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Engels-Domínguez N, Koops EA, Hsieh S, Wiklund EE, Schultz AP, Riphagen JM, Prokopiou PC, Hanseeuw BJ, Rentz DM, Sperling RA, Johnson KA, Jacobs HIL. Lower in vivo locus coeruleus integrity is associated with lower cortical thickness in older individuals with elevated Alzheimer's pathology: a cohort study. Alzheimers Res Ther 2024; 16:129. [PMID: 38886798 PMCID: PMC11181564 DOI: 10.1186/s13195-024-01500-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Autopsy work indicates that the widely-projecting noradrenergic pontine locus coeruleus (LC) is among the earliest regions to accumulate hyperphosphorylated tau, a neuropathological Alzheimer's disease (AD) hallmark. This early tau deposition is accompanied by a reduced density of LC projections and a reduction of norepinephrine's neuroprotective effects, potentially compromising the neuronal integrity of LC's cortical targets. Previous studies suggest that lower magnetic resonance imaging (MRI)-derived LC integrity may signal cortical tissue degeneration in cognitively healthy, older individuals. However, whether these observations are driven by underlying AD pathology remains unknown. To that end, we examined potential effect modifications by cortical beta-amyloid and tau pathology on the association between in vivo LC integrity, as quantified by LC MRI signal intensity, and cortical neurodegeneration, as indexed by cortical thickness. METHODS A total of 165 older individuals (74.24 ± 9.72 years, ~ 60% female, 10% cognitively impaired) underwent whole-brain and dedicated LC 3T-MRI, Pittsburgh Compound-B (PiB, beta-amyloid) and Flortaucipir (FTP, tau) positron emission tomography. Linear regression analyses with bootstrapped standard errors (n = 2000) assessed associations between bilateral cortical thickness and i) LC MRI signal intensity and, ii) LC MRI signal intensity interacted with cortical FTP or PiB (i.e., EC FTP, IT FTP, neocortical PiB) in the entire sample and a low beta-amyloid subsample. RESULTS Across the entire sample, we found a direct effect, where lower LC MRI signal intensity was associated with lower mediolateral temporal cortical thickness. Evaluation of potential effect modifications by FTP or PiB revealed that lower LC MRI signal intensity was related to lower cortical thickness, particularly in individuals with elevated (EC, IT) FTP or (neocortical) PiB. The latter result was present starting from subthreshold PiB values. In low PiB individuals, lower LC MRI signal intensity was related to lower EC cortical thickness in the context of elevated EC FTP. CONCLUSIONS Our findings suggest that LC-related cortical neurodegeneration patterns in older individuals correspond to regions representing early Braak stages and may reflect a combination of LC projection density loss and emergence of cortical AD pathology. This provides a novel understanding that LC-related cortical neurodegeneration may signal downstream consequences of AD-related pathology, rather than being exclusively a result of aging.
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Affiliation(s)
- Nina Engels-Domínguez
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands
| | - Elouise A Koops
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Stephanie Hsieh
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Emma E Wiklund
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Aaron P Schultz
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Joost M Riphagen
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Prokopis C Prokopiou
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA
| | - Bernard J Hanseeuw
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Cliniques Universitaires Saint-Luc, Brussels, Belgium
| | - Dorene M Rentz
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Heidi I L Jacobs
- The Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA.
- Faculty of Health, Medicine and Life Sciences, School for Mental Health and Neuroscience, Alzheimer Centre Limburg, Maastricht University, Maastricht, The Netherlands.
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2
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Spotorno N, Strandberg O, Stomrud E, Janelidze S, Blennow K, Nilsson M, van Westen D, Hansson O. Diffusion MRI tracks cortical microstructural changes during the early stages of Alzheimer's disease. Brain 2024; 147:961-969. [PMID: 38128551 PMCID: PMC10907088 DOI: 10.1093/brain/awad428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 11/02/2023] [Accepted: 12/03/2023] [Indexed: 12/23/2023] Open
Abstract
There is increased interest in developing markers reflecting microstructural changes that could serve as outcome measures in clinical trials. This is especially important after unexpected results in trials evaluating disease-modifying therapies targeting amyloid-β (Aβ), where morphological metrics from MRI showed increased volume loss despite promising clinical treatment effects. In this study, changes over time in cortical mean diffusivity, derived using diffusion tensor imaging, were investigated in a large cohort (n = 424) of non-demented participants from the Swedish BioFINDER study. Participants were stratified following the Aβ/tau (AT) framework. The results revealed a widespread increase in mean diffusivity over time, including both temporal and parietal cortical regions, in Aβ-positive but still tau-negative individuals. These increases were steeper in Aβ-positive and tau-positive individuals and robust to the inclusion of cortical thickness in the model. A steeper increase in mean diffusivity was also associated with both changes over time in fluid markers reflecting astrocytic activity (i.e. plasma level of glial fibrillary acidic protein and CSF levels of YKL-40) and worsening of cognitive performance (all P < 0.01). By tracking cortical microstructural changes over time and possibly reflecting variations related to the astrocytic response, cortical mean diffusivity emerges as a promising marker for tracking treatments-induced microstructural changes in clinical trials.
