151
|
Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, Dalca AV, Eickhoff S, Fox M, Franke L, Golland P, Haehn D, Iglesias JE, O’Donnell LJ, Ou Y, Rathi Y, Siddiqi SH, Sun H, Westover MB, Whitfield-Gabrieli S, Gollub RL. How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics 2022; 20:943-964. [PMID: 35347570 PMCID: PMC9515245 DOI: 10.1007/s12021-022-09572-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
Collapse
Affiliation(s)
- Nalini M. Singh
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordan B. Harrod
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Sandya Subramanian
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Mitchell Robinson
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Ken Chang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | | | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital and Harvard Medical School, 02115 Boston, USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, London, UK ,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114 USA ,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, MA 02115 Boston, USA
| | - Yangming Ou
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Shan H. Siddiqi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Haoqi Sun
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | - M. Brandon Westover
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | | | - Randy L. Gollub
- Department of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114 USA
| |
Collapse
|
152
|
Kurth F, Levitt JG, Gaser C, Alger J, Loo SK, Narr KL, O'Neill J, Luders E. Preliminary evidence for a lower brain age in children with attention-deficit/hyperactivity disorder. Front Psychiatry 2022; 13:1019546. [PMID: 36532197 PMCID: PMC9755736 DOI: 10.3389/fpsyt.2022.1019546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/14/2022] [Indexed: 12/05/2022] Open
Abstract
Attention-deficit hyperactivity disorder (ADHD) is a debilitating disorder with apparent roots in abnormal brain development. Here, we quantified the level of individual brain maturation in children with ADHD using structural neuroimaging and a recently developed machine learning algorithm. More specifically, we compared the BrainAGE index between three groups matched for chronological age (mean ± SD: 11.86 ± 3.25 years): 89 children diagnosed with ADHD, 34 asymptomatic siblings of those children with ADHD, and 21 unrelated healthy control children. Brains of children with ADHD were estimated significantly younger (-0.85 years) than brains of healthy controls (Cohen's d = -0.33; p = 0.028, one-tailed), while there were no significant differences between unaffected siblings and healthy controls. In addition, more severe ADHD symptoms were significantly associated with younger appearing brains. Altogether, these results are in line with the proposed delay of individual brain maturation in children with ADHD. However, given the relatively small sample size (N = 144), the findings should be considered preliminary and need to be confirmed in future studies.
Collapse
Affiliation(s)
- Florian Kurth
- School of Psychology, University of Auckland, Auckland, New Zealand
| | - Jennifer G Levitt
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.,Department of Neurology, Jena University Hospital, Jena, Germany
| | - Jeffry Alger
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sandra K Loo
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L Narr
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Joseph O'Neill
- Division of Child and Adolescent Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States
| | - Eileen Luders
- School of Psychology, University of Auckland, Auckland, New Zealand.,Laboratory of Neuro Imaging, School of Medicine, University of Southern California, Los Angeles, CA, United States.,Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| |
Collapse
|
153
|
Sadeghi I, Gispert JD, Palumbo E, Muñoz-Aguirre M, Wucher V, D'Argenio V, Santpere G, Navarro A, Guigo R, Vilor-Tejedor N. Brain transcriptomic profiling reveals common alterations across neurodegenerative and psychiatric disorders. Comput Struct Biotechnol J 2022; 20:4549-4561. [PMID: 36090817 PMCID: PMC9428860 DOI: 10.1016/j.csbj.2022.08.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 08/16/2022] [Accepted: 08/16/2022] [Indexed: 11/29/2022] Open
Abstract
Neurodegenerative and neuropsychiatric disorders (ND-NPs) are multifactorial, polygenic and complex behavioral phenotypes caused by brain abnormalities. Large-scale collaborative efforts have tried to identify the genetic architecture of these conditions. However, the specific and shared underlying molecular pathobiology of brain illnesses is not clear. Here, we examine transcriptome-wide characterization of eight conditions, using a total of 2,633 post-mortem brain samples from patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Pathological Aging (PA), Autism Spectrum Disorder (ASD), Schizophrenia (Scz), Major Depressive Disorder (MDD), and Bipolar Disorder (BP)–in comparison with 2,078 brain samples from matched control subjects. Similar transcriptome alterations were observed between NDs and NPs with the top correlations obtained between Scz-BP, ASD-PD, AD-PD, and Scz-ASD. Region-specific comparisons also revealed shared transcriptome alterations in frontal and temporal lobes across NPs and NDs. Co-expression network analysis identified coordinated dysregulations of cell-type-specific modules across NDs and NPs. This study provides a transcriptomic framework to understand the molecular alterations of NPs and NDs through their shared- and specific gene expression in the brain.
Collapse
|
154
|
Ballester PL, Romano MT, de Azevedo Cardoso T, Hassel S, Strother SC, Kennedy SH, Frey BN. Brain age in mood and psychotic disorders: a systematic review and meta-analysis. Acta Psychiatr Scand 2022; 145:42-55. [PMID: 34510423 DOI: 10.1111/acps.13371] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 09/05/2021] [Accepted: 09/07/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p < 0.01) presenting the largest effect, followed by bipolar disorder (1.93; [0.53; 3.34]; p < 0.01) and major depressive disorder (1.12; [0.41; 1.83]; p < 0.01). The brain age gap was larger in older compared to younger individuals. CONCLUSION Individuals with mood and psychotic disorders may undergo a process of accelerated brain aging reflected in patterns captured by neuroimaging data. The brain age gap tends to be more pronounced in older individuals, indicating a possible cumulative biological effect of illness burden.
Collapse
Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Maria T Romano
- Integrated Science Undergraduate Program, McMaster University, Hamilton, Ontario, Canada
| | - Taiane de Azevedo Cardoso
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
| | - Stefanie Hassel
- Mathison Centre for Mental Health Research and Education, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.,Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada.,Mood Disorders Treatment and Research Centre, and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| |
Collapse
|
155
|
Schindler LS, Subramaniapillai S, Barth C, van der Meer D, Pedersen ML, Kaufmann T, Maximov II, Linge J, Leinhard OD, Beck D, Gurholt TP, Voldsbekk I, Suri S, Ebmeier KP, Draganski B, Andreassen OA, Westlye LT, de Lange AMG. Associations between abdominal adipose tissue, reproductive span, and brain characteristics in post-menopausal women. Neuroimage Clin 2022; 36:103239. [PMID: 36451350 PMCID: PMC9668664 DOI: 10.1016/j.nicl.2022.103239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022]
Abstract
The menopause transition involves changes in oestrogens and adipose tissue distribution, which may influence female brain health post-menopause. Although increased central fat accumulation is linked to risk of cardiometabolic diseases, adipose tissue also serves as the primary biosynthesis site of oestrogens post-menopause. It is unclear whether different types of adipose tissue play diverging roles in female brain health post-menopause, and whether this depends on lifetime oestrogen exposure, which can have lasting effects on the brain and body even after menopause. Using the UK Biobank sample, we investigated associations between brain characteristics and visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) in 10,251 post-menopausal females, and assessed whether the relationships varied depending on length of reproductive span (age at menarche to age at menopause). To parse the effects of common genetic variation, we computed polygenic scores for reproductive span. The results showed that higher VAT and ASAT were both associated with higher grey and white matter brain age, and greater white matter hyperintensity load. The associations varied positively with reproductive span, indicating more prominent associations between adipose tissue and brain measures in females with a longer reproductive span. The effects were in general small, but could not be fully explained by genetic variation or relevant confounders. Our findings indicate that associations between abdominal adipose tissue and brain health post-menopause may partly depend on individual differences in cumulative oestrogen exposure during reproductive years, emphasising the complexity of neural and endocrine ageing processes in females.
Collapse
Affiliation(s)
- Louise S Schindler
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Sivaniya Subramaniapillai
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Mads L Pedersen
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Jennifer Linge
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Olof Dahlqvist Leinhard
- AMRA Medical AB, Linköping, Sweden; Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Tiril P Gurholt
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Dept. of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| |
Collapse
|
156
|
Chen YC, Arnatkevičiūtė A, McTavish E, Pang JC, Chopra S, Suo C, Fornito A, Aquino KM. The individuality of shape asymmetries of the human cerebral cortex. eLife 2022; 11:75056. [PMID: 36197720 PMCID: PMC9668337 DOI: 10.7554/elife.75056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 10/04/2022] [Indexed: 01/05/2023] Open
Abstract
Asymmetries of the cerebral cortex are found across diverse phyla and are particularly pronounced in humans, with important implications for brain function and disease. However, many prior studies have confounded asymmetries due to size with those due to shape. Here, we introduce a novel approach to characterize asymmetries of the whole cortical shape, independent of size, across different spatial frequencies using magnetic resonance imaging data in three independent datasets. We find that cortical shape asymmetry is highly individualized and robust, akin to a cortical fingerprint, and identifies individuals more accurately than size-based descriptors, such as cortical thickness and surface area, or measures of inter-regional functional coupling of brain activity. Individual identifiability is optimal at coarse spatial scales (~37 mm wavelength), and shape asymmetries show scale-specific associations with sex and cognition, but not handedness. While unihemispheric cortical shape shows significant heritability at coarse scales (~65 mm wavelength), shape asymmetries are determined primarily by subject-specific environmental effects. Thus, coarse-scale shape asymmetries are highly personalized, sexually dimorphic, linked to individual differences in cognition, and are primarily driven by stochastic environmental influences.
Collapse
Affiliation(s)
- Yu-Chi Chen
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Monash Data Futures Institute, Monash UniversityMelbourneAustralia
| | - Aurina Arnatkevičiūtė
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Eugene McTavish
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Healthy Brain and Mind Research Centre, Faculty of Health Sciences, Australian Catholic UniversityFitzroyAustralia
| | - James C Pang
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Sidhant Chopra
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,Department of Psychology, Yale UniversityNew HavenUnited States
| | - Chao Suo
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,BrainPark, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia
| | - Kevin M Aquino
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityMelbourneAustralia,Monash Biomedical Imaging, Monash UniversityMelbourneAustralia,School of Physics, University of SydneySydneyAustralia,Center of Excellence for Integrative Brain Function, University of SydneySydneyAustralia,BrainKey IncSan FranciscoUnited States
| | | |
Collapse
|
157
|
He S, Grant PE, Ou Y. Global-Local Transformer for Brain Age Estimation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:213-224. [PMID: 34460370 PMCID: PMC9746186 DOI: 10.1109/tmi.2021.3108910] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer.
Collapse
|
158
|
Jawinski P, Markett S, Drewelies J, Düzel S, Demuth I, Steinhagen-Thiessen E, Wagner GG, Gerstorf D, Lindenberger U, Gaser C, Kühn S. Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II. Front Aging Neurosci 2022; 14:791222. [PMID: 35936763 PMCID: PMC9355695 DOI: 10.3389/fnagi.2022.791222] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.
Collapse
Affiliation(s)
- Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Johanna Drewelies
- Department of Psychology, Humboldt-Universität zu Berlin, Berlin, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Ilja Demuth
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health at Charité - Universitätsmedizin Berlin, BCRT-Berlin Institute of Health Center for Regenerative Therapies, Berlin, Germany
| | - Elisabeth Steinhagen-Thiessen
- Division of Lipid Metabolism, Department of Endocrinology and Metabolic Diseases, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Gert G Wagner
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany.,Federal Institute for Population Research (BiB), Berlin, Germany
| | - Denis Gerstorf
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,German Socio-Economic Panel Study (SOEP), Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, Department of Psychiatry and Neurology, Jena University Hospital, Jena, Germany
| | - Simone Kühn
- Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany.,Department of Psychiatry and Psychotherapy, University Clinic Hamburg Eppendorf, Hamburg, Germany
| |
Collapse
|
159
|
Lund MJ, Alnæs D, de Lange AMG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin 2021; 33:102921. [PMID: 34959052 PMCID: PMC8718718 DOI: 10.1016/j.nicl.2021.102921] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health. METHODS We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance. RESULTS Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN. DISCUSSION Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.
Collapse
Affiliation(s)
- Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway.
