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Sibilia F, Jost-Mousseau C, Banaschewski T, Barker GJ, Büchel C, Desrivières S, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Artiges E, Nees F, Orfanos DP, Poustka L, Millenet S, Fröhner JH, Smolka MN, Walter H, Whelan R, Schumann G, Bokde AL. The relationship between negative life events and cortical structural connectivity in adolescents. IBRO Neurosci Rep 2024; 16:201-210. [PMID: 38348392 PMCID: PMC10859284 DOI: 10.1016/j.ibneur.2024.01.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 01/27/2024] [Indexed: 02/15/2024] Open
Abstract
Adolescence is a crucial period for physical and psychological development. The impact of negative life events represents a risk factor for the onset of neuropsychiatric disorders. This study aims to investigate the relationship between negative life events and structural brain connectivity, considering both graph theory and connectivity strength. A group (n = 487) of adolescents from the IMAGEN Consortium was divided into Low and High Stress groups. Brain networks were extracted at an individual level, based on morphological similarity between grey matter regions with regions defined using an atlas-based region of interest (ROI) approach. Between-group comparisons were performed with global and local graph theory measures in a range of sparsity levels. The analysis was also performed in a larger sample of adolescents (n = 976) to examine linear correlations between stress level and network measures. Connectivity strength differences were investigated with network-based statistics. Negative life events were not found to be a factor influencing global network measures at any sparsity level. At local network level, between-group differences were found in centrality measures of the left somato-motor network (a decrease of betweenness centrality was seen at sparsity 5%), of the bilateral central visual and the left dorsal attention network (increase of degree at sparsity 10% at sparsity 30% respectively). Network-based statistics analysis showed an increase in connectivity strength in the High stress group in edges connecting the dorsal attention, limbic and salience networks. This study suggests negative life events alone do not alter structural connectivity globally, but they are associated to connectivity properties in areas involved in emotion and attention.
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Affiliation(s)
- Francesca Sibilia
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Coline Jost-Mousseau
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | | | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Sabina Millenet
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
| | - Juliane H. Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Michael N. Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - IMAGEN Consortium
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
- Paris Institute of Technology for Life, Food and Environmental Sciences, Paris, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159 Mannheim, Germany
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, United Kingdom
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131 Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
- Departments of Psychiatry and Psychology, University of Vermont, 05405 Burlington, VT, USA
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
- Charité – Universitätsmedizin Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes – Sorbonne Paris Cité; and Maison de Solenn, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Saclay, University Paris Descartes; and AP-HP.Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 “Neuroimaging & Psychiatry”, University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Psychiatry Department 91G16, Orsay Hospital, France
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Ireland
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Torbati ME, Minhas DS, Laymon CM, Maillard P, Wilson JD, Chen CL, Crainiceanu CM, DeCarli CS, Hwang SJ, Tudorascu DL. MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data. Med Image Anal 2023; 89:102926. [PMID: 37595405 PMCID: PMC10529705 DOI: 10.1016/j.media.2023.102926] [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: 07/27/2022] [Revised: 06/06/2023] [Accepted: 08/03/2023] [Indexed: 08/20/2023]
Abstract
Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.
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Affiliation(s)
| | - Davneet S Minhas
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Charles M Laymon
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Pauline Maillard
- Department of Neurology, University of California Davis, Davis, CA 95816, USA
| | - James D Wilson
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Chang-Le Chen
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Charles S DeCarli
- Department of Neurology, University of California Davis, Davis, CA 95816, USA
| | - Seong Jae Hwang
- Department of Artificial Intelligence, Yonsei University, Seoul, South Korea
| | - Dana L Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA; Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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Li Y, Zhi W, Qi B, Wang L, Hu X. Update on neurobiological mechanisms of fear: illuminating the direction of mechanism exploration and treatment development of trauma and fear-related disorders. Front Behav Neurosci 2023; 17:1216524. [PMID: 37600761 PMCID: PMC10433239 DOI: 10.3389/fnbeh.2023.1216524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/18/2023] [Indexed: 08/22/2023] Open
Abstract
Fear refers to an adaptive response in the face of danger, and the formed fear memory acts as a warning when the individual faces a dangerous situation again, which is of great significance to the survival of humans and animals. Excessive fear response caused by abnormal fear memory can lead to neuropsychiatric disorders. Fear memory has been studied for a long time, which is of a certain guiding effect on the treatment of fear-related disorders. With continuous technological innovations, the study of fear has gradually shifted from the level of brain regions to deeper neural (micro) circuits between brain regions and even within single brain regions, as well as molecular mechanisms. This article briefly outlines the basic knowledge of fear memory and reviews the neurobiological mechanisms of fear extinction and relapse, which aims to provide new insights for future basic research on fear emotions and new ideas for treating trauma and fear-related disorders.
