1
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Aumont E, Bedard MA, Bussy A, Arias JF, Tissot C, Hall BJ, Therriault J, Rahmouni N, Stevenson J, Servaes S, Macedo AC, Vitali P, Poltronetti NM, Fliaguine O, Trudel L, Gauthier S, Chakravarty MM, Rosa-Neto P. Hippocampal atrophy over two years in relation to tau, amyloid-β and memory in older adults. Neurobiol Aging 2025; 146:48-57. [PMID: 39631245 DOI: 10.1016/j.neurobiolaging.2024.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 09/27/2024] [Accepted: 11/18/2024] [Indexed: 12/07/2024]
Abstract
In this longitudinal brain imaging study, we aimed to characterize hippocampal tau accumulation and subfield atrophy relative to cortical amyloid-β and memory performance. We measured tau-PET in regions associated with Braak stages I to VI, global amyloid-PET burden, hippocampal subfield volumes and memory assessments from 173 participants aged 55-85. Eighty-six of these participants were tested again two years later. Tau-PET change in the Braak II region, corresponding to the hippocampus and the entorhinal cortex, was significantly associated with the cornu ammonis 1 (CA1) atrophy and memory score. This CA1 atrophy did not significantly mediate the association between tau and memory, nor did global amyloid-PET burden correlate with tau-PET changes in the Braak II region. Longitudinal hippocampal tau accumulation is amyloid-β-independent and co-localized with subfield atrophy. As tau-associated memory decline seems to be independent from hippocampal atrophy, other mechanisms could contribute to the deficit.
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Affiliation(s)
- Etienne Aumont
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada
| | - Marc-André Bedard
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada; McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada
| | - Aurélie Bussy
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada; Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R3, Canada
| | - Jaime Fernandez Arias
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Cecile Tissot
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada; Lawrence Berkeley National Laboratory, Berkeley, CA 94720, United States
| | - Brandon J Hall
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Joseph Therriault
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Nesrine Rahmouni
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Jenna Stevenson
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Stijn Servaes
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Arthur C Macedo
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Paolo Vitali
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | | | - Olga Fliaguine
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal, QC H2X 3P2, Canada
| | - Lydia Trudel
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada
| | - Mallar M Chakravarty
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada; Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R3, Canada; Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Pedro Rosa-Neto
- McGill University Research Centre for Studies in Aging, McGill University, Montréal, QC H4H 1R3, Canada; Montreal Neurological Institute, McGill University, Montréal, QC H3A 2B4, Canada; Department of neurology and neurosurgery, McGill University, Montréal, QC H3A 1A1, Canada.
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2
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Wu YK, Zhu LL, Li JT, Li Q, Dai YR, Li K, Mitchell PB, Si TM, Su YA. Striatal Functional Alterations Link to Distinct Symptomatology Across Mood States in Bipolar Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:777-785. [PMID: 38703823 DOI: 10.1016/j.bpsc.2024.04.014] [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: 02/15/2024] [Revised: 04/07/2024] [Accepted: 04/22/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND As a central hub in cognitive and emotional brain circuits, the striatum is considered likely to be integrally involved in the psychopathology of bipolar disorder (BD). However, it remains unclear how alterations in striatal function contribute to distinct symptomatology of BD during different mood states. METHODS Behavioral assessment (i.e., emotional symptoms and cognitive performance) and neuroimaging data were collected from 125 participants comprising 31 (hypo)manic, 31 depressive, and 31 euthymic patients with BD, and 32 healthy control participants. We compared the functional connectivity (FC) of striatal subregions across BD mood states with healthy control participants and then used a multivariate data-driven approach to explore dimensional associations between striatal connectivity and behavioral performance. Finally, we compared the FC and behavioral composite scores, which reflect the individual weighted representation of the associations, among different mood states. RESULTS Patients in all mood states exhibited increased FC between the bilateral ventral rostral putamen and ventrolateral thalamus. Bipolar (hypo)mania uniquely exhibited increased ventral rostral putamen connectivity and superior ventral striatum connectivity. One latent component was identified, whereby increased FCs of striatal subregions were associated with distinct psychopathological symptomatology (more manic symptoms, elevated positive mood, less depressive symptoms, and worse cognitive performance). Patients with bipolar (hypo)mania had the highest FC and behavioral composite scores while bipolar patients with depression had the lowest scores. CONCLUSIONS Our data demonstrated both trait features of BD and state features specific to bipolar (hypo)mania. The findings underscored the fundamental role of the striatum in the pathophysiological processes underlying specific symptomatology across all mood states.