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany.
| |
Collapse
|
160
|
Trofimova IN, Gaykalova AA. Emotionality vs. Other Biobehavioural Traits: A Look at Neurochemical Biomarkers for Their Differentiation. Front Psychol 2021; 12:781631. [PMID: 34987450 PMCID: PMC8720768 DOI: 10.3389/fpsyg.2021.781631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 12/01/2021] [Indexed: 12/15/2022] Open
Abstract
This review highlights the differential contributions of multiple neurochemical systems to temperament traits related and those that are unrelated to emotionality, even though these systems have a significant overlap. The difference in neurochemical biomarkers of these traits is analysed from the perspective of the neurochemical model, Functional Ensemble of Temperament (FET) that uses multi-marker and constructivism principles. Special attention is given to a differential contribution of hypothalamic-pituitary hormones and opioid neuropeptides implicated in both emotional and non-emotional regulation. The review highlights the role of the mu-opioid receptor system in dispositional emotional valence and the role of the kappa-opioid system in dispositional perceptual and behavioural alertness. These opioid receptor (OR) systems, microbiota and cytokines are produced in three neuroanatomically distinct complexes in the brain and the body, which all together integrate dispositional emotionality. In contrast, hormones could be seen as neurochemical biomarkers of non-emotional aspects of behavioural regulation related to the construction of behaviour in fast-changing and current situations. As examples of the role of hormones, the review summarised their contribution to temperament traits of Sensation Seeking (SS) and Empathy (EMP), which FET considers as non-emotionality traits related to behavioural orientation. SS is presented here as based on (higher) testosterone (fluctuating), adrenaline and (low) cortisol systems, and EMP, as based on (higher) oxytocin, reciprocally coupled with vasopressin and (lower) testosterone. Due to the involvement of gonadal hormones, there are sex and age differences in these traits that could be explained by evolutionary theory. There are, therefore, specific neurochemical biomarkers differentiating (OR-based) dispositional emotionality and (hormones-based) body's regulation in fast-changing events. Here we propose to consider dispositional emotionality associated with OR systems as emotionality in a true sense, whereas to consider hormonal ensembles regulating SS and EMP as systems of behavioural orientation and not emotionality.
Collapse
Affiliation(s)
- Irina N. Trofimova
- Laboratory of Collective Intelligence, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | | |
Collapse
|
161
|
Warped Bayesian linear regression for normative modelling of big data. Neuroimage 2021; 245:118715. [PMID: 34798518 DOI: 10.1016/j.neuroimage.2021.118715] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 11/20/2022] Open
Abstract
Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges. So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the 'normal' trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest. Here, we present a novel framework based on Bayesian linear regression with likelihood warping that allows us to address these problems, that is, to correctly model non-Gaussian predictive distributions and scale normative modelling elegantly to big data cohorts. In addition, this method provides likelihood-based statistics, which are useful for model selection. To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals. The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.
Collapse
|
162
|
Popescu SG, Glocker B, Sharp DJ, Cole JH. Local Brain-Age: A U-Net Model. Front Aging Neurosci 2021; 13:761954. [PMID: 34966266 PMCID: PMC8710767 DOI: 10.3389/fnagi.2021.761954] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
We propose a new framework for estimating neuroimaging-derived "brain-age" at a local level within the brain, using deep learning. The local approach, contrary to existing global methods, provides spatial information on anatomical patterns of brain ageing. We trained a U-Net model using brain MRI scans from n = 3,463 healthy people (aged 18-90 years) to produce individualised 3D maps of brain-predicted age. When testing on n = 692 healthy people, we found a median (across participant) mean absolute error (within participant) of 9.5 years. Performance was more accurate (MAE around 7 years) in the prefrontal cortex and periventricular areas. We also introduce a new voxelwise method to reduce the age-bias when predicting local brain-age "gaps." To validate local brain-age predictions, we tested the model in people with mild cognitive impairment or dementia using data from OASIS3 (n = 267). Different local brain-age patterns were evident between healthy controls and people with mild cognitive impairment or dementia, particularly in subcortical regions such as the accumbens, putamen, pallidum, hippocampus, and amygdala. Comparing groups based on mean local brain-age over regions-of-interest resulted in large effects sizes, with Cohen's d values >1.5, for example when comparing people with stable and progressive mild cognitive impairment. Our local brain-age framework has the potential to provide spatial information leading to a more mechanistic understanding of individual differences in patterns of brain ageing in health and disease.
Collapse
Affiliation(s)
- Sebastian G. Popescu
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Imperial College London, London, United Kingdom
| | - Ben Glocker
- Biomedical Image Analysis Group, Imperial College London, London, United Kingdom
| | - David J. Sharp
- Computational, Cognitive & Clinical Neuroimaging Laboratory, Imperial College London, London, United Kingdom
- Care Research & Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| |
Collapse
|
163
|
Gialluisi A, Santoro A, Tirozzi A, Cerletti C, Donati MB, de Gaetano G, Franceschi C, Iacoviello L. Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology. Ageing Res Rev 2021; 72:101502. [PMID: 34700008 DOI: 10.1016/j.arr.2021.101502] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 10/18/2021] [Accepted: 10/20/2021] [Indexed: 01/09/2023]
Abstract
Estimators of biological age (BA) - defined as the hypothetical underlying age of an organism - have attracted more and more attention in the last years, especially after the advent of new algorithms based on machine learning and genetic markers. While different aging clocks reportedly predict mortality in the general population, very little is known on their overlap. Here we review the evidence reported so far to support the existence of a partial overlap among different BA acceleration estimators, both from an epidemiological and a genetic perspective. On the epidemiological side, we review evidence supporting shared and independent influence on mortality risk of different aging clocks - including telomere length, brain, blood and epigenetic aging - and provide an overview of how an important exposure like diet may affect the different aging systems. On the genetic side, we apply linkage disequilibrium score regression analyses to support the existence of partly shared genomic overlap among these aging clocks. Through multivariate analysis of published genetic associations with these clocks, we also identified the most associated variants, genes, and pathways, which may affect common mechanisms underlying biological aging of different systems within the body. Based on our analyses, the most implicated pathways were involved in inflammation, lipid and carbohydrate metabolism, suggesting them as potential molecular targets for future anti-aging interventions. Overall, this review is meant as a contribution to the knowledge on the overlap of aging clocks, trying to clarify their shared biological basis and epidemiological implications.
Collapse
Affiliation(s)
| | - Aurelia Santoro
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy; Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate), University of Bologna, Bologna 40126, Italy
| | - Alfonsina Tirozzi
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy
| | - Chiara Cerletti
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy
| | | | | | - Claudio Franceschi
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy; Laboratory of Systems Medicine of Healthy Aging and Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS NEUROMED, Pozzilli, Italy; Department of Medicine and Surgery, University of Insubria, Varese, Italy
| |
Collapse
|
164
|
Li B, Jang I, Riphagen J, Almaktoum R, Yochim KM, Ances BM, Bookheimer SY, Salat DH. Identifying individuals with Alzheimer's disease-like brains based on structural imaging in the Human Connectome Project Aging cohort. Hum Brain Mapp 2021; 42:5535-5546. [PMID: 34582057 PMCID: PMC8559490 DOI: 10.1002/hbm.25626] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022] Open
Abstract
Given the difficulty in factoring out typical age effects from subtle Alzheimer's disease (AD) effects on brain structure, identification of very early, as well as younger preclinical “at‐risk” individuals has unique challenges. We examined whether age‐correction procedures could be used to better identify individuals at very early potential risk from adults who did not have any existing cognitive diagnosis. First, we obtained cross‐sectional age effects for each structural feature using data from a selected portion of the Human Connectome Project Aging (HCP‐A) cohort. After age detrending, we weighted AD structural deterioration with patterns quantified from data of the Alzheimer's Disease Neuroimaging Initiative. Support vector machine was then used to classify individuals with brains that most resembled atrophy in AD across the entire HCP‐A sample. Additionally, we iteratively adjusted the pipeline by removing individuals classified as AD‐like from the HCP‐A cohort to minimize atypical brain structural contributions to the age detrending. The classifier had a mean cross‐validation accuracy of 94.0% for AD recognition. It also could identify mild cognitive impairment with more severe AD‐specific biomarkers and worse cognition. In an independent HCP‐A cohort, 8.8% were identified as AD‐like, and they trended toward worse cognition. An “AD risk” score derived from the machine learning models also significantly correlated with cognition. This work provides a proof of concept for the potential to use structural brain imaging to identify asymptomatic individuals at young ages who show structural brain patterns similar to AD and are potentially at risk for a future clinical disorder.
Collapse
Affiliation(s)
- Binyin Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ikbeom Jang
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Joost Riphagen
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Randa Almaktoum
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Kathryn Morrison Yochim
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Beau M Ances
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Susan Y Bookheimer
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, USA
| | - David H Salat
- MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.,Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, Massachusetts, USA
| | | |
Collapse
|
165
|
Sendi MSE, Salat DH, Calhoun VD. Brain age gap difference between healthy and mild dementia subjects: Functional network connectivity analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1636-1639. [PMID: 34891599 DOI: 10.1109/embc46164.2021.9630648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Brain age gap, the difference between an individual's brain predicted age and their chronological age, is used as a biomarker of brain disease and aging. To date, although previous studies used structural magnetic resonance imaging (MRI) data to predict brain age, less work has used functional network connectivity (FNC) estimated from functional MRI to predict brain age and its association with Alzheimer's disease progression. This study used FNC estimated from 951 normal cognitive functions (NCF) individuals aged 42-95 years to train a support vector regression (SVR) to predict brain age. In the next step, we tested the trained model on two unseen datasets, including NCF and mild dementia (MD) subjects with similar age distribution (between 50-80 years old, N=70). The mean brain age gap for the NCF and MD groups was -2.25 and 2.08, respectively. We also found a significant difference between the brain age gap of NCF and MD groups. This piece of evidence introduces the brain age gap estimated from FNC as a biomarker of Alzheimer's disease progression.
Collapse
|
166
|
Man W, Ding H, Chai C, An X, Liu F, Qin W, Yu C. Brain age gap as a potential biomarker for schizophrenia: A multi-site structural MRI study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4060-4063. [PMID: 34892121 DOI: 10.1109/embc46164.2021.9631085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Gray matter atrophy in schizophrenia has been widely recognized; however, it remains controversial whether it reflects a neurodegenerative condition. Recent studies have suggested that the brain age gap (BAG) between the predicted and chronological ones may serve as a biomarker for early-stage neurodegeneration. Nevertheless, it is unknown its value for schizophrenia diagnosis and the potential meaning. We included structural MRI datasets from 8 independent sites in the current study, including 501 schizophrenia patients (SZ) and 512 healthy controls (HC). We first applied support vector regression (SVR) to train the age prediction model of the controls using the gray matter volume (GMV) and apply this model to predict the age of the SZ. Meta-analysis identified the SZ had significantly higher BAG than the HC (Cohen's d = 0.38, 95% confidence level = [0.19, 0.57]), and this trend was reliably repeated in each site. Furthermore, logistic regression demonstrated BAG can significantly discriminate the SZ from the HC (OR = 1.07, P = 7.14 × 10-8). Finally, the linear regression study demonstrated a significant negative correlation between the BAG and gray matter volume in both groups, especially at the subcortical regions and prefrontal cortex (P<0.05, corrected using the family-wise error method).Clinical Relevance- This multi-site study suggested that the brain age gap derived from machine learning can be taken as a potential biomarker for schizophrenia, which is significantly associated with brain gray matter atrophy.
Collapse
|
167
|
Ballester PL, Suh JS, Nogovitsyn N, Hassel S, Strother SC, Arnott SR, Minuzzi L, Sassi RB, Lam RW, Milev R, Müller DJ, Taylor VH, Kennedy SH, Frey BN. Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report. NEUROIMAGE-CLINICAL 2021; 32:102864. [PMID: 34710675 PMCID: PMC8556529 DOI: 10.1016/j.nicl.2021.102864] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 10/08/2021] [Accepted: 10/18/2021] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response. METHODS Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data. RESULTS Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD. CONCLUSION We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.
