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Plini ERG, O’Hanlon E, Boyle R, Sibilia F, Rikhye G, Kenney J, Whelan R, Melnychuk MC, Robertson IH, Dockree PM. Examining the Role of the Noradrenergic Locus Coeruleus for Predicting Attention and Brain Maintenance in Healthy Old Age and Disease: An MRI Structural Study for the Alzheimer's Disease Neuroimaging Initiative. Cells 2021; 10:1829. [PMID: 34359997 PMCID: PMC8306442 DOI: 10.3390/cells10071829] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 07/08/2021] [Accepted: 07/08/2021] [Indexed: 12/18/2022] Open
Abstract
The noradrenergic theory of Cognitive Reserve (Robertson, 2013-2014) postulates that the upregulation of the locus coeruleus-noradrenergic system (LC-NA) originating in the brainstem might facilitate cortical networks involved in attention, and protracted activation of this system throughout the lifespan may enhance cognitive stimulation contributing to reserve. To test the above-mentioned theory, a study was conducted on a sample of 686 participants (395 controls, 156 mild cognitive impairment, 135 Alzheimer's disease) investigating the relationship between LC volume, attentional performance and a biological index of brain maintenance (BrainPAD-an objective measure, which compares an individual's structural brain health, reflected by their voxel-wise grey matter density, to the state typically expected at that individual's age). Further analyses were carried out on reserve indices including education and occupational attainment. Volumetric variation across groups was also explored along with gender differences. Control analyses on the serotoninergic (5-HT), dopaminergic (DA) and cholinergic (Ach) systems were contrasted with the noradrenergic (NA) hypothesis. The antithetic relationships were also tested across the neuromodulatory subcortical systems. Results supported by Bayesian modelling showed that LC volume disproportionately predicted higher attentional performance as well as biological brain maintenance across the three groups. These findings lend support to the role of the noradrenergic system as a key mediator underpinning the neuropsychology of reserve, and they suggest that early prevention strategies focused on the noradrenergic system (e.g., cognitive-attentive training, physical exercise, pharmacological and dietary interventions) may yield important clinical benefits to mitigate cognitive impairment with age and disease.
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Affiliation(s)
- Emanuele R. G. Plini
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Erik O’Hanlon
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
- Department of Psychiatry, Royal College of Surgeons in Ireland, Hospital Rd, Beaumont, 9QRH+4F Dublin, Ireland
- Department of Psychiatry, School of Medicine Dublin, Trinity College Dublin, 152-160 Pearse St, 8QV3+99 Dublin, Ireland;
| | - Rory Boyle
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Francesca Sibilia
- Department of Psychiatry, School of Medicine Dublin, Trinity College Dublin, 152-160 Pearse St, 8QV3+99 Dublin, Ireland;
| | - Gaia Rikhye
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Joanne Kenney
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Robert Whelan
- Department of Psychology, Global Brain Health Institute, Trinity College Dublin, Lloyd Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland;
| | - Michael C. Melnychuk
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
| | - Ian H. Robertson
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
- Department of Psychology, Global Brain Health Institute, Trinity College Dublin, Lloyd Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland;
| | - Paul M. Dockree
- Department of Psychology, Trinity College Institute of Neuroscience, Trinity College Dublin, Llyod Building, 42A Pearse St, 8PVX+GJ Dublin, Ireland; (E.O.); (R.B.); (G.R.); (J.K.); (M.C.M.); (I.H.R.); (P.M.D.)
