1
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Schwarz C, Franz CE, Kremen WS, Vuoksimaa E. Reserve, resilience and maintenance of episodic memory and other cognitive functions in aging. Neurobiol Aging 2024; 140:60-69. [PMID: 38733869 DOI: 10.1016/j.neurobiolaging.2024.04.011] [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: 08/14/2023] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
We tested if cognitive and brain reserve and maintenance explain individual differences in episodic memory and other cognitive domains from late middle to early older adulthood. We used The Vietnam Era Twin Study of Aging data (n=1604 men) with episodic memory measured at mean ages of 56, 62 and 68 years, and magnetic resonance imaging data for a subsample of participants (n=321). Cognitive reserve -young adult general cognitive ability at a mean age of 20 years and, to a lesser degree, educational attainment- was positively related to episodic memory performance at each assessment, but not to memory change. We found no evidence for the associations of brain reserve or brain maintenance on memory change. Results were highly similar when looking at processing speed, executive function and verbal fluency. In conclusion, higher young adult cognitive reserve was related to better episodic memory in midlife and older adulthood, but it did not confer better cognitive maintenance with respect to memory. This supports the importance of early cognitive development in dementia prevention.
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Affiliation(s)
- Claudia Schwarz
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Carol E Franz
- Department of Psychiatry and Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - William S Kremen
- Department of Psychiatry and Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland; Department of Psychiatry and Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla, CA, USA.
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2
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Levman J, McCann B, Baumer N, Lam MY, Shiohama T, Cogger L, MacDonald A, Takahashi E. Structural Magnetic Resonance Imaging-Based Surface Morphometry Analysis of Pediatric Down Syndrome. BIOLOGY 2024; 13:575. [PMID: 39194513 DOI: 10.3390/biology13080575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 07/10/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024]
Abstract
Down syndrome (DS) is a genetic disorder characterized by intellectual disability whose etiology includes an additional partial or full copy of chromosome 21. Brain surface morphometry analyses can potentially assist in providing a better understanding of structural brain differences, and may help characterize DS-specific neurodevelopment. We performed a retrospective surface morphometry study of 73 magnetic resonance imaging (MRI) examinations of DS patients (aged 1 day to 22 years) and compared them to a large cohort of 993 brain MRI examinations of neurotypical participants, aged 1 day to 32 years. Surface curvature measurements, absolute surface area measurements, and surface areas as a percentage of total brain surface area (%TBSA) were extracted from each brain region in each examination. Results demonstrate broad reductions in surface area and abnormalities of surface curvature measurements across the brain in DS. After adjusting our regional surface area measurements as %TBSA, abnormally increased presentation in DS relative to neurotypical controls was observed in the left precentral, bilateral entorhinal, left parahippocampal, and bilateral perirhinal cortices, as well as Brodmann's area 44 (left), and the right temporal pole. Findings suggest the presence of developmental abnormalities of regional %TBSA in DS that can be characterized from clinical MRI examinations.
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Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Boston, MA 02129, USA
- Nova Scotia Health Authority, Halifax, NS B3H 1V8, Canada
| | - Bernadette McCann
- Department of Human Kinetics, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
| | - Nicole Baumer
- Department of Neurology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115, USA
| | - Melanie Y Lam
- Department of Human Kinetics, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
| | - Tadashi Shiohama
- Department of Pediatrics, Graduate School of Medicine, Chiba University, Chiba 260-8677, Japan
| | - Liam Cogger
- Department of Education, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
| | - Allissa MacDonald
- Department of Biology, St. Francis Xavier University, Antigonish, NS B2G 2W5, Canada
| | - Emi Takahashi
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, Boston, MA 02129, USA
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA 02215, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
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3
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Gazula H, Tregidgo HFJ, Billot B, Balbastre Y, Williams-Ramirez J, Herisse R, Deden-Binder LJ, Casamitjana A, Melief EJ, Latimer CS, Kilgore MD, Montine M, Robinson E, Blackburn E, Marshall MS, Connors TR, Oakley DH, Frosch MP, Young SI, Van Leemput K, Dalca AV, Fischl B, MacDonald CL, Keene CD, Hyman BT, Iglesias JE. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. eLife 2024; 12:RP91398. [PMID: 38896568 PMCID: PMC11186625 DOI: 10.7554/elife.91398] [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] [Indexed: 06/21/2024] Open
Abstract
We present open-source tools for three-dimensional (3D) analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (1) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (2) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite 'FreeSurfer' (https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools).
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Affiliation(s)
- Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Henry FJ Tregidgo
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Benjamin Billot
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
| | - Yael Balbastre
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | | | - Rogeny Herisse
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Lucas J Deden-Binder
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Adria Casamitjana
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
- Biomedical Imaging Group, Universitat Politècnica de CatalunyaBarcelonaSpain
| | - Erica J Melief
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Caitlin S Latimer
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Mitchell D Kilgore
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Mark Montine
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Eleanor Robinson
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Emily Blackburn
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
| | - Michael S Marshall
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Theresa R Connors
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Derek H Oakley
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Matthew P Frosch
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Sean I Young
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Neuroscience and Biomedical Engineering, Aalto UniversityEspooFinland
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
| | | | - C Dirk Keene
- BioRepository and Integrated Neuropathology (BRaIN) Laboratory and Precision Neuropathology Core, UW School of MedicineSeattleUnited States
| | - Bradley T Hyman
- Massachusetts Alzheimer Disease Research Center, MGH and Harvard Medical SchoolCharlestownUnited States
| | - Juan E Iglesias
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical SchoolCharlestownUnited States
- Centre for Medical Image Computing, University College LondonLondonUnited Kingdom
- Computer Science and Artificial Intelligence Laboratory, MITCambridgeUnited States
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4
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Quek Y, Fung YL, Bourgeat P, Vogrin SJ, Collins SJ, Bowden SC. Combining neuropsychological assessment and structural neuroimaging to identify early Alzheimer's disease in a memory clinic cohort. Brain Behav 2024; 14:e3505. [PMID: 38688879 PMCID: PMC11061200 DOI: 10.1002/brb3.3505] [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: 12/20/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 05/02/2024] Open
Abstract
INTRODUCTION The current study examined the contributions of comprehensive neuropsychological assessment and volumetric assessment of selected mesial temporal subregions on structural magnetic resonance imaging (MRI) to identify patients with amnestic mild cognitive impairment (aMCI) and mild probable Alzheimer's disease (AD) dementia in a memory clinic cohort. METHODS Comprehensive neuropsychological assessment and automated entorhinal, transentorhinal, and hippocampal volume measurements were conducted in 40 healthy controls, 38 patients with subjective memory symptoms, 16 patients with aMCI, 16 patients with mild probable AD dementia. Multinomial logistic regression was used to compare the neuropsychological and MRI measures. RESULTS Combining the neuropsychological and MRI measures improved group membership prediction over the MRI measures alone but did not improve group membership prediction over the neuropsychological measures alone. CONCLUSION Comprehensive neuropsychological assessment was an important tool to evaluate cognitive impairment. The mesial temporal volumetric MRI measures contributed no diagnostic value over and above the determinations made through neuropsychological assessment.