Collapse
Affiliation(s)
- Pedro L Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Jee Su Suh
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Nikita Nogovitsyn
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stephen C Strother
- Rotman Research Institute, Baycrest, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, ON, Canada
| | | | - Luciano Minuzzi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada
| | - Roberto B Sassi
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Queen's University, and Providence Care, Kingston, ON, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Valerie H Taylor
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Sidney H Kennedy
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Centre for Mental Health, University Health Network, Toronto, ON, Canada; Krembil Research Institute, University Health Network, Toronto, ON, Canada; Centre for Depression and Suicide Studies, and Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Benicio N Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Women's Health Concerns Clinic, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
| | | |
Collapse
|
168
|
Brain-predicted age difference is associated with cognitive processing in later-life. Neurobiol Aging 2021; 109:195-203. [PMID: 34775210 DOI: 10.1016/j.neurobiolaging.2021.10.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 01/08/2023]
Abstract
Brain age is a neuroimaging-based biomarker of aging. This study examined whether the difference between brain age and chronological age (brain-PAD) is associated with cognitive function at baseline and longitudinally. Participants were relatively healthy, predominantly white community-dwelling older adults (n = 531, aged ≥70 years), with high educational attainment (61% ≥12 years) and socioeconomic status (59% ≥75th percentile). Brain age was estimated from T1-weighted magnetic resonance images using an algorithm by Cole et al., 2018. After controlling for age, gender, education, depression and body mass index, brain-PAD was negatively associated with psychomotor speed (Symbol Digit Modalities Test) at baseline (Bonferroni p < 0.006), but was not associated with baseline verbal fluency (Controlled Oral Word Association Test), delayed recall (Hopkins Learning Test Revised), or general cognitive status (Mini-Mental State Examination). Baseline brain-PAD was not associated with 3-year change in cognition (Bonferroni p > 0.006). These findings indicate that even in relatively healthy older people, accelerated brain aging is associated with worse psychomotor speed, but future longitudinal research into changes in brain-PAD is needed.
Collapse
|
169
|
Brain age estimation at tract group level and its association with daily life measures, cardiac risk factors and genetic variants. Sci Rep 2021; 11:20563. [PMID: 34663856 PMCID: PMC8523533 DOI: 10.1038/s41598-021-99153-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 09/14/2021] [Indexed: 11/08/2022] Open
Abstract
Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.
Collapse
|
170
|
Structural and functional motor-network disruptions predict selective action-concept deficits: Evidence from frontal lobe epilepsy. Cortex 2021; 144:43-55. [PMID: 34637999 DOI: 10.1016/j.cortex.2021.08.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 07/12/2021] [Accepted: 08/05/2021] [Indexed: 12/22/2022]
Abstract
Built on neurodegenerative lesions models, the disrupted motor grounding hypothesis (DMGH) posits that motor-system alterations selectively impair action comprehension. However, major doubts remain concerning the dissociability, neural signatures, and etiological generalizability of such deficits. Few studies have compared action-concept outcomes between disorders affecting and sparing motor circuitry, and none has examined their multimodal network predictors via data-driven approaches. Here, we first assessed action- and object-concept processing in patients with frontal lobe epilepsy (FLE), patients with posterior cortex epilepsy (PCE), and healthy controls. Then, we examined structural and functional network signatures via diffusion tensor imaging and resting-state connectivity measures. Finally, we used these measures to predict behavioral performance with an XGBoost machine learning regression algorithm. Relative to controls, FLE (but not PCE) patients exhibited selective action-concept deficits together with structural and functional abnormalities along motor networks. The XGBoost model reached a significantly large effect size only for action-concept outcomes in FLE, mainly predicted by structural (cortico-spinal tract, anterior thalamic radiation, uncinate fasciculus) and functional (M1-parietal/supramarginal connectivity) motor networks. These results extend the DMGH, suggesting that action-concept deficits are dissociable markers of frontal/motor (relative to posterior) disruptions, directly related to the structural and functional integrity of motor networks, and traceable beyond canonical movement disorders.
Collapse
|
171
|
Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine 2021; 72:103600. [PMID: 34614461 PMCID: PMC8498228 DOI: 10.1016/j.ebiom.2021.103600] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
Collapse
Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK; Department of General Psychology, University of Padua, Italy
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
| |
Collapse
|
172
|
Eickhoff CR, Hoffstaedter F, Caspers J, Reetz K, Mathys C, Dogan I, Amunts K, Schnitzler A, Eickhoff SB. Advanced brain ageing in Parkinson's disease is related to disease duration and individual impairment. Brain Commun 2021; 3:fcab191. [PMID: 34541531 PMCID: PMC8445399 DOI: 10.1093/braincomms/fcab191] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/22/2021] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
Machine learning can reliably predict individual age from MRI data, revealing that patients with neurodegenerative disorders show an elevated biological age. A surprising gap in the literature, however, pertains to Parkinson's disease. Here, we evaluate brain age in two cohorts of Parkinson's patients and investigated the relationship between individual brain age and clinical characteristics. We assessed 372 patients with idiopathic Parkinson's disease, newly diagnosed cases from the Parkinson's Progression Marker Initiative database and a more chronic local sample, as well as age- and sex-matched healthy controls. Following morphometric preprocessing and atlas-based compression, individual brain age was predicted using a multivariate machine learning model trained on an independent, multi-site reference sample. Across cohorts, healthy controls were well predicted with a mean error of 4.4 years. In turn, Parkinson's patients showed a significant (controlling for age, gender and site) increase in brain age of ∼3 years. While this effect was already present in the newly diagnosed sample, advanced biological age was significantly related to disease duration as well as worse cognitive and motor impairment. While biological age is increased in patients with Parkinson's disease, the effect is at the lower end of what is found for other neurological and psychiatric disorders. We argue that this may reflect a heterochronicity between forebrain atrophy and small but behaviourally salient midbrain pathology. Finally, we point to the need to disentangle physiological ageing trajectories, lifestyle effects and core pathological changes.
Collapse
Affiliation(s)
- Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Julian Caspers
- Institute of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kathrin Reetz
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | | | - Imis Dogan
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Department of Neurology, University Hospital RWTH Aachen, Aachen, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7, INM-11), Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| |
Collapse
|
173
|
Drobinin V, Van Gestel H, Helmick CA, Schmidt MH, Bowen CV, Uher R. The developmental brain age is associated with adversity, depression, and functional outcomes among adolescents. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:406-414. [PMID: 34555562 DOI: 10.1016/j.bpsc.2021.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 09/03/2021] [Accepted: 09/07/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Most psychiatric disorders emerge in the second decade of life. In the present study we examined whether environmental adversity, developmental antecedents, major depressive disorder (MDD), and functional impairment correlate with deviation from normative brain development in adolescence. METHODS We trained a brain age prediction model using 189 structural MRI brain features in 1299 typically developing adolescents (age range 9-19 years old, M = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (M = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (M = 13.6, SD = 2.82). We tested associations between the brain-age-gap and adversity, early antecedents, depression, and functional impairment. RESULTS We accurately predicted chronological age in typically developing adolescents (mean absolute error (MAE) = 1.53 years). The model generalized to the validation set (MAE = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (MAE = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intra class correlation = 0.94). Experience of environmental advertises (β = 0.18, 95% CI [0.04, 0.31], p = 0.02), diagnosis of MDD (β = 0.61, 95% CI [0.23, 0.99], p = 0.01) and functional impairment (β = 0.16, 95% CI [0.05, 0.27], p = 0.01) were associated with a positive brain-age-gap. CONCLUSIONS Risk factors, diagnosis, and impact of mental illness are associated with an older appearing brain during development.
Collapse
Affiliation(s)
| | | | - Carl A Helmick
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Matthias H Schmidt
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Chris V Bowen
- Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada.
| |
Collapse
|
174
|
Gonneaud J, Baria AT, Pichet Binette A, Gordon BA, Chhatwal JP, Cruchaga C, Jucker M, Levin J, Salloway S, Farlow M, Gauthier S, Benzinger TLS, Morris JC, Bateman RJ, Breitner JCS, Poirier J, Vachon-Presseau E, Villeneuve S. Accelerated functional brain aging in pre-clinical familial Alzheimer's disease. Nat Commun 2021; 12:5346. [PMID: 34504080 PMCID: PMC8429427 DOI: 10.1038/s41467-021-25492-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 08/06/2021] [Indexed: 01/02/2023] Open
Abstract
Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18-94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
Collapse
Affiliation(s)
- Julie Gonneaud
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | - Alex T Baria
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Alexa Pichet Binette
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Brian A Gordon
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jasmeer P Chhatwal
- Brigham and Women's Hospital-Massachusetts General Hospital, Boston, MA, USA
| | - Carlos Cruchaga
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Mathias Jucker
- Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Johannes Levin
- Ludwig-Maximilians-Universität München, German Center for Neurodegenerative Diseases and Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | | | - Martin Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Serge Gauthier
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C S Breitner
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Judes Poirier
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Etienne Vachon-Presseau
- Department of Anesthesia, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Faculty of Dentistry, McGill University, Montreal, QC, Canada
- Alan Edwards Centre for Research on Pain, McGill University, Montreal, QC, Canada
| | - Sylvia Villeneuve
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| |
Collapse
|
175
|
Brain age and Alzheimer's-like atrophy are domain-specific predictors of cognitive impairment in Parkinson's disease. Neurobiol Aging 2021; 109:31-42. [PMID: 34649002 DOI: 10.1016/j.neurobiolaging.2021.08.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 08/27/2021] [Accepted: 08/31/2021] [Indexed: 11/20/2022]
Abstract
Recently, it was shown that patients with Parkinson's disease (PD) who exhibit an "Alzheimer's disease (AD)-like" pattern of brain atrophy are at greater risk for future cognitive decline. This study aimed to investigate whether this association is domain-specific and whether atrophy associated with brain aging also relates to cognitive impairment in PD. SPARE-AD, an MRI index capturing AD-like atrophy, and atrophy-based estimates of brain age were computed from longitudinal structural imaging data of 178 PD patients and 84 healthy subjects from the LANDSCAPE cohort. All patients underwent an extensive neuropsychological test battery. Patients diagnosed with mild cognitive impairment or dementia were found to have higher SPARE-AD scores as compared to patients with normal cognition and healthy controls. All patient groups showed increased brain age. SPARE-AD predicted impairment in memory, language and executive functions, whereas advanced brain age was associated with deficits in attention and working memory. Data suggest that SPARE-AD and brain age are differentially related to domain-specific cognitive decline in PD. The underlying pathomechanisms remain to be determined.
