1
|
Zhang D, She Y, Sun J, Cui Y, Yang X, Zeng X, Qin W. Brain Age Estimation from Overnight Sleep Electroencephalography with Multi-Flow Sequence Learning. Nat Sci Sleep 2024; 16:879-896. [PMID: 38974693 PMCID: PMC11227046 DOI: 10.2147/nss.s463495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Purpose This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data. Methods We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution. Results We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals. Conclusion The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.
Collapse
Affiliation(s)
- Di Zhang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Yichong She
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Jinbo Sun
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Yapeng Cui
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Xuejuan Yang
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Xiao Zeng
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| | - Wei Qin
- Engineering Research Center of Molecular and Neuro Imaging of the Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi, 710126, People’s Republic of China
- Intelligent Non-Invasive Neuromodulation Technology and Transformation Joint Laboratory, Xidian University, Xi’an, People’s Republic of China
| |
Collapse
|
2
|
Wittens MMJ, Denissen S, Sima DM, Fransen E, Niemantsverdriet E, Bastin C, Benoit F, Bergmans B, Bier JC, de Deyn PP, Deryck O, Hanseeuw B, Ivanoiu A, Picard G, Ribbens A, Salmon E, Segers K, Sieben A, Struyfs H, Thiery E, Tournoy J, van Binst AM, Versijpt J, Smeets D, Bjerke M, Nagels G, Engelborghs S. Brain age as a biomarker for pathological versus healthy ageing - a REMEMBER study. Alzheimers Res Ther 2024; 16:128. [PMID: 38877568 PMCID: PMC11179390 DOI: 10.1186/s13195-024-01491-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/04/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVES This study aimed to evaluate the potential clinical value of a new brain age prediction model as a single interpretable variable representing the condition of our brain. Among many clinical use cases, brain age could be a novel outcome measure to assess the preventive effect of life-style interventions. METHODS The REMEMBER study population (N = 742) consisted of cognitively healthy (HC,N = 91), subjective cognitive decline (SCD,N = 65), mild cognitive impairment (MCI,N = 319) and AD dementia (ADD,N = 267) subjects. Automated brain volumetry of global, cortical, and subcortical brain structures computed by the CE-labeled and FDA-cleared software icobrain dm (dementia) was retrospectively extracted from T1-weighted MRI sequences that were acquired during clinical routine at participating memory clinics from the Belgian Dementia Council. The volumetric features, along with sex, were combined into a weighted sum using a linear model, and were used to predict 'brain age' and 'brain predicted age difference' (BPAD = brain age-chronological age) for every subject. RESULTS MCI and ADD patients showed an increased brain age compared to their chronological age. Overall, brain age outperformed BPAD and chronological age in terms of classification accuracy across the AD spectrum. There was a weak-to-moderate correlation between total MMSE score and both brain age (r = -0.38,p < .001) and BPAD (r = -0.26,p < .001). Noticeable trends, but no significant correlations, were found between BPAD and incidence of conversion from MCI to ADD, nor between BPAD and conversion time from MCI to ADD. BPAD was increased in heavy alcohol drinkers compared to non-/sporadic (p = .014) and moderate (p = .040) drinkers. CONCLUSIONS Brain age and associated BPAD have the potential to serve as indicators for, and to evaluate the impact of lifestyle modifications or interventions on, brain health.
Collapse
Affiliation(s)
- Mandy M J Wittens
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Stijn Denissen
- icometrix, Leuven, Belgium
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Diana M Sima
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Erik Fransen
- Centre of Medical Genetics, University of Antwerp, and Antwerp University Hospital - UZA, Edegem, Belgium
| | | | - Christine Bastin
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
| | - Florence Benoit
- Geriatrics Department, Brugmann University Hospital, Universite Libre de Bruxelles, Brussels, Belgium
| | - Bruno Bergmans
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Jean-Christophe Bier
- Neurological department H. U. B. - Erasme Hospital - Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Peter Paul de Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Antwerp, 2610, Belgium
- Memory Clinic, Ziekenhuisnetwerk, Antwerp, Belgium
| | - Olivier Deryck
- Neurology Department, AZ St-Jan Brugge, Brugge, Belgium
- Ghent University Hospital, Ghent, Belgium
| | - Bernard Hanseeuw
- Institute of Neuroscience, Université Catholique de Louvain, Brussels, 1200, Belgium
- Department of Neurology, Clinique Universitaires Saint-Luc, Brussels, 1200, Belgium
- WELBIO Department, WEL Research Institute, Wavre, 1300, Belgium
| | - Adrian Ivanoiu
- Department of Neurology, Cliniques Universitaires St Luc, and Institute of Neuroscience, Université Catholique de Louvain, Woluwe-Saint-Lambert (Brussels), Belgium
| | - Gaëtane Picard
- Department of Neurology, Clinique Saint-Pierre, Ottignies, Belgium
| | | | - Eric Salmon
- GIGA-CRC-IVI, Liège University, Allée du Six Août, 8, Liège, 4000, Belgium
- Department of Neurology, Memory Clinic, Centre Hospitalier Universitaire (CHU) Liège, Liège, Belgium
| | - Kurt Segers
- Memory Clinic - Neurology and Geriatrics Department, CHU Brugmann, Van Gehuchtenplein 4, Brussels, 1020, Belgium
| | - Anne Sieben
- Neuropathology Lab, IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital - UZA, Antwerp, Belgium
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Johnson and Johnson Innovative Medicine, Beerse, Belgium
| | - Evert Thiery
- Department of Neurology, University Hospital Ghent, Ghent University, Ghent, Belgium
| | - Jos Tournoy
- Department of Chronic Diseases, Metabolism and Ageing, Geriatric Medicine and Memory Clinic, University Hospitals Leuven and KU Leuven, Louvain, Belgium
| | - Anne-Marie van Binst
- Radiology Department, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Jan Versijpt
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
| | - Dirk Smeets
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- icometrix, Leuven, Belgium
| | - Maria Bjerke
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium
- Department of Clinical Chemistry, Laboratory of Neurochemistry, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
| | - Guy Nagels
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium
- St. Edmund Hall, University of Oxford, Oxford, UK
- AIMS lab, Center for Neurosciences (C4N), Vrije Universiteit Brussel, UZ Brussel, Brussels, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium.
