Connect 2019 03 16;9(2):113-127. Epub 2018 Nov 16.

1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Rapid advances in neuroimaging and network science have produced powerful tools and measures to appreciate human brain organization at multiple spatial and temporal scales. It is now possible to obtain increasingly meaningful representations of whole-brain structural and functional brain networks and to formally assess macroscale principles of network topology. In addition to its utility in characterizing healthy brain organization, individual variability, and life span-related changes, there is high promise of network neuroscience for the conceptualization and, ultimately, management of brain disorders. In the current review, we argue for a science of the human brain that, while strongly embracing macroscale connectomics, also recommends awareness of brain properties derived from meso- and microscale resolutions. Such features include MRI markers of tissue microstructure, local functional properties, as well as information from nonimaging domains, including cellular, genetic, or chemical data. Integrating these measures with connectome models promises to better define the individual elements that constitute large-scale networks, and clarify the notion of connection strength among them. By enriching the description of large-scale networks, this approach may improve our understanding of fundamental principles of healthy brain organization. Notably, it may also better define the substrate of prevalent brain disorders, including stroke, autism, as well as drug-resistant epilepsies that are each characterized by intriguing interactions between local anomalies and network-level perturbations.
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http://dx.doi.org/10.1089/brain.2018.0587DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6444904PMC
March 2019

Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling.

Front Neuroinform 2018 12;12:39. Epub 2018 Jul 12.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.

Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent multiple-template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large template library, as segmentation suffers when the boundaries of template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-template segmentation, could maintain accurate performance even with a 50% template library size.
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http://dx.doi.org/10.3389/fninf.2018.00039DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6052096PMC
July 2018