Collapse
|
176
|
Han LKM, Dinga R, Hahn T, Ching CRK, Eyler LT, Aftanas L, Aghajani M, Aleman A, Baune BT, Berger K, Brak I, Filho GB, Carballedo A, Connolly CG, Couvy-Duchesne B, Cullen KR, Dannlowski U, Davey CG, Dima D, Duran FLS, Enneking V, Filimonova E, Frenzel S, Frodl T, Fu CHY, Godlewska BR, Gotlib IH, Grabe HJ, Groenewold NA, Grotegerd D, Gruber O, Hall GB, Harrison BJ, Hatton SN, Hermesdorf M, Hickie IB, Ho TC, Hosten N, Jansen A, Kähler C, Kircher T, Klimes-Dougan B, Krämer B, Krug A, Lagopoulos J, Leenings R, MacMaster FP, MacQueen G, McIntosh A, McLellan Q, McMahon KL, Medland SE, Mueller BA, Mwangi B, Osipov E, Portella MJ, Pozzi E, Reneman L, Repple J, Rosa PGP, Sacchet MD, Sämann PG, Schnell K, Schrantee A, Simulionyte E, Soares JC, Sommer J, Stein DJ, Steinsträter O, Strike LT, Thomopoulos SI, van Tol MJ, Veer IM, Vermeiren RRJM, Walter H, van der Wee NJA, van der Werff SJA, Whalley H, Winter NR, Wittfeld K, Wright MJ, Wu MJ, Völzke H, Yang TT, Zannias V, de Zubicaray GI, Zunta-Soares GB, Abé C, Alda M, Andreassen OA, Bøen E, Bonnin CM, Canales-Rodriguez EJ, Cannon D, Caseras X, Chaim-Avancini TM, Elvsåshagen T, Favre P, Foley SF, Fullerton JM, Goikolea JM, Haarman BCM, Hajek T, Henry C, Houenou J, Howells FM, Ingvar M, Kuplicki R, Lafer B, Landén M, Machado-Vieira R, Malt UF, McDonald C, Mitchell PB, Nabulsi L, Otaduy MCG, Overs BJ, Polosan M, Pomarol-Clotet E, Radua J, Rive MM, Roberts G, Ruhe HG, Salvador R, Sarró S, Satterthwaite TD, Savitz J, Schene AH, Schofield PR, Serpa MH, Sim K, Soeiro-de-Souza MG, Sutherland AN, Temmingh HS, Timmons GM, Uhlmann A, Vieta E, Wolf DH, Zanetti MV, Jahanshad N, Thompson PM, Veltman DJ, Penninx BWJH, Marquand AF, Cole JH, Schmaal L. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Mol Psychiatry 2021; 26:5124-5139. [PMID: 32424236 PMCID: PMC8589647 DOI: 10.1038/s41380-020-0754-0] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 04/01/2020] [Accepted: 04/23/2020] [Indexed: 01/15/2023]
Abstract
Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.
Collapse
Grants
- RF1 AG041915 NIA NIH HHS
- G0802594 Medical Research Council
- R01 MH083968 NIMH NIH HHS
- MR/L010305/1 Medical Research Council
- R01 MH116147 NIMH NIH HHS
- T32 AG058507 NIA NIH HHS
- R01 HD050735 NICHD NIH HHS
- R21 MH113871 NIMH NIH HHS
- T35 AG026757 NIA NIH HHS
- R56 AG058854 NIA NIH HHS
- K23 MH090421 NIMH NIH HHS
- Wellcome Trust
- R61 AT009864 NCCIH NIH HHS
- P41 EB015922 NIBIB NIH HHS
- P20 GM121312 NIGMS NIH HHS
- R37 MH101495 NIMH NIH HHS
- P41 RR008079 NCRR NIH HHS
- T32 MH073526 NIMH NIH HHS
- 104036/Z/14/Z Wellcome Trust
- UL1 TR001872 NCATS NIH HHS
- Department of Health
- U54 EB020403 NIBIB NIH HHS
- R01 MH117601 NIMH NIH HHS
- MR/R024790/2 Medical Research Council
- K01 MH117442 NIMH NIH HHS
- R01 MH085734 NIMH NIH HHS
- R21 AT009173 NCCIH NIH HHS
- RF1 AG051710 NIA NIH HHS
- R01 AG059874 NIA NIH HHS
- CC was supported by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- Russian Science Foundation (RSF)
- The study was supported by a grant from the German Federal Ministry of Education and Research (BMBF; grant FKZ-01ER0816 and FKZ-01ER1506)
- Dr. Busatto was supported by the funding agencies FAPESP and CNPq, Brazil
- Department of Health | National Health and Medical Research Council (NHMRC)
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- This study was funded by National Health and Medical Research Council of Australia (NHMRC) Project Grants 1064643 (Principal Investigator BJH) and 1024570 (Principal Investigator CGD).
- Science Foundation Ireland (SFI)
- This work was supported by NIH grant R37 MH101495
- The Study of Health in Pomerania (SHIP) is part of the Community Medicine Research net (CMR) (http://www.medizin.uni-greifswald.de/icm) of the University Medicine Greifswald, which is supported by the German Federal State of Mecklenburg- West Pomerania. MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. This study was further supported by the EU-JPND Funding for BRIDGET (FKZ:01ED1615).
- Gratama Foundation, the Netherlands (2012/35 to NG)
- This work was partially supported by the Deutsche Forschungsgemeinschaft (DFG) via grants to OG (GR1950/5-1 and GR1950/10-1).
- This study was supported by the following National Health and Medical Research Council funding sources: Programme Grant (no. 566529), Centres of Clinical Research Excellence Grant (no. 264611), Australia Fellowship (no. 511921) and Clinical Research Fellowship (no. 402864).
- This study was funded by the National Institute of Mental health grant K23MH090421 (D. Cullen) and Biotechnology Research Center grant P41RR008079 (Center for Magnetic Resonance Research), the National Alliance for Research on Schizophrenia and Depression, the University of Minnesota Graduate School, and the Minnesota Medical Foundation. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute.
- This work was funded by the German Research Foundation (DFG, grant FOR2107 KR 3822/7-2 to AK; FOR2107 KI 588/14-2 to TK and FOR2107 JA 1890/7-2 to AJ)
- The research leading to these results was supported by IMAGEMEND, which received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 602450. This paper reflects only the author’s views and the European Union is not liable for any use that may be made of the information contained therein. This work was also supported by a Wellcome Trust Strategic Award 104036/Z/14/Z
- The QTIM dataset was supported by the Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064) and US National Institute of Child Health and Human Development(RO1HD050735)
- MJP was funded by Ministerio de Ciencia e Innovación of Spanish Government (ISCIII) through a "Miguel Servet II" (CP16/00020)
- Jair C. Soares supported by the Pat Rutherford Chair in Psychiatry, UTHealth. Jair Soares has received research support from Allergan, Pfizer, Johnson & Johnson, Alquermes and COMPASS. He is a member of the speakers’ bureaus for Sunovion and Sanofi and he is a consultant for Johnson & Johnson.
- The QTIM dataset was supported by the Australian National Health and Medical Research Council (Project Grants No. 496682 and 1009064) and US National Institute of Child Health and Human Development (RO1HD050735)
- SIT was supported in part by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- The CODE cohort was collected from studies funded by Lundbeck and the German Research Foundation (WA 1539/4-1, SCHN 1205/3-1, SCHR443/11-1)
- Canadian Institutes of Health Research (142255)
- Fundet by Research Council of Norway (223273, 248778, 273291), NIH (ENIGMA grants)
- Funded by the South-Eastern Norway Regional Health Authority and a research grant from Mrs. Throne-Holst.
- This work was supported by the Health Research Board, Ireland and the Irish Research Council
- The Cardiff dataset was supported through a 2010 NARSAD Young Investigator Award (ref: 17319) to Dr. Xavier Caseras
- This work was supported by the FRM (Fondation pour la recherche Biomédicale) "Bio-informatique pour la biologie" 2014 grant
- Canadian Institutes of Health Research (103703, 106469), Nova Scotia Health Research Foundation, Dalhousie Clinical Research Scholarship to T. Hajek, Brain & Behavior Research Foundation (formerly NARSAD) 2007 Young Investigator and 2015 Independent Investigator Awards to T. Hajek
- This work was supported by the University Research Council of the University of Cape Town and the National Research Foundation of South Africa.
- Australian NHMRC Program Grant 1037196 and Project Grants 1063960 and 1066177.
- This work was supported by research grants from Grenoble University Hospital
- This work was supported by the Generalitat de Catalunya (2014 SGR 1573) and Instituto de Salud Carlos III (CPII16/00018) and (PI14/01151 and PI14/01148).
- The DIADE dataset was suported by a ZonMW OOG 2007 grant (100-002-034). HG Ruhe was supported by a ZonMW VENI grant (016.126.059)
- JS is supported by the National Institute of General Medical Sciences (P20GM121312) and the National Insitute of Mental Health (R21MH113871)
- Dr. Mauricio was supported by the funding agencies CAPES, Brazil
- This study was supported by R01MH083968, Desert-Pacific Mental Illness Research Education and Clinical Center, and the US National Science Foundation (Science Gateways Community Institutes; XSEDE).
- GT's work was supported by the National Institutes of Health, Grant T35 AG026757/AG/NIA and the University of California San Diego, Stein Institute for Research on Aging
- "EV thanks the support of the Spanish Ministry of Science, Innovation and Universities (PI15/00283) integrated into the Plan Nacional de I+D+I y cofinanciado por el ISCIII-Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER); CIBERSAM; and the Comissionat per a Universitats i Recerca del DIUE de la Generalitat de Catalunya to the Bipolar Disorders Group (2017 SGR 1365) and the project SLT006/17/00357, from PERIS 2016-2020 (Departament de Salut). CERCA Programme/Generalitat de Catalunya. "
- Dr. Zanetti was supported by FAPESP, Brazil (grant no. 2013/03905-4).
- NIH grants R01 MH117601, R01 AG059874, U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- PT was supported in part by NIH grants U54 EB020403, RF1 AG041915, RF1AG051710, P41EB015922, R01MH116147, and R56AG058854
- Dr Cole is funded by a UKRI Innovation Fellowship
- This work was supported by NIH grants U54 EB020403 and R01 MH116147. LS is supported by a NHMRC Career Development Fellowship (1140764).
Collapse
Affiliation(s)
- Laura K M Han
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & GGZinGeest, Amsterdam, The Netherlands.
| | - Richard Dinga
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & GGZinGeest, Amsterdam, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Tim Hahn
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lisa T Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - Lyubomir Aftanas
- FSSBI "Scientific Research Institute of Physiology & Basic Medicine", Laboratory of Affective, Cognitive & Translational Neuroscience, Novosibirsk, Russia
- Department of Neuroscience, Novosibirsk State University, Novosibirsk, Russia
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & GGZinGeest, Amsterdam, The Netherlands
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Clinical and Developmental Neuropsychology, University of Groningen, Groningen, The Netherlands
| | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, VIC, Australia
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Ivan Brak
- FSSBI "Scientific Research Institute of Physiology & Basic Medicine", Laboratory of Affective, Cognitive & Translational Neuroscience, Novosibirsk, Russia
- Laboratory of Experimental & Translational Neuroscience, Novosibirsk State University, Novosibirsk, Russia
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Angela Carballedo
- Department for Psychiatry, Trinity College Dublin, Dublin, Ireland
- North Dublin Mental Health Services, Dublin, Ireland
| | - Colm G Connolly
- Department of Biomedical Sciences, Florida State University, Tallahassee, FL, USA
| | | | - Kathryn R Cullen
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Christopher G Davey
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Danai Dima
- Department of Psychology, School of Arts and Social Sciences, City, University of London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
| | - Fabio L S Duran
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Verena Enneking
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Elena Filimonova
- FSSBI "Scientific Research Institute of Physiology & Basic Medicine", Laboratory of Affective, Cognitive & Translational Neuroscience, Novosibirsk, Russia
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Thomas Frodl
- Department for Psychiatry, Trinity College Dublin, Dublin, Ireland
- Department of Psychiatry and Psychotherapy, Otto von Guericke University (OVGU), Magdeburg, Germany
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany
| | - Cynthia H Y Fu
- Centre for Affective Disorders, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- School of Psychology, University of East London, London, UK
| | | | - Ian H Gotlib
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center of Neurodegenerative Diseases (DZNE) Site Rostock/Greifswald, Greifswald, Germany
| | - Nynke A Groenewold
- Interdisciplinary Center Psychopathology and Emotion regulation (ICPE), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | | | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Ben J Harrison
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Sean N Hatton
- Youth Mental Health Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Department of Neuroscience, University of California San Diego, San Diego, CA, USA
| | - Marco Hermesdorf
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Ian B Hickie
- Youth Mental Health Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Tiffany C Ho
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Psychiatry & Behavioral Sciences, Standord University, Stanford, CA, USA
| | - Norbert Hosten
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Andreas Jansen
- Department of Psychiatry, Philipps-University Marburg, Marburg, Germany
| | - Claas Kähler
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry, Philipps-University Marburg, Marburg, Germany
| | | | - Bernd Krämer
- Section for Experimental Psychopathology and Neuroimaging, Department of Psychiatry, University of Heidelberg, Heidelberg, Germany
| | - Axel Krug
- Department of Psychiatry, Philipps-University Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jim Lagopoulos
- Youth Mental Health Team, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
- Sunshine Coast Mind and Neuroscience Institute, University of the Sunshine Coast QLD, Sippy Downs, QLD, Australia
| | - Ramona Leenings
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Frank P MacMaster
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada
- Addictions and Mental Health Strategic Clinical Network, Calgary, AB, Canada
| | - Glenda MacQueen
- Department of Psychiatry, University of Calgary, Calgary, AB, Canada
| | - Andrew McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Quinn McLellan
- Departments of Psychiatry and Pediatrics, University of Calgary, Calgary, AB, Canada
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
| | - Katie L McMahon
- School of Clinical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Instititute, Brisbane, QLD, Australia
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - Benson Mwangi
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Evgeny Osipov
- Laboratory of Experimental & Translational Neuroscience, Novosibirsk State University, Novosibirsk, Russia
| | - Maria J Portella
- Institut d'Investigació Biomèdica Sant Pau, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red de Salud Mental, Cibersam, Spain
| | - Elena Pozzi
- Department of Psychiatry and Behavioral Sciences, University of Minnesota Medical School, Minneapolis, Minnesota, USA
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, VIC, Australia
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
| | - Jonathan Repple
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Pedro G P Rosa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Matthew D Sacchet
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | | | - Knut Schnell
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
- Department of Psychiatry and Psychotherapy, Asklepios Fachklinikum Göttingen, Göttingen, Germany
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
| | - Egle Simulionyte
- Department of Psychology, Neuroscience & Behaviour, McMaster University, Hamilton, ON, Canada
| | - Jair C Soares
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jens Sommer
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Dan J Stein
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- SA MRC Unit on Risk and Resilience, University of Cape Town, Cape Town, South Africa
| | - Olaf Steinsträter
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Lachlan T Strike
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marie-José van Tol
- Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Ilya M Veer
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Robert R J M Vermeiren
- Department of Child Psychiatry, University Medical Center, Leiden, The Netherlands
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Nic J A van der Wee
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven J A van der Werff
- Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Heather Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - Nils R Winter
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center of Neurodegenerative Diseases (DZNE) Site Rostock/Greifswald, Greifswald, Germany
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Mon-Ju Wu
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Tony T Yang
- Department of Psychiatry, Division of Child and Adolescent Psychiatry, UCSF School of Medicine, UCSF, San Francisco, CA, USA
| | | | - Greig I de Zubicaray
- Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
- Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Giovana B Zunta-Soares
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Christoph Abé
- Department of Clinical Neuroscience, Osher Center, Karolinska Institutet, Stockholm, Sweden
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Erlend Bøen
- Clinic for Mental Health and Dependency, C-L psychiatry and Psychosomatic Unit, Oslo University Hospital, Oslo, Norway
| | - Caterina M Bonnin
- Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | | | - Dara Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33, Galway, Ireland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - Tiffany M Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Torbjørn Elvsåshagen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Pauline Favre
- UNIACT, Psychiatry Team, Neurospin, Atomic Energy Commission, Gif-Sur-Yvette, France
- Translational Psychiatry Team, Pôle de psychiatrie, Faculté de Médecine, APHP, Hôpitaux Universitaires Mondor, INSERM, U955, Créteil, France
| | - Sonya F Foley
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
| | - Janice M Fullerton
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Jose M Goikolea
- Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Bartholomeus C M Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Tomas Hajek
- Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
| | - Chantal Henry
- Université de Paris, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neuroscience, F-75014, Paris, France
| | - Josselin Houenou
- UNIACT, Psychiatry Team, Neurospin, Atomic Energy Commission, Gif-Sur-Yvette, France
- Translational Psychiatry Team, Pôle de psychiatrie, Faculté de Médecine, APHP, Hôpitaux Universitaires Mondor, INSERM, U955, Créteil, France
| | - Fleur M Howells
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Martin Ingvar
- Department of Clinical Neuroscience, Osher Center, Karolinska Institutet, Stockholm, Sweden
| | | | - Beny Lafer
- Department of Psychiatry, School of Medicine, University of Sao Paulo (FMUSP), Sao Paulo, Brazil
| | - Mikael Landén
- Department of Clinical Neuroscience, Osher Center, Karolinska Institutet, Stockholm, Sweden
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Rodrigo Machado-Vieira
- Department of Psychiatry, School of Medicine, University of Sao Paulo (FMUSP), Sao Paulo, Brazil
| | - Ulrik F Malt
- Department of Clinical Neuroscience, University of Oslo, Oslo, Norway
- Clinic for Psychiatry and Dependency, C-L psychiatry and Psychosomatic Unit, Oslo University Hospital, Oslo, Norway
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33, Galway, Ireland
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Kingsford, Sydney, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, Sydney, NSW, Australia
| | - Leila Nabulsi
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, H91 TK33, Galway, Ireland
| | - Maria Concepcion Garcia Otaduy
- Instituto de Radiologia, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Bronwyn J Overs
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
| | - Mircea Polosan
- Department of Psychiatry and Neurology, CHU Grenoble Alpes, Université Grenoble Alpes, F-38000, Grenoble, France
- Inserm 1216, Grenoble Institut des Neurosciences, GIN, F-38000, Grenoble, France
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Catalonia, Spain
| | - Joaquim Radua
- Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Maria M Rive
- Department of Psychiatry, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
| | - Gloria Roberts
- School of Psychiatry, University of New South Wales, Kingsford, Sydney, NSW, Australia
- Black Dog Institute, Prince of Wales Hospital, Randwick, Sydney, NSW, Australia
| | - Henricus G Ruhe
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Catalonia, Spain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research Foundation, CIBERSAM, Barcelona, Catalonia, Spain
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvannia Perelman School of Medicine, Philadelphia, PA, USA
| | - Jonathan Savitz
- Laureate Institute for Brain Research, Tulsa, OK, USA
- Oxley College of Health Sciences, The University of Tulsa, Tulsa, OK, USA
| | - Aart H Schene
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter R Schofield
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
- School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mauricio H Serpa
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
| | - Kang Sim
- West Region and Research Division, Institute of Mental Health, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | | | - Ashley N Sutherland
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - Henk S Temmingh
- Section for Experimental Psychopathology and Neuroimaging, Department of Psychiatry, University of Heidelberg, Heidelberg, Germany
- Valkenberg Psychiatric Hospital, Cape Town, South Africa
| | - Garrett M Timmons
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - Anne Uhlmann
- Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Eduard Vieta
- Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Catalonia, Spain
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvannia Perelman School of Medicine, Philadelphia, PA, USA
| | - Marcus V Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM-21), Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, Brazil
- Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, Sao Paulo, SP, Brazil
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & GGZinGeest, Amsterdam, The Netherlands
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam Public Health and Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit & GGZinGeest, Amsterdam, The Netherlands
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - James H Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College, London, UK
- 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
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| |
Collapse
|
177
|
Voldsbekk I, Barth C, Maximov II, Kaufmann T, Beck D, Richard G, Moberget T, Westlye LT, de Lange AG. A history of previous childbirths is linked to women's white matter brain age in midlife and older age. Hum Brain Mapp 2021; 42:4372-4386. [PMID: 34118094 PMCID: PMC8356991 DOI: 10.1002/hbm.25553] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 05/12/2021] [Accepted: 05/27/2021] [Indexed: 02/06/2023] Open
Abstract
Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter and white matter ageing trajectories later in life. Utilising global and regional brain age prediction based on multi-shell diffusion-weighted imaging data, we investigated the association between previous childbirths and white matter brain age in 8,895 women in the UK Biobank cohort (age range = 54-81 years). The results showed that number of previous childbirths was negatively associated with white matter brain age, potentially indicating a protective effect of parity on white matter later in life. Both global white matter and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global white matter association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain white matter characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence women's white matter trajectories across the lifespan.
Collapse
Affiliation(s)
- Irene Voldsbekk
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Claudia Barth
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of Psychiatry and PsychotherapyUniversity of TübingenTübingenGermany
| | - Dani Beck
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- Sunnaas Rehabilitation Hospital HTOsloNorway
| | - Genevieve Richard
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - Torgeir Moberget
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- NORMENT, Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| |
Collapse
|
178
|
Cheng W, Frei O, van der Meer D, Wang Y, O’Connell KS, Chu Y, Bahrami S, Shadrin AA, Alnæs D, Hindley GFL, Lin A, Karadag N, Fan CC, Westlye LT, Kaufmann T, Molden E, Dale AM, Djurovic S, Smeland OB, Andreassen OA. Genetic Association Between Schizophrenia and Cortical Brain Surface Area and Thickness. JAMA Psychiatry 2021; 78:1020-1030. [PMID: 34160554 PMCID: PMC8223140 DOI: 10.1001/jamapsychiatry.2021.1435] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 04/28/2021] [Indexed: 01/03/2023]
Abstract
Importance Schizophrenia is a complex heritable disorder associated with many genetic variants, each with a small effect. While cortical differences between patients with schizophrenia and healthy controls are consistently reported, the underlying molecular mechanisms remain elusive. Objective To investigate the extent of shared genetic architecture between schizophrenia and brain cortical surface area (SA) and thickness (TH) and to identify shared genomic loci. Design, Setting, and Participants Independent genome-wide association study data on schizophrenia (Psychiatric Genomics Consortium and CLOZUK: n = 105 318) and SA and TH (UK Biobank: n = 33 735) were obtained. The extent of polygenic overlap was investigated using MiXeR. The specific shared genomic loci were identified by conditional/conjunctional false discovery rate analysis and were further examined in 3 independent cohorts. Data were collected from December 2019 to February 2021, and data analysis was performed from May 2020 to February 2021. Main Outcomes and Measures The primary outcomes were estimated fractions of polygenic overlap between schizophrenia, total SA, and average TH and a list of functionally characterized shared genomic loci. Results Based on genome-wide association study data from 139 053 participants, MiXeR estimated schizophrenia to be more polygenic (9703 single-nucleotide variants [SNVs]) than total SA (2101 SNVs) and average TH (1363 SNVs). Most SNVs associated with total SA (1966 of 2101 [93.6%]) and average TH (1322 of 1363 [97.0%]) may be associated with the development of schizophrenia. Subsequent conjunctional false discovery rate analysis identified 44 and 23 schizophrenia risk loci shared with total SA and average TH, respectively. The SNV associations of shared loci between schizophrenia and total SA revealed en masse concordant association between the discovery and independent cohorts. After removing high linkage disequilibrium regions, such as the major histocompatibility complex region, the shared loci were enriched in immunologic signature gene sets. Polygenic overlap and shared loci between schizophrenia and schizophrenia-associated regions of interest for SA (superior frontal and middle temporal gyri) and for TH (superior temporal, inferior temporal, and superior frontal gyri) were also identified. Conclusions and Relevance This study demonstrated shared genetic loci between cortical morphometry and schizophrenia, among which a subset are associated with immunity. These findings provide an insight into the complex genetic architecture and associated with schizophrenia.
Collapse
Affiliation(s)
- Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Yunpeng Wang
- Centre for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Kevin S. O’Connell
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Yunhan Chu
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Shahram Bahrami
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A. Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F. L. Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King’s College London, London, United Kingdom
| | - Aihua Lin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Naz Karadag
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Chun-Chieh Fan
- Population Neuroscience and Genetics Lab, University of California, San Diego, La Jolla
- Center for Human Development, University of California, San Diego, La Jolla
| | - Lars T. Westlye
- NORMENT, 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
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
- Department of Pharmacy, University of Oslo, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, San Diego, La Jolla
- Department of Psychiatry, University of California, San Diego, La Jolla
- Department of Neurosciences, University of California San Diego, La Jolla
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Olav B. Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| |
Collapse
|
179
|
Gialluisi A, Di Castelnuovo A, Costanzo S, Bonaccio M, Persichillo M, Magnacca S, De Curtis A, Cerletti C, Donati MB, de Gaetano G, Capobianco E, Iacoviello L. Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing. Eur J Epidemiol 2021; 37:35-48. [PMID: 34453631 DOI: 10.1007/s10654-021-00797-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 08/07/2021] [Indexed: 01/05/2023]
Abstract
Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.
Collapse
Affiliation(s)
- Alessandro Gialluisi
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy.
| | | | - Simona Costanzo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Marialaura Bonaccio
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Mariarosaria Persichillo
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | | | - Amalia De Curtis
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Chiara Cerletti
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Maria Benedetta Donati
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Giovanni de Gaetano
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
| | - Enrico Capobianco
- Institute of Data Science and Computing, University of Miami, Miami, FL, USA
| | - Licia Iacoviello
- Department of Epidemiology and Prevention, IRCCS Neuromed, Via dell´Elettronica, 86077, Pozzilli, Italy
- Department of Medicine and Surgery, Research Center in Epidemiology and Preventive Medicine (EPIMED), University of Insubria, Varese, Italy
| |
Collapse
|
180
|
Bonfanti L, Charvet CJ. Brain Plasticity in Humans and Model Systems: Advances, Challenges, and Future Directions. Int J Mol Sci 2021; 22:9358. [PMID: 34502267 PMCID: PMC8431131 DOI: 10.3390/ijms22179358] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/26/2021] [Accepted: 08/26/2021] [Indexed: 12/20/2022] Open
Abstract
Plasticity, and in particular, neurogenesis, is a promising target to treat and prevent a wide variety of diseases (e.g., epilepsy, stroke, dementia). There are different types of plasticity, which vary with age, brain region, and species. These observations stress the importance of defining plasticity along temporal and spatial dimensions. We review recent studies focused on brain plasticity across the lifespan and in different species. One main theme to emerge from this work is that plasticity declines with age but that we have yet to map these different forms of plasticity across species. As part of this effort, we discuss our recent progress aimed to identify corresponding ages across species, and how this information can be used to map temporal variation in plasticity from model systems to humans.
Collapse
Affiliation(s)
- Luca Bonfanti
- Department of Veterinary Sciences, University of Turin, Largo Braccini 2, 10095 Grugliasco, TO, Italy
- Neuroscience Institute Cavalieri Ottolenghi (NICO), Regione Gonzole 10, 10043 Orbassano, TO, Italy
| | | |
Collapse
|
181
|
Fisch L, Leenings R, Winter NR, Dannlowski U, Gaser C, Cole JH, Hahn T. Editorial: Predicting Chronological Age From Structural Neuroimaging: The Predictive Analytics Competition 2019. Front Psychiatry 2021; 12:710932. [PMID: 34421686 PMCID: PMC8374100 DOI: 10.3389/fpsyt.2021.710932] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 07/09/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Lukas Fisch
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Nils R. Winter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- Department of Neurology, Jena University Hospital, Jena, Germany
| | - James H. Cole
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Computational, Cognitive and Clinical Neuroimaging Laboratory, Department King's College, London, United Kingdom
| | - Tim Hahn
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| |
Collapse
|
182
|
de Lange AMG, Kaufmann T, Quintana DS, Winterton A, Andreassen OA, Westlye LT, Ebmeier KP. Prominent health problems, socioeconomic deprivation, and higher brain age in lonely and isolated individuals: A population-based study. Behav Brain Res 2021; 414:113510. [PMID: 34358570 DOI: 10.1016/j.bbr.2021.113510] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 07/09/2021] [Accepted: 07/31/2021] [Indexed: 01/12/2023]
Abstract
Loneliness is linked to increased risk for Alzheimer's disease, but little is known about factors potentially contributing to adverse brain health in lonely individuals. In this study, we used data from 24,867 UK Biobank participants to investigate risk factors related to loneliness and estimated brain age based on neuroimaging data. The results showed that on average, individuals who self-reported loneliness on a single yes/no item scored higher on neuroticism, depression, social isolation, and socioeconomic deprivation, performed less physical activity, and had higher BMI compared to individuals who did not report loneliness. In line with studies pointing to a genetic overlap of loneliness with neuroticism and depression, permutation feature importance ranked these factors as the most important for classifying lonely vs. not lonely individuals (ROC AUC = 0.83). While strongly linked to loneliness, neuroticism and depression were not associated with brain age estimates. Conversely, objective social isolation showed a main effect on brain age, and individuals reporting both loneliness and social isolation showed higher brain age relative to controls - as part of a prominent risk profile with elevated scores on socioeconomic deprivation and unhealthy lifestyle behaviours, in addition to neuroticism and depression. While longitudinal studies are required to determine causality, this finding may indicate that the combination of social isolation and a genetic predisposition for loneliness involves a risk for adverse brain health. Importantly, the results underline the complexity in associations between loneliness and adverse health outcomes, where observed risks likely depend on a combination of interlinked variables including genetic as well as social, behavioural, physical, and socioeconomic factors.
Collapse
Affiliation(s)
- Ann-Marie G de Lange
- Department of Psychiatry, University of Oxford, Oxford, UK; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Tübingen Center for Mental Health, Dept. of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Daniel S Quintana
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Adriano Winterton
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- 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
| | - Lars T Westlye
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | | |
Collapse
|
183
|
Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, Yang Y, Stein EA, Bradberry CW, Ben Hamed S, Jedema HP, Poirier C. Strengths and challenges of longitudinal non-human primate neuroimaging. Neuroimage 2021; 236:118009. [PMID: 33794361 PMCID: PMC8270888 DOI: 10.1016/j.neuroimage.2021.118009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 01/20/2023] Open
Abstract
Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.
Collapse
Affiliation(s)
- Xiaowei Song
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Pamela García-Saldivar
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Nathan Kindred
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, United Kingdom
| | - Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille/CNRS, Institut Language, Communication and the Brain 13331 Marseille, France
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Charles W Bradberry
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France
| | - Hank P Jedema
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA.
| | - Colline Poirier
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom.
| |
Collapse
|
184
|
He S, Pereira D, David Perez J, Gollub RL, Murphy SN, Prabhu S, Pienaar R, Robertson RL, Ellen Grant P, Ou Y. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Med Image Anal 2021; 72:102091. [PMID: 34038818 PMCID: PMC8316301 DOI: 10.1016/j.media.2021.102091] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/10/2021] [Accepted: 04/14/2021] [Indexed: 12/31/2022]
Abstract
Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.
Collapse
Affiliation(s)
- Sheng He
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Diana Pereira
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Juan David Perez
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Randy L Gollub
- Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA, USA
| | - Shawn N Murphy
- Massachusetts General Hospital and Harvard Medical School, 55 Fruit St., Boston, MA, USA
| | - Sanjay Prabhu
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Rudolph Pienaar
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Richard L Robertson
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - P Ellen Grant
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA
| | - Yangming Ou
- Boston Children's Hospital and Harvard Medical School, 300 Longwood Ave., Boston, MA, USA.
| |
Collapse
|
185
|
Higgins-Chen AT, Thrush KL, Levine ME. Aging biomarkers and the brain. Semin Cell Dev Biol 2021; 116:180-193. [PMID: 33509689 PMCID: PMC8292153 DOI: 10.1016/j.semcdb.2021.01.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/18/2021] [Accepted: 01/19/2021] [Indexed: 12/15/2022]
Abstract
Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.
Collapse
Affiliation(s)
- Albert T Higgins-Chen
- Department of Psychiatry, Yale University School of Medicine, 300 George St, Suite 901, New Haven, CT 06511, USA.
| | - Kyra L Thrush
- Program in Computational Biology and Bioinformatics, Yale University, 300 George St, Suite 501, New Haven, CT 06511, USA.
| | - Morgan E Levine
- Department of Pathology, Yale University School of Medicine, 310 Cedar Street, Suite LH 315A, New Haven, CT 06520, USA.
| |
Collapse
|
186
|
Sanders AM, Richard G, Kolskår K, Ulrichsen KM, Kaufmann T, Alnæs D, Beck D, Dørum ES, de Lange AMG, Egil Nordvik J, Westlye LT. Linking objective measures of physical activity and capability with brain structure in healthy community dwelling older adults. Neuroimage Clin 2021; 31:102767. [PMID: 34330086 PMCID: PMC8329542 DOI: 10.1016/j.nicl.2021.102767] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/28/2022]
Abstract
Maintaining high levels of daily activity and physical capability have been proposed as important constituents to promote healthy brain and cognitive aging. Studies investigating the associations between brain health and physical activity in late life have, however, mainly been based on self-reported data or measures designed for clinical populations. In the current study, we examined cross-sectional associations between physical activity, recorded by an ankle-positioned accelerometer for seven days, physical capability (grip strength, postural control, and walking speed), and neuroimaging based surrogate markers of brain health in 122 healthy older adults aged 65-88 years. We used a multimodal brain imaging approach offering complementary structural MRI based indicators of brain health: global white matter fractional anisotropy (FA) and mean diffusivity (MD) based on diffusion tensor imaging, and subcortical and global brain age based on brain morphology inferred from T1-weighted MRI data. In addition, based on the results from the main analysis, follow-up regression analysis was performed to test for association between the volume of key subcortical regions of interest (hippocampus, caudate, thalamus and cerebellum) and daily steps, and a follow-up voxelwise analysis to test for associations between walking speed and FA across the white matter Tract-Based Spatial Statistics (TBSS) skeleton. The analyses revealed a significant association between global FA and walking speed, indicating higher white matter integrity in people with higher pace. Voxelwise analysis supported widespread significant associations. We also found a significant interaction between sex and subcortical brain age on number of daily steps, indicating younger-appearing brains in more physically active women, with no significant associations among men. These results provide insight into the intricate associations between different measures of brain and physical health in old age, and corroborate established public health advice promoting physical activity.
Collapse
Affiliation(s)
- Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway
| | - Knut Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Dani Beck
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Ann-Marie G de Lange
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | | | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway; KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| |
Collapse
|
187
|
Han LKM, Schnack HG, Brouwer RM, Veltman DJ, van der Wee NJA, van Tol MJ, Aghajani M, Penninx BWJH. Contributing factors to advanced brain aging in depression and anxiety disorders. Transl Psychiatry 2021; 11:402. [PMID: 34290222 PMCID: PMC8295382 DOI: 10.1038/s41398-021-01524-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 05/26/2021] [Accepted: 07/05/2021] [Indexed: 02/07/2023] Open
Abstract
Depression and anxiety are common and often comorbid mental health disorders that represent risk factors for aging-related conditions. Brain aging has shown to be more advanced in patients with major depressive disorder (MDD). Here, we extend prior work by investigating multivariate brain aging in patients with MDD, anxiety disorders, or both, and examine which factors contribute to older-appearing brains. Adults aged 18-57 years from the Netherlands Study of Depression and Anxiety underwent structural MRI. A pretrained brain-age prediction model based on >2000 samples from the ENIGMA consortium was applied to obtain brain-predicted age differences (brain PAD, predicted brain age minus chronological age) in 65 controls and 220 patients with current MDD and/or anxiety. Brain-PAD estimates were associated with clinical, somatic, lifestyle, and biological factors. After correcting for antidepressant use, brain PAD was significantly higher in MDD (+2.78 years, Cohen's d = 0.25, 95% CI -0.10-0.60) and anxiety patients (+2.91 years, Cohen's d = 0.27, 95% CI -0.08-0.61), compared with controls. There were no significant associations with lifestyle or biological stress systems. A multivariable model indicated unique contributions of higher severity of somatic depression symptoms (b = 4.21 years per unit increase on average sum score) and antidepressant use (-2.53 years) to brain PAD. Advanced brain aging in patients with MDD and anxiety was most strongly associated with somatic depressive symptomatology. We also present clinically relevant evidence for a potential neuroprotective antidepressant effect on the brain-PAD metric that requires follow-up in future research.