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152
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Sun T, Zhan J, Li F, Ji C, Wu H. Environmentally relevant concentrations of microplastics influence the locomotor activity of aquatic biota. JOURNAL OF HAZARDOUS MATERIALS 2021; 414:125581. [PMID: 34030420 DOI: 10.1016/j.jhazmat.2021.125581] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/09/2021] [Accepted: 03/02/2021] [Indexed: 05/17/2023]
Abstract
The occurrence of microplastics (MPs) in various marine and freshwater matrices has attracted great attention. However, the effect of MPs in natural environment on the locomotor performance of aquatic biota is still controversial. Therefore, this meta-analysis was conducted, involving 116 effect sizes from 2347 samples, to quantitatively evaluate the alteration in locomotor behavior of aquatic organisms induced by MPs at environmentally relevant concentrations (≤ 1 mg/L, median = 0.125 mg/L). It was shown that MP exposure significantly inhibited the average speed and moved distance of aquatic organisms by 5% and 8% (p < 0.05), respectively, compared with the control, resulting in an obvious reduction of locomotor ability by 6% (p < 0.05). Egger's test indicated that the results were stable without publication bias (p > 0.05). The complex influence of MPs on the locomotor ability were characterized through random-effects meta-regression analyses, presenting size-, time-, concentration-dependent manners and multi-factors interactions. In addition, several physiological changes, including energy reserve reduction, metabolism disorder, gut microbiota dysbiosis, inflammation response, neurotoxic response, and oxidative stress, of aquatic organisms triggered by MP exposure at environmentally relevant concentrations were also provided, which might account for the MPs-induced locomotor activity decline.
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Affiliation(s)
- Tao Sun
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Junfei Zhan
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Fei Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China
| | - Chenglong Ji
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China
| | - Huifeng Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS); Shandong Key Laboratory of Coastal Environmental Processes, YICCAS, Yantai 264003, PR China; Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, PR China; Center for Ocean Mega-Science, Chinese Academy of Sciences (CAS), Qingdao 266071, PR China.
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153
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Butler ER, Chen A, Ramadan R, Le TT, Ruparel K, Moore TM, Satterthwaite TD, Zhang F, Shou H, Gur RC, Nichols TE, Shinohara RT. Pitfalls in brain age analyses. Hum Brain Mapp 2021; 42:4092-4101. [PMID: 34190372 PMCID: PMC8357007 DOI: 10.1002/hbm.25533] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/08/2021] [Accepted: 04/29/2021] [Indexed: 01/02/2023] Open
Abstract
Over the past decade, there has been an abundance of research on the difference between age and age predicted using brain features, which is commonly referred to as the “brain age gap.” Researchers have identified that the brain age gap, as a linear transformation of an out‐of‐sample residual, is dependent on age. As such, any group differences on the brain age gap could simply be due to group differences on age. To mitigate the brain age gap's dependence on age, it has been proposed that age be regressed out of the brain age gap. If this modified brain age gap is treated as a corrected deviation from age, model accuracy statistics such as R2 will be artificially inflated to the extent that it is highly improbable that an R2 value below .85 will be obtained no matter the true model accuracy. Given the limitations of proposed brain age analyses, further theoretical work is warranted to determine the best way to quantify deviation from normality.
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Affiliation(s)
- Ellyn R. Butler
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew Chen
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Rabie Ramadan
- Mathematics DepartmentTemple UniversityPhiladelphiaPennsylvaniaUSA
| | - Trang T. Le
- Department of Biostatistics, Epidemiology and InformaticsInstitute for Biomedical Informatics, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kosha Ruparel
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tyler M. Moore
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics & Neuroimaging Center, Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Fengqing Zhang
- Department of PsychologyDrexel UniversityPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Brain Behavior Laboratory, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Thomas E. Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and DiscoveryUniversity of OxfordOxfordUK
- FMRIB, Wellcome Centre for Integrative NeuroimagingOxfordUK
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor, Center for Clinical Epidemiology and BiostatisticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and AnalyticsDepartment of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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154
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Beheshti I, Ganaie MA, Paliwal V, Rastogi A, Razzak I, Tanveer M. Predicting brain age using machine learning algorithms: A comprehensive evaluation. IEEE J Biomed Health Inform 2021; 26:1432-1440. [PMID: 34029201 DOI: 10.1109/jbhi.2021.3083187] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Machine learning (ML) algorithms play a vital role in brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (N = 788) as a training set followed by different regression algorithms (18 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimers disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms (mean absolute error (MAE) from 4.63 to 7.14 yrs, R2 from 0.76 to 0.88). The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE = 4.63 yrs, R2 = 0.88, 95% CI = [-1.26, 1.42]) and Binary Decision Tree algorithm (MAE = 7.14 yrs, R2 = 0.76, 95% CI = [-1.50, 2.62]), respectively. Our experimental results demonstrate that prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.
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155
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Karim HT, Ly M, Yu G, Krafty R, Tudorascu DL, Aizenstein HJ, Andreescu C. Aging faster: worry and rumination in late life are associated with greater brain age. Neurobiol Aging 2021; 101:13-21. [PMID: 33561786 PMCID: PMC8122027 DOI: 10.1016/j.neurobiolaging.2021.01.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/05/2021] [Accepted: 01/09/2021] [Indexed: 01/07/2023]
Abstract
Older adults with anxiety have lower gray matter brain volume-a component of accelerated aging. We have previously validated a machine learning model to predict brain age, an estimate of an individual's age based on voxel-wise gray matter images. We investigated associations between brain age and anxiety, depression, stress, and emotion regulation. We recruited 78 participants (≥50 years) along a wide range of worry severity. We collected imaging data and computed voxel-wise gray matter images, which were input into an existing machine learning model to estimate brain age. We conducted a multivariable linear regression between brain age and age, sex, race, education, worry, anxiety, depression, rumination, neuroticism, stress, reappraisal, and suppression. We found that greater brain age was significantly associated with greater age, male sex, greater worry, greater rumination, and lower suppression. Male sex, worry, and rumination are associated with accelerated aging in late life and expressive suppression may have a protective effect. These results provide evidence for the transdiagnostic model of negative repetitive thoughts, which are associated with cognitive decline, amyloid, and tau.
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Affiliation(s)
- Helmet T Karim
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Maria Ly
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gary Yu
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Robert Krafty
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Dana L Tudorascu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Howard J Aizenstein
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Carmen Andreescu
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
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156
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Lee WH, Antoniades M, Schnack HG, Kahn RS, Frangou S. Brain age prediction in schizophrenia: Does the choice of machine learning algorithm matter? Psychiatry Res Neuroimaging 2021; 310:111270. [PMID: 33714090 PMCID: PMC8056405 DOI: 10.1016/j.pscychresns.2021.111270] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 03/03/2021] [Accepted: 03/04/2021] [Indexed: 12/27/2022]
Abstract
Brain-predicted age difference (brainPAD) has been used in schizophrenia to assess individual-level deviation in the biological age of the patients' brain (i.e., brain-age) from normative reference brain structural datasets. There is marked inter-study variation in brainPAD in schizophrenia which is commonly attributed to sample heterogeneity. However, the potential contribution of the different machine learning algorithms used for brain-age estimation has not been systematically evaluated. Here, we aimed to assess variation in brain-age estimated by six commonly used algorithms [ordinary least squares regression, ridge regression, least absolute shrinkage and selection operator regression, elastic-net regression, linear support vector regression, and relevance vector regression] when applied to the same brain structural features from the same sample. To assess reproducibility we used data from two publically available samples of healthy individuals (n = 1092 and n = 492) and two further samples, from the Icahn School of Medicine at Mount Sinai (ISMMS) and the Center of Biomedical Research Excellence (COBRE), comprising both patients with schizophrenia (n = 90 and n = 76) and healthy individuals (n = 200 and n = 87). Performance similarity across algorithms was compared within each sample using correlation analyses and hierarchical clustering. Across all samples ordinary least squares regression, the only algorithm without a penalty term, performed markedly worse. All other algorithms showed comparable performance but they still yielded variable brain-age estimates despite being applied to the same data. Although brainPAD was consistently higher in patients with schizophrenia, it varied by algorithm from 3.8 to 5.2 years in the ISMMS sample and from to 4.5 to 11.7 years in the COBRE sample. Algorithm choice introduces variations in brain-age and may confound inter-study comparisons when assessing brainPAD in schizophrenia.
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Affiliation(s)
- Won Hee Lee
- Department of Software Convergence, Kyung Hee University, Yongin, Republic of Korea
| | - Mathilde Antoniades
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States
| | - Hugo G Schnack
- Department of Psychiatry, UMCU Brain Center, University Medical Center Utrecht, Netherlands
| | - Rene S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1425 Madison Avenue, New York, NY 20019, United States; Department of Psychiatry, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Canada.
