Publications by authors named "Seok-Jun Hong"

41 Publications

Mapping functional gradients of the striatal circuit using simultaneous microelectric stimulation and ultrahigh-field fMRI in non-human primates.

Neuroimage 2021 Apr 18;236:118077. Epub 2021 Apr 18.

Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea,. Electronic address:

Advances in functional magnetic resonance imaging (fMRI) have significantly enhanced our understanding of the striatal system of both humans and non-human primates (NHP) over the last few decades. However, its circuit-level functional anatomy remains poorly understood, partly because in-vivo fMRI cannot directly perturb a brain system and map its casual input-output relationship. Also, routine 3T fMRI has an insufficient spatial resolution. We performed electrical microstimulation (EM) of the striatum in lightly-anesthetized NHPs while simultaneously mapping whole-brain activation, using contrast-enhanced fMRI at ultra-high-field 7T. By stimulating multiple positions along the striatum's main (dorsal-to-ventral) axis, we revealed its complex functional circuit concerning mutually connected subsystems in both cortical and subcortical areas. Indeed, within the striatum, there were distinct brain activation patterns across different stimulation sites. Specifically, dorsal stimulation revealed a medial-to-lateral elongated shape of activation in upper caudate and putamen areas, whereas ventral stimulation evoked areas confined to the medial and lower caudate. Such dorsoventral gradients also appeared in neocortical and thalamic activations, indicating consistent embedding profiles of the striatal system across the whole brain. These findings reflect different forms of within-circuit and inter-regional neuronal connectivity between the dorsal and ventromedial striatum. These patterns both shared and contrasted with previous anatomical tract-tracing and in-vivo resting-state fMRI studies. Our approach of combining microstimulation and whole-brain fMRI mapping in NHPs provides a unique opportunity to integrate our understanding of a targeted brain area's meso- and macro-scale functional systems.
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http://dx.doi.org/10.1016/j.neuroimage.2021.118077DOI Listing
April 2021

Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism.

Nat Commun 2021 04 13;12(1):2225. Epub 2021 Apr 13.

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

The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.
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http://dx.doi.org/10.1038/s41467-021-21732-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044226PMC
April 2021

Decomposing complex links between the childhood environment and brain structure in school-aged youth.

Dev Cogn Neurosci 2021 Apr 22;48:100919. Epub 2021 Jan 22.

Department of Psychology, Yale University, New Haven, CT, USA. Electronic address:

Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called 'Similarity Network Fusion', which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with greater myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with greater cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures are differentially associated with alterations in structural neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.
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http://dx.doi.org/10.1016/j.dcn.2021.100919DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7868609PMC
April 2021

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

Cross-species functional alignment reveals evolutionary hierarchy within the connectome.

Neuroimage 2020 12 9;223:117346. Epub 2020 Sep 9.

Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.

Evolution provides an important window into how cortical organization shapes function and vice versa. The complex mosaic of changes in brain morphology and functional organization that have shaped the mammalian cortex during evolution, complicates attempts to chart cortical differences across species. It limits our ability to fully appreciate how evolution has shaped our brain, especially in systems associated with unique human cognitive capabilities that lack anatomical homologues in other species. Here, we develop a function-based method for cross-species alignment that enables the quantification of homologous regions between humans and rhesus macaques, even when their location is decoupled from anatomical landmarks. Critically, we find cross-species similarity in functional organization reflects a gradient of evolutionary change that decreases from unimodal systems and culminates with the most pronounced changes in posterior regions of the default mode network (angular gyrus, posterior cingulate and middle temporal cortices). Our findings suggest that the establishment of the default mode network, as the apex of a cognitive hierarchy, has changed in a complex manner during human evolution - even within subnetworks.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117346DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7871099PMC
December 2020

Toward a connectivity gradient-based framework for reproducible biomarker discovery.

Neuroimage 2020 12 1;223:117322. Epub 2020 Sep 1.

Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, NY, USA. Electronic address:

Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117322DOI Listing
December 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

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury.

Cereb Cortex 2020 11;30(12):6238-6253

Laboratory of Neuro Imaging at USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, 2025 Zonal Ave, Los Angeles, CA 90033, USA.