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Affiliation(s)
- Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Lin-Lin Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Qian Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - You-Ran Dai
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, China
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, New South Wales, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, New South Wales, Australia
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China.
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Dohm-Hansen S, English JA, Lavelle A, Fitzsimons CP, Lucassen PJ, Nolan YM. The 'middle-aging' brain. Trends Neurosci 2024; 47:259-272. [PMID: 38508906 DOI: 10.1016/j.tins.2024.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/09/2024] [Accepted: 02/05/2024] [Indexed: 03/22/2024]
Abstract
Middle age has historically been an understudied period of life compared to older age, when cognitive and brain health decline are most pronounced, but the scope for intervention may be limited. However, recent research suggests that middle age could mark a shift in brain aging. We review emerging evidence on multiple levels of analysis indicating that midlife is a period defined by unique central and peripheral processes that shape future cognitive trajectories and brain health. Informed by recent developments in aging research and lifespan studies in humans and animal models, we highlight the utility of modeling non-linear changes in study samples with wide subject age ranges to distinguish life stage-specific processes from those acting linearly throughout the lifespan.
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Affiliation(s)
- Sebastian Dohm-Hansen
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Jane A English
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland; INFANT Research Centre, University College Cork, Cork, Ireland
| | - Aonghus Lavelle
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland
| | - Carlos P Fitzsimons
- Swammerdam Institute for Life Sciences, Brain Plasticity Group, University of Amsterdam, Amsterdam, The Netherlands
| | - Paul J Lucassen
- Swammerdam Institute for Life Sciences, Brain Plasticity Group, University of Amsterdam, Amsterdam, The Netherlands
| | - Yvonne M Nolan
- Department of Anatomy and Neuroscience, University College Cork, Cork, Ireland; APC Microbiome Ireland, University College Cork, Cork, Ireland.
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4
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Haast RAM, Kashyap S, Ivanov D, Yousif MD, DeKraker J, Poser BA, Khan AR. Insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI. Proc Natl Acad Sci U S A 2024; 121:e2310044121. [PMID: 38446857 PMCID: PMC10945835 DOI: 10.1073/pnas.2310044121] [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: 06/19/2023] [Accepted: 12/26/2023] [Indexed: 03/08/2024] Open
Abstract
We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 tesla arterial spin labeling (ASL) data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and already detectable with 50 perfusion-weighted images per subject, acquired in 5 min. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometric properties, macrovasculature, and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterizing hippocampal perfusion.
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Affiliation(s)
- Roy A. M. Haast
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
- Krembil Brain Institute, University Health Network, Toronto, ONM5G 2C4, Canada
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
| | - Mohamed D. Yousif
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
| | - Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill University, Montreal, QCH3A 0G4, Canada
| | - Benedikt A. Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht6200, The Netherlands
| | - Ali R. Khan
- Centre of Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ONN6A 3K7, Canada
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5
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Wu YK, Su YA, Zhu LL, Yan C, Li JT, Lin JY, Chen J, Chen L, Li K, Stein DJ, Si TM. A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder. Transl Psychiatry 2024; 14:136. [PMID: 38443354 PMCID: PMC10915152 DOI: 10.1038/s41398-024-02838-7] [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: 03/11/2023] [Revised: 02/11/2024] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.