Collapse
Affiliation(s)
- Laura K. M. Han
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Hugo G. Schnack
- grid.7692.a0000000090126352Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Rachel M. Brouwer
- grid.7692.a0000000090126352Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Utrecht, Netherlands ,grid.12380.380000 0004 1754 9227Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, VU University, Amsterdam, The Netherlands
| | - Dick J. Veltman
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Nic J. A. van der Wee
- grid.5132.50000 0001 2312 1970Leiden Institute for Brain and Cognition, Leiden University, Leiden, The Netherlands ,grid.10419.3d0000000089452978Department of Psychiatry, University Medical Center Leiden, Leiden, The Netherlands
| | - Marie-José van Tol
- grid.4830.f0000 0004 0407 1981Cognitive Neuroscience Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Moji Aghajani
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands ,grid.5132.50000 0001 2312 1970Institute of Education & Child Studies, Section Forensic Family & Youth Care, Leiden University, Leiden, The Netherlands
| | - Brenda W. J. H. Penninx
- grid.484519.5Department of Psychiatry, Amsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
188
|
Luna A, Bernanke J, Kim K, Aw N, Dworkin JD, Cha J, Posner J. Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth. Hum Brain Mapp 2021; 42:4568-4579. [PMID: 34240783 PMCID: PMC8410534 DOI: 10.1002/hbm.25565] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 05/03/2021] [Accepted: 06/08/2021] [Indexed: 01/10/2023] Open
Abstract
Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held‐out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr = .012) and lower functioning on the Children's Global Assessment Scale (pcorr = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr = .008). Brain age prediction was more accurate using ComBat‐harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.
Collapse
Affiliation(s)
- Alex Luna
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Joel Bernanke
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Kakyeong Kim
- Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea
| | - Natalie Aw
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Jordan D Dworkin
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Jiook Cha
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,Department of Brain and Cognitive Sciences, Seoul National University, Seoul, South Korea.,Data Science Institute, Columbia University, New York, New York, USA.,Department of Psychology, Seoul National University, Seoul, South Korea
| | - Jonathan Posner
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| |
Collapse
|
189
|
Zhao Q, Liu Z, Adeli E, Pohl KM. Longitudinal self-supervised learning. Med Image Anal 2021; 71:102051. [PMID: 33882336 PMCID: PMC8184636 DOI: 10.1016/j.media.2021.102051] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 02/19/2021] [Accepted: 03/24/2021] [Indexed: 11/16/2022]
Abstract
Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to identify changes and consistencies across the multiple MRIs acquired of each individual over time. Specifically, we propose a new definition of disentanglement by formulating a multivariate mapping between factors (e.g., brain age) associated with an MRI and a latent image representation. Then, factors that evolve across acquisitions of longitudinal sequences are disentangled from that mapping by self-supervised learning in such a way that changes in a single factor induce change along one direction in the representation space. We implement this model, named Longitudinal Self-Supervised Learning (LSSL), via a standard autoencoding structure with a cosine loss to disentangle brain age from the image representation. We apply LSSL to two longitudinal neuroimaging studies to highlight its strength in extracting the brain-age information from MRI and revealing informative characteristics associated with neurodegenerative and neuropsychological disorders. Moreover, the representations learned by LSSL facilitate supervised classification by recording faster convergence and higher (or similar) prediction accuracy compared to several other representation learning techniques.
Collapse
Affiliation(s)
- Qingyu Zhao
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA
| | - Zixuan Liu
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Ehsan Adeli
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Kilian M Pohl
- Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, USA; Center for Biomedical Sciences, SRI International, Menlo Park, CA 95025, USA.
| |
Collapse
|
190
|
Lin CW, Chang LC, Ma T, Oh H, French B, Puralewski R, Mathews F, Fang Y, Lewis DA, Kennedy JL, Mueller D, Marshe VS, Jaffe A, Chen Q, Ursini G, Weinberger D, Newman AB, Lenze EJ, Nikolova YS, Tseng GC, Sibille E. Older molecular brain age in severe mental illness. Mol Psychiatry 2021; 26:3646-3656. [PMID: 32632206 PMCID: PMC7785531 DOI: 10.1038/s41380-020-0834-1] [Citation(s) in RCA: 16] [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: 06/27/2019] [Revised: 06/03/2020] [Accepted: 06/25/2020] [Indexed: 12/15/2022]
Abstract
Psychiatric disorders are associated with accelerated aging and enhanced risk for neurodegenerative disorders. Brain aging is associated with molecular, cellular, and structural changes that are robust on the group level, yet show substantial inter-individual variability. Here we assessed deviations in gene expression from normal age-dependent trajectories, and tested their validity as predictors of risk for major mental illnesses and neurodegenerative disorders. We performed large-scale gene expression and genotype analyses in postmortem samples of two frontal cortical brain regions from 214 control subjects aged 20-90 years. Individual estimates of "molecular age" were derived from age-dependent genes, identified by robust regression analysis. Deviation from chronological age was defined as "delta age". Genetic variants associated with deviations from normal gene expression patterns were identified by expression quantitative trait loci (cis-eQTL) of age-dependent genes or genome-wide association study (GWAS) on delta age, combined into distinct polygenic risk scores (PRScis-eQTL and PRSGWAS), and tested for predicting brain disorders or pathology in independent postmortem expression datasets and clinical cohorts. In these validation datasets, molecular ages, defined by 68 and 76 age-related genes for two brain regions respectively, were positively correlated with chronological ages (r = 0.88/0.91), elevated in bipolar disorder (BP) and schizophrenia (SCZ), and unchanged in major depressive disorder (MDD). Exploratory analyses in independent clinical datasets show that PRSs were associated with SCZ and MDD diagnostics, and with cognition in SCZ and pathology in Alzheimer's disease (AD). These results suggest that older molecular brain aging is a common feature of severe mental illnesses and neurodegeneration.
Collapse
Affiliation(s)
- Chien-Wei Lin
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Lun-Ching Chang
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Department of Mathematical Sciences, Florida Atlantic University, Boca Raton, FL, 33431, USA
| | - Tianzhou Ma
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD, 20742, USA
| | - Hyunjung Oh
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada
| | - Beverly French
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA
| | - Rachel Puralewski
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA
| | - Fasil Mathews
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA
| | - Yusi Fang
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA
| | - James L Kennedy
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada
| | - Daniel Mueller
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada
| | - Victoria S Marshe
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Andrew Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Daniel Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Anne B Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Eric J Lenze
- Department of Psychiatry, Washington University, St. Louis, MO, 63130, USA
| | - Yuliya S Nikolova
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada
| | - George C Tseng
- Department of Biostatistics, Graduate school of Public Health, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Etienne Sibille
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, 15312, USA.
- Campbell Family Mental Health Research Institute of CAMH, Department of Psychiatry, University of Toronto, Toronto, M5T1R8, ON, Canada.
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, M5T1R8, ON, Canada.
| |
Collapse
|
191
|
Abstract
OBJECTIVES Cannabis is a known teratogen. Data availability addressing both major congenital anomalies and cannabis use allowed us to explore their geospatial relationships. METHODS Data for the years 1998 to 2009 from Canada Health and Statistics Canada was analyzed in R. Maps have been drawn and odds ratios, principal component analysis, correlation matrices, least squares regression and geospatial regression analyses have been conducted using the R packages base, dplyr, epiR, psych, ggplot2, colorplaner and the spml and spreml functions from package splm. RESULTS Mapping showed cannabis use was more common in the northern Territories of Canada in the Second National Survey of Cannabis Use 2018. Total congenital anomalies, all cardiovascular defects, orofacial clefts, Downs syndrome and gastroschisis were all found to be more common in these same regions and rose as a function of cannabis exposure. When Canada was dichotomized into high and low cannabis use zones by Provinces v Territories the Territories had a higher rate of total congenital anomalies 450.026 v 390.413 (O.R. = 1.16 95%C.I. 1.08-1.25, P = 0.000058; attributable fraction in exposed 13.25%, 95%C.I. 7.04-19.04%). In geospatial analysis in a spreml spatial error model cannabis was significant both alone as a main effect (P < 2.0 × 10) and in all its first and second order interactions with both tobacco and opioids from P < 2.0 × 10. CONCLUSION These results show that the northern Territories of Canada share a higher rate of cannabis use together with elevated rates of total congenital anomalies, all cardiovascular defects, Down's syndrome and gastroschisis. This is the second report of a significant association between cannabis use and both total defects and all cardiovascular anomalies and the fourth published report of a link with Downs syndrome and thereby direct major genotoxicity. The correlative relationships described in this paper are confounded by many features of social disadvantage in Canada's northern territories. However, in the context of a similar broad spectrum of defects described both in animals and in epidemiological reports from Hawaii, Colorado, USA and Australia they are cause for particular concern and indicate further research.
Collapse
|
192
|
Tombor L, Kakuszi B, Papp S, Réthelyi J, Bitter I, Czobor P. Atypical resting-state gamma band trajectory in adult attention deficit/hyperactivity disorder. J Neural Transm (Vienna) 2021; 128:1239-1248. [PMID: 34164742 PMCID: PMC8321998 DOI: 10.1007/s00702-021-02368-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/18/2021] [Indexed: 11/24/2022]
Abstract
Decreased gamma activity has been reported both in children and adults with attention deficit/hyperactivity disorder (ADHD). However, while ADHD is a lifelong neurodevelopmental disorder, our insight into the associations of spontaneous gamma band activity with age is limited, especially in adults. Therefore, we conducted an explorative study to investigate trajectories of resting gamma activity in adult ADHD patients (N = 42) versus matched healthy controls (N = 59). We investigated the relationship of resting gamma activity (30–48 Hz) with age in four right hemispheric electrode clusters where diminished gamma power in ADHD had previously been demonstrated by our group. We found significant non-linear association between resting gamma power and age in the lower frequency gamma1 range (30–39 Hz) in ADHD as compared to controls in all investigated locations. Resting gamma1 increased with age and was significantly lower in ADHD than in control subjects from early adulthood. We found no significant association between gamma activity and age in the gamma2 range (39–48 Hz). Alterations of gamma band activity might reflect altered cortical network functioning in adult ADHD relative to controls. Our results reveal that abnormal gamma power is present at all ages, highlighting the lifelong nature of ADHD. Nonetheless, longitudinal studies are needed to confirm our results.
Collapse
Affiliation(s)
- László Tombor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary.
| | - Brigitta Kakuszi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary
| | - Szilvia Papp
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary
| | - János Réthelyi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary
| | - István Bitter
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary
| | - Pál Czobor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa utca 6., Budapest, U1083, Hungary
| |
Collapse
|
193
|
Delayed brain development of Rolandic epilepsy profiled by deep learning-based neuroanatomic imaging. Eur Radiol 2021; 31:9628-9637. [PMID: 34018056 DOI: 10.1007/s00330-021-08048-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/30/2021] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Although Rolandic epilepsy (RE) has been regarded as a brain developmental disorder, neuroimaging studies have not yet ascertained whether RE has brain developmental delay. This study employed deep learning-based neuroanatomic biomarker to measure the changed feature of "brain age" in RE. METHODS The study constructed a 3D-CNN brain age prediction model through 1155 cases of typically developing children's morphometric brain MRI from open-source datasets and further applied to a local dataset of 167 RE patients and 107 typically developing children. The brain-predicted age difference was measured to quantitatively estimate brain age changes in RE and further investigated the relevancies with cognitive and clinical variables. RESULTS The brain age estimation network model presented a good performance for brain age prediction in typically developing children. The children with RE showed a 0.45-year delay of brain age by contrast with typically developing children. Delayed brain age was associated with neuroanatomic changes in the Rolandic regions and also associated with cognitive dysfunction of attention. CONCLUSION This study provided neuroimaging evidence to support the notion that RE has delayed brain development. KEY POINTS • The children with Rolandic epilepsy showed imaging phenotypes of delayed brain development with increased GM volume and decreased WM volume in the Rolandic regions. • The children with Rolandic epilepsy had a 0.45-year delay of brain-predicted age by comparing with typically developing children, using 3D-CNN-based brain age prediction model. • The delayed brain age was associated with morphometric changes in the Rolandic regions and attentional deficit in Rolandic epilepsy.