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Affiliation(s)
- Yi‐En Quek
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Yi Leng Fung
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
| | - Pierrick Bourgeat
- The Australian e‐Health Research CentreCSIRO Health and BiosecurityHerstonQueenslandAustralia
| | - Simon J. Vogrin
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
| | - Steven J. Collins
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
- Department of MedicineThe Royal Melbourne HospitalThe University of MelbourneParkvilleVictoriaAustralia
| | - Stephen C. Bowden
- Melbourne School of Psychological SciencesThe University of MelbourneParkvilleVictoriaAustralia
- Department of Clinical NeurosciencesSt. Vincent's Hospital MelbourneFitzroyVictoriaAustralia
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5
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Moodie JE, Harris SE, Harris MA, Buchanan CR, Davies G, Taylor A, Redmond P, Liewald DCM, Valdés Hernández MDC, Shenkin S, Russ TC, Muñoz Maniega S, Luciano M, Corley J, Stolicyn A, Shen X, Steele D, Waiter G, Sandu A, Bastin ME, Wardlaw JM, McIntosh A, Whalley H, Tucker‐Drob EM, Deary IJ, Cox SR. General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning. Hum Brain Mapp 2024; 45:e26641. [PMID: 38488470 PMCID: PMC10941541 DOI: 10.1002/hbm.26641] [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: 09/27/2023] [Revised: 01/29/2024] [Accepted: 02/18/2024] [Indexed: 03/18/2024] Open
Abstract
Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components: gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 29 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.18 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.
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Affiliation(s)
- Joanna E. Moodie
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Sarah E. Harris
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Mathew A. Harris
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Colin R. Buchanan
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Gail Davies
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Adele Taylor
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - David C. M. Liewald
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Maria del C. Valdés Hernández
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Susan Shenkin
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
- Ageing and Health Research Group, Usher InstituteUniversity of EdinburghUK
| | - Tom C. Russ
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
- Alzheimer Scotland Dementia Research CentreUniversity of EdinburghUK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Michelle Luciano
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Janie Corley
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Aleks Stolicyn
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Xueyi Shen
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Douglas Steele
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Gordon Waiter
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Anca‐Larisa Sandu
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
| | - Mark E. Bastin
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | - Joanna M. Wardlaw
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
- Centre for Clinical Brain SciencesUniversity of EdinburghUK
| | | | | | | | - Ian J. Deary
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
| | - Simon R. Cox
- Lothian Birth Cohorts, Department of PsychologyThe University of EdinburghEdinburghUK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) CollaborationEdinburghUK
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6
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Li X, Ng KK, Wong JJY, Zhou JH, Yow WQ. Brain gray matter morphometry relates to onset age of bilingualism and theory of mind in young and older adults. Sci Rep 2024; 14:3193. [PMID: 38326334 PMCID: PMC10850089 DOI: 10.1038/s41598-023-48710-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 11/29/2023] [Indexed: 02/09/2024] Open
Abstract
Lifelong bilingualism may result in neural reserve against decline not only in the general cognitive domain, but also in social cognitive functioning. In this study, we show the brain structural correlates that are associated with second language age of acquisition (L2AoA) and theory of mind (the ability to reason about mental states) in normal aging. Participants were bilingual adults (46 young, 50 older) who completed a theory-of-mind task battery, a language background questionnaire, and an anatomical MRI scan to obtain cortical morphometric features (i.e., gray matter volume, thickness, and surface area). Findings indicated a theory-of-mind decline in older adults compared to young adults, controlling for education and general cognition. Importantly, earlier L2AoA and better theory-of-mind performance were associated with larger volume, higher thickness, and larger surface area in the bilateral temporal, medial temporal, superior parietal, and prefrontal brain regions. These regions are likely to be involved in mental representations, language, and cognitive control. The morphometric association with L2AoA in young and older adults were comparable, but its association with theory of mind was stronger in older adults than young adults. The results demonstrate that early bilingual acquisition may provide protective benefits to intact theory-of-mind abilities against normal age-related declines.
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Affiliation(s)
- Xiaoqian Li
- Humanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Joey Ju Yu Wong
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
| | - W Quin Yow
- Humanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore.
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7
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Gazula H, Tregidgo HFJ, Billot B, Balbastre Y, William-Ramirez J, Herisse R, Deden-Binder LJ, Casamitjana A, Melief EJ, Latimer CS, Kilgore MD, Montine M, Robinson E, Blackburn E, Marshall MS, Connors TR, Oakley DH, Frosch MP, Young SI, Van Leemput K, Dalca AV, FIschl B, Mac Donald CL, Keene CD, Hyman BT, Iglesias JE. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.08.544050. [PMID: 37333251 PMCID: PMC10274889 DOI: 10.1101/2023.06.08.544050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite "FreeSurfer" ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).
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8
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Berisha DE, Rizvi B, Chappel-Farley MG, Tustison N, Taylor L, Dave A, Sattari NS, Chen IY, Lui KK, Janecek JC, Keator D, Neikrug AB, Benca RM, Yassa MA, Mander BA. Cerebrovascular pathology mediates associations between hypoxemia during rapid eye movement sleep and medial temporal lobe structure and function in older adults. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.28.577469. [PMID: 38328085 PMCID: PMC10849660 DOI: 10.1101/2024.01.28.577469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Obstructive sleep apnea (OSA) is common in older adults and is associated with medial temporal lobe (MTL) degeneration and memory decline in aging and Alzheimer's disease (AD). However, the underlying mechanisms linking OSA to MTL degeneration and impaired memory remains unclear. By combining magnetic resonance imaging (MRI) assessments of cerebrovascular pathology and MTL structure with clinical polysomnography and assessment of overnight emotional memory retention in older adults at risk for AD, cerebrovascular pathology in fronto-parietal brain regions was shown to statistically mediate the relationship between OSA-related hypoxemia, particularly during rapid eye movement (REM) sleep, and entorhinal cortical thickness. Reduced entorhinal cortical thickness was, in turn, associated with impaired overnight retention in mnemonic discrimination ability across emotional valences for high similarity lures. These findings identify cerebrovascular pathology as a contributing mechanism linking hypoxemia to MTL degeneration and impaired sleep-dependent memory in older adults.
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Affiliation(s)
- Destiny E. Berisha
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Batool Rizvi
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Miranda G. Chappel-Farley
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Nicholas Tustison
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - Lisa Taylor
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Abhishek Dave
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
| | - Negin S. Sattari
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ivy Y. Chen
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Kitty K. Lui
- San Diego State University/University of California San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, CA, 92093, USA
| | - John C. Janecek
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
| | - David Keator
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ariel B. Neikrug
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
| | - Ruth M. Benca
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, 53706, WI, USA
- Department of Psychiatry and Behavioral Medicine, Wake Forest University, Winston-Salem, NC, 27109, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
| | - Michael A. Yassa
- Department of Neurobiology and Behavior, University of California Irvine, Irvine CA, 92697, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Neurology, University of California Irvine, Irvine CA, 92697, USA
| | - Bryce A. Mander
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine CA, 92697, USA
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine CA, 92697, USA
- Department of Cognitive Sciences, University of California Irvine, Irvine CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine CA, 92697, USA
- Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine CA, 92697, USA
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9
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Yu H, Ding Y, Wei Y, Dyrba M, Wang D, Kang X, Xu W, Zhao K, Liu Y. Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type. Hum Brain Mapp 2023; 44:6364-6374. [PMID: 37846762 PMCID: PMC10681645 DOI: 10.1002/hbm.26512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/10/2023] [Accepted: 09/25/2023] [Indexed: 10/18/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most prevalent forms of dementia in older individuals. Convergent evidence suggests structural connectome abnormalities in specific brain regions are linked to AD progression. The biological basis underpinnings of these connectome changes, however, have remained elusive. We utilized an individual regional mean connectivity strength (RMCS) derived from a regional radiomics similarity network to capture altered morphological connectivity in 1654 participants (605 normal controls, 766 mild cognitive impairment [MCI], and 283 AD). Then, we also explored the biological basis behind these morphological changes through gene enrichment analysis and cell-specific analysis. We found that RMCS probes of the hippocampus and medial temporal lobe were significantly altered in AD and MCI, with these differences being spatially related to the expression of AD-risk genes. In addition, gene enrichment analysis revealed that the modulation of chemical synaptic transmission is the most relevant biological process associated with the altered RMCS in AD. Notably, neuronal cells were found to be the most pertinent cells in the altered RMCS. Our findings shed light on understanding the biological basis of structural connectome changes in AD, which may ultimately lead to more effective diagnostic and therapeutic strategies for this devastating disease.