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157
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Anatürk M, Kaufmann T, Cole JH, Suri S, Griffanti L, Zsoldos E, Filippini N, Singh‐Manoux A, Kivimäki M, Westlye LT, Ebmeier KP, de Lange AG. Prediction of brain age and cognitive age: Quantifying brain and cognitive maintenance in aging. Hum Brain Mapp 2021; 42:1626-1640. [PMID: 33314530 PMCID: PMC7978127 DOI: 10.1002/hbm.25316] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 11/27/2020] [Accepted: 12/01/2020] [Indexed: 12/17/2022] Open
Abstract
The concept of brain maintenance refers to the preservation of brain integrity in older age, while cognitive reserve refers to the capacity to maintain cognition in the presence of neurodegeneration or aging-related brain changes. While both mechanisms are thought to contribute to individual differences in cognitive function among older adults, there is currently no "gold standard" for measuring these constructs. Using machine-learning methods, we estimated brain and cognitive age based on deviations from normative aging patterns in the Whitehall II MRI substudy cohort (N = 537, age range = 60.34-82.76), and tested the degree of correspondence between these constructs, as well as their associations with premorbid IQ, education, and lifestyle trajectories. In line with established literature highlighting IQ as a proxy for cognitive reserve, higher premorbid IQ was linked to lower cognitive age independent of brain age. No strong evidence was found for associations between brain or cognitive age and lifestyle trajectories from midlife to late life based on latent class growth analyses. However, post hoc analyses revealed a relationship between cumulative lifestyle measures and brain age independent of cognitive age. In conclusion, we present a novel approach to characterizing brain and cognitive maintenance in aging, which may be useful for future studies seeking to identify factors that contribute to brain preservation and cognitive reserve mechanisms in older age.
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Affiliation(s)
- Melis Anatürk
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
| | - James H. Cole
- Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
| | - Sana Suri
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Ludovica Griffanti
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Enikő Zsoldos
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Nicola Filippini
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Archana Singh‐Manoux
- Epidemiology of Ageing and Neurodegenerative diseasesUniversité de Paris, INSERM U1153ParisFrance
- Department of Epidemiology and Public HealthUniversity College LondonLondonUK
| | - Mika Kivimäki
- Department of Epidemiology and Public HealthUniversity College LondonLondonUK
| | - Lars T. Westlye
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | | | - Ann‐Marie G. de Lange
- Department of PsychiatryUniversity of OxfordOxfordUK
- NORMENT, Institute of Clinical MedicineUniversity of Oslo, & Division of Mental Health and Addiction, Oslo University HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
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158
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Subramaniapillai S, Rajagopal S, Snytte J, Otto AR, Einstein G, Rajah MN. Sex differences in brain aging among adults with family history of Alzheimer's disease and APOE4 genetic risk. Neuroimage Clin 2021; 30:102620. [PMID: 33857772 PMCID: PMC8065341 DOI: 10.1016/j.nicl.2021.102620] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 03/02/2021] [Accepted: 03/03/2021] [Indexed: 12/03/2022]
Abstract
Emerging evidence suggests that Alzheimer's Disease (AD) risk factors may differentially contribute to disease trajectory in women than men. Determining the effect of AD risk factors on brain aging in women, compared to men, is critical for understanding whether there are sex differences in the pathways towards AD in cognitively intact but at-risk adults. Brain Age Gap (BAG) is a concept used increasingly as a measure of brain health; BAG is defined as the difference between predicted age (based on structural MRI) and chronological age, with negative values reflecting preserved brain health with age. Using BAG, we investigated whether there were sex differences in the brain effects of AD risk factors (i.e., family history of AD, and carrying an apolipoprotein E ε4 allele [+APOE4]) in cognitively intact adults, and if this relationship was moderated by modifiable factors (i.e. body mass index [BMI], blood pressure and physical activity). We undertook a cross-sectional study of structural MRIs from 1067 cognitively normal adults across four neuroimaging datasets. An elastic net regression model found that women with a family history of AD and +APOE4 genotype had more advanced brain aging than their male counterparts. In a sub-cohort of women with those risk factors, higher BMI was associated with less brain aging whereas lower BMI was not. In a sub-cohort of women and men with +APOE4, engaging in physical activity was more beneficial to men's brain aging than women's. Our results demonstrate that AD risk factors are associated with greater brain aging in women than men, although there may be more unexplored modifiable factors that influence this relationship. These findings suggest that the complex interplay between unmodifiable and modifiable AD risk factors can potentially protect against brain aging in women and men.