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.
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http://dx.doi.org/10.1093/cercor/bhaa181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7609941PMC
November 2020

Toward Neurosubtypes in Autism.

Biol Psychiatry 2020 07 22;88(1):111-128. Epub 2020 Apr 22.

Autism Center, Child Mind Institute, New York. Electronic address:

There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.
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http://dx.doi.org/10.1016/j.biopsych.2020.03.022DOI Listing
July 2020

Sleep Disturbance and Its Clinical Implication in Patients with Adult Spinal Deformity: Comparison with Lumbar Spinal Stenosis.

Pain Res Manag 2020 13;2020:6294151. Epub 2020 Apr 13.

Department of Anesthesiology & Pain Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 134-701, Republic of Korea.

Purpose: The purpose of this study was to investigate the prevalence of sleep disturbance and its clinical implication in patients with ASD.

Methods: A total of 44 patients with ASD and 137 patients with lumbar spinal stenosis (LSS) were enrolled in the study. Forty four patients were selected from the LSS group after propensity score matching. Global Pittsburgh Sleep Quality Index (PSQI) score, demographic data, visual analog scale (VAS) score for back and leg pain, Oswestry Disability Index (ODI), and EuroQol 5-dimension questionnaire (EQ-5D) were compared between both groups. Multiple regression analysis was performed with VAS for back pain as the dependent variable and age, sex, PSQI, and VAS for leg pain as the independent variables in the ASD group.

Results: 33 (75.0%) and 32 (72.7%) patients were classified as poor sleepers in the ASD group and the LSS group, respectively. In the ASD group, the VAS score for back pain was 7.7 ± 1.7 in the poor sleeper group and 5.6 ± 2.2 in the nonpoor sleeper group. In the LSS group, poor sleep quality was associated with the ODI score, ODI score without a sleep component, and EQ-5D. The regression model for predicting VAS for back pain in the ASD group suggested that poor sleep quality and increased leg pain were associated with increased back pain.

Conclusions: Because sleep quality is a critical factor in augmenting back pain in patients with ASD, this study underlines the need to investigate sleep quality during the routine examination of patients with ASD.
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http://dx.doi.org/10.1155/2020/6294151DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178501PMC
October 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

BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets.

Commun Biol 2020 03 5;3(1):103. Epub 2020 Mar 5.

McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Understanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.
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http://dx.doi.org/10.1038/s42003-020-0794-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7058611PMC
March 2020

Volume change in amygdala enlargement as a prognostic factor in patients with temporal lobe epilepsy: A longitudinal study.

Epilepsia 2020 01 11;61(1):70-80. Epub 2019 Dec 11.

Department of Neurology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.

Objective: Considering the clinical heterogeneity of temporal lobe epilepsy with amygdala enlargement (TLE-AE), identifying distinct prognostic subgroups of TLE-AE has clinical implications. Until now, baseline volume of the enlarged amygdala (EAV) has consistently failed to predict prognosis in TLE-AE. Based on studies suggesting that patients responsive to antiepileptic drugs (AEDs) exhibit remission of AE on follow-up imaging, we investigated whether reduction rate of EAV is predictive of long-term prognosis in TLE-AE.

Methods: Sixty-one consecutive patients with two separate magnetic resonance imaging (MRI) scans were enrolled. To utilize longitudinally measured biomarkers in prediction, the period beyond the first MRI acquisition was split into two periods: the "observation window" (period between the two MRIs) and "prediction window" (follow-up period beyond the second MRI). Patients were classified according to their AED responsiveness during the observation window, and AED-responsive patients were further subdivided by initial seizure frequency: (a) AED-responsive patients presenting with low-frequency seizures (<5 seizures/3 mo; Group A, n = 25), (b) high-frequency seizures (≥5 seizures/3 mo; Group B, n = 23), and (c) patients with poor initial treatment response (Group C, n = 13). Multivariate logistic regression models were constructed for identification of prognostic factors. Along with factors obtained at baseline, factors derived from the observation window (annual percentage change of EAV [APCEAV] and initial AED responsiveness) were also considered as potential predictors.