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Affiliation(s)
- Huiying Yu
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Yanhui Ding
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Yongbin Wei
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)RostockGermany
| | - Dong Wang
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Xiaopeng Kang
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
| | - Weizhi Xu
- School of Information Science and EngineeringShandong Normal UniversityJinanChina
| | - Kun Zhao
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijingChina
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesBeijingChina
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10
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Debiasi G, Mazzonetto I, Bertoldo A. The effect of processing pipelines, input images and age on automatic cortical morphology estimates. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107825. [PMID: 37806120 DOI: 10.1016/j.cmpb.2023.107825] [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: 06/04/2023] [Revised: 09/01/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. METHODS For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. RESULTS Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. CONCLUSIONS Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.
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Affiliation(s)
- Giulia Debiasi
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Ilaria Mazzonetto
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, via Gradenigo 6/b, Padova 35131, Italy; Padova Neuroscience Center (PNC), University of Padova, via Orus 2/b, Padova 35131, Italy.
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11
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Zhang X, Zhong Y, He K. The causal effects between selenium levels and the brain cortical structure: A two-sample Mendelian randomization study. Brain Behav 2023; 13:e3296. [PMID: 37904336 PMCID: PMC10726828 DOI: 10.1002/brb3.3296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/03/2023] [Accepted: 10/12/2023] [Indexed: 11/01/2023] Open
Abstract
Extensive research has demonstrated the critical role of selenium (Se) and selenoproteins in brain function and cognition. However, the impact of Se on brain cortical structure remains enigmatic. Therefore, this study used Mendelian randomization (MR) analysis to investigate the causal effect between Se levels and brain cortical structure. METHODS This study utilizes 11 genetic variants associated with Se level variations, extracted from a large-scale genome-wide association study (GWAS) encompassed circulating Se levels (n = 5477) and toenail Se levels (n = 4162) in the European population. Outcome data were sourced from the summary statistics of the ENIGMA Consortium, comprising GWAS data from 51,666 individuals. The variables include cortical surface area (SA), thickness (TH) at the global level, and 34 functional cortical regions evaluated by magnetic resonance imaging. The inverse-variance-weighted method was used as the primary estimate. Additionally, sensitivity analyses were conducted to detect potential violations of assumptions underlying MR. RESULTS At the global level, Se levels were not correlated with SA but showed a significant negative correlation with TH (β = -0.00485 mm, SE = 0.00192, p = .0115). Heterogeneity was observed across different brain regions, with positive correlations found between Se levels and the TH of the parahippocampal gyrus, superior frontal gyrus, and frontal pole, whereas negative correlations were found with the TH of the inferior parietal lobe and middle temporal lobe. Regarding SA, Se levels exhibit positive correlations with the pars triangularis, caudal anterior cingulate, inferior parietal lobe, and banks of the superior temporal sulcus. Conversely, negative correlations were observed with the medial orbitofrontal cortex, posterior cingulate gyrus, insula, and the middle, superior, and transverse gyrus of the temporal lobe. No pleiotropy was detected. RESULTS This MR study indicated that Se levels causally influence the brain cortical structure.
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Affiliation(s)
- Xiaowei Zhang
- Department of NeurosurgeryThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Yuqing Zhong
- The First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
| | - Kejun He
- Department of NeurosurgeryThe First Affiliated Hospital of Sun Yat‐sen UniversityGuangzhouChina
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12
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Simpson S, Eskandaripour M, Levine B. Effects of Healthy and Neuropathological Aging on Autobiographical Memory: A Meta-Analysis of Studies Using the Autobiographical Interview. J Gerontol B Psychol Sci Soc Sci 2023; 78:1617-1624. [PMID: 37224530 PMCID: PMC10561892 DOI: 10.1093/geronb/gbad077] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Indexed: 05/26/2023] Open
Abstract
OBJECTIVES A meta-analytic review was conducted to assess the effects of healthy aging, amnestic mild cognitive impairment (MCI), and Alzheimer's disease (AD) on naturalistic autobiographical memory using the Autobiographical Interview, a widely used, standardized assessment that derives measures of internal (episodic) and external (nonepisodic) details from freely recalled autobiographical narratives. METHODS A comprehensive literature search identified 21 aging, 6 MCI, and 7 AD studies (total N = 1,556 participants). Summary statistics for internal and external details for each comparison (younger vs older or MCI/AD vs age-matched comparison groups) and effect size statistics were extracted and summarized using Hedges' g (random effects model) and adjusted for the presence of publication bias. RESULTS The pattern of reduced internal and elevated external details in aging was robust and consistent across nearly all 21 studies. MCI and-to a greater extent-AD were associated with reduced internal details, whereas the external detail elevation faded with MCI and AD. Although there was evidence of publication bias on reporting of internal detail effects, these effects remained robust after correction. DISCUSSION The canonical changes to episodic memory observed in aging and neurodegenerative disease are mirrored in the free recall of real-life events. Our findings indicate that the onset of neuropathology overwhelms the capacity of older adults to draw upon distributed neural systems to elaborate on past experiences, including both episodic details specific to identified events and nonepisodic content characteristic of healthy older adults' autobiographical narratives.
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Affiliation(s)
- Stephanie Simpson
- Rotman Research Institute at Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Mona Eskandaripour
- Rotman Research Institute at Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Brian Levine
- Rotman Research Institute at Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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13
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Moodie JE, Harris SE, Harris MA, Buchanan CR, Davies G, Taylor A, Redmond P, Liewald D, del C Valdés Hernández M, Shenkin S, Russ TC, Muñoz Maniega S, Luciano M, Corley J, Stolicyn A, Shen X, Steele D, Waiter G, Sandu-Giuraniuc A, Bastin ME, Wardlaw JM, McIntosh A, Whalley H, Tucker-Drob EM, Deary IJ, Cox SR. General and specific patterns of cortical gene expression as spatial correlates of complex cognitive functioning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532915. [PMID: 36993650 PMCID: PMC10055068 DOI: 10.1101/2023.03.16.532915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene expression varies across the brain. This spatial patterning denotes specialised support for particular brain functions. However, the way that a given gene's expression fluctuates across the brain may be governed by general rules. Quantifying patterns of spatial covariation across genes would offer insights into the molecular characteristics of brain areas supporting, for example, complex cognitive functions. Here, we use principal component analysis to separate general and unique gene regulatory associations with cortical substrates of cognition. We find that the region-to-region variation in cortical expression profiles of 8235 genes covaries across two major principal components : gene ontology analysis suggests these dimensions are characterised by downregulation and upregulation of cell-signalling/modification and transcription factors. We validate these patterns out-of-sample and across different data processing choices. Brain regions more strongly implicated in general cognitive functioning (g; 3 cohorts, total meta-analytic N = 39,519) tend to be more balanced between downregulation and upregulation of both major components (indicated by regional component scores). We then identify a further 41 genes as candidate cortical spatial correlates of g, beyond the patterning of the two major components (|β| range = 0.15 to 0.53). Many of these genes have been previously associated with clinical neurodegenerative and psychiatric disorders, or with other health-related phenotypes. The results provide insights into the cortical organisation of gene expression and its association with individual differences in cognitive functioning.