- Department of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel), Brussels, Belgium.
- Neuroprotection and Neuromodulation (NEUR) Research Group, Center for Neurosciences (C4N), Vrije, Universiteit Brussel (VUB), Brussels, Belgium.
| |
Collapse
|
3
|
Kamarajan C, Ardekani BA, Pandey AK, Meyers JL, Chorlian DB, Kinreich S, Pandey G, Richard C, de Viteri SS, Kuang W, Porjesz B. Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.01.582844. [PMID: 38496639 PMCID: PMC10942318 DOI: 10.1101/2024.03.01.582844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline.
Collapse
Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Babak A. Ardekani
- Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
- Department of Psychiatry, Grossman School of Medicine, New York University, New York, NY 10016, USA
| | - Ashwini K. Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L. Meyers
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B. Chorlian
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Christian Richard
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| |
Collapse
|
4
|
Kim HW, Kim HJ, Lee H, Yang H, Rieu Z, Lee JH. Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment. Sci Rep 2023; 13:22467. [PMID: 38105274 PMCID: PMC10725862 DOI: 10.1038/s41598-023-49465-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023] Open
Abstract
Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining 'survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.
Collapse
Affiliation(s)
- Hye Weon Kim
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyung-Ji Kim
- Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University, Uijeongbu, Korea
| | - Hyunji Lee
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Hyeonsik Yang
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - ZunHyan Rieu
- Research Institute, Neurophet Inc., Seoul, 06234, Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Korea.
| |
Collapse
|
5
|
Kerber B, Hepp T, Küstner T, Gatidis S. Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population. PLoS One 2023; 18:e0292993. [PMID: 37934735 PMCID: PMC10629654 DOI: 10.1371/journal.pone.0292993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 10/03/2023] [Indexed: 11/09/2023] Open
Abstract
Aging is an important risk factor for disease, leading to morphological change that can be assessed on Computed Tomography (CT) scans. We propose a deep learning model for automated age estimation based on CT- scans of the thorax and abdomen generated in a clinical routine setting. These predictions could serve as imaging biomarkers to estimate a "biological" age, that better reflects a patient's true physical condition. A pre-trained ResNet-18 model was modified to predict chronological age as well as to quantify its aleatoric uncertainty. The model was trained using 1653 non-pathological CT-scans of the thorax and abdomen of subjects aged between 20 and 85 years in a 5-fold cross-validation scheme. Generalization performance as well as robustness and reliability was assessed on a publicly available test dataset consisting of thorax-abdomen CT-scans of 421 subjects. Score-CAM saliency maps were generated for interpretation of model outputs. We achieved a mean absolute error of 5.76 ± 5.17 years with a mean uncertainty of 5.01 ± 1.44 years after 5-fold cross-validation. A mean absolute error of 6.50 ± 5.17 years with a mean uncertainty of 6.39 ± 1.46 years was obtained on the test dataset. CT-based age estimation accuracy was largely uniform across all age groups and between male and female subjects. The generated saliency maps highlighted especially the lumbar spine and abdominal aorta. This study demonstrates, that accurate and generalizable deep learning-based automated age estimation is feasible using clinical CT image data. The trained model proved to be robust and reliable. Methods of uncertainty estimation and saliency analysis improved the interpretability.