Results: Favorable initial treatment response and faster volume reduction rate (APCEAV ≤ -5.0%/y) were identified as factors predictive of achieving overall seizure freedom. Among the AED-responsive patients, Group A (low-frequency seizures) showed slower remission of AE and higher rate of seizure recurrence, whereas Group B (high-frequency seizures) exhibited faster remission of AE and lower rate of seizure recurrence.

Significance: Faster recuperation of AE in patients with initial high-frequency seizures may be indicative of seizure-induced changes. As volume reduction rate serves as a prognostic marker in TLE-AE, short-term MRI follow-up may be useful in prognostication.
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http://dx.doi.org/10.1111/epi.16400DOI Listing
January 2020

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

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

Multiscale Structure-Function Gradients in the Neonatal Connectome.

Cereb Cortex 2020 01;30(1):47-58

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

The adult functional connectome is well characterized by a macroscale spatial gradient of connectivity traversing from unimodal toward higher-order transmodal cortices that recapitulates known principles of hierarchical organization and myelination patterns. Despite an emerging literature assessing connectome properties in neonates, the presence of connectome gradients and particularly their correspondence to microstructure remains largely unknown. We derived connectome gradients using unsupervised techniques applied to functional connectivity data from 40 term-born neonates. A series of cortex-wide analysis examined associations to magnetic resonance imaging-derived morphological parameters (cortical thickness, sulcal depth, curvature), measures of tissue microstructure (intracortical T1w/T2w intensity, superficial white matter diffusion parameters), and subcortico-cortical functional connectivity. Our findings indicate that the primary neonatal connectome gradient runs between sensorimotor and visual anchors and captures specific associations to cortical and superficial white matter microstructure as well as thalamo-cortical connectivity. A second gradient indicated an anterior-to-posterior asymmetry in macroscale connectivity alongside an immature differentiation between unimodal and transmodal areas, indicating a connectome-level circuitry en route to an adult-like organization. Our findings reveal an important coordination of structural and functional interactions in the neonatal connectome across spatial scales. Observed associations were replicable across individual neonates, suggesting consistency and generalizability.
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http://dx.doi.org/10.1093/cercor/bhz069DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029695PMC
January 2020

The Change of Endotracheal Tube Cuff Pressure During Laparoscopic Surgery.

Open Med (Wars) 2019 30;14:431-436. Epub 2019 May 30.

Department of Anesthesiology and Pain medicine, Hallym University School of Medicine, Chuncheon Sacred Heart Hospital, 77 Sakju-ro, Chuncheon, 24253, South Korea.

Background: We evaluated the endotracheal tube cuff pressure (P) changes during pneumoperitoneum for laparoscopic cholecystectomy and the correlations between body mass index (BMI), pneumoperitoneum time, and P changes.

Methods: Total 60 patients undergoing laparoscopic cholecystectomy were allocated to either a study group (BMI ≥ 25 kg/m) or a control group (BMI < 25 kg/m). The endotracheal intubation was performed with a high-volume low-pressure cuffed oral endotracheal tube. A manometer was connected to the pilot balloon using a 3-way stopcock and the cuff was inflated. The change in P was defined as the difference between the pressure just before intra-abdominal CO insufflation and the pressure before CO desufflation.

Results: P increased to 5.3 ± 3.6 cmHO in the study group and 5.7 ± 5.4 cmHO in the control group. There was no significant difference between two groups. While BMI was not correlated with change in P (r = 0.022, = 0.867), there was a significant correlation between change in P and the duration of pneumoperitoneum (r = 0.309, = 0.016).

Conclusion: The change in P was not affected by BMI and was significantly correlated with pneumoperitoneum time. We recommend regular measurement and adjustment of P during laparoscopic surgery.
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http://dx.doi.org/10.1515/med-2019-0046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6555239PMC
May 2019

Best cut-off point of the cervical facet joint area as a new morphological measurement tool to predict cervical foraminal stenosis.

J Pain Res 2019 24;12:1325-1330. Epub 2019 Apr 24.

Department of Anesthesiology and Pain Medicine, Kangdong Sacred Heart Hospital, Hallym University, College of Medicine, Seoul, Republic of Korea.