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Affiliation(s)
- Joanna E. Moodie
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Sarah E. Harris
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Mathew A. Harris
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Colin R. Buchanan
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Gail Davies
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Adele Taylor
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Paul Redmond
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - David Liewald
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Maria del C Valdés Hernández
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Susan Shenkin
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
- Ageing and Health Research Group, Usher Institute, University of Edinburgh, UK
| | - Tom C. Russ
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, UK
| | - Susana Muñoz Maniega
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Michelle Luciano
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Aleks Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Douglas Steele
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Gordon Waiter
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Anca Sandu-Giuraniuc
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Mark E. Bastin
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Joanna M. Wardlaw
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Andrew McIntosh
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Heather Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | | | - Ian J. Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Simon R. Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
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14
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Coleman C, Wang M, Wang E, Micallef C, Shao Z, Vicari JM, Li Y, Yu K, Cai D, Peng J, Haroutunian V, Fullard JF, Bendl J, Zhang B, Roussos P. Multi-omic atlas of the parahippocampal gyrus in Alzheimer's disease. Sci Data 2023; 10:602. [PMID: 37684260 PMCID: PMC10491684 DOI: 10.1038/s41597-023-02507-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia worldwide, with a projection of 151 million cases by 2050. Previous genetic studies have identified three main genes associated with early-onset familial Alzheimer's disease, however this subtype accounts for less than 5% of total cases. Next-generation sequencing has been well established and holds great promise to assist in the development of novel therapeutics as well as biomarkers to prevent or slow the progression of this devastating disease. Here we present a public resource of functional genomic data from the parahippocampal gyrus of 201 postmortem control, mild cognitively impaired (MCI) and AD individuals from the Mount Sinai brain bank, of which whole-genome sequencing (WGS), and bulk RNA sequencing (RNA-seq) were previously published. The genomic data include bulk proteomics and DNA methylation, as well as cell-type-specific RNA-seq and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) data. We have performed extensive preprocessing and quality control, allowing the research community to access and utilize this public resource available on the Synapse platform at https://doi.org/10.7303/syn51180043.2 .
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Affiliation(s)
- Claire Coleman
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Erming Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Courtney Micallef
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhiping Shao
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - James M Vicari
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Yuxin Li
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Kaiwen Yu
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Dongming Cai
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
- Alzheimer Disease Research Center, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Junmin Peng
- Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
- Center for Proteomics and Metabolomics, St. Jude Children's Research Hospital, Memphis, TN, 38105, USA
| | - Vahram Haroutunian
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F Fullard
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jaroslav Bendl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Icahn Institute of Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- James J Peters VA Medical Center, Research & Development, Bronx, NY, 10468, USA.
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15
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Rezaeyan A, Asadi S, Kamrava SK, Zare-Sadeghi A. Brain structural analysis in patients with post-traumatic anosmia: Voxel-based and surface-based morphometry. J Neuroradiol 2023; 50:482-491. [PMID: 36610937 DOI: 10.1016/j.neurad.2022.11.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE AND BACKGROUND Voxel-based morphometry (VBM) and surfaced-based morphometry (SBM) investigate the characteristics of gray matter (GM) in various diseases such as post-traumatic anosmia (PTA). This study uses SBM and VBM to examine neuroanatomical measurements of GM and its functional correlates in patients with PTA. METHODS MRI images and olfactory test results were collected from 39 PTA patients and 39 healthy controls. Sniffin' Sticks test was used to assess olfactory function. GM structure was analyzed using CAT12 and FreeSurfer, and olfactory bulb (OB) volume and olfactory sulcus (OS) depth were calculated using 3D-Slicer. RESULTS Anosmic patients showed lower scores in the Sniffin' Sticks olfactory test, as well as reduction of OB volume and OS depth compared to control subjects. In these patients, overlapping changes were found between the VBM and SBM findings in the areas with significant effects, in particular, orbitofrontal cortex, superior and middle frontal gyrus, superior and middle temporal gyrus, anterior cingulate cortex, and insular cortex. Using SBM, decreased cortical thickness clusters were located in inferior and superior parietal gyrus. Further analysis in the region of interest demonstrated correlations between the orbitofrontal cortex and odor threshold score as well as the middle frontal gyrus and smell loss duration. CONCLUSION These findings show that the morphological alterations in the OB, OS, and the central olfactory pathways might contribute to the pathogenic mechanism of olfactory dysfunction after head injury.
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Affiliation(s)
- Abolhasan Rezaeyan
- Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran; Department of Medical Physics, Faculty of Medicine, Fasa University of Medical Sciences, Fasa, Iran
| | - Somayeh Asadi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - S Kamran Kamrava
- ENT and Head and Neck Research Center and Department, The Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Arash Zare-Sadeghi
- Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; ENT and Head and Neck Research Center and Department, The Five Senses Institute, Iran University of Medical Sciences, Tehran, Iran; Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran.
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16
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Harrison JR, Foley SF, Baker E, Bracher-Smith M, Holmans P, Stergiakouli E, Linden DEJ, Caseras X, Jones DK, Escott-Price V. Pathway-specific polygenic scores for Alzheimer's disease are associated with changes in brain structure in younger and older adults. Brain Commun 2023; 5:fcad229. [PMID: 37744023 PMCID: PMC10517196 DOI: 10.1093/braincomms/fcad229] [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: 11/14/2022] [Revised: 05/17/2023] [Accepted: 08/23/2023] [Indexed: 09/26/2023] Open
Abstract
Genome-wide association studies have identified multiple Alzheimer's disease risk loci with small effect sizes. Polygenic risk scores, which aggregate these variants, are associated with grey matter structural changes. However, genome-wide scores do not allow mechanistic interpretations. The present study explored associations between disease pathway-specific scores and grey matter structure in younger and older adults. Data from two separate population cohorts were used as follows: the Avon Longitudinal Study of Parents and Children, mean age 19.8, and UK Biobank, mean age 64.4 (combined n = 18 689). Alzheimer's polygenic risk scores were computed using the largest genome-wide association study of clinically assessed Alzheimer's to date. Relationships between subcortical volumes and cortical thickness, pathway-specific scores and genome-wide scores were examined. Increased pathway-specific scores were associated with reduced cortical thickness in both the younger and older cohorts. For example, the reverse cholesterol transport pathway score showed evidence of association with lower left middle temporal cortex thickness in the younger Avon participants (P = 0.034; beta = -0.013, CI -0.025, -0.001) and in the older UK Biobank participants (P = 0.019; beta = -0.003, CI -0.005, -4.56 × 10-4). Pathway scores were associated with smaller subcortical volumes, such as smaller hippocampal volume, in UK Biobank older adults. There was also evidence of positive association between subcortical volumes in Avon younger adults. For example, the tau protein-binding pathway score was negatively associated with left hippocampal volume in UK Biobank (P = 8.35 × 10-05; beta = -11.392, CI -17.066, -5.718) and positively associated with hippocampal volume in the Avon study (P = 0.040; beta = 51.952, CI 2.445, 101.460). The immune response score had a distinct pattern of association, being only associated with reduced thickness in the right posterior cingulate in older and younger adults (P = 0.011; beta = -0.003, CI -0.005, -0.001 in UK Biobank; P = 0.034; beta = -0.016, CI -0.031, -0.001 in the Avon study). The immune response score was associated with smaller subcortical volumes in the older adults, but not younger adults. The disease pathway scores showed greater evidence of association with imaging phenotypes than the genome-wide score. This suggests that pathway-specific polygenic methods may allow progress towards a mechanistic understanding of structural changes linked to polygenic risk in pre-clinical Alzheimer's disease. Pathway-specific profiling could further define pathophysiology in individuals, moving towards precision medicine in Alzheimer's disease.
