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Krüger J, Opfer R, Spies L, Hedderich D, Buchert R. Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network. Eur Radiol 2024; 34:3578-3587. [PMID: 37943313 PMCID: PMC11166757 DOI: 10.1007/s00330-023-10356-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 08/19/2023] [Accepted: 08/26/2023] [Indexed: 11/10/2023]
Abstract
OBJECTIVES Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM). MATERIALS AND METHODS The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods. RESULTS The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM. CONCLUSION CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM. CLINICAL RELEVANCE STATEMENT A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases. KEY POINTS • The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry.
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Affiliation(s)
| | | | | | - Dennis Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistr. 52, 20246, Hamburg, Germany.
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Arizono E, Sato N, Shigemoto Y, Kimura Y, Chiba E, Maki H, Matsuda H, Takeshita E, Shimizu-Motohashi Y, Sasaki M, Saito K. Brain structural changes in alternating hemiplegia of childhood using single-case voxel-based morphometry analysis. Int J Dev Neurosci 2023; 83:665-673. [PMID: 37604479 DOI: 10.1002/jdn.10295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/24/2023] [Accepted: 07/03/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND AND PURPOSE Alternating hemiplegia of childhood (AHC) is a rare neurodevelopmental disease caused by ATP1A3 mutations. Using voxel-based morphometry (VBM) analysis, we compared an AHC patient cohort with controls. Additionally, with single-case VBM analysis, we assessed the associations between clinical severity and brain volume in patients with AHC. MATERIALS AND METHODS To investigate structural brain changes in gray matter (GM) and white matter (WM) volumes between 9 patients with AHC and 20 age-matched controls, VBM analysis was performed using three-dimensional T1-weighted magnetic resonance imaging. Single-case VBM analysis was also performed on nine patients with AHC to investigate the associations between the respective volumes of GM/WM differences and the motor level, cognitive level, and status epilepticus severity in patients with AHC. RESULTS Compared with controls, patients with AHC showed significant GM volume reductions in both hippocampi and diffuse cerebellum, and there were WM reductions in both cerebral hemispheres. In patients with AHC, cases with more motor dysfunction, the less GM/WM volume of cerebellum was shown. Three of the six cases with cognitive dysfunction showed a clear GM volume reduction in the insulae. Five of the six cases with status epilepticus showed the GM volume reduction in hippocampi. One case had severe status epilepticus without motor dysfunction and showed no cerebellar atrophy. CONCLUSION With single-case VBM analysis, we could show the association between region-specific changes in brain volume and the severity of various clinical symptoms even in a small sample of subjects.
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Affiliation(s)
- Elly Arizono
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
- Department of Radiology, Tokyo Medical University, Tokyo, Japan
| | - Noriko Sato
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Yoko Shigemoto
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Yukio Kimura
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Emiko Chiba
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hiroyuki Maki
- Department of Radiology, National Center Hospital, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hiroshi Matsuda
- Department of Biofunctional Imaging, Fukushima Medical University, Fukushima, Japan
| | - Eri Takeshita
- Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yuko Shimizu-Motohashi
- Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Masayuki Sasaki
- Department of Child Neurology, National Center Hospital, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kazuhiro Saito
- Department of Radiology, Tokyo Medical University, Tokyo, Japan
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3
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Ceceli AO, Huang Y, Kronberg G, Malaker P, Miller P, King SG, Gaudreault PO, McClain N, Gabay L, Vasa D, Newcorn JH, Ekin D, Alia-Klein N, Goldstein RZ. Common and distinct fronto-striatal volumetric changes in heroin and cocaine use disorders. Brain 2023; 146:1662-1671. [PMID: 36200376 PMCID: PMC10319776 DOI: 10.1093/brain/awac366] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/11/2022] [Accepted: 09/08/2022] [Indexed: 12/30/2022] Open
Abstract
Different drugs of abuse impact the morphology of fronto-striatal dopaminergic targets in both common and unique ways. While dorsal striatal volume tracks with addiction severity across drug classes, opiates impact ventromedial prefrontal cortex (vmPFC) and nucleus accumbens (NAcc) neuroplasticity in preclinical models, and psychostimulants alter inhibitory control, rooted in cortical regions such as the inferior frontal gyrus (IFG). We hypothesized parallel grey matter volume changes associated with human heroin or cocaine use disorder: lower grey matter volume of vmPFC/NAcc in heroin use disorder and IFG in cocaine use disorder, and putamen grey matter volume to be associated with addiction severity measures (including craving) across both. In this cross-sectional study, we quantified grey matter volume (P < 0.05-corrected) in age/sex/IQ-matched individuals with heroin use disorder (n = 32, seven females), cocaine use disorder (n = 32, six females) and healthy controls (n = 32, six females) and compared fronto-striatal volume between groups using voxel-wise general linear models and non-parametric permutation-based tests. Overall, individuals with heroin use disorder had smaller vmPFC and NAcc/putamen volumes than healthy controls. Bilateral lower IFG grey matter volume patterns were specifically evident in cocaine versus heroin use disorders. Correlations between addiction severity measures and putamen grey matter volume did not reach nominal significance level in this sample. These results indicate alterations in dopamine-innervated regions (in the vmPFC and NAcc) in heroin addiction. For the first time we demonstrate lower IFG grey matter volume specifically in cocaine compared with heroin use disorder, suggesting a signature of reduced inhibitory control, which remains to be tested directly using select behavioural measures. Overall, results suggest substance-specific volumetric changes in human psychostimulant or opiate addiction, with implications for fine-tuning biomarker and treatment identification by primary drug of abuse.
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Affiliation(s)
- Ahmet O Ceceli
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Yuefeng Huang
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Greg Kronberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pias Malaker
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pazia Miller
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah G King
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | | | - Natalie McClain
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lily Gabay
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Devarshi Vasa
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jeffrey H Newcorn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Defne Ekin
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nelly Alia-Klein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Rita Z Goldstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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4
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Piredda GF, Caneschi S, Hilbert T, Bonanno G, Joseph A, Egger K, Peter J, Klöppel S, Jehli E, Grieder M, Slotboom J, Seiffge D, Goeldlin M, Hoepner R, Willems T, Vulliemoz S, Seeck M, Venkategowda PB, Corredor Jerez RA, Maréchal B, Thiran JP, Wiest R, Kober T, Radojewski P. Submillimeter T 1 atlas for subject-specific abnormality detection at 7T. Magn Reson Med 2023; 89:1601-1616. [PMID: 36478417 DOI: 10.1002/mrm.29540] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/14/2022] [Accepted: 11/07/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE Studies at 3T have shown that T1 relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T1 values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T. METHODS T1 maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T1 values was established by modeling the T1 inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept. RESULTS The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results. CONCLUSION A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland.,CIBM-AIT, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Samuele Caneschi
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gabriele Bonanno
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Arun Joseph
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Bern, Switzerland.,Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Magnetic Resonance Methodology, Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Karl Egger
- Department of Neuroradiology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jessica Peter
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Elisabeth Jehli
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.,Department of Neurosurgery, University Hospital of Zurich, Zurich, Switzerland
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Johannes Slotboom
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - David Seiffge
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Martina Goeldlin
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Robert Hoepner
- Department of Neurology, University Hospital Bern, Inselspital, University of Bern, Bern, Switzerland
| | - Tom Willems
- Institute of Psychology, University of Bern, Bern, Switzerland
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, Geneva, Switzerland
| | | | - Ricardo A Corredor Jerez
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Bénédicte Maréchal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Roland Wiest
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Piotr Radojewski
- Translational Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, University of Bern, Bern, Switzerland
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5
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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6
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Morita T, Hirose S, Kimura N, Takemura H, Asada M, Naito E. Hyper-Adaptation in the Human Brain: Functional and Structural Changes in the Foot Section of the Primary Motor Cortex in a Top Wheelchair Racing Paralympian. Front Syst Neurosci 2022; 16:780652. [PMID: 35498215 PMCID: PMC9039206 DOI: 10.3389/fnsys.2022.780652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 03/18/2022] [Indexed: 11/27/2022] Open
Abstract
The human brain has the capacity to drastically alter its somatotopic representations in response to congenital or acquired limb deficiencies and dysfunctions. The main purpose of the present study was to elucidate such extreme adaptability in the brain of an active top wheelchair racing Paralympian (participant P1) who has congenital paraplegia (dysfunction of bilateral lower limbs). Participant P1 has undergone long-term wheelchair racing training using bilateral upper limbs and has won a total of 19 medals in six consecutive summer Paralympic games as of 2021. We examined the functional and structural changes in the foot section of the primary motor cortex (M1) in participant P1 as compared to able-bodied control participants. We also examined the functional and structural changes in three other individuals (participants P2, P3, and P4) with acquired paraplegia, who also had long-term non-use period of the lower limbs and had undergone long-term training for wheelchair sports (but not top athletes at the level of participant P1). We measured brain activity in all the participants using functional magnetic resonance imaging (MRI) when bimanual wrist extension-flexion movement was performed, and the structural MRI images were collected. Compared to 37 control participants, participant P1 showed significantly greater activity in the M1 foot section during the bimanual task, and significant local GM expansion in this section. Significantly greater activity in the M1 foot section was also observed in participant P4, but not in P2 and P3, and the significant local GM expansion was observed in participant P2, but not in P3 and P4. Thus, functional or structural change was observed in an acquired paraplegic participant, but was not observed in all the paraplegic participants. The functional and structural changes typically observed in participant P1 may represent extreme adaptability of the human brain. We discuss the results in terms of a new idea of hyper-adaptation.