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Affiliation(s)
- Sivaniya Subramaniapillai
- Department of Psychology, McGill University, 2001 Avenue McGill College, Montréal, QC H3A 1G1, Canada; Brain Imaging Centre, Douglas Institute Research Centre, 6875 LaSalle Blvd Verdun, Montréal, QC H4H 1R3, Canada.
| | - Sricharana Rajagopal
- Brain Imaging Centre, Douglas Institute Research Centre, 6875 LaSalle Blvd Verdun, Montréal, QC H4H 1R3, Canada
| | - Jamie Snytte
- Department of Psychology, McGill University, 2001 Avenue McGill College, Montréal, QC H3A 1G1, Canada; Brain Imaging Centre, Douglas Institute Research Centre, 6875 LaSalle Blvd Verdun, Montréal, QC H4H 1R3, Canada
| | - A Ross Otto
- Department of Psychology, McGill University, 2001 Avenue McGill College, Montréal, QC H3A 1G1, Canada
| | - Gillian Einstein
- Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada; Rotman Research Institute, Baycrest Hospital, 3560 Bathurst St, Toronto, ON M6A 2E1, Canada; Tema Genus, Linköping University, TEMA-huset, Entrance 37, Room E433, Campus Valla, Linköping, Sweden
| | - M Natasha Rajah
- Brain Imaging Centre, Douglas Institute Research Centre, 6875 LaSalle Blvd Verdun, Montréal, QC H4H 1R3, Canada; Department of Psychiatry, Faculty of Medicine, McGill University, 1033 Avenue des Pins, Montréal, QC H3A 1A1, Canada.
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159
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Dunås T, Wåhlin A, Nyberg L, Boraxbekk CJ. Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance. Cereb Cortex 2021; 31:3393-3407. [PMID: 33690853 PMCID: PMC8196254 DOI: 10.1093/cercor/bhab019] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 01/20/2021] [Accepted: 01/20/2021] [Indexed: 12/11/2022] Open
Abstract
Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.
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Affiliation(s)
- Tora Dunås
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Centre for Demographic and Ageing Research (CEDAR), Umeå University, S-901 87 Umeå, Sweden
| | - Anders Wåhlin
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden
| | - Lars Nyberg
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, S-901 87 Umeå, Sweden
| | - Carl-Johan Boraxbekk
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, S-901 87 Umeå, Sweden.,Department of Radiation Sciences, Umeå University, S-901 87 Umeå, Sweden.,Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, DK-2650 Hvidovre, Denmark.,Institute of Sports Medicine Copenhagen (ISMC), Copenhagen University Hospital Bispebjerg, DK-2400 Copenhagen, Denmark
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160
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Ballester PL, da Silva LT, Marcon M, Esper NB, Frey BN, Buchweitz A, Meneguzzi F. Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability. Front Psychiatry 2021; 12:598518. [PMID: 33716814 PMCID: PMC7949912 DOI: 10.3389/fpsyt.2021.598518] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 01/22/2021] [Indexed: 11/13/2022] Open
Abstract
Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians. Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site. Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model. Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.