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Affiliation(s)
- Ying Li
- College of Education, Hebei University, Baoding, China
- Laboratory of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Weijia Zhi
- Laboratory of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Bing Qi
- College of Education, Hebei University, Baoding, China
| | - Lifeng Wang
- Laboratory of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing, China
| | - Xiangjun Hu
- College of Education, Hebei University, Baoding, China
- Laboratory of Experimental Pathology, Beijing Institute of Radiation Medicine, Beijing, China
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4
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Boer OD, El Marroun H, H A Franken I. Brain Morphology Predictors of Alcohol, Tobacco, and Cannabis Use in Adolescence: A Systematic Review. Brain Res 2022; 1795:148020. [PMID: 35853511 DOI: 10.1016/j.brainres.2022.148020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/09/2022] [Accepted: 07/14/2022] [Indexed: 11/16/2022]
Abstract
In the last decade, extensive research has emerged on the predictive value of brain morphology for substance use initiation and related problems during adolescence. This systematic review provides an overview of longitudinal studies on pre-existing brain variations and later initiation of alcohol, tobacco, and cannabis use (N = 18). Adolescent structural neuroimaging studies that started before substance use initiation suggest that a smaller anterior cingulate cortex (ACC) volume, thicker or smaller superior frontal gyrus, and larger nucleus accumbens (NAcc) volume are associated with future alcohol use. Also, both smaller and larger orbitofrontal cortex (OFC) volumes were associated with future cannabis and combined alcohol/cannabis use. Smaller amygdala volumes were related to future daily tobacco smoking. These findings could point to specific vulnerabilities for adolescent substance use, as these brain areas are involved in cognitive control (ACC), reward (NAcc), motivation (OFC), and emotional memory (amygdala). However, the reported findings were inconsistent in directionality and laterality, and the largest study on alcohol use predictors reported null findings. Therefore, large population-based longitudinal studies should investigate the robustness and mechanisms of these associations. We suggested future research directions regarding sample selection, timing of baseline and follow-up measurements, and a harmonization approach of study methods.
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Affiliation(s)
- Olga D Boer
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Science, Erasmus University Rotterdam, 3000 DR, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus MC, Sophia Children's Hospital, 3000 CB, Rotterdam, the Netherlands.
| | - Hanan El Marroun
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Science, Erasmus University Rotterdam, 3000 DR, Rotterdam, the Netherlands; Department of Child and Adolescent Psychiatry, University Medical Center Rotterdam, Erasmus MC, Sophia Children's Hospital, 3000 CB, Rotterdam, the Netherlands.
| | - Ingmar H A Franken
- Department of Psychology, Education and Child Studies, Erasmus School of Social and Behavioral Science, Erasmus University Rotterdam, 3000 DR, Rotterdam, the Netherlands.
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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Constructing the rodent stereotaxic brain atlas: a survey. SCIENCE CHINA-LIFE SCIENCES 2021; 65:93-106. [PMID: 33860452 DOI: 10.1007/s11427-020-1911-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/03/2021] [Indexed: 12/22/2022]
Abstract
The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.
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Isherwood SJS, Bazin PL, Alkemade A, Forstmann BU. Quantity and quality: Normative open-access neuroimaging databases. PLoS One 2021; 16:e0248341. [PMID: 33705468 PMCID: PMC7951909 DOI: 10.1371/journal.pone.0248341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Accepted: 02/24/2021] [Indexed: 11/19/2022] Open
Abstract
The focus of this article is to compare twenty normative and open-access neuroimaging databases based on quantitative measures of image quality, namely, signal-to-noise (SNR) and contrast-to-noise ratios (CNR). We further the analysis through discussing to what extent these databases can be used for the visualization of deeper regions of the brain, such as the subcortex, as well as provide an overview of the types of inferences that can be drawn. A quantitative comparison of contrasts including T1-weighted (T1w) and T2-weighted (T2w) images are summarized, providing evidence for the benefit of ultra-high field MRI. Our analysis suggests a decline in SNR in the caudate nuclei with increasing age, in T1w, T2w, qT1 and qT2* contrasts, potentially indicative of complex structural age-dependent changes. A similar decline was found in the corpus callosum of the T1w, qT1 and qT2* contrasts, though this relationship is not as extensive as within the caudate nuclei. These declines were accompanied by a declining CNR over age in all image contrasts. A positive correlation was found between scan time and the estimated SNR as well as a negative correlation between scan time and spatial resolution. Image quality as well as the number and types of contrasts acquired by these databases are important factors to take into account when selecting structural data for reuse. This article highlights the opportunities and pitfalls associated with sampling existing databases, and provides a quantitative backing for their usage.
