1
|
Roy JC, Hédouin R, Desmidt T, Dam S, Mirea-Grivel I, Weyl L, Bannier E, Barantin L, Drapier D, Batail JM, David R, Coloigner J, Robert GH. Quantifying Apathy in Late-Life Depression: Unraveling Neurobehavioral Links Through Daily Activity Patterns and Brain Connectivity Analysis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:639-649. [PMID: 38615911 DOI: 10.1016/j.bpsc.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/04/2024] [Accepted: 04/04/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Better understanding apathy in late-life depression would help improve prediction of poor prognosis of diseases such as dementia. Actimetry provides an objective and ecological measure of apathy from patients' daily motor activity. We aimed to determine whether patterns of motor activity were associated with apathy and brain connectivity in networks that underlie goal-directed behaviors. METHODS Resting-state functional magnetic resonance imaging and diffusion magnetic resonance imaging were collected from 38 nondemented participants with late-life depression. Apathy was evaluated using the diagnostic criteria for apathy, Apathy Evaluation Scale, and Apathy Motivation Index. Functional principal components (fPCs) of motor activity were derived from actimetry recordings taken for 72 hours. Associations between fPCs and apathy were estimated by linear regression. Subnetworks whose connectivity was significantly associated with fPCs were identified via threshold-free network-based statistics. The relationship between apathy and microstructure metrics was estimated along fibers by diffusion tensor imaging and a multicompartment model called neurite orientation dispersion and density imaging via tractometry. RESULTS We found 2 fPCs associated with apathy: mean diurnal activity, negatively associated with Apathy Evaluation Scale scores, and an early chronotype, negatively associated with Apathy Motivation Index scores. Mean diurnal activity was associated with increased connectivity in the default mode, cingulo-opercular, and frontoparietal networks, while chronotype was associated with a more heterogeneous connectivity pattern in the same networks. We did not find significant associations between microstructural metrics and fPCs. CONCLUSIONS Our findings suggest that mean diurnal activity and chronotype could provide indirect ambulatory measures of apathy in late-life depression, associated with modified functional connectivity of brain networks that underlie goal-directed behaviors.
Collapse
Affiliation(s)
- Jean-Charles Roy
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France; Centre d'Investigation Clinique 1414, Centre Hospitalier Universitaire de Rennes, Institut National de la Santé et de la Recherche Médicale (INSERM), Rennes, France; Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France.
| | - Renaud Hédouin
- Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Thomas Desmidt
- CHU de Tours, Tours, France; UMR 1253, iBrain, Université de Tours, INSERM, Tours, France; Centre d'Investigation Clinique 1415, CHU de Tours, INSERM, Tours, France
| | - Sébastien Dam
- Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Iris Mirea-Grivel
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France
| | - Louise Weyl
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France
| | - Elise Bannier
- Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France; CHU de Rennes, Service de Radiologie, Rennes, France
| | - Laurent Barantin
- CHU de Tours, Tours, France; UMR 1253, iBrain, Université de Tours, INSERM, Tours, France
| | - Dominique Drapier
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France; Centre d'Investigation Clinique 1414, Centre Hospitalier Universitaire de Rennes, Institut National de la Santé et de la Recherche Médicale (INSERM), Rennes, France; Faculté de Médecine, Rennes Université, Rennes, France
| | - Jean-Marie Batail
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France; Centre d'Investigation Clinique 1414, Centre Hospitalier Universitaire de Rennes, Institut National de la Santé et de la Recherche Médicale (INSERM), Rennes, France; Faculté de Médecine, Rennes Université, Rennes, France
| | - Renaud David
- CHU de Nice, Université Côte d'Azur, Nice, France
| | - Julie Coloigner
- Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France
| | - Gabriel H Robert
- Centre Hospitalier Guillaume Régnier, Pôle Hospitalo-Universitaire de Psychiatrie Adulte, Rennes, France; Centre d'Investigation Clinique 1414, Centre Hospitalier Universitaire de Rennes, Institut National de la Santé et de la Recherche Médicale (INSERM), Rennes, France; Université de Rennes, Inria, Centre National de la Recherche Scientifique, IRISA, INSERM, Empenn U1228 ERL, Rennes, France; Faculté de Médecine, Rennes Université, Rennes, France
| |
Collapse
|
2
|
Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
Collapse
Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| |
Collapse
|
3
|