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Affiliation(s)
- Pedro L. Ballester
- Neuroscience Graduate Program, McMaster University, Hamilton, ON, Canada
| | - Laura Tomaz da Silva
- School of Technology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Matheus Marcon
- School of Technology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
- BRAINS - Brain Institute of Rio Grande do Sul, Porto Alegre, Brazil
| | - Nathalia Bianchini Esper
- BRAINS - Brain Institute of Rio Grande do Sul, Porto Alegre, Brazil
- Graduate School of Medicine, School of Medicine, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Benicio N. Frey
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, ON, Canada
| | - Augusto Buchweitz
- BRAINS - Brain Institute of Rio Grande do Sul, Porto Alegre, Brazil
- Mood Disorders Program, Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
- Graduate School of Psychology, School of Health and Life Sciences, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
| | - Felipe Meneguzzi
- School of Technology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Brazil
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161
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Apolipoprotein ɛ4 Status and Brain Structure 12 Months after Mild Traumatic Injury: Brain Age Prediction Using Brain Morphometry and Diffusion Tensor Imaging. J Clin Med 2021; 10:jcm10030418. [PMID: 33499167 PMCID: PMC7865561 DOI: 10.3390/jcm10030418] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/15/2021] [Accepted: 01/18/2021] [Indexed: 12/15/2022] Open
Abstract
Background: Apolipoprotein E (APOE) ɛ4 is associated with poor outcome following moderate to severe traumatic brain injury (TBI). There is a lack of studies investigating the influence of APOE ɛ4 on intracranial pathology following mild traumatic brain injury (MTBI). This study explores the association between APOE ɛ4 and MRI measures of brain age prediction, brain morphometry, and diffusion tensor imaging (DTI). Methods: Patients aged 16 to 65 with acute MTBI admitted to the trauma center were included. Multimodal MRI was performed 12 months after injury and associated with APOE ɛ4 status. Corrections for multiple comparisons were done using false discovery rate (FDR). Results: Of included patients, 123 patients had available APOE, volumetric, and DTI data of sufficient quality. There were no differences between APOE ɛ4 carriers (39%) and non-carriers in demographic and clinical data. Age prediction revealed high accuracy both for the DTI-based and the brain morphometry based model. Group comparisons revealed no significant differences in brain-age gap between ɛ4 carriers and non-carriers, and no significant differences in conventional measures of brain morphometry and volumes. Compared to non-carriers, APOE ɛ4 carriers showed lower fractional anisotropy (FA) in the hippocampal part of the cingulum bundle, which did not remain significant after FDR adjustment. Conclusion: APOE ɛ4 carriers might be vulnerable to reduced neuronal integrity in the cingulum. Larger cohort studies are warranted to replicate this finding.
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162
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Kuo CY, Tai TM, Lee PL, Tseng CW, Chen CY, Chen LK, Lee CK, Chou KH, See S, Lin CP. Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework. Front Psychiatry 2021; 12:626677. [PMID: 33833699 PMCID: PMC8021919 DOI: 10.3389/fpsyt.2021.626677] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/22/2021] [Indexed: 01/02/2023] Open
Abstract
Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R 2 = 0.88; support vector regression, MAE = 4.42 years, R 2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R 2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.
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Affiliation(s)
- Chen-Yuan Kuo
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | - Pei-Lin Lee
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | | | | | - Liang-Kung Chen
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | | | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Simon See
- NVIDIA AI Technology Center, NVIDIA, Taipei, Taiwan
| | - Ching-Po Lin
- Aging and Health Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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163
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Rokicki J, Wolfers T, Nordhøy W, Tesli N, Quintana DS, Alnaes D, Richard G, de Lange AMG, Lund MJ, Norbom L, Agartz I, Melle I, Naerland T, Selbaek G, Persson K, Nordvik JE, Schwarz E, Andreassen OA, Kaufmann T, Westlye LT. Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders. Hum Brain Mapp 2020; 42:1714-1726. [PMID: 33340180 PMCID: PMC7978139 DOI: 10.1002/hbm.25323] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/20/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022] Open
Abstract
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
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Affiliation(s)
- Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (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
| | - Thomas Wolfers
- Norwegian Centre for Mental Disorders Research (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
| | - Wibeke Nordhøy
- Department of Diagnostic Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Daniel S Quintana
- Norwegian Centre for Mental Disorders Research (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
| | - Dag Alnaes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (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.,Department of Psychiatry, University of Oxford, Oxford, UK
| | - Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Linn Norbom
- Norwegian Centre for Mental Disorders Research (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.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (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.,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway.,Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, and Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Terje Naerland
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Geir Selbaek
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway.,Department of Geriatric Medicine, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Karin Persson
- Norwegian National Advisory Unit On Ageing and Health, Vestfold Hospital Trust, Tønsberg, Norway
| | | | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (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
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (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
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164
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Kolbeinsson A, Filippi S, Panagakis Y, Matthews PM, Elliott P, Dehghan A, Tzoulaki I. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep 2020; 10:19940. [PMID: 33203906 PMCID: PMC7672070 DOI: 10.1038/s41598-020-76518-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023] Open
Abstract
Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict "healthy" brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.