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Affiliation(s)
- Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 214 28 Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
| | - Markus Nilsson
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, 221 85 Lund, Sweden
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
- Diagnostic Radiology, Institution for Clinical Sciences, Lund University, 221 85 Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, 223 62 Lund, Sweden
- Memory Clinic, Skåne University Hospital, 214 28 Malmö, Sweden
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3
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Rodriguez-Vieitez E, Vannini P, Montal V, Graff C. Cortical microstructural imaging from diffusion MRI: towards sensitive biomarkers for clinical trials. Brain 2024; 147:746-748. [PMID: 38408356 DOI: 10.1093/brain/awae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 02/13/2024] [Indexed: 02/28/2024] Open
Abstract
This scientific commentary refers to ‘Diffusion MRI tracks cortical microstructural changes during the early stages of Alzheimer’s disease’ by Spotorno et al. (https://doi.org/10.1093/brain/awad428).
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Affiliation(s)
- Elena Rodriguez-Vieitez
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Patrizia Vannini
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Caroline Graff
- Division of Neurogeriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Stockholm, Sweden
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4
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Silva-Rudberg JA, Salardini E, O'Dell RS, Chen MK, Ra J, Georgelos JK, Morehouse MR, Melino KP, Varma P, Toyonaga T, Nabulsi NB, Huang Y, Carson RE, van Dyck CH, Mecca AP. Assessment of Gray Matter Microstructure and Synaptic Density in Alzheimer's Disease: A Multimodal Imaging Study With DTI and SV2A PET. Am J Geriatr Psychiatry 2024; 32:17-28. [PMID: 37673749 PMCID: PMC10840732 DOI: 10.1016/j.jagp.2023.08.002] [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: 05/25/2023] [Revised: 07/19/2023] [Accepted: 08/05/2023] [Indexed: 09/08/2023]
Abstract
OBJECTIVE Multimodal imaging techniques have furthered our understanding of how different aspects of Alzheimer's disease (AD) pathology relate to one another. Diffusion tensor imaging (DTI) measures such as mean diffusivity (MD) may be a surrogate measure of the changes in gray matter structure associated with AD. Positron emission tomography (PET) imaging of synaptic vesicle glycoprotein 2A (SV2A) has been used to quantify synaptic loss, which is the major pathological correlate of cognitive impairment in AD. In this study, we investigated the relationship between gray matter microstructure and synaptic density. METHODS DTI was used to measure MD and [11C]UCB-J PET to measure synaptic density in 33 amyloid-positive participants with AD and 17 amyloid-negative cognitively normal (CN) participants aged 50-83. Univariate regression analyses were used to assess the association between synaptic density and MD in both the AD and CN groups. RESULTS Hippocampal MD was inversely associated with hippocampal synaptic density in participants with AD (r = -0.55, p <0.001, df = 31) but not CN (r = 0.13, p = 0.62, df = 15). Exploratory analyses across other regions known to be affected in AD suggested widespread inverse associations between synaptic density and MD in the AD group. CONCLUSION In the setting of AD, an increase in gray matter MD is inversely associated with synaptic density. These co-occurring changes may suggest a link between synaptic loss and gray matter microstructural changes in AD. Imaging studies of gray matter microstructure and synaptic density may allow important insights into AD-related neuropathology.
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Affiliation(s)
- Jason A Silva-Rudberg
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT.
| | - Elaheh Salardini
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Ryan S O'Dell
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Jocelyn Ra
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Jamie K Georgelos
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Mackenzie R Morehouse
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Kaitlyn P Melino
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT
| | - Pradeep Varma
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Nabeel B Nabulsi
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Yiyun Huang
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging (M-KC, PV, TT, NBN, YH, REC), Yale University School of Medicine, New Haven, CT
| | - Christopher H van Dyck
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Neuroscience (CHvD), Yale University School of Medicine, New Haven, CT; Department of Neurology (CHvD), Yale University School of Medicine, New Haven, CT
| | - Adam P Mecca
- Alzheimer's Disease Research Unit (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT; Department of Psychiatry (JAS-R, ES, RSO, JR, JKG, MRM, KPM, CHvD, APM), Yale University School of Medicine, New Haven, CT.