One of the main factor of cervical foraminal stenosis (CFS) is the hypertrophic change of the cervical facet joint. In order to analyze the connection between CFS and the facet joint hypertrophy, we devised a new morphological parameter, called the cervical facet joint cross-sectional area (CFJA). The CFJA has not yet been investigated for its association with CFS. We hypothesized that the CFJA is an important morphologic parameter in the diagnosis of CFS. All patients over 50 years of age were included. Data regarding the CFJA were collected from 160 subjects with CFS. A total of 162 control individuals underwent cervical spine magnetic resonance imaging (CMRI) as part of a routine medical examination. Axial T2-weighted CMRI images were acquired from all subjects. We used a picture archiving system to analyze the cross-sectional area of the bone margin of the cervical facet joint at the level of the most stenotic cervical spine in the axial plane. The average CFJA was 109.07±20.91 mm in the control group, and 126.75±22.59 mm in the CFS group. The CFS group was found to have significantly higher levels of the CFJA (<0.001) than the control group. ROC curve estimation was used to verify the validity of the CFJA as a new predictor of CFS. In the CFS group, the best cut off-point was 113.14 mm, with sensitivity =70.6%, specificity =68.6%, and AUC =0.72 (95% CI, 0.66-0.77). CFJA high values were closely associated with a possibility of CFS. We concluded CFJA is easy to use, fast, and useful new morphological parameter to predict CFS.
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http://dx.doi.org/10.2147/JPR.S204567DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6497142PMC
April 2019

Microstructural and functional gradients are increasingly dissociated in transmodal cortices.

PLoS Biol 2019 05 20;17(5):e3000284. Epub 2019 May 20.

McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.
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http://dx.doi.org/10.1371/journal.pbio.3000284DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6544318PMC
May 2019

Atypical functional connectome hierarchy in autism.

Nat Commun 2019 03 4;10(1):1022. Epub 2019 Mar 4.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, H3A2B4, Montreal, Canada.

One paradox of autism is the co-occurrence of deficits in sensory and higher-order socio-cognitive processing. Here, we examined whether these phenotypical patterns may relate to an overarching system-level imbalance-specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. Combining connectome gradient and stepwise connectivity analysis based on task-free functional magnetic resonance imaging (fMRI), we demonstrated atypical connectivity transitions between sensory and higher-order default mode regions in a large cohort of individuals with autism relative to typically-developing controls. Further analyses indicated that reduced differentiation related to perturbed stepwise connectivity from sensory towards transmodal areas, as well as atypical long-range rich-club connectivity. Supervised pattern learning revealed that hierarchical features predicted deficits in social cognition and low-level behavioral symptoms, but not communication-related symptoms. Our findings provide new evidence for imbalances in network hierarchy in autism, which offers a parsimonious reference frame to consolidate its diverse features.
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http://dx.doi.org/10.1038/s41467-019-08944-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399265PMC
March 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

The Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity.

Cereb Cortex 2019 09;29(10):4415-4425

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

In autism spectrum disorders (ASDs), the majority of neuroimaging studies have focused on the analysis of cortical morphology. White matter changes remain less understood, particularly their association to cortical structure and function. Here, we focused on region that has gained only little attention in ASD neuroimaging: the superficial white matter (SWM) immediately beneath the cortical interface, a compartment playing a prominent role in corticogenesis that incorporates long- and short-range fibers implicated in corticocortical connectivity. Studying a multicentric dataset of ASD and neurotypical controls, we harnessed surface-based techniques to aggregate microstructural SWM diffusion features. Multivariate analysis revealed SWM anomalies in ASD compared with controls in medial parietal and temporoparietal regions. Effects were similar in children and adolescents/adults and consistent across sites. Although SWM anomalies were more confined when correcting for cortical thickness and surface area, findings were overall robust. Diffusion anomalies modulated functional connectivity reductions in ASD and related to symptom severity. Furthermore, mediation models indicated a link between SWM changes, functional connectivity, and symptom load. Analyses targeting the SWM offer a novel perspective on the interplay between structural and functional network perturbations in ASD, highlighting a potentially important neurobiological substrate contributing to its diverse behavioral phenotype.
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http://dx.doi.org/10.1093/cercor/bhy321DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735258PMC
September 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

Topographic principles of cortical fluid-attenuated inversion recovery signal in temporal lobe epilepsy.

