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Bolzan A, Benoit J, Pizzo F, Makhalova J, Villeneuve N, Carron R, Scavarda D, Bartolomei F, Lagarde S. Correspondence between scalp-EEG and stereoelectroencephalography seizure-onset patterns in patients with MRI-negative drug-resistant focal epilepsy. Epilepsia Open 2024; 9:568-581. [PMID: 38148028 PMCID: PMC10984298 DOI: 10.1002/epi4.12886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/28/2023] [Accepted: 12/14/2023] [Indexed: 12/28/2023] Open
Abstract
OBJECTIVE Our objective was to evaluate the relationship between scalp-EEG and stereoelectroencephalography (SEEG) seizure-onset patterns (SOP) in patients with MRI-negative drug-resistant focal epilepsy. METHODS We analyzed retrospectively 41 patients without visible lesion on brain MRI who underwent video-EEG followed by SEEG. We defined five types of SOPs on scalp-EEG and eight types on SEEG. We examined how various clinical variables affected scalp-EEG SOPs. RESULTS The most prevalent scalp SOPs were rhythmic sinusoidal activity (56.8%), repetitive epileptiform discharges (22.7%), and paroxysmal fast activity (15.9%). The presence of paroxysmal fast activity on scalp-EEG was always seen without delay from clinical onset and correlated with the presence of low-voltage fast activity in SEEG (sensitivity = 22.6%, specificity = 100%). The main factor explaining the discrepancy between the scalp and SEEG SOPs was the delay between clinical and scalp-EEG onset. There was a correlation between the scalp and SEEG SOPs when the scalp onset was simultaneous with the clinical onset (p = 0.026). A significant delay between clinical and scalp discharge onset was observed in 25% of patients and featured always with a rhythmic sinusoidal activity on scalp, corresponding to similar morphology of the discharge on SEEG. The presence of repetitive epileptiform discharges on scalp was associated with an underlying focal cortical dysplasia (sensitivity = 30%, specificity = 90%). There was no significant association between the scalp SOP and the epileptogenic zone location (deep or superficial), or surgical outcome. SIGNIFICANCE In patients with MRI-negative focal epilepsy, scalp SOP could suggest the SEEG SOP and some etiology (focal cortical dysplasia) but has no correlation with surgical prognosis. Scalp SOP correlates with the SEEG SOP in cases of simultaneous EEG and clinical onset; otherwise, scalp SOP reflects the propagation of the SEEG discharge. PLAIN LANGUAGE SUMMARY We looked at the correspondence between the electrical activity recorded during the start of focal seizure using scalp and intracerebral electrodes in patients with no visible lesion on MRI. If there is a fast activity on scalp, it reflects similar activity inside the brain. We found a good correspondence between scalp and intracerebral electrical activity for cases without significant delay between clinical and scalp electrical onset (seen in 75% of the cases we studied). Visualizing repetitive epileptic activity on scalp could suggest a particular cause of the epilepsy: a subtype of brain malformation called focal cortical dysplasia.
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Affiliation(s)
- Anna Bolzan
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
| | - Jeanne Benoit
- CHU de Nice, Epileptology DepartmentUniversité Côte d'Azur, UMR2CA (URRIS)NiceFrance
| | - Francesca Pizzo
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Julia Makhalova
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- APHM, Timone Hospital, CEMEREMMarseilleFrance
| | | | - Romain Carron
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- APHM, Timone Hospital, Stereotactic and Functional Neurosurgery, Gamma UnitMarseilleFrance
| | - Didier Scavarda
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- APHM, Timone Hospital, Paediatric NeurosurgeryMarseilleFrance
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
| | - Stanislas Lagarde
- APHM, Timone Hospital, Epileptology and Cerebral RhythmologyMarseilleFrance
- Aix Marseille Univ, INSERM, INS, Inst Neurosci SystMarseilleFrance
- University Hospitals of Geneva (HUG), University of Geneva (UNIGE)GenevaSwitzerland
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Kronlage C, Heide EC, Hagberg GE, Bender B, Scheffler K, Martin P, Focke N. MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy. PLoS One 2024; 19:e0296843. [PMID: 38330027 PMCID: PMC10852321 DOI: 10.1371/journal.pone.0296843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 12/19/2023] [Indexed: 02/10/2024] Open
Abstract
In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).
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Affiliation(s)
- Cornelius Kronlage
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Ev-Christin Heide
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Gisela E. Hagberg
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Benjamin Bender
- Department of Neuroradiology, University of Tuebingen, Tuebingen, Germany
| | - Klaus Scheffler
- High-Field MR Centre, Max-Planck-Institute for Biological Cybernetics, Tuebingen, Germany
- Department for Biomedical Magnetic Resonances, University of Tuebingen, Tuebingen, Germany
| | - Pascal Martin
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
| | - Niels Focke
- Department of Neurology and Epileptology, Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany
- Clinic of Neurology, University Medical Center Goettingen, Goettingen, Germany
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3
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Jin B, Xu J, Wang C, Wang S, Li H, Chen C, Ye L, He C, Cheng H, Zhang L, Wang S, Wang J, Aung T. Functional profile of perilesional gray matter in focal cortical dysplasia: an fMRI study. Front Neurosci 2024; 18:1286302. [PMID: 38318464 PMCID: PMC10838983 DOI: 10.3389/fnins.2024.1286302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 01/08/2024] [Indexed: 02/07/2024] Open
Abstract
Objectives We aim to investigate the functional profiles of perilesional gray matter (GM) in epileptic patients with focal cortical dysplasia (FCD) and to correlate these profiles with FCD II subtypes, surgical outcomes, and different antiseizure medications (ASMs) treatment response patterns. Methods Nine patients with drug-responsive epilepsy and 30 patients with drug-resistant epilepsy (11 were histologically confirmed FCD type IIa, 19 were FCD type IIb) were included. Individual-specific perilesional GM and contralateral homotopic GM layer masks were generated. These masks underwent a two-voxel (2 mm) dilation from the FCD lesion and contralateral homotopic region, resulting in 10 GM layers (20 mm). Layer 1, the innermost, progressed to Layer 10, the outermost. Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) analyses were conducted to assess the functional characteristics of ipsilateral perilesional GM and contralateral homotopic GM. Results Compared to the contralateral homotopic GM, a significant reduction of ALFF was detected at ipsilateral perilesional GM layer 1 to 6 in FCD type IIa (after Bonferroni correction p < 0.005, paired t-test), whereas a significant decrease was observed at ipsilateral perilesional GM layer 1 to 2 in FCD type IIb (after Bonferroni correction p < 0.005, paired t-test). Additionally, a significant decrease of the ReHo was detected at ipsilateral perilesional GM layer 1 compared to the CHRs in FCD type IIb. Notably, complete resection of functional perilesional GM alterations did not correlate with surgical outcomes. Compared to the contralateral homotopic GM, a decreased ALFF in the ipsilateral perilesional GM layer was detected in drug-responsive patients, whereas decreased ALFF in the ipsilateral perilesional GM layer 1-6 and decreased ReHo at ipsilateral perilesional GM layer 1 were observed in drug-resistant patients (after Bonferroni correction p < 0.005, paired t-test). Conclusion Our findings indicate distinct functional profiles of perilesional GM based on FCD histological subtypes and ASMs' response patterns. Importantly, our study illustrates that the identified functional alterations in perilesional GM may not provide sufficient evidence to determine the epileptogenic boundary required for surgical resection.
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Affiliation(s)
- Bo Jin
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiahui Xu
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chao Wang
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shan Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hong Li
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Cong Chen
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Linqi Ye
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenmin He
- Department of Radiology, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Hui Cheng
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Lisan Zhang
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuang Wang
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jin Wang
- Department of Neurology, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Thandar Aung
- Department of Neurology, Epilepsy Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
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Lee HM, Hong SJ, Gill R, Caldairou B, Wang I, Zhang JG, Deleo F, Schrader D, Bartolomei F, Guye M, Cho KH, Barba C, Sisodiya S, Jackson G, Hogan RE, Wong-Kisiel L, Cascino GD, Schulze-Bonhage A, Lopes-Cendes I, Cendes F, Guerrini R, Bernhardt B, Bernasconi N, Bernasconi A. Multimodal mapping of regional brain vulnerability to focal cortical dysplasia. Brain 2023; 146:3404-3415. [PMID: 36852571 PMCID: PMC10393418 DOI: 10.1093/brain/awad060] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 01/17/2023] [Accepted: 02/02/2023] [Indexed: 03/01/2023] Open
Abstract
Focal cortical dysplasia (FCD) type II is a highly epileptogenic developmental malformation and a common cause of surgically treated drug-resistant epilepsy. While clinical observations suggest frequent occurrence in the frontal lobe, mechanisms for such propensity remain unexplored. Here, we hypothesized that cortex-wide spatial associations of FCD distribution with cortical cytoarchitecture, gene expression and organizational axes may offer complementary insights into processes that predispose given cortical regions to harbour FCD. We mapped the cortex-wide MRI distribution of FCDs in 337 patients collected from 13 sites worldwide. We then determined its associations with (i) cytoarchitectural features using histological atlases by Von Economo and Koskinas and BigBrain; (ii) whole-brain gene expression and spatiotemporal dynamics from prenatal to adulthood stages using the Allen Human Brain Atlas and PsychENCODE BrainSpan; and (iii) macroscale developmental axes of cortical organization. FCD lesions were preferentially located in the prefrontal and fronto-limbic cortices typified by low neuron density, large soma and thick grey matter. Transcriptomic associations with FCD distribution uncovered a prenatal component related to neuroglial proliferation and differentiation, likely accounting for the dysplastic makeup, and a postnatal component related to synaptogenesis and circuit organization, possibly contributing to circuit-level hyperexcitability. FCD distribution showed a strong association with the anterior region of the antero-posterior axis derived from heritability analysis of interregional structural covariance of cortical thickness, but not with structural and functional hierarchical axes. Reliability of all results was confirmed through resampling techniques. Multimodal associations with cytoarchitecture, gene expression and axes of cortical organization indicate that prenatal neurogenesis and postnatal synaptogenesis may be key points of developmental vulnerability of the frontal lobe to FCD. Concordant with a causal role of atypical neuroglial proliferation and growth, our results indicate that FCD-vulnerable cortices display properties indicative of earlier termination of neurogenesis and initiation of cell growth. They also suggest a potential contribution of aberrant postnatal synaptogenesis and circuit development to FCD epileptogenicity.