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Affiliation(s)
- Tomoyo Morita
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
| | - Satoshi Hirose
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Otemon Gakuin University, Faculty of Psychology, Osaka, Japan
| | - Nodoka Kimura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan
- Department of Physiological Sciences, School of Life Science, SOKENDAI (The Graduate University for Advanced Studies), Kanagawa, Japan
| | - Minoru Asada
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
- International Professional University of Technology in Osaka, Osaka, Japan
| | - Eiichi Naito
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology (NICT), Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
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Scarpazza C, Miolla A, Zampieri I, Melis G, Sartori G, Ferracuti S, Pietrini P. Translational Application of a Neuro-Scientific Multi-Modal Approach Into Forensic Psychiatric Evaluation: Why and How? Front Psychiatry 2021; 12:597918. [PMID: 33613339 PMCID: PMC7892615 DOI: 10.3389/fpsyt.2021.597918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Accepted: 01/14/2021] [Indexed: 01/01/2023] Open
Abstract
A prominent body of literature indicates that insanity evaluations, which are intended to provide influential expert reports for judges to reach a decision "beyond any reasonable doubt," suffer from a low inter-rater reliability. This paper reviews the limitations of the classical approach to insanity evaluation and the criticisms to the introduction of neuro-scientific approach in court. Here, we explain why in our opinion these criticisms, that seriously hamper the translational implementation of neuroscience into the forensic setting, do not survive scientific scrutiny. Moreover, we discuss how the neuro-scientific multimodal approach may improve the inter-rater reliability in insanity evaluation. Critically, neuroscience does not aim to introduce a brain-based concept of insanity. Indeed, criteria for responsibility and insanity are and should remain clinical. Rather, following the falsificationist approach and the convergence of evidence principle, the neuro-scientific multimodal approach is being proposed as a way to improve reliability of insanity evaluation and to mitigate the influence of cognitive biases on the formulation of insanity opinions, with the final aim to reduce errors and controversies.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Padova, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alessio Miolla
- Department of General Psychology, University of Padova, Padova, Italy
| | - Ilaria Zampieri
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Giulia Melis
- Department of General Psychology, University of Padova, Padova, Italy
| | - Giuseppe Sartori
- Department of General Psychology, University of Padova, Padova, Italy
| | - Stefano Ferracuti
- Department of Human Neurosciences, “Sapienza” University of Rome, Rome, Italy
| | - Pietro Pietrini
- Molecular Mind Laboratory, IMT School for Advanced Studies Lucca, Lucca, Italy
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8
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Scarpazza C, Zampieri I, Miolla A, Melis G, Pietrini P, Sartori G. A multidisciplinary approach to insanity assessment as a way to reduce cognitive biases. Forensic Sci Int 2020; 319:110652. [PMID: 33360246 DOI: 10.1016/j.forsciint.2020.110652] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/07/2020] [Accepted: 12/08/2020] [Indexed: 12/21/2022]
Abstract
Insanity assessment requires the evaluation of the psychopathological condition that underlies the mens rea. Psychopathological evaluation may be quite challenging due to (i) absence of biomarkers; (ii) low inter-rater reliability; (iii) presence of cognitive bias. This intrinsic low reliability of forensic psychiatric diagnosis does impact on insanity assessment, leading to arbitrary and unjust legal outcomes for the examinee. Thus, strategies to improve the reliability of insanity evaluation are strongly needed. A multidisciplinary approach has been proposed as a way to enrich clinical diagnosis with reliable and biologically founded data, thus minimizing subjectivity, reducing controversies and increasing inter-subject concordance in insanity assessment. By discussing a real case, here we show how the convergence of multiple indices can produce evidence that cannot be denied without introducing logical fallacies. Applying this approach, the forensic discussion will move from the presence/absence of psychopathology to the impact of psychopathology on insanity. This article illustrates how a multidisciplinary evaluation, which integrates neuroscientific methods with the classical insanity assessment, may lead to a more accurate approach in insanity evaluation. Critically, this approach will minimize the impact of cognitive bias on insanity opinion and thus result in an improvement of the whole criminal justice process.
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Affiliation(s)
- Cristina Scarpazza
- Department of General Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy.
| | - Ilaria Zampieri
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, Italy.
| | - Alessio Miolla
- Department of General Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy.
| | - Giulia Melis
- Department of General Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy.
| | - Pietro Pietrini
- Molecular Mind Lab, IMT School for Advanced Studies Lucca, Lucca, Italy.
| | - Giuseppe Sartori
- Department of General Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy.
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9
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Riederer F, Seiger R, Lanzenberger R, Pataraia E, Kasprian G, Michels L, Beiersdorf J, Kollias S, Czech T, Hainfellner J, Baumgartner C. Voxel-Based Morphometry-from Hype to Hope. A Study on Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy. AJNR Am J Neuroradiol 2020; 41:987-993. [PMID: 32522839 DOI: 10.3174/ajnr.a6545] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 03/18/2020] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE Automated volumetry of the hippocampus is considered useful to assist the diagnosis of hippocampal sclerosis in temporal lobe epilepsy. However, voxel-based morphometry is rarely used for individual subjects because of high rates of false-positives. We investigated whether an approach with high dimensional warping to the template and nonparametric statistics would be useful to detect hippocampal atrophy in patients with hippocampal sclerosis. MATERIALS AND METHODS We performed single-subject voxel-based morphometry with nonparametric statistics within the framework of Statistical Parametric Mapping to compare MRI from 26 well-characterized patients with temporal lobe epilepsy individually against a group of 110 healthy controls. The following statistical threshold was used: P < .05 corrected for multiple comparisons with family-wise error over the region of interest right and left hippocampus. RESULTS The sensitivity for the detection of atrophy related to hippocampal sclerosis was 0.92 (95% CI, 0.67-0.99) for the right hippocampus and 0.60 (0.31-0.83) for the left, and the specificity for volume changes was 0.98 (0.93-0.99). All clusters of decreased hippocampal volumes were correctly lateralized to the seizure focus. Hippocampal volume decrease was in accordance with neuronal cell loss on histology reports. CONCLUSIONS Nonparametric voxel-based morphometry is sensitive and specific for hippocampal atrophy in patients with mesial temporal lobe epilepsy and may be useful in clinical practice.
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Affiliation(s)
- F Riederer
- From the Hietzing Hospital with Neurological Center Rosenhügel & Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology (F.R., J.B., C.B.), Vienna, Austria .,Faculty of Medicine (F.R.), University of Zurich, Zurich, Switzerland
| | - R Seiger
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy (R.S., R.L.)
| | - R Lanzenberger
- Neuroimaging Labs, Department of Psychiatry and Psychotherapy (R.S., R.L.)
| | | | | | - L Michels
- Clinic of Neuroradiology (L.M., S.K.), University Hospital Zurich, Zurich, Switzerland
| | - J Beiersdorf
- From the Hietzing Hospital with Neurological Center Rosenhügel & Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology (F.R., J.B., C.B.), Vienna, Austria
| | - S Kollias
- Clinic of Neuroradiology (L.M., S.K.), University Hospital Zurich, Zurich, Switzerland
| | | | - J Hainfellner
- and Institute of Neurology (J.H.), Medical University of Vienna, Vienna, Austria
| | - C Baumgartner
- From the Hietzing Hospital with Neurological Center Rosenhügel & Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology (F.R., J.B., C.B.), Vienna, Austria.,Medical Faculty (C.B.), Sigmund Freud Private University, Vienna, Austria
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10
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Külsgaard HC, Orlando JI, Bendersky M, Princich JP, Manzanera LSR, Vargas A, Kochen S, Larrabide I. Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study. J Neurol Sci 2020; 420:117220. [PMID: 33183776 DOI: 10.1016/j.jns.2020.117220] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 10/06/2020] [Accepted: 11/02/2020] [Indexed: 12/21/2022]
Abstract
Single subject VBM (SS-VBM), has been used as an alternative tool to standard VBM for single case studies. However, it has the disadvantage of producing an excessively large number of false positive detections. In this study we propose a machine learning technique widely used for automated data classification, namely Support Vector Machine (SVM), to refine the findings produced by SS-VBM. A controlled set of experiments was conducted to evaluate the proposed approach using three-dimensional T1 MRI scans from control subjects collected from the publicly available IXI dataset. The scans were artificially atrophied at different locations and with different sizes to mimic the behavior of neurological disorders. Results empirically demonstrated that the proposed method is able to significantly reduce the amount of false positive clusters (p < 0.05), with no statistical differences in the true positive findings (p > 0.05). This evidence was observed to be consistent for different atrophied areas and sizes of atrophies. This approach could be potentially be applied to alleviate the intensive manual analysis that radiologists and clinicians have to perform to filter out miss-detections of SS-VBM, increasing its usability for image reading.