Collapse
Affiliation(s)
- Bjarne Kerber
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Tobias Hepp
- Max Planck Institute for Intelligent Systems, Tuebingen, Germany
| | - Thomas Küstner
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany
- Max Planck Institute for Intelligent Systems, Tuebingen, Germany
| |
Collapse
|
6
|
Dular L, Pernuš F, Špiclin Ž. Extensive T1-weighted MRI Preprocessing Improves Generalizability of Deep Brain Age Prediction Models. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.10.540134. [PMID: 37214863 PMCID: PMC10197652 DOI: 10.1101/2023.05.10.540134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain age is an estimate of chronological age obtained from T1-weighted magnetic resonance images (T1w MRI) and represents a simple diagnostic biomarker of brain ageing and associated diseases. While the current best accuracy of brain age predictions on T1w MRIs of healthy subjects ranges from two to three years, comparing results from different studies is challenging due to differences in the datasets, T1w preprocessing pipelines, and performance metrics used. This paper investigates the impact of T1w image preprocessing on the performance of four deep learning brain age models presented in recent literature. Four preprocessing pipelines were evaluated, differing in terms of registration, grayscale correction, and software implementation. The results showed that the choice of software or preprocessing steps can significantly affect the prediction error, with a maximum increase of 0.7 years in mean absolute error (MAE) for the same model and dataset. While grayscale correction had no significant impact on MAE, the affine registration, compared to the rigid registration of T1w images to brain atlas was shown to statistically significantly improve MAE. Models trained on 3D images with isotropic 1 mm3 resolution exhibited less sensitivity to the T1w preprocessing variations compared to 2D models or those trained on downsampled 3D images. Some proved invariant to the preprocessing pipeline, however only after offset correction. Our findings generally indicate that extensive T1w preprocessing enhances the MAE, especially when applied to a new dataset. This runs counter to prevailing research literature which suggests that models trained on minimally preprocessed T1w scans are better poised for age predictions on MRIs from unseen scanners. Regardless of model or T1w preprocessing used, we show that to enable generalization of model's performance on a new dataset with either the same or different T1w preprocessing than the one applied in model training, some form of offset correction should be applied.
Collapse
Affiliation(s)
- Lara Dular
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Franjo Pernuš
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| | - Žiga Špiclin
- University of Ljubljana, Faculty of Electrical Engineering, Tržaška cesta 25, Ljubljana 1000, Slovenia
| |
Collapse
|
7
|
Hu L, Wan Q, Huang L, Tang J, Huang S, Chen X, Bai X, Kong L, Deng J, Liang H, Liu G, Liu H, Lu L. MRI-based brain age prediction model for children under 3 years old using deep residual network. Brain Struct Funct 2023; 228:1771-1784. [PMID: 37603065 DOI: 10.1007/s00429-023-02686-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
Collapse
Affiliation(s)
- Lianting Hu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Qirong Wan
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, 4330060, Hubei, China
| | - Li Huang
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Jiajie Tang
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Shuai Huang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xuanhui Chen
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Xiaohe Bai
- School of Physical Sciences, University of California San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Lingcong Kong
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
| | - Jingyi Deng
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China
| | - Huiying Liang
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Guangjian Liu
- Medical Big Data Center, Guangdong Provincial People's Hospital/Guangdong Academy of Medical Sciences, Guangzhou, 510080, Guangdong, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, Guangdong, China
| | - Hongsheng Liu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
| | - Long Lu
- Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
- School of Information Management, Wuhan University, Wuhan, 430072, Hubei, China.
| |
Collapse
|
8
|
Chu C, Holst SC, Elmenhorst EM, Foerges AL, Li C, Lange D, Hennecke E, Baur DM, Beer S, Hoffstaedter F, Knudsen GM, Aeschbach D, Bauer A, Landolt HP, Elmenhorst D. Total Sleep Deprivation Increases Brain Age Prediction Reversibly in Multisite Samples of Young Healthy Adults. J Neurosci 2023; 43:2168-2177. [PMID: 36804738 PMCID: PMC10039745 DOI: 10.1523/jneurosci.0790-22.2023] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 01/13/2023] [Accepted: 01/18/2023] [Indexed: 02/22/2023] Open
Abstract
Sleep loss pervasively affects the human brain at multiple levels. Age-related changes in several sleep characteristics indicate that reduced sleep quality is a frequent characteristic of aging. Conversely, sleep disruption may accelerate the aging process, yet it is not known what will happen to the age status of the brain if we can manipulate sleep conditions. To tackle this question, we used an approach of brain age to investigate whether sleep loss would cause age-related changes in the brain. We included MRI data of 134 healthy volunteers (mean chronological age of 25.3 between the age of 19 and 39 years, 42 females/92 males) from five datasets with different sleep conditions. Across three datasets with the condition of total sleep deprivation (>24 h of prolonged wakefulness), we consistently observed that total sleep deprivation increased brain age by 1-2 years regarding the group mean difference with the baseline. Interestingly, after one night of recovery sleep, brain age was not different from baseline. We also demonstrated the associations between the change in brain age after total sleep deprivation and the sleep variables measured during the recovery night. By contrast, brain age was not significantly changed by either acute (3 h time-in-bed for one night) or chronic partial sleep restriction (5 h time-in-bed for five continuous nights). Together, the convergent findings indicate that acute total sleep loss changes brain morphology in an aging-like direction in young participants and that these changes are reversible by recovery sleep.SIGNIFICANCE STATEMENT Sleep is fundamental for humans to maintain normal physical and psychological functions. Experimental sleep deprivation is a variable-controlling approach to engaging the brain among different sleep conditions for investigating the responses of the brain to sleep loss. Here, we quantified the response of the brain to sleep deprivation by using the change of brain age predictable with brain morphologic features. In three independent datasets, we consistently found increased brain age after total sleep deprivation, which was associated with the change in sleep variables. Moreover, no significant change in brain age was found after partial sleep deprivation in another two datasets. Our study provides new evidence to explain the brainwide effect of sleep loss in an aging-like direction.
Collapse
Affiliation(s)
- Congying Chu
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Sebastian C Holst
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
| | - Eva-Maria Elmenhorst
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
- Institute for Occupational, Social and Environmental Medicine, Medical Faculty, RWTH Aachen University, 52074 Aachen, Germany
| | - Anna L Foerges
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Neurophysiology, Institute of Zoology (Bio-II), RWTH Aachen University, 52074 Aachen, Germany
| | - Changhong Li
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Denise Lange
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
| | - Eva Hennecke
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
| | - Diego M Baur
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
| | - Simone Beer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Forschungszentrum Jülich, 52428 Jülich, Germany
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Institute of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Daniel Aeschbach
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, 51147 Cologne, Germany
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, Massachusetts 02115
- Division of Sleep Medicine, Harvard Medical School, Boston, Massachusetts 02115
- Institute of Experimental Epileptology and Cognition Research, Faculty of Medicine, University of Bonn, 53127, Bonn, Germany
| | - Andreas Bauer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Neurological Department, Medical Faculty, Heinrich-Heine-University, 40225 Düsseldorf, Germany
| | - Hans-Peter Landolt
- Institute of Pharmacology and Toxicology, University of Zurich, CH-8006 Zurich, Switzerland
- Sleep & Health Zurich, University Center of Competence, University of Zurich, Zurich, Switzerland
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, 52428 Jülich, Germany
- Department of Nuclear Medicine, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
- Division of Medical Psychology, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, 53127 Germany
| |
Collapse
|
9
|
Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants. Transl Psychiatry 2022; 12:397. [PMID: 36130921 PMCID: PMC9492670 DOI: 10.1038/s41398-022-02162-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/04/2022] [Accepted: 09/08/2022] [Indexed: 11/12/2022] Open
Abstract
Major depressive disorder (MDD) is one of the most common mental health conditions that has been intensively investigated for its association with brain atrophy and mortality. Recent studies suggest that the deviation between the predicted and the chronological age can be a marker of accelerated brain aging to characterize MDD. However, current conclusions are usually drawn based on structural MRI information collected from Caucasian participants. The universality of this biomarker needs to be further validated by subjects with different ethnic/racial backgrounds and by different types of data. Here we make use of the REST-meta-MDD, a large scale resting-state fMRI dataset collected from multiple cohort participants in China. We develop a stacking machine learning model based on 1101 healthy controls, which estimates a subject's chronological age from fMRI with promising accuracy. The trained model is then applied to 1276 MDD patients from 24 sites. We observe that MDD patients exhibit a +4.43 years (p < 0.0001, Cohen's d = 0.31, 95% CI: 2.23-3.88) higher brain-predicted age difference (brain-PAD) compared to controls. In the MDD subgroup, we observe a statistically significant +2.09 years (p < 0.05, Cohen's d = 0.134525) brain-PAD in antidepressant users compared to medication-free patients. The statistical relationship observed is further checked by three different machine learning algorithms. The positive brain-PAD observed in participants in China confirms the presence of accelerated brain aging in MDD patients. The utilization of functional brain connectivity for age estimation verifies existing findings from a new dimension.
Collapse
|
10
|
Brink-Kjaer A, Leary EB, Sun H, Westover MB, Stone KL, Peppard PE, Lane NE, Cawthon PM, Redline S, Jennum P, Sorensen HBD, Mignot E. Age estimation from sleep studies using deep learning predicts life expectancy. NPJ Digit Med 2022; 5:103. [PMID: 35869169 PMCID: PMC9307657 DOI: 10.1038/s41746-022-00630-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 06/10/2022] [Indexed: 11/11/2022] Open
Abstract
Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.
Collapse
Affiliation(s)
- Andreas Brink-Kjaer
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
| | - Eileen B Leary
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Katie L Stone
- Research Institute, California Pacific Medical Center, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Paul E Peppard
- Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Nancy E Lane
- Department of Medicine, University of Davis School of Medicine, Sacramento, CA, USA
| | - Peggy M Cawthon
- Research Institute, California Pacific Medical Center, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark
| | - Helge B D Sorensen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Emmanuel Mignot
- Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
| |
Collapse
|
11
|
de Lange AG, Anatürk M, Rokicki J, Han LKM, Franke K, Alnæs D, Ebmeier KP, Draganski B, Kaufmann T, Westlye LT, Hahn T, Cole JH. Mind the gap: Performance metric evaluation in brain-age prediction. Hum Brain Mapp 2022; 43:3113-3129. [PMID: 35312210 PMCID: PMC9188975 DOI: 10.1002/hbm.25837] [Citation(s) in RCA: 44] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/04/2022] [Accepted: 03/06/2022] [Indexed: 12/21/2022] Open
Abstract
Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance.
Collapse
Affiliation(s)
- Ann‐Marie G. de Lange
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanne,Department of PsychologyUniversity of OsloOslo,Department of PsychiatryUniversity of OxfordOxford
| | - Melis Anatürk
- Department of PsychiatryUniversity of OxfordOxford,Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
| | - Jaroslav Rokicki
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway,Centre of Research and Education in Forensic PsychiatryOslo University HospitalOsloNorway
| | - Laura K. M. Han
- Department of PsychiatryAmsterdam University Medical Centers, Vrije Universiteit and GGZ inGeest, Amsterdam NeuroscienceAmsterdamThe Netherlands
| | - Katja Franke
- Structural Brain Mapping Group, Department of NeurologyJena University HospitalJenaGermany
| | - Dag Alnæs
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | | | - Bogdan Draganski
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesLausanne University Hospital (CHUV) and University of LausanneLausanne,Department of NeurologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway,Tübingen Center for Mental Health, Department of Psychiatry and PsychotherapyUniversity of TübingenTübingenGermany
| | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOslo,NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway,KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Tim Hahn
- Institute of Translational PsychiatryUniversity of MünsterMünsterGermany
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK,Dementia Research Centre, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| |
Collapse
|
12
|
Clausen AN, Fercho KA, Monsour M, Disner S, Salminen L, Haswell CC, Rubright EC, Watts AA, Buckley MN, Maron-Katz A, Sierk A, Manthey A, Suarez-Jimenez B, Olatunji BO, Averill CL, Hofmann D, Veltman DJ, Olson EA, Li G, Forster GL, Walter H, Fitzgerald J, Théberge J, Simons JS, Bomyea JA, Frijling JL, Krystal JH, Baker JT, Phan KL, Ressler K, Han LKM, Nawijn L, Lebois LAM, Schmaal L, Densmore M, Shenton ME, van Zuiden M, Stein M, Fani N, Simons RM, Neufeld RWJ, Lanius R, van Rooij S, Koch SBJ, Bonomo S, Jovanovic T, deRoon-Cassini T, Ely TD, Magnotta VA, He X, Abdallah CG, Etkin A, Schmahl C, Larson C, Rosso IM, Blackford JU, Stevens JS, Daniels JK, Herzog J, Kaufman ML, Olff M, Davidson RJ, Sponheim SR, Mueller SC, Straube T, Zhu X, Neria Y, Baugh LA, Cole JH, Thompson PM, Morey RA. Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups. Brain Behav 2022; 12:e2413. [PMID: 34907666 PMCID: PMC8785613 DOI: 10.1002/brb3.2413] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Revised: 09/03/2021] [Accepted: 10/15/2021] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.
Collapse
Affiliation(s)
- Ashley N Clausen
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA.,Kansas City VA Medical Center, Kansas City, Missouri, USA
| | - Kelene A Fercho
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Civil Aerospace Medical Institute, US Federal Aviation Administration, Oklahoma City, Oklahoma, USA.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - Molly Monsour
- Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Seth Disner
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, USA.,Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
| | - Lauren Salminen
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Courtney C Haswell
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Emily Clarke Rubright
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Amanda A Watts
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - M Nicole Buckley
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA
| | - Adi Maron-Katz
- Department of Psychiatry and Behavioral Sciences, Stanford University of Medicine, Stanford, California, USA
| | - Anika Sierk
- University Medical Centre Charite, Berlin, Germany
| | | | - Benjamin Suarez-Jimenez
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Bunmi O Olatunji
- Department of Psychology, Vanderbilt University, Nashville, Tennessee, USA
| | - Christopher L Averill
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - David Hofmann
- Institute of Medical Psychology and Systems Neuroscience, University of Muenster, Muenster, Germany
| | - Dick J Veltman
- Department of Psychiatry, Amsterdam University Medical Centers, Location VU University Medical Center, VU University, Amsterdam, The Netherlands
| | - Elizabeth A Olson
- Harvard Medical School, Boston, Massachusetts, USA.,McLean Hospital, Belmont, Massachusetts, USA
| | - Gen Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Gina L Forster
- Brain Health Research Centre, Department of Anatomy, University of Otago, Dunedin, New Zealand.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA
| | | | | | - Jean Théberge
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Imaging Division, Lawson Health Research Institute, London, Ontario, Canada
| | - Jeffrey S Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - Jessica A Bomyea
- UC San Diego Department of Psychiatry, San Deigo, California, USA.,VA San Diego Healthcare System Center of Excellence for Stress and Mental Health, San Deigo, California, USA
| | - Jessie L Frijling
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - John H Krystal
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Justin T Baker
- Institute for Technology in Psychiatry, McLean Hospital, Harvard University, Belmont, Massachusetts, USA
| | - K Luan Phan
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, Ohio, USA
| | - Kerry Ressler
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Laura K M Han
- Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Laura Nawijn
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, Location VU University Medical Center, VU University, Amsterdam, The Netherlands
| | - Lauren A M Lebois
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Division of Depression and Anxiety Disorders, McLean Hospital, Belmont, Massachusetts, USA
| | - Lianne Schmaal
- Centre for Youth Mental Health, The University of Melbourne, Melbourne, Australia.,Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Australia
| | - Maria Densmore
- Department of Psychiatry, Western University, London, Ontario, Canada
| | - Martha E Shenton
- Department of Psychiatry, VA Boston Healthcare System, Brockton, Massachusetts, USA.,Departments of Psychiatry & Radiology, Harvard Medical School, Boston, Massachusetts, USA.,Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Mirjam van Zuiden