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Affiliation(s)
- Hyo M Lee
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
- Center for Neuroscience Imaging, Research Institute for Basic Science, Department of Global Biomedical Engineering, SungKyunKwan University, Suwon, KoreaSuwon, Korea
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Irene Wang
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jian-guo Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Francesco Deleo
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico C. Besta, Milano, Italy
| | - Dewi Schrader
- Department of Pediatrics, British Columbia Children’s Hospital, Vancouver, Canada
| | - Fabrice Bartolomei
- Aix Marseille Univ, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, 13005, France
| | - Maxime Guye
- Aix Marseille University, CNRS, CRMBM UMR 7339, Marseille, France
| | - Kyoo Ho Cho
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
| | - Carmen Barba
- Meyer Children's Hospital IRCCS, Florence, Italy
- University of Florence, 50121 Florence, Italy
| | - Sanjay Sisodiya
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, UK
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health and The University of Melbourne, Victoria, Australia
| | - R Edward Hogan
- Department of Neurology, Washington University School of Medicine, St Louis, MO, USA
| | | | | | | | - Iscia Lopes-Cendes
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas SP, Brazil
| | - Fernando Cendes
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas SP, Brazil
| | - Renzo Guerrini
- Meyer Children's Hospital IRCCS, Florence, Italy
- University of Florence, 50121 Florence, Italy
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute, McGill University, Montreal, Canada
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Villaseñor PJ, Cortés-Servín D, Pérez-Moriel A, Aquiles A, Luna-Munguía H, Ramirez-Manzanares A, Coronado-Leija R, Larriva-Sahd J, Concha L. Multi-tensor diffusion abnormalities of gray matter in an animal model of cortical dysplasia. Front Neurol 2023; 14:1124282. [PMID: 37342776 PMCID: PMC10278582 DOI: 10.3389/fneur.2023.1124282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 04/18/2023] [Indexed: 06/23/2023] Open
Abstract
Focal cortical dysplasias are a type of malformations of cortical development that are a common cause of drug-resistant focal epilepsy. Surgical treatment is a viable option for some of these patients, with their outcome being highly related to complete surgical resection of lesions visible in magnetic resonance imaging (MRI). However, subtle lesions often go undetected on conventional imaging. Several methods to analyze MRI have been proposed, with the common goal of rendering subtle cortical lesions visible. However, most image-processing methods are targeted to detect the macroscopic characteristics of cortical dysplasias, which do not always correspond to the microstructural disarrangement of these cortical malformations. Quantitative analysis of diffusion-weighted MRI (dMRI) enables the inference of tissue characteristics, and novel methods provide valuable microstructural features of complex tissue, including gray matter. We investigated the ability of advanced dMRI descriptors to detect diffusion abnormalities in an animal model of cortical dysplasia. For this purpose, we induced cortical dysplasia in 18 animals that were scanned at 30 postnatal days (along with 19 control animals). We obtained multi-shell dMRI, to which we fitted single and multi-tensor representations. Quantitative dMRI parameters derived from these methods were queried using a curvilinear coordinate system to sample the cortical mantle, providing inter-subject anatomical correspondence. We found region- and layer-specific diffusion abnormalities in experimental animals. Moreover, we were able to distinguish diffusion abnormalities related to altered intra-cortical tangential fibers from those associated with radial cortical fibers. Histological examinations revealed myelo-architectural abnormalities that explain the alterations observed through dMRI. The methods for dMRI acquisition and analysis used here are available in clinical settings and our work shows their clinical relevance to detect subtle cortical dysplasias through analysis of their microstructural properties.
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Affiliation(s)
- Paulina J. Villaseñor
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - David Cortés-Servín
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ana Aquiles
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Hiram Luna-Munguía
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ricardo Coronado-Leija
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Jorge Larriva-Sahd
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
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Fountain C, Ghuman H, Paldino M, Tamber M, Panigrahy A, Modo M. Acquisition and Analysis of Excised Neocortex from Pediatric Patients with Focal Cortical Dysplasia Using Mesoscale Diffusion MRI. Diagnostics (Basel) 2023; 13:diagnostics13091529. [PMID: 37174921 PMCID: PMC10177920 DOI: 10.3390/diagnostics13091529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/01/2023] [Accepted: 04/15/2023] [Indexed: 05/15/2023] Open
Abstract
Non-invasive classification of focal cortical dysplasia (FCD) subtypes remains challenging from a radiology perspective. Quantitative imaging biomarkers (QIBs) have the potential to distinguish subtypes that lack pathognomonic features and might help in defining the extent of abnormal connectivity associated with each FCD subtype. A key motivation of diagnostic imaging is to improve the localization of a "lesion" that can guide the surgical resection of affected tissue, which is thought to cause seizures. Conversely, surgical resections to eliminate or reduce seizures provided unique opportunities to develop magnetic resonance imaging (MRI)-based QIBs by affording long scan times to evaluate multiple contrast mechanisms at the mesoscale (0.5 mm isotropic voxel dimensions). Using ex vivo hybrid diffusion tensor imaging on a 9.4 T MRI scanner, the grey to white matter ratio of scalar indices was lower in the resected middle temporal gyrus (MTG) of two neuropathologically confirmed cases of FCD compared to non-diseased control postmortem fixed temporal lobes. In contrast, fractional anisotropy was increased within FCD and also adjacent white matter tracts. Connectivity (streamlines/mm3) in the MTG was higher in FCD, suggesting that an altered connectivity at the lesion locus can potentially provide a tangible QIB to distinguish and characterize FCD abnormalities. However, as illustrated here, a major challenge for a robust tractographical comparison lies in the considerable differences in the ex vivo processing of bioptic and postmortem samples. Mesoscale diffusion MRI has the potential to better define and characterize epileptic tissues obtained from surgical resection to advance our understanding of disease etiology and treatment.
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Affiliation(s)
- Chandler Fountain
- Department of Radiology and Medical Imaging, University of Virginia Health System, 1215 Lee St, Chartlottesville, VA 22903, USA
| | - Harmanvir Ghuman
- Department of Bioengineering, University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pititsburgh, PA 15260, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, 450 Technology Drive, Suite 300, Pittsburgh, PA 15219, USA
| | - Michael Paldino
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
| | - Mandeep Tamber
- Department of Neurological Surgery, University of Pittsburgh, 200 Lothrop Street, Suite B 400, Pittsburgh, PA 15213, USA
| | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
| | - Michel Modo
- Department of Bioengineering, University of Pittsburgh, 302 Benedum Hall, 3700 O'Hara Street, Pititsburgh, PA 15260, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, 450 Technology Drive, Suite 300, Pittsburgh, PA 15219, USA
- Department of Radiology, University of Pittsburgh, PUH Suite E204, 200 Lothrop Street, Pittsburgh, PA 15213, USA
- Centre for the Neural Basis of Behavior, University of Pittsburgh and Carnegie Mellon University, 4074 Biomedical Science Tower 3, Pittsburgh, PA 15261, USA
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Choi JY, Hu S, Su TY, Murakami H, Tang Y, Blümcke I, Najm I, Sakaie K, Jones S, Griswold M, Wang ZI, Ma D. Normative quantitative relaxation atlases for characterization of cortical regions using magnetic resonance fingerprinting. Cereb Cortex 2023; 33:3562-3574. [PMID: 35945683 PMCID: PMC10068276 DOI: 10.1093/cercor/bhac292] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/14/2022] Open
Abstract
Quantitative magnetic resonance (MR) has been used to study cyto- and myelo-architecture of the human brain non-invasively. However, analyzing brain cortex using high-resolution quantitative MR acquisition can be challenging to perform using 3T clinical scanners. MR fingerprinting (MRF) is a highly efficient and clinically feasible quantitative MR technique that simultaneously provides T1 and T2 relaxation maps. Using 3D MRF from 40 healthy subjects (mean age = 25.6 ± 4.3 years) scanned on 3T magnetic resonance imaging, we generated whole-brain gyral-based normative MR relaxation atlases and investigated cortical-region-based T1 and T2 variations. Gender and age dependency of T1 and T2 variations were additionally analyzed. The coefficient of variation of T1 and T2 for each cortical-region was 3.5% and 7.3%, respectively, supporting low variability of MRF measurements across subjects. Significant differences in T1 and T2 were identified among 34 brain regions (P < 0.001), lower in the precentral, postcentral, paracentral lobule, transverse temporal, lateral occipital, and cingulate areas, which contain sensorimotor, auditory, visual, and limbic functions. Significant correlations were identified between age and T1 and T2 values. This study established whole-brain MRF T1 and T2 atlases of healthy subjects using a clinical 3T scanner, which can provide a quantitative and region-specific baseline for future brain studies and pathology detection.
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Affiliation(s)
- Joon Yul Choi
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Siyuan Hu
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Ting-Yu Su
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Hiroatsu Murakami
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Yingying Tang
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Department of Neurology, West China Hospital of Sichuan University, 37 Guoxue Ln, Wuhou District, Chengdu, Sichuan 610041, China
| | - Ingmar Blümcke
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
- Imaging Institute, Cleveland Clinic, 1950 E 89th St U Bldg, Cleveland, OH 44195, United States
| | - Imad Najm
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Ken Sakaie
- Department of Neuropathology, University of Erlangen, Schlobplatz 4, Erlangen 91054, Germany
| | - Stephen Jones
- Department of Neuropathology, University of Erlangen, Schlobplatz 4, Erlangen 91054, Germany
| | - Mark Griswold
- Department of Radiology, Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH 44106, United States
| | - Zhong Irene Wang
- Charles Shor Epilepsy Center, Neurological Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH 44106, United States
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
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8
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Gennari AG, Cserpan D, Stefanos-Yakoub I, Kottke R, O’Gorman Tuura R, Ramantani G. Diffusion tensor imaging discriminates focal cortical dysplasia from normal brain parenchyma and differentiates between focal cortical dysplasia types. Insights Imaging 2023; 14:36. [PMID: 36826756 PMCID: PMC9958211 DOI: 10.1186/s13244-023-01368-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 12/29/2022] [Indexed: 02/25/2023] Open
Abstract
OBJECTIVES Although diffusion tensor imaging (DTI) may facilitate the identification of cytoarchitectural changes associated with focal cortical dysplasia (FCD), the predominant aetiology of paediatric structural epilepsy, its potential has thus far remained unexplored in this population. Here, we investigated whether DTI indices can differentiate FCD from contralateral brain parenchyma (CBP) and whether clinical features affect these indices. METHODS In this single-centre, retrospective study, we considered children and adolescents with FCD-associated epilepsy who underwent brain magnetic resonance (MRI), including DTI. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, and radial diffusivity, were calculated in both FCD and CBP. The DTI indices best discriminating between FCD and CBP were subsequently used to assess the link between DTI and selected clinical and lesion-related parameters. RESULTS We enrolled 32 patients (20 male; median age at MRI 4 years), including 15 with histologically confirmed FCD. FA values were lower (p = 0.03), whereas MD values were higher in FCD than in CBP (p = 0.04). The difference in FA values between FCD and CBP was more pronounced for a positive vs. negative history of status epilepticus (p = 0.004). Among histologically confirmed cases, the difference in FA values between FCD and CBP was more pronounced for type IIb versus type I FCD (p = 0.03). CONCLUSIONS FA and MD discriminate between FCD and CBP, while FA differentiates between FCD types. Status epilepticus increases differences in FA, potentially reflecting changes induced in the brain. Our findings support the potential of DTI to serve as a non-invasive biomarker to characterise FCD in the paediatric population.