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5
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Burnham SC, Iaccarino L, Pontecorvo MJ, Fleisher AS, Lu M, Collins EC, Devous MD. A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles. Brain Commun 2023; 6:fcad305. [PMID: 38187878 PMCID: PMC10768888 DOI: 10.1093/braincomms/fcad305] [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: 09/13/2023] [Accepted: 11/14/2023] [Indexed: 01/09/2024] Open
Abstract
Alzheimer's disease is defined by the presence of β-amyloid plaques and neurofibrillary tau tangles potentially preceding clinical symptoms by many years. Previously only detectable post-mortem, these pathological hallmarks are now identifiable using biomarkers, permitting an in vivo definitive diagnosis of Alzheimer's disease. 18F-flortaucipir (previously known as 18F-T807; 18F-AV-1451) was the first tau positron emission tomography tracer to be introduced and is the only Food and Drug Administration-approved tau positron emission tomography tracer (Tauvid™). It has been widely adopted and validated in a number of independent research and clinical settings. In this review, we present an overview of the published literature on flortaucipir for positron emission tomography imaging of neurofibrillary tau tangles. We considered all accessible peer-reviewed literature pertaining to flortaucipir through 30 April 2022. We found 474 relevant peer-reviewed publications, which were organized into the following categories based on their primary focus: typical Alzheimer's disease, mild cognitive impairment and pre-symptomatic populations; atypical Alzheimer's disease; non-Alzheimer's disease neurodegenerative conditions; head-to-head comparisons with other Tau positron emission tomography tracers; and technical considerations. The available flortaucipir literature provides substantial evidence for the use of this positron emission tomography tracer in assessing neurofibrillary tau tangles in Alzheimer's disease and limited support for its use in other neurodegenerative disorders. Visual interpretation and quantitation approaches, although heterogeneous, mostly converge and demonstrate the high diagnostic and prognostic value of flortaucipir in Alzheimer's disease.
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Affiliation(s)
| | | | | | | | - Ming Lu
- Avid, Eli Lilly and Company, Philadelphia, PA 19104, USA
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6
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Gallagher RL, Koscik RL, Moody JF, Vogt NM, Adluru N, Kecskemeti SR, Van Hulle CA, Chin NA, Asthana S, Kollmorgen G, Suridjan I, Carlsson CM, Johnson SC, Dean DC, Zetterberg H, Blennow K, Alexander AL, Bendlin BB. Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status. Alzheimers Res Ther 2023; 15:180. [PMID: 37848950 PMCID: PMC10583332 DOI: 10.1186/s13195-023-01281-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 07/27/2023] [Indexed: 10/19/2023]
Abstract
BACKGROUND Alzheimer's disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. METHODS Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). RESULTS Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. CONCLUSION Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged.
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Affiliation(s)
- Rigina L Gallagher
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Rebecca Langhough Koscik
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Jason F Moody
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
| | - Nicholas M Vogt
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Medical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Nagesh Adluru
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | | | - Carol A Van Hulle
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Nathaniel A Chin
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
| | - Sanjay Asthana
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | | | | | - Cynthia M Carlsson
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | - Sterling C Johnson
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Wisconsin Alzheimer's Institute, Madison, WI, USA
- Veterans Administration, Madison, WI, USA
| | - Douglas C Dean
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | - Henrik Zetterberg
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Andrew L Alexander
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA
- Waisman Research Center, Madison, WI, USA
| | - Barbara B Bendlin
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA.
- Wisconsin Alzheimer's Disease Research Center, Madison, WI, USA.
- Wisconsin Alzheimer's Institute, Madison, WI, USA.