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Affiliation(s)
- Arinbjörn Kolbeinsson
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK.
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK.
| | - Sarah Filippi
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- Department of Mathematics, Imperial College London, London, SW7 2AZ, UK
| | - Yannis Panagakis
- Department of Computing, Imperial College London, London, SW7 2AZ, UK
- Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - Paul M Matthews
- Department of Brain Sciences, Burlington Danes Building, Imperial College London, London, W12 0NN, UK
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
- National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, Exhibition Road, London, SW7 2AZ, UK
- Health Data Research UK London at Imperial College London, Exhibition Road, London, SW7 2AZ, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, St Mary's Campus, Norfolk Place, London, W2 1PG, UK
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
- UK Dementia Research Institute at Imperial College, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
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165
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Nenning KH, Xu T, Schwartz E, Arroyo J, Woehrer A, Franco AR, Vogelstein JT, Margulies DS, Liu H, Smallwood J, Milham MP, Langs G. Joint embedding: A scalable alignment to compare individuals in a connectivity space. Neuroimage 2020; 222:117232. [PMID: 32771618 PMCID: PMC7779372 DOI: 10.1016/j.neuroimage.2020.117232] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/31/2020] [Accepted: 08/02/2020] [Indexed: 11/15/2022] Open
Abstract
A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.
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Affiliation(s)
- Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jesus Arroyo
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Hesheng Liu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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166
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de Lange AMG, Anatürk M, Suri S, Kaufmann T, Cole JH, Griffanti L, Zsoldos E, Jensen DEA, Filippini N, Singh-Manoux A, Kivimäki M, Westlye LT, Ebmeier KP. Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study. Neuroimage 2020; 222:117292. [PMID: 32835819 PMCID: PMC8121758 DOI: 10.1016/j.neuroimage.2020.117292] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/21/2022] Open
Abstract
Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.
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Affiliation(s)
- Ann-Marie G de Lange
- Department of Psychiatry, University of Oxford, Oxford, UK; Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Melis Anatürk
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Sana Suri
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - James H Cole
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Ludovica Griffanti
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Enikő Zsoldos
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Daria E A Jensen
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Nicola Filippini
- Department of Psychiatry, University of Oxford, Oxford, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Archana Singh-Manoux
- Epidemiology of Ageing and Neurodegenerative Diseases, Universit de Paris, INSERM U1153, Paris France; Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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167
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de Lange AG, Barth C, Kaufmann T, Anatürk M, Suri S, Ebmeier KP, Westlye LT. The maternal brain: Region-specific patterns of brain aging are traceable decades after childbirth. Hum Brain Mapp 2020; 41:4718-4729. [PMID: 32767637 PMCID: PMC7555081 DOI: 10.1002/hbm.25152] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 06/30/2020] [Accepted: 07/16/2020] [Indexed: 12/11/2022] Open
Abstract
Pregnancy involves maternal brain adaptations, but little is known about how parity influences women's brain aging trajectories later in life. In this study, we replicated previous findings showing less apparent brain aging in women with a history of childbirths, and identified regional brain aging patterns linked to parity in 19,787 middle- and older-aged women. Using novel applications of brain-age prediction methods, we found that a higher number of previous childbirths were linked to less apparent brain aging in striatal and limbic regions. The strongest effect was found in the accumbens-a key region in the mesolimbic reward system, which plays an important role in maternal behavior. While only prospective longitudinal studies would be conclusive, our findings indicate that subcortical brain modulations during pregnancy and postpartum may be traceable decades after childbirth.
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Affiliation(s)
- Ann‐Marie G. de Lange
- Department of PsychiatryUniversity of OxfordOxfordUK
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Claudia Barth
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Tobias Kaufmann
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and AddictionOslo University HospitalOsloNorway
| | - Melis Anatürk
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | - Sana Suri
- Department of PsychiatryUniversity of OxfordOxfordUK
- Wellcome Centre for Integrative NeuroimagingUniversity of OxfordOxfordUK
| | | | - Lars T. Westlye
- Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and AddictionOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
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