Epilepsia 2018 03 31;59(3):627-635. Epub 2018 Jan 31.

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

Objective: In drug-resistant temporal lobe epilepsy (TLE), relative to the large number of whole-brain morphological studies, neocortical T2 changes have not been systematically investigated. The aim of this study was to assess the anatomical principles that govern the distribution of neocortical T2-weighted fluid-attenuated inversion recovery (FLAIR) signal intensity and uncover its topographic principles.

Methods: Using a surface-based sampling scheme, we mapped neocortical FLAIR intensity of 61 TLE patients relative to 38 healthy controls imaged at 3 T. To address topographic principles of the susceptibility to FLAIR signal changes in TLE, we assessed associations with normative data on tissue composition using 2 complementary approaches. First, we evaluated whether the degree of TLE-related FLAIR intensity changes differed across cytoarchitectonic classes as defined by Von Economo-Koskinas taxonomy. Second, as a proxy to map regions with similar intracortical composition, we carried out a FLAIR intensity covariance paradigm in controls by seeding systematically from all cortical regions, and identified those networks that were the best spatial predictors of the between-group FLAIR changes.

Results: Increased intensities were observed in bilateral limbic and paralimbic cortices (hippocampus, parahippocampus, cingulate, temporopolar, insular, orbitofrontal). Effect sizes were highest in periallocortical limbic and insular classes as defined by the Von Economo-Koskinas cytoarchitectonic taxonomy. Furthermore, systematic FLAIR intensity covariance analysis in healthy controls revealed that similarity patterns characteristic of limbic cortices, most notably the hippocampus, served as sensitive predictors for the topography of FLAIR hypersignal in patients. FLAIR intensity findings were robust against correction for morphological confounds. Patients with a history of febrile convulsions showed more marked signal changes in parahippocampal and retrosplenial cortices, known to be strongly connected to the hippocampus.

Significance: FLAIR intensity mapping and covariance analysis provide a model of TLE gray matter pathology based on shared vulnerability of periallocortical and limbic cortices.
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http://dx.doi.org/10.1111/epi.14017DOI Listing
March 2018

Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder.

Cereb Cortex 2018 10;28(10):3578-3588

Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada.

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.
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http://dx.doi.org/10.1093/cercor/bhx229DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7190887PMC
October 2018

The spectrum of structural and functional network alterations in malformations of cortical development.

Brain 2017 Aug;140(8):2133-2143

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

Neuroimaging studies of malformations of cortical development have mainly focused on the characterization of the primary lesional substrate, while whole-brain investigations remain scarce. Our purpose was to assess large-scale brain organization in prevalent cortical malformations. Based on experimental evidence suggesting that distributed effects of focal insults are modulated by stages of brain development, we postulated differential patterns of network anomalies across subtypes of malformations. We studied a cohort of patients with focal cortical dysplasia type II (n = 63), subcortical nodular heterotopia (n = 44), and polymicrogyria (n = 34), and compared them to 82 age- and sex-matched controls. Graph theoretical analysis of structural covariance networks indicated a consistent rearrangement towards a regularized architecture characterized by increased path length and clustering, as well as disrupted rich-club topology, overall suggestive of inefficient global and excessive local connectivity. Notably, we observed a gradual shift in network reconfigurations across subgroups, with only subtle changes in focal cortical dysplasia type II, moderate effects in heterotopia and maximal effects in polymicrogyria. Analysis of resting state functional connectivity also revealed gradual network changes, with most marked rearrangement in polymicrogyria; contrary to findings in the structural domain, however, functional architecture was characterized by decreases in both local and global parameters. Diverging results in the structural and functional domain were supported by formal structure-function coupling analysis. Our findings support the concept that time of insult during corticogenesis impacts the severity of topological network reconfiguration. Specifically, late-stage malformations, typified by polymicrogyria, may selectively disrupt the formation of large-scale cortico-cortical networks and thus lead to a more profound impact on whole-brain organization than early stage disturbances of predominantly radial migration patterns observed in cortical dysplasia type II, which likely affect a relatively confined cortical territory.
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http://dx.doi.org/10.1093/brain/awx145DOI Listing
August 2017

Preferential susceptibility of limbic cortices to microstructural damage in temporal lobe epilepsy: A quantitative T1 mapping study.