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - Murray Stein
- UC San Diego Department of Family Medicine and Public Health, San Deigo, California, USA.,UC San Diego Department of Psychiatry, San Deigo, California, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Raluca M Simons
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Department of Psychology, University of South Dakota, Vermillion, South Dakota, USA
| | - Richard W J Neufeld
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada.,Department of Psychology, University of British Columbia, Okanagan, Canada.,Department of Psychology, Western University, London, Ontario, Canada
| | - Ruth Lanius
- Department of Neuroscience, Western University, London, Ontario, Canada.,Department of Psychiatry, Western University, London, Ontario, Canada
| | - Sanne van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Saskia B J Koch
- Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands.,Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Serena Bonomo
- New York State Psychiatric Institute, New York, New York, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, Michigan, USA
| | | | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Vincent A Magnotta
- Departments of Radiology, Psychiatry and Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Xiaofu He
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Chadi G Abdallah
- Clinical Neuroscience Division, National Center for PTSD, West Haven, Connecticut, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA.,Michael E, DeBakey VA Medical Center, Houston, Texas, USA.,Menninger Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University of Medicine, Stanford, California, USA.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California, USA
| | - Christian Schmahl
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | | | - Isabelle M Rosso
- Harvard Medical School, Boston, Massachusetts, USA.,McLean Hospital, Belmont, Massachusetts, USA
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA.,Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Julia Herzog
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
| | - Milissa L Kaufman
- Division of Women's Mental Health, McLean Hospital, Belmont, Massachusetts, USA
| | - Miranda Olff
- ARQ National Psychotrauma Centrum, Diemen, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, Location Academic Medical Center, Amsterdam Neuroscience, University of Amsterdam, Amsterdam, The Netherlands
| | - Richard J Davidson
- Center for Healthy Minds, Departments of Psychology and Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Scott R Sponheim
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, Minnesota, USA.,Minneapolis VA Health Care System, Minneapolis, Minnesota, USA
| | - Sven C Mueller
- Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium.,Department of Personality, Psychological Assessment and Treatment, University of Deusto, Bilbao, Spain
| | - Thomas Straube
- Institute of Medical Psychology and Systems Neuroscience, University of Muenster, Muenster, Germany
| | - Xi Zhu
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Yuval Neria
- Columbia University Medical Center, Manhattan, New York, USA.,New York State Psychiatric Institute, New York, New York, USA
| | - Lee A Baugh
- Center for Brain and Behavior Research, University of South Dakota, Vermillion, South Dakota, USA.,Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, South Dakota, USA.,Sioux Falls VA Health Care System, Sioux Falls, South Dakota, USA
| | - James H Cole
- Centre for Medical Image Computing, Computer Science, University College London, London, UK.,Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Rajendra A Morey
- VA Mid-Atlantic Mental Illness Research, Education, and Clinical Center, Durham, North Carolina, USA.,Duke University Brain Imaging and Analysis Center, Durham, North Carolina, USA.,Kansas City VA Medical Center, Kansas City, Missouri, USA.,ARQ National Psychotrauma Centrum, Diemen, The Netherlands.,Department of Psychiatry, Amsterdam University Medical Centers, VU University Medical Center, GGZ inGeest, Amsterdam Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
13
|
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: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [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
|
14
|
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: 123] [Impact Index Per Article: 41.0] [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
|
15
|
Wrigglesworth J, Ward P, Harding IH, Nilaweera D, Wu Z, Woods RL, Ryan J. Factors associated with brain ageing - a systematic review. BMC Neurol 2021; 21:312. [PMID: 34384369 PMCID: PMC8359541 DOI: 10.1186/s12883-021-02331-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 06/24/2021] [Indexed: 11/10/2022] Open
Abstract
Background Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing. Methods This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible. Results A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable. Conclusion This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets. Trial registration A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-021-02331-4.
Collapse
Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Phillip Ward
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.,Turner Institute for Brain and Mental Health, Monash University, Clayton, Victoria, 3800, Australia.,Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Victoria , 3800, , Australia
| | - Ian H Harding
- Monash Biomedical Imaging, Monash University, Clayton, Victoria, 3168, Australia.,Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, 3004, Australia
| | - Dinuli Nilaweera
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Zimu Wu
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Robyn L Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, 3004, Australia.
| |
Collapse
|
16
|
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
|
17
|
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: 27] [Impact Index Per Article: 9.0] [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
|
18
|
Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, Knight SP, Carey D, Robertson IH, Emek-Savaş DD, Stern Y, Kenny RA, Whelan R. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav 2021; 15:327-345. [PMID: 32141032 DOI: 10.1007/s11682-020-00260-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.