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Affiliation(s)
- Antonio Giulio Gennari
- Department of Neuropediatrics, University Children's Hospital Zurich, Steinwiesstrasse 75, 8032, Zurich, Switzerland. .,MR-Research Centre, University Children's Hospital Zurich, Zurich, Switzerland.
| | - Dorottya Cserpan
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Ilona Stefanos-Yakoub
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland
| | - Raimund Kottke
- grid.412341.10000 0001 0726 4330Department of Diagnostic Imaging, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Ruth O’Gorman Tuura
- grid.412341.10000 0001 0726 4330MR-Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412341.10000 0001 0726 4330Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Georgia Ramantani
- grid.412341.10000 0001 0726 4330Department of Neuropediatrics, University Children’s Hospital Zurich, Steinwiesstrasse 75, 8032 Zurich, Switzerland ,grid.7400.30000 0004 1937 0650University of Zurich, Zurich, Switzerland ,grid.412341.10000 0001 0726 4330Children’s Research Centre, University Children’s Hospital Zurich, Zurich, Switzerland
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9
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Wang Y, Royer J, Park BY, Vos de Wael R, Larivière S, Tavakol S, Rodriguez-Cruces R, Paquola C, Hong SJ, Margulies DS, Smallwood J, Valk SL, Evans AC, Bernhardt BC. Long-range functional connections mirror and link microarchitectural and cognitive hierarchies in the human brain. Cereb Cortex 2023; 33:1782-1798. [PMID: 35596951 PMCID: PMC9977370 DOI: 10.1093/cercor/bhac172] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Higher-order cognition is hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, it is unknown how computational advantages of long-range connections reflect cortical microstructure and microcircuitry. METHODS We investigated this question by (i) profiling long-range cortical connectivity using resting-state functional magnetic resonance imaging (MRI) and cortico-cortical geodesic distance mapping, (ii) assessing how long-range connections reflect local brain microarchitecture, and (iii) examining the microarchitectural similarity of regions connected through long-range connections. RESULTS Analysis of 2 independent datasets indicated that sensory/motor areas had more clustered short-range connections, while transmodal association systems hosted distributed, long-range connections. Meta-analytical decoding suggested that this topographical difference mirrored shifts in cognitive function, from perception/action towards emotional/social processing. Analysis of myelin-sensitive in vivo MRI as well as postmortem histology and transcriptomics datasets established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Notably, long-range connections were found to link spatially remote regions of association cortex with an unexpectedly similar microarchitecture. CONCLUSIONS By mapping covarying topographies of long-range functional connections and cortical microcircuits, the current work provides insights into structure-function relations in human neocortex.
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Affiliation(s)
- Yezhou Wang
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Department of Data Science, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
| | - Daniel S Margulies
- Cognitive Neuroanatomy Lab, Integrative Neuroscience and Cognition Centre, University of Paris and CRNS, INCC - UMR 8002, Rue des Saint-Pères 75006, Paris
| | - Jonathan Smallwood
- Department of Psychology, Queen's University, 62 Arch Street, Humphrey Hall, Room 232 Kingston, Ontario K7L 3N6, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A. Leipzig D-04103, Germany.,Institute of Systems Neuroscience, Heinrich Heine University, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Alan C Evans
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
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10
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Zhang S, Luo Y, Zhao Y, Zhu F, Jiang X, Wang X, Mo T, Zeng H. Prognostic analysis in children with focal cortical dysplasia II undergoing epilepsy surgery: Clinical and radiological factors. Front Neurol 2023; 14:1123429. [PMID: 36949857 PMCID: PMC10025379 DOI: 10.3389/fneur.2023.1123429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 02/06/2023] [Indexed: 03/08/2023] Open
Abstract
OBJECTIVE The aim of this study was to investigate the value of clinical profiles and radiological findings in assessing postsurgical outcomes in children with focal cortical dysplasia (FCD) II while exploring prognostic predictors of this disease. METHODS We retrospectively reviewed 50 patients with postoperative pathologically confirmed FCD II from January 2016 to June 2021. The clinical profiles and preoperative radiological findings were measured and analyzed. The patients were classified into four classes based on the Engel Class Outcome System at the last follow-up. For the analysis, the patients were divided into two categories based on Engel I and Engel II-IV, namely, seizure-free and non-seizure-free groups. Qualitative and quantitative factors were subsequently compared by groups using comparative statistics. Receiver operating characteristic (ROC) curves were used to identify the predictors of prognosis in children with FCD II. RESULTS Thirty-seven patients (74%) had Engel class I outcomes. The minimum postsurgical follow-up was 1 year. At the epilepsy onset, patients who attained seizure freedom were older and less likely to have no apparent lesions on the preoperative MRI ("MRI-negative"). The non-seizure-free group exhibited a higher gray matter signal intensity ratio (GR) on 3D T1-MPRAGE images (p = 0.006), with a lower GR on T2WI images (p = 0.003) and FLAIR images (p = 0.032). The ROC curve indicated that the model that combined the GR value of all MRI sequences (AUC, 0.87; 95% CI, 0.77-0.97; p < 0.001; 86% sensitivity, 85% specificity) was able to predict prognosis accurately. CONCLUSION A lower age at the onset or the MRI-negative finding of FCD lesions suggests a poor prognosis for children with FCD II. The model consisting of GR values from three MRI sequences facilitates the prognostic assessment of FCD II patients with subtle MRI abnormalities to prevent worse outcomes.
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Affiliation(s)
- Siqi Zhang
- Shantou University Medical College, Shantou University, Shantou, China
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Yi Luo
- Shantou University Medical College, Shantou University, Shantou, China
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Yilin Zhao
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Fengjun Zhu
- Department of Epilepsy Surgical Department, Shenzhen Children's Hospital, Shenzhen, China
| | - Xianping Jiang
- Department of Pathology, Shenzhen Children's Hospital, Shenzhen, China
| | - Xiaoyu Wang
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Tong Mo
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
| | - Hongwu Zeng
- Department of Radiology, Shenzhen Children's Hospital, Shenzhen, China
- *Correspondence: Hongwu Zeng
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11
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Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis. Heliyon 2022; 8:e12276. [PMID: 36582679 PMCID: PMC9793282 DOI: 10.1016/j.heliyon.2022.e12276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 05/19/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.
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12
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Jeong JW, Lee MH, Kuroda N, Sakakura K, O'Hara N, Juhasz C, Asano E. Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy. IEEE J Biomed Health Inform 2022; 26:5529-5539. [PMID: 35925854 PMCID: PMC9710730 DOI: 10.1109/jbhi.2022.3196330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.
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13
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Mo J, Zhang J, Hu W, Sang L, Zheng Z, Zhou W, Wang H, Zhu J, Zhang C, Wang X, Zhang K. Automated Detection and Surgical Planning for Focal Cortical Dysplasia with Multicenter Validation. Neurosurgery 2022; 91:799-807. [PMID: 36135782 DOI: 10.1227/neu.0000000000002113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/20/2022] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND In patients with surgically amenable focal cortical dysplasia (FCD), subtle neuroimaging representation and the risk of open surgery lead to gaps in surgical treatment and delays in surgery. OBJECTIVE To construct an integrated platform that can accurately detect FCD and automatically establish trajectory planning for magnetic resonance-guided laser interstitial thermal therapy. METHODS This multicenter study included retrospective patients to train the automated detection model, prospective patients for model evaluation, and an additional cohort for construction of the automated trajectory planning algorithm. For automated detection, we evaluated the performance and generalization of the conventional neural network in different multicenter cohorts. For automated trajectory planning, feasibility/noninferiority and safety score were calculated to evaluate the clinical value. RESULTS Of the 260 patients screened for eligibility, 202 were finally included. Eighty-eight patients were selected for conventional neural network training, 88 for generalizability testing, and 26 for the establishment of an automated trajectory planning algorithm. The model trained using preprocessed and multimodal neuroimaging displayed the best performance in diagnosing FCD (figure of merit = 0.827 and accuracy range = 75.0%-91.7% across centers). None of the clinical variables had a significant effect on prediction performance. Moreover, the automated trajectory was feasible and noninferior to the manual trajectory (χ2 = 3.540, P = .060) and significantly safer (overall: test statistic = 30.423, P < .001). CONCLUSION The integrated platform validated based on multicenter, prospective cohorts exhibited advantages of easy implementation, high performance, and generalizability, thereby indicating its potential in the diagnosis and minimally invasive treatment of FCD.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Lin Sang
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Zhong Zheng
- Department of Neurosurgery, Beijing Fengtai Hospital, Beijing, China
| | - Wenjing Zhou
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Haixiang Wang
- Epilepsy Center, Tsinghua University Yuquan Hospital, Beijing, China
| | - Junming Zhu
- Epilepsy Center, Department of Neurosurgery, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
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14
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Říha P, Doležalová I, Mareček R, Lamoš M, Bartoňová M, Kojan M, Mikl M, Gajdoš M, Vojtíšek L, Bartoň M, Strýček O, Pail M, Brázdil M, Rektor I. Multimodal combination of neuroimaging methods for localizing the epileptogenic zone in MR-negative epilepsy. Sci Rep 2022; 12:15158. [PMID: 36071087 PMCID: PMC9452535 DOI: 10.1038/s41598-022-19121-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/10/2022] Open
Abstract
The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.
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Affiliation(s)
- Pavel Říha
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Irena Doležalová
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Radek Mareček
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Lamoš
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Michaela Bartoňová
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Kojan
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Michal Mikl
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Martin Gajdoš
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Lubomír Vojtíšek
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Marek Bartoň
- Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ondřej Strýček
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Pail
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Milan Brázdil
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Ivan Rektor
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic. .,Multimodal and Functional Neuroimaging Research Group, CEITEC-Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
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15
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Foit NA, Yung S, Lee HM, Bernasconi A, Bernasconi N, Hong SJ. A whole-brain 3D myeloarchitectonic atlas: Mapping the Vogt-Vogt legacy to the cortical surface. Neuroimage 2022; 263:119617. [PMID: 36084859 DOI: 10.1016/j.neuroimage.2022.119617] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 09/03/2022] [Accepted: 09/05/2022] [Indexed: 11/18/2022] Open
Abstract
Building precise and detailed parcellations of anatomically and functionally distinct brain areas has been a major focus in Neuroscience. Pioneer anatomists parcellated the cortical manifold based on extensive histological studies of post-mortem brain, harnessing local variations in cortical cyto- and myeloarchitecture to define areal boundaries. Compared to the cytoarchitectonic field, where multiple neuroimaging studies have recently translated this old legacy data into useful analytical resources, myeloarchitectonics, which parcellate the cortex based on the organization of myelinated fibers, has received less attention. Here, we present the neocortical surface-based myeloarchitectonic atlas based on the histology-derived maps of the Vogt-Vogt school and its 2D translation by Nieuwenhuys. In addition to a myeloarchitectonic parcellation, our package includes intracortical laminar profiles of myelin content based on Vogt-Vogt-Hopf original publications. Histology-derived myelin density mapped on our atlas demonstrated a close overlap with in vivo quantitative MRI markers for myelin and relates to cytoarchitectural features. Complementing the existing battery of approaches for digital cartography, the whole-brain myeloarchitectonic atlas offers an opportunity to validate imaging surrogate markers of myelin in both health and disease.