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Affiliation(s)
- Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Lin-Lin Zhu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - ChaoGan Yan
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing-Yu Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - JingXu Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Lin Chen
- Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China
| | - Ke Li
- PLA Strategic support Force Characteristic Medical Center, Beijing, 100101, China
| | - Dan J Stein
- Neuroscience Institute, Department of Psychiatry and Mental Health, South African Medical Research Council (SAMRC), Unit on Risk and Resilience in Mental Disorders, University of Cape Town, Cape Town, South Africa
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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6
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Aumont E, Bussy A, Bedard MA, Bezgin G, Therriault J, Savard M, Fernandez Arias J, Sziklas V, Vitali P, Poltronetti NM, Pallen V, Thomas E, Gauthier S, Kobayashi E, Rahmouni N, Stevenson J, Tissot C, Chakravarty MM, Rosa-Neto P. Hippocampal subfield associations with memory depend on stimulus modality and retrieval mode. Brain Commun 2023; 5:fcad309. [PMID: 38035364 PMCID: PMC10681971 DOI: 10.1093/braincomms/fcad309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 09/26/2023] [Accepted: 11/08/2023] [Indexed: 12/02/2023] Open
Abstract
Hippocampal atrophy is a well-known feature of age-related memory decline, and hippocampal subfields may contribute differently to this decline. In this cross-sectional study, we investigated the associations between hippocampal subfield volumes and performance in free recall and recognition memory tasks in both verbal and visual modalities in older adults without dementia. We collected MRIs from 97 (41 males) right-handed participants aged over 60. We segmented the right and left hippocampi into (i) dentate gyrus and cornu ammonis 4 (DG/CA4); (ii) CA2 and CA3 (CA2/CA3); (iii) CA1; (iv) strata radiatum, lacunosum and moleculare; and (v) subiculum. Memory was assessed with verbal free recall and recognition tasks, as well as visual free recall and recognition tasks. Amyloid-β and hippocampal tau positivity were assessed using [18F]AZD4694 and [18F]MK6240 PET tracers, respectively. The verbal free recall and verbal recognition performances were positively associated with CA1 and strata radiatum, lacunosum and moleculare volumes. The verbal free recall and visual free recall were positively correlated with the right DG/CA4. The visual free recall, but not verbal free recall, was also associated with the right CA2/CA3. The visual recognition was not significantly associated with any subfield volume. Hippocampal tau positivity, but not amyloid-β positivity, was associated with reduced DG/CA4, CA2/CA3 and strata radiatum, lacunosum and moleculare volumes. Our results suggest that memory performances are linked to specific subfields. CA1 appears to contribute to the verbal modality, irrespective of the free recall or recognition mode of retrieval. In contrast, DG/CA4 seems to be involved in the free recall mode, irrespective of verbal or visual modalities. These results are concordant with the view that DG/CA4 plays a primary role in encoding a stimulus' distinctive attributes, and that CA2/CA3 could be instrumental in recollecting a visual memory from one of its fragments. Overall, we show that hippocampal subfield segmentation can be useful for detecting early volume changes and improve our understanding of the hippocampal subfields' roles in memory.
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Affiliation(s)
- Etienne Aumont
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal H2X 3P2, Canada
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Aurélie Bussy
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada
- Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R2, Canada
| | - Marc-André Bedard
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal H2X 3P2, Canada
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Gleb Bezgin
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Joseph Therriault
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Melissa Savard
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Jaime Fernandez Arias
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Viviane Sziklas
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Paolo Vitali
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | | | - Vanessa Pallen
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
| | - Emilie Thomas
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
| | - Serge Gauthier
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Nesrine Rahmouni
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Jenna Stevenson
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Cecile Tissot
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
| | - Mallar M Chakravarty
- Cerebral Imaging Center, Douglas Research Center, Montreal, QC H4H 1R3, Canada
- Computational Brain Anatomy (CoBrALab) Laboratory, Montreal, QC H4H 1R2, Canada
- Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Pedro Rosa-Neto
- NeuroQAM Research Centre, Université du Québec à Montréal (UQAM), Montreal H2X 3P2, Canada
- McGill University Research Centre for Studies in Aging, McGill University, Montreal, QC H4H 1R3, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC H3A 1A1, Canada
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7
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Parent O, Bussy A, Devenyi GA, Dai A, Costantino M, Tullo S, Salaciak A, Bedford S, Farzin S, Béland ML, Valiquette V, Villeneuve S, Poirier J, Tardif CL, Dadar M, Chakravarty MM. Assessment of white matter hyperintensity severity using multimodal magnetic resonance imaging. Brain Commun 2023; 5:fcad279. [PMID: 37953840 PMCID: PMC10636521 DOI: 10.1093/braincomms/fcad279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 09/05/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
