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Dear R, Wagstyl K, Seidlitz J, Markello RD, Arnatkevičiūtė A, Anderson KM, Bethlehem RAI, Raznahan A, Bullmore ET, Vértes PE. Cortical gene expression architecture links healthy neurodevelopment to the imaging, transcriptomics and genetics of autism and schizophrenia. Nat Neurosci 2024:10.1038/s41593-024-01624-4. [PMID: 38649755 DOI: 10.1038/s41593-024-01624-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 03/18/2024] [Indexed: 04/25/2024]
Abstract
Human brain organization involves the coordinated expression of thousands of genes. For example, the first principal component (C1) of cortical transcription identifies a hierarchy from sensorimotor to association regions. In this study, optimized processing of the Allen Human Brain Atlas revealed two new components of cortical gene expression architecture, C2 and C3, which are distinctively enriched for neuronal, metabolic and immune processes, specific cell types and cytoarchitectonics, and genetic variants associated with intelligence. Using additional datasets (PsychENCODE, Allen Cell Atlas and BrainSpan), we found that C1-C3 represent generalizable transcriptional programs that are coordinated within cells and differentially phased during fetal and postnatal development. Autism spectrum disorder and schizophrenia were specifically associated with C1/C2 and C3, respectively, across neuroimaging, differential expression and genome-wide association studies. Evidence converged especially in support of C3 as a normative transcriptional program for adolescent brain development, which can lead to atypical supragranular cortical connectivity in people at high genetic risk for schizophrenia.
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Affiliation(s)
- Richard Dear
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | | | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ross D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | | | | | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
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2
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Eriksson MH, Prentice F, Piper RJ, Wagstyl K, Adler S, Chari A, Booth J, Moeller F, Das K, Eltze C, Cooray G, Perez Caballero A, Menzies L, McTague A, Shavel-Jessop S, Tisdall MM, Cross JH, Martin Sanfilippo P, Baldeweg T. Long-term neuropsychological trajectories in children with epilepsy: does surgery halt decline? Brain 2024:awae121. [PMID: 38643018 DOI: 10.1093/brain/awae121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/29/2024] [Accepted: 03/16/2024] [Indexed: 04/22/2024] Open
Abstract
Neuropsychological impairments are common in children with drug-resistant epilepsy. It has been proposed that epilepsy surgery may alleviate these impairments by providing seizure freedom; however, findings from prior studies have been inconsistent. We mapped long-term neuropsychological trajectories in children before and after undergoing epilepsy surgery, to measure the impact of disease course and surgery on functioning. We performed a retrospective cohort study of 882 children who had undergone epilepsy surgery at Great Ormond Street Hospital (1990-2018). We extracted patient information and neuropsychological functioning - obtained from IQ tests (domains: Full-Scale IQ, Verbal IQ, Performance IQ, Working Memory, and Processing Speed) and tests of academic attainment (Reading, Spelling and Numeracy) - and investigated changes in functioning using regression analyses. We identified 500 children (248 females) who had undergone epilepsy surgery (median age at surgery = 11.9 years, interquartile range = [7.8,15.0]) and neuropsychology assessment. These children showed declines in all domains of neuropsychological functioning in the time leading up to surgery (all p-values ≤ 0.001; e.g., βFSIQ = -1.9, SEFSIQ = 0.3, pFSIQ < 0.001). Children lost on average one to four points per year, depending on the domain considered; 27-43% declined by 10 or more points from their first to their last preoperative assessment. At the time of presurgical evaluation, most children (46-60%) scored one or more standard deviations below the mean (<85) on the different neuropsychological domains; 37% of these met the threshold for intellectual disability (Full-Scale IQ < 70). On a group level, there was no change in performance from pre- to postoperative assessment on any of the domains (all p-values > 0.128). However, children who became seizure-free through surgery showed higher postoperative neuropsychological performance (e.g., rrb-FSIQ = 0.37, p < 0.001). These children continued to demonstrate improvements in neuropsychological functioning over the course of their long-term follow-up (e.g., βFSIQ = 0.9, SEFSIQ = 0.3, pFSIQ = 0.004). Children who had discontinued antiseizure medication (ASM) treatment at one-year follow-up showed an eight-to-13-point advantage in postoperative Working Memory, Processing Speed, and Numeracy, and greater improvements in Verbal IQ, Working Memory, Reading, and Spelling (all p-values < 0.034) over the postoperative period compared to children who were seizure-free and still receiving ASMs. In conclusion, by providing seizure freedom and the opportunity for ASM cessation, epilepsy surgery may not only halt but reverse the downward trajectory that children with drug-resistant epilepsy display in neuropsychological functioning. To halt this decline as soon as possible, or potentially prevent it from occurring in the first place, children with focal epilepsy should be considered for epilepsy surgery as early as possible after diagnosis.
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Affiliation(s)
- Maria H Eriksson
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neuropsychology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
- Department of Neurology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Freya Prentice
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neuropsychology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Rory J Piper
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Konrad Wagstyl
- Imaging Neuroscience, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Sophie Adler
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Aswin Chari
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - John Booth
- Digital Research Environment, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Friederike Moeller
- Department of Neurophysiology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Krishna Das
- Department of Neurology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
- Department of Neurophysiology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Christin Eltze
- Department of Neurophysiology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Gerald Cooray
- Department of Neurophysiology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
- Clinical Neuroscience, Karolinska Institutet, Solna 171 77, Sweden
| | - Ana Perez Caballero
- North Thames Genomic Laboratory Hub, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Lara Menzies
- Department of Clinical Genetics, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Amy McTague
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Clinical Genetics, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Sara Shavel-Jessop
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neuropsychology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Martin M Tisdall
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neurosurgery, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - J Helen Cross
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neurology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
- Department of Neurosurgery, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
- Young Epilepsy, Lingfield RH7 6PW, UK
| | - Patricia Martin Sanfilippo
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neuropsychology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
| | - Torsten Baldeweg
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Neuropsychology, Great Ormond Street Hospital for Children, London WC1N 3JH, UK
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Wagstyl K, Adler S, Seidlitz J, Vandekar S, Mallard TT, Dear R, DeCasien AR, Satterthwaite TD, Liu S, Vértes PE, Shinohara RT, Alexander-Bloch A, Geschwind DH, Raznahan A. Transcriptional cartography integrates multiscale biology of the human cortex. eLife 2024; 12:RP86933. [PMID: 38324465 PMCID: PMC10945526 DOI: 10.7554/elife.86933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College LondonLondonUnited Kingdom
| | - Sophie Adler
- UCL Great Ormond Street Institute for Child HealthHolbornUnited Kingdom
| | - Jakob Seidlitz
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt UniversityNashvilleUnited States
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General HospitalBostonUnited States
- Department of Psychiatry, Harvard Medical SchoolBostonUnited States
| | - Richard Dear
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Alex R DeCasien
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of MedicinePhiladelphiaUnited States
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
| | - Petra E Vértes
- Department of Psychiatry, University of CambridgeCambridgeUnited Kingdom
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of PennsylvaniaPhiladelphiaUnited States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of PhiladelphiaPhiladelphiaUnited States
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute, Program in Neurogenetics, Department of Neurology and Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental HealthBethesdaUnited States
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Mellor S, Timms RC, O'Neill GC, Tierney TM, Spedden ME, Brookes MJ, Wagstyl K, Barnes GR. Combining OPM and lesion mapping data for epilepsy surgery planning: a simulation study. Sci Rep 2024; 14:2882. [PMID: 38311614 PMCID: PMC10838931 DOI: 10.1038/s41598-024-51857-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
When planning for epilepsy surgery, multiple potential sites for resection may be identified through anatomical imaging. Magnetoencephalography (MEG) using optically pumped sensors (OP-MEG) is a non-invasive functional neuroimaging technique which could be used to help identify the epileptogenic zone from these candidate regions. Here we test the utility of a-priori information from anatomical imaging for differentiating potential lesion sites with OP-MEG. We investigate a number of scenarios: whether to use rigid or flexible sensor arrays, with or without a-priori source information and with or without source modelling errors. We simulated OP-MEG recordings for 1309 potential lesion sites identified from anatomical images in the Multi-centre Epilepsy Lesion Detection (MELD) project. To localise the simulated data, we used three source inversion schemes: unconstrained, prior source locations at centre of the candidate sites, and prior source locations within a volume around the lesion location. We found that prior knowledge of the candidate lesion zones made the inversion robust to errors in sensor gain, orientation and even location. When the reconstruction was too highly restricted and the source assumptions were inaccurate, the utility of this a-priori information was undermined. Overall, we found that constraining the reconstruction to the region including and around the participant's potential lesion sites provided the best compromise of robustness against modelling or measurement error.
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Affiliation(s)
- Stephanie Mellor
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK.
| | - Ryan C Timms
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - George C O'Neill
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Meaghan E Spedden
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
- UCL Great Ormond Street Institute for Child Health, University College London, 30 Guilford St, London, WC1N 1EH, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
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5
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Chari A, Adler S, Wagstyl K, Seunarine K, Tahir MZ, Moeller F, Thornton R, Boyd S, Das K, Cooray G, Smith S, D'Arco F, Baldeweg T, Eltze C, Cross JH, Tisdall MM. Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm. Dev Med Child Neurol 2024; 66:216-225. [PMID: 37559345 DOI: 10.1111/dmcn.15727] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 06/11/2023] [Accepted: 07/03/2023] [Indexed: 08/11/2023]
Abstract
AIM To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. WHAT THIS PAPER ADDS The focal cortical dysplasia detection algorithm collocated with the seizure-onset zone (SOZ) in 4 out of 19 patients. The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug-resistant epilepsy. The patient with an altered resection due to the algorithm was seizure-free 1 year after resective surgery. Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified.
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Affiliation(s)
- Aswin Chari
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
| | - Sophie Adler
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
| | - Konrad Wagstyl
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kiran Seunarine
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
| | - M Zubair Tahir
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
| | | | - Rachel Thornton
- Department of Neurophysiology, Addenbrooke's Hospital, Cambridge, UK
| | - Steward Boyd
- Department of Neurophysiology, Great Ormond Street Hospital, London, UK
| | - Krishna Das
- Department of Neurophysiology, Great Ormond Street Hospital, London, UK
- Department of Neurology, Great Ormond Street Hospital, London, UK
| | - Gerald Cooray
- Department of Neurophysiology, Great Ormond Street Hospital, London, UK
| | - Stuart Smith
- Department of Neurophysiology, Great Ormond Street Hospital, London, UK
| | - Felice D'Arco
- Department of Neuroradiology, Great Ormond Street Hospital, London, UK
| | - Torsten Baldeweg
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
| | - Christin Eltze
- Department of Neurology, Great Ormond Street Hospital, London, UK
| | - J Helen Cross
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
- Department of Neurology, Great Ormond Street Hospital, London, UK
| | - Martin M Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Institute of Child Health, University College London, London, UK
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Snyder WE, Vértes PE, Kyriakopoulou V, Wagstyl K, Williams LZJ, Moraczewski D, Thomas AG, Karolis VR, Seidlitz J, Rivière D, Robinson EC, Mangin JF, Raznahan A, Bullmore ET. A bipolar taxonomy of adult human brain sulcal morphology related to timing of fetal sulcation and trans-sulcal gene expression gradients. bioRxiv 2023:2023.12.19.572454. [PMID: 38168226 PMCID: PMC10760196 DOI: 10.1101/2023.12.19.572454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (N=34,725; 45-82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bipolar distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex; complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (N=228; 21-36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes.
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Affiliation(s)
- William E Snyder
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Dustin Moraczewski
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Adam G Thomas
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, Maryland, USA
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Jakob Seidlitz
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Jean-Francois Mangin
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, 91191, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD, USA
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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7
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Saberi A, Paquola C, Wagstyl K, Hettwer MD, Bernhardt BC, Eickhoff SB, Valk SL. The regional variation of laminar thickness in the human isocortex is related to cortical hierarchy and interregional connectivity. PLoS Biol 2023; 21:e3002365. [PMID: 37943873 PMCID: PMC10684102 DOI: 10.1371/journal.pbio.3002365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 11/28/2023] [Accepted: 10/06/2023] [Indexed: 11/12/2023] Open
Abstract
The human isocortex consists of tangentially organized layers with unique cytoarchitectural properties. These layers show spatial variations in thickness and cytoarchitecture across the neocortex, which is thought to support function through enabling targeted corticocortical connections. Here, leveraging maps of the 6 cortical layers based on 3D human brain histology, we aimed to quantitatively characterize the systematic covariation of laminar structure in the cortex and its functional consequences. After correcting for the effect of cortical curvature, we identified a spatial pattern of changes in laminar thickness covariance from lateral frontal to posterior occipital regions, which differentiated the dominance of infra- versus supragranular layer thickness. Corresponding to the laminar regularities of cortical connections along cortical hierarchy, the infragranular-dominant pattern of laminar thickness was associated with higher hierarchical positions of regions, mapped based on resting-state effective connectivity in humans and tract-tracing of structural connections in macaques. Moreover, we show that regions with similar laminar thickness patterns have a higher likelihood of structural connections and strength of functional connections. In sum, here we characterize the organization of laminar thickness in the human isocortex and its association with cortico-cortical connectivity, illustrating how laminar organization may provide a foundational principle of cortical function.
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Affiliation(s)
- Amin Saberi
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Casey Paquola
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College London, London, United Kingdom
| | - Meike D. Hettwer
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Max Planck School of Cognition, Leipzig, Germany
| | - Boris C. Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Simon B. Eickhoff
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sofie L. Valk
- Otto Hahn Research Group for Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neurosciences and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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9
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Wagstyl K, Raznahan A. Subcortical Anatomy in Neurogenetic Disorders: New Findings and Future Questions. Am J Psychiatry 2023; 180:634-635. [PMID: 37654115 DOI: 10.1176/appi.ajp.20230555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London (Wagstyl); Section on Developmental Neurogenomics, Human Genetics Branch, NIMH, Bethesda, Md. (Raznahan)
| | - Armin Raznahan
- Wellcome Centre for Human Neuroimaging, University College London, London (Wagstyl); Section on Developmental Neurogenomics, Human Genetics Branch, NIMH, Bethesda, Md. (Raznahan)
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10
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Eriksson MH, Whitaker KJ, Booth J, Piper RJ, Chari A, Sanfilippo PM, Caballero AP, Menzies L, McTague A, Adler S, Wagstyl K, Tisdall MM, Cross JH, Baldeweg T. Pediatric epilepsy surgery from 2000 to 2018: Changes in referral and surgical volumes, patient characteristics, genetic testing, and postsurgical outcomes. Epilepsia 2023; 64:2260-2273. [PMID: 37264783 PMCID: PMC7615891 DOI: 10.1111/epi.17670] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 05/25/2023] [Accepted: 05/31/2023] [Indexed: 06/03/2023]
Abstract
OBJECTIVE Neurosurgery is a safe and effective form of treatment for select children with drug-resistant epilepsy. Still, there is concern that it remains underutilized, and that seizure freedom rates have not improved over time. We investigated referral and surgical practices, patient characteristics, and postoperative outcomes over the past two decades. METHODS We performed a retrospective cohort study of children referred for epilepsy surgery at a tertiary center between 2000 and 2018. We extracted information from medical records and analyzed temporal trends using regression analyses. RESULTS A total of 1443 children were evaluated for surgery. Of these, 859 (402 females) underwent surgical resection or disconnection at a median age of 8.5 years (interquartile range [IQR] = 4.6-13.4). Excluding palliative procedures, 67% of patients were seizure-free and 15% were on no antiseizure medication (ASM) at 1-year follow-up. There was an annual increase in the number of referrals (7%, 95% confidence interval [CI] = 5.3-8.6; p < .001) and surgeries (4% [95% CI = 2.9-5.6], p < .001) over time. Duration of epilepsy and total number of different ASMs trialed from epilepsy onset to surgery were, however, unchanged, and continued to exceed guidelines. Seizure freedom rates were also unchanged overall but showed improvement (odds ratio [OR] 1.09, 95% CI = 1.01-1.18; p = .027) after adjustment for an observed increase in complex cases. Children who underwent surgery more recently were more likely to be off ASMs postoperatively (OR 1.04, 95% CI = 1.01-1.08; p = .013). There was a 17% annual increase (95% CI = 8.4-28.4, p < .001) in children identified to have a genetic cause of epilepsy, which was associated with poor outcome. SIGNIFICANCE Children with drug-resistant epilepsy continue to be put forward for surgery late, despite national and international guidelines urging prompt referral. Seizure freedom rates have improved over the past decades, but only after adjustment for a concurrent increase in complex cases. Finally, genetic testing in epilepsy surgery patients has expanded considerably over time and shows promise in identifying patients in whom surgery is less likely to be successful.
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Affiliation(s)
- Maria H Eriksson
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neuropsychology, Great Ormond Street Hospital NHS Trust, London, UK
- The Alan Turing Institute, London, UK
- Department of Neurology, Great Ormond Street Hospital NHS Trust, London, UK
| | | | - John Booth
- Digital Research Environment, Great Ormond Street Hospital NHS Trust, London, UK
| | - Rory J Piper
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neurosurgery, Great Ormond Street Hospital NHS Trust, London, UK
| | - Aswin Chari
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neurosurgery, Great Ormond Street Hospital NHS Trust, London, UK
| | - Patricia Martin Sanfilippo
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neuropsychology, Great Ormond Street Hospital NHS Trust, London, UK
| | - Ana Perez Caballero
- North Thames Genomic Laboratory Hub, Great Ormond Street Hospital NHS Trust, London, UK
| | - Lara Menzies
- Department of Clinical Genetics, Great Ormond Street Hospital NHS Trust, London, UK
| | - Amy McTague
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neurology, Great Ormond Street Hospital NHS Trust, London, UK
| | - Sophie Adler
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
| | - Konrad Wagstyl
- Imaging Neuroscience, UCL Queen Square Institute of Neurology, London, UK
| | - Martin M Tisdall
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neurosurgery, Great Ormond Street Hospital NHS Trust, London, UK
| | - J Helen Cross
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neurology, Great Ormond Street Hospital NHS Trust, London, UK
- Young Epilepsy, Lingfield, UK
| | - Torsten Baldeweg
- Developmental Neurosciences Research and Teaching Department, UCL Great Ormond Street Institute of Child Health, London, UK
- Department of Neuropsychology, Great Ormond Street Hospital NHS Trust, London, UK
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11
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Eriksson MH, Ripart M, Piper RJ, Moeller F, Das KB, Eltze C, Cooray G, Booth J, Whitaker KJ, Chari A, Martin Sanfilippo P, Perez Caballero A, Menzies L, McTague A, Tisdall MM, Cross JH, Baldeweg T, Adler S, Wagstyl K. Predicting seizure outcome after epilepsy surgery: Do we need more complex models, larger samples, or better data? Epilepsia 2023; 64:2014-2026. [PMID: 37129087 PMCID: PMC10952307 DOI: 10.1111/epi.17637] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/30/2023] [Accepted: 05/01/2023] [Indexed: 05/03/2023]
Abstract
OBJECTIVE The accurate prediction of seizure freedom after epilepsy surgery remains challenging. We investigated if (1) training more complex models, (2) recruiting larger sample sizes, or (3) using data-driven selection of clinical predictors would improve our ability to predict postoperative seizure outcome using clinical features. We also conducted the first substantial external validation of a machine learning model trained to predict postoperative seizure outcome. METHODS We performed a retrospective cohort study of 797 children who had undergone resective or disconnective epilepsy surgery at a tertiary center. We extracted patient information from medical records and trained three models-a logistic regression, a multilayer perceptron, and an XGBoost model-to predict 1-year postoperative seizure outcome on our data set. We evaluated the performance of a recently published XGBoost model on the same patients. We further investigated the impact of sample size on model performance, using learning curve analysis to estimate performance at samples up to N = 2000. Finally, we examined the impact of predictor selection on model performance. RESULTS Our logistic regression achieved an accuracy of 72% (95% confidence interval [CI] = 68%-75%, area under the curve [AUC] = .72), whereas our multilayer perceptron and XGBoost both achieved accuracies of 71% (95% CIMLP = 67%-74%, AUCMLP = .70; 95% CIXGBoost own = 68%-75%, AUCXGBoost own = .70). There was no significant difference in performance between our three models (all p > .4) and they all performed better than the external XGBoost, which achieved an accuracy of 63% (95% CI = 59%-67%, AUC = .62; pLR = .005, pMLP = .01, pXGBoost own = .01) on our data. All models showed improved performance with increasing sample size, but limited improvements beyond our current sample. The best model performance was achieved with data-driven feature selection. SIGNIFICANCE We show that neither the deployment of complex machine learning models nor the assembly of thousands of patients alone is likely to generate significant improvements in our ability to predict postoperative seizure freedom. We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.
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Affiliation(s)
- Maria H. Eriksson
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- The Alan Turing InstituteLondonUK
| | - Mathilde Ripart
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Rory J. Piper
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | | | - Krishna B. Das
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Christin Eltze
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
| | - Gerald Cooray
- Department of NeurophysiologyGreat Ormond Street HospitalLondonUK
- Clinical NeuroscienceKarolinska InstituteSolnaSweden
| | - John Booth
- Digital Research EnvironmentGreat Ormond Street HospitalLondonUK
| | | | - Aswin Chari
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - Patricia Martin Sanfilippo
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | | | - Lara Menzies
- Department of Clinical GeneticsGreat Ormond Street HospitalLondonUK
| | - Amy McTague
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
| | - Martin M. Tisdall
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
| | - J. Helen Cross
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeurologyGreat Ormond Street HospitalLondonUK
- Department of NeurosurgeryGreat Ormond Street HospitalLondonUK
- Young EpilepsyLingfieldUK
| | - Torsten Baldeweg
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
- Department of NeuropsychologyGreat Ormond Street HospitalLondonUK
| | - Sophie Adler
- Developmental Neurosciences Research & Teaching DepartmentUCL Great Ormond Street Institute of Child HealthLondonUK
| | - Konrad Wagstyl
- Imaging NeuroscienceUCL Queen Square Institute of NeurologyLondonUK
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12
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Walger L, Adler S, Wagstyl K, Henschel L, David B, Borger V, Hattingen E, Vatter H, Elger CE, Baldeweg T, Rosenow F, Urbach H, Becker A, Radbruch A, Surges R, Reuter M, Cendes F, Wang ZI, Huppertz HJ, Rüber T. Artificial intelligence for the detection of focal cortical dysplasia: Challenges in translating algorithms into clinical practice. Epilepsia 2023; 64:1093-1112. [PMID: 36721976 DOI: 10.1111/epi.17522] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/02/2023]
Abstract
Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common pathologies causing pharmacoresistant focal epilepsy. Resective neurosurgery yields high success rates, especially if the full extent of the lesion is correctly identified and completely removed. The visual assessment of magnetic resonance imaging does not pinpoint the FCD in 30%-50% of cases, and half of all patients with FCD are not amenable to epilepsy surgery, partly because the FCD could not be sufficiently localized. Computational approaches to FCD detection are an active area of research, benefitting from advancements in computer vision. Automatic FCD detection is a significant challenge and one of the first clinical grounds where the application of artificial intelligence may translate into an advance for patients' health. The emergence of new methods from the combination of health and computer sciences creates novel challenges. Imaging data need to be organized into structured, well-annotated datasets and combined with other clinical information, such as histopathological subtypes or neuroimaging characteristics. Algorithmic output, that is, model prediction, requires a technically correct evaluation with adequate metrics that are understandable and usable for clinicians. Publication of code and data is necessary to make research accessible and reproducible. This critical review introduces the field of automatic FCD detection, explaining underlying medical and technical concepts, highlighting its challenges and current limitations, and providing a perspective for a novel research environment.
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Affiliation(s)
- Lennart Walger
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Sophie Adler
- University College London Great Ormond Street Institute for Child Health, London, UK
| | | | | | - Bastian David
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Valeri Borger
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Elke Hattingen
- Department of Neuroradiology, University Hospital and Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Hartmut Vatter
- Department of Neurosurgery, University Hospital Bonn, Bonn, Germany
| | - Christian E Elger
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Torsten Baldeweg
- University College London Great Ormond Street Institute for Child Health, London, UK
| | - Felix Rosenow
- Epilepsy Center Frankfurt Rhine-Main and Department of Neurology, Goethe University and University Hospital Frankfurt, Frankfurt am Main, Germany.,LOEWE Center for Personalized Translational Epilepsy Research, Goethe University, Frankfurt am Main, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center, University of Freiburg, Freiburg, Germany
| | - Albert Becker
- Department of Neuropathology, University Hospital Bonn, Bonn, Germany
| | | | - Rainer Surges
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
| | - Martin Reuter
- German Center for Neurodegenerative Diseases, Bonn, Germany.,A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
| | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas, Brazil
| | - Zhong Irene Wang
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, Ohio, USA
| | | | - Theodor Rüber
- Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
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13
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Spitzer H, Ripart M, Whitaker K, D’Arco F, Mankad K, Chen AA, Napolitano A, De Palma L, De Benedictis A, Foldes S, Humphreys Z, Zhang K, Hu W, Mo J, Likeman M, Davies S, Güttler C, Lenge M, Cohen NT, Tang Y, Wang S, Chari A, Tisdall M, Bargallo N, Conde-Blanco E, Pariente JC, Pascual-Diaz S, Delgado-Martínez I, Pérez-Enríquez C, Lagorio I, Abela E, Mullatti N, O’Muircheartaigh J, Vecchiato K, Liu Y, Caligiuri ME, Sinclair B, Vivash L, Willard A, Kandasamy J, McLellan A, Sokol D, Semmelroch M, Kloster AG, Opheim G, Ribeiro L, Yasuda C, Rossi-Espagnet C, Hamandi K, Tietze A, Barba C, Guerrini R, Gaillard WD, You X, Wang I, González-Ortiz S, Severino M, Striano P, Tortora D, Kälviäinen R, Gambardella A, Labate A, Desmond P, Lui E, O’Brien T, Shetty J, Jackson G, Duncan JS, Winston GP, Pinborg LH, Cendes F, Theis FJ, Shinohara RT, Cross JH, Baldeweg T, Adler S, Wagstyl K. Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study. Brain 2022; 145:3859-3871. [PMID: 35953082 PMCID: PMC9679165 DOI: 10.1093/brain/awac224] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 04/22/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.
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Affiliation(s)
- Hannah Spitzer
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
| | - Mathilde Ripart
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | | | - Felice D’Arco
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Kshitij Mankad
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital, Rome 00165, Italy
| | - Luca De Palma
- Rare and Complex Epilepsies, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Alessandro De Benedictis
- Neurosurgery Unit, Department of Neurosciences, Bambino Gesù Children’s Hospital, IRCCS, Rome 00165, Italy
| | - Stephen Foldes
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Zachary Humphreys
- Barrow Neurological Institute at Phoenix Children’s Hospital, Phoenix, AZ 85016, USA
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Jiajie Mo
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100054, China
| | - Marcus Likeman
- Bristol Royal Hospital for Children, Bristol BS2 8BJ, UK
| | - Shirin Davies
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, Cardiff and Vale University Health Board, University Hospital of Wales, Cardiff CF14 4XW, UK
| | | | - Matteo Lenge
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Nathan T Cohen
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, Chengdu 610093, China
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Shan Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
- Department of Neurology, Epilepsy Center, Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Aswin Chari
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Martin Tisdall
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Nuria Bargallo
- Department of Neuroradiology, Hospital Clinic Barcelona and Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid 28029, Spain
| | | | | | - Saül Pascual-Diaz
- Magnetic Resonance Imaging, Core Facility, IDIBAPS, Barcelona 08036, Spain
| | | | | | | | - Eugenio Abela
- Center for Neuropsychiatry and Intellectual Disability, Psychiatrische Dienste Aargau AG, Windisch 5120, Switzerland
| | - Nandini Mullatti
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Jonathan O’Muircheartaigh
- Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
| | - Katy Vecchiato
- Department of Perinatal Imaging and Health, St. Thomas’ Hospital, King’s College London, London SE1 7EH, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yawu Liu
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
| | - Maria Eugenia Caligiuri
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro 88100, Italy
| | - Ben Sinclair
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Lucy Vivash
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Neurology, Monash University, Melbourne, VIC 3004, Australia
| | - Anna Willard
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
| | - Jothy Kandasamy
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Ailsa McLellan
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Drahoslav Sokol
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Mira Semmelroch
- The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052, Australia
| | - Ane G Kloster
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Giske Opheim
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Department of Neuroradiology, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
| | - Letícia Ribeiro
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Clarissa Yasuda
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | | | - Khalid Hamandi
- School of Psychology, Cardiff University Brain Research Imaging Centre, Cardiff CF24 4HQ, UK
- The Welsh Epilepsy Unit, University Hospital of Wales, Cardiff CF14 4XW, UK
| | - Anna Tietze
- Charité University Hospital, Berlin 10117, Germany
| | - Carmen Barba
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | - Renzo Guerrini
- Neuroscience Department, Children’s Hospital Meyer-University of Florence, Florence 50139, Italy
| | | | - Xiaozhen You
- Center for Neuroscience, Children’s National Hospital, Washington, DC 20012, USA
| | - Irene Wang
- Epilepsy Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Sofía González-Ortiz
- Department of Neuroradiology, Hospital del Mar, Barcelona 08003, Spain
- Magnetic Resonance Imaging Core Facility, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona 08036, Spain
| | | | - Pasquale Striano
- IRCCS Istituto Giannina Gaslini, Genova 16147, Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
| | | | - Reetta Kälviäinen
- Department of Neurology, University of Eastern Finland, Kuopio 70210, Finland
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Kuopio 70210, Finland
| | - Antonio Gambardella
- Institute of Neurology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro 88100, Italy
| | - Angelo Labate
- Neurology Unit, Department of BIOMORF, University of Messina, Messina 98168, Italy
| | - Patricia Desmond
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Elaine Lui
- Department of Radiology, The Royal Melbourne Hospital, University of Melbourne, Parkville, VIC 3050, Australia
| | - Terence O’Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia
- Department of Medicine, The Royal Melbourne Hospital, Parkville, VIC, 3052, Australia
| | - Jay Shetty
- Royal Hospital for Children and Young People, Edinburgh EH16 4TJ, UK
| | - Graeme Jackson
- The Florey Institute of Neuroscience and Mental Health, Austin Campus, Heidelberg, VIC 3071, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC 3084, Australia
| | - John S Duncan
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Gavin P Winston
- UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
- Department of Medicine, Division of Neurology, Queen’s University, Kingston, ON, Canada K7L 3N6
| | - Lars H Pinborg
- Neurobiology Research Unit, Copenhagen University Hospital—Rigshospitalet, Copenhagen 2100, Denmark
- Epilepsy Clinic, Department of Neurology, Copenhagen University Hospital—Rigshopsitalet, Copenhagen 2100, Denmark
| | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas 13083-888, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas, Campinas 13083-888, Brazil
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich 85764, Germany
- Department of Mathematics, Technical University of Munich, Garching 85748, Germany
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - J Helen Cross
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Young Epilepsy, Lingfield, Surrey RH7 6PW, UK
| | - Torsten Baldeweg
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Great Ormond Street Hospital NHS Foundation Trust, London WC1N 3JH, UK
| | - Sophie Adler
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
| | - Konrad Wagstyl
- Department of Developmental Neuroscience, UCL Great Ormond Street Institute for Child Health, London WC1N 1EH, UK
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
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14
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Cohen NT, You X, Krishnamurthy M, Sepeta LN, Zhang A, Oluigbo C, Whitehead MT, Gholipour T, Baldeweg T, Wagstyl K, Adler S, Gaillard WD. Networks Underlie Temporal Onset of Dysplasia-Related Epilepsy: A MELD Study. Ann Neurol 2022; 92:503-511. [PMID: 35726354 PMCID: PMC10410674 DOI: 10.1002/ana.26442] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/22/2022] [Accepted: 06/10/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The purpose of this study was to evaluate if focal cortical dysplasia (FCD) co-localization to cortical functional networks is associated with the temporal distribution of epilepsy onset in FCD. METHODS International (20 center), retrospective cohort from the Multi-Centre Epilepsy Lesion Detection (MELD) project. Patients included if >3 years old, had 3D pre-operative T1 magnetic resonance imaging (MRI; 1.5 or 3 T) with radiologic or histopathologic FCD after surgery. Images processed using the MELD protocol, masked with 3D regions-of-interest (ROI), and co-registered to fsaverage_sym (symmetric template). FCDs were then co-localized to 1 of 7 distributed functional cortical networks. Negative binomial regression evaluated effect of FCD size, network, histology, and sulcal depth on age of epilepsy onset. From this model, predictive age of epilepsy onset was calculated for each network. RESULTS Three hundred eighty-eight patients had median age seizure onset 5 years (interquartile range [IQR] = 3-11 years), median age at pre-operative scan 18 years (IQR = 11-28 years). FCDs co-localized to the following networks: limbic (90), default mode (87), somatomotor (65), front parietal control (52), ventral attention (32), dorsal attention (31), and visual (31). Larger lesions were associated with younger age of onset (p = 0.01); age of epilepsy onset was associated with dominant network (p = 0.04) but not sulcal depth or histology. Sensorimotor networks had youngest onset; the limbic network had oldest age of onset (p values <0.05). INTERPRETATION FCD co-localization to distributed functional cortical networks is associated with age of epilepsy onset: sensory neural networks (somatomotor and visual) with earlier onset, and limbic latest onset. These variations may reflect developmental differences in synaptic/white matter maturation or network activation and may provide a biological basis for age-dependent epilepsy onset expression. ANN NEUROL 2022;92:503-511.