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Affiliation(s)
- Niels A Foit
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada; Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg, Germany
| | - Seles Yung
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada; Center for the Developing Brain, Child Mind Institute, NY, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea.
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16
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Guerrero-Gonzalez JM, Yeske B, Kirk GR, Bell MJ, Ferrazzano PA, Alexander AL. Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI. Neuroimage 2022; 260:119475. [PMID: 35840117 PMCID: PMC9531540 DOI: 10.1016/j.neuroimage.2022.119475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 06/27/2022] [Accepted: 07/11/2022] [Indexed: 12/04/2022] Open
Abstract
Imaging-based quantitative measures from diffusion-weighted MRI (dMRI) offer the ability to non-invasively extract microscopic information from human brain tissues. Group-level comparisons of such measures represent an important approach to investigate abnormal brain conditions. These types of analyses are especially useful when the regions of abnormality spatially coincide across subjects. When this is not true, approaches for individualized analyses are necessary. Here we present a framework for single-subject multidimensional analysis based on the Mahalanobis distance. This is conducted along specific white matter pathways represented by tractography-derived streamline bundles. A definition for abnormality was constructed from Wilk’s criterion, which accounts for normative sample size, number of features used in the Mahalanobis distance, and multiple comparisons. One example of a condition exhibiting high heterogeneity across subjects is traumatic brain injury (TBI). Using the Mahalanobis distance computed from the three eigenvalues of the diffusion tensor along the cingulum, uncinate, and parcellated corpus callosum tractograms, 8 severe TBI patients were individually compared to a normative sample of 49 healthy controls. For all TBI patients, the analyses showed statistically significant deviations from the normative data at one or multiple locations along the analyzed bundles. The detected anomalies were widespread across the analyzed tracts, consistent with the expected heterogeneity that is hallmark of TBI. Each of the controls subjects was also compared to the remaining 48 subjects in the control group in a leave-one-out fashion. Only two segments were identified as abnormal out of the entire analysis in the control group, thus the method also demonstrated good specificity.
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Affiliation(s)
- Jose M Guerrero-Gonzalez
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
| | - Benjamin Yeske
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Gregory R Kirk
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Michael J Bell
- Critical Care Medicine, Children's National Medical Center, Washington, DC, USA
| | - Peter A Ferrazzano
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
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Shi D, Zhang H, Wang G, Wang S, Yao X, Li Y, Guo Q, Zheng S, Ren K. Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis. Front Aging Neurosci 2022; 14:806828. [PMID: 35309885 PMCID: PMC8928361 DOI: 10.3389/fnagi.2022.806828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 12/03/2022] Open
Abstract
Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuang Zheng
- School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- *Correspondence: Ke Ren,
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18
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Lee HM, Fadaie F, Gill R, Caldairou B, Sziklas V, Crane J, Hong SJ, Bernhardt BC, Bernasconi A, Bernasconi N. Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification. Brain 2021; 145:897-908. [PMID: 34849619 DOI: 10.1093/brain/awab425] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/28/2021] [Accepted: 10/28/2021] [Indexed: 11/14/2022] Open
Abstract
In drug-resistant temporal lobe epilepsy (TLE), precise predictions of drug response, surgical outcome, and cognitive dysfunction at an individual level remain challenging. A possible explanation may lie in the dominant "one-size-fits-all" group-level analytical approaches that does not allow parsing inter-individual variations along the disease spectrum. Conversely, analyzing inter-patient heterogeneity is increasingly recognized as a step towards person-centered care. Here, we utilized unsupervised machine learning to estimate latent relations (or disease factors) from 3 T multimodal MRI features (cortical thickness, hippocampal volume, FLAIR, T1/FLAIR, diffusion parameters) representing whole-brain patterns of structural pathology in 82 TLE patients. We assessed the specificity of our approach against age- and sex-matched healthy individuals and a cohort of frontal lobe epilepsy patients with histologically-verified focal cortical dysplasia. We identified four latent disease factors variably co-expressed within each patient and characterized by ipsilateral hippocampal microstructural alterations, loss of myelin and atrophy (Factor-1), bilateral paralimbic and hippocampal gliosis (Factor-2), bilateral neocortical atrophy (Factor-3), bilateral white matter microstructural alterations (Factor-4). Bootstrap analysis and parameter variations supported high stability and robustness of these factors. Moreover, they were not expressed in healthy controls and only negligibly in disease controls, supporting specificity. Supervised classifiers trained on latent disease factors could predict patient-specific drug-response in 76 ± 3% and postsurgical seizure outcome in 88 ± 2%, outperforming classifiers that did not operate on latent factor information. Latent factor models predicted inter-patient variability in cognitive dysfunction (verbal IQ: r = 0.40 ± 0.03; memory: r = 0.35 ± 0.03; sequential motor tapping: r = 0.36 ± 0.04), again outperforming baseline learners. Data-driven analysis of disease factors provides a novel appraisal of the continuum of interindividual variability, which is likely determined by multiple interacting pathological processes. Incorporating interindividual variability is likely to improve clinical prognostics.
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Affiliation(s)
- Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research Institute for Basic Science, Department of Biomedical Engineering, Sungkyunkwan University Suwon South Korea
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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19
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MRI of focal cortical dysplasia. Neuroradiology 2021; 64:443-452. [PMID: 34839379 PMCID: PMC8850246 DOI: 10.1007/s00234-021-02865-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/17/2021] [Indexed: 11/09/2022]
Abstract
Focal cortical dysplasia (FCD) are histopathologically categorized in ILAE type I to III. Mild malformations of cortical development (mMCD) including those with oligodendroglial hyperplasia (MOGHE) are to be integrated into this classification yet. Only FCD type II have distinctive MRI and molecular genetics alterations so far. Subtle FCD including FCD type II located in the depth of a sulcus are often overlooked requiring the use of dedicated sequences (MP2RAGE, FLAWS, EDGE) and/or voxel (VBM)- or surface-based (SBM) postprocessing. The added value of 7 Tesla MRI has to be proven yet.
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20
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Gill RS, Lee HM, Caldairou B, Hong SJ, Barba C, Deleo F, D'Incerti L, Mendes Coelho VC, Lenge M, Semmelroch M, Schrader DV, Bartolomei F, Guye M, Schulze-Bonhage A, Urbach H, Cho KH, Cendes F, Guerrini R, Jackson G, Hogan RE, Bernasconi N, Bernasconi A. Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia. Neurology 2021; 97:e1571-e1582. [PMID: 34521691 DOI: 10.1212/wnl.0000000000012698] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 07/26/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVE To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD). METHODS We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity. RESULTS Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls. DISCUSSION This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.
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Affiliation(s)
- Ravnoor Singh Gill
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Hyo-Min Lee
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Benoit Caldairou
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Seok-Jun Hong
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Carmen Barba
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Francesco Deleo
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Ludovico D'Incerti
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Vanessa Cristina Mendes Coelho
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Matteo Lenge
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Mira Semmelroch
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Dewi Victoria Schrader
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Fabrice Bartolomei
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Maxime Guye
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Andreas Schulze-Bonhage
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Horst Urbach
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Kyoo Ho Cho
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Fernando Cendes
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Renzo Guerrini
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Graeme Jackson
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - R Edward Hogan
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Neda Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO
| | - Andrea Bernasconi
- From the Neuroimaging of Epilepsy Laboratory (R.S.G., H.-M.L., B.C., S.-J.H., N.B., A.B.), Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada; Pediatric Neurology Unit and Laboratories (C.B., M.L., R.G.), Children's Hospital A. Meyer-University of Florence, Italy; Epilepsy Unit (F.D.) and Neuroradiology (L.D.), Fondazione IRCCS Istituto Neurologico C. Besta, Milan, Italy; Department of Neurology (V.C.M.C., F.C.), University of Campinas, Brazil; The Florey Institute of Neuroscience and Mental Health and The University of Melbourne (M.S., G.J.), Victoria, Australia; Department of Pediatrics (D.V.S.), British Columbia Children's Hospital, Vancouver, Canada; Aix Marseille University (F.B.), INSERM UMR 1106, Institut de Neurosciences des Systèmes; Aix Marseille University (M.G.), CNRS, CRMBM UMR 7339, Marseille, France; Freiburg Epilepsy Center (A.S.-B., H.U.), Universitätsklinikum Freiburg, Germany; Department of Neurology (K.H.C.), Yonsei University College of Medicine, Seoul, Korea; and Department of Neurology (R.E.H.), Washington University School of Medicine, St. Louis, MO.
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21
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Nowak A, Bala A. Occult focal cortical dysplasia may predict poor outcome of surgery for drug-resistant mesial temporal lobe epilepsy. PLoS One 2021; 16:e0257678. [PMID: 34591859 PMCID: PMC8483375 DOI: 10.1371/journal.pone.0257678] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 09/07/2021] [Indexed: 11/18/2022] Open
Abstract
PURPOSE The results of surgery in patients with mesial temporal lobe epilepsy (MTLE) associated with hippocampal sclerosis (HS) are favorable, with a success rate over 70% following resection. An association of HS with focal cortical dysplasia (FCD) in the temporal lobe is one of the potential causes for poor surgical outcome in MTLE. We aimed to analyzed seizure outcome in a population of MTLE patients and recognize the role of occult FCD in achieving postoperative seizure control. METHODS We retrospectively analyzed postoperative outcomes for 82 consecutive adult patients with the syndrome of MTLE due to HS, who had no concomitant lesions within temporal lobe in MRI and who underwent surgical treatment in the years 2005-2016, and correlated factors associated with seizure relapse. RESULTS At the latest follow-up evaluation after surgery, 59 (72%) were free of disabling seizures (Engel Class I) and 48 (58,5%) had an Engel Class Ia. HS associated with FCD in neocortical structures were noted in 33 patients (40%). Analyzes have shown that dual pathology was the most significant negative predictive factor for Engel class I and Engel class Ia outcome. CONCLUSIONS The incidence of dual pathology in patients with temporal lobe epilepsy seems to be underestimated. An incomplete epileptogenic zone resection of occult focal temporal dysplasia within temporal lobe is supposed to be the most important negative prognostic factor for seizure freedom after epilepsy surgery in MTLE-HS patients. The study indicates the need to improve diagnostics for other temporal lobe pathologies, despite the typical clinical and radiological picture of MTLE-HS.
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Affiliation(s)
- Arkadiusz Nowak
- Department of Neurosurgery, Medical University of Warsaw, Warsaw, Poland
- * E-mail:
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22
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Fadaie F, Lee HM, Caldairou B, Gill RS, Sziklas V, Crane J, Bernhardt BC, Hong SJ, Bernasconi A, Bernasconi N. Atypical functional connectome hierarchy impacts cognition in temporal lobe epilepsy. Epilepsia 2021; 62:2589-2603. [PMID: 34490890 DOI: 10.1111/epi.17032] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 06/24/2021] [Accepted: 07/26/2021] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Drug-resistant temporal lobe epilepsy (TLE) is typically associated with hippocampal pathology. However, widespread network alterations are increasingly recognized and suggested to perturb cognitive function in multiple domains. Here we tested (1) whether TLE shows atypical cortical hierarchical organization, differentiating sensory and higher order systems; and (2) whether atypical hierarchy predicts cognitive impairment. METHODS We studied 72 well-characterized drug-resistant TLE patients and 41 healthy controls, statistically matched for age and sex, using multimodal magnetic resonance imaging analysis and cognitive testing. To model cortical hierarchical organization in vivo, we used a bidirectional stepwise functional connectivity analysis tapping into the differentiation between sensory/unimodal and paralimbic/transmodal cortices. Linear models compared patients to controls. Finally, we assessed associations of functional anomalies to cortical atrophy and microstructural anomalies, as well as clinical and cognitive parameters. RESULTS Compared to controls, TLE presented with bidirectional disruptions of sensory-paralimbic functional organization. Stepwise connectivity remained segregated within paralimbic and salience networks at the top of the hierarchy, and sensorimotor and dorsal attention at the bottom. Whereas paralimbic segregation was associated with atypical cortical myeloarchitecture and hippocampal atrophy, dysconnectivity of sensorimotor cortices reflected diffuse cortical thinning. The degree of abnormal hierarchical organization in sensory-petal streams covaried, with broad cognitive impairments spanning sensorimotor, attention, fluency, and visuoconstructional ability and memory, and was more marked in patients with longer disease duration and Engel I outcome. SIGNIFICANCE Our findings show atypical functional integration between paralimbic/transmodal and sensory/unimodal systems in TLE. Differential associations with paralimbic microstructure and sensorimotor atrophy suggest that system-level imbalance likely reflects complementary structural processes, but ultimately accounts for a broad spectrum of cognitive impairments. Hierarchical contextualization of cognitive deficits promises to open new avenues for personalized counseling in TLE.
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Affiliation(s)
- Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Hyo M Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ravnoor S Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Medical Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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23
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Perilesional white matter integrity in drug-resistant epilepsy related to focal cortical dysplasia. Seizure 2021; 91:484-489. [PMID: 34343860 DOI: 10.1016/j.seizure.2021.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/04/2021] [Accepted: 07/23/2021] [Indexed: 11/24/2022] Open
Abstract
PURPOSE We aimed to investigate the differences of white matter (WM) between the focal cortical dysplasia (FCD) patients with drug-resistant epilepsy and those with drug-responsive epilepsy. METHODS Thirty epileptic patients with MRI-identified or histologically proven FCD were consecutively enrolled. Fractional anisotropy (FA) and mean diffusivity (MD) of the ipsilateral perilesional WM and contralateral homotopic WM layer masks were computed and corrected by the FA/MD of the corresponding hemispheric WM. The difference was evaluated using paired t-tests. The FA, MD and volumes of hemispheric WM and corpus callosum were also calculated. RESULTS Patients with drug-resistant epilepsy showed significantly decreased FA and increased MD among ipsilateral perilesional WM layer 1 and 2, while patients with drug-responsive epilepsy showed decreased FA in only ipsilateral perilesional WM layer l, compared to remaining ipsilateral perilesional WM layers and contralateral layers 1 through 6. The integrity and volumes of the hemispheric WM and corpus callosum were similar between the two groups. CONCLUSION We demonstrated that the WM microstructural alterations differed between epileptic patients with FCD according to their antiepileptic drug responses. More extensive perilesional WM abnormality is observed in patients with drug-resistant epilepsy related to FCD.
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24
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Khalilov VS, Kholin AA, Kislyakov AN, Bakaeva BR, Medvedeva NA, Panova KI, Zavadenko NN. [The structural basis of epileptogenesis in an adult patient: the comprehensive diagnostics experience]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:27-33. [PMID: 34283526 DOI: 10.17116/jnevro202112106127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
On the example of the diagnosis of the structural basis of focal epilepsy in an adult patient, the possibilities of a multimodal and interdisciplinary approach to diagnosis, combining the latest methods of neuroimaging with the results of neurophysiological examinations, are considered. The interaction and high qualification of specialists in epileptology, neuroradiology and pathomorphology provide a high probability to determine the cause of the focal forms of epilepsy. Along with the introduction of super-inductive MR systems, it is important to use their capabilities correctly and optimize the scanning protocol for the individual characteristics of the patient. With a long-term pharmacoresistant course of focal epilepsy, accompanied by low quality of life, the progression of neurological deficits and the aggravation of cognitive and personality problems in patients, it becomes obvious that these patients, even with negative results of standard MRI, are candidates for a more in-depth comprehensive examination to identify the structural basis of epileptogenesis and subsequent epileptic surgery. However, the problem of epilepsy pseudoresistance should be considered, when a patient with uncontrolled seizures takes antiepileptic drugs for a long time in inadequate dosages. Modern comprehensive diagnostics offers new rational approaches to antiepileptic therapy indication on the part of the doctor, as well as to improve the patient's compliance to the treatment.
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Affiliation(s)
- V S Khalilov
- Federal Research and Clinical Center for children and adolescents, Moscow, Russia.,Pirogov Russian National Research Medical University, Moscow, Russia
| | - A A Kholin
- Pirogov Russian National Research Medical University, Moscow, Russia.,Russian Children Clinical Hospital of the Pirogov Russian National Research Medical University, Moscow, Russia
| | | | - B R Bakaeva
- Federal Research and Clinical Center for specialized types of medical care and medical technologies, Moscow, Russia
| | - N A Medvedeva
- Federal Research and Clinical Center for children and adolescents, Moscow, Russia
| | | | - N N Zavadenko
- Pirogov Russian National Research Medical University, Moscow, Russia
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25
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Pedersen M, Zalesky A. Intracranial brain stimulation modulates fMRI-based network switching. Neurobiol Dis 2021; 156:105401. [PMID: 34023395 DOI: 10.1016/j.nbd.2021.105401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/26/2021] [Accepted: 05/19/2021] [Indexed: 10/21/2022] Open
Abstract
The extent to which functional MRI (fMRI) reflects direct neuronal changes remains unknown. Using 160 simultaneous electrical stimulation (es-fMRI) and intracranial brain stimulation recordings acquired in 26 individuals with epilepsy (with varying electrode locations), we tested whether brain networks dynamically change during intracranial brain stimulation, aiming to establish whether switching between brain networks is reduced after intracranial brain stimulation. As the brain spontaneously switches between a repertoire of intrinsic functional network configurations and the rate of switching is likely increased in epilepsy, we hypothesised that intracranial stimulation would reduce the brain's switching rate, thus potentially normalising aberrant brain network dynamics. To test this hypothesis, we quantified the rate that brain regions changed networks over time in response to brain stimulation, using network switching applied to multilayer modularity analysis of time-resolved es-fMRI connectivity. Network switching and synchrony was decreased after the first brain stimulation, followed by a more consistent pattern of network switching over time. This change was commonly observed in cortical networks and adjacent to the electrode targets. Our results suggest that neuronal perturbation is likely to modulate large-scale brain networks, and multilayer network modelling may be used to inform the clinical efficacy of brain stimulation in epilepsy.
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Affiliation(s)
- Mangor Pedersen
- Department of Psychology and Neuroscience, Auckland University of Technology (AUT), Auckland, New Zealand.
| | - Andrew Zalesky
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, VIC, Australia; Melbourne School of Engineering, The University of Melbourne, VIC, Australia
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26
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Dalboni da Rocha JL, Coutinho G, Bramati I, Moll FT, Sitaram R. Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders. Brain Imaging Behav 2021; 14:641-652. [PMID: 30519999 DOI: 10.1007/s11682-018-0002-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer's disease and healthy controls, 83% between Alzheimer's disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others.
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Affiliation(s)
- Josué Luiz Dalboni da Rocha
- Brain and Language Lab, Department of Clinical Neuroscience, University of Geneva, Geneva, Switzerland.,Department of Biomedical Engineering, University of Florida, Gainesville, USA
| | - Gabriel Coutinho
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Ivanei Bramati
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil
| | - Fernanda Tovar Moll
- D'Or Institute for Research and Education, Rio de Janeiro, Brazil.,Federal Univerisity of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Ranganatha Sitaram
- Institute for Biological and Medical Engineering, Schools of Engineering, Biology and Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile. .,Department of Psychiatry and Section of Neuroscience, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile. .,Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.
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27
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Di Sapia R, Moro F, Montanarella M, Iori V, Micotti E, Tolomeo D, Wang KKW, Vezzani A, Ravizza T, Zanier ER. In-depth characterization of a mouse model of post-traumatic epilepsy for biomarker and drug discovery. Acta Neuropathol Commun 2021; 9:76. [PMID: 33902685 PMCID: PMC8073903 DOI: 10.1186/s40478-021-01165-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 03/19/2021] [Indexed: 12/11/2022] Open
Abstract
Post-traumatic epilepsy (PTE) accounts for 5% of all epilepsies and 10–20% of the acquired forms. The latency between traumatic brain injury (TBI) and epilepsy onset in high-risk patients offers a therapeutic window for intervention to prevent or improve the disease course. However, progress towards effective treatments has been hampered by the lack of sensitive prognostic biomarkers of PTE, and of therapeutic targets. There is therefore a pressing clinical need for preclinical PTE models suitable for biomarker discovery and drug testing. We characterized in-depth a model of severe TBI induced by controlled cortical impact evolving into PTE in CD1 adult male mice. To identify sensitive measures predictive of PTE development and severity, TBI mice were longitudinally monitored by video-electrocorticography (ECoG), examined by MRI, and tested for sensorimotor and cognitive deficits and locomotor activity. At the end of the video-ECoG recording mice were killed for brain histological analysis. PTE occurred in 58% of mice with frequent motor seizures (one seizure every other day), as determined up to 5 months post-TBI. The weight loss of PTE mice in 1 week after TBI correlated with the number of spontaneous seizures at 5 months. Moreover, the recovery rate of the sensorimotor deficit detected by the SNAP test before the predicted time of epilepsy onset was significantly lower in PTE mice than in those without epilepsy. Neuroscore, beam walk and cognitive deficit were similar in all TBI mice. The increase in the contusion volume, the volume of forebrain regions contralateral to the lesioned hemisphere and white matter changes over time assessed by MRI were similar in PTE and no-PTE mice. However, brain histology showed a more pronounced neuronal cell loss in the cortex and hippocampus contralateral to the injured hemisphere in PTE than in no-PTE mice. The extensive functional and neuropathological characterization of this TBI model, provides a tool to identify sensitive measures of epilepsy development and severity clinically useful for increasing PTE prediction in high-risk TBI patients. The high PTE incidence and spontaneous seizures frequency in mice provide an ideal model for biomarker discovery and for testing new drugs.