Collapse
Affiliation(s)
- Rory Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Lee Jollans
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, München, Germany
| | - Laura M Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Rossella Rizzo
- Physics Department, University of Calabria, Rende, CS, Italy
| | - Görsev G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Department of Neurology, Dokuz Eylul University Medical School, İzmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, İzmir, Turkey
| | - Jason P McMorrow
- Centre for Advanced Medical Imaging, St. James's Hospital, Dublin 8, Ireland
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Silvin P Knight
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
| | - Daniel Carey
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - Ian H Robertson
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Derya D Emek-Savaş
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, İzmir, Turkey
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Rose Anne Kenny
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Mercer's Institute for Successful Ageing, St. James's Hospital, Dublin 8, Ireland
| | - Robert Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
| |
Collapse
|
19
|
Baecker L, Dafflon J, da Costa PF, Garcia-Dias R, Vieira S, Scarpazza C, Calhoun VD, Sato JR, Mechelli A, Pinaya WHL. Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data. Hum Brain Mapp 2021; 42:2332-2346. [PMID: 33738883 PMCID: PMC8090783 DOI: 10.1002/hbm.25368] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/27/2021] [Accepted: 01/31/2021] [Indexed: 12/26/2022] Open
Abstract
Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such “brain age prediction” vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47–73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole‐brain region‐based or voxel‐based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross‐validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel‐level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.
Collapse
Affiliation(s)
- Lea Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Jessica Dafflon
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pedro F da Costa
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Rafael Garcia-Dias
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Sandra Vieira
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Cristina Scarpazza
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Department of General Psychology, University of Padua, Padua, Italy
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Atlanta, Georgia, USA.,Georgia Institute of Technology, Emory University, Georgia, USA
| | - João R Sato
- Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Paulo, Brazil
| | - Andrea Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Walter H L Pinaya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Center of Mathematics, Computing and Cognition, Universidade Federal do ABC, São Paulo, Brazil.,Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| |
Collapse
|
20
|
Bittner N, Jockwitz C, Franke K, Gaser C, Moebus S, Bayen UJ, Amunts K, Caspers S. When your brain looks older than expected: combined lifestyle risk and BrainAGE. Brain Struct Funct 2021; 226:621-645. [PMID: 33423086 PMCID: PMC7981332 DOI: 10.1007/s00429-020-02184-6] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 11/24/2020] [Indexed: 12/25/2022]
Abstract
Lifestyle may be one source of unexplained variance in the great interindividual variability of the brain in age-related structural differences. While physical and social activity may protect against structural decline, other lifestyle behaviors may be accelerating factors. We examined whether riskier lifestyle correlates with accelerated brain aging using the BrainAGE score in 622 older adults from the 1000BRAINS cohort. Lifestyle was measured using a combined lifestyle risk score, composed of risk (smoking, alcohol intake) and protective variables (social integration and physical activity). We estimated individual BrainAGE from T1-weighted MRI data indicating accelerated brain atrophy by higher values. Then, the effect of combined lifestyle risk and individual lifestyle variables was regressed against BrainAGE. One unit increase in combined lifestyle risk predicted 5.04 months of additional BrainAGE. This prediction was driven by smoking (0.6 additional months of BrainAGE per pack-year) and physical activity (0.55 less months in BrainAGE per metabolic equivalent). Stratification by sex revealed a stronger association between physical activity and BrainAGE in males than females. Overall, our observations may be helpful with regard to lifestyle-related tailored prevention measures that slow changes in brain structure in older adults.
Collapse
Affiliation(s)
- Nora Bittner
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany
| | - Christiane Jockwitz
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany
| | - Katja Franke
- Structural Brain Mapping Group, University Hospital Jena, 07743, Jena, Germany
| | - Christian Gaser
- Structural Brain Mapping Group, University Hospital Jena, 07743, Jena, Germany
| | - Susanne Moebus
- Institute of Urban Public Health, University of Duisburg-Essen, 45122, Essen, Germany
| | - Ute J Bayen
- Mathematical and Cognitive Psychology, Institute for Experimental Psychology, Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany.,Cecile and Oskar Vogt Institute for Brain Research, Medical Faculty, Heinrich-Heine University Düsseldorf, 40225, Düsseldorf, Germany.,JARA-BRAIN, Juelich-Aachen Research Alliance, 52425, Jülich, Germany
| | - Svenja Caspers
- Institute for Anatomy I, Medical Faculty, Heinrich-Heine University Düsseldorf, Universitätstr. 1, 40225, Düsseldorf, Germany. .,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany. .,JARA-BRAIN, Juelich-Aachen Research Alliance, 52425, Jülich, Germany.
| |
Collapse
|
21
|
Dafflon J, Pinaya WHL, Turkheimer F, Cole JH, Leech R, Harris MA, Cox SR, Whalley HC, McIntosh AM, Hellyer PJ. An automated machine learning approach to predict brain age from cortical anatomical measures. Hum Brain Mapp 2020; 41:3555-3566. [PMID: 32415917 PMCID: PMC7416036 DOI: 10.1002/hbm.25028] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/10/2020] [Accepted: 04/21/2020] [Indexed: 12/31/2022] Open
Abstract
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.