White matter hyperintensities are radiological abnormalities reflecting cerebrovascular dysfunction detectable using MRI. White matter hyperintensities are often present in individuals at the later stages of the lifespan and in prodromal stages in the Alzheimer's disease spectrum. Tissue alterations underlying white matter hyperintensities may include demyelination, inflammation and oedema, but these are highly variable by neuroanatomical location and between individuals. There is a crucial need to characterize these white matter hyperintensity tissue alterations in vivo to improve prognosis and, potentially, treatment outcomes. How different MRI measure(s) of tissue microstructure capture clinically-relevant white matter hyperintensity tissue damage is currently unknown. Here, we compared six MRI signal measures sampled within white matter hyperintensities and their associations with multiple clinically-relevant outcomes, consisting of global and cortical brain morphometry, cognitive function, diagnostic and demographic differences and cardiovascular risk factors. We used cross-sectional data from 118 participants: healthy controls (n = 30), individuals at high risk for Alzheimer's disease due to familial history (n = 47), mild cognitive impairment (n = 32) and clinical Alzheimer's disease dementia (n = 9). We sampled the median signal within white matter hyperintensities on weighted MRI images [T1-weighted (T1w), T2-weighted (T2w), T1w/T2w ratio, fluid-attenuated inversion recovery (FLAIR)] as well as the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. qT2* and fluid-attenuated inversion recovery signals within white matter hyperintensities displayed different age- and disease-related trends compared to normal-appearing white matter signals, suggesting sensitivity to white matter hyperintensity-specific tissue deterioration. Further, white matter hyperintensity qT2*, particularly in periventricular and occipital white matter regions, was consistently associated with all types of clinically-relevant outcomes in both univariate and multivariate analyses and across two parcellation schemes. qT1 and fluid-attenuated inversion recovery measures showed consistent clinical relationships in multivariate but not univariate analyses, while T1w, T2w and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to white matter hyperintensities. Our results suggest that combining volumetric and signal measures of white matter hyperintensity should be considered to fully characterize the severity of white matter hyperintensities in vivo. These findings may have implications in determining the reversibility of white matter hyperintensities and the potential efficacy of cardio- and cerebrovascular treatments.
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Affiliation(s)
- Olivier Parent
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Aurélie Bussy
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Gabriel Allan Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Dai
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Alyssa Salaciak
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Saashi Bedford
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sarah Farzin
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Marie-Lise Béland
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
| | - Vanessa Valiquette
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Sylvia Villeneuve
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
| | - Judes Poirier
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Center for the Studies in the Prevention of Alzheimer's Disease, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Molecular Neurobiology Unit, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Department of Medicine, McGill University, Montreal, Quebec H4A 3J1, Canada
| | - Christine Lucas Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Quebec H3A 2B4, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - Mahsa Dadar
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec H4H 1R3, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec H3A 1A1, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Quebec H3A 2B4, Canada
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8
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Haast RAM, Kashyap S, Ivanov D, Yousif MD, DeKraker J, Poser BA, Khan AR. Novel insights into hippocampal perfusion using high-resolution, multi-modal 7T MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.19.549533. [PMID: 37503042 PMCID: PMC10370151 DOI: 10.1101/2023.07.19.549533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
We present a comprehensive study on the non-invasive measurement of hippocampal perfusion. Using high-resolution 7 Tesla arterial spin labelling data, we generated robust perfusion maps and observed significant variations in perfusion among hippocampal subfields, with CA1 exhibiting the lowest perfusion levels. Notably, these perfusion differences were robust and detectable even within five minutes and just fifty perfusion-weighted images per subject. To understand the underlying factors, we examined the influence of image quality metrics, various tissue microstructure and morphometry properties, macrovasculature and cytoarchitecture. We observed higher perfusion in regions located closer to arteries, demonstrating the influence of vascular proximity on hippocampal perfusion. Moreover, ex vivo cytoarchitectonic features based on neuronal density differences appeared to correlate stronger with hippocampal perfusion than morphometric measures like gray matter thickness. These findings emphasize the interplay between microvasculature, macrovasculature, and metabolic demand in shaping hippocampal perfusion. Our study expands the current understanding of hippocampal physiology and its relevance to neurological disorders. By providing in vivo evidence of perfusion differences between hippocampal subfields, our findings have implications for diagnosis and potential therapeutic interventions. In conclusion, our study provides a valuable resource for extensively characterising hippocampal perfusion.