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Affiliation(s)
| | - José I Orlando
- Pladema Institute - UNICEN/CONICET, Tandil, Buenos Aires, Argentina
| | - Mariana Bendersky
- ENyS - UNAJ/CONICET, Florencio Varela, Buenos Aires, Argentina; III Normal Anatomy Department, School of Medicine, University of Buenos Aires, Buenos Aires, Argentina
| | - Juan P Princich
- ENyS - UNAJ/CONICET, Florencio Varela, Buenos Aires, Argentina
| | | | | | - Silvia Kochen
- ENyS - UNAJ/CONICET, Florencio Varela, Buenos Aires, Argentina
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11
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Garcia-Dias R, Scarpazza C, Baecker L, Vieira S, Pinaya WHL, Corvin A, Redolfi A, Nelson B, Crespo-Facorro B, McDonald C, Tordesillas-Gutiérrez D, Cannon D, Mothersill D, Hernaus D, Morris D, Setien-Suero E, Donohoe G, Frisoni G, Tronchin G, Sato J, Marcelis M, Kempton M, van Haren NEM, Gruber O, McGorry P, Amminger P, McGuire P, Gong Q, Kahn RS, Ayesa-Arriola R, van Amelsvoort T, Ortiz-García de la Foz V, Calhoun V, Cahn W, Mechelli A. Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners. Neuroimage 2020; 220:117127. [PMID: 32634595 PMCID: PMC7573655 DOI: 10.1016/j.neuroimage.2020.117127] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 06/08/2020] [Accepted: 06/30/2020] [Indexed: 02/05/2023] Open
Abstract
•We present Neuroharmony, a harmonization tool for images from unseen scanners. •We developed Neuroharmony using a total of 15,026 sMRI images. •The tool was able to reduce scanner-related bias from unseen scans. •Neuroharmony represents a significant step towards imaging-based clinical tools. •Neuroharmony is available at https://github.com/garciadias/Neuroharmony .
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Affiliation(s)
- Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom.
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Department of General Psychology, University of Padova, Via Venezia 8, Padova, Italy
| | - Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom; Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Aiden Corvin
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Departamento de Psiquiatria, Universidad de Sevilla, Instituto de Biomedicina de Sevilla (IBIS), Spain; Hospital Universitario Virgen del Rocío, Sevilla, Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Diana Tordesillas-Gutiérrez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, Spain
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - David Mothersill
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Derek Morris
- Discipline of Biochemistry & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Esther Setien-Suero
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Gary Donohoe
- School of Psychology & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - Giovanni Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Ageing, University Hospitals and University of Geneva, Geneva, Switzerland; Laboratory of Alzheimer's Neuroimaging and Epidemiology - LANE, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Giulia Tronchin
- Clinical Neuroimaging Laboratory, School of Medicine & Center for Neuroimaging and Cognitive Genomics, NUI Galway University, Galway, Ireland
| | - João Sato
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, Santo André, Brazil
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Matthew Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Centre - Sophia Children's Hospital, Rotterdam, Netherlands
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany; Center for Translational Research in Systems Neuroscience and Psychiatry, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Germany
| | - Patrick McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Paul Amminger
- Orygen, The National Centre of Excellence in Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Philip McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China; Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - René S Kahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Therese van Amelsvoort
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, Maastricht, the Netherlands
| | - Victor Ortiz-García de la Foz
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain; Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia; State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Wiepke Cahn
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht, the Netherlands
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, SE5 8AF, United Kingdom
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12
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Scarpazza C, Ha M, Baecker L, Garcia-Dias R, Pinaya WHL, Vieira S, Mechelli A. Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders. Transl Psychiatry 2020; 10:107. [PMID: 32313006 PMCID: PMC7170931 DOI: 10.1038/s41398-020-0798-6] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/25/2020] [Indexed: 12/14/2022] Open
Abstract
A pivotal aim of psychiatric and neurological research is to promote the translation of the findings into clinical practice to improve diagnostic and prognostic assessment of individual patients. Structural neuroimaging holds much promise, with neuroanatomical measures accounting for up to 40% of the variance in clinical outcome. Building on these findings, a number of imaging-based clinical tools have been developed to make diagnostic and prognostic inferences about individual patients from their structural Magnetic Resonance Imaging scans. This systematic review describes and compares the technical characteristics of the available tools, with the aim to assess their translational potential into real-world clinical settings. The results reveal that a total of eight tools. All of these were specifically developed for neurological disorders, and as such are not suitable for application to psychiatric disorders. Furthermore, most of the tools were trained and validated in a single dataset, which can result in poor generalizability, or using a small number of individuals, which can cause overoptimistic results. In addition, all of the tools rely on two strategies to detect brain abnormalities in single individuals, one based on univariate comparison, and the other based on multivariate machine-learning algorithms. We discuss current barriers to the adoption of these tools in clinical practice and propose a checklist of pivotal characteristics that should be included in an "ideal" neuroimaging-based clinical tool for brain disorders.
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Affiliation(s)
- C Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK.
- Department of General Psychology, University of Padova, Padova, Italy.
| | - M Ha
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - L Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - R Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - W H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
- Center of Mathematics, Computing, and Cognition, Universidade Federal do ABC, São Bernardo do Campo, SP, Brazil
| | - S Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - A Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
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13
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Peyre H, Mohanpuria N, Jednoróg K, Heim S, Grande M, van Ermingen-Marbach M, Altarelli I, Monzalvo K, Williams CM, Germanaud D, Toro R, Ramus F. Neuroanatomy of dyslexia: An allometric approach. Eur J Neurosci 2020; 52:3595-3609. [PMID: 31991019 DOI: 10.1111/ejn.14690] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 01/04/2020] [Accepted: 01/17/2020] [Indexed: 01/06/2023]
Abstract
Despite evidence for a difference in total brain volume between dyslexic and good readers, no previous neuroimaging study examined differences in allometric scaling (i.e. differences in the relationship between regional and total brain volumes) between dyslexic and good readers. The present study aims to fill this gap by testing differences in allometric scaling and regional brain volume differences in dyslexic and good readers. Object-based morphometry analysis was used to determine grey and white matter volumes of the four lobes, the cerebellum and limbic structures in 130 dyslexic and 106 good readers aged 8-14 years. Data were collected across three countries (France, Poland and Germany). Three methodological approaches were used as follows: principal component analysis (PCA), linear regression and multiple-group confirmatory factor analysis (MGCFA). Difference in total brain volume between good and dyslexic readers was Cohen's d = 0.39. We found no difference in allometric scaling, nor in regional brain volume between dyslexic and good readers. Results of our three methodological approaches (PCA, linear regression and MGCFA) were consistent. This study provides evidence for total brain volume differences between dyslexic and control children, but no evidence for differences in the volumes of the four lobes, the cerebellum or limbic structures, once allometry is taken into account. It also finds no evidence for a difference in allometric relationships between the groups. We highlight the methodological interest of the MGCFA approach to investigate such research issues.
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Affiliation(s)
- Hugo Peyre
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France.,Neurodiderot, INSERM UMR 1141, Paris Diderot University, Paris, France.,Department of Child and Adolescent Psychiatry, Robert Debré Hospital, APHP, Paris, France
| | - Neha Mohanpuria
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
| | - Katarzyna Jednoróg
- Laboratory of Language Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences (PAS), Warsaw, Poland
| | - Stefan Heim
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine (INM-1), Helmholtz-Gemeinschaft Deutscher Forschungszentren (HZ), Jülich, Germany
| | - Marion Grande
- Department of Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Muna van Ermingen-Marbach
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.,Department of Neurology, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Irene Altarelli
- Laboratory for the Psychology of Child Development and Education, CNRS UMR 8240, Université de Paris, Paris, France
| | - Karla Monzalvo
- INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France
| | - Camille Michèle Williams
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
| | - David Germanaud
- Neurodiderot, INSERM UMR 1141, Paris Diderot University, Paris, France.,INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France.,Department of Pediatric Neurology and Metabolic Diseases, Robert Debré Hospital, APHP, Paris, France.,INSERM, CEA, UMR 1129, Sorbonne Paris Cité University (USPC), Paris, France
| | - Roberto Toro
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - Franck Ramus
- Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL University, Paris, France
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14
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Piredda GF, Hilbert T, Granziera C, Bonnier G, Meuli R, Molinari F, Thiran JP, Kober T. Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology. Magn Reson Med 2019; 83:337-351. [PMID: 31418910 DOI: 10.1002/mrm.27927] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/08/2019] [Accepted: 07/12/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times. METHODS Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T1 and T2 inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept. RESULTS The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T1 and 4 ms for T2 ), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores ~2-4) were observed in clinically plausible regions (between lesions, corpus callosum). CONCLUSIONS Brain T1 and T2 quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.
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Affiliation(s)
- Gian Franco Piredda
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tom Hilbert
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Cristina Granziera
- Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland.,Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Guillaume Bonnier
- Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Swiss Center for Electronics and Microtechnology, Neuchatel, Switzerland
| | - Reto Meuli
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Filippo Molinari
- Biolab, Department of Electronics and Telecommunication, Polytechnic University of Turin, Turin, Italy
| | - Jean-Philippe Thiran
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.,Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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15
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Urgen BM, Topac Y, Ustun FS, Demirayak P, Oguz KK, Kansu T, Saygi S, Ozcelik T, Boyaci H, Doerschner K. Homozygous LAMC3 mutation links to structural and functional changes in visual attention networks. Neuroimage 2019; 190:242-253. [DOI: 10.1016/j.neuroimage.2018.03.077] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 03/09/2018] [Accepted: 03/31/2018] [Indexed: 01/26/2023] Open
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16
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Bonnier G, Fischi-Gomez E, Roche A, Hilbert T, Kober T, Krueger G, Granziera C. Personalized pathology maps to quantify diffuse and focal brain damage. NEUROIMAGE-CLINICAL 2018; 21:101607. [PMID: 30502080 PMCID: PMC6413479 DOI: 10.1016/j.nicl.2018.11.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 10/02/2018] [Accepted: 11/18/2018] [Indexed: 01/04/2023]
Abstract
Background and objectives Quantitative MRI (qMRI) permits the quantification of brain changes compatible with inflammation, degeneration and repair in multiple sclerosis (MS) patients. In this study, we propose a new method to provide personalized maps of tissue alterations and longitudinal brain changes based on different qMRI metrics, which provide complementary information about brain pathology. Methods We performed baseline and two-years follow-up on (i) 13 relapsing-remitting MS patients and (ii) four healthy controls. A group consisting of up to 65 healthy controls was used to compute the reference distribution of qMRI metrics in healthy tissue. All subjects underwent 3T MRI examinations including T1, T2, T2* relaxation and Magnetization Transfer Ratio (MTR) imaging. We used a recent partial volume estimation algorithm to estimate the concentration of different brain tissue types on T1 maps; then, we computed a deviation map (z-score map) for each contrast at both time-points. Finally, we subtracted those deviation maps only for voxels showing a significant difference with healthy tissue in one of the time points, to obtain a difference map for each subject. Results and conclusion Control subjects did not show any significant z-score deviations or longitudinal z-score changes. On the other hand, MS patients showed brain regions with cross-sectional and longitudinal concomitant increase in T1, T2, T2* z-scores and decrease of MTR z-scores, suggesting brain tissue degeneration/loss. In the lesion periphery, we observed areas with cross-sectional and longitudinal decreased T1/T2 and slight decrease in T2* most likely related to iron accumulation. Moreover, we measured longitudinal decrease in T1, T2 - and to a lesser extent in T2* - as well as a concomitant increase in MTR, suggesting remyelination/repair. In summary, we have developed a method that provides whole-brain personalized maps of cross-sectional and longitudinal changes in MS patients, which are computed in patient space. These maps may open new perspectives to complement and support radiological evaluation of brain damage for a given patient.
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Affiliation(s)
- G Bonnier
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
| | - E Fischi-Gomez
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - A Roche
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Hilbert
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - T Kober
- Advanced Clinical Imaging Technology (HC CEMEA SUI DI PI), Siemens Healthcare AG, Switzerland; Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - G Krueger
- Siemens Healthcare AG (HC CEMEA DI), Zürich, Switzerland
| | - C Granziera
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States; Neurologic Clinic and Policlinic, Departments of Medicine, Clinical Research and Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
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17
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Demirayak P, Onat OE, Gevrekci AÖ, Gülsüner S, Uysal H, Bilgen RS, Doerschner K, Özçelik TS, Boyacı H. Abnormal subcortical activity in congenital mirror movement disorder with RAD51 mutation. ACTA ACUST UNITED AC 2018; 24:392-401. [PMID: 30406765 DOI: 10.5152/dir.2018.18096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
PURPOSE Congenital mirror movement disorder (CMMD) is characterized by unintended, nonsuppressible, homologous mirroring activity contralateral to the movement on the intended side of the body. In healthy controls, unilateral movements are accompanied with predominantly contralateral cortical activity, whereas in CMMD, in line with the abnormal behavior, bilateral cortical activity is observed for unilateral motor tasks. However, task-related activities in subcortical structures, which are known to play critical roles in motor actions, have not been investigated in CMMD previously. METHODS We investigated the functional activation patterns of the motor components in CMMD patients. By using linkage analysis and exome sequencing, common mutations were revealed in seven affected individuals from the same family. Next, using functional magnetic resonance imaging (fMRI) we investigated cortical and subcortical activity during manual motor actions in two right-handed affected brothers and sex, age, education, and socioeconomically matched healthy individuals. RESULTS Genetic analyses revealed heterozygous RAD51 c.401C>T mutation which cosegregated with the phenotype in two affected members of the family. Consistent with previous literature, our fMRI results on these two affected individuals showed that mirror movements were closely related to abnormal cortical activity in M1 and SMA during unimanual movements. Furthermore, we have found previously unknown abnormal task-related activity in subcortical structures. Specifically, we have found increased and bilateral activity during unimanual movements in thalamus, striatum, and globus pallidus in CMMD patients. CONCLUSION These findings reveal further neural correlates of CMMD, and may guide our understanding of the critical roles of subcortical structures for unimanual movements in healthy individuals.
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Affiliation(s)
- Pınar Demirayak
- Neuroscience Graduate Program, Bilkent University; A.S. Brain Research Center and National Magnetic Resonance Research Center, Bilkent University, Ankara, Turkey
| | - Onur Emre Onat
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
| | | | - Süleyman Gülsüner
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Hilmi Uysal
- Department of Neurology, Akdeniz University School of Medicine, Antalya, Turkey
| | - Rengin S Bilgen
- Department of Neurology, Bezmialem University School of Medicine, İstanbul, Turkey
| | - Katja Doerschner
- Neuroscience Graduate Program, A.S. Brain Research Center and National Magnetic Resonance Research Center and Psychology, Bilkent University, Ankara, Turkey; Department of Psychology, JL Giessen University, Giessen, Germany
| | - Tayfun S Özçelik
- Department of Molecular Biology and Genetics, Bilkent University, Ankara, Turkey
| | - Hüseyin Boyacı
- Neuroscience Graduate Program, A.S. Brain Research Center and National Magnetic Resonance Research Center and Psychology, Bilkent University, Ankara, Turkey; Department of Psychology, JL Giessen University, Giessen, Germany
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18
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Scarpazza C, Ferracuti S, Miolla A, Sartori G. The charm of structural neuroimaging in insanity evaluations: guidelines to avoid misinterpretation of the findings. Transl Psychiatry 2018; 8:227. [PMID: 30367031 PMCID: PMC6203853 DOI: 10.1038/s41398-018-0274-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 08/24/2018] [Accepted: 09/10/2018] [Indexed: 01/03/2023] Open
Abstract
Despite the popularity of structural neuroimaging techniques in twenty-first-century research, its results have had limited translational impact in real-world settings, where inferences need to be made at the individual level. Structural neuroimaging methods are now introduced frequently to aid in assessing defendants for insanity in criminal forensic evaluations, with the aim of providing "convergence" of evidence on the mens rea of the defendant. This approach may provide pivotal support for judges' decisions. Although neuroimaging aims to reduce uncertainty and controversies in legal settings and to increase the objectivity of criminal rulings, the application of structural neuroimaging in forensic settings is hampered by cognitive biases in the evaluation of evidence that lead to misinterpretation of the imaging results. It is thus increasingly important to have clear guidelines on the correct ways to apply and interpret neuroimaging evidence. In the current paper, we review the literature concerning structural neuroimaging in court settings with the aim of identifying rules for its correct application and interpretation. These rules, which aim to decrease the risk of biases, focus on the importance of (i) descriptive diagnoses, (ii) anatomo-clinical correlation, (iii) brain plasticity and (iv) avoiding logical fallacies, such as reverse inference. In addition, through the analysis of real forensic cases, we describe errors frequently observed due to incorrect interpretations of imaging. Clear guidelines for both the correct circumstances for introducing neuroimaging and its eventual interpretation are defined.
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Affiliation(s)
- C Scarpazza
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padova, Italy.
- Department of Psychosis Studies, Institute of Psychiatry, Psychosis and Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, UK.
| | - S Ferracuti
- Department of Human Neuroscience, Sapienza University of Rome, P.le A Moro 5, 00185, Roma, Italy
| | - A Miolla
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padova, Italy
| | - G Sartori
- Department of General Psychology, University of Padua, Via Venezia 8, 35131, Padova, Italy
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19
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Vanasse TJ, Fox PM, Barron DS, Robertson M, Eickhoff SB, Lancaster JL, Fox PT. BrainMap VBM: An environment for structural meta-analysis. Hum Brain Mapp 2018; 39:3308-3325. [PMID: 29717540 PMCID: PMC6866579 DOI: 10.1002/hbm.24078] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 03/29/2018] [Accepted: 03/30/2018] [Indexed: 12/14/2022] Open
Abstract
The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.