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28
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Girardi-Schappo M, Fadaie F, Lee HM, Caldairou B, Sziklas V, Crane J, Bernhardt BC, Bernasconi A, Bernasconi N. Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia 2021; 62:1022-1033. [PMID: 33705572 DOI: 10.1111/epi.16864] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 12/23/2022]
Abstract
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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Affiliation(s)
- Mauricio Girardi-Schappo
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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Brain functional connectivity patterns in focal cortical dysplasia related epilepsy. Seizure 2021; 87:1-6. [PMID: 33636448 DOI: 10.1016/j.seizure.2021.02.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 02/05/2021] [Accepted: 02/06/2021] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) appears to be strongly associated with intractable epilepsy. Although patients with FCD are candidates for epilepsy surgery, gray matter structural abnormalities can extend beyond the primary lesion, which makes surgery less effective. The objective of this study was to evaluate functional connectivity patterns in epilepsy associated with FCD to explore the underlying pathological mechanism of this disorder. METHODS A total of 34 patients (14 men) with FCD and epilepsy [mean age ± standard deviation (SD), 24.5 ± 9.8 years; range, 8-47 years] and 34 age-matched healthy controls (14 men, 24.6 ± 9.7 years) underwent functional magnetic resonance imaging. Independent component analysis (ICA), seed-based functional connectivity, and graph theory were applied to analyze functional connectivity patterns in the brain. RESULTS Patients showed more connections among dorsal attention network, anterior default mode network, and sensorimotor brain networks than healthy controls based on ICA. Analysis of connectivity between regions of interest (ROIs) showed greater functional connectivity in patients between frontal and temporal regions, but lower connectivity between the cerebellum and frontal regions. The normalized characteristic path length was significantly higher in group of patients, but the two groups showed no significant differences in global or regional efficiency, clustering coefficient or characteristic path length. CONCLUSIONS Analysis of ICA-derived and ROI-based functional connectivity suggests that disrupted interactions and dysconnectivity in large-scale neural networks and frontotemporal-cerebellar regions may contribute to underlying pathological mechanisms in FCD-related epilepsy.
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Snyder K, Whitehead EP, Theodore WH, Zaghloul KA, Inati SJ, Inati SK. Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach. NEUROIMAGE-CLINICAL 2021; 30:102565. [PMID: 33556791 PMCID: PMC7887437 DOI: 10.1016/j.nicl.2021.102565] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 11/21/2020] [Accepted: 01/11/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Focal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection. METHODS Standardized MPRAGE, T2 and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector. RESULTS FCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity. SIGNIFICANCE A normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.
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Affiliation(s)
- Kathryn Snyder
- EEG Section, Office of the Clinical Director, NINDS, National Institutes of Health, United States
| | | | - William H Theodore
- Clinical Epilepsy Section, NINDS, National Institutes of Health, United States
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, United States
| | - Souheil J Inati
- Office of the Clinical Director, NINDS, National Institutes of Health, United States
| | - Sara K Inati
- EEG Section, Office of the Clinical Director, NINDS, National Institutes of Health, United States.
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31
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Liu T, Liang H, Cui J, Sun K, Zhang S, Yuan L, Wang Y, Xue R, Liang S. Clinical Application of 7T Magnetic Resonance Imaging in Patients with Focal Cortical Dysplasia IIa and Epilepsy. Stereotact Funct Neurosurg 2020; 99:212-220. [PMID: 33311023 DOI: 10.1159/000511152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 08/25/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND Focal cortical dysplasia (FCD) is one of the most important pathogenic findings in patients with extratemporal lobe epilepsy. Magnetic resonance imaging (MRI)-negative is the most important negative factor to predict postoperative seizure freedom; however, FCD-I and part of FCD-IIa are MRI-negative on routine MRI. OBJECTIVES To explore the diagnostic values of 7T MRI and its new scan sequences in epilepsy patients with FCD-IIa. METHODS To include patients with focal seizure and suspicious focal abnormal imaging on 3T MRI during preoperative evaluation and perform a 7T MRI scan with white matter-suppressed (WMS) and gray-white matter tissue border enhancement (GWBE) sequences, resective epilepsy surgery, and postoperative pathological finding of FCD-IIa. The preoperative qualitative and localization significance of 7T MRI and 3T MRI in lesions with FCD-IIa was compared, and then, the imaging characteristics of lesions with FCD-IIa on 7T MRI were analyzed. RESULTS Ten cases were enrolled in this study. Seven tesla MRI presented high spatial resolutions and a high signal-to-noise ratio. WMS and GWBE could selectively suppress the signal of special tissue and improved the possibility of FCD findings. FCD-IIa showed abnormal thickness of gray matter and a blurring border and was hypointense on 7T MRI compared with 3T MRI. Seven patients showed improvement in the qualitative diagnosis strength grade of FCD, and 6 subjects showed improvement in the localization strength grade of the lesion border after careful reading of the 7T MR images. Significant differences were found in the qualitative diagnosis of FCD (p < 0.05) and localization of the lesion border (p < 0.05) between the neuroimaging diagnoses based on 3T MRI and the findings based on 7T MRI. CONCLUSION 7T MRI with WMS and GWBE sequences shows application value in the preoperative imaging diagnosis of lesions with FCD-IIa in epilepsy patients.
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Affiliation(s)
- Tinghong Liu
- Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Huilou Liang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jianfei Cui
- Neurosurgery Department, Luoyang Center Hospital Affiliated to Zhengzhou University, Luoyang, China
| | - Kaibao Sun
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shaohui Zhang
- Neurosurgery Department, Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Liu Yuan
- Neurosurgery Department, Fourth Medical Center of PLA General Hospital, Beijing, China
| | - Yangshuo Wang
- Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - Rong Xue
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, .,University of Chinese Academy of Sciences, Beijing, China, .,Beijing Institute for Brain Disorders, Beijing, China,
| | - Shuli Liang
- Functional Neurosurgery Department, National Center for Children's Health, Beijing Children's Hospital, Capital Medical University, Beijing, China
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Larivière S, Bernasconi A, Bernasconi N, Bernhardt BC. Connectome biomarkers of drug-resistant epilepsy. Epilepsia 2020; 62:6-24. [PMID: 33236784 DOI: 10.1111/epi.16753] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/29/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023]
Abstract
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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Affiliation(s)
- Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Guo Y, Liu Y, Ming W, Wang Z, Zhu J, Chen Y, Yao L, Ding M, Shen C. Distinguishing Focal Cortical Dysplasia From Glioneuronal Tumors in Patients With Epilepsy by Machine Learning. Front Neurol 2020; 11:548305. [PMID: 33329300 PMCID: PMC7732488 DOI: 10.3389/fneur.2020.548305] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/14/2020] [Indexed: 11/13/2022] Open
Abstract
Purpose: We are aiming to build a supervised machine learning-based classifier, in order to preoperatively distinguish focal cortical dysplasia (FCD) from glioneuronal tumors (GNTs) in patients with epilepsy. Methods: This retrospective study was comprised of 96 patients who underwent epilepsy surgery, with the final neuropathologic diagnosis of either an FCD or GNTs. Seven classical machine learning algorithms (i.e., Random Forest, SVM, Decision Tree, Logistic Regression, XGBoost, LightGBM, and CatBoost) were employed and trained by our dataset to get the classification model. Ten features [i.e., Gender, Past history, Age at seizure onset, Course of disease, Seizure type, Seizure frequency, Scalp EEG biomarkers, MRI features, Lesion location, Number of antiepileptic drug (AEDs)] were analyzed in our study. Results: We enrolled 56 patients with FCD and 40 patients with GNTs, which included 29 with gangliogliomas (GGs) and 11 with dysembryoplasic neuroepithelial tumors (DNTs). Our study demonstrated that the Random Forest-based machine learning model offered the best predictive performance on distinguishing the diagnosis of FCD from GNTs, with an F1-score of 0.9180 and AUC value of 0.9340. Furthermore, the most discriminative factor between FCD and GNTs was the feature "age at seizure onset" with the Chi-square value of 1,213.0, suggesting that patients who had a younger age at seizure onset were more likely to be diagnosed as FCD. Conclusion: The Random Forest-based machine learning classifier can accurately differentiate FCD from GNTs in patients with epilepsy before surgery. This might lead to improved clinician confidence in appropriate surgical planning and treatment outcomes.
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Affiliation(s)
- Yi Guo
- Department of General Practice, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yushan Liu
- School of Computer Science, Fudan University, Shanghai, China
| | - Wenjie Ming
- Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Department of Neurology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Zhongjin Wang
- Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Department of Neurology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Junming Zhu
- Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Department of Neurosurgery, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Yang Chen
- School of Computer Science, Fudan University, Shanghai, China
| | - Lijun Yao
- Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Meiping Ding
- Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Department of Neurology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Chunhong Shen
- Epilepsy Center, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Department of Neurology, School of Medicine, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
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Hong SJ, Hyung B, Paquola C, Bernhardt BC. The Superficial White Matter in Autism and Its Role in Connectivity Anomalies and Symptom Severity. Cereb Cortex 2020; 29:4415-4425. [PMID: 30566613 DOI: 10.1093/cercor/bhy321] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/11/2018] [Accepted: 11/26/2018] [Indexed: 12/29/2022] Open
Abstract
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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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada.,Center for the Developing Brain, Child Mind Institute, 101 E 56th St, New York, NY 10022, USA
| | - Brian Hyung
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
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Zhang H, Mo J, Jiang H, Li Z, Hu W, Zhang C, Wang Y, Wang X, Liu C, Zhao B, Zhang J, Zhang K. Deep Learning Model for the Automated Detection and Histopathological Prediction of Meningioma. Neuroinformatics 2020; 19:393-402. [PMID: 32974873 DOI: 10.1007/s12021-020-09492-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2020] [Indexed: 01/12/2023]
Abstract
The volumetric assessment and accurate grading of meningiomas before surgery are highly relevant for therapy planning and prognosis prediction. This study was to design a deep learning algorithm and evaluate the performance in detecting meningioma lesions and grade classification. In total, 5088 patients with histopathologically confirmed meningioma were retrospectively included. The pyramid scene parsing network (PSPNet) was trained to automatically detect and delineate the meningiomas. The results were compared to manual segmentations by evaluating the mean intersection over union (mIoU). The performance of grade classification was evaluated by accuracy. For the automated detection and segmentation of meningiomas, the mean pixel accuracy, tumor accuracy, background accuracy and mIoU were 99.68%, 81.36%, 99.88% and 81.36% for all patients; 99.52%, 84.86%, 99.93% and 84.86% for grade I meningiomas; 99.57%, 80.11%, 99.92% and 80.12% for grade II meningiomas; and 99.75%, 78.40%, 99.99% and 78.40% for grade III meningiomas, respectively. For grade classification, the accuracy values of the training and test datasets were 99.93% and 81.52% for all patients; 99.98% and 98.51% for grade I meningiomas; 99.91% and 66.67% for grade II meningiomas; and 99.88% and 73.91% for grade III meningiomas, respectively. The automated detection, segmentation and grade classification of meningiomas based on deep learning were accurate and reliable and may improve the monitoring and treatment of this frequently occurring tumor entity. Furthermore, the method could function as a useful tool for preassessment and preselection for radiologists, offering auxiliary information for clinical decision making in presurgical evaluation.