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Affiliation(s)
- Roy A M Haast
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Mohamed D Yousif
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
| | - Jordan DeKraker
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Ali R Khan
- Centre of Functional and Metabolic Mapping, Western University, London, Ontario, Canada
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9
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Kalantar-Hormozi H, Patel R, Dai A, Ziolkowski J, Dong HM, Holmes A, Raznahan A, Devenyi GA, Chakravarty MM. A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework. Neuroimage 2023; 268:119885. [PMID: 36657692 DOI: 10.1016/j.neuroimage.2023.119885] [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: 10/11/2022] [Revised: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 01/18/2023] Open
Abstract
Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.
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Affiliation(s)
- Hadis Kalantar-Hormozi
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada.
| | - Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Alyssa Dai
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada
| | - Justine Ziolkowski
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada
| | - Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Department of Psychology, Yale University, New Haven, USA
| | - Avram Holmes
- Department of Psychology, Yale University, New Haven, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health (NIMH), Bethesda, MD, USA
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada; Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, QC, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada
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10
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da Silva DJF, Torres JL, Ericeira LP, Jardim NYV, da Costa VO, Carvalho JPR, Corrêa PGR, Bento-Torres J, Picanço-Diniz CW, Bento-Torres NVO. Pilates and Cognitive Stimulation in Dual Task an Intervention Protocol to Improve Functional Abilities and Minimize the Rate of Age-Related Cognitive Decline in Postmenopausal Women. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13333. [PMID: 36293914 PMCID: PMC9603464 DOI: 10.3390/ijerph192013333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
UNLABELLED It is already known the effectiveness of Pilates training on cognitive and functional abilities. It is also known that dual-task exercise and cognitive stimuli improve cognition and functional capacity. However, no previous report combined cognitive stimuli and Pilates in dual task and measured its effects on the cognitive and physical performances of postmenopausal women. OBJECTIVE To apply an interventional dual-task (PILATES-COG) protocol and to evaluate its influence on memory, language, and functional physical performances on healthy, community-dwelling postmenopausal older women. METHODS 47 women with amenorrhea for at least 12 months participated in this study. Those allocated on the PILATES-COG group underwent a 12-week, twice a week regimen of 50 min sessions of simultaneous mat Pilates exercise program and cognitive tasks. Cognitive and physical functional performance were assessed. Two-way mixed ANOVA was used for data analysis, and Bonferroni post hoc tests were used for within- and between-group comparisons. RESULTS The PILATES-COG group showed significant improvement after the intervention in semantic verbal fluency (p < 0.001; ηρ² = 0.268), phonological verbal fluency (p < 0.019; ηρ² = 0.143), immediate memory (p < 0.001; ηρ² = 0.258), evocation memory (p < 0.001 ηρ² = 0.282), lower-limb muscle strength (p < 0.001; ηρ² = 0.447), balance (p < 0.001; ηρ² = 0.398), and dual-ask cost (p < 0.05; ηρ² = 0.111) assessments on healthy, community-dwelling postmenopausal older women. CONCLUSION This is the first report of a feasible and effective approach using Pilates and cognitive stimulation in dual task for the reduction of age-related cognitive decline and the improvement of physical functional performance in healthy postmenopausal women.
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Affiliation(s)
- Daniel José Fontel da Silva
- Graduate Program in Human Movement Sciences, Federal University of Pará, Belém 66075-110, Brazil
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Juliana Lima Torres
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Luiza Pimentel Ericeira
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Naina Yuki Vieira Jardim
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Victor Oliveira da Costa
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Josilayne Patrícia Ramos Carvalho
- Graduate Program in Human Movement Sciences, Federal University of Pará, Belém 66075-110, Brazil
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Paola Geaninne Reis Corrêa
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - João Bento-Torres
- Graduate Program in Human Movement Sciences, Federal University of Pará, Belém 66075-110, Brazil
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Cristovam Wanderley Picanço-Diniz
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
| | - Natáli Valim Oliver Bento-Torres
- Graduate Program in Human Movement Sciences, Federal University of Pará, Belém 66075-110, Brazil
- Neurodegeneration and Infection Research Laboratory, Institute of Biological Science, João de Barros Barreto University Hospital, Federal University of Pará, Belém 66073-005, Brazil
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11
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Patel R, Mackay CE, Jansen MG, Devenyi GA, O'Donoghue MC, Kivimäki M, Singh-Manoux A, Zsoldos E, Ebmeier KP, Chakravarty MM, Suri S. Inter- and intra-individual variation in brain structural-cognition relationships in aging. Neuroimage 2022; 257:119254. [PMID: 35490915 PMCID: PMC9393406 DOI: 10.1016/j.neuroimage.2022.119254] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/14/2022] [Accepted: 04/16/2022] [Indexed: 01/21/2023] Open
Abstract
The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure.