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Affiliation(s)
- Judith R Harrison
- Institute of Neuroscience, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, CF24 4HQ, UK
| | - Sonya F Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, CF24 4HQ, UK
| | - Emily Baker
- Dementia Research Institute & MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Matthew Bracher-Smith
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Evie Stergiakouli
- Bristol Population Health Science Institute, Bristol University, Oakfield House, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, BS8 2BN, UK
| | - David E J Linden
- School for Mental Health and Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, CF24 4HQ, UK
- Mary MacKillop Institute for Health Research, Australian Catholic University, 5/215 Spring St, Melbourne, VIC 3000, Australia
| | - Valentina Escott-Price
- Dementia Research Institute & MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, CF24 4HQ, UK
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17
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Cannizzaro A, Ronat L, El Haffaf LM, Hanganu A. Associations between neuropsychiatric symptoms of affective and vegetative domains and brain morphology in aging people with mild cognitive impairment and Alzheimer's disease. Int J Geriatr Psychiatry 2023; 38:e5952. [PMID: 37351584 DOI: 10.1002/gps.5952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 06/05/2023] [Indexed: 06/24/2023]
Abstract
OBJECTIVE Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and even more in Alzheimer's disease (AD). The symptom-based cluster including nighttime disturbances, depression, appetite changes, anxiety, and apathy (affective and vegetative symptoms) was associated with an increased risk of dementia in MCI and has common neuroanatomical associations. Our objective was to investigate the differences in brain morphology associations with affective and vegetative symptoms between three groups: cognitively normal older adults (CN), MCI and AD. MATERIAL AND METHODS Alzheimer's Disease Neuroimaging Initiative data of 223 CN, 367 MCI and 175 AD, including cortical volumes, surface areas and thicknesses and severity scores of the five NPS were analyzed. A whole-brain vertex-wise general linear model was performed to test for intergroup differences (CN-MCI, CN-AD, AD-MCI) in brain morphology associations with five NPS. Multiple regressions were conducted to investigate cortical change as a function of NPS severity in the AD-MCI contrast. RESULTS We found (1) signature differences in nighttime disturbances associations with prefrontal regions in AD-MCI, (2) signature differences in NPS associations with temporal regions in AD-MCI for depression and in CN-AD for anxiety, (3) decreased temporal metrics in MCI as nighttime disturbances and depression severity increased, (4) decreased pars triangularis metrics in AD as nighttime disturbances and apathy severity increased. CONCLUSION Each NPS seems to have a signature on brain morphology. Affective and vegetative NPS were primarily associated with prefrontal and temporal regions. These signatures open the possibility of potential future assessments of links between brain morphology and NPS on an individual level.
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Affiliation(s)
- Adriana Cannizzaro
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
| | - Lucas Ronat
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté de Médecine, Département de Médecine, Université de Montréal, Montreal, Quebec, Canada
| | - Lyna Mariam El Haffaf
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
| | - Alexandru Hanganu
- Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Faculté des Arts et des Sciences, Département de Psychologie, Université de Montréal, Montreal, Quebec, Canada
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18
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Chadwick W, Maudsley S, Hull W, Havolli E, Boshoff E, Hill MDW, Goetghebeur PJD, Harrison DC, Nizami S, Bedford DC, Coope G, Real K, Thiemermann C, Maycox P, Carlton M, Cole SL. The oDGal Mouse: A Novel, Physiologically Relevant Rodent Model of Sporadic Alzheimer's Disease. Int J Mol Sci 2023; 24:ijms24086953. [PMID: 37108119 PMCID: PMC10138655 DOI: 10.3390/ijms24086953] [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: 03/03/2023] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 04/29/2023] Open
Abstract
Sporadic Alzheimer's disease (sAD) represents a serious and growing worldwide economic and healthcare burden. Almost 95% of current AD patients are associated with sAD as opposed to patients presenting with well-characterized genetic mutations that lead to AD predisposition, i.e., familial AD (fAD). Presently, the use of transgenic (Tg) animals overexpressing human versions of these causative fAD genes represents the dominant research model for AD therapeutic development. As significant differences in etiology exist between sAD and fAD, it is perhaps more appropriate to develop novel, more sAD-reminiscent experimental models that would expedite the discovery of effective therapies for the majority of AD patients. Here we present the oDGal mouse model, a novel model of sAD that displays a range of AD-like pathologies as well as multiple cognitive deficits reminiscent of AD symptomology. Hippocampal cognitive impairment and pathology were delayed with N-acetyl-cysteine (NaC) treatment, which strongly suggests that reactive oxygen species (ROS) are the drivers of downstream pathologies such as elevated amyloid beta and hyperphosphorylated tau. These features demonstrate a desired pathophenotype that distinguishes our model from current transgenic rodent AD models. A preclinical model that presents a phenotype of non-genetic AD-like pathologies and cognitive deficits would benefit the sAD field, particularly when translating therapeutics from the preclinical to the clinical phase.
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Affiliation(s)
- Wayne Chadwick
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2000 Antwerp, Belgium
| | - William Hull
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Enes Havolli
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Eugene Boshoff
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark D W Hill
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | | | - David C Harrison
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sohaib Nizami
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - David C Bedford
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Gareth Coope
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Katia Real
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Christoph Thiemermann
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Peter Maycox
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark Carlton
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sarah L Cole
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
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19
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Neuschwander P, Schmitt R, Jagoda L, Kurthen I, Giroud N, Meyer M. Different neuroanatomical correlates for temporal and spectral supra-threshold auditory tasks and speech in noise recognition in older adults with hearing impairment. Eur J Neurosci 2023; 57:981-1002. [PMID: 36683390 DOI: 10.1111/ejn.15922] [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: 04/10/2021] [Revised: 08/20/2022] [Accepted: 01/06/2023] [Indexed: 01/24/2023]
Abstract
Varying degrees of pure-tone hearing loss in older adults are differentially associated with cortical volume (CV) and thickness (CT) within and outside of the auditory pathway. This study addressed the question to what degree supra-threshold auditory performance (i.e., temporal compression and frequency selectivity) as well as speech in noise (SiN) recognition are associated with neurostructural correlates in a sample of 59 healthy older adults with mild to moderate pure-tone hearing loss. Using surface-based morphometry on T1-weighted MRI images, CT, CV, and surface area (CSA) of several regions-of-interest were obtained. The results showed distinct neurostructural patterns for the different tasks in terms of involved regions as well as morphometric parameters. While pure-tone averages (PTAs) positively correlated with CT in a right hemisphere superior temporal sulcus and gyrus cluster, supra-threshold auditory perception additionally extended significantly to CV and CT in left and right superior temporal clusters including Heschl's gyrus and sulcus, the planum polare and temporale. For SiN recognition, we found significant correlations with an auditory-related CT cluster and furthermore with language-related areas in the prefrontal cortex. Taken together, our results show that different auditory abilities are differently associated with cortical morphology in older adults with hearing impairment. Still, a common pattern is that greater PTAs and poorer supra-threshold auditory performance as well as poorer SiN recognition are all related to cortical thinning and volume loss but not to changes in CSA. These results support the hypothesis that mostly CT undergoes alterations in the context of auditory decline, while CSA remains stable.