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Affiliation(s)
- Nathan T Cohen
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Xiaozhen You
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Manu Krishnamurthy
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Leigh N Sepeta
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Anqing Zhang
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
- Division of Biostatistics and Study Methodology, Children's National Research Institute, Washington, DC
| | - Chima Oluigbo
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
- Department of Neurosurgery, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Matthew T Whitehead
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
- Department of Neuroradiology, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
| | - Taha Gholipour
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
- George Washington University Epilepsy Center, The George Washington University School of Medicine, Washington, DC
| | - Torsten Baldeweg
- Great Ormond Street Institute for Child Health, University College of London, London, UK
| | | | - Sophie Adler
- Great Ormond Street Institute for Child Health, University College of London, London, UK
| | - William D Gaillard
- Center for Neuroscience Research, Children's National Hospital, The George Washington University School of Medicine, Washington, DC
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15
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 400] [Impact Index Per Article: 200.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Chari A, Adler S, Wagstyl K, Seunarine K, Marcus H, Baldeweg T, Tisdall M. IDEAL approach to the evaluation of machine learning technology in epilepsy surgery: protocol for the MAST trial. BMJ Surg Interv Health Technologies 2022; 4:e000109. [PMID: 35136859 PMCID: PMC8796270 DOI: 10.1136/bmjsit-2021-000109] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/10/2022] [Indexed: 12/03/2022] Open
Abstract
Epilepsy and epilepsy surgery lend themselves well to the application of machine learning (ML) and artificial intelligence (AI) technologies. This is evidenced by the plethora of tools developed for applications such as seizure detection and analysis of imaging and electrophysiological data. However, few of these tools have been directly used to guide patient management. In recent years, the Idea, Development, Exploration, Assessment, Long-Term Follow-Up (IDEAL) collaboration has formalised stages for the evaluation of surgical innovation and medical devices, and, in many ways, this pragmatic framework is also applicable to ML/AI technology, balancing innovation and safety. In this protocol paper, we outline the preclinical (IDEAL stage 0) evaluation and the protocol for a prospective (IDEAL stage 1/2a) study to evaluate the utility of an ML lesion detection algorithm designed to detect focal cortical dysplasia from structural MRI, as an adjunct in the planning of stereoelectroencephalography trajectories in children undergoing intracranial evaluation for drug-resistant epilepsy.
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Affiliation(s)
- Aswin Chari
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sophie Adler
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kiran Seunarine
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Hani Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Torsten Baldeweg
- Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Martin Tisdall
- Department of Neurosurgery, Great Ormond Street Hospital, London, UK
- Developmental Neuroscience, Great Ormond Street Institute of Child Health, University College London, London, UK
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Wagstyl K, Whitaker K, Raznahan A, Seidlitz J, Vértes PE, Foldes S, Humphreys Z, Hu W, Mo J, Likeman M, Davies S, Lenge M, Cohen NT, Tang Y, Wang S, Ripart M, Chari A, Tisdall M, Bargallo N, Conde‐Blanco E, Pariente JC, Pascual‐Diaz S, Delgado‐Martínez I, Pérez‐Enríquez C, Lagorio I, Abela E, Mullatti N, O'Muircheartaigh J, Vecchiato K, Liu Y, Caligiuri M, Sinclair B, Vivash L, Willard A, Kandasamy J, McLellan A, Sokol D, Semmelroch M, Kloster A, Opheim G, Yasuda C, Zhang K, Hamandi K, Barba C, Guerrini R, Gaillard WD, You X, Wang I, González‐Ortiz S, Severino M, Striano P, Tortora D, Kalviainen R, Gambardella A, Labate A, Desmond P, Lui E, O'Brien T, Shetty J, Jackson G, Duncan JS, Winston GP, Pinborg L, Cendes F, Cross JH, Baldeweg T, Adler S. Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: A MELD study. Epilepsia 2022; 63:61-74. [PMID: 34845719 PMCID: PMC8916105 DOI: 10.1111/epi.17130] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/07/2021] [Accepted: 11/08/2021] [Indexed: 02/05/2023]
Abstract
OBJECTIVE Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome. METHODS The MELD (Multi-centre Epilepsy Lesion Detection) project collated a retrospective cohort of 580 patients with epilepsy attributed to FCD from 20 epilepsy centers worldwide. Magnetic resonance imaging-based maps of individual FCDs with accompanying demographic, clinical, and surgical information were collected. We mapped the distribution of FCDs, examined for associations between clinical factors and lesion location, and developed a predictive model of postsurgical seizure freedom. RESULTS FCDs were nonuniformly distributed, concentrating in the superior frontal sulcus, frontal pole, and temporal pole. Epilepsy onset was typically before the age of 10 years. Earlier epilepsy onset was associated with lesions in primary sensory areas, whereas later epilepsy onset was associated with lesions in association cortices. Lesions in temporal and occipital lobes tended to be larger than frontal lobe lesions. Seizure freedom rates varied with FCD location, from around 30% in visual, motor, and premotor areas to 75% in superior temporal and frontal gyri. The predictive model of postsurgical seizure freedom had a positive predictive value of 70% and negative predictive value of 61%. SIGNIFICANCE FCD location is an important determinant of its size, the age at epilepsy onset, and the likelihood of seizure freedom postsurgery. Our atlas of lesion locations can be used to guide the radiological search for subtle lesions in individual patients. Our atlas of regional seizure freedom rates and associated predictive model can be used to estimate individual likelihoods of postsurgical seizure freedom. Data-driven atlases and predictive models are essential for evidence-based, precision medicine and risk counseling in epilepsy.
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McColgan P, Helbling S, Vaculčiaková L, Pine K, Wagstyl K, Attar FM, Edwards L, Papoutsi M, Wei Y, Van den Heuvel MP, Tabrizi SJ, Rees G, Weiskopf N. Relating quantitative 7T MRI across cortical depths to cytoarchitectonics, gene expression and connectomics. Hum Brain Mapp 2021; 42:4996-5009. [PMID: 34272784 PMCID: PMC8449108 DOI: 10.1002/hbm.25595] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 06/26/2021] [Accepted: 07/06/2021] [Indexed: 12/24/2022] Open
Abstract
Ultra-high field MRI across the depth of the cortex has the potential to provide anatomically precise biomarkers and mechanistic insights into neurodegenerative disease like Huntington's disease that show layer-selective vulnerability. Here we compare multi-parametric mapping (MPM) measures across cortical depths for a 7T 500 μm whole brain acquisition to (a) layer-specific cell measures from the von Economo histology atlas, (b) layer-specific gene expression, using the Allen Human Brain atlas and (c) white matter connections using high-fidelity diffusion tractography, at a 1.3 mm isotropic voxel resolution, from a 300mT/m Connectom MRI system. We show that R2*, but not R1, across cortical depths is highly correlated with layer-specific cell number and layer-specific gene expression. R1- and R2*-weighted connectivity strength of cortico-striatal and intra-hemispheric cortical white matter connections was highly correlated with grey matter R1 and R2* across cortical depths. Limitations of the layer-specific relationships demonstrated are at least in part related to the high cross-correlations of von Economo atlas cell counts and layer-specific gene expression across cortical layers. These findings demonstrate the potential and limitations of combining 7T MPMs, gene expression and white matter connections to provide an anatomically precise framework for tracking neurodegenerative disease.