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Affiliation(s)
- Thomas J. Vanasse
- Research Imaging Institute, University of Texas Health Science Center at San AntonioSan AntonioTexas
- Department of RadiologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexas
| | - P. Mickle Fox
- Research Imaging Institute, University of Texas Health Science Center at San AntonioSan AntonioTexas
| | - Daniel S. Barron
- Department of PsychiatryYale University School of MedicineNew HavenConnecticut
| | - Michaela Robertson
- Research Imaging Institute, University of Texas Health Science Center at San AntonioSan AntonioTexas
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7)Research Centre JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University DüsseldorfDüsseldorfGermany
| | - Jack L. Lancaster
- Research Imaging Institute, University of Texas Health Science Center at San AntonioSan AntonioTexas
- Department of RadiologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexas
| | - Peter T. Fox
- Research Imaging Institute, University of Texas Health Science Center at San AntonioSan AntonioTexas
- Department of RadiologyUniversity of Texas Health Science Center at San AntonioSan AntonioTexas
- South Texas Veterans Health Care SystemSan AntonioTexas
- Shenzhen Institute of Neuroscience, Shenzhen UniversityShenzhen ChinaPeople's Republic of China
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20
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Patil I, Zanon M, Novembre G, Zangrando N, Chittaro L, Silani G. Neuroanatomical basis of concern-based altruism in virtual environment. Neuropsychologia 2018; 116:34-43. [DOI: 10.1016/j.neuropsychologia.2017.02.015] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2016] [Revised: 02/09/2017] [Accepted: 02/21/2017] [Indexed: 01/18/2023]
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21
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Greve DN, Fischl B. False positive rates in surface-based anatomical analysis. Neuroimage 2018; 171:6-14. [PMID: 29288131 PMCID: PMC5857431 DOI: 10.1016/j.neuroimage.2017.12.072] [Citation(s) in RCA: 150] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 12/06/2017] [Accepted: 12/21/2017] [Indexed: 10/18/2022] Open
Abstract
The false positive rates (FPR) for surface-based group analysis of cortical thickness, surface area, and volume were evaluated for parametric and non-parametric clusterwise correction for multiple comparisons for a range of smoothing levels and cluster-forming thresholds (CFT) using real data under group assignments that should not yield significant results. For whole cortical surface analysis, thickness showed modest inflation in parametric FPRs above the nominal level (10% versus 5%). Surface area and volume FPRs were much higher (20-30%). In the analysis of interhemispheric thickness asymmetries, FPRs were well controlled by parametric correction, but FPRs for surface area and volume asymmetries were still inflated. In all cases, non-parametric permutation adequately controlled the FPRs. It was found that inflated parametric FPRs were caused by violations in the parametric assumptions, namely a heavier-than-Gaussian spatial correlation. The non-Gaussian spatial correlation originates from anatomical features unique to individuals (e.g., a patch of cortex slightly thicker or thinner than average) and is not a by-product of scanning or processing. Thickness performed better than surface area and volume because thickness does not require a Jacobian correction.
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Affiliation(s)
- Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA
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22
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Scarpazza C, Pellegrini S, Pietrini P, Sartori G. The Role of Neuroscience in the Evaluation of Mental Insanity: on the Controversies in Italy. NEUROETHICS-NETH 2017. [DOI: 10.1007/s12152-017-9349-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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23
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Wise T, Radua J, Via E, Cardoner N, Abe O, Adams TM, Amico F, Cheng Y, Cole JH, de Azevedo Marques Périco C, Dickstein DP, Farrow TFD, Frodl T, Wagner G, Gotlib IH, Gruber O, Ham BJ, Job DE, Kempton MJ, Kim MJ, Koolschijn PCMP, Malhi GS, Mataix-Cols D, McIntosh AM, Nugent AC, O'Brien JT, Pezzoli S, Phillips ML, Sachdev PS, Salvadore G, Selvaraj S, Stanfield AC, Thomas AJ, van Tol MJ, van der Wee NJA, Veltman DJ, Young AH, Fu CH, Cleare AJ, Arnone D. Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis. Mol Psychiatry 2017; 22:1455-1463. [PMID: 27217146 PMCID: PMC5622121 DOI: 10.1038/mp.2016.72] [Citation(s) in RCA: 372] [Impact Index Per Article: 53.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/01/2016] [Accepted: 03/23/2016] [Indexed: 12/11/2022]
Abstract
Finding robust brain substrates of mood disorders is an important target for research. The degree to which major depression (MDD) and bipolar disorder (BD) are associated with common and/or distinct patterns of volumetric changes is nevertheless unclear. Furthermore, the extant literature is heterogeneous with respect to the nature of these changes. We report a meta-analysis of voxel-based morphometry (VBM) studies in MDD and BD. We identified studies published up to January 2015 that compared grey matter in MDD (50 data sets including 4101 individuals) and BD (36 data sets including 2407 individuals) using whole-brain VBM. We used statistical maps from the studies included where available and reported peak coordinates otherwise. Group comparisons and conjunction analyses identified regions in which the disorders showed common and distinct patterns of volumetric alteration. Both disorders were associated with lower grey-matter volume relative to healthy individuals in a number of areas. Conjunction analysis showed smaller volumes in both disorders in clusters in the dorsomedial and ventromedial prefrontal cortex, including the anterior cingulate cortex and bilateral insula. Group comparisons indicated that findings of smaller grey-matter volumes relative to controls in the right dorsolateral prefrontal cortex and left hippocampus, along with cerebellar, temporal and parietal regions were more substantial in major depression. These results suggest that MDD and BD are characterised by both common and distinct patterns of grey-matter volume changes. This combination of differences and similarities has the potential to inform the development of diagnostic biomarkers for these conditions.
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Affiliation(s)
- T Wise
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - J Radua
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Research Unit, FIDMAG Germanes Hospitalàries – CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - E Via
- Mental Health, Parc Taulí Sabadell-CIBERSAM, University Hospital, Sabadell, Barcelona, Spain
- Department of Psychiatry, Bellvitge University Hospital-IDIBELL, Barcelona, Spain
| | - N Cardoner
- Mental Health, Parc Taulí Sabadell-CIBERSAM, University Hospital, Sabadell, Barcelona, Spain
| | - O Abe
- Department of Radiology, Nihon University School of Medicine, Tokyo, Japan
| | - T M Adams
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - F Amico
- Department of Psychiatry, Trinity College Institute of Neuroscience, Trinity College, Dublin, Ireland
| | - Y Cheng
- Department of Psychiatry, The 1st Affiliated Hospital of Kunming Medical University, Kunming, PR China
| | - J H Cole
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK
| | - C de Azevedo Marques Périco
- Department of Neuroscience, Medical School, Fundação do ABC, Santo André, SP, Brazil
- ABC Center of Studies on Mental Health, Santo André, SP, Brazil
| | - D P Dickstein
- PediMIND Program, Bradley Hospital, Department of Psychiatry, Brown University, East Providence, RI, USA
| | - T F D Farrow
- Academic Clinical Neurology, Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - T Frodl
- Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
- Department of Psychiatry, University of Dublin, Trinity College, Dublin, Ireland
| | - G Wagner
- Psychiatric Brain and Body Research Group Jena, Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - I H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - O Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University Hospital, Heidelberg, Germany
| | - B J Ham
- Department of Psychiatry, Korea University College of Medicine, Seoul, South Korea
| | - D E Job
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, UK
- Scottish Imaging Network–A Platform for Scientific Excellence (SINAPSE), Giffnock, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - M J Kempton
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - M J Kim
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - P C M P Koolschijn
- Department of Psychology, Dutch Autism and ADHD Research Center, Brain and Cognition, University of Amsterdam, Amsterdam, The Netherlands
| | - G S Malhi
- CADE Clinic, Discipline of Psychiatry, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - D Mataix-Cols
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - A M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - A C Nugent
- Experimental Therapeutics and Pathophysiology Branch, Intramural Research Program, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - J T O'Brien
- Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - S Pezzoli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Department of Neuroscience, Medical School, University of Sheffield, Sheffield, UK
| | - M L Phillips
- Department of Psychiatry, University of Pittsburgh, School of Medicine, Pittsburgh, PA
| | - P S Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia
| | - G Salvadore
- Janssen Research and Development, Titusville, NJ, USA
| | - S Selvaraj
- Department of Psychiatry and Behavioral Sciences, Center of Excellence on Mood Disorders, Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - A C Stanfield
- The Patrick Wild Centre, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - A J Thomas
- Institute for Ageing and Health, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - M J van Tol
- NeuroImaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - N J A van der Wee
- Department of Psychiatry, Leiden University Medical Center, Leiden University, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden, The Netherlands
| | - D J Veltman
- Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - A H Young
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - C H Fu
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- School of Psychology, University of East London, London, UK
| | - A J Cleare
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - D Arnone
- Department of Psychological Medicine, Centre for Affective Disorders, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
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Chen Z, Chen X, Liu M, Liu S, Ma L, Yu S. Volume gain of periaqueductal gray in medication-overuse headache. J Headache Pain 2017; 18:12. [PMID: 28144808 PMCID: PMC5285292 DOI: 10.1186/s10194-016-0715-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 12/12/2016] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Periaqueductal gray (PAG) is a substantial descending pain modulatory center, and previous voxel-based morphometry study confirmed the clusters with increased volume in PAG region in medication-overuse headache (MOH). The aim of this study is to investigate altered PAG volume in MOH using an automated PAG segment method to measure the true PAG volume. METHODS High resolution three-dimensional T1-weighted fast spoiled gradient recalled echo MR images were obtained from 22 patients with MOH and 22 normal controls (NC). PAG template was created based on ICBM 152 gray template, and the individual PAG was generated by applying the deformation field from structural image segment to the PAG template, and individual PAG volume was calculated. RESULTS There was a significant increased volume of PAG in MOH (0.366 ± 0.005 ml) than that in NC (0.341 ± 0.005 ml)(P < 0.05). There was no significant correlation between the PAG volume and the clinical variables in MOH patients (P > 0.05). The area of receiver operating characteristic (ROC) curve was 0.845, and the cut-off of PAG volume was 0.341 ml with sensitivity 95.5% and specificity 63.6%. CONCLUSION The present study demonstrated that the PAG volume gain was confirmed in MOH patients, and the automated individual PAG volume measure may be considered as a simple and effective imaging biomarker in MOH diagnosis.