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7
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Chouliaras L, O'Brien JT. The use of neuroimaging techniques in the early and differential diagnosis of dementia. Mol Psychiatry 2023; 28:4084-4097. [PMID: 37608222 PMCID: PMC10827668 DOI: 10.1038/s41380-023-02215-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/24/2023]
Abstract
Dementia is a leading cause of disability and death worldwide. At present there is no disease modifying treatment for any of the most common types of dementia such as Alzheimer's disease (AD), Vascular dementia, Lewy Body Dementia (LBD) and Frontotemporal dementia (FTD). Early and accurate diagnosis of dementia subtype is critical to improving clinical care and developing better treatments. Structural and molecular imaging has contributed to a better understanding of the pathophysiology of neurodegenerative dementias and is increasingly being adopted into clinical practice for early and accurate diagnosis. In this review we summarise the contribution imaging has made with particular focus on multimodal magnetic resonance imaging (MRI) and positron emission tomography imaging (PET). Structural MRI is widely used in clinical practice and can help exclude reversible causes of memory problems but has relatively low sensitivity for the early and differential diagnosis of dementia subtypes. 18F-fluorodeoxyglucose PET has high sensitivity and specificity for AD and FTD, while PET with ligands for amyloid and tau can improve the differential diagnosis of AD and non-AD dementias, including recognition at prodromal stages. Dopaminergic imaging can assist with the diagnosis of LBD. The lack of a validated tracer for α-synuclein or TAR DNA-binding protein 43 (TDP-43) imaging remain notable gaps, though work is ongoing. Emerging PET tracers such as 11C-UCB-J for synaptic imaging may be sensitive early markers but overall larger longitudinal multi-centre cross diagnostic imaging studies are needed.
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Affiliation(s)
- Leonidas Chouliaras
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Specialist Dementia and Frailty Service, Essex Partnership University NHS Foundation Trust, St Margaret's Hospital, Epping, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
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8
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Bagwe PV, Deshpande RD, Juhasz G, Sathaye S, Joshi SV. Uncovering the Significance of STEP61 in Alzheimer's Disease: Structure, Substrates, and Interactome. Cell Mol Neurobiol 2023; 43:3099-3113. [PMID: 37219664 DOI: 10.1007/s10571-023-01364-2] [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: 03/17/2023] [Accepted: 05/15/2023] [Indexed: 05/24/2023]
Abstract
STEP (STriatal-Enriched Protein Tyrosine Phosphatase) is a brain-specific phosphatase that plays an important role in controlling signaling molecules involved in neuronal activity and synaptic development. The striatum is the main location of the STEP enzyme. An imbalance in STEP61 activity is a risk factor for Alzheimer's disease (AD). It can contribute to the development of numerous neuropsychiatric diseases, including Parkinson's disease (PD), schizophrenia, fragile X syndrome (FXS), Huntington's disease (HD), alcoholism, cerebral ischemia, and stress-related diseases. The molecular structure, chemistry, and molecular mechanisms associated with STEP61's two major substrates, Alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPAr) and N-methyl-D-aspartate receptors (NMDARs), are crucial in understanding the relationship between STEP61 and associated illnesses. STEP's interactions with its substrate proteins can alter the pathways of long-term potentiation and long-term depression. Therefore, understanding the role of STEP61 in neurological illnesses, particularly Alzheimer's disease-associated dementia, can provide valuable insights for possible therapeutic interventions. This review provides valuable insights into the molecular structure, chemistry, and molecular mechanisms associated with STEP61. This brain-specific phosphatase controls signaling molecules involved in neuronal activity and synaptic development. This review can aid researchers in gaining deep insights into the complex functions of STEP61.
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Affiliation(s)
- Pritam V Bagwe
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, 400019, India
| | - Radni D Deshpande
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, 400019, India
| | - Gabor Juhasz
- Clinical Research Unit (CRU Global Hungary Ltd.), Budapest, Hungary
| | - Sadhana Sathaye
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, 400019, India.
| | - Shreerang V Joshi
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Matunga, Mumbai, 400019, India.