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Affiliation(s)
- Hua Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Han Jiang
- OpenBayes Joint Laboratory For Artificial Intelligence, Tianjin University, Tianjin, China
| | - Zhuyun Li
- OpenBayes Joint Laboratory For Artificial Intelligence, Tianjin University, Tianjin, China.,Graduate School of IPS, Waseda University, Fukuoka, Japan
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. .,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. .,China National Clinical Research Center for Neurological Diseases, Beijing, China.
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China. .,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China. .,China National Clinical Research Center for Neurological Diseases, Beijing, China.
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Deleo F, Hong SJ, Fadaie F, Caldairou B, Krystal S, Bernasconi N, Bernasconi A. Whole-brain multimodal MRI phenotyping of periventricular nodular heterotopia. Neurology 2020; 95:e2418-e2426. [PMID: 32817185 DOI: 10.1212/wnl.0000000000010648] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Accepted: 05/26/2020] [Indexed: 12/14/2022] Open
Abstract
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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Affiliation(s)
- Francesco Deleo
- 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
| | - Seok-Jun Hong
- 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
| | - Fatemeh Fadaie
- 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
| | - Benoit Caldairou
- 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
| | - Sidney Krystal
- 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
| | - Neda Bernasconi
- 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
| | - Andrea Bernasconi
- 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.
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Aydin Ü, Pellegrino G, Ali OBK, Abdallah C, Dubeau F, Lina JM, Kobayashi E, Grova C. Magnetoencephalography resting state connectivity patterns as indicatives of surgical outcome in epilepsy patients. J Neural Eng 2020; 17:035007. [PMID: 32191632 DOI: 10.1088/1741-2552/ab8113] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Focal epilepsy is a disorder affecting several brain networks; however, epilepsy surgery usually targets a restricted region, the so-called epileptic focus. There is a growing interest in embedding resting state (RS) connectivity analysis into pre-surgical workup. APPROACH In this retrospective study, we analyzed Magnetoencephalography (MEG) long-range RS functional connectivity patterns in patients with drug-resistant focal epilepsy. MEG recorded prior to surgery from seven seizure-free (Engel Ia) and five non seizure-free (Engel III or IV) patients were analyzed (minimum 2-years post-surgical follow-up). MEG segments without any detectable epileptic activity were source localized using wavelet-based Maximum Entropy on the Mean method. Amplitude envelope correlation in the theta (4-8 Hz), alpha (8-13 Hz), and beta (13-26 Hz) bands were used for assessing connectivity. MAIN RESULTS For seizure-free patients, we found an isolated epileptic network characterized by weaker connections between the brain region where interictal epileptic discharges (IED) are generated and the rest of the cortex, when compared to connectivity between the corresponding contralateral homologous region and the rest of the cortex. Contrarily, non seizure-free patients exhibited a widespread RS epileptic network characterized by stronger connectivity between the IED generator and the rest of the cortex, in comparison to the contralateral region and the cortex. Differences between the two seizure outcome groups concerned mainly distant long-range connections and were found in the alpha-band. SIGNIFICANCE Importantly, these connectivity patterns suggest specific mechanisms describing the underlying organization of the epileptic network and were detectable at the individual patient level, supporting the prospect use of MEG connectivity patterns in epilepsy to predict post-surgical seizure outcome.
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Affiliation(s)
- Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada. Authors to whom any correspondence should be addressed
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Lorio S, Adler S, Gunny R, D'Arco F, Kaden E, Wagstyl K, Jacques TS, Clark CA, Cross JH, Baldeweg T, Carmichael DW. MRI profiling of focal cortical dysplasia using multi-compartment diffusion models. Epilepsia 2020; 61:433-444. [PMID: 32065673 PMCID: PMC7154549 DOI: 10.1111/epi.16451] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/26/2020] [Accepted: 01/27/2020] [Indexed: 12/11/2022]
Abstract
Objective Focal cortical dysplasia (FCD) lesion detection and subtyping remain challenging on conventional MRI. New diffusion models such as the spherical mean technique (SMT) and neurite orientation dispersion and density imaging (NODDI) provide measurements that potentially produce more specific maps of abnormal tissue microstructure. This study aims to assess the SMT and NODDI maps for computational and radiological lesion characterization compared to standard fractional anisotropy (FA) and mean diffusivity (MD). Methods SMT, NODDI, FA, and MD maps were calculated for 33 pediatric patients with suspected FCD (18 histologically confirmed). Two neuroradiologists scored lesion visibility on clinical images and diffusion maps. Signal profile changes within lesions and homologous regions were quantified using a surface‐based approach. Diffusion parameter changes at multiple cortical depths were statistically compared between FCD type IIa and type IIb. Results Compared to fluid‐attenuated inversion recovery (FLAIR) or T1‐weighted imaging, lesions conspicuity on NODDI intracellular volume fraction (ICVF) maps was better/equal/worse in 5/14/14 patients, respectively, while on SMT intra‐neurite volume fraction (INVF) in 3/3/27. Compared to FA or MD, lesion conspicuity on the ICVF was better/equal/worse in 27/4/2, while on the INVF in 20/7/6. Quantitative signal profiling demonstrated significant ICVF and INVF reductions in the lesions, whereas SMT microscopic mean, radial, and axial diffusivities were significantly increased. FCD type IIb exhibited greater changes than FCD type IIa. No changes were detected on FA or MD profiles. Significance FCD lesion‐specific signal changes were found in ICVF and INVF but not in FA and MD maps. ICVF and INVF showed greater contrast than FLAIR in some cases and had consistent signal changes specific to FCD, suggesting that they could improve current presurgical pediatric epilepsy imaging protocols and can provide features useful for automated lesion detection.
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Affiliation(s)
- Sara Lorio
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, London, UK
| | - Sophie Adler
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | | | | | - Enrico Kaden
- Centre for Medical Image Computing, University College London, London, UK
| | - Konrad Wagstyl
- Brain Mapping Unit, Institute of Psychiatry, University of Cambridge, Cambridge, UK
| | - Thomas S Jacques
- Developmental Biology and Cancer Programme, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Department of Histopathology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Chris A Clark
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Judith Helen Cross
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Torsten Baldeweg
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - David W Carmichael
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, London, UK
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Nolan RL, Brandmeir N, Tucker ES, Magruder JL, Lee MR, Chen G, Lewis JW. Functional and resting-state characterizations of a periventricular heterotopic nodule associated with epileptogenic activity. Neurosurg Focus 2020; 48:E10. [PMID: 32006947 DOI: 10.3171/2019.11.focus19765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 11/05/2019] [Indexed: 11/06/2022]
Abstract
The object of this study was to extensively characterize a region of periventricular nodular heterotopia (PVNH) in an epilepsy patient to reveal its possible neurocognitive functional role(s). The authors used 3-T MRI approaches to exhaustively characterize a single, right hemisphere heterotopion in a high-functioning adult male with medically responsive epilepsy, which had manifested during late adolescence. The heterotopion proved to be spectroscopically consistent with a cortical-like composition and was interconnected with nearby ipsilateral cortical fundi, as revealed by fiber tractography (diffusion-weighted imaging) and resting-state functional connectivity MRI (rsfMRI). Moreover, the region of PVNH demonstrated two novel characterizations for a heterotopion. First, functional MRI (fMRI), as distinct from rsfMRI, showed that the heterotopion was significantly modulated while the patient watched animated video scenes of biological motion (i.e., cartoons). Second, rsfMRI, which demonstrated correlated brain activity during a task-negative state, uniquely showed directionality within an interconnected network, receiving positive path effects from patent cortical and cerebellar foci while outputting only negative path effects to specific brain foci.These findings are addressed in the context of the impact on noninvasive presurgical brain mapping strategies for adult and pediatric patient workups, as well as the impact of this study on an understanding of the functional cortical architecture underlying cognition from a neurodiversity and evolutionary perspective.
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Affiliation(s)
| | - Nicholas Brandmeir
- 2Neurosurgery, Rockefeller Neuroscience Institute and Center for Advanced Imaging at West Virginia University
| | | | - John L Magruder
- 3Department of Pediatrics, West Virginia University, Morgantown, West Virginia; and
| | - Mark R Lee
- 2Neurosurgery, Rockefeller Neuroscience Institute and Center for Advanced Imaging at West Virginia University
| | - Gang Chen
- 4Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland
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Hong SJ, Lee HM, Gill R, Crane J, Sziklas V, Bernhardt BC, Bernasconi N, Bernasconi A. A connectome-based mechanistic model of focal cortical dysplasia. Brain 2020; 142:688-699. [PMID: 30726864 DOI: 10.1093/brain/awz009] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 09/07/2018] [Accepted: 11/19/2018] [Indexed: 11/13/2022] Open
Abstract
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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Affiliation(s)
- Seok-Jun Hong
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Hyo-Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Ravnoor Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Joelle Crane
- Department of Psychology, Neuropsychology Unit, McGill University, Montreal, Quebec, Canada
| | - Viviane Sziklas
- Department of Psychology, Neuropsychology Unit, McGill University, Montreal, Quebec, Canada
| | - Boris C Bernhardt
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre and Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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41
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Mo J, Liu Z, Sun K, Ma Y, Hu W, Zhang C, Wang Y, Wang X, Liu C, Zhao B, Zhang K, Zhang J, Tian J. Automated detection of hippocampal sclerosis using clinically empirical and radiomics features. Epilepsia 2019; 60:2519-2529. [PMID: 31769021 DOI: 10.1111/epi.16392] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 10/27/2019] [Accepted: 10/28/2019] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. METHODS Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. RESULTS The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. SIGNIFICANCE Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.