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Affiliation(s)
- Zhiye Chen
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.,Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xiaoyan Chen
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Mengqi Liu
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Shuangfeng Liu
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Lin Ma
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
| | - Shengyuan Yu
- Department of Neurology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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25
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Imaging the neuroplastic effects of ketamine with VBM and the necessity of placebo control. Neuroimage 2016; 147:198-203. [PMID: 27986606 DOI: 10.1016/j.neuroimage.2016.12.032] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Revised: 10/19/2016] [Accepted: 12/12/2016] [Indexed: 11/20/2022] Open
Abstract
In the last years a plethora of studies have investigated morphological changes induced by behavioural or pharmacological interventions using structural T1-weighted MRI and voxel-based morphometry (VBM). Ketamine is thought to exert its antidepressant action by restoring neuroplasticity. In order to test for acute impact of a single ketamine infusion on grey matter volume we performed a placebo-controlled, double-blind investigation in healthy volunteers using VBM. 28 healthy individuals underwent two MRI sessions within a timeframe of 2 weeks, each consisting of two structural T1-weighted MRIs within a single session, one before and one 45min after infusion of S-ketamine (bolus of 0.11mg/kg, followed by an maintenance infusion of 0.12mg/kg) or placebo (0.9% NaCl infusion) using a crossover design. In the repeated-measures ANOVA with time (post-infusion/pre-infusion) and medication (placebo/ketamine) as factors, no significant effect of interaction and no effect of medication was found (FWE-corrected). Importantly, further post-hoc t-tests revealed a strong "decrease" of grey matter both in the placebo and the ketamine condition over time. This effect was evident mainly in frontal and temporal regions bilaterally with t-values ranging from 4.95 to 5.31 (FWE-corrected at p<0.05 voxel level). The vulnerabilities of VBM have been repeatedly demonstrated, with reports of influence of blood flow, tissue water and direct effects of pharmacological compounds on the MRI signal. Here again, we highlight that the relationship between intervention and VBM results is apparently subject to a number of physiological influences, which are partly unknown. Future studies focusing on the effects of ketamine on grey matter should try to integrate known influential factors such as blood flow into analysis. Furthermore, the results of this study highlight the importance of a carefully performed placebo condition in pharmacological fMRI studies.
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Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc Natl Acad Sci U S A 2016; 113:7900-5. [PMID: 27357684 DOI: 10.1073/pnas.1602413113] [Citation(s) in RCA: 2237] [Impact Index Per Article: 279.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The most widely used task functional magnetic resonance imaging (fMRI) analyses use parametric statistical methods that depend on a variety of assumptions. In this work, we use real resting-state data and a total of 3 million random task group analyses to compute empirical familywise error rates for the fMRI software packages SPM, FSL, and AFNI, as well as a nonparametric permutation method. For a nominal familywise error rate of 5%, the parametric statistical methods are shown to be conservative for voxelwise inference and invalid for clusterwise inference. Our results suggest that the principal cause of the invalid cluster inferences is spatial autocorrelation functions that do not follow the assumed Gaussian shape. By comparison, the nonparametric permutation test is found to produce nominal results for voxelwise as well as clusterwise inference. These findings speak to the need of validating the statistical methods being used in the field of neuroimaging.
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Maumet C, Maurel P, Ferré JC, Barillot C. An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling. Neuroimage 2016; 134:424-433. [PMID: 27039702 DOI: 10.1016/j.neuroimage.2016.03.054] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 03/16/2016] [Accepted: 03/21/2016] [Indexed: 12/13/2022] Open
Abstract
In this paper, we introduce a new locally multivariate procedure to quantitatively extract voxel-wise patterns of abnormal perfusion in individual patients. This a contrario approach uses a multivariate metric from the computer vision community that is suitable to detect abnormalities even in the presence of closeby hypo- and hyper-perfusions. This method takes into account local information without applying Gaussian smoothing to the data. Furthermore, to improve on the standard a contrario approach, which assumes white noise, we introduce an updated a contrario approach that takes into account the spatial coherency of the noise in the probability estimation. Validation is undertaken on a dataset of 25 patients diagnosed with brain tumours and 61 healthy volunteers. We show how the a contrario approach outperforms the massively univariate general linear model usually employed for this type of analysis.
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Affiliation(s)
- Camille Maumet
- University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VisAGeS Project Team, F-35042 Rennes, France; Warwick Manufacturing Group, University of Warwick, CV4 7AL Coventry, United Kingdom.
| | - Pierre Maurel
- University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VisAGeS Project Team, F-35042 Rennes, France
| | - Jean-Christophe Ferré
- University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VisAGeS Project Team, F-35042 Rennes, France; CHU Rennes, Department of Neuroradiology, F-35033 Rennes, France
| | - Christian Barillot
- University of Rennes 1, Faculty of Medicine, F-35043 Rennes, France; INSERM, U746, F-35042 Rennes, France; CNRS, IRISA, UMR 6074, F-35042 Rennes, France; INRIA, VisAGeS Project Team, F-35042 Rennes, France
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Scarpazza C, Nichols TE, Seramondi D, Maumet C, Sartori G, Mechelli A. When the Single Matters more than the Group (II): Addressing the Problem of High False Positive Rates in Single Case Voxel Based Morphometry Using Non-parametric Statistics. Front Neurosci 2016; 10:6. [PMID: 26834533 PMCID: PMC4724722 DOI: 10.3389/fnins.2016.00006] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 01/08/2016] [Indexed: 01/08/2023] Open
Abstract
In recent years, an increasing number of studies have used Voxel Based Morphometry (VBM) to compare a single patient with a psychiatric or neurological condition of interest against a group of healthy controls. However, the validity of this approach critically relies on the assumption that the single patient is drawn from a hypothetical population with a normal distribution and variance equal to that of the control group. In a previous investigation, we demonstrated that family-wise false positive error rate (i.e., the proportion of statistical comparisons yielding at least one false positive) in single case VBM are much higher than expected (Scarpazza et al., 2013). Here, we examine whether the use of non-parametric statistics, which does not rely on the assumptions of normal distribution and equal variance, would enable the investigation of single subjects with good control of false positive risk. We empirically estimated false positive rates (FPRs) in single case non-parametric VBM, by performing 400 statistical comparisons between a single disease-free individual and a group of 100 disease-free controls. The impact of smoothing (4, 8, and 12 mm) and type of pre-processing (Modulated, Unmodulated) was also examined, as these factors have been found to influence FPRs in previous investigations using parametric statistics. The 400 statistical comparisons were repeated using two independent, freely available data sets in order to maximize the generalizability of the results. We found that the family-wise error rate was 5% for increases and 3.6% for decreases in one data set; and 5.6% for increases and 6.3% for decreases in the other data set (5% nominal). Further, these results were not dependent on the level of smoothing and modulation. Therefore, the present study provides empirical evidence that single case VBM studies with non-parametric statistics are not susceptible to high false positive rates. The critical implication of this finding is that VBM can be used to characterize neuroanatomical alterations in individual subjects as long as non-parametric statistics are employed.