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Affiliation(s)
- Raihaan Patel
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Québec, H4H 1R3, Canada; Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, H3A 2B4, Canada
| | - Clare E Mackay
- Department of Psychiatry, Warneford Hospital, University of Oxford, OX3 7JX, Oxford, United Kingdom; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX3 7JX, Oxford, United Kingdom
| | - Michelle G Jansen
- Donders Centre for Cognition, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Québec, H4H 1R3, Canada; Department of Psychiatry, McGill University, Montréal, Québec, H3A 1A1, Canada
| | - M Clare O'Donoghue
- Department of Psychiatry, Warneford Hospital, University of Oxford, OX3 7JX, Oxford, United Kingdom; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX3 7JX, Oxford, United Kingdom
| | - Mika Kivimäki
- Department of Epidemiology and Public Health, University College London, WC1E 6BT, London, United Kingdom
| | - Archana Singh-Manoux
- Department of Epidemiology and Public Health, University College London, WC1E 6BT, London, United Kingdom; Université de Paris, Inserm U1153, Epidemiology of Ageing and Neurodegenerative diseases, 7501020, Paris, France
| | - Enikő Zsoldos
- Department of Psychiatry, Warneford Hospital, University of Oxford, OX3 7JX, Oxford, United Kingdom; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX3 7JX, Oxford, United Kingdom; Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX3 9DU, Oxford, UK
| | - Klaus P Ebmeier
- Department of Psychiatry, Warneford Hospital, University of Oxford, OX3 7JX, Oxford, United Kingdom
| | - M Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Québec, H4H 1R3, Canada; Department of Biological and Biomedical Engineering, McGill University, Montréal, Québec, H3A 2B4, Canada; Department of Psychiatry, McGill University, Montréal, Québec, H3A 1A1, Canada
| | - Sana Suri
- Department of Psychiatry, Warneford Hospital, University of Oxford, OX3 7JX, Oxford, United Kingdom; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, OX3 7JX, Oxford, United Kingdom.
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12
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Majrashi NA, Alyami AS, Shubayr NA, Alenezi MM, Waiter GD. Amygdala and subregion volumes are associated with photoperiod and seasonal depressive symptoms: A cross-sectional study in the UK Biobank cohort. Eur J Neurosci 2022; 55:1388-1404. [PMID: 35165958 PMCID: PMC9304295 DOI: 10.1111/ejn.15624] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/16/2022] [Accepted: 02/07/2022] [Indexed: 12/02/2022]
Abstract
Although seasonal changes in amygdala volume have been demonstrated in animals, seasonal differences in human amygdala subregion volumes have yet to be investigated. Amygdala volume has also been linked to depressed mood. Therefore, we hypothesised that differences in photoperiod would predict differences in amygdala or subregion volumes and that this association would be linked to depressed mood. 10,033 participants ranging in age from 45 to 79 years were scanned by MRI in a single location. Amygdala subregion volumes were obtained using automated processing and segmentation algorithms. A mediation analysis tested whether amygdala volume mediated the relationship between photoperiod and mood. Photoperiod was positively associated with total amygdala volume (p < .001). Multivariate (GLM) analyses revealed significant effects of photoperiod across all amygdala subregion volumes for both hemispheres (p < .001). Post hoc univariate regression analyses revealed significant associations of photoperiod with each amygdala subregion volume (p < .001). PLS showed the highest loadings of amygdala subregions in lateral nucleus, ABN, basal nucleus, CAT, PLN, AAA, central nucleus, cortical nucleus and medial nucleus for left hemisphere and ABN, lateral nucleus, CAT, PLN, cortical nucleus, AAA, central nucleus and medial nucleus for right hemisphere. There were no significant associations between photoperiod and mood nor between mood scores and amygdala volumes, and due to the lack of these associations, the mediation hypothesis was not supported. This study is the first to demonstrate an association between photoperiod and amygdala volume. These findings add to the evidence supporting the role of photoperiod on brain structural plasticity.