Neuroimage 2018 11 3;182:294-303. Epub 2017 Jun 3.

Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada. Electronic address:

The majority of MRI studies in temporal lobe epilepsy (TLE) have utilized morphometry to map widespread cortical alterations. Morphological markers, such as cortical thickness or grey matter density, reflect combinations of biological events largely driven by overall cortical geometry rather than intracortical tissue properties. Because of its sensitivity to intracortical myelin, quantitative measurement of longitudinal relaxation time (qT) provides and an in vivo proxy for cortical microstructure. Here, we mapped the regional distribution of qT in a consecutive cohort of 24 TLE patients and 20 healthy controls. Compared to controls, patients presented with a strictly ipsilateral distribution of qT increases in temporopolar, parahippocampal and orbitofrontal cortices. Supervised statistical learning applied to qT maps could lateralize the seizure focus in 92% of patients. Intracortical profiling of qT along streamlines perpendicular to the cortical mantle revealed marked effects in upper levels that tapered off at the white matter interface. Findings remained robust after correction for cortical thickness and interface blurring, suggesting independence from previously reported morphological anomalies in this disorder. Mapping of qT along hippocampal subfield surfaces revealed marked increases in anterior portions of the ipsilateral CA1-3 and DG that were also robust against correction for atrophy. Notably, in operated patients, qualitative histopathological analysis of myelin stains in resected hippocampal specimens confirmed disrupted internal architecture and fiber organization. Both hippocampal and neocortical qT anomalies were more severe in patients with early disease onset. Finally, analysis of resting-state connectivity from regions of qT increases revealed altered intrinsic functional network embedding in patients, particularly to prefrontal networks. Analysis of qT suggests a preferential susceptibility of ipsilateral limbic cortices to microstructural damage, possibly related to disrupted myeloarchitecture. These alterations may reflect atypical neurodevelopment and affect the integrity of fronto-limbic functional networks.
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http://dx.doi.org/10.1016/j.neuroimage.2017.06.002DOI Listing
November 2018

Multimodal MRI profiling of focal cortical dysplasia type II.

Neurology 2017 Feb 27;88(8):734-742. Epub 2017 Jan 27.

From the Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital (S.-J.H., B.C.B., B.C., J.A.H., M.C.G., N.B., A.B.), Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre (S.-J.H., B.C.B., B.C., D.S., N.B., A.B.), and Department of Pathology (M.C.G.), McGill University, Montreal, Canada.

Objective: To characterize in vivo MRI signatures of focal cortical dysplasia (FCD) type IIA and type IIB through combined analysis of morphology, intensity, microstructure, and function.

Methods: We carried out a multimodal 3T MRI profiling of 33 histologically proven FCD type IIA (9) and IIB (24) lesions. A multisurface approach operating on manual consensus labels systematically sampled intracortical and subcortical lesional features. Geodesic distance mapping quantified the same features in the lesion perimeter. Logistic regression assessed the relationship between MRI and histology, while supervised pattern learning was used for individualized subtype prediction.

Results: FCD type IIB was characterized by abnormal morphology, intensity, diffusivity, and function across all surfaces, while type IIA lesions presented only with increased fluid-attenuated inversion recovery signal and reduced diffusion anisotropy close to the gray-white matter interface. Similar to lesional patterns, perilesional anomalies were more marked in type IIB extending up to 16 mm. Structural MRI markers correlated with categorical histologic characteristics. A profile-based classifier predicted FCD subtypes with equal sensitivity of 85%, while maintaining a high specificity of 94% against healthy and disease controls.

Conclusions: Image processing applied to widely available MRI contrasts has the ability to dissociate FCD subtypes at a mesoscopic level. Integrating in vivo staging of pathologic traits with automated lesion detection is likely to provide an objective definition of lesional boundary and assist emerging approaches, such as minimally invasive thermal ablation, which do not supply tissue specimen.
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http://dx.doi.org/10.1212/WNL.0000000000003632DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5344077PMC
February 2017