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Affiliation(s)
- Peter McColgan
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Saskia Helbling
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Lenka Vaculčiaková
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Kerrin Pine
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Konrad Wagstyl
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | | | - Luke Edwards
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Marina Papoutsi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Yongbin Wei
- Vrije Universiteit AmsterdamComplex Traits Genetics LabAmsterdamNetherlands
| | | | - Sarah J Tabrizi
- Huntington's Disease Research Centre, Institute of NeurologyUniversity College LondonLondon
| | - Geraint Rees
- The Wellcome Centre for Human Neuroimaging, Institute of NeurologyUniversity College LondonLondonUK
| | - Nikolaus Weiskopf
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Felix Bloch Institute for Solid State PhysicsFaculty of Physics and Earth Sciences, Leipzig UniversityLeipzigGermany
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19
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Paquola C, Royer J, Lewis LB, Lepage C, Glatard T, Wagstyl K, DeKraker J, Toussaint PJ, Valk SL, Collins L, Khan AR, Amunts K, Evans AC, Dickscheid T, Bernhardt B. The BigBrainWarp toolbox for integration of BigBrain 3D histology with multimodal neuroimaging. eLife 2021; 10:e70119. [PMID: 34431476 PMCID: PMC8445620 DOI: 10.7554/elife.70119] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/23/2021] [Indexed: 01/03/2023] Open
Abstract
Neuroimaging stands to benefit from emerging ultrahigh-resolution 3D histological atlases of the human brain; the first of which is 'BigBrain'. Here, we review recent methodological advances for the integration of BigBrain with multi-modal neuroimaging and introduce a toolbox, 'BigBrainWarp', that combines these developments. The aim of BigBrainWarp is to simplify workflows and support the adoption of best practices. This is accomplished with a simple wrapper function that allows users to easily map data between BigBrain and standard MRI spaces. The function automatically pulls specialised transformation procedures, based on ongoing research from a wide collaborative network of researchers. Additionally, the toolbox improves accessibility of histological information through dissemination of ready-to-use cytoarchitectural features. Finally, we demonstrate the utility of BigBrainWarp with three tutorials and discuss the potential of the toolbox to support multi-scale investigations of brain organisation.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Lindsay B Lewis
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Claude Lepage
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Tristan Glatard
- Department of Computer Science and Software Engineering, Concordia UniversityMontrealCanada
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College LondonLondonUnited Kingdom
| | - Jordan DeKraker
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
- Brain and Mind Institute, University of Western OntarioOntarioCanada
| | - Paule-J Toussaint
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Sofie L Valk
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- Institute of Neuroscience and Medicine (INM-7), Forschungszentrum JülichJülichGermany
| | - Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Alan C Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum JülichJülichGermany
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill UniversityMontréalCanada
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20
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Yang S, Wagstyl K, Meng Y, Zhao X, Li J, Zhong P, Li B, Fan YS, Chen H, Liao W. Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices. Cell Rep 2021; 36:109582. [PMID: 34433023 DOI: 10.1016/j.celrep.2021.109582] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 05/28/2021] [Accepted: 07/30/2021] [Indexed: 10/20/2022] Open
Abstract
The topological organization of the cerebral cortex provides hierarchical axes, namely gradients, which reveal systematic variations of brain structure and function. However, the hierarchical organization of macroscopic brain morphology and how it constrains cortical function along the organizing axes remains unclear. We map the gradient of cortical morphometric similarity (MS) connectome, combining multiple features conceptualized as a "fingerprint" of an individual's brain. The principal MS gradient is anchored by motor and sensory cortices at two extreme ends, which are reliable and reproducible. Notably, divergences between motor and sensory hierarchies are consistent with the laminar histological thickness gradient but contrary to the canonical functional connectivity (FC) gradient. Moreover, the MS dissociates with FC gradients in the higher-order association cortices. The MS gradient recapitulates fundamental properties of cortical organization, from gene expression and cyto- and myeloarchitecture to evolutionary expansion. Collectively, our findings provide a heuristic hierarchical organization of cortical morphological neuromarkers.
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Affiliation(s)
- Siqi Yang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Konrad Wagstyl
- Wellcome Trust Centre for Neuroimaging, University College London, London, UK
| | - Yao Meng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Xiaopeng Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Peng Zhong
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Bing Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Yun-Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China.
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China; MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu 611731, P.R. China.
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21
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Huber LR, Poser BA, Bandettini PA, Arora K, Wagstyl K, Cho S, Goense J, Nothnagel N, Morgan AT, van den Hurk J, Müller AK, Reynolds RC, Glen DR, Goebel R, Gulban OF. LayNii: A software suite for layer-fMRI. Neuroimage 2021; 237:118091. [PMID: 33991698 PMCID: PMC7615890 DOI: 10.1016/j.neuroimage.2021.118091] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 01/06/2023] Open
Abstract
High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain 'layerification' and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.
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Affiliation(s)
| | - Benedikt A Poser
- MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | | | - Kabir Arora
- MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Shinho Cho
- CMRR, University of Minneapolis, MN, USA
| | | | | | | | | | | | | | | | - Rainer Goebel
- MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands; Brain Innovation, Maastricht, the Netherlands
| | - Omer Faruk Gulban
- MBIC, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands; Brain Innovation, Maastricht, the Netherlands
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22
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Vasung L, Rollins CK, Yun HJ, Velasco-Annis C, Zhang J, Wagstyl K, Evans A, Warfield SK, Feldman HA, Grant PE, Gholipour A. Quantitative In vivo MRI Assessment of Structural Asymmetries and Sexual Dimorphism of Transient Fetal Compartments in the Human Brain. Cereb Cortex 2021; 30:1752-1767. [PMID: 31602456 DOI: 10.1093/cercor/bhz200] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 08/02/2019] [Accepted: 08/05/2019] [Indexed: 12/19/2022] Open
Abstract
Structural asymmetries and sexual dimorphism of the human cerebral cortex have been identified in newborns, infants, children, adolescents, and adults. Some of these findings were linked with cognitive and neuropsychiatric disorders, which have roots in altered prenatal brain development. However, little is known about structural asymmetries or sexual dimorphism of transient fetal compartments that arise in utero. Thus, we aimed to identify structural asymmetries and sexual dimorphism in the volume of transient fetal compartments (cortical plate [CP] and subplate [SP]) across 22 regions. For this purpose, we used in vivo structural T2-weighted MRIs of 42 healthy fetuses (16.43-36.86 gestational weeks old, 15 females). We found significant leftward asymmetry in the volume of the CP and SP in the inferior frontal gyrus. The orbitofrontal cortex showed significant rightward asymmetry in the volume of CP merged with SP. Males had significantly larger volumes in regions belonging to limbic, occipital, and frontal lobes, which were driven by a significantly larger SP. Lastly, we did not observe sexual dimorphism in the growth trajectories of the CP or SP. In conclusion, these results support the hypothesis that structural asymmetries and sexual dimorphism in relative volumes of cortical regions are present during prenatal brain development.
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Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin K Rollins
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,McGill Centre for Integrative Neuroscience/Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | | | - Alan Evans
- McGill Centre for Integrative Neuroscience/Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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23
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Lepage C, Wagstyl K, Jung B, Seidlitz J, Sponheim C, Ungerleider L, Wang X, Evans AC, Messinger A. CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques. Neuroimage 2021; 227:117622. [PMID: 33301944 PMCID: PMC7615896 DOI: 10.1016/j.neuroimage.2020.117622] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/27/2020] [Accepted: 11/28/2020] [Indexed: 12/21/2022] Open
Abstract
The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.