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Williams ME, Elman JA, Bell TR, Dale AM, Eyler LT, Fennema-Notestine C, Franz CE, Gillespie NA, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Higher cortical thickness/volume in Alzheimer's-related regions: protective factor or risk factor? Neurobiol Aging 2023; 129:185-194. [PMID: 37343448 PMCID: PMC10676195 DOI: 10.1016/j.neurobiolaging.2023.05.004] [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: 11/03/2022] [Revised: 04/18/2023] [Accepted: 05/03/2023] [Indexed: 06/23/2023]
Abstract
Some evidence suggests a biphasic pattern of changes in cortical thickness wherein higher, rather than lower, thickness is associated with very early Alzheimer's disease (AD) pathology. We examined whether integrating information from AD brain signatures based on mean diffusivity (MD) can aid in the interpretation of cortical thickness/volume as a risk factor for future AD-related changes. Participants were 572 men in the Vietnam Era Twin Study of Aging who were cognitively unimpaired at baseline (mean age = 56 years; range = 51-60). Individuals with both high thickness/volume signatures and high MD signatures at baseline had lower cortical thickness/volume in AD signature regions and lower episodic memory performance 12 years later compared to those with high thickness/volume and low MD signatures at baseline. Groups did not differ in level of young adult cognitive reserve. Our findings are in line with a biphasic model in which increased cortical thickness may precede future decline and establish the value of examining cortical MD alongside cortical thickness to identify subgroups with differential risk for poorer brain and cognitive outcomes.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, CA, USA
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, CA, USA
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
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Williams ME, Gillespie NA, Bell TR, Dale AM, Elman JA, Eyler LT, Fennema-Notestine C, Franz CE, Hagler DJ, Lyons MJ, McEvoy LK, Neale MC, Panizzon MS, Reynolds CA, Sanderson-Cimino M, Kremen WS. Genetic and Environmental Influences on Structural and Diffusion-Based Alzheimer's Disease Neuroimaging Signatures Across Midlife and Early Old Age. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:918-927. [PMID: 35738479 PMCID: PMC9827615 DOI: 10.1016/j.bpsc.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 05/04/2022] [Accepted: 06/07/2022] [Indexed: 01/27/2023]
Abstract
BACKGROUND Composite scores of magnetic resonance imaging-derived metrics in brain regions associated with Alzheimer's disease (AD), commonly termed AD signatures, have been developed to distinguish early AD-related atrophy from normal age-associated changes. Diffusion-based gray matter signatures may be more sensitive to early AD-related changes compared with thickness/volume-based signatures, demonstrating their potential clinical utility. The timing of early (i.e., midlife) changes in AD signatures from different modalities and whether diffusion- and thickness/volume-based signatures each capture unique AD-related phenotypic or genetic information remains unknown. METHODS Our validated thickness/volume signature, our novel mean diffusivity (MD) signature, and a magnetic resonance imaging-derived measure of brain age were used in biometrical analyses to examine genetic and environmental influences on the measures as well as phenotypic and genetic relationships between measures over 12 years. Participants were 736 men from 3 waves of the Vietnam Era Twin Study of Aging (VETSA) (baseline/wave 1: mean age [years] = 56.1, SD = 2.6, range = 51.1-60.2). Subsequent waves occurred at approximately 5.7-year intervals. RESULTS MD and thickness/volume signatures were highly heritable (56%-72%). Baseline MD signatures predicted thickness/volume signatures over a decade later, but baseline thickness/volume signatures showed a significantly weaker relationship with future MD signatures. AD signatures and brain age were correlated, but each measure captured unique phenotypic and genetic variance. CONCLUSIONS Cortical MD and thickness/volume AD signatures are heritable, and each signature captures unique variance that is also not explained by brain age. Moreover, results are in line with changes in MD emerging before changes in cortical thickness, underscoring the utility of MD as a very early predictor of AD risk.
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Affiliation(s)
- McKenna E Williams
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California.
| | - Nathan A Gillespie
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Tyler R Bell
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Anders M Dale
- Department of Radiology, University of California San Diego, San Diego, California; Department of Neuroscience, University of California San Diego, San Diego, California
| | - Jeremy A Elman
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Lisa T Eyler
- Desert Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare System, San Diego, California
| | - Christine Fennema-Notestine
- Department of Psychiatry, University of California San Diego, San Diego, California; Department of Radiology, University of California San Diego, San Diego, California
| | - Carol E Franz
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Donald J Hagler
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, Massachusetts
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, San Diego, California
| | - Michael C Neale
- Virginia Institute for Psychiatric and Behavior Genetics, Virginia Commonwealth University, Richmond, Virginia
| | - Matthew S Panizzon
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
| | - Chandra A Reynolds
- Department of Psychology, University of California Riverside, Riverside, California
| | - Mark Sanderson-Cimino
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California; Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - William S Kremen
- Center for Behavior Genetics of Aging, University of California San Diego, San Diego, California; Department of Psychiatry, University of California San Diego, San Diego, California
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Gagliardi G, Rodriguez-Vieitez E, Montal V, Sepulcre J, Diez I, Lois C, Hanseeuw B, Schultz AP, Properzi MJ, Papp KV, Marshall GA, Fortea J, Johnson KA, Sperling RA, Vannini P. Cortical microstructural changes predict tau accumulation and episodic memory decline in older adults harboring amyloid. COMMUNICATIONS MEDICINE 2023; 3:106. [PMID: 37528163 PMCID: PMC10394044 DOI: 10.1038/s43856-023-00324-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/19/2023] [Indexed: 08/03/2023] Open