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Affiliation(s)
- Pia Neuschwander
- Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Raffael Schmitt
- Neuroscience of Speech & Hearing, Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Laura Jagoda
- Division of Neuropsychology, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Ira Kurthen
- Developmental Psychology: Infancy and Childhood, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Nathalie Giroud
- Neuroscience of Speech & Hearing, Department of Computational Linguistics, University of Zurich, Zurich, Switzerland
| | - Martin Meyer
- Evolutionary Neuroscience of Language, Department of Comparative Language Science, University of Zurich, Zurich, Switzerland.,Center for the Interdisciplinary Study of Language Evolution (ISLE), University of Zurich, Zurich, Switzerland.,Cognitive Psychology Unit, Alpen-Adria University of Klagenfurt, Klagenfurt, Austria
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20
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Garg N, Choudhry MS, Bodade RM. A review on Alzheimer's disease classification from normal controls and mild cognitive impairment using structural MR images. J Neurosci Methods 2023; 384:109745. [PMID: 36395961 DOI: 10.1016/j.jneumeth.2022.109745] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is an irreversible neurodegenerative brain disorder that degrades the memory and cognitive ability in elderly people. The main reason for memory loss and reduction in cognitive ability is the structural changes in the brain that occur due to neuronal loss. These structural changes are most conspicuous in the hippocampus, cortex, and grey matter and can be assessed by using neuroimaging techniques viz. Positron Emission Tomography (PET), structural Magnetic Resonance Imaging (MRI) and functional MRI (fMRI), etc. Out of these neuroimaging techniques, structural MRI has evolved as the best technique as it indicates the best soft tissue contrast and high spatial resolution which is important for AD detection. Currently, the focus of researchers is on predicting the conversion of Mild Cognitive Impairment (MCI) into AD. MCI represents the transition state between expected cognitive changes with normal aging and Alzheimer's disease. Not every MCI patient progresses into Alzheimer's disease. MCI can develop into stable MCI (sMCI, patients are called non-converters) or into progressive MCI (pMCI, patients are diagnosed as MCI converters). This paper discusses the prognosis of MCI to AD conversion and presents a review of structural MRI-based studies for AD detection. AD detection framework includes feature extraction, feature selection, and classification process. This paper reviews the studies for AD detection based on different feature extraction methods and machine learning algorithms for classification. The performance of various feature extraction methods has been compared and it has been observed that the wavelet transform-based feature extraction method would give promising results for AD classification. The present study indicates that researchers are successful in classifying AD from Normal Controls (NrmC) but, it still requires a lot of work to be done for MCI/ NrmC and MCI/AD, which would help in detecting AD at its early stage.
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Affiliation(s)
- Neha Garg
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Mahipal Singh Choudhry
- Delhi Technological University, Department of Electronics and Communication, Delhi 110042, India.
| | - Rajesh M Bodade
- Military College of Telecommunication Engineering (MCTE), Mhow, Indore 453441, Madhya Pradesh, India.
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21
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Longitudinal Changes in Cortical Surface Area Associated With Transition to Psychosis in Adolescents at Clinical High Risk for the Disease. J Am Acad Child Adolesc Psychiatry 2023; 62:593-600. [PMID: 36638884 DOI: 10.1016/j.jaac.2023.01.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/22/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023]
Abstract
OBJECTIVE Identifying biomarkers of transition to psychosis in individuals at clinical high risk for psychosis (CHR-P) is essential to understanding the mechanisms underlying the disease. Although cross-sectional abnormalities in cortical surface area (CSA) have been demonstrated in individuals at CHR-P who transition to psychosis (CHR-P-T) compared with those who do not (CHR-P-NT), how CSA longitudinally develops remains unclear, especially in younger individuals. We set out to compare CSA in adolescents at CHR-P and healthy controls (HC) over 2 points in time. METHOD A longitudinal multicenter study was performed in adolescents at CHR-P in comparison to HC and according to transition to psychosis. Magnetic resonance imaging scans were acquired at baseline, at 18-month follow-up, or at the time of transition. Images were pre-processed and hemisphere and regional CSA were computed using FreeSurfer. Between-group analyses were performed with linear mixed-effects models. RESULTS A total of 313 scans (107 CHR-P and 102 HC) were included in the analysis. At 18 months, the rate of transition to psychosis in CHR-P was 23.4%. Adolescents at CHR-P-T presented greater age-related decrease in CSA in the left parietal and occipital lobes compared with HC, and in the bilateral parietal lobe and right frontal lobe relative to CHR-P-NT. These results were not influenced by antipsychotic treatment, cannabis use, or intelligence quotient (IQ). CONCLUSION Adolescents at CHR-P that developed a psychotic disorder presented different developmental trajectories of CSA relative to those who did not. A relatively greater decrease in CSA in the parietal and frontal lobes may index clinical transition to psychosis in adolescents at CHR-P.
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22
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Lu J, Huang R, Peng Y, Zhang J, Liang K, Wang Y, Feng Y, Wang Z. Mendelian Randomization Analyses Accounting for Causal Effect of COVID-19 on Brain Imaging-Derived Phenotypes. J Alzheimers Dis 2023; 96:1059-1070. [PMID: 37955088 DOI: 10.3233/jad-230626] [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] [Indexed: 11/14/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) has been a major challenge to global health and a financial burden. Little is known regarding the possible causal effects of COVID-19 on the macro- and micro-structures of the human brain. OBJECTIVE To determine the causal links between susceptibility, hospitalization, and the severity of COVID-19 and brain imaging-derived phenotypes (IDPs). METHODS Mendelian randomization (MR) analyses were performed to investigate the causal effect of three COVID-19 exposures (SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19) on brain structure employing summary datasets of genome-wide association studies. RESULTS In terms of cortical phenotypes, hospitalization due to COVID-19 was associated with a global decrease in the surface area (SA) of the cortex structure (β= -624.77, 95% CI: -1227.88 to -21.66, p = 0.042). At the regional level, SARS-CoV-2 infection was found to have a nominally causal effect on the thickness (TH) of the postcentral region (β= -0.004, 95% CI: -0.007 to -0.001, p = 0.01), as well as eight other IDPs. Hospitalized COVID-19 has a nominally causal relationship with TH of postcentral (β= -0.004, 95% CI: -0.007 to -0.001, p = 0.01) and other 6 IDPs. The nominally causal effects of critical COVID-19 on TH of medial orbitofrontal (β=0.004, 95% CI: 0.001to 0.007, p = 0.004) and other 7 IDPs were revealed. CONCLUSIONS Our study provides compelling genetic evidence supporting causal relationships between three COVID-19 traits and brain IDPs. This discovery holds promise for enhancing predictions and interventions in brain imaging.
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Affiliation(s)
- Jiajie Lu
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Rihong Huang
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Yuecheng Peng
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, China
| | - Jinming Zhang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Kairong Liang
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yezhong Wang
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yi Feng
- Department of Thoracic Surgery and Oncology, China State Key Laboratory of Respiratory Disease & National Clinical Research Center for Respiratory Disease, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Guangzhou Institute of Respiratory Health, Guangzhou, China
| | - Zhaotao Wang
- Institute of Neuroscience, Department of Neurosurgery, the Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Department of Clinical Medicine, The Second Clinical School of Guangzhou Medical University, Guangzhou, China
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23
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Cheong Y, Nishitani S, Yu J, Habata K, Kamiya T, Shiotsu D, Omori IM, Okazawa H, Tomoda A, Kosaka H, Jung M. The effects of epigenetic age and its acceleration on surface area, cortical thickness, and volume in young adults. Cereb Cortex 2022; 32:5654-5663. [PMID: 35196707 DOI: 10.1093/cercor/bhac043] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/20/2022] [Accepted: 01/21/2022] [Indexed: 01/25/2023] Open
Abstract
DNA methylation age has been used in recent studies as an epigenetic marker of accelerated cellular aging, whose contribution to the brain structural changes was lately acknowledged. We aimed to characterize the association of epigenetic age (i.e. estimated DNA methylation age) and its acceleration with surface area, cortical thickness, and volume in healthy young adults. Using the multi-tissue method (Horvath S. DNA methylation age of human tissues and cell types. 2013. Genome Biol 14), epigenetic age was computed with saliva sample. Epigenetic age acceleration was derived from residuals after adjusting epigenetic age for chronological age. Multiple regression models were computed for 148 brain regions for surface area, cortical thickness, and volume using epigenetic age or accelerated epigenetic age as a predictor and controlling for sex. Epigenetic age was associated with surface area reduction of the left insula. It was also associated with cortical thinning and volume reduction in multiple regions, with prominent changes of cortical thickness in the left temporal regions and of volume in the bilateral orbital gyri. Finally, accelerated epigenetic age was negatively associated with right cuneus gyrus volume. Our findings suggest that understanding the mechanisms of epigenetic age acceleration in young individuals may yield valuable insights into the relationship between epigenetic aging and the cortical change and on the early development of neurocognitive pathology among young adults.