Collapse
|
194
|
Zeighami Y, Evans AC. Association vs. Prediction: The Impact of Cortical Surface Smoothing and Parcellation on Brain Age. Front Big Data 2021; 4:637724. [PMID: 34027399 PMCID: PMC8131952 DOI: 10.3389/fdata.2021.637724] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 04/06/2021] [Indexed: 11/15/2022] Open
Abstract
Association and prediction studies of the brain target the biological consequences of aging and their impact on brain function. Such studies are conducted using different smoothing levels and parcellations at the preprocessing stage, on which their results are dependent. However, the impact of these parameters on the relationship between association values and prediction accuracy is not established. In this study, we used cortical thickness and its relationship with age to investigate how different smoothing and parcellation levels affect the detection of age-related brain correlates as well as brain age prediction accuracy. Our main measures were resel numbers—resolution elements—and age-related variance explained. Using these common measures enabled us to directly compare parcellation and smoothing effects in both association and prediction studies. In our sample of N = 608 participants with age range 18–88, we evaluated age-related cortical thickness changes as well as brain age prediction. We found a negative relationship between prediction performance and correlation values for both parameters. Our results also quantify the relationship between delta age estimates obtained based on different processing parameters. Furthermore, with the direct comparison of the two approaches, we highlight the importance of correct choice of smoothing and parcellation parameters in each task, and how they can affect the results of the analysis in opposite directions.
Collapse
Affiliation(s)
- Yashar Zeighami
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, QC, Canada
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, QC, Canada
| |
Collapse
|
195
|
Woo MS, Ufer F, Rothammer N, Di Liberto G, Binkle L, Haferkamp U, Sonner JK, Engler JB, Hornig S, Bauer S, Wagner I, Egervari K, Raber J, Duvoisin RM, Pless O, Merkler D, Friese MA. Neuronal metabotropic glutamate receptor 8 protects against neurodegeneration in CNS inflammation. J Exp Med 2021; 218:e20201290. [PMID: 33661276 PMCID: PMC7938362 DOI: 10.1084/jem.20201290] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 12/17/2020] [Accepted: 02/02/2021] [Indexed: 12/13/2022] Open
Abstract
Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system with continuous neuronal loss. Treatment of clinical progression remains challenging due to lack of insights into inflammation-induced neurodegenerative pathways. Here, we show that an imbalance in the neuronal receptor interactome is driving glutamate excitotoxicity in neurons of MS patients and identify the MS risk-associated metabotropic glutamate receptor 8 (GRM8) as a decisive modulator. Mechanistically, GRM8 activation counteracted neuronal cAMP accumulation, thereby directly desensitizing the inositol 1,4,5-trisphosphate receptor (IP3R). This profoundly limited glutamate-induced calcium release from the endoplasmic reticulum and subsequent cell death. Notably, we found Grm8-deficient neurons to be more prone to glutamate excitotoxicity, whereas pharmacological activation of GRM8 augmented neuroprotection in mouse and human neurons as well as in a preclinical mouse model of MS. Thus, we demonstrate that GRM8 conveys neuronal resilience to CNS inflammation and is a promising neuroprotective target with broad therapeutic implications.
Collapse
Affiliation(s)
- Marcel S. Woo
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Friederike Ufer
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Nicola Rothammer
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Giovanni Di Liberto
- Division of Clinical Pathology, Department of Pathology and Immunology, Geneva Faculty of Medicine, Geneva, Switzerland
| | - Lars Binkle
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Undine Haferkamp
- Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany
| | - Jana K. Sonner
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Broder Engler
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Sönke Hornig
- Experimentelle Neuropädiatrie, Klinik für Kinder und Jugendmedizin, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Simone Bauer
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| | - Ingrid Wagner
- Division of Clinical Pathology, Department of Pathology and Immunology, Geneva Faculty of Medicine, Geneva, Switzerland
| | - Kristof Egervari
- Division of Clinical Pathology, Department of Pathology and Immunology, Geneva Faculty of Medicine, Geneva, Switzerland
| | - Jacob Raber
- Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR
- Department of Neurology, Oregon Health & Science University, Portland, OR
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Portland, OR
| | - Robert M. Duvoisin
- Department of Chemical Physiology and Biochemistry, Oregon Health & Science University, Portland, OR
| | - Ole Pless
- Fraunhofer Institute for Translational Medicine and Pharmacology, Hamburg, Germany
| | - Doron Merkler
- Division of Clinical Pathology, Department of Pathology and Immunology, Geneva Faculty of Medicine, Geneva, Switzerland
| | - Manuel A. Friese
- Institut für Neuroimmunologie und Multiple Sklerose, Zentrum für Molekulare Neurobiologie Hamburg, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Germany
| |
Collapse
|
196
|
Han S, Chen Y, Zheng R, Li S, Jiang Y, Wang C, Fang K, Yang Z, Liu L, Zhou B, Wei Y, Pang J, Li H, Zhang Y, Cheng J. The stage-specifically accelerated brain aging in never-treated first-episode patients with depression. Hum Brain Mapp 2021; 42:3656-3666. [PMID: 33932251 PMCID: PMC8249899 DOI: 10.1002/hbm.25460] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2021] [Revised: 04/02/2021] [Accepted: 04/15/2021] [Indexed: 12/29/2022] Open
Abstract
Depression associated with structural brain abnormalities is hypothesized to be related with accelerated brain aging. However, there is far from a unified conclusion because of clinical variations such as medication status, cumulative illness burden. To explore whether brain age is accelerated in never‐treated first‐episode patients with depression and its association with clinical characteristics, we constructed a prediction model where gray matter volumes measured by voxel‐based morphometry derived from T1‐weighted MRI scans were treated as features. The prediction model was first validated using healthy controls (HCs) in two Chinese Han datasets (Dataset 1, N = 130 for HCs and N = 195 for patients with depression; Dataset 2, N = 270 for HCs) separately or jointly, then the trained prediction model using HCs (N = 400) was applied to never‐treated first‐episode patients with depression (N = 195). The brain‐predicted age difference (brain‐PAD) scores defined as the difference between predicted brain age and chronological age, were calculated for all participants and compared between patients with age‐, gender‐, educational level‐matched HCs in Dataset 1. Overall, patients presented higher brain‐PAD scores suggesting patients with depression having an “older” brain than expected. More specially, this difference occurred at illness onset (illness duration <3 months) and following 2 years then disappeared as the illness further advanced (>2 years) in patients. This phenomenon was verified by another data‐driven method and significant correlation between brain‐PAD scores and illness duration in patients. Our results reveal that accelerated brain aging occurs at illness onset and suggest it is a stage‐dependent phenomenon in depression.
Collapse
Affiliation(s)
- Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Ruiping Zheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Shuying Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Keke Fang
- Phase I Clinical Research Center, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Jianyue Pang
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hengfen Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.,Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China.,Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China.,Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China.,Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China.,Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China.,Key Laboratory of Imaging Intelligence Research Medicine of Henan Province
| |
Collapse
|
197
|
Teeuw J, Ori APS, Brouwer RM, de Zwarte SMC, Schnack HG, Hulshoff Pol HE, Ophoff RA. Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia. Schizophr Res 2021; 231:189-197. [PMID: 33882370 DOI: 10.1016/j.schres.2021.04.005] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 03/02/2021] [Accepted: 04/07/2021] [Indexed: 02/07/2023]
Abstract
Schizophrenia patients show signs of accelerated aging in cognitive and physiological domains. Both schizophrenia and accelerated aging, as measured by MRI brain images and epigenetic clocks, are correlated with increased mortality. However, the association between these aging measures have not yet been studied in schizophrenia patients. In schizophrenia patients and healthy subjects, accelerated aging was assessed in brain tissue using a longitudinal MRI (N = 715 scans; mean scan interval 3.4 year) and in blood using two epigenetic age clocks (N = 172). Differences ('gaps') between estimated ages and chronological ages were calculated, as well as the acceleration rate of brain aging. The correlations between these aging measures as well as with polygenic risk scores for schizophrenia (PRS; N = 394) were investigated. Brain aging and epigenetic aging were not significantly correlated. Polygenic risk for schizophrenia was significantly correlated with brain age gap, brain age acceleration rate, and negatively correlated with DNAmAge gap, but not with PhenoAge gap. However, after controlling for disease status and multiple comparisons correction, these effects were no longer significant. Our results imply that the (accelerated) aging observed in the brain and blood reflect distinct biological processes. Our findings will require replication in a larger cohort.
Collapse
Affiliation(s)
- Jalmar Teeuw
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Anil P S Ori
- Center for Neurobehavioral Genetics, University of California, Los Angeles, United States
| | - Rachel M Brouwer
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Sonja M C de Zwarte
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hugo G Schnack
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hilleke E Hulshoff Pol
- Department of Psychiatry, UMC Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Roel A Ophoff
- Center for Neurobehavioral Genetics, University of California, Los Angeles, United States; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
| |
Collapse
|
198
|
Bjørnebekk A, Kaufmann T, Hauger LE, Klonteig S, Hullstein IR, Westlye LT. Long-term Anabolic-Androgenic Steroid Use Is Associated With Deviant Brain Aging. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 6:579-589. [PMID: 33811018 DOI: 10.1016/j.bpsc.2021.01.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/02/2020] [Accepted: 01/04/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND High-dose long-term use of anabolic-androgenic steroids (AASs) may cause a range of adverse effects, including brain and cognitive abnormalities. We performed age prediction based on brain scans to test whether prolonged AAS use is associated with accentuated brain aging. METHODS T1-weighted magnetic resonance imaging (3D MPRAGE [magnetization-prepared rapid acquisition gradient-echo]) scans were obtained from male weightlifters with a history of prolonged AAS use (n = 130) or no AAS use (n = 99). We trained machine learning models on combinations of regional brain volumes, cortical thickness, and surface area in an independent training set of 1838 healthy male subjects (18-92 years of age) and predicted brain age for each participant in our study. Including cross-sectional and longitudinal (mean interval = 3.5 years, n = 76) magnetic resonance imaging data, we used linear mixed-effects models to compare the gap between chronological age and predicted brain age (the brain age gap [BAG]) for the two groups and tested for group differences in the rate of change in BAG. We tested for associations between apparent brain aging and AAS use duration, pattern of administration, and dependence. RESULTS AAS users had higher BAG compared with weightlifting control subjects, which was associated with dependency and longer history of use. Group differences in BAG could not be explained by other substance use, general cognitive abilities, or depression. While longitudinal analysis revealed no evidence of increased brain aging in the overall AAS group, accelerated brain aging was seen with longer AAS exposure. CONCLUSIONS The findings suggest that long-term high-dose AAS use may have adverse effects on brain aging, potentially linked to dependency and exaggerated use of AASs.
Collapse
Affiliation(s)
- Astrid Bjørnebekk
- Anabolic Androgenic Steroid Research Group, Section for Clinical Addiction Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Lisa E Hauger
- Anabolic Androgenic Steroid Research Group, Section for Clinical Addiction Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Sandra Klonteig
- Anabolic Androgenic Steroid Research Group, Section for Clinical Addiction Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingunn R Hullstein
- Norwegian Doping Control Laboratory, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, and Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; K.G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| |
Collapse
|
199
|
Schmidt MV, Koutsouleris N. Promises and Pitfalls of the New Era of Computational Behavioral Neuroscience. Biol Psychiatry 2021; 89:845-846. [PMID: 33858591 DOI: 10.1016/j.biopsych.2021.02.965] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 02/19/2021] [Indexed: 11/16/2022]
Affiliation(s)
- Mathias V Schmidt
- Max Planck Institute for Psychiatry, Ludwig-Maximilian-University, Munich, Germany
| | - Nikolaos Koutsouleris
- Max Planck Institute for Psychiatry, Ludwig-Maximilian-University, Munich, Germany; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| |
Collapse
|
200
|
Lee WH, Antoniades M, Schnack HG, Kahn RS, Frangou S. Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter? Psychiatry Res Neuroimaging 2021; 310:111270. [PMID: 33714090 PMCID: PMC8056405 DOI: 10.1016/j.pscychresns.2021.111270] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 12/27/2022]
Abstract
Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals (n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia (n = 90 and n = 76) and healthy individuals (n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.
Collapse
Affiliation(s)
- Won Hee Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Mathilde Antoniades
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Netherlands
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States; Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Canada.
| |
Collapse
|