Abstract
INTRODUCTION Non-invasive diffusion-weighted imaging (DWI) to assess brain microstructural changes via cortical mean diffusivity (cMD) has been shown to be cross-sectionally associated with tau in cognitively normal older adults, suggesting that it might be an early marker of neuronal injury. Here, we investigated how regional cortical microstructural changes measured by cMD are related to the longitudinal accumulation of regional tau as well as to episodic memory decline in cognitively normal individuals harboring amyloid pathology. METHODS 122 cognitively normal participants from the Harvard Aging Brain Study underwent DWI, T1w-MRI, amyloid and tau PET imaging, and Logical Memory Delayed Recall (LMDR) assessments. We assessed whether the interaction of baseline amyloid status and cMD (in entorhinal and inferior-temporal cortices) was associated with longitudinal regional tau accumulation and with longitudinal LMDR using separate linear mixed-effects models. RESULTS We find a significant interaction effect of the amyloid status and baseline cMD in predicting longitudinal tau in the entorhinal cortex (p = 0.044) but not the inferior temporal lobe, such that greater baseline cMD values predicts the accumulation of entorhinal tau in amyloid-positive participants. Moreover, we find a significant interaction effect of the amyloid status and baseline cMD in the entorhinal cortex (but not inferior temporal cMD) in predicting longitudinal LMDR (p < 0.001), such that baseline entorhinal cMD predicts the episodic memory decline in amyloid-positive participants. CONCLUSIONS The combination of amyloidosis and elevated cMD in the entorhinal cortex may help identify individuals at short-term risk of tau accumulation and Alzheimer's Disease-related episodic memory decline, suggesting utility in clinical trials.
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Affiliation(s)
- Geoffroy Gagliardi
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Elena Rodriguez-Vieitez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Stockholm, 14152, Sweden
| | - Victor Montal
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, 08041, Spain
- Centre of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, 28031, Spain
| | - Jorge Sepulcre
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Ibai Diez
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Cristina Lois
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Bernard Hanseeuw
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
- Saint Luc University Hospital, Université Catholique de Louvain, Brussels, 1200, Belgium
| | - Aaron P Schultz
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA
| | - Michael J Properzi
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Kathryn V Papp
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Gad A Marshall
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, 08041, Spain
- Centre of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, 28031, Spain
| | - Keith A Johnson
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Gordon Center for Medical Imaging, Boston, MA, 02114, USA
| | - Reisa A Sperling
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Patrizia Vannini
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, 02129, USA.
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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12
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Wang L, Zhou C, Zhang W, Zhang M, Cheng W, Feng J. Association of Cortical and Subcortical Microstructure With Clinical Progression and Fluid Biomarkers in Patients With Parkinson Disease. Neurology 2023; 101:e300-e310. [PMID: 37202161 PMCID: PMC10382272 DOI: 10.1212/wnl.0000000000207408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 03/28/2023] [Indexed: 05/20/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Mean diffusivity (MD) of diffusion MRI (dMRI) has been used to measure cortical and subcortical microstructural properties. This study investigated relationships of cortical and subcortical MD, clinical progression, and fluid biomarkers in Parkinson disease (PD). METHODS This longitudinal study using data from the Parkinson's Progression Markers Initiative was collected from April 2011 to July 2022. Clinical symptoms were assessed with Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS) and Montreal Cognitive Assessment (MoCA) scores. Clinical assessments were followed up to 5 years. Linear mixed-effects (LME) models were performed to examine associations of MD and the annual rate of changes in clinical scores. Partial correlation analysis was conducted to examine the associations of MD and fluid biomarker levels. RESULTS A total of 174 patients with PD (age 61.9 ± 9.7 years, 63% male) with baseline dMRI and at least 2 years of clinical follow-up were included. Results of LME models revealed a significant association between MD values, predominantly in subcortical regions, temporal lobe, occipital lobe, and frontal lobe, and annual rate of changes in clinical scores (UPDRS-Part-I, standardized β > 2.35; UPDRS-Part-II, standardized β > 2.34; postural instability and gait disorder score, standardized β > 2.47; MoCA, standardized β < -2.42; all p < 0.05, false discovery rate [FDR] corrected). In addition, MD was associated with the levels of neurofilament light chain in serum (r > 0.22) and α-synuclein (right putamen r = 0.31), β-amyloid 1-42 (left hippocampus r = -0.30), phosphorylated tau at 181 threonine position (r > 0.26), and total tau (r > 0.23) in CSF at baseline (all p < 0.05, FDR corrected). Furthermore, the β coefficients derived from MD and annual rate of changes in the clinical score recapitulated the spatial distribution of dopamine (DAT, D1, and D2), glutamate (mGluR5 and NMDA), serotonin (5-HT1a and 5-HT2a), cannabinoid (CB1), and γ-amino butyric acid A receptor neurotransmitter receptors/transporters (p < 0.05, FDR corrected) derived from PET scans in the brain of healthy volunteers. DISCUSSION In this cohort study, cortical and subcortical MD values at baseline were associated with clinical progression and baseline fluid biomarkers, suggesting that microstructural properties could be useful for stratification of patients with fast clinical progression.