Triana AM, Saramäki J, Glerean E, Hayward NMEA. Neuroscience meets behavior: A systematic literature review on magnetic resonance imaging of the brain combined with real-world digital phenotyping. Hum Brain Mapp 2024; 45:e26620. [PMID: 38436603 PMCID: PMC10911114 DOI: 10.1002/hbm.26620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 01/19/2024] [Accepted: 01/24/2024] [Indexed: 03/05/2024] Open
Abstract
A primary goal of neuroscience is to understand the relationship between the brain and behavior. While magnetic resonance imaging (MRI) examines brain structure and function under controlled conditions, digital phenotyping via portable automatic devices (PAD) quantifies behavior in real-world settings. Combining these two technologies may bridge the gap between brain imaging, physiology, and real-time behavior, enhancing the generalizability of laboratory and clinical findings. However, the use of MRI and data from PADs outside the MRI scanner remains underexplored. Herein, we present a Preferred Reporting Items for Systematic Reviews and Meta-Analysis systematic literature review that identifies and analyzes the current state of research on the integration of brain MRI and PADs. PubMed and Scopus were automatically searched using keywords covering various MRI techniques and PADs. Abstracts were screened to only include articles that collected MRI brain data and PAD data outside the laboratory environment. Full-text screening was then conducted to ensure included articles combined quantitative data from MRI with data from PADs, yielding 94 selected papers for a total of N = 14,778 subjects. Results were reported as cross-frequency tables between brain imaging and behavior sampling methods and patterns were identified through network analysis. Furthermore, brain maps reported in the studies were synthesized according to the measurement modalities that were used. Results demonstrate the feasibility of integrating MRI and PADs across various study designs, patient and control populations, and age groups. The majority of published literature combines functional, T1-weighted, and diffusion weighted MRI with physical activity sensors, ecological momentary assessment via PADs, and sleep. The literature further highlights specific brain regions frequently correlated with distinct MRI-PAD combinations. These combinations enable in-depth studies on how physiology, brain function and behavior influence each other. Our review highlights the potential for constructing brain-behavior models that extend beyond the scanner and into real-world contexts.
Collapse
Affiliation(s)
- Ana María Triana
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Jari Saramäki
- Department of Computer Science, School of ScienceAalto UniversityEspooFinland
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, School of ScienceAalto UniversityEspooFinland
| | | |
Collapse
|
4
|
Lavie I, Schnaider Beeri M, Schwartz Y, Soleimani L, Heymann A, Azuri J, Ravona-Springer R. Decrease in Gait Speed Over Time Is Associated With Increase in Number of Depression Symptoms in Older Adults With Type 2 Diabetes. J Gerontol A Biol Sci Med Sci 2023; 78:1504-1512. [PMID: 36626301 PMCID: PMC10395563 DOI: 10.1093/gerona/glad008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND We examined the cross-sectional and longitudinal relationships of motor functions with depression in older adults with type 2 diabetes (T2D). METHODS Participants (n = 984) were from the longitudinal Israel Diabetes and Cognitive Decline (IDCD) study. They were initially cognitively normal and underwent evaluations of motor functions (grip strength and gait speed) and of depression (using the 15-item version of the Geriatric Depression Scale [GDS]) approximately every 18 months. We applied Hierarchical Linear Mixed Models (HLMM) to investigate the associations between motor functions and depression adjusting for sociodemographic, cardiovascular factors, overall cognitive score, and subjective report of exhaustion. RESULTS Participants' baseline characteristics were 72 (±5) years of age (59.6% males), 13 (±4) years of education, Mini-Mental Status Exam (MMSE) score of 28.01 (±1.78), and a GDS score of (2 ± 2.00), consistent with normal cognitive status and lack of major affective symptomatology. Slower gait speed at baseline was associated with higher GDS scores (p = .001) and with their increase over time (p = .049). A decrease in walking speed from baseline was associated with an increase in GDS scores (p = .015). Lower grip strength at baseline was associated with higher GDS scores (p = .002), but not with trajectories in GDS scores over time. A faster decrease in grip strength from baseline was associated with a faster increase in GDS scores (p = .022). CONCLUSIONS Both gait speed and grip strength are cross-sectionally associated with depression. However, only gait speed and its decrease over time can potentially be used to predict incident depression symptoms, thus facilitating the introduction of depression prevention strategies.
Collapse
Affiliation(s)
- Inbar Lavie
- Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel
| | - Michal Schnaider Beeri
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Baruch Ivcher School of Psychology, Interdisciplinary Center, Herzliya, Israel
| | - Yonathan Schwartz
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
| | - Laili Soleimani
- The Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anthony Heymann
- Department of Family Medicine, Tel Aviv University, Tel Aviv, Israel
- Maccabi Healthcare Services, Israel
| | - Joseph Azuri
- Department of Family Medicine, Tel Aviv University, Tel Aviv, Israel
- Maccabi Healthcare Services, Israel
| | - Ramit Ravona-Springer
- Sackler School of Medicine, Tel-Aviv University, Tel Aviv, Israel
- The Joseph Sagol Neuroscience Center, Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel
- Psychiatric Division, Sheba Medical Center, Tel-Hashomer, Israel
| |
Collapse
|
5
|
Leonardsen EH, Vidal-Piñeiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, de Lange AMG, Kaufmann T, Taschler B, Smith SM, Andreassen OA, Wolfers T, Westlye LT, Wang Y. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry 2023; 28:3111-3120. [PMID: 37165155 PMCID: PMC10615751 DOI: 10.1038/s41380-023-02087-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/18/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023]
Abstract
The difference between chronological age and the apparent age of the brain estimated from brain imaging data-the brain age gap (BAG)-is widely considered a general indicator of brain health. Converging evidence supports that BAG is sensitive to an array of genetic and nongenetic traits and diseases, yet few studies have examined the genetic architecture and its corresponding causal relationships with common brain disorders. Here, we estimate BAG using state-of-the-art neural networks trained on brain scans from 53,542 individuals (age range 3-95 years). A genome-wide association analysis across 28,104 individuals (40-84 years) from the UK Biobank revealed eight independent genomic regions significantly associated with BAG (p < 5 × 10-8) implicating neurological, metabolic, and immunological pathways - among which seven are novel. No significant genetic correlations or causal relationships with BAG were found for Parkinson's disease, major depressive disorder, or schizophrenia, but two-sample Mendelian randomization indicated a causal influence of AD (p = 7.9 × 10-4) and bipolar disorder (p = 1.35 × 10-2) on BAG. These results emphasize the polygenic architecture of brain age and provide insights into the causal relationship between selected neurological and neuropsychiatric disorders and BAG.
Collapse
Affiliation(s)
- Esten H Leonardsen
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - James M Roe
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Olena Iakunchykova
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, 1015, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, OX1 2JD, Oxford, UK
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Bernd Taschler
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, OX3 9DU, Oxford, United Kingdom
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72074, Tübingen, Germany
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, 0317, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, 0317, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, 0317, Oslo, Norway.
| |
Collapse
|
6
|
Sanders AM, Richard G, Kolskår K, Ulrichsen KM, Alnaes D, Beck D, Dørum ES, Engvig A, Lund MJ, Nordhøy W, Pedersen ML, Rokicki J, Nordvik JE, Westlye LT. Associations between everyday activities and arterial spin labeling-derived cerebral blood flow: A longitudinal study in community-dwelling elderly volunteers. Hum Brain Mapp 2023; 44:3377-3393. [PMID: 36947581 PMCID: PMC10171542 DOI: 10.1002/hbm.26287] [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: 09/26/2022] [Revised: 02/20/2023] [Accepted: 03/08/2023] [Indexed: 03/23/2023] Open
Abstract
Cerebral blood flow (CBF) is critical for brain metabolism and function. Age-related changes in CBF are associated with increased risk of neurocognitive disorders and vascular events such as stroke. Identifying correlates and positive modifiers of age-related changes in CBF before the emergence of incipient clinical decline may inform public health advice and clinical practice. Former research has been inconclusive regarding the association between regular physical activity and CBF, and there is a lack of studies on the association between level of everyday activities and CBF, in older adults. To investigate these relationships, 118 healthy community-dwelling adults (65-89 years) underwent pseudo-continuous arterial spin labeling (ASL) MRI, neurocognitive, physical, and activity assessments at baseline. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Cross-sectional analysis revealed credible evidence for positive associations between time spent on low intensity physical activity and CBF in multiple cortical and subcortical regions, time spent on moderate to vigorous intensity physical activity and accumbens CBF, participation in social activity and CBF in multiple cortical regions, and between reading and thalamic CBF, indicating higher regional CBF in more active adults. Longitudinal analysis revealed anecdotal evidence for an interaction between time and baseline level of gardening on occipital and parietal CBF, and baseline reading on pallidum CBF, indicating more change in CBF in adults with lower level of activity. The findings support that malleable lifestyle factors contribute to healthy brain aging, with relevance for public health guidelines.
Collapse
Affiliation(s)
- Anne-Marthe Sanders
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Knut Kolskår
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Kristine M Ulrichsen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Dag Alnaes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Oslo New University College, Oslo, Norway
| | - Dani Beck
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Erlend S Dørum
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Sunnaas Rehabilitation Hospital HT, Nesodden, Norway
| | - Andreas Engvig
- Section for Preventive Cardiology, Department of Endocrinology, Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
| | - Martina Jonette Lund
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Wibeke Nordhøy
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Physics and Computational Radiology, Div. of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Mads L Pedersen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Jaroslav Rokicki
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jan Egil Nordvik
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
- Norwegian Directorate of Health, Oslo, Norway
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| |
Collapse
|
7
|
Yang X, Cheng B, Yang J, Cheng S, Pan C, Zhao Y, Zhang H, Liu L, Meng P, Zhang J, Zhang Z, Li C, Chen Y, He D, Wen Y, Jia Y, Liu H, Zhang F. Assessing the interaction effects of brain structure longitudinal changes and life environmental factors on depression and anxiety. Hum Brain Mapp 2023; 44:1227-1238. [PMID: 36416531 PMCID: PMC9875931 DOI: 10.1002/hbm.26153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Revised: 10/16/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Disrupted brain structures and several life environmental factors have been shown to influence depression and anxiety, but their interactions with anxiety and depression remain elusive. Genome-wide association study datasets of 15 brain structure longitudinal changes (N = 15,640) were obtained from the published study. Genotype and phenotype-related data of depression, anxiety, and life environmental factors (including smoking, alcohol drinking, coffee intake, maternal smoking, physical activity, vitamin D, insomnia, sleep duration, and family satisfaction) were collected from UK Biobank. We calculated the polygenic risk scores (PRS) of 15 brain structure changes and then conducted linear regression analyses to explore the interactions of brain structure changes and life environmental factors on depression and anxiety using 15 brain structure change-related PRS, life environmental factors and interactions of them as instrumental variables, and depression score or anxiety score as outcomes. Sex stratification in all analyses was performed to reveal sex-specific differences in the interactions. We found 14 shared interactions related to both depression and anxiety in total sample, such as alcohol drinking × cerebellum white matter 3 (WM; beta = -.003, p = .018 for depression; beta = -003, p = .008 for anxiety) and maternal smoking × nucleus accumbens 2 (beta = .088, p = .002 for depression; beta = .070, p = .008 for anxiety). We also observed sex-specific differences in the interactions, for instance, alcohol drinking × cerebellum WM 3 was negatively associated with depression and anxiety in males (beta = -.004, p = .020 for depression; beta = -.005, p = .002 for anxiety). Our study results reveal the important interactions between brain structure changes and several life environmental factors on depression and anxiety, which may help to explore the pathogenesis of depression and anxiety.
Collapse
Affiliation(s)
- Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jian Yang
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.,Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
8
|
Sex-specific relationships between obesity, physical activity, and gray and white matter volume in cognitively unimpaired older adults. GeroScience 2023:10.1007/s11357-023-00734-4. [PMID: 36781598 PMCID: PMC10400512 DOI: 10.1007/s11357-023-00734-4] [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: 08/09/2022] [Accepted: 01/17/2023] [Indexed: 02/15/2023] Open
Abstract
Independently, obesity and physical activity (PA) influence cerebral structure in aging, yet their interaction has not been investigated. We examined sex differences in the relationships among PA, obesity, and cerebral structure in aging with 340 participants who completed magnetic resonance imaging (MRI) acquisition to quantify grey matter volume (GMV) and white matter volume (WMV). Height and weight were measured to calculate body mass index (BMI). A PA questionnaire was used to estimate weekly Metabolic Equivalents. The relationships between BMI, PA, and their interaction on GMV Regions of Interest (ROIs) and WMV ROIs were examined. Increased BMI was associated with higher GMV in females, an inverse U relationship was found between PA and GMV in females, and the interaction indicated that regardless of BMI greater PA was associated with enhanced GMV. Males demonstrated an inverse U shape between BMI and GMV, and in males with high PA and had normal weight demonstrated greater GMV than normal weight low PA revealed by the interaction. WMV ROIs had a linear relationship with moderate PA in females, whereas in males, increased BMI was associated with lower WMV as well as a positive relationship with moderate PA and WMV. Males and females have unique relationships among GMV, PA and BMI, suggesting sex-aggregated analyses may lead to biased or non-significant results. These results suggest higher BMI, and PA are associated with increased GMV in females, uniquely different from males, highlighting the importance of sex-disaggregated models. Future work should include other imaging parameters, such as perfusion, to identify if these differences co-occur in the same regions as GMV.
Collapse
|
9
|
Longitudinal brain age prediction and cognitive function after stroke. Neurobiol Aging 2023; 122:55-64. [PMID: 36502572 DOI: 10.1016/j.neurobiolaging.2022.10.007] [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: 03/25/2022] [Revised: 09/19/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022]
Abstract
Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD.
Collapse
|
10
|
Wrigglesworth J, Ryan J, Ward PGD, Woods RL, Storey E, Egan GF, Murray A, Espinoza SE, Shah RC, Trevaks RE, Ward SA, Harding IH. Health-related heterogeneity in brain aging and associations with longitudinal change in cognitive function. Front Aging Neurosci 2023; 14:1063721. [PMID: 36688169 PMCID: PMC9846261 DOI: 10.3389/fnagi.2022.1063721] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/29/2022] [Indexed: 01/05/2023] Open
Abstract
Introduction Neuroimaging-based 'brain age' can identify individuals with 'advanced' or 'resilient' brain aging. Brain-predicted age difference (brain-PAD) is predictive of cognitive and physical health outcomes. However, it is unknown how individual health and lifestyle factors may modify the relationship between brain-PAD and future cognitive or functional performance. We aimed to identify health-related subgroups of older individuals with resilient or advanced brain-PAD, and determine if membership in these subgroups is differentially associated with changes in cognition and frailty over three to five years. Methods Brain-PAD was predicted from T1-weighted images acquired from 326 community-dwelling older adults (73.8 ± 3.6 years, 42.3% female), recruited from the larger ASPREE (ASPirin in Reducing Events in the Elderly) trial. Participants were grouped as having resilient (n=159) or advanced (n=167) brain-PAD, and latent class analysis (LCA) was performed using a set of cognitive, lifestyle, and health measures. We examined associations of class membership with longitudinal change in cognitive function and frailty deficit accumulation index (FI) using linear mixed models adjusted for age, sex and education. Results Subgroups of resilient and advanced brain aging were comparable in all characteristics before LCA. Two typically similar latent classes were identified for both subgroups of brain agers: class 1 were characterized by low prevalence of obesity and better physical health and class 2 by poor cardiometabolic, physical and cognitive health. Among resilient brain agers, class 1 was associated with a decrease in cognition, and class 2 with an increase over 5 years, though was a small effect that was equivalent to a 0.04 standard deviation difference per year. No significant class distinctions were evident with FI. For advanced brain agers, there was no evidence of an association between class membership and changes in cognition or FI. Conclusion These results demonstrate that the relationship between brain age and cognitive trajectories may be influenced by other health-related factors. In particular, people with age-resilient brains had different trajectories of cognitive change depending on their cognitive and physical health status at baseline. Future predictive models of aging outcomes will likely be aided by considering the mediating or synergistic influence of multiple lifestyle and health indices alongside brain age.
Collapse
Affiliation(s)
- Jo Wrigglesworth
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Joanne Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Phillip G. D. Ward
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Robyn L. Woods
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Elsdon Storey
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Gary F. Egan
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Australian Research Council Centre of Excellence for Integrative Brain Function, Clayton, Vic, Australia
| | - Anne Murray
- Hennepin Healthcare and Berman Center for Outcomes & Clinical Research, Hennepin Healthcare Research Institute, Minneapolis, MN, United States
- Department of Medicine, Division of Geriatrics, Hennepin Healthcare, University of Minnesota, Minneapolis, MN, United States
| | - Sara E. Espinoza
- Division of Geriatrics, Gerontology & Palliative Medicine, Barshop Institute for Longevity and Aging Studies, University of Texas Health Science Center, Houston, TX, United States
- Geriatric Research, Education & Clinical Center, South Texas Veterans Health Care System, San Antonio, TX, United States
| | - Raj C. Shah
- Department of Family & Preventive Medicine and the Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, United States
| | - Ruth E. Trevaks
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Stephanie A. Ward
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
- Centre for Healthy Brain Ageing (CHeBA), University of New South Wales, Sydney, NSW, Australia
- Department of Geriatric Medicine, Prince of Wales Hospital, Randwick, NSW, Australia
| | - Ian H. Harding
- Monash Biomedical Imaging, Monash University, Clayton, Vic, Australia
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, VIC, Australia
| |
Collapse
|
11
|
Sone D, Beheshti I. Neuroimaging-Based Brain Age Estimation: A Promising Personalized Biomarker in Neuropsychiatry. J Pers Med 2022; 12:jpm12111850. [PMID: 36579560 PMCID: PMC9695293 DOI: 10.3390/jpm12111850] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/10/2022] Open
Abstract
It is now possible to estimate an individual's brain age via brain scans and machine-learning models. This validated technique has opened up new avenues for addressing clinical questions in neurology, and, in this review, we summarize the many clinical applications of brain-age estimation in neuropsychiatry and general populations. We first provide an introduction to typical neuroimaging modalities, feature extraction methods, and machine-learning models that have been used to develop a brain-age estimation framework. We then focus on the significant findings of the brain-age estimation technique in the field of neuropsychiatry as well as the usefulness of the technique for addressing clinical questions in neuropsychiatry. These applications may contribute to more timely and targeted neuropsychiatric therapies. Last, we discuss the practical problems and challenges described in the literature and suggest some future research directions.
Collapse
Affiliation(s)
- Daichi Sone
- Department of Psychiatry, Jikei University School of Medicine, Tokyo 105-8461, Japan
- Correspondence: ; Tel.: +81-03-3433
| | - Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, Winnipeg, MB R3E 3P5, Canada
| |
Collapse
|