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Affiliation(s)
- Scott Jie Shen Isherwood
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Pierre-Louis Bazin
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Anneke Alkemade
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
| | - Birte Uta Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, The Netherlands
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Madan CR. Age-related decrements in cortical gyrification: Evidence from an accelerated longitudinal dataset. Eur J Neurosci 2020; 53:1661-1671. [PMID: 33171528 DOI: 10.1111/ejn.15039] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 10/25/2020] [Accepted: 10/31/2020] [Indexed: 01/05/2023]
Abstract
Cortical gyrification has been found to decrease due to aging, but thus far this has only been examined in cross-sectional samples. Interestingly, the topography of these age-related differences in gyrification follows a distinct gradient along the cortex relative to age effects on cortical thickness, likely suggesting a different underlying neurobiological mechanism. Here I examined several aspects of gyrification in an accelerated longitudinal dataset of 280 healthy adults aged 45-92 with an interval between first and last MRI sessions of up to 10 years (total of 815 MRI sessions). Results suggest that age changes in sulcal morphology underlie these changes in gyrification.
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de Lange AMG, Anatürk M, Suri S, Kaufmann T, Cole JH, Griffanti L, Zsoldos E, Jensen DEA, Filippini N, Singh-Manoux A, Kivimäki M, Westlye LT, Ebmeier KP. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. Neuroimage 2020; 222:117292. [PMID: 32835819 PMCID: PMC8121758 DOI: 10.1016/j.neuroimage.2020.117292] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022] Open
Abstract
Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
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Affiliation(s)
- Ann-Marie G de Lange
- Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Melis Anatürk
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ludovica Griffanti
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Daria E A Jensen
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Nicola Filippini
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Archana Singh-Manoux
- Epidemiology of Ageing and Neurodegenerative Diseases, Universit de Paris, INSERM U1153, Paris France; Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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10
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Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction. Brain Sci 2020; 10:brainsci10060364. [PMID: 32545374 PMCID: PMC7349402 DOI: 10.3390/brainsci10060364] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023] Open
Abstract
Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies.
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Abstract
OBJECTIVES With an increasing aging population, it is important to understand biological markers of aging. Subcortical volume is known to differ with age; additionally considering shape-related characteristics may provide a better index of age-related differences. Fractal dimensionality is more sensitive to age-related differences, but is borne out of mathematical principles, rather than neurobiological relevance. We considered four distinct measures of shape and how they relate to aging and fractal dimensionality: surface-to-volume ratio, sphericity, long-axis curvature, and surface texture. METHODS Structural MRIs from a combined sample of over 600 healthy adults were used to measure age-related differences in the structure of the thalamus, putamen, caudate, and hippocampus. For each, volume and fractal dimensionality were calculated, as well as four distinct shape measures. These measures were examined for their utility in explaining age-related variability in brain structure. RESULTS The four shape measures were able to account for 80%-90% of the variance in fractal dimensionality. Of the distinct shape measures, surface-to-volume ratio was the most sensitive biomarker. CONCLUSION Though volume is often used to characterize inter-individual differences in subcortical structures, our results demonstrate that additional measures can be useful complements. Our results indicate that shape characteristics are useful biological markers of aging.
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Affiliation(s)
- Christopher R Madan
- a School of Psychology , University of Nottingham , Nottingham , UK.,b Department of Psychology , Boston College , Chestnut Hill , MA , USA
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12
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Abstract
While it is well established that cortical morphology differs in relation to a variety of inter-individual factors, it is often characterized using estimates of volume, thickness, surface area, or gyrification. Here we developed a computational approach for estimating sulcal width and depth that relies on cortical surface reconstructions output by FreeSurfer. While other approaches for estimating sulcal morphology exist, studies often require the use of multiple brain morphology programs that have been shown to differ in their approaches to localize sulcal landmarks, yielding morphological estimates based on inconsistent boundaries. To demonstrate the approach, sulcal morphology was estimated in three large sample of adults across the lifespan, in relation to aging. A fourth sample is additionally used to estimate test–retest reliability of the approach. This toolbox is now made freely available as supplemental to this paper: https://cmadan.github.io/calcSulc/.