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Affiliation(s)
- Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Zhenyu Liu
- CAS, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Kai Sun
- CAS, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China.,Engineering Research Center of Molecular and Neuroimaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Yanshan Ma
- Epilepsy Center, Peking University First Hospital Fengtai Hospital, Beijing, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chao Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Yao Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chang Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Baotian Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Jie Tian
- CAS, Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
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Hong SJ, Valk SL, Di Martino A, Milham MP, Bernhardt BC. Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder. Cereb Cortex 2019; 28:3578-3588. [PMID: 28968847 DOI: 10.1093/cercor/bhx229] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Accepted: 08/23/2017] [Indexed: 12/15/2022] Open
Abstract
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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Affiliation(s)
- Seok-Jun Hong
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
| | - Sofie L Valk
- Department of Social Neuroscience, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1a, Leipzig, Germany
| | - Adriana Di Martino
- Department of Child and Adolescent Psychiatry, Child Study Center at NYU Langone Health, 1 Park Avenue, New York, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, 445 Park Avenue, New York, NY, USA.,Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Rd, Orangeburg, New York, NY, USA
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, QC, Canada
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43
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Avakyan GN, Blinov DV, Alikhanov AA, Perepelova EM, Perepelov VA, Burd SG, Lebedeva AV, Avakyan GG. Recommendations of the Russian League Against Epilepsy (RLAE) on the use of magnetic resonance imaging in the diagnosis of epilepsy. ACTA ACUST UNITED AC 2019. [DOI: 10.17749/2077-8333.2019.11.3.208-232] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Introduction. The MRI method has revolutionized the diagnosis of epilepsy. However, the widespread adoption of MRI in clinical practice is slowed by an insufficient number of high-field MRI scanners, a shortage of trained specialists, and the lack of standard examination protocols. The aim of this article is to present the Recommendations of the Russian League Against Epilepsy (RLAE) on the use of magnetic resonance imaging in the diagnosis of epilepsy.Materials and methods. As a structural element of the International League Against Epilepsy (ILAE), the RLAE considers it important to adapt the Protocol developed by ILAE for specialists in Russia and EAEU countries. The working group analyzed and generalized the clinical practice existing in the Russian Federation, the Republic of Kazakhstan, the Republic of Belarus and the Republic of Uzbekistan. These recommendations are intended for doctors in specialized centers of epilepsy surgery, and for doctors in general medical centers. The recommendations are applicable primarily to adult patients, but the general principles are relevant to children as well.Results. In all patients with convulsive seizures shortly after the first seizure, or patients diagnosed with epilepsy who have an unexplained increase in the frequency of seizures, rapid decrease in cognitive functions or the appearance / worsening of neuropsychiatric symptoms, the RLAE recommends using a unified MR protocol for the neuroimaging of structural sequences in epilepsy with three-dimensional pulse sequences T1 and T2 FLAIR with isotropic voxel 1 × 1 × 1 mm3 and two-dimensional T2- weighted pulse sequences with a pixel size of 1 × 1 mm2 or less. The MRI examination should be combined with EEG or EEG-video monitoring. Using this protocol allows one to set a unified standard for examining patients with epilepsy in order to detect (with high sensitivity) brain lesions playing a key role in the occurrence of seizures. Here, all 13 recommendations are presented.Conclusion. Implementation of these recommendations in clinical practice will improve the access to high-tech medical care and optimize health care costs.
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Affiliation(s)
- G. N. Avakyan
- Pirogov Russian National Research Medical University
| | - D. V. Blinov
- Institute for Preventive and Social Medicine;
Moscow Haass Medical – Social Institute;
Lapino Clinic Hospital, MD Medical Group
| | | | | | | | - S. G. Burd
- Pirogov Russian National Research Medical University
| | | | - G. G. Avakyan
- Pirogov Russian National Research Medical University
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Gyebnár G, Klimaj Z, Entz L, Fabó D, Rudas G, Barsi P, Kozák LR. Personalized microstructural evaluation using a Mahalanobis-distance based outlier detection strategy on epilepsy patients' DTI data - Theory, simulations and example cases. PLoS One 2019; 14:e0222720. [PMID: 31545838 PMCID: PMC6756533 DOI: 10.1371/journal.pone.0222720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 09/05/2019] [Indexed: 11/19/2022] Open
Abstract
Quantitative MRI methods have recently gained extensive interest and are seeing substantial developments; however, their application in single patient vs control group comparisons is often limited by inherent statistical difficulties. One such application is detecting malformations of cortical development (MCDs) behind drug resistant epilepsies, a task that, especially when based solely on conventional MR images, may represent a serious challenge. We aimed to develop a novel straightforward voxel-wise evaluation method based on the Mahalanobis-distance, combining quantitative MRI data into a multidimensional parameter space and detecting lesion voxels as outliers. Simulations with standard multivariate Gaussian distribution and resampled DTI-eigenvalue data of 45 healthy control subjects determined the optimal critical value, cluster size threshold, and the expectable lesion detection performance through ROC-analyses. To reduce the effect of false positives emanating from registration artefacts and gyrification differences, an automatic classification method was applied, fine-tuned using a leave-one-out strategy based on diffusion and T1-weighted data of the controls. DWI processing, including thorough corrections and robust tensor fitting was performed with ExploreDTI, spatial coregistration was achieved with the DARTEL tools of SPM12. Additional to simulations, clusters of outlying diffusion profile, concordant with neuroradiological evaluation and independent calculations with the MAP07 toolbox were identified in 12 cases of a 13 patient example population with various types of MCDs. The multidimensional approach proved sufficiently sensitive in pinpointing regions of abnormal tissue microstructure using DTI data both in simulations and in the heterogeneous example population. Inherent limitations posed by registration artefacts, age-related differences, and the different or mixed pathologies limit the generalization of specificity estimation. Nevertheless, the proposed statistical method may aid the everyday examination of individual subjects, ever so more upon extending the framework with quantitative information from other modalities, e.g. susceptibility mapping, relaxometry, or perfusion.
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Affiliation(s)
- Gyula Gyebnár
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
- * E-mail:
| | - Zoltán Klimaj
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - László Entz
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Dániel Fabó
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Gábor Rudas
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Péter Barsi
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
| | - Lajos R. Kozák
- Magnetic Resonance Research Centre, Semmelweis University, Budapest, Hungary
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Bernasconi A, Cendes F, Theodore WH, Gill RS, Koepp MJ, Hogan RE, Jackson GD, Federico P, Labate A, Vaudano AE, Blümcke I, Ryvlin P, Bernasconi N. 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; 60:1054-1068. [PMID: 31135062 DOI: 10.1111/epi.15612] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 04/23/2019] [Accepted: 04/24/2019] [Indexed: 01/01/2023]
Abstract
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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Affiliation(s)
- Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas, Brazil
| | - William H Theodore
- Clinical Epilepsy Section, National Institutes of Health, Bethesda, Maryland
| | - Ravnoor S Gill
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | | | - Robert Edward Hogan
- Department of Neurology, Washington University School of Medicine, St Louis, Missouri
| | - Graeme D Jackson
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Heidelberg, Victoria, Australia
| | - Paolo Federico
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Angelo Labate
- Institute of Neurology, University of Catanzaro, Catanzaro, Italy
| | - Anna Elisabetta Vaudano
- Neurology Unit, Azienda Ospedaliero Universitaria, University of Modena and Reggio Emilia, Modena, Italy
| | - Ingmar Blümcke
- Department of Neuropathology, University Hospital Erlangen, Erlangen, Germany
| | - Philippe Ryvlin
- Clinical Neurosciences, Lausanne University Hospital, Lausanne, Switzerland
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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46
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Bernhardt BC, Fadaie F, Liu M, Caldairou B, Gu S, Jefferies E, Smallwood J, Bassett DS, Bernasconi A, Bernasconi N. Temporal lobe epilepsy: Hippocampal pathology modulates connectome topology and controllability. Neurology 2019; 92:e2209-e2220. [PMID: 31004070 DOI: 10.1212/wnl.0000000000007447] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 01/08/2019] [Indexed: 01/05/2023] Open
Abstract
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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Affiliation(s)
- Boris C Bernhardt
- 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
| | - Fatemeh Fadaie
- 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
| | - Min Liu
- 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
| | - Benoit Caldairou
- 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
| | - Shi Gu
- 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
| | - Elizabeth Jefferies
- 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
| | - Jonathan Smallwood
- 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
| | - Danielle S Bassett
- 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
| | - Andrea Bernasconi
- 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
| | - Neda Bernasconi
- 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.
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Malformations of cortical development: New surgical advances. Rev Neurol (Paris) 2019; 175:183-188. [DOI: 10.1016/j.neurol.2019.01.392] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/23/2018] [Accepted: 01/04/2019] [Indexed: 11/18/2022]
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Abstract
PURPOSE OF REVIEW MRI has a crucial position in the diagnostic routine of epilepsy patients. It relevantly contributes to etiological diagnostics and is indispensable in presurgical evaluation. As modern MRI research has been a boon to clinical neuroscience in general, it also holds the promise of enhancing diagnostics of epilepsy patients; i.e. increasing the diagnostic yield while decreasing the number of MRI-negative patients. Its rapid progress, however, has caused uncertainty about which of its latest developments already are of clinical interest and which still are of academic interest. It is the purpose of this review to clarify what, to the authors' mind, good practice of MRI in epilepsy patient care is today and what it might be tomorrow. RECENT FINDINGS Progress of diagnostic MRI in epilepsy patients is driven by development of scanner hardware, scanner sequence and data postprocessing. Ultra high-field MRI and elaborate sequences provide datasets of novel quality which can be fed into postprocessing programs extracting pathognomonic features of structural or functional anatomy. The integration of these features by means of computerized classifiers yield previously unsurpassed diagnostic validity. Enthusiasm about Diffusion Tensor Imaging and functional MRI in the evaluation before epilepsy surgery is quelled. SUMMARY The application of an epilepsy tailored MRI protocol at 3 Tesla followed by meticulous expert evaluation early after the onset of epilepsy is most crucial. It is hoped that future research will result in MRI workups more standardized than today and widely used postprocessing routines analyzing co-registered three-dimensional volumes from different modalities.
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Mo JJ, Zhang JG, Li WL, Chen C, Zhou NJ, Hu WH, Zhang C, Wang Y, Wang X, Liu C, Zhao BT, Zhou JJ, Zhang K. Clinical Value of Machine Learning in the Automated Detection of Focal Cortical Dysplasia Using Quantitative Multimodal Surface-Based Features. Front Neurosci 2019; 12:1008. [PMID: 30686974 PMCID: PMC6336916 DOI: 10.3389/fnins.2018.01008] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 12/14/2018] [Indexed: 01/18/2023] Open
Abstract
Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value. Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis. Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair). Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.
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Affiliation(s)
- Jia-Jie Mo
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jian-Guo Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wen-Ling Li
- Department of Functional Neurosurgery, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Chao Chen
- Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China
| | - Na-Jing Zhou
- Department of Pharmacology, Hebei Medical University, Shijiazhuang, China
| | - Wen-Han Hu
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chao Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yao Wang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chang Liu
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bao-Tian Zhao
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun-Jian Zhou
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kai Zhang
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Larivière S, Vos de Wael R, Paquola C, Hong SJ, Mišić B, Bernasconi N, Bernasconi A, Bonilha L, Bernhardt BC. Microstructure-Informed Connectomics: Enriching Large-Scale Descriptions of Healthy and Diseased Brains. Brain Connect 2018; 9:113-127. [PMID: 30079754 DOI: 10.1089/brain.2018.0587] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
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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Affiliation(s)
- Sara Larivière
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos de Wael
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Casey Paquola
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.,2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Bratislav Mišić
- 3 Network Neuroscience Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Neda Bernasconi
- 2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Andrea Bernasconi
- 2 NeuroImaging of Epilepsy Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Leonardo Bonilha
- 4 Department of Neurosciences, Medical University of South Carolina, Charleston, South Carolina
| | - Boris C Bernhardt
- 1 Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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