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Affiliation(s)
- Naif A Majrashi
- Diagnostic Radiography Technology (DRT) Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia.,Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
| | - Ali S Alyami
- Diagnostic Radiography Technology (DRT) Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Nasser A Shubayr
- Diagnostic Radiography Technology (DRT) Department, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia.,Medical Research Center, Jazan University, Jazan, Saudi Arabia
| | - Meshaal M Alenezi
- Radiology Department, King Khalid Hospital in Hail, Ministry of Health, Hail, Saudi Arabia
| | - Gordon D Waiter
- Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK
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13
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Chauveau L, Kuhn E, Palix C, Felisatti F, Ourry V, de La Sayette V, Chételat G, de Flores R. Medial Temporal Lobe Subregional Atrophy in Aging and Alzheimer's Disease: A Longitudinal Study. Front Aging Neurosci 2021; 13:750154. [PMID: 34720998 PMCID: PMC8554299 DOI: 10.3389/fnagi.2021.750154] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Medial temporal lobe (MTL) atrophy is a key feature of Alzheimer's disease (AD), however, it also occurs in typical aging. To enhance the clinical utility of this biomarker, we need to better understand the differential effects of age and AD by encompassing the full AD-continuum from cognitively unimpaired (CU) to dementia, including all MTL subregions with up-to-date approaches and using longitudinal designs to assess atrophy more sensitively. Age-related trajectories were estimated using the best-fitted polynomials in 209 CU adults (aged 19–85). Changes related to AD were investigated among amyloid-negative (Aβ−) (n = 46) and amyloid-positive (Aβ+) (n = 14) CU, Aβ+ patients with mild cognitive impairment (MCI) (n = 33) and AD (n = 31). Nineteen MCI-to-AD converters were also compared with 34 non-converters. Relationships with cognitive functioning were evaluated in 63 Aβ+ MCI and AD patients. All participants were followed up to 47 months. MTL subregions, namely, the anterior and posterior hippocampus (aHPC/pHPC), entorhinal cortex (ERC), Brodmann areas (BA) 35 and 36 [as perirhinal cortex (PRC) substructures], and parahippocampal cortex (PHC), were segmented from a T1-weighted MRI using a new longitudinal pipeline (LASHiS). Statistical analyses were performed using mixed models. Adult lifespan models highlighted both linear (PRC, BA35, BA36, PHC) and nonlinear (HPC, aHPC, pHPC, ERC) trajectories. Group comparisons showed reduced baseline volumes and steeper volume declines over time for most of the MTL subregions in Aβ+ MCI and AD patients compared to Aβ− CU, but no differences between Aβ− and Aβ+ CU or between Aβ+ MCI and AD patients (except in ERC). Over time, MCI-to-AD converters exhibited a greater volume decline than non-converters in HPC, aHPC, and pHPC. Most of the MTL subregions were related to episodic memory performances but not to executive functioning or speed processing. Overall, these results emphasize the benefits of studying MTL subregions to distinguish age-related changes from AD. Interestingly, MTL subregions are unequally vulnerable to aging, and those displaying non-linear age-trajectories, while not damaged in preclinical AD (Aβ+ CU), were particularly affected from the prodromal stage (Aβ+ MCI). This volume decline in hippocampal substructures might also provide information regarding the conversion from MCI to AD-dementia. All together, these findings provide new insights into MTL alterations, which are crucial for AD-biomarkers definition.
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Affiliation(s)
- Léa Chauveau
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Elizabeth Kuhn
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Cassandre Palix
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | | | - Valentin Ourry
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France.,U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Vincent de La Sayette
- U1077 NIMH, Inserm, Caen-Normandie University, École Pratique des Hautes Études, Caen, France
| | - Gaël Chételat
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
| | - Robin de Flores
- U1237 PhIND, Inserm, Caen-Normandie University, GIP Cyceron, Caen, France
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