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Affiliation(s)
- Linbo Wang
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom
| | - Cheng Zhou
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom
| | - Wei Zhang
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom
| | - Minming Zhang
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom
| | - Wei Cheng
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain-Inspired Intelligence (L.W., W.Z., W.C., J.F.), Fudan University; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) (L.W., W.Z., W.C., J.F.), Ministry of Education; MOE Frontiers Center for Brain Science (L.W., W.Z., W.C., J.F.), Fudan University; Zhangjiang Fudan International Innovation Center (L.W., W.Z., W.C., J.F.), Shanghai; Department of Radiology (C.Z., M.Z.), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence (W.C.), Zhejiang Normal University, Jinhua, China; and Department of Computer Science (J.F.), University of Warwick, Coventry, United Kingdom.
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Chen D, Li J, Liu H, Liu X, Zhang C, Luo H, Wei Y, Xi Y, Liang H, Zhang Q. Genome-Wide Epistasis Study of Cerebrospinal Fluid Hyperphosphorylated Tau in ADNI Cohort. Genes (Basel) 2023; 14:1322. [PMID: 37510227 PMCID: PMC10379656 DOI: 10.3390/genes14071322] [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: 06/01/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is the main cause of dementia worldwide, and the genetic mechanism of which is not yet fully understood. Much evidence has accumulated over the past decade to suggest that after the first large-scale genome-wide association studies (GWAS) were conducted, the problem of "missing heritability" in AD is still a great challenge. Epistasis has been considered as one of the main causes of "missing heritability" in AD, which has been largely ignored in human genetics. The focus of current genome-wide epistasis studies is usually on single nucleotide polymorphisms (SNPs) that have significant individual effects, and the amount of heritability explained by which was very low. Moreover, AD is characterized by progressive cognitive decline and neuronal damage, and some studies have suggested that hyperphosphorylated tau (P-tau) mediates neuronal death by inducing necroptosis and inflammation in AD. Therefore, this study focused on identifying epistasis between two-marker interactions at marginal main effects across the whole genome using cerebrospinal fluid (CSF) P-tau as quantitative trait (QT). We sought to detect interactions between SNPs in a multi-GPU based linear regression method by using age, gender, and clinical diagnostic status (cds) as covariates. We then used the STRING online tool to perform the PPI network and identify two-marker epistasis at the level of gene-gene interaction. A total of 758 SNP pairs were found to be statistically significant. Particularly, between the marginal main effect SNP pairs, highly significant SNP-SNP interactions were identified, which explained a relatively high variance at the P-tau level. In addition, 331 AD-related genes were identified, 10 gene-gene interaction pairs were replicated in the PPI network. The identified gene-gene interactions and genes showed associations with AD in terms of neuroinflammation and neurodegeneration, neuronal cells activation and brain development, thereby leading to cognitive decline in AD, which is indirectly associated with the P-tau pathological feature of AD and in turn supports the results of this study. Thus, the results of our study might be beneficial for explaining part of the "missing heritability" of AD.
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Affiliation(s)
- Dandan Chen
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
- School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China
| | - Jin Li
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Hongwei Liu
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Xiaolong Liu
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Chenghao Zhang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Haoran Luo
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yiming Wei
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Yang Xi
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
| | - Hong Liang
- College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin 132012, China
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14
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Niu X, Guo Y, Chang Z, Li T, Chen Y, Zhang X, Ni H. The correlation between changes in gray matter microstructure and cerebral blood flow in Alzheimer's disease. Front Aging Neurosci 2023; 15:1205838. [PMID: 37333456 PMCID: PMC10272452 DOI: 10.3389/fnagi.2023.1205838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/20/2023] Open
Abstract
Objective To investigate the relationship between changes in cerebral blood flow (CBF) and gray matter (GM) microstructure in Alzheimer's disease (AD) and mild cognitive impairment (MCI). Methods A recruited cohort of 23 AD patients, 40 MCI patients, and 37 normal controls (NCs) underwent diffusional kurtosis imaging (DKI) for microstructure evaluation and pseudo-continuous arterial spin labeling (pCASL) for CBF assessment. We investigated the differences in diffusion- and perfusion-related parameters across the three groups, including CBF, mean diffusivity (MD), mean kurtosis (MK), and fractional anisotropy (FA). These quantitative parameters were compared using volume-based analyses for the deep GM and surface-based analyses for the cortical GM. The correlation between CBF, diffusion parameters, and cognitive scores was assessed using Spearman coefficients, respectively. The diagnostic performance of different parameters was investigated with k-nearest neighbor (KNN) analysis, using fivefold cross-validation to generate the mean accuracy (mAcc), mean precision (mPre), and mean area under the curve (mAuc). Results In the cortical GM, CBF reduction primarily occurred in the parietal and temporal lobes. Microstructural abnormalities were predominantly noted in the parietal, temporal, and frontal lobes. In the deep GM, more regions showed DKI and CBF parametric changes at the MCI stage. MD showed most of the significant abnormalities among all the DKI metrics. The MD, FA, MK, and CBF values of many GM regions were significantly correlated with cognitive scores. In the whole sample, the MD, FA, and MK were associated with CBF in most evaluated regions, with lower CBF values associated with higher MD, lower FA, or lower MK values in the left occipital lobe, left frontal lobe, and right parietal lobe. CBF values performed best (mAuc = 0.876) for distinguishing the MCI from the NC group. Last, MD values performed best (mAuc = 0.939) for distinguishing the AD from the NC group. Conclusion Gray matter microstructure and CBF are closely related in AD. Increased MD, decreased FA, and MK are accompanied by decreased blood perfusion throughout the AD course. Furthermore, CBF values are valuable for the predictive diagnosis of MCI and AD. GM microstructural changes are promising as novel neuroimaging biomarkers of AD.
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Affiliation(s)
- Xiaoxi Niu
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Ying Guo
- First Central Clinical College, Tianjin Medical University, Tianjin, China
| | - Zhongyu Chang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Tongtong Li
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | | | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
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15
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Ding W, Ren P, Yi L, Si Y, Yang F, Li Z, Bao H, Yan S, Zhang X, Li S, Liang X, Yao L. Association of cortical and subcortical microstructure with disease severity: impact on cognitive decline and language impairments in frontotemporal lobar degeneration. Alzheimers Res Ther 2023; 15:58. [PMID: 36941645 PMCID: PMC10029187 DOI: 10.1186/s13195-023-01208-7] [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: 07/18/2022] [Accepted: 03/13/2023] [Indexed: 03/23/2023]
Abstract
BACKGROUND Cortical and subcortical microstructural modifications are critical to understanding the pathogenic changes in frontotemporal lobar degeneration (FTLD) subtypes. In this study, we investigated cortical and subcortical microstructure underlying cognitive and language impairments across behavioral variant of frontotemporal dementia (bvFTD), semantic variant of primary progressive aphasia (svPPA), and nonfluent variant of primary progressive aphasia (nfvPPA) subtypes. METHODS The current study characterized 170 individuals with 3 T MRI structural and diffusion-weighted imaging sequences as portion of the Frontotemporal Lobar Degeneration Neuroimaging Initiative study: 41 bvFTD, 35 nfvPPA, 34 svPPA, and 60 age-matched cognitively unimpaired controls. To determine the severity of the disease, clinical dementia rating plus national Alzheimer's coordinating center behavior and language domains sum of boxes scores were used; other clinical measures, including the Boston naming test and verbal fluency test, were also evaluated. We computed surface-based cortical thickness and cortical and subcortical microstructural metrics using tract-based spatial statistics and explored their relationships with clinical and cognitive assessments. RESULTS Compared with controls, those with FTLD showed substantial cortical mean diffusivity alterations extending outside the regions with cortical thinning. Tract-based spatial statistics revealed that anomalies in subcortical white matter diffusion were widely distributed across the frontotemporal and parietal areas. Patients with bvFTD, nfvPPA, and svPPA exhibited distinct patterns of cortical and subcortical microstructural abnormalities, which appeared to correlate with disease severity, and separate dimensions of language functions. CONCLUSIONS Our findings imply that cortical and subcortical microstructures may serve as sensitive biomarkers for the investigation of neurodegeneration-associated microstructural alterations in FTLD subtypes. Flowchart of the study design (see materials and methods for detailed description).
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Affiliation(s)
- Wencai Ding
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Peng Ren
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, 150001, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Yao Si
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Fan Yang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Zhipeng Li
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, 150001, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, 150001, China
| | - Shi Yan
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Xinyu Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China
| | - Siyang Li
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, 150001, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, 150001, China.
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China.
| | - Lifen Yao
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150001, China.
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16
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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: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [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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