Collapse
Affiliation(s)
- Jessica Dafflon
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Walter H. L. Pinaya
- Department of Psychosis StudiesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
- Center of Mathematics, Computation and CognitionUniversidade Federal do ABCSanto AndréBrazil
| | - Federico Turkheimer
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - James H. Cole
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Robert Leech
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | | | - Simon R. Cox
- Lothian Birth Cohorts group, Department of PsychologyUniversity of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | | | | | - Peter J. Hellyer
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| |
Collapse
|
22
|
Brink-Kjaer A, Mignot E, Sorensen HBD, Jennum P. Predicting Age with Deep Neural Networks from Polysomnograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:146-149. [PMID: 33017951 DOI: 10.1109/embc44109.2020.9176254] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines. We validated the framework by predicting the age of subjects. We designed a hierarchical attention network architecture, which can be pre-trained to predict labels based on 5-minute epochs of data and fine-tuned to predict based on whole-night polysomnography recordings. The model was trained on 511 recordings from the Cleveland Family study and tested on 146 test subjects aged between 6 to 88 years. The proposed network achieved a mean absolute error of 7.36 years and a correlation to true age of 0.857. Sleep can be analyzed using our end-to-end deep learning framework, which we expect can generalize to learning other subject-specific labels such as sleep disorders. The difference in the predicted and chronological age is further proposed as an estimate of biological age.
Collapse
|
23
|
Cole JH, Raffel J, Friede T, Eshaghi A, Brownlee WJ, Chard D, De Stefano N, Enzinger C, Pirpamer L, Filippi M, Gasperini C, Rocca MA, Rovira A, Ruggieri S, Sastre-Garriga J, Stromillo ML, Uitdehaag BMJ, Vrenken H, Barkhof F, Nicholas R, Ciccarelli O. Longitudinal Assessment of Multiple Sclerosis with the Brain-Age Paradigm. Ann Neurol 2020; 88:93-105. [PMID: 32285956 DOI: 10.1002/ana.25746] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 04/06/2020] [Accepted: 04/09/2020] [Indexed: 12/18/2022]
Abstract
OBJECTIVE During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. METHODS In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. RESULTS Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). INTERPRETATION The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.
Collapse
Affiliation(s)
- James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Computational, Cognitive, and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK
| | - Joel Raffel
- Centre for Neuroinflammation and Neurodegeneration, Faculty of Medicine, Imperial College London, London, UK
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Wallace J Brownlee
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| | - Declan Chard
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Christian Enzinger
- Research Unit for Neural Repair and Plasticity, Department of Neurology and Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria
| | - Lukas Pirpamer
- Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Claudio Gasperini
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Maria Assunta Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Alex Rovira
- MR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Serena Ruggieri
- Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy
| | - Jaume Sastre-Garriga
- Department of Neurology / Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | | | | | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
- National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK
| | - Richard Nicholas
- Centre for Neuroinflammation and Neurodegeneration, Faculty of Medicine, Imperial College London, London, UK
- Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, UK
| | - Olga Ciccarelli
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
| |
Collapse
|
24
|
Da Costa PF, Dafflon J, Pinaya WHL. Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019. Front Psychiatry 2020; 11:604478. [PMID: 33343431 PMCID: PMC7738323 DOI: 10.3389/fpsyt.2020.604478] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 11/09/2020] [Indexed: 01/03/2023] Open
Abstract
As we age, our brain structure changes and our cognitive capabilities decline. Although brain aging is universal, rates of brain aging differ markedly, which can be associated with pathological mechanism of psychiatric and neurological diseases. Predictive models have been applied to neuroimaging data to learn patterns associated with this variability and develop a neuroimaging biomarker of the brain condition. Aiming to stimulate the development of more accurate brain-age predictors, the Predictive Analytics Competition (PAC) 2019 provided a challenge that included a dataset of 2,640 participants. Here, we present our approach which placed between the top 10 of the challenge. We developed an ensemble of shallow machine learning methods (e.g., Support Vector Regression and Decision Tree-based regressors) that combined voxel-based and surface-based morphometric data. We used normalized brain volume maps (i.e., gray matter, white matter, or both) and features of cortical regions and anatomical structures, like cortical thickness, volume, and mean curvature. In order to fine-tune the hyperparameters of the machine learning methods, we combined the use of genetic algorithms and grid search. Our ensemble had a mean absolute error of 3.7597 years on the competition, showing the potential that shallow methods still have in predicting brain-age.
Collapse
Affiliation(s)
- Pedro F Da Costa
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,Centre for Brain and Cognitive Development, Birkbeck College, London, United Kingdom
| | - Jessica Dafflon
- Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Walter H L Pinaya
- Department of Biomedical Engineering, King's College London, London, United Kingdom
| |
Collapse
|