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13
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King DJ, Ellis KR, Seri S, Wood AG. A systematic review of cross-sectional differences and longitudinal changes to the morphometry of the brain following paediatric traumatic brain injury. NEUROIMAGE-CLINICAL 2019; 23:101844. [PMID: 31075554 PMCID: PMC6510969 DOI: 10.1016/j.nicl.2019.101844] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 04/26/2019] [Accepted: 04/29/2019] [Indexed: 01/27/2023]
Abstract
Paediatric traumatic brain injury (pTBI) is a leading cause of disability for children and young adults. Children are a uniquely vulnerable group with the disease process that occurs following a pTBI interacting with the trajectory of normal brain development. Quantitative MRI post-injury has suggested a long-term, neurodegenerative effect of TBI on the morphometry of the brain, in both adult and childhood TBI. Changes to the brain beyond that of anticipated, age-dependant differences may allow us to estimate the state of the brain post-injury and produce clinically relevant predictions for long-term outcome. The current review synthesises the existing literature to assess whether, following pTBI, the morphology of the brain exhibits either i) longitudinal change and/or ii) differences compared to healthy controls and outcomes. The current literature suggests that morphometric differences from controls are apparent cross-sectionally at both acute and late-chronic timepoints post-injury, thus suggesting a non-transient effect of injury. Developmental trajectories of morphometry are altered in TBI groups compared to patients, and it is unlikely that typical maturation overcomes damage post-injury, or even 'catches up' with that of typically-developing peers. However, there is limited evidence for diverted developmental trajectories being associated with cognitive impairment post-injury. The current review also highlights the apparent challenges to the existing literature and potential methods by which these can be addressed.
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Affiliation(s)
- D J King
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - K R Ellis
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - S Seri
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK
| | - A G Wood
- School of Life and Health Sciences & Aston Brain Centre, Aston University, Birmingham, UK; Child Neuropsychology, Clinical Sciences, Murdoch Children's Research Institute, Melbourne, Australia.
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Keshavan A, Poline JB. From the Wet Lab to the Web Lab: A Paradigm Shift in Brain Imaging Research. Front Neuroinform 2019; 13:3. [PMID: 30881299 PMCID: PMC6405692 DOI: 10.3389/fninf.2019.00003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 01/22/2019] [Indexed: 01/08/2023] Open
Abstract
Web technology has transformed our lives, and has led to a paradigm shift in the computational sciences. As the neuroimaging informatics research community amasses large datasets to answer complex neuroscience questions, we find that the web is the best medium to facilitate novel insights by way of improved collaboration and communication. Here, we review the landscape of web technologies used in neuroimaging research, and discuss future applications, areas for improvement, and the limitations of using web technology in research. Fully incorporating web technology in our research lifecycle requires not only technical skill, but a widespread culture change; a shift from the small, focused "wet lab" to a multidisciplinary and largely collaborative "web lab."
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Affiliation(s)
- Anisha Keshavan
- Department of Speech and Hearing, Institute for Neuroengineering, eScience Institute, University of Washington, Seattle, WA, United States
| | - Jean-Baptiste Poline
- Faculty of Medicine, McConnell Brain Imaging Centre, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States
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15
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Greshake Tzovaras B, Tzovara A. The Personal Data Is Political. PHILOSOPHICAL STUDIES SERIES 2019. [DOI: 10.1007/978-3-030-04363-6_8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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16
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Richard G, Kolskår K, Sanders AM, Kaufmann T, Petersen A, Doan NT, Monereo Sánchez J, Alnæs D, Ulrichsen KM, Dørum ES, Andreassen OA, Nordvik JE, Westlye LT. Assessing distinct patterns of cognitive aging using tissue-specific brain age prediction based on diffusion tensor imaging and brain morphometry. PeerJ 2018; 6:e5908. [PMID: 30533290 PMCID: PMC6276592 DOI: 10.7717/peerj.5908] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 10/10/2018] [Indexed: 01/26/2023] Open
Abstract
Multimodal imaging enables sensitive measures of the architecture and integrity of the human brain, but the high-dimensional nature of advanced brain imaging features poses inherent challenges for the analyses and interpretations. Multivariate age prediction reduces the dimensionality to one biologically informative summary measure with potential for assessing deviations from normal lifespan trajectories. A number of studies documented remarkably accurate age prediction, but the differential age trajectories and the cognitive sensitivity of distinct brain tissue classes have yet to be adequately characterized. Exploring differential brain age models driven by tissue-specific classifiers provides a hitherto unexplored opportunity to disentangle independent sources of heterogeneity in brain biology. We trained machine-learning models to estimate brain age using various combinations of FreeSurfer based morphometry and diffusion tensor imaging based indices of white matter microstructure in 612 healthy controls aged 18-87 years. To compare the tissue-specific brain ages and their cognitive sensitivity, we applied each of the 11 models in an independent and cognitively well-characterized sample (n = 265, 20-88 years). Correlations between true and estimated age and mean absolute error (MAE) in our test sample were highest for the most comprehensive brain morphometry (r = 0.83, CI:0.78-0.86, MAE = 6.76 years) and white matter microstructure (r = 0.79, CI:0.74-0.83, MAE = 7.28 years) models, confirming sensitivity and generalizability. The deviance from the chronological age were sensitive to performance on several cognitive tests for various models, including spatial Stroop and symbol coding, indicating poorer performance in individuals with an over-estimated age. Tissue-specific brain age models provide sensitive measures of brain integrity, with implications for the study of a range of brain disorders.