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Affiliation(s)
- Claude Lepage
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Konrad Wagstyl
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Xindi Wang
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
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24
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Seidlitz J, Nadig A, Liu S, Bethlehem RAI, Vértes PE, Morgan SE, Váša F, Romero-Garcia R, Lalonde FM, Clasen LS, Blumenthal JD, Paquola C, Bernhardt B, Wagstyl K, Polioudakis D, de la Torre-Ubieta L, Geschwind DH, Han JC, Lee NR, Murphy DG, Bullmore ET, Raznahan A. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat Commun 2020; 11:3358. [PMID: 32620757 PMCID: PMC7335069 DOI: 10.1038/s41467-020-17051-5] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2019] [Accepted: 05/11/2020] [Indexed: 11/29/2022] Open
Abstract
Neurodevelopmental disorders have a heritable component and are associated with region specific alterations in brain anatomy. However, it is unclear how genetic risks for neurodevelopmental disorders are translated into spatially patterned brain vulnerabilities. Here, we integrated cortical neuroimaging data from patients with neurodevelopmental disorders caused by genomic copy number variations (CNVs) and gene expression data from healthy subjects. For each of the six investigated disorders, we show that spatial patterns of cortical anatomy changes in youth are correlated with cortical spatial expression of CNV genes in neurotypical adults. By transforming normative bulk-tissue cortical expression data into cell-type expression maps, we link anatomical change maps in each analysed disorder to specific cell classes as well as the CNV-region genes they express. Our findings reveal organizing principles that regulate the mapping of genetic risks onto regional brain changes in neurogenetic disorders. Our findings will enable screening for candidate molecular mechanisms from readily available neuroimaging data.
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Affiliation(s)
- Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Ajay Nadig
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | | | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- School of Mathematical Sciences, Queen Mary University of London, London, UK
- The Alan Turing Institute, London, UK
| | - Sarah E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - František Váša
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | | | - François M Lalonde
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Liv S Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Jonathan D Blumenthal
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, QC, Canada
| | - Damon Polioudakis
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Luis de la Torre-Ubieta
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Joan C Han
- Departments of Pediatrics and Physiology, University of Tennessee Health Science Center and Le Bonheur Children's Foundation Research Institute, Memphis, TN, USA
- Pediatrics and Developmental Neuropsychiatry Branch, National Institute of Mental Health, NIH, Bethesda, MD, USA
- Unit on Metabolism and Neuroendocrinology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Nancy R Lee
- Department of Psychology, Drexel University, Philadelphia, PA, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD, USA.
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25
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Wagstyl K, Adler S, Pimpel B, Chari A, Seunarine K, Lorio S, Thornton R, Baldeweg T, Tisdall M. Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study. Epilepsia 2020; 61:1406-1416. [PMID: 32533794 PMCID: PMC8432161 DOI: 10.1111/epi.16574] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/18/2020] [Accepted: 05/18/2020] [Indexed: 02/06/2023]
Abstract
Objective This retrospective, cross‐sectional study evaluated the feasibility and potential benefits of incorporating deep‐learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug‐resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. Methods A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross‐validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier‐predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier‐predicted lesions was considered colocalization. Results In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the controls (specificity = 100%). Of the total 34 sEEG patients, 21 patients had a focal cortical SOZ, of whom eight were histopathologically confirmed as having an FCD. The algorithm correctly detected seven of eight of these FCDs (86%). In patients with histopathologically heterogeneous focal cortical lesions, there was colocalization between classifier output and SOZ contacts in 62%. In three patients, the electroclinical profile was indicative of focal epilepsy, but no SOZ was localized on sEEG. In these patients, the classifier identified additional abnormalities that had not been implanted. Significance There was a high degree of colocalization between automated lesion detection and sEEG. We have created a framework for incorporation of deep‐learning–based MRI lesion detection into sEEG implantation planning. Our findings support the prospective evaluation of automated MRI analysis to plan optimal electrode trajectories.
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Affiliation(s)
- Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Sophie Adler
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Birgit Pimpel
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Aswin Chari
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,Great Ormond Street Hospital, London, UK
| | - Kiran Seunarine
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Sara Lorio
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Rachel Thornton
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,Great Ormond Street Hospital, London, UK
| | - Torsten Baldeweg
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Martin Tisdall
- Developmental Neurosciences, Great Ormond Street Institute of Child Health, University College London, London, UK.,Great Ormond Street Hospital, London, UK
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26
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Bonaiuto JJ, Afdideh F, Ferez M, Wagstyl K, Mattout J, Bonnefond M, Barnes GR, Bestmann S. Estimates of cortical column orientation improve MEG source inversion. Neuroimage 2020; 216:116862. [PMID: 32305564 PMCID: PMC8417767 DOI: 10.1016/j.neuroimage.2020.116862] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 04/07/2020] [Accepted: 04/14/2020] [Indexed: 01/06/2023] Open
Abstract
Determining the anatomical source of brain activity non-invasively measured from EEG or MEG sensors is challenging. In order to simplify the source localization problem, many techniques introduce the assumption that current sources lie on the cortical surface. Another common assumption is that this current flow is orthogonal to the cortical surface, thereby approximating the orientation of cortical columns. However, it is not clear which cortical surface to use to define the current source locations, and normal vectors computed from a single cortical surface may not be the best approximation to the orientation of cortical columns. We compared three different surface location priors and five different approaches for estimating dipole vector orientation, both in simulations and visual and motor evoked MEG responses. We show that models with source locations on the white matter surface and using methods based on establishing correspondences between white matter and pial cortical surfaces dramatically outperform models with source locations on the pial or combined pial/white surfaces and which use methods based on the geometry of a single cortical surface in fitting evoked visual and motor responses. These methods can be easily implemented and adopted in most M/EEG analysis pipelines, with the potential to significantly improve source localization of evoked responses.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France.
| | - Fardin Afdideh
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Maxime Ferez
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge, CB2 0SZ, UK; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Mathilde Bonnefond
- Université Claude Bernard Lyon 1, Université de Lyon, France; Lyon Neuroscience Research Center, CRNL, Brain Dynamics and Cognition Team, INSERM U1028, CNRS UMR5292, Lyon, France
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3AR, UK; Dept of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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27
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Paquola C, Bethlehem RAI, Seidlitz J, Wagstyl K, Romero-Garcia R, Whitaker KJ, Vos de Wael R, Williams GB, Vértes PE, Margulies DS, Bernhardt B, Bullmore ET. Shifts in myeloarchitecture characterise adolescent development of cortical gradients. eLife 2019; 8:e50482. [PMID: 31724948 PMCID: PMC6855802 DOI: 10.7554/elife.50482] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 11/02/2019] [Indexed: 11/21/2022] Open
Abstract
We studied an accelerated longitudinal cohort of adolescents and young adults (n = 234, two time points) to investigate dynamic reconfigurations in myeloarchitecture. Intracortical profiles were generated using magnetization transfer (MT) data, a myelin-sensitive magnetic resonance imaging contrast. Mixed-effect models of depth specific intracortical profiles demonstrated two separate processes i) overall increases in MT, and ii) flattening of the MT profile related to enhanced signal in mid-to-deeper layers, especially in heteromodal and unimodal association cortices. This development was independent of morphological changes. Enhanced MT in mid-to-deeper layers was found to spatially co-localise specifically with gene expression markers of oligodendrocytes. Interregional covariance analysis revealed that these intracortical changes contributed to a gradual differentiation of higher-order from lower-order systems. Depth-dependent trajectories of intracortical myeloarchitectural development contribute to the maturation of structural hierarchies in the human neocortex, providing a model for adolescent development that bridges microstructural and macroscopic scales of brain organisation.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Richard AI Bethlehem
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Autism Research Centre, Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
| | - Jakob Seidlitz
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Developmental Neurogenomics UnitNational Institute of Mental HealthBethesdaUnited States
| | - Konrad Wagstyl
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Kirstie J Whitaker
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- The Alan Turing InstituteLondonUnited Kingdom
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Guy B Williams
- Department of Clinical Neurosciences, Wolfson Brain Imaging CentreUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Petra E Vértes
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- The Alan Turing InstituteLondonUnited Kingdom
| | - Daniel S Margulies
- FrontlabInstitut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225ParisFrance
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Edward T Bullmore
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Department of Clinical Neurosciences, Wolfson Brain Imaging CentreUniversity of CambridgeCambridgeUnited Kingdom
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29
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Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong SJ, Seidlitz J, Bullmore ET, Evans AC, Misic B, Margulies DS, Smallwood J, Bernhardt BC. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol 2019; 17:e3000284. [PMID: 31107870 PMCID: PMC6544318 DOI: 10.1371/journal.pbio.3000284] [Citation(s) in RCA: 210] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/31/2019] [Accepted: 05/08/2019] [Indexed: 01/10/2023] Open
Abstract
While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos De Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Konrad Wagstyl
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
| | - Richard A. I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
- Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Cambridge, United Kingdom
| | - Edward T. Bullmore
- Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Cambridge, United Kingdom
| | - Alan C. Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | | | | | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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30
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Wagstyl K, Lepage C, Bludau S, Zilles K, Fletcher PC, Amunts K, Evans AC. Mapping Cortical Laminar Structure in the 3D BigBrain. Cereb Cortex 2018; 28:2551-2562. [PMID: 29901791 PMCID: PMC5998962 DOI: 10.1093/cercor/bhy074] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Revised: 03/08/2018] [Accepted: 03/13/2018] [Indexed: 11/30/2022] Open
Abstract
Histological sections offer high spatial resolution to examine laminar architecture of the human cerebral cortex; however, they are restricted by being 2D, hence only regions with sufficiently optimal cutting planes can be analyzed. Conversely, noninvasive neuroimaging approaches are whole brain but have relatively low resolution. Consequently, correct 3D cross-cortical patterns of laminar architecture have never been mapped in histological sections. We developed an automated technique to identify and analyze laminar structure within the high-resolution 3D histological BigBrain. We extracted white matter and pial surfaces, from which we derived histologically verified surfaces at the layer I/II boundary and within layer IV. Layer IV depth was strongly predicted by cortical curvature but varied between areas. This fully automated 3D laminar analysis is an important requirement for bridging high-resolution 2D cytoarchitecture and in vivo 3D neuroimaging. It lays the foundation for in-depth, whole-brain analyses of cortical layering.
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Affiliation(s)
- Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Claude Lepage
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Sebastian Bludau
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen, Germany
- JARA—Translational Brain Medicine, Aachen, Aachen, Germany
| | - Paul C Fletcher
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- C. and O. Vogt-Institute for Brain Research, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Alan C Evans
- Department of Neurology and Neurosurgery, Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
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31
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Jin B, Krishnan B, Adler S, Wagstyl K, Hu W, Jones S, Najm I, Alexopoulos A, Zhang K, Zhang J, Ding M, Wang S, Wang ZI. Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia 2018; 59:982-992. [PMID: 29637549 DOI: 10.1111/epi.14064] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2018] [Indexed: 01/13/2023]
Abstract
OBJECTIVE Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers. METHODS Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation. RESULTS The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance. SIGNIFICANCE Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy.
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Affiliation(s)
- Bo Jin
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China.,Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Balu Krishnan
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | - Sophie Adler
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Great Ormond Street Hospital for Children, London, UK
| | - Konrad Wagstyl
- Developmental Neurosciences, UCL Great Ormond Street Institute of Child Health, University College London, London, UK.,Brain Mapping Unit, Institute of Psychiatry, University of Cambridge, Cambridge, UK
| | - Wenhan Hu
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Stephen Jones
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Imad Najm
- Epilepsy Center, Cleveland Clinic, Cleveland, OH, USA
| | | | - Kai Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Neurosurgery, Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Meiping Ding
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Shuang Wang
- Department of Neurology, School of Medicine, Epilepsy Center, Second Affiliated Hospital, Zhejiang University, Hangzhou, China
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Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S, Messinger A, Leopold DA, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Raznahan A, Bullmore ET. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron 2017; 97:231-247.e7. [PMID: 29276055 DOI: 10.1016/j.neuron.2017.11.039] [Citation(s) in RCA: 209] [Impact Index Per Article: 29.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 10/05/2017] [Accepted: 11/22/2017] [Indexed: 12/22/2022]
Abstract
Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
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Affiliation(s)
- Jakob Seidlitz
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.
| | - František Váša
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Maxwell Shinn
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Kirstie J Whitaker
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Petra E Vértes
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Liv Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - David A Leopold
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Edward T Bullmore
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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Vértes PE, Rittman T, Whitaker KJ, Romero-Garcia R, Váša F, Kitzbichler MG, Wagstyl K, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Bullmore ET. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos Trans R Soc Lond B Biol Sci 2017; 371:rstb.2015.0362. [PMID: 27574314 PMCID: PMC5003862 DOI: 10.1098/rstb.2015.0362] [Citation(s) in RCA: 126] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2016] [Indexed: 12/22/2022] Open
Abstract
Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.
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Affiliation(s)
- Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Kirstie J Whitaker
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - František Váša
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - Konrad Wagstyl
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, UK Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK Immuno-Psychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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Huntenburg J, Wagstyl K, Steele C, Funck T, Bethlehem R, Foubet O, Larrat B, Borrell V, Bazin PL. Laminar Python: tools for cortical depth-resolved analysis of high-resolution brain imaging data in Python. RIO 2017. [DOI: 10.3897/rio.3.e12346] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Ronan L, Alexander-Bloch AF, Wagstyl K, Farooqi S, Brayne C, Tyler LK, Fletcher PC. Obesity associated with increased brain age from midlife. Neurobiol Aging 2016; 47:63-70. [PMID: 27562529 PMCID: PMC5082766 DOI: 10.1016/j.neurobiolaging.2016.07.010] [Citation(s) in RCA: 132] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Revised: 07/14/2016] [Accepted: 07/15/2016] [Indexed: 01/30/2023]
Abstract
Common mechanisms in aging and obesity are hypothesized to increase susceptibility to neurodegeneration, however, direct evidence in support of this hypothesis is lacking. We therefore performed a cross-sectional analysis of magnetic resonance image-based brain structure on a population-based cohort of healthy adults. Study participants were originally part of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) and included 527 individuals aged 20-87 years. Cortical reconstruction techniques were used to generate measures of whole-brain cerebral white-matter volume, cortical thickness, and surface area. Results indicated that cerebral white-matter volume in overweight and obese individuals was associated with a greater degree of atrophy, with maximal effects in middle-age corresponding to an estimated increase of brain age of 10 years. There were no similar body mass index-related changes in cortical parameters. This study suggests that at a population level, obesity may increase the risk of neurodegeneration.
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Affiliation(s)
- Lisa Ronan
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK.
| | | | - Konrad Wagstyl
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK
| | - Sadaf Farooqi
- Department of Clinical Biochemistry, Institute of Metabolic Sciences, Cambridge, UK
| | - Carol Brayne
- Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Lorraine K Tyler
- MRC Cognition and Brain Sciences Unit, Cambridge Center for Ageing and Neuroscience (Cam-CAN), Cambridge, UK
| | - Paul C Fletcher
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK
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Wagstyl K, Ronan L, Whitaker KJ, Goodyer IM, Roberts N, Crow TJ, Fletcher PC. Multiple markers of cortical morphology reveal evidence of supragranular thinning in schizophrenia. Transl Psychiatry 2016; 6:e780. [PMID: 27070408 PMCID: PMC4872401 DOI: 10.1038/tp.2016.43] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2016] [Accepted: 02/19/2016] [Indexed: 02/05/2023] Open
Abstract
In vivo structural neuroimaging can reliably identify changes to cortical morphology and its regional variation but cannot yet relate these changes to specific cortical layers. We propose, however, that by synthesizing principles of cortical organization, including relative contributions of different layers to sulcal and gyral thickness, regional patterns of variation in thickness of different layers across the cortical sheet and profiles of layer variation across functional hierarchies, it is possible to develop indirect morphological measures as markers of more specific cytoarchitectural changes. We developed four indirect measures sensitive to changes specifically occurring in supragranular cortical layers, and applied these to test the hypothesis that supragranular layers are disproportionately affected in schizophrenia. Our findings from the four different measures converge to indicate a predominance of supragranular thinning in schizophrenia, independent of medication and illness duration. We propose that these indirect measures offer novel ways of identifying layer-specific cortical changes, offering complementary in vivo observations to existing post-mortem studies.
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Affiliation(s)
- K Wagstyl
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK,Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Sir William Hardy Building, Downing Street, Cambridge, CB2 3EB, UK. E-mail:
| | - L Ronan
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - K J Whitaker
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - I M Goodyer
- Developmental and Life-course Research Group, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - N Roberts
- Clinical Research Imaging Centre, School of Clinical Sciences, University of Edinburgh, Edinburgh, UK
| | - T J Crow
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, UK
| | - P C Fletcher
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK,Cambridge and Peterborough Foundation Trust, Cambridge, UK
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