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Affiliation(s)
- Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK
| | - Thomas E Nichols
- Department of Statistics, University of WarwickCoventry, UK; Warwick Manufacturing Group, University of WarwickCoventry, UK
| | - Donato Seramondi
- Department of Human and Social Sciences, University of Bergamo Bergamo, Italy
| | - Camille Maumet
- Warwick Manufacturing Group, University of Warwick Coventry, UK
| | | | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London London, UK
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29
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Impaired functional differentiation for categories of objects in the ventral visual stream: A case of developmental visual impairment. Neuropsychologia 2015; 77:52-61. [DOI: 10.1016/j.neuropsychologia.2015.08.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2015] [Revised: 07/17/2015] [Accepted: 08/09/2015] [Indexed: 11/22/2022]
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Taubert M, Wenzel U, Draganski B, Kiebel SJ, Ragert P, Krug J, Villringer A. Investigating Neuroanatomical Features in Top Athletes at the Single Subject Level. PLoS One 2015; 10:e0129508. [PMID: 26079870 PMCID: PMC4469455 DOI: 10.1371/journal.pone.0129508] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2015] [Accepted: 05/08/2015] [Indexed: 11/25/2022] Open
Abstract
In sport events like Olympic Games or World Championships competitive athletes keep pushing the boundaries of human performance. Compared to team sports, high achievements in many athletic disciplines depend solely on the individual's performance. Contrasting previous research looking for expertise-related differences in brain anatomy at the group level, we aim to demonstrate changes in individual top athlete's brain, which would be averaged out in a group analysis. We compared structural magnetic resonance images (MRI) of three professional track-and-field athletes to age-, gender- and education-matched control subjects. To determine brain features specific to these top athletes, we tested for significant deviations in structural grey matter density between each of the three top athletes and a carefully matched control sample. While total brain volumes were comparable between athletes and controls, we show regional grey matter differences in striatum and thalamus. The demonstrated brain anatomy patterns remained stable and were detected after 2 years with Olympic Games in between. We also found differences in the fusiform gyrus in two top long jumpers. We interpret our findings in reward-related areas as correlates of top athletes' persistency to reach top-level skill performance over years.
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Affiliation(s)
- Marco Taubert
- Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Uwe Wenzel
- Institute of General Kinesiology and Athletics Training, University of Leipzig, Leipzig, Germany
| | - Bogdan Draganski
- Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- LREN, Département des Neurosciences Cliniques, CHUV, Université de Lausanne, Lausanne, Switzerland
| | - Stefan J. Kiebel
- Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Psychology, Neuroimaging Center, Technical University, Dresden, Germany
| | - Patrick Ragert
- Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of General Kinesiology and Athletics Training, University of Leipzig, Leipzig, Germany
| | - Jürgen Krug
- Institute of General Kinesiology and Athletics Training, University of Leipzig, Leipzig, Germany
| | - Arno Villringer
- Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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31
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False positive rates in Voxel-based Morphometry studies of the human brain: should we be worried? Neurosci Biobehav Rev 2015; 52:49-55. [PMID: 25701614 DOI: 10.1016/j.neubiorev.2015.02.008] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2014] [Revised: 02/10/2015] [Accepted: 02/11/2015] [Indexed: 11/21/2022]
Abstract
Voxel-based Morphometry (VBM) is a widely used automated technique for the analysis of neuroanatomical images. Despite its popularity within the neuroimaging community, there are outstanding concerns about its potential susceptibility to false positive findings. Here we review the main methodological factors that are known to influence the results of VBM studies comparing two groups of subjects. We then use two large, open-access data sets to empirically estimate false positive rates and how these depend on sample size, degree of smoothing and modulation. Our review and investigation provide three main results: (i) when groups of equal size are compared false positive rate is not higher than expected, i.e. about 5%; (ii) the sample size, degree of smoothing and modulation do not appear to influence false positive rate; (iii) when they exist, false positive findings are randomly distributed across the brain. These results provide reassurance that VBM studies comparing groups are not vulnerable to the higher than expected false positive rates that are evident in single case VBM.
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32
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Does anodal transcranial direct current stimulation modulate sensory perception and pain? A meta-analysis study. Clin Neurophysiol 2014; 125:1847-58. [DOI: 10.1016/j.clinph.2014.01.020] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Revised: 01/01/2014] [Accepted: 01/09/2014] [Indexed: 11/21/2022]
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33
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Hosseini SMH, Kramer JH, Kesler SR. Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease. Front Aging Neurosci 2014; 6:231. [PMID: 25206335 PMCID: PMC4143724 DOI: 10.3389/fnagi.2014.00231] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 08/11/2014] [Indexed: 01/18/2023] Open
Abstract
Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.
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Affiliation(s)
- S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine Stanford, CA, USA
| | - Joel H Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, CA, USA
| | - Shelli R Kesler
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine Stanford, CA, USA ; Stanford Cancer Institute Palo Alto, CA, USA
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34
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Adolf D, Weston S, Baecke S, Luchtmann M, Bernarding J, Kropf S. Increasing the reliability of data analysis of functional magnetic resonance imaging by applying a new blockwise permutation method. Front Neuroinform 2014; 8:72. [PMID: 25165444 PMCID: PMC4131278 DOI: 10.3389/fninf.2014.00072] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2014] [Accepted: 07/25/2014] [Indexed: 11/13/2022] Open
Abstract
A recent paper by Eklund et al. (2012) showed that up to 70% false positive results may occur when analyzing functional magnetic resonance imaging (fMRI) data using the statistical parametric mapping (SPM) software, which may mainly be caused by insufficient compensation for the temporal correlation between successive scans. Here, we show that a blockwise permutation method can be an effective alternative to the standard correction method for the correlated residuals in the general linear model, assuming an AR(1)-model as used in SPM for analyzing fMRI data. The blockwise permutation approach including a random shift developed by our group (Adolf et al., 2011) accounts for the temporal correlation structure of the data without having to provide a specific definition of the underlying autocorrelation model. 1465 publicly accessible resting-state data sets were re-analyzed, and the results were compared with those of Eklund et al. (2012). It was found that with the new permutation method the nominal familywise error rate for the detection of activated voxels could be maintained approximately under even the most critical conditions in which Eklund et al. found the largest deviations from the nominal error level. Thus, the method presented here can serve as a tool to ameliorate the quality and reliability of fMRI data analyses.
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Affiliation(s)
- Daniela Adolf
- Institute for Biometry and Medical Informatics, Otto von Guericke University Magdeburg Magdeburg, Germany
| | - Snezhana Weston
- Institute for Biometry and Medical Informatics, Otto von Guericke University Magdeburg Magdeburg, Germany
| | - Sebastian Baecke
- Institute for Biometry and Medical Informatics, Otto von Guericke University Magdeburg Magdeburg, Germany
| | - Michael Luchtmann
- Department of Neurosurgery, Otto von Guericke University Magdeburg Magdeburg, Germany
| | - Johannes Bernarding
- Institute for Biometry and Medical Informatics, Otto von Guericke University Magdeburg Magdeburg, Germany
| | - Siegfried Kropf
- Institute for Biometry and Medical Informatics, Otto von Guericke University Magdeburg Magdeburg, Germany
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35
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Wilke M, Rose DF, Holland SK, Leach JL. Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population. Hum Brain Mapp 2014; 35:3199-215. [PMID: 25050423 PMCID: PMC6869842 DOI: 10.1002/hbm.22395] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Revised: 07/24/2013] [Accepted: 08/02/2013] [Indexed: 11/07/2022] Open
Abstract
Automated morphometric approaches are used to detect epileptogenic structural abnormalities in 3D MR images in adults, using the variance of a control population to obtain z-score maps in an individual patient. Due to the substantial changes the developing human brain undergoes, performing such analyses in children is challenging. This study investigated six features derived from high-resolution T1 datasets in four groups: normal children (1.5T or 3T data), normal clinical scans (3T data), and patients with structural brain lesions (3T data), with each n = 10. Normative control data were obtained from the NIH study on normal brain development (n = 401). We show that control group size substantially influences the captured variance, directly impacting the patient's z-scores. Interestingly, matching on gender does not seem to be beneficial, which was unexpected. Using data obtained at higher field scanners produces slightly different base rates of suprathreshold voxels, as does using clinically derived normal studies, suggesting a subtle but systematic effect of both factors. Two approaches for controlling suprathreshold voxels in a multidimensional approach (combining features and requiring a minimum cluster size) were shown to be substantial and effective in reducing this number. Finally, specific strengths and limitations of such an approach could be demonstrated in individual cases.
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Affiliation(s)
- Marko Wilke
- Department of Pediatric Neurology and Developmental MedicineChildren's Hospital, University of TübingenGermany
- Experimental Pediatric Neuroimaging, Children's Hospital and Department of NeuroradiologyUniversity of TübingenGermany
- Department of NeurologyCincinnati Children's Hospital Medical CenterCincinnatiOhio
- Department of RadiologyCincinnati Children's Hospital Medical CenterCincinnatiOhio
| | - Douglas F. Rose
- Department of NeurologyCincinnati Children's Hospital Medical CenterCincinnatiOhio
| | - Scott K. Holland
- Pediatric Neuroimaging Research ConsortiumCincinnati Children's Hospital Medical CenterCincinnatiOhio
| | - James L. Leach
- Department of RadiologyCincinnati Children's Hospital Medical CenterCincinnatiOhio
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36
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Harnessing graphics processing units for improved neuroimaging statistics. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2014; 13:587-97. [PMID: 23625719 DOI: 10.3758/s13415-013-0165-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.