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Affiliation(s)
- Yongjeon Cheong
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu 41062, South Korea
| | - Shota Nishitani
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui 910-1193, Japan.,Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka 565-0871, Japan
| | - Jinyoung Yu
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu 41062, South Korea
| | - Kaie Habata
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan
| | - Taku Kamiya
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan
| | - Daichi Shiotsu
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan
| | - Ichiro M Omori
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan
| | - Hidehiko Okazawa
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan.,Biomedical Imaging Research Center, University of Fukui, Eiheiji, Fukui 910-1193, Japan
| | - Akemi Tomoda
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui 910-1193, Japan.,Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka 565-0871, Japan
| | - Hirotaka Kosaka
- Research Center for Child Mental Development, University of Fukui, Eiheiji, Fukui 910-1193, Japan.,Division of Developmental Higher Brain Functions, United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, Osaka 565-0871, Japan.,Department of Neuropsychiatry, University of Fukui, 23-3 Matsuokashimoaizuki, Eiheiji, Fukui 910-1193, Japan
| | - Minyoung Jung
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu 41062, South Korea
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24
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Genetically predicted telomere length and Alzheimer’s disease endophenotypes: a Mendelian randomization study. Alzheimers Res Ther 2022; 14:167. [PMID: 36345036 PMCID: PMC9641781 DOI: 10.1186/s13195-022-01101-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 10/16/2022] [Indexed: 11/09/2022]
Abstract
Telomere length (TL) is associated with biological aging, consequently influencing the risk of age-related diseases such as Alzheimer’s disease (AD). We aimed to evaluate the potential causal role of TL in AD endophenotypes (i.e., cognitive performance, N = 2233; brain age and AD-related signatures, N = 1134; and cerebrospinal fluid biomarkers (CSF) of AD and neurodegeneration, N = 304) through a Mendelian randomization (MR) analysis. Our analysis was conducted in the context of the ALFA (ALzheimer and FAmilies) study, a population of cognitively healthy individuals at risk of AD. A total of 20 single nucleotide polymorphisms associated with TL were used to determine the effect of TL on AD endophenotypes. Analyses were adjusted by age, sex, and years of education. Stratified analyses by APOE-ɛ4 status and polygenic risk score of AD were conducted. MR analysis revealed significant associations between genetically predicted longer TL and lower levels of CSF Aβ and higher levels of CSF NfL only in APOE-ɛ4 non-carriers. Moreover, inheriting longer TL was associated with greater cortical thickness in age and AD-related brain signatures and lower levels of CSF p-tau among individuals at a high genetic predisposition to AD. Further observational analyses are warranted to better understand these associations.
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25
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Sanabria-Diaz G, Etter MM, Melie-Garcia L, Lieb JM, Psychogios MN, Hutter G, Granziera C. Brain cortical alterations in COVID-19 patients with neurological symptoms. Front Neurosci 2022; 16:992165. [PMID: 36340780 PMCID: PMC9630324 DOI: 10.3389/fnins.2022.992165] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 09/28/2022] [Indexed: 07/29/2023] Open
Abstract
Background Growing evidence suggests that the central nervous system is affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), since infected patients suffer from acute and long-term neurological sequelae. Nevertheless, it is currently unknown whether the virus affects the brain cortex. The purpose of this study was to assess the cortical gray matter volume, the cortical thickness, and the cortical surface area in a group of SARS-CoV-2 infected patients with neurological symptoms compared to healthy control subjects. Additionally, we analyzed the cortical features and the association with inflammatory biomarkers in the cerebrospinal fluid (CSF) and plasma. Materials and methods Thirty-three patients were selected from a prospective cross-sectional study cohort during the ongoing pandemic (August 2020-April 2021) at the university hospitals of Basel and Zurich (Switzerland). The group included patients with different neurological symptom severity (Class I: nearly asymptomatic/mild symptoms, II: moderate symptoms, III: severe symptoms). Thirty-three healthy age and sex-matched subjects that underwent the same MRI protocol served as controls. For each anatomical T1w MPRAGE image, regional cortical gray matter volume, thickness, and surface area were computed with FreeSurfer. Using a linear regression model, cortical measures were compared between groups (patients vs. controls; Class I vs. II-III), with age, sex, MRI magnetic field strength, and total intracranial volume/mean thickness/total surface area as covariates. In a subgroup of patients, the association between cortical features and clinical parameters was assessed using partial correlation adjusting for the same covariates. P-values were corrected using a false discovery rate (FDR). Results Our findings revealed a lower cortical volume in COVID-19 patients' orbitofrontal, frontal, and cingulate regions than in controls (p < 0.05). Regional gray matter volume and thickness decreases were negatively associated with CSF total protein levels, the CSF/blood-albumin ratio, and CSF EN-RAGE levels. Conclusion Our data suggest that viral-triggered inflammation leads to neurotoxic damage in some cortical areas during the acute phase of a COVID-19 infection in patients with neurological symptoms.
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Affiliation(s)
- Gretel Sanabria-Diaz
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Manina Maja Etter
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Division of Neurosurgery, University Hospital Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
| | - Johanna M. Lieb
- Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Marios-Nikos Psychogios
- Department of Neuroradiology, Clinic of Radiology and Nuclear Medicine, University Hospital Basel, Basel, Switzerland
| | - Gregor Hutter
- Brain Tumor Immunotherapy Lab, Department of Biomedicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Division of Neurosurgery, University Hospital Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel, University of Basel, Basel, Switzerland
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26
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Zhuang K, Chen X, Cassady KE, Baker SL, Jagust WJ. Metacognition, cortical thickness, and tauopathy in aging. Neurobiol Aging 2022; 118:44-54. [PMID: 35868093 PMCID: PMC9979699 DOI: 10.1016/j.neurobiolaging.2022.06.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Revised: 06/17/2022] [Accepted: 06/21/2022] [Indexed: 11/30/2022]
Abstract
We investigated self-rating of cognitive task performance (self-appraisal) and the difference between self-rating and actual task performance (appraisal discrepancy) in cognitively healthy older adults and their relationship with cortical thickness and Alzheimer's disease (AD) biomarkers, amyloid and tau. All participants (N = 151) underwent neuropsychological testing and 1.5T structural magnetic resonance imaging. A subset (N = 66) received amyloid-PET with [11C] PiB and tau-PET with [18F] Flortaucipir. We found that worse performers had lower self-appraisal ratings, but still overestimated their performance, consistent with the Dunning-Kruger effect. Self-appraisal rating and appraisal discrepancy revealed distinct relationships with cortical thickness and AD pathology. Greater appraisal discrepancy, indicating overestimation, was related to thinning of inferior-lateral temporal, fusiform, and rostral anterior cingulate cortices. Lower self-appraisal was associated with higher entorhinal and inferior temporal tau. These results suggest that overestimation could implicate structural atrophy beyond AD pathology, while lower self-appraisal could indicate early behavioral alteration due to AD pathology, supporting the notion of subjective cognitive decline prior to objective deficits.