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Affiliation(s)
- Geneviève Richard
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Knut Kolskår
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Anne-Marthe Sanders
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anders Petersen
- Center for Visual Cognition, Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Nhat Trung Doan
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jennifer Monereo Sánchez
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Kristine M. Ulrichsen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Erlend S. Dørum
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Ole A. Andreassen
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | - Lars T. Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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17
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Yang J, Zhang M, Ahn H, Zhang Q, Jin TB, Li I, Nemesure M, Joshi N, Jiang H, Miller JM, Ogden RT, Petkova E, Milak MS, Sublette ME, Sullivan GM, Trivedi MH, Weissman M, McGrath PJ, Fava M, Kurian BT, Pizzagalli DA, Cooper CM, McInnis M, Oquendo MA, Mann JJ, Parsey RV, DeLorenzo C. Development and evaluation of a multimodal marker of major depressive disorder. Hum Brain Mapp 2018; 39:4420-4439. [PMID: 30113112 DOI: 10.1002/hbm.24282] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 05/16/2018] [Accepted: 06/04/2018] [Indexed: 12/30/2022] Open
Abstract
This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.
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Affiliation(s)
- Jie Yang
- Department of Family, Population and Preventive Medicine, Stony Brook University, New York, New York
| | - Mengru Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Hongshik Ahn
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Qing Zhang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Tony B Jin
- Department of Psychiatry, Stony Brook University, New York, New York
| | - Ien Li
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey
| | - Matthew Nemesure
- Integrative Neuroscience Program, Binghamton University, Binghamton, New York
| | - Nandita Joshi
- Department of Electrical and Computer Engineering, Stony Brook University, New York, New York
| | - Haoran Jiang
- Department of Applied Mathematics and Statistics, Stony Brook University, New York, New York
| | - Jeffrey M Miller
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Eva Petkova
- Department of Child & Adolescent Psychiatry, Department of Population Health, New York University, New York, New York
| | - Matthew S Milak
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Gregory M Sullivan
- Chief Medical Officer, Clinical Research and Development program, Tonix Pharmaceuticals, Inc., New York, New York
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Myrna Weissman
- Department of Psychiatry, Columbia University, New York, New York
| | | | - Maurizio Fava
- Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Benji T Kurian
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | | | - Crystal M Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Melvin McInnis
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan
| | - Maria A Oquendo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Joseph John Mann
- Department of Psychiatry, Columbia University, New York, New York
| | - Ramin V Parsey
- Department of Psychiatry, Stony Brook University, New York, New York
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18
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Madan CR. Age differences in head motion and estimates of cortical morphology. PeerJ 2018; 6:e5176. [PMID: 30065858 PMCID: PMC6065477 DOI: 10.7717/peerj.5176] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 06/16/2018] [Indexed: 01/20/2023] Open
Abstract
Cortical morphology is known to differ with age, as measured by cortical thickness, fractal dimensionality, and gyrification. However, head motion during MRI scanning has been shown to influence estimates of cortical thickness as well as increase with age. Studies have also found task-related differences in head motion and relationships between body–mass index (BMI) and head motion. Here I replicated these prior findings, as well as several others, within a large, open-access dataset (Centre for Ageing and Neuroscience, CamCAN). This is a larger dataset than these results have been demonstrated previously, within a sample size of more than 600 adults across the adult lifespan. While replicating prior findings is important, demonstrating these key findings concurrently also provides an opportunity for additional related analyses: critically, I test for the influence of head motion on cortical fractal dimensionality and gyrification; effects were statistically significant in some cases, but small in magnitude.
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19
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Madan CR, Kensinger EA. Predicting age from cortical structure across the lifespan. Eur J Neurosci 2018; 47:399-416. [PMID: 29359873 PMCID: PMC5835209 DOI: 10.1111/ejn.13835] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 01/22/2023]
Abstract
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
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Affiliation(s)
- Christopher R. Madan
- School of Psychology, University of Nottingham, Nottingham, UK
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
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