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37
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Adjamian P, Hall DA, Palmer AR, Allan TW, Langers DRM. Neuroanatomical abnormalities in chronic tinnitus in the human brain. Neurosci Biobehav Rev 2014; 45:119-33. [PMID: 24892904 PMCID: PMC4148481 DOI: 10.1016/j.neubiorev.2014.05.013] [Citation(s) in RCA: 81] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Revised: 05/19/2014] [Accepted: 05/25/2014] [Indexed: 12/23/2022]
Abstract
We review brain anatomical studies of tinnitus. We evaluate the “gating mechanism” in light of the evidence from these studies. We discuss the results and the possible causes of disparity between findings. Overall, the evidence for structural abnormalities in tinnitus is unconvincing. We identify methodological concerns and suggest strategies for future research.
In this paper, we review studies that have investigated brain morphology in chronic tinnitus in order to better understand the underlying pathophysiology of the disorder. Current consensus is that tinnitus is a disorder involving a distributed network of peripheral and central pathways in the nervous system. However, the precise mechanism remains elusive and it is unclear which structures are involved. Given that brain structure and function are highly related, identification of anatomical differences may shed light upon the mechanism of tinnitus generation and maintenance. We discuss anatomical changes in the auditory cortex, the limbic system, and prefrontal cortex, among others. Specifically, we discuss the gating mechanism of tinnitus and evaluate the evidence in support of the model from studies of brain anatomy. Although individual studies claim significant effects related to tinnitus, outcomes are divergent and even contradictory across studies. Moreover, results are often confounded by the presence of hearing loss. We conclude that, at present, the overall evidence for structural abnormalities specifically related to tinnitus is poor. As this area of research is expanding, we identify some key considerations for research design and propose strategies for future research.
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Affiliation(s)
- Peyman Adjamian
- MRC Institute of Hearing Research, University Park, Nottingham NG7 2RD, United Kingdom.
| | - Deborah A Hall
- National Institute for Health Research (NIHR), Nottingham Hearing Biomedical Research Unit, University of Nottingham, Ropewalk House, 113 The Ropewalk, Nottingham NG1 5DU, United Kingdom; Otology and Hearing Group, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom
| | - Alan R Palmer
- MRC Institute of Hearing Research, University Park, Nottingham NG7 2RD, United Kingdom
| | - Thomas W Allan
- MRC Institute of Hearing Research, University Park, Nottingham NG7 2RD, United Kingdom
| | - Dave R M Langers
- National Institute for Health Research (NIHR), Nottingham Hearing Biomedical Research Unit, University of Nottingham, Ropewalk House, 113 The Ropewalk, Nottingham NG1 5DU, United Kingdom; Otology and Hearing Group, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom
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38
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Ziegler G, Ridgway GR, Dahnke R, Gaser C. Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects. Neuroimage 2014; 97:333-48. [PMID: 24742919 PMCID: PMC4077633 DOI: 10.1016/j.neuroimage.2014.04.018] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 02/25/2014] [Accepted: 04/04/2014] [Indexed: 01/24/2023] Open
Abstract
Structural imaging based on MRI is an integral component of the clinical assessment of patients with potential dementia. We here propose an individualized Gaussian process-based inference scheme for clinical decision support in healthy and pathological aging elderly subjects using MRI. The approach aims at quantitative and transparent support for clinicians who aim to detect structural abnormalities in patients at risk of Alzheimer's disease or other types of dementia. Firstly, we introduce a generative model incorporating our knowledge about normative decline of local and global gray matter volume across the brain in elderly. By supposing smooth structural trajectories the models account for the general course of age-related structural decline as well as late-life accelerated loss. Considering healthy subjects' demography and global brain parameters as informative about normal brain aging variability affords individualized predictions in single cases. Using Gaussian process models as a normative reference, we predict new subjects' brain scans and quantify the local gray matter abnormalities in terms of Normative Probability Maps (NPM) and global z-scores. By integrating the observed expectation error and the predictive uncertainty, the local maps and global scores exploit the advantages of Bayesian inference for clinical decisions and provide a valuable extension of diagnostic information about pathological aging. We validate the approach in simulated data and real MRI data. We train the GP framework using 1238 healthy subjects with ages 18–94 years, and predict in 415 independent test subjects diagnosed as healthy controls, Mild Cognitive Impairment and Alzheimer's disease. We propose an approach to support individualized clinical decisions in elderlies. Gaussian process models are used to build a normative generative model of aging. It affords probabilistic predictions of local gray matter volume in subjects. We validate the model using simulated and large real MRI data samples.
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Affiliation(s)
- G Ziegler
- Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK; Department of Psychiatry, Jena University Hospital, Jena, Germany.
| | - G R Ridgway
- Wellcome Trust Center for Neuroimaging, Institute of Neurology, London, UK
| | - R Dahnke
- Department of Psychiatry, Jena University Hospital, Jena, Germany
| | - C Gaser
- Department of Psychiatry, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany
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39
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A novel presenilin 1 mutation (Ala275Val) as cause of early-onset familial Alzheimer disease. Neurosci Lett 2014; 566:115-9. [DOI: 10.1016/j.neulet.2014.02.034] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2013] [Revised: 01/22/2014] [Accepted: 02/17/2014] [Indexed: 11/23/2022]
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40
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Long-term intensive training induced brain structural changes in world class gymnasts. Brain Struct Funct 2013; 220:625-44. [DOI: 10.1007/s00429-013-0677-5] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2013] [Accepted: 11/11/2013] [Indexed: 11/26/2022]
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41
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Fusar-Poli P, Radua J, Frascarelli M, Mechelli A, Borgwardt S, Di Fabio F, Biondi M, Ioannidis JPA, David SP. Evidence of reporting biases in voxel-based morphometry (VBM) studies of psychiatric and neurological disorders. Hum Brain Mapp 2013; 35:3052-65. [PMID: 24123491 PMCID: PMC6869352 DOI: 10.1002/hbm.22384] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2013] [Revised: 06/19/2013] [Accepted: 07/23/2013] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVES To evaluate whether biases may influence the findings of whole-brain structural imaging literature. METHODS Forty-seven whole-brain voxel-based meta-analyses including voxel-based morphometry (VBM) studies in neuropsychiatric conditions were included, for a total of 324 individual VBM studies. The total sample size, the overall number of foci, and different moderators were extracted both at the level of the individual studies and at the level of the meta-analyses. RESULTS Sample size ranged from 12 to 545 (median n = 47) per VBM study. The median number of reported foci per study was six. VBM studies with larger sample sizes reported only slightly more abnormalities than smaller studies (2% increase in the number of foci per 10-patients increase in sample size). A similar pattern was seen in several analyses according to different moderator variables with some possible modulating evidence for the statistical threshold employed, publication year and number of coauthors. Whole-brain meta-analyses (median sample size n = 534) found fewer foci (median = 3) than single studies and overall they showed no significant increase in the number of foci with increasing sample size. Meta-analyses with ≥10 VBM studies reported a median of three foci and showed a significant increase with increasing sample size, while there was no relationship between sample size and number of foci (median = 5) in meta-analyses with <10 VBM studies. CONCLUSIONS The number of foci reported in small VBM studies and even in meta-analyses with few studies may often be inflated. This picture is consistent with reporting biases affecting small studies.
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Affiliation(s)
- Paolo Fusar-Poli
- Department of Psychosis Studies, Institute of Psychiatry, King's College London, United Kingdom; OASIS team, South London and the Maudsley NHS Foundation Trust, London, United Kingdom
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Memarian N, Thompson PM, Engel J, Staba RJ. Quantitative analysis of structural neuroimaging of mesial temporal lobe epilepsy. ACTA ACUST UNITED AC 2013; 5. [PMID: 24319498 DOI: 10.2217/iim.13.28] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Mesial temporal lobe epilepsy (MTLE) is the most common of the surgically remediable drug-resistant epilepsies. MRI is the primary diagnostic tool to detect anatomical abnormalities and, when combined with EEG, can more accurately identify an epileptogenic lesion, which is often hippocampal sclerosis in cases of MTLE. As structural imaging technology has advanced the surgical treatment of MTLE and other lesional epilepsies, so too have the analysis techniques that are used to measure different structural attributes of the brain. These techniques, which are reviewed here and have been used chiefly in basic research of epilepsy and in studies of MTLE, have identified different types and the extent of anatomical abnormalities that can extend beyond the affected hippocampus. These results suggest that structural imaging and sophisticated imaging analysis could provide important information to identify networks capable of generating spontaneous seizures and ultimately help guide surgical therapy that improves postsurgical seizure-freedom outcomes.
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Affiliation(s)
- Negar Memarian
- Department of Neurology, Reed, Neurological Research Center, Suite, 2155, University of California, 710 Westwood Plaza, Los Angeles, CA 90095, USA
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