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Affiliation(s)
- Kailin Zhuang
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Xi Chen
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Kaitlin E Cassady
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Suzanne L Baker
- Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - William J Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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27
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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: 4.5] [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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28
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Perrenoud Q, Leclerc C, Geoffroy H, Vitalis T, Richetin K, Rampon C, Gallopin T. Molecular and electrophysiological features of GABAergic neurons in the dentate gyrus reveal limited homology with cortical interneurons. PLoS One 2022; 17:e0270981. [PMID: 35802727 PMCID: PMC9269967 DOI: 10.1371/journal.pone.0270981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 06/21/2022] [Indexed: 11/18/2022] Open
Abstract
GABAergic interneurons tend to diversify into similar classes across telencephalic regions. However, it remains unclear whether the electrophysiological and molecular properties commonly used to define these classes are discriminant in the hilus of the dentate gyrus. Here, using patch-clamp combined with single cell RT-PCR, we compare the relevance of commonly used electrophysiological and molecular features for the clustering of GABAergic interneurons sampled from the mouse hilus and primary sensory cortex. While unsupervised clustering groups cortical interneurons into well-established classes, it fails to provide a convincing partition of hilar interneurons. Statistical analysis based on resampling indicates that hilar and cortical GABAergic interneurons share limited homology. While our results do not invalidate the use of classical molecular marker in the hilus, they indicate that classes of hilar interneurons defined by the expression of molecular markers do not exhibit strongly discriminating electrophysiological properties.
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Affiliation(s)
- Quentin Perrenoud
- Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Clémence Leclerc
- Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, Paris, France
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, France
| | - Hélène Geoffroy
- Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Tania Vitalis
- Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, Paris, France
| | - Kevin Richetin
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, France
| | - Claire Rampon
- Centre de Recherches sur la Cognition Animale (CRCA), Centre de Biologie Intégrative (CBI), Université de Toulouse; CNRS, UPS, France
| | - Thierry Gallopin
- Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, Paris, France
- * E-mail:
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29
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Rus-Oswald OG, Benner J, Reinhardt J, Bürki C, Christiner M, Hofmann E, Schneider P, Stippich C, Kressig RW, Blatow M. Musicianship-Related Structural and Functional Cortical Features Are Preserved in Elderly Musicians. Front Aging Neurosci 2022; 14:807971. [PMID: 35401149 PMCID: PMC8990841 DOI: 10.3389/fnagi.2022.807971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Professional musicians are a model population for exploring basic auditory function, sensorimotor and multisensory integration, and training-induced neuroplasticity. The brain of musicians exhibits distinct structural and functional cortical features; however, little is known about how these features evolve during aging. This multiparametric study aimed to examine the functional and structural neural correlates of lifelong musical practice in elderly professional musicians. Methods Sixteen young musicians, 16 elderly musicians (age >70), and 15 elderly non-musicians participated in the study. We assessed gray matter metrics at the whole-brain and region of interest (ROI) levels using high-resolution magnetic resonance imaging (MRI) with the Freesurfer automatic segmentation and reconstruction pipeline. We used BrainVoyager semiautomated segmentation to explore individual auditory cortex morphotypes. Furthermore, we evaluated functional blood oxygenation level-dependent (BOLD) activations in auditory and non-auditory regions by functional MRI (fMRI) with an attentive tone-listening task. Finally, we performed discriminant function analyses based on structural and functional ROIs. Results A general reduction of gray matter metrics distinguished the elderly from the young subjects at the whole-brain level, corresponding to widespread natural brain atrophy. Age- and musicianship-dependent structural correlations revealed group-specific differences in several clusters including superior, middle, and inferior frontal as well as perirolandic areas. In addition, the elderly musicians exhibited increased gyrification of auditory cortex like the young musicians. During fMRI, the elderly non-musicians activated predominantly auditory regions, whereas the elderly musicians co-activated a much broader network of auditory association areas, primary and secondary motor areas, and prefrontal and parietal regions like, albeit weaker, the young musicians. Also, group-specific age- and musicianship-dependent functional correlations were observed in the frontal and parietal regions. Moreover, discriminant function analysis could separate groups with high accuracy based on a set of specific structural and functional, mainly temporal and occipital, ROIs. Conclusion In conclusion, despite naturally occurring senescence, the elderly musicians maintained musicianship-specific structural and functional cortical features. The identified structural and functional brain regions, discriminating elderly musicians from non-musicians, might be of relevance for the aging musicians’ brain. To what extent lifelong musical activity may have a neuroprotective impact needs to be addressed further in larger longitudinal studies.
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Affiliation(s)
- Oana G. Rus-Oswald
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
- *Correspondence: Oana G. Rus-Oswald,
| | - Jan Benner
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Jan Benner,
| | - Julia Reinhardt
- Department of Neuroradiology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zürich, Switzerland
- Division of Diagnostic and Interventional Neuroradiology, Department of Radiology, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Cardiology and Cardiovascular Research Institute Basel, University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Orthopedic Surgery and Traumatology, University Hospital of Basel, University of Basel, Basel, Switzerland
| | - Céline Bürki
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
| | - Markus Christiner
- Centre for Systematic Musicology, University of Graz, Graz, Austria
- Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Elke Hofmann
- Academy of Music, University of Applied Sciences and Arts Northwestern Switzerland (FHNW), Basel, Switzerland
| | - Peter Schneider
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- Centre for Systematic Musicology, University of Graz, Graz, Austria
- Vitols Jazeps Latvian Academy of Music, Riga, Latvia
| | - Christoph Stippich
- Department of Neuroradiology and Radiology, Kliniken Schmieder, Allensbach, Germany
| | - Reto W. Kressig
- University Department of Geriatric Medicine FELIX PLATTER, Basel, Switzerland
| | - Maria Blatow
- Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Neurocenter, Cantonal Hospital Lucerne, University of Lucerne, Lucerne, Switzerland
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30
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Tarawneh HY, Menegola HK, Peou A, Tarawneh H, Jayakody DMP. Central Auditory Functions of Alzheimer's Disease and Its Preclinical Stages: A Systematic Review and Meta-Analysis. Cells 2022; 11:1007. [PMID: 35326458 PMCID: PMC8947537 DOI: 10.3390/cells11061007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 03/01/2022] [Accepted: 03/12/2022] [Indexed: 01/01/2023] Open
Abstract
In 2020, 55 million people worldwide were living with dementia, and this number is projected to reach 139 million in 2050. However, approximately 75% of people living with dementia have not received a formal diagnosis. Hence, they do not have access to treatment and care. Without effective treatment in the foreseeable future, it is essential to focus on modifiable risk factors and early intervention. Central auditory processing is impaired in people diagnosed with Alzheimer's disease (AD) and its preclinical stages and may manifest many years before clinical diagnosis. This study systematically reviewed central auditory processing function in AD and its preclinical stages using behavioural central auditory processing tests. Eleven studies met the full inclusion criteria, and seven were included in the meta-analyses. The results revealed that those with mild cognitive impairment perform significantly worse than healthy controls within channel adaptive tests of temporal response (ATTR), time-compressed speech test (TCS), Dichotic Digits Test (DDT), Dichotic Sentence Identification (DSI), Speech in Noise (SPIN), and Synthetic Sentence Identification-Ipsilateral Competing Message (SSI-ICM) central auditory processing tests. In addition, this analysis indicates that participants with AD performed significantly worse than healthy controls in DDT, DSI, and SSI-ICM tasks. Clinical implications are discussed in detail.
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Affiliation(s)
- Hadeel Y. Tarawneh
- Ear Science Institute Australia, Subiaco, WA 6008, Australia; (H.Y.T.); (H.K.M.); (A.P.); (H.T.)
- Ear Sciences Centre, School of Surgery, University of Western Australia, Crawley, WA 6009, Australia
- School of Human Sciences, The University of Western Australia, Crawley, WA 6009, Australia
| | - Holly K. Menegola
- Ear Science Institute Australia, Subiaco, WA 6008, Australia; (H.Y.T.); (H.K.M.); (A.P.); (H.T.)
- Ear Sciences Centre, School of Surgery, University of Western Australia, Crawley, WA 6009, Australia
| | - Andrew Peou
- Ear Science Institute Australia, Subiaco, WA 6008, Australia; (H.Y.T.); (H.K.M.); (A.P.); (H.T.)
- School of Human Sciences, The University of Western Australia, