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Gardner M, Shinohara RT, Bethlehem RAI, Romero-Garcia R, Warrier V, Dorfschmidt L, Shanmugan S, Thompson P, Seidlitz J, Alexander-Bloch AF, Chen AA. ComBatLS: A location- and scale-preserving method for multi-site image harmonization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.21.599875. [PMID: 39131292 PMCID: PMC11312440 DOI: 10.1101/2024.06.21.599875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.
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Affiliation(s)
- Margaret Gardner
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
- Center for Biomedical Imaging Computing and Analytics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, USA
| | | | - Rafael Romero-Garcia
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, ES
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Varun Warrier
- Department of Psychology, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Lena Dorfschmidt
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Sheila Shanmugan
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA
| | - Paul Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jakob Seidlitz
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aaron F Alexander-Bloch
- Brain-Gene-Development Lab, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew A Chen
- Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
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del Ser T, Valeriano-Lorenzo E, Jáñez-Escalada L, Ávila-Villanueva M, Frades B, Zea MA, Valentí M, Zhang L, Fernández-Blázquez MA. Dimensions of cognitive reserve and their predictive power of cognitive performance and decline in the elderly. FRONTIERS IN DEMENTIA 2023; 2:1099059. [PMID: 39081990 PMCID: PMC11285562 DOI: 10.3389/frdem.2023.1099059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 08/03/2023] [Indexed: 08/02/2024]
Abstract
Background The relative importance of different components of cognitive reserve (CR), as well as their differences by gender, are poorly established. Objective To explore several dimensions of CR, their differences by gender, and their effects on cognitive performance and trajectory in a cohort of older people without relevant psychiatric, neurologic, or systemic conditions. Methods Twenty-one variables related to the education, occupation, social activities, and life habits of 1,093 home-dwelling and cognitively healthy individuals, between 68 and 86 years old, were explored using factorial analyses to delineate several dimensions of CR. These dimensions were contrasted with baseline cognitive performance, follow-up over 5 years of participants' cognitive trajectory, conversion to mild cognitive impairment (MCI), and brain volumes using regression and growth curve models, controlling for gender, age, marital status, number of medications, trait anxiety, depression, and ApoE genotype. Results Five highly intercorrelated dimensions of CR were identified, with some differences in their structure and effects based on gender. Three of them, education/occupation, midlife cognitive activities, and leisure activities, were significantly associated with late-life cognitive performance, accounting for more than 20% of its variance. The education/occupation had positive effect on the rate of cognitive decline during the 5-year follow up in individuals with final diagnosis of MCI but showed a reduced risk for MCI in men. None of these dimensions showed significant relationships with gray or white matter volumes. Conclusion Proxy markers of CR can be represented by five interrelated dimensions. Education/occupation, midlife cognitive activities, and leisure activities are associated with better cognitive performance in old age and provide a buffer against cognitive impairment. Education/occupation may delay the clinical onset of MCI and is also associated with the rate of change in cognitive performance.
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Affiliation(s)
- Teodoro del Ser
- Clinical Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
| | | | - Luis Jáñez-Escalada
- Clinical Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
- Institute of Knowledge Technology, Complutense University, Madrid, Spain
| | | | - Belén Frades
- Clinical Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
| | - María-Ascensión Zea
- Clinical Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
| | - Meritxell Valentí
- Clinical Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
| | - Linda Zhang
- Neuroimaging Department, Alzheimer's Center Reina Sofia—CIEN Foundation, Madrid, Spain
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Xie Y, Sun J, Man W, Zhang Z, Zhang N. Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance. Mol Autism 2023; 14:27. [PMID: 37507798 PMCID: PMC10375633 DOI: 10.1186/s13229-023-00558-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person's perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. METHODS We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter-regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. RESULTS The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. LIMITATIONS There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. CONCLUSIONS ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
- Department of Radiology, Tianjin First Central Hospital, Tianjin, 300192, China
| | - Zhang Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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Fingelkurts AA, Fingelkurts AA. Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications. Brain Sci 2023; 13:brainsci13030520. [PMID: 36979330 PMCID: PMC10046544 DOI: 10.3390/brainsci13030520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan. METHODS AND OBJECTIVE Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months). RESULTS We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately. CONCLUSIONS Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.
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Jockwitz C, Krämer C, Stumme J, Dellani P, Moebus S, Bittner N, Caspers S. Characterization of the angular gyrus in an older adult population: a multimodal multilevel approach. Brain Struct Funct 2023; 228:83-102. [PMID: 35904594 DOI: 10.1007/s00429-022-02529-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 06/26/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus (AG) has been associated with multiple cognitive functions, such as language, spatial and memory functions. Since the AG is thought to be a cross-modal hub region suffering from significant age-related structural atrophy, it may also play a key role in age-related cognitive decline. However, the exact relation between structural atrophy of the AG and cognitive decline in older adults is not fully understood, which may be related to two aspects: First, the AG is cytoarchitectonically divided into two areas, PGa and PGp, potentially sub-serving different cognitive functions. Second, the older adult population is characterized by high between-subjects variability which requires targeting individual phenomena during the aging process. We therefore performed a multimodal (gray matter volume [GMV], resting-state functional connectivity [RSFC] and structural connectivity [SC]) characterization of AG subdivisions PGa and PGp in a large older adult population, together with relations to age, cognition and lifestyle on the group level. Afterwards, we switched the perspective to the individual, which is especially important when it comes to the assessment of individual patients. The AG can be considered a heterogeneous structure in of the older brain: we found the different AG parts to be associated with different patterns of whole-brain GMV associations as well as their associations with RSFC, and SC patterns. Similarly, differential effects of age, cognition and lifestyle on the GMV of AG subdivisions were observed. This suggests each region to be structurally and functionally differentially involved in the older adult's brain network architecture, which was supported by differential molecular and genetic patterns, derived from the EBRAINS multilevel atlas framework. Importantly, individual profiles deviated considerably from the global conclusion drawn from the group study. Hence, general observations within the older adult population need to be carefully considered, when addressing individual conditions in clinical practice.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany. .,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
| | - Camilla Krämer
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Johanna Stumme
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Paulo Dellani
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Susanne Moebus
- Institute of Urban Public Health, University of Duisburg-Essen, Essen, Germany
| | - Nora Bittner
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
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Markov NT, Lindbergh CA, Staffaroni AM, Perez K, Stevens M, Nguyen K, Murad NF, Fonseca C, Campisi J, Kramer J, Furman D. Age-related brain atrophy is not a homogenous process: Different functional brain networks associate differentially with aging and blood factors. Proc Natl Acad Sci U S A 2022; 119:e2207181119. [PMID: 36459652 PMCID: PMC9894212 DOI: 10.1073/pnas.2207181119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 10/04/2022] [Indexed: 12/04/2022] Open
Abstract
Aging is characterized by a progressive loss of brain volume at an estimated rate of 5% per decade after age 40. While these morphometric changes, especially those affecting gray matter and atrophy of the temporal lobe, are predictors of cognitive performance, the strong association with aging obscures the potential parallel, but more specific role, of individual subject physiology. Here, we studied a cohort of 554 human subjects who were monitored using structural MRI scans and blood immune protein concentrations. Using machine learning, we derived a cytokine clock (CyClo), which predicted age with good accuracy (Mean Absolute Error = 6 y) based on the expression of a subset of immune proteins. These proteins included, among others, Placenta Growth Factor (PLGF) and Vascular Endothelial Growth Factor (VEGF), both involved in angiogenesis, the chemoattractant vascular cell adhesion molecule 1 (VCAM-1), the canonical inflammatory proteins interleukin-6 (IL-6) and tumor necrosis factor alpha (TNFα), the chemoattractant IP-10 (CXCL10), and eotaxin-1 (CCL11), previously involved in brain disorders. Age, sex, and the CyClo were independently associated with different functionally defined cortical networks in the brain. While age was mostly correlated with changes in the somatomotor system, sex was associated with variability in the frontoparietal, ventral attention, and visual networks. Significant canonical correlation was observed for the CyClo and the default mode, limbic, and dorsal attention networks, indicating that immune circulating proteins preferentially affect brain processes such as focused attention, emotion, memory, response to social stress, internal evaluation, and access to consciousness. Thus, we identified immune biomarkers of brain aging which could be potential therapeutic targets for the prevention of age-related cognitive decline.
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Affiliation(s)
- Nikola T. Markov
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Cutter A. Lindbergh
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT06030
| | - Adam M. Staffaroni
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - Kevin Perez
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- University of Lausanne, LausanneCH-1015, Switzerland
| | - Michael Stevens
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Khiem Nguyen
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- Nguyen Tat Thanh Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City70000, Vietnam
| | - Natalia F. Murad
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Corrina Fonseca
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - Judith Campisi
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
| | - Joel Kramer
- Department of Neurology, Memory and Aging Center, University of California San Francisco, Weill Institute for Neurosciences, San Francisco, CA94158
| | - David Furman
- Buck AI Platform, Buck Institute for Research on Aging, Novato, CA94945
- Instituto de Investigaciones en Medicina Traslacional, Universidad Austral, Consejo Nacional de Investigaciones Científicas y Técnicas, Pilar1629, Argentina
- Stanford 1000 Immunomes Project, Stanford University School of Medicine, Stanford, CA94305
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7
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Dima D, Modabbernia A, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes‐Eizagirre A, Alnæs D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur‐Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales‐Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim‐Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo‐Facorro B, Crivello F, Crone EA, Dannlowski U, Dale AM, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros‐Bergman H, Fisher SE, Fouche J, Franke B, Frodl T, Fuentes‐Claramonte P, Glahn DC, Gotlib IH, Grabe H, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho B, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch K, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez‐Zalacaín I, Mataix‐Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer‐Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol‐Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez‐Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano‐Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas‐Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Williams SCR, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, Frangou S. Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3-90 years. Hum Brain Mapp 2022; 43:452-469. [PMID: 33570244 PMCID: PMC8675429 DOI: 10.1002/hbm.25320] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/27/2020] [Accepted: 12/06/2020] [Indexed: 12/25/2022] Open
Abstract
Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
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Affiliation(s)
- Danai Dima
- Department of Psychology, School of Arts and Social SciencesCity University of LondonLondonUK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | | | | | | | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Moji Aghajani
- Department of Psychiatry, Amsterdam University Medical CentreLocation VUmcAmsterdamNetherlands
- Institute of Education & Child StudiesSection Forensic Family & Youth Care, Leiden UniversityNetherlands
| | - Theophilus N. Akudjedu
- Institute of Medical Imaging and Visualisation, Department of Medical Science and Public Health, Faculty of Health and Social SciencesBournemouth UniversityPooleUK
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandDublinIreland
| | - Anton Albajes‐Eizagirre
- FIDMAG Germanes HospitalàriesMadridSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Institute of Clinical MedicineUniversity of OsloOsloNorway
| | | | - Micael Andersson
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
| | - Nancy C. Andreasen
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityMannheimGermany
| | - Nuria Bargallo
- Imaging Diagnostic Centre, Hospital ClinicBarcelona University ClinicBarcelonaSpain
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityMannheimGermany
| | - Ramona Baur‐Streubel
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWurzburgGermany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Aurora Bonvino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Stefan Borgwardt
- Department of Psychiatry & PsychotherapyUniversity of LübeckLubeckGermany
| | - Josiane Bourque
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityMannheimGermany
| | - Alan Breier
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia
| | - Rachel M. Brouwer
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Jan K. Buitelaar
- Donders Center of Medical NeurosciencesRadboud UniversityNijmegenNetherlands
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Randy L. Buckner
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Vincent Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, USA Neurology, Radiology, Psychiatry and Biomedical EngineeringEmory UniversityAtlantaGeorgiaUSA
| | | | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandDublinIreland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUK
| | | | - Simon Cervenka
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- Stockholm Health Care ServicesStockholm RegionStockholmSweden
| | - Tiffany M. Chaim‐Avancini
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Victoria Chubar
- Department of NeuroscienceKU Leuven, Mind‐Body Research GroupLeuvenBelgium
| | - Vincent P. Clark
- Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
- Mind Research NetworkAlbuquerqueNew MexicoUSA
| | - Patricia Conrod
- Department of PsychiatryUniversité de MontréalMontrealCanada
| | - Annette Conzelmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity of TübingenTubingenGermany
| | - Benedicto Crespo‐Facorro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- HU Virgen del Rocio, IBiS, University of SevillaSevillaSpain
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxTalenceFrance
| | - Eveline A. Crone
- Erasmus School of Social and Behavioural SciencesErasmus University RotterdamRotterdamNetherlands
- Faculteit der Sociale Wetenschappen, Instituut PsychologieUniversiteit LeidenLeidenNetherlands
| | - Udo Dannlowski
- Department of Psychiatry and PsychotherapyUniversity of MünsterMunsterGermany
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, Department of Neuroscience and Department of RadiologyUniversity of California‐San DiegoLa JollaCaliforniaUSA
| | | | - Eco J. C. de Geus
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Lieuwe de Haan
- Academisch Medisch CentrumUniversiteit van AmsterdamAmsterdamNetherlands
| | - Greig I. de Zubicaray
- Faculty of Health, Institute of Health and Biomedical InnovationQueensland University of TechnologyBrisbaneAustralia
| | - Anouk den Braber
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Erin W. Dickie
- Kimel Family Translational Imaging Genetics LaboratoryCampbell Family Mental Health Research Institute, CAMHTorontoCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Annabella Di Giorgio
- Biological Psychiatry Lab, Fondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Erlend S. Dørum
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- Sunnaas Rehabilitation Hospital HTNesoddenNorway
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental NeurosciencesTechnische Universität DresdenDresdenGermany
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Thomas Espeseth
- Department of PsychologyUniversity of OsloOsloNorway
- Bjørknes CollegeOsloNorway
| | - Helena Fatouros‐Bergman
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- Stockholm Health Care ServicesStockholm RegionStockholmSweden
| | - Simon E. Fisher
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Jean‐Paul Fouche
- Department of Psychiatry and Mental HealthUniversity of Cape TownRondeboschSouth Africa
| | - Barbara Franke
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
- Department of PsychiatryRadboud University Medical CenterNijmegenNetherlands
| | - Thomas Frodl
- Department of Psychiatry and PsychotherapyOtto von Guericke University MagdeburgMagdeburgGermany
| | - Paola Fuentes‐Claramonte
- FIDMAG Germanes HospitalàriesMadridSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - David C. Glahn
- Department of Psychiatry, Tommy Fuss Center for Neuropsychiatric Disease Research Boston Children's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Ian H. Gotlib
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Hans‐Jörgen Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Oliver Grimm
- Department for Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum FrankfurtGoethe UniversitatFrankfurtGermany
| | - Nynke A. Groenewold
- Department of Psychiatry and Mental HealthUniversity of Cape TownRondeboschSouth Africa
- Neuroscience InstituteUniversity of Cape TownRondeboschSouth Africa
| | | | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Patricia Gruner
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Learning Based Recovery CenterVA Connecticut Health SystemNew HavenConnecticutUSA
| | - Rachel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tim Hahn
- Department of Psychiatry and PsychotherapyUniversity of MünsterMunsterGermany
| | - Ben J. Harrison
- Melbourne Neuropsychiatry CenterUniversity of MelbourneMelbourneAustralia
| | - Catharine A. Hartman
- Interdisciplinary Center Psychopathology and Emotion regulationUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sean N. Hatton
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Andreas Heinz
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | - Dirk J. Heslenfeld
- Departments of Experimental and Clinical PsychologyVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Derrek P. Hibar
- Personalized HealthcareGenentech, IncSouth San FranciscoCaliforniaUSA
| | - Ian B. Hickie
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Beng‐Choon Ho
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Pieter J. Hoekstra
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityMannheimGermany
| | - Avram J. Holmes
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Martine Hoogman
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Psychiatry and Mental HealthUniversity of Cape TownRondeboschSouth Africa
| | - Norbert Hosten
- Norbert Institute of Diagnostic Radiology and NeuroradiologyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
| | - Fleur M. Howells
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
- Department for Psychiatry, Psychosomatics and Psychotherapy, Universitätsklinikum FrankfurtGoethe UniversitatFrankfurtGermany
| | | | - Chaim Huyser
- Bascule, Academic Centre for Children and Adolescent PsychiatryDuivendrechtNetherlands
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Terry L. Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and RadiologyUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- Stockholm Health Care ServicesStockholm RegionStockholmSweden
| | - John A. Joska
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Rene Kahn
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Kalnin
- Department of RadiologyOhio State University College of MedicineColumbusOhioUSA
| | - Ryota Kanai
- Department of NeuroinformaticsAraya, IncTokyoJapan
| | - Marieke Klein
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Psychiatry and Mental HealthUniversity of Cape TownRondeboschSouth Africa
- Department of PsychiatryUniversity of California San DiegoLa JollaCaliforniaUSA
| | | | - Laura Koenders
- Department of PsychiatryUniversity of MelbourneMelbourneAustralia
| | - Sanne Koops
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Bernd Krämer
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Jim Lagopoulos
- Sunshine Coast Mind and Neuroscience, Thompson InstituteUniversity of the Sunshine CoastSunshine CoastAustralia
| | - Luisa Lázaro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of Child and Adolescent Psychiatry and PsychologyHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Irina Lebedeva
- Mental Health Research CenterRussian Academy of Medical SciencesMoskvaRussia
| | - Won Hee Lee
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Klaus‐Peter Lesch
- Department of Psychiatry, Psychosomatics and PsychotherapyJulius‐Maximilians Universität WürzburgWurzburgGermany
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | | | - Sophie Maingault
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxTalenceFrance
| | - Nicholas G. Martin
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteBrisbaneAustralia
| | - Ignacio Martínez‐Zalacaín
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - David Mataix‐Cols
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- Stockholm Health Care ServicesStockholm RegionStockholmSweden
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxTalenceFrance
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandDublinIreland
| | - Brenna C. McDonald
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Katie L. McMahon
- School of Clinical Sciences, Institute of Health and Biomedical InnovationQueensland University of TechnologyBrisbaneAustralia
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandDublinIreland
| | - Susanne Meinert
- Department of Psychiatry and PsychotherapyUniversity of MünsterMunsterGermany
| | - José M. Menchón
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Sarah E. Medland
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteBrisbaneAustralia
| | - Andreas Meyer‐Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Jilly Naaijen
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Pablo Najt
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandDublinIreland
| | - Tomohiro Nakao
- Department of Clinical MedicineKyushu UniversityKyushuJapan
| | | | - Lars Nyberg
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
- Department of Radiation Sciences, Umeå Center for Functional Brain ImagingUmeå UniversityUmeåSweden
| | - Jaap Oosterlaan
- Department of Clinical NeuropsychologyAmsterdam University Medical Centre, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Víctor Ortiz‐García de la Foz
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of Psychiatry, University Hospital “Marques de Valdecilla”Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Instituto de Salud Carlos IIIMadridSpain
| | - Yannis Paloyelis
- Department of Neuroimaging, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Paul Pauli
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWurzburgGermany
- Centre of Mental HealthUniversity of WürzburgWurzburgGermany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes HospitalàriesMadridSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Maria J. Portella
- FIDMAG Germanes HospitalàriesMadridSpain
- Department of Psychiatry, Hospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant PauUniversitat Autònoma de BarcelonaBarcelonaSpain
| | - Steven G. Potkin
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
| | - Joaquim Radua
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
- Department of Psychosis Studies, Institute of PsychiatryPsychology & Neuroscience, King's College LondonLondonUK
| | - Andreas Reif
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Daniel A. Rinker
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Joshua L. Roffman
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Matthew D. Sacchet
- Center for Depression, Anxiety, and Stress ResearchMcLean Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia
| | | | - Pascual Sánchez‐Juan
- Department of Psychiatry, University Hospital “Marques de Valdecilla”Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED)MadridSpain
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Lianne Schmaal
- Orygen, The National Centre of Excellence in Youth Mental HealthParkvilleAustralia
- Centre for Youth Mental HealthThe University of MelbourneMelbourneAustralia
| | - Knut Schnell
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
| | - Gunter Schumann
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
- Centre for Population Neuroscience and Precision Medicine, Institute of PsychiatryPsychology & Neuroscience, King's College LondonLondonUK
| | - Kang Sim
- Institute of Mental HealthSingaporeSingapore
| | - Jordan W. Smoller
- Center for Genomic MedicineMassachusetts General HospitalBostonMassachusettsUSA
| | - Iris Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit GroningenUniversity Medical Center GroningenGöttingenNetherlands
| | - Carles Soriano‐Mas
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | | | | | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Henk S. Temmingh
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Diana Tordesillas‐Gutiérrez
- FIDMAG Germanes HospitalàriesMadridSpain
- Neuroimaging Unit, Technological FacilitiesValdecilla Biomedical Research Institute IDIVALSantanderSpain
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia
| | - Jessica A. Turner
- College of Arts and SciencesGeorgia State UniversityAtlantaGeorgiaUSA
| | - Anne Uhlmann
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Odile A. van den Heuvel
- Department of Psychiatry, Amsterdam University Medical CentreLocation VUmcAmsterdamNetherlands
| | - Dennis van den Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical MedicineUniversity of OsloOsloNorway
- Division of Mental Health and Addiction, Institute of Clinical MedicineUniversity of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Nic J. A. van der Wee
- Department of PsychiatryLeiden University Medical CenterLeidenNetherlands
- Leiden Institute for Brain and CognitionLeidenNetherlands
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical Center, Sophia Children's HospitalRotterdamThe Netherlands
| | - Dennis van't Ent
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Theo G. M. van Erp
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
| | - Ilya M. Veer
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | - Dick J. Veltman
- Department of Psychiatry, Amsterdam University Medical CentreLocation VUmcAmsterdamNetherlands
| | - Aristotle Voineskos
- Faculty of Health, Institute of Health and Biomedical InnovationQueensland University of TechnologyBrisbaneAustralia
- Kimel Family Translational Imaging Genetics LaboratoryCampbell Family Mental Health Research Institute, CAMHTorontoCanada
| | - Henry Völzke
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
- German Centre for Cardiovascular Research (DZHK), partner site GreifswaldGreifswaldGermany
- German Center for Diabetes Research (DZD), partner site GreifswaldGreifswaldGermany
| | - Henrik Walter
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | | | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of MedicineNorthwestern UniversityChicagoIllinoisUSA
| | - Yang Wang
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Thomas H. Wassink
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesSydneyAustralia
| | - John D. West
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Lars T. Westlye
- Biological Psychiatry Lab, Fondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
| | | | - Lara M. Wierenga
- Developmental and Educational Psychology UnitInstitute of Psychology, Leiden UniversityLeidenNetherlands
| | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | - Katharina Wittfeld
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Amanda Worker
- Department of Neuroimaging, Institute of Psychiatry, Psychology and NeuroscienceKing's College LondonLondonUK
| | | | - Kun Yang
- National High Magnetic Field LaboratoryFlorida State UniversityTallahasseeFloridaUSA
| | - Yulyia Yoncheva
- Department of Child and Adolescent PsychiatryChild Study Center, NYU Langone HealthNew YorkNew YorkUSA
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
- Instituto de Ensino e Pesquisa, Hospital Sírio‐LibanêsSão PauloBrazil
| | - Georg C. Ziegler
- Division of Molecular Psychiatry, Center of Mental HealthUniversity of WürzburgWurzburgGermany
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Sophia Frangou
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverCanada
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Borys D, Kijonka M, Psiuk-Maksymowicz K, Gorczewski K, Zarudzki L, Sokol M, Swierniak A. Non-parametric MRI Brain Atlas for the Polish Population. Front Neuroinform 2021; 15:684759. [PMID: 34690731 PMCID: PMC8526931 DOI: 10.3389/fninf.2021.684759] [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: 03/23/2021] [Accepted: 09/02/2021] [Indexed: 11/13/2022] Open
Abstract
Introduction: The application of magnetic resonance imaging (MRI) to acquire detailed descriptions of the brain morphology in vivo is a driving force in brain mapping research. Most atlases are based on parametric statistics, however, the empirical results indicate that the population brain tissue distributions do not exhibit exactly a Gaussian shape. Our aim was to verify the population voxel-wise distribution of three main tissue classes: gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), and to construct the brain templates for the Polish (Upper Silesian) healthy population with the associated non-parametric tissue probability maps (TPMs) taking into account the sex and age influence. Material and Methods: The voxel-wise distributions of these tissues were analyzed using the Shapiro-Wilk test. The non-parametric atlases were generated from 96 brains of the ethnically homogeneous, neurologically healthy, and radiologically verified group examined in a 3-Tesla MRI system. The standard parametric tissue proportion maps were also calculated for the sake of comparison. The maps were compared using the Wilcoxon signed-rank test and Kolmogorov-Smirnov test. The volumetric results segmented with the parametric and non-parametric templates were also analyzed. Results: The results confirmed that in each brain structure (regardless of the studied sub-population) the data distribution is skewed and apparently not Gaussian. The determined non-parametric and parametric templates were statistically compared, and significant differences were found between the maps obtained using both measures (the maps of GM, WM, and CSF). The impacts of applying the parametric and non-parametric TPMs on the segmentation process were also compared. The GM volumes are significantly greater when using the non-parametric atlas in the segmentation procedure, while the CSF volumes are smaller. Discussion and Conclusion: To determine the population atlases the parametric measures are uncritically and widely used. However, our findings suggest that the mean and parametric measures of such skewed distribution may not be the most appropriate summary statistic to find the best spatial representations of the structures in a standard space. The non-parametric methodology is more relevant and universal than the parametric approach in constructing the MRI brain atlases.
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Affiliation(s)
- Damian Borys
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
| | - Marek Kijonka
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Krzysztof Psiuk-Maksymowicz
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
| | - Kamil Gorczewski
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Maria Sokol
- Department of Medical Physics, Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch, Gliwice, Poland
| | - Andrzej Swierniak
- Faculty of Automatic Control, Electronics and Computer Science, Department of Systems Biology and Engineering, Silesian University of Technology, Gliwice, Poland.,Biotechnology Center, Silesian University of Technology, Gliwice, Poland
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9
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Deep characterization of individual brain-phenotype relations using a multilevel atlas. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2021.04.016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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10
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Higher Framingham Risk Scores are associated with greater loss of brain volume over time in multiple sclerosis. Mult Scler Relat Disord 2021; 54:103088. [PMID: 34186319 DOI: 10.1016/j.msard.2021.103088] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 05/28/2021] [Accepted: 06/13/2021] [Indexed: 12/19/2022]
Abstract
BACKGROUND Few studies have evaluated the association between comorbidities associated with increased vascular risk and brain volume changes in multiple sclerosis (MS). To date, findings have not been consistent with respect to which comorbidities are associated with lower brain volumes or whether comorbidities associated with increased vascular risk are associated with greater brain volume loss over time. OBJECTIVES We aimed to evaluate the association between the Framingham Risk Score (FRS) which evaluates vascular risk and normalized whole brain volume in MS. METHODS We included 98 participants with MS who underwent two brain MRIs two years apart, from which whole brain volumes were calculated. Each participant reported their comorbidities and medications taken. Blood pressure, height and weight were recorded and we calculated the FRS. We tested the association between the FRS at baseline and brain volume at the second time point using quantile regression adjusting for baseline normalized brain volume, age, gender and use of disease-modifying therapy. RESULTS As the FRS increased, brain volume was lower, both at enrollment (β= -0.24; 95%CI: -0.42, -0.04) and at follow-up (-0.27; 95%CI: -0.45, -0.08). After further adjustment for age, gender, and use of disease modifying therapy, higher FRS remained associated with lower brain volume at follow-up at the 90th percentile of brain volume (β= -2.22; 95%CI: -3.40, -1.04) but not at the 10th or 50th percentiles. CONCLUSION Higher FRS were associated with lower brain volumes in persons with MS at baseline, and with brain volume loss over time. This effect was most pronounced for persons with higher brain volumes at baseline, which suggests that prevention, detection and effective management of comorbidities associated with vascular risk in people with MS is particularly important early in the disease course.
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11
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Jockwitz C, Mérillat S, Liem F, Oschwald J, Amunts K, Jäncke L, Caspers S. Generalizing Longitudinal Age Effects on Brain Structure - A Two-Study Comparison Approach. Front Hum Neurosci 2021; 15:635687. [PMID: 33935669 PMCID: PMC8085300 DOI: 10.3389/fnhum.2021.635687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
Cross-sectional studies indicate that normal aging is accompanied by decreases in brain structure. Longitudinal studies, however, are relatively rare and inconsistent regarding their outcomes. Particularly the heterogeneity of methods, sample characteristics and the high inter-individual variability in older adults prevent the deduction of general trends. Therefore, the current study aimed to compare longitudinal age-related changes in brain structure (measured through cortical thickness) in two large independent samples of healthy older adults (n = 161 each); the Longitudinal Healthy Aging Brain (LHAB) database project at the University of Zurich, Switzerland, and 1000BRAINS at the Research Center Juelich, Germany. Annual percentage changes in the two samples revealed stable to slight decreases in cortical thickness over time. After correction for major covariates, i.e., baseline age, sex, education, and image quality, sample differences were only marginally present. Results suggest that general trends across time might be generalizable over independent samples, assuming the same methodology is used, and similar sample characteristics are present.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Susan Mérillat
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Franziskus Liem
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Jessica Oschwald
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,C. and O. Vogt Institute for Brain Research, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Lutz Jäncke
- University Research Priority Program "Dynamics of Healthy Aging", University of Zurich, Zurich, Switzerland.,Division of Neuropsychology, University of Zurich, Zurich, Switzerland
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
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12
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Jockwitz C, Caspers S. Resting-state networks in the course of aging-differential insights from studies across the lifespan vs. amongst the old. Pflugers Arch 2021; 473:793-803. [PMID: 33576851 PMCID: PMC8076139 DOI: 10.1007/s00424-021-02520-7] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 01/05/2021] [Accepted: 01/11/2021] [Indexed: 11/29/2022]
Abstract
Resting-state functional connectivity (RSFC) has widely been used to examine reorganization of functional brain networks during normal aging. The extraction of generalizable age trends, however, is hampered by differences in methodological approaches, study designs and sample characteristics. Distinct age ranges of study samples thereby represent an important aspect between studies especially due to the increase in inter-individual variability over the lifespan. The current review focuses on comparing age-related differences in RSFC in the course of the whole adult lifespan versus later decades of life. We summarize and compare studies assessing age-related differences in within- and between-network RSFC of major resting-state brain networks. Differential effects of the factor age on resting-state networks can be identified when comparing studies focusing on younger versus older adults with studies investigating effects within the older adult population. These differential effects pertain to higher order and primary processing resting-state networks to a varying extent. Especially during later decades of life, other factors beyond age might come into play to understand the high inter-individual variability in RSFC.
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Affiliation(s)
- C Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany. .,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.
| | - S Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Institute for Anatomy I, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany.,JARA-Brain, Jülich-Aachen Research Alliance, Jülich, Germany
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13
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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.
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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.
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14
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Caspi Y, Brouwer RM, Schnack HG, van de Nieuwenhuijzen ME, Cahn W, Kahn RS, Niessen WJ, van der Lugt A, Pol HH. Changes in the intracranial volume from early adulthood to the sixth decade of life: A longitudinal study. Neuroimage 2020; 220:116842. [PMID: 32339774 DOI: 10.1016/j.neuroimage.2020.116842] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 03/04/2020] [Accepted: 04/06/2020] [Indexed: 01/09/2023] Open
Abstract
Normal brain-aging occurs at all structural levels. Excessive pathophysiological changes in the brain, beyond the normal one, are implicated in the etiology of brain disorders such as severe forms of the schizophrenia spectrum and dementia. To account for brain-aging in health and disease, it is critical to study the age-dependent trajectories of brain biomarkers at various levels and among different age groups. The intracranial volume (ICV) is a key biological marker, and changes in the ICV during the lifespan can teach us about the biology of development, aging, and gene X environment interactions. However, whether ICV changes with age in adulthood is not resolved. Applying a semi-automatic in-house-built algorithm for ICV extraction on T1w MR brain scans in the Dutch longitudinal cohort (GROUP), we measured ICV changes. Individuals between the ages of 16 and 55 years were scanned up to three consecutive times with 3.32±0.32 years between consecutive scans (N = 482, 359, 302). Using the extracted ICVs, we calculated ICV longitudinal aging-trajectories based on three analysis methods; direct calculation of ICV differences between the first and the last scan, fitting all ICV measurements of individuals to a straight line, and applying a global linear mixed model fitting. We report statistically significant increase in the ICV in adulthood until the fourth decade of life (average change +0.03%/y, or about 0.5 ml/y, at age 20), and decrease in the ICV afterward (-0.09%/y, or about -1.2 ml/y, at age 55). To account for previous cross-sectional reports of ICV changes, we analyzed the same data using a cross-sectional approach. Our cross-sectional analysis detected ICV changes consistent with the previously reported cross-sectional effect. However, the reported amount of cross-sectional changes within this age range was significantly larger than the longitudinal changes. We attribute the cross-sectional results to a generational effect. In conclusion, the human intracranial volume does not stay constant during adulthood but instead shows a small increase during young adulthood and a decrease thereafter from the fourth decade of life. The age-related changes in the longitudinalmeasure are smaller than those reported using cross-sectional approaches and unlikely to affect structural brain imaging studies correcting for intracranial volume considerably. As to the possible mechanisms involved, this awaits further study, although thickening of the meninges and skull bones have been proposed, as well as a smaller amount of brain fluids addition above the overall loss of brain tissue.
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Affiliation(s)
- Yaron Caspi
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands.
| | - Rachel M Brouwer
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands
| | - Hugo G Schnack
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands
| | | | - Wiepke Cahn
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands
| | - René S Kahn
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC: University Medical Center Rotterdam, the Netherlands
| | - Aad van der Lugt
- Department of Radiology and Nuclear Medicine, Erasmus MC: University Medical Center Rotterdam, the Netherlands
| | - Hilleke Hulshoff Pol
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, the Netherlands.
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15
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Doucet GE, Moser DA, Rodrigue A, Bassett DS, Glahn DC, Frangou S. Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features. Cereb Cortex 2020; 29:852-862. [PMID: 30462205 PMCID: PMC6319174 DOI: 10.1093/cercor/bhy287] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/25/2018] [Indexed: 11/29/2022] Open
Abstract
The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person’s brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.
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Affiliation(s)
- Gaelle E Doucet
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Dominik A Moser
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amanda Rodrigue
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA.,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA.,Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David C Glahn
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Olin Neuropsychiatric Institute, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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16
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The Whole Picture: From Isolated to Global MRI Measures of Neurovascular and Neurodegenerative Disease. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020. [PMID: 31894568 DOI: 10.1007/978-3-030-31904-5_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Structural magnetic resonance imaging (MRI) has been used to characterise the appearance of the brain in cerebral small vessel disease (SVD), ischaemic stroke, cognitive impairment, and dementia. SVD is a major cause of stroke and dementia; features of SVD include white matter hyperintensities (WMH) of presumed vascular origin, lacunes of presumed vascular origin, microbleeds, and perivascular spaces. Cognitive impairment and dementia have traditionally been stratified into subtypes of varying origin, e.g., vascular dementia versus dementia of the Alzheimer's type (Alzheimer's disease; AD). Vascular dementia is caused by reduced blood flow in the brain, often as a result of SVD, and AD is thought to have its genesis in the accumulation of tau and amyloid-beta leading to brain atrophy. But after early seminal studies in the 1990s found neurovascular disease features in around 30% of AD patients, it is becoming recognised that so-called "mixed pathologies" (of vascular and neurodegenerative origin) exist in many more patients diagnosed with stroke, only one type of dementia, or cognitive impairment. On the back of these discoveries, attempts have recently been made to quantify the full extent of degenerative and vascular disease in the brain in vivo on MRI. The hope being that these "global" methods may one day lead to better diagnoses of disease and provide more sensitive measurements to detect treatment effects in clinical trials. Indeed, the "Total MRI burden of cerebral small vessel disease", the "Brain Health Index" (BHI), and "MRI measure of degenerative and cerebrovascular pathology in Alzheimer disease" have all been shown to have stronger associations with clinical and cognitive phenotypes than individual brain MRI features. This chapter will review individual structural brain MRI features commonly seen in SVD, stroke, and dementia. The relationship between these features and differing clinical and cognitive phenotypes will be discussed along with developments in their measurement and quantification. The chapter will go on to review emerging methods for quantifying the collective burden of structural brain MRI findings and how these "whole picture" methods may lead to better diagnoses of neurovascular and neurodegenerative disorders.
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17
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Jockwitz C, Mérillat S, Liem F, Oschwald J, Amunts K, Caspers S, Jäncke L. Generalizing age effects on brain structure and cognition: A two-study comparison approach. Hum Brain Mapp 2019; 40:2305-2319. [PMID: 30666760 PMCID: PMC6590363 DOI: 10.1002/hbm.24524] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 11/27/2018] [Accepted: 01/08/2019] [Indexed: 01/06/2023] Open
Abstract
Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross-validating age-related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well-being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age-related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two-study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age-related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.
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Affiliation(s)
- Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
| | - Susan Mérillat
- University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Franziskus Liem
- University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Jessica Oschwald
- University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.,JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany.,Institute for Anatomy I, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Lutz Jäncke
- University Research Priority Program Dynamics of Healthy Aging, University of Zurich, Zurich, Switzerland.,Division of Neuropsychology, University of Zurich, Zurich, Switzerland
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18
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Abellaneda-Pérez K, Vaqué-Alcázar L, Vidal-Piñeiro D, Jannati A, Solana E, Bargalló N, Santarnecchi E, Pascual-Leone A, Bartrés-Faz D. Age-related differences in default-mode network connectivity in response to intermittent theta-burst stimulation and its relationships with maintained cognition and brain integrity in healthy aging. Neuroimage 2018; 188:794-806. [PMID: 30472372 DOI: 10.1016/j.neuroimage.2018.11.036] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Revised: 11/21/2018] [Accepted: 11/21/2018] [Indexed: 12/22/2022] Open
Abstract
The default-mode network (DMN) is affected by advancing age, where particularly long-range connectivity has been consistently reported to be reduced as compared to young individuals. We examined whether there were any differences in the effects of intermittent theta-burst stimulation (iTBS) in DMN connectivity between younger and older adults, its associations with cognition and brain integrity, as well as with long-term cognitive status. Twenty-four younger and 27 cognitively normal older adults were randomly assigned to receive real or sham iTBS over the left inferior parietal lobule between two resting-state functional magnetic resonance imaging (rs-fMRI) acquisitions. Three years later, those older adults who had received real iTBS underwent a cognitive follow-up assessment. Among the younger adults, functional connectivity increased following iTBS in distal DMN areas from the stimulation site. In contrast, older adults exhibited increases in connectivity following iTBS in proximal DMN regions. Moreover, older adults with functional responses to iTBS resembling those of the younger participants exhibited greater brain integrity and higher cognitive performance at baseline and at the 3-year follow-up, along with less cognitive decline. Finally, we observed that 'young-like' functional responses to iTBS were also related to the educational background attained amongst older adults. The present study reveals that functional responses of the DMN to iTBS are modulated by age. Furthermore, combining iTBS and rs-fMRI in older adults may allow characterizing distinctive cognitive profiles in aging and its progression, probably reflecting network plasticity systems that may entail a neurobiological substrate of cognitive reserve.
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Affiliation(s)
- Kilian Abellaneda-Pérez
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Lídia Vaqué-Alcázar
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Ali Jannati
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Elisabeth Solana
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Núria Bargalló
- Hospital Clínic de Barcelona, Magnetic Resonance Image Core Facility (IDIBAPS), Barcelona, Spain; Hospital Clínic de Barcelona, Neuroradiology Section, Radiology Service, Centre de Diagnòstic per la Imatge, Barcelona, Spain
| | - Emiliano Santarnecchi
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Siena Brain Investigation and Neuromodulation Laboratory, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Autonomous University of Barcelona, Institut Universitari de Neurorehabilitació Guttmann, Badalona, Spain
| | - David Bartrés-Faz
- Department of Medicine, Faculty of Medicine and Health Sciences, Institute of Neurosciences, University of Barcelona, Barcelona, Spain; Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain; Autonomous University of Barcelona, Institut Universitari de Neurorehabilitació Guttmann, Badalona, Spain.
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19
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Jannusch K, Jockwitz C, Bidmon HJ, Moebus S, Amunts K, Caspers S. A Complex Interplay of Vitamin B1 and B6 Metabolism with Cognition, Brain Structure, and Functional Connectivity in Older Adults. Front Neurosci 2017; 11:596. [PMID: 29163003 PMCID: PMC5663975 DOI: 10.3389/fnins.2017.00596] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2017] [Accepted: 10/10/2017] [Indexed: 11/13/2022] Open
Abstract
Aging is associated with brain atrophy, functional brain network reorganization and decline of cognitive performance, albeit characterized by high interindividual variability. Among environmental influencing factors accounting for this variability, nutrition and particularly vitamin supply is thought to play an important role. While evidence exists that supplementation of vitamins B6 and B1 might be beneficial for cognition and brain structure, at least in deficient states and neurodegenerative diseases, little is known about this relation during healthy aging and in relation to reorganization of functional brain networks. We thus assessed the relation between blood levels of vitamins B1 and B6 and cognitive performance, cortical folding, and functional resting-state connectivity in a large sample of older adults (N > 600; age: 55-85 years), drawn from the population-based 1000BRAINS study. In addition to blood sampling, subjects underwent structural and functional resting-state neuroimaging as well as extensive neuropsychological testing in the domains of executive functions, (working) memory, attention, and language. Brain regions showing changes in the local gyrification index as calculated using FreeSurfer in relation to vitamin levels were used for subsequent seed-based resting-state functional connectivity analysis. For B6, a positive correlation with local cortical folding was found throughout the brain, while only slight changes in functional connectivity were observed. Contrarily, for B1, a negative correlation with cortical folding as well as problem solving and visuo-spatial working memory performance was found, which was accompanied by pronounced increases of interhemispheric and decreases of intrahemispheric functional connectivity. While the effects for B6 expand previous knowledge on beneficial effects of B6 supplementation on brain structure, they also showed that additional effects on cognition might not be recognizable in healthy older subjects with normal B6 blood levels. The cortical atrophy and pronounced functional reorganization associated with B1, contrarily, was more in line with the theory of a disturbed B1 metabolism in older adults, leading to B1 utilization deficits, and thus, an effective B1 deficiency in the brain, despite normal to high-normal blood levels.
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Affiliation(s)
- Kai Jannusch
- C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- JARA–BRAIN, Jülich Aachen Research Alliance, Research Centre Jülich, Jülich, Germany
| | - Hans-Jürgen Bidmon
- C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Katrin Amunts
- C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- JARA–BRAIN, Jülich Aachen Research Alliance, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- JARA–BRAIN, Jülich Aachen Research Alliance, Research Centre Jülich, Jülich, Germany
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Brain structural differences between 73- and 92-year olds matched for childhood intelligence, social background, and intracranial volume. Neurobiol Aging 2017; 62:146-158. [PMID: 29149632 PMCID: PMC5759896 DOI: 10.1016/j.neurobiolaging.2017.10.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 09/05/2017] [Accepted: 10/06/2017] [Indexed: 01/17/2023]
Abstract
Fully characterizing age differences in the brain is a key task for combating aging-related cognitive decline. Using propensity score matching on 2 independent, narrow-age cohorts, we used data on childhood cognitive ability, socioeconomic background, and intracranial volume to match participants at mean age of 92 years (n = 42) to very similar participants at mean age of 73 years (n = 126). Examining a variety of global and regional structural neuroimaging variables, there were large differences in gray and white matter volumes, cortical surface area, cortical thickness, and white matter hyperintensity volume and spatial extent. In a mediation analysis, the total volume of white matter hyperintensities and total cortical surface area jointly mediated 24.9% of the relation between age and general cognitive ability (tissue volumes and cortical thickness were not significant mediators in this analysis). These findings provide an unusual and valuable perspective on neurostructural aging, in which brains from the 8th and 10th decades of life differ widely despite the same cognitive, socioeconomic, and brain-volumetric starting points.
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At least eighty percent of brain grey matter is modifiable by physical activity: A review study. Behav Brain Res 2017; 332:204-217. [DOI: 10.1016/j.bbr.2017.06.002] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Revised: 05/27/2017] [Accepted: 06/03/2017] [Indexed: 12/12/2022]
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Shenkin SD, Pernet C, Nichols TE, Poline JB, Matthews PM, van der Lugt A, Mackay C, Lanyon L, Mazoyer B, Boardman JP, Thompson PM, Fox N, Marcus DS, Sheikh A, Cox SR, Anblagan D, Job DE, Dickie DA, Rodriguez D, Wardlaw JM. Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group. Neuroimage 2017; 153:399-409. [PMID: 28232121 PMCID: PMC5798604 DOI: 10.1016/j.neuroimage.2017.02.030] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 02/03/2017] [Accepted: 02/12/2017] [Indexed: 12/27/2022] Open
Abstract
Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.
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Affiliation(s)
- Susan D Shenkin
- Geriatric Medicine, University of Edinburgh, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4SB, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK.
| | - Cyril Pernet
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Edinburgh Imaging, University of Edinburgh, UK
| | - Thomas E Nichols
- Department of Statistics & WMG, University of Warwick, Coventry CV4 7AL, UK
| | - Jean-Baptiste Poline
- Henry H. Wheeler, Jr. Brain Imaging Center Helen Wills Neuroscience Institute, University of California, 132 Barker Hall, Office 210S, MC 3190, Berkeley, CA, USA
| | - Paul M Matthews
- Division of Brain Sciences, Department of Medicine, Imperial College, London W12 0NN, UK
| | - Aad van der Lugt
- Department of Radiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Clare Mackay
- Department of Psychiatry, University of Oxford, UK
| | - Linda Lanyon
- International Neuroinformatics Coordinating Facility, Karolinska Institutet, Nobels väg 15A, 17177 Stockholm, Sweden
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des maladies neurodégénératives, Université de Bordeaux, CEA, CNRS, UMR5293, France
| | - James P Boardman
- MRC Centre for Reproductive Health, Centre for Clinical Brain Sciences, 47 Little France Crescent, Edinburgh EH16 4TJ, UK
| | - Paul M Thompson
- Keck USC School of Medicine; NIH ENIGMA Center for Worldwide Medicine, Imaging and Genomics; Professor of Neurology, Psychiatry, Radiology, Pediatrics, Engineering & Ophthalmology; USC Imaging Genetics Center, Marina del Rey, CA, USA
| | - Nick Fox
- Dementia Research Centre, Institute of Neurology, University College London, 8-11 Queen Square, London WC1N 3BG, UK
| | - Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Aziz Sheikh
- Centre for Medical Informatics, Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, UK
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK
| | - Devasuda Anblagan
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Edinburgh Imaging, University of Edinburgh, UK
| | - Dominic E Job
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Edinburgh Imaging, University of Edinburgh, UK
| | - David Alexander Dickie
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - David Rodriguez
- Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Edinburgh Imaging, University of Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK; Scottish Imaging Network, a Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh,UK; Department of Neuroimaging Sciences, Centre for Clinical Brain Sciences, University of Edinburgh, UK; Edinburgh Imaging, University of Edinburgh, UK
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Influence of age and cognitive performance on resting-state brain networks of older adults in a population-based cohort. Cortex 2017; 89:28-44. [PMID: 28192723 DOI: 10.1016/j.cortex.2017.01.008] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Revised: 03/11/2016] [Accepted: 01/13/2017] [Indexed: 11/23/2022]
Abstract
Aging leads to global changes in brain structure and cognitive performance, with reorganization of functional brain networks. Importantly, these age-related changes show higher inter-individual variability in older subjects. To particularly address this variability is a challenge for studies on lifetime trajectories from early to late adulthood. The present study therefore had a dedicated focus on late adulthood to characterize the functional connectivity in resting-state networks (RSFC) in relation to age and cognitive performance in 711 older adults (55-85 years) from the 1000BRAINS project. The executive, left and right frontoparietal resting-state (RS) networks showed age-related increases in RSFC. However, older adults did not show changes in RSFC in the default mode network (DMN). Furthermore, lower performance in working memory (WM) was associated with higher RSFC in the left frontoparietal RS network. The results suggest age-related compensatory increases in RSFC which might help to maintain cognitive performance. Nevertheless, the negative correlation between RSFC and WM performance hints at limited cognitive reserve capacity in lower performing older adults. Consequently, the current results provide evidence for a functional reorganization of the brain until late adulthood that might additionally explain parts of the variability of cognitive abilities in older adults.
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Dickie DA, Shenkin SD, Anblagan D, Lee J, Blesa Cabez M, Rodriguez D, Boardman JP, Waldman A, Job DE, Wardlaw JM. Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging. Front Neuroinform 2017; 11:1. [PMID: 28154532 PMCID: PMC5244468 DOI: 10.3389/fninf.2017.00001] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 01/04/2017] [Indexed: 11/17/2022] Open
Abstract
Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2* sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.
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Affiliation(s)
- David Alexander Dickie
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - Susan D. Shenkin
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Geriatric Medicine Unit, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, The University of EdinburghEdinburgh, UK
| | - Devasuda Anblagan
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - Juyoung Lee
- Graduate Training Centre of Neuroscience, International Max Planck Research School, University of TübingenTübingen, Germany
| | - Manuel Blesa Cabez
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - David Rodriguez
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - James P. Boardman
- MRC Centre for Reproductive Health, Queen's Medical Research InstituteEdinburgh, UK
| | - Adam Waldman
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
| | - Dominic E. Job
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
| | - Joanna M. Wardlaw
- Brain Research Imaging Centre, Neuroimaging Sciences, Centre for Clinical Brain Sciences, Royal Infirmary of Edinburgh, The University of EdinburghEdinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationGlasgow, UK
- Department of Psychology, Centre for Cognitive Ageing and Cognitive Epidemiology, The University of EdinburghEdinburgh, UK
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25
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ENIGMA and the individual: Predicting factors that affect the brain in 35 countries worldwide. Neuroimage 2017; 145:389-408. [PMID: 26658930 PMCID: PMC4893347 DOI: 10.1016/j.neuroimage.2015.11.057] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 10/16/2015] [Accepted: 11/23/2015] [Indexed: 11/22/2022] Open
Abstract
In this review, we discuss recent work by the ENIGMA Consortium (http://enigma.ini.usc.edu) - a global alliance of over 500 scientists spread across 200 institutions in 35 countries collectively analyzing brain imaging, clinical, and genetic data. Initially formed to detect genetic influences on brain measures, ENIGMA has grown to over 30 working groups studying 12 major brain diseases by pooling and comparing brain data. In some of the largest neuroimaging studies to date - of schizophrenia and major depression - ENIGMA has found replicable disease effects on the brain that are consistent worldwide, as well as factors that modulate disease effects. In partnership with other consortia including ADNI, CHARGE, IMAGEN and others1, ENIGMA's genomic screens - now numbering over 30,000 MRI scans - have revealed at least 8 genetic loci that affect brain volumes. Downstream of gene findings, ENIGMA has revealed how these individual variants - and genetic variants in general - may affect both the brain and risk for a range of diseases. The ENIGMA consortium is discovering factors that consistently affect brain structure and function that will serve as future predictors linking individual brain scans and genomic data. It is generating vast pools of normative data on brain measures - from tens of thousands of people - that may help detect deviations from normal development or aging in specific groups of subjects. We discuss challenges and opportunities in applying these predictors to individual subjects and new cohorts, as well as lessons we have learned in ENIGMA's efforts so far.
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26
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Irimia A, Torgerson CM, Goh SYM, Van Horn JD. Statistical estimation of physiological brain age as a descriptor of senescence rate during adulthood. Brain Imaging Behav 2016; 9:678-89. [PMID: 25376330 DOI: 10.1007/s11682-014-9321-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Mapping aging-related brain structure and connectivity changes can be helpful for assessing physiological brain age (PBA), which is distinct from chronological age (CA) because genetic and environmental factors affect individuals differently. This study proposes an approach whereby structural and connectomic information can be combined to estimate PBA as an early biomarker of brain aging. In a cohort of 136 healthy adults, magnetic resonance and diffusion tensor imaging are respectively used to measure cortical thickness over the entire cortical mantle as well as connectivity properties (mean connectivity density and mean fractional anisotropy) for white matter connections. Using multivariate regression, these measurements are then employed to (1) illustrate how CA can be predicated--and thereby also how PBA can be estimated--and to conclude that (2) healthy aging is associated with significant connectome changes during adulthood. Our study illustrates a connectomically-informed statistical approach to PBA estimation, with potential applicability to the clinical identification of patients who exhibit accelerated brain aging, and who are consequently at higher risk for developing mild cognitive impairment or dementia.
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Affiliation(s)
- Andrei Irimia
- Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Room 102, MC 9232, Los Angeles, CA, 90089-9235, USA
| | - Carinna M Torgerson
- Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Room 102, MC 9232, Los Angeles, CA, 90089-9235, USA
| | - S-Y Matthew Goh
- Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Room 102, MC 9232, Los Angeles, CA, 90089-9235, USA
| | - John D Van Horn
- Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, 2001 North Soto Street, Room 102, MC 9232, Los Angeles, CA, 90089-9235, USA.
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Job DE, Dickie DA, Rodriguez D, Robson A, Danso S, Pernet C, Bastin ME, Boardman JP, Murray AD, Ahearn T, Waiter GD, Staff RT, Deary IJ, Shenkin SD, Wardlaw JM. A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS). Neuroimage 2016; 144:299-304. [PMID: 26794641 DOI: 10.1016/j.neuroimage.2016.01.027] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 01/05/2016] [Accepted: 01/08/2016] [Indexed: 11/16/2022] Open
Abstract
The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available. BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of 'textual data', such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of 'what is normal' at different ages.
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Affiliation(s)
- Dominic E Job
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom.
| | - David Alexander Dickie
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom
| | - David Rodriguez
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom
| | - Andrew Robson
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom
| | - Sammy Danso
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom
| | - Cyril Pernet
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom
| | - Mark E Bastin
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom
| | - James P Boardman
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; MRC Centre for Reproductive Health, University of Edinburgh, United Kingdom
| | - Alison D Murray
- Aberdeen Biomedical Imaging Centre, Lilian Sutton Building, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, United Kingdom
| | - Trevor Ahearn
- Medical Physics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen AB25 2ZN, United Kingdom
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, Lilian Sutton Building, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, United Kingdom
| | - Roger T Staff
- Medical Physics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen AB25 2ZN, United Kingdom
| | - Ian J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
| | - Susan D Shenkin
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom; Geriatric Medicine Unit, University of Edinburgh, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh EH16 4TJ, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
| | - Joanna M Wardlaw
- Brain Research Imaging Centre (BRIC), & Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh, Division of Clinical Neurosciences, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, United Kingdom; Scottish Imaging Network, 15 Redburn Avenue, Giffnock, Glasgow G46 6RH, United Kingdom; Centre for Cognitive Ageing and Cognitive Epidemiology, Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, United Kingdom
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Dickie DA, Mikhael S, Job DE, Wardlaw JM, Laidlaw DH, Bastin ME. Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI. Magn Reson Imaging 2015; 33:1299-1305. [PMID: 26253778 DOI: 10.1016/j.mri.2015.07.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 07/31/2015] [Accepted: 07/31/2015] [Indexed: 12/13/2022]
Abstract
Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25-64years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P<0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR<0.05) declines in gray matter proportion with age identified with permutation testing inference (N≈6000) were approximately twice the size of those identified with parametric inference (N=3221voxels). Permutation testing with 10000 (N=6251voxels) and 20000 (N=6233voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N=7117voxels) than with ≥10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified.
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Affiliation(s)
- David A Dickie
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE), 15 Redburn Avenue, Giffnock, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.
| | - Shadia Mikhael
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE), 15 Redburn Avenue, Giffnock, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Dominic E Job
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE), 15 Redburn Avenue, Giffnock, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE), 15 Redburn Avenue, Giffnock, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - David H Laidlaw
- Computer Science Department, Brown University, Providence, RI, United States
| | - Mark E Bastin
- Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom; Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE), 15 Redburn Avenue, Giffnock, United Kingdom; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
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29
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Dickie DA, Job DE, Gonzalez DR, Shenkin SD, Wardlaw JM. Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method. PLoS One 2015; 10:e0127939. [PMID: 26023913 PMCID: PMC4449178 DOI: 10.1371/journal.pone.0127939] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2014] [Accepted: 04/20/2015] [Indexed: 12/03/2022] Open
Abstract
Introduction Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients. Methods Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. Results The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. Discussion To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.
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Affiliation(s)
- David Alexander Dickie
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- * E-mail:
| | - Dominic E. Job
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - David Rodriguez Gonzalez
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
| | - Susan D. Shenkin
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) collaboration, Glasgow, United Kingdom
| | - Joanna M. Wardlaw
- Neuroimaging Sciences, Centre for Clinical Brain Sciences (CCBS), The University of Edinburgh Medical School, Edinburgh, United Kingdom
- Geriatric Medicine Unit, The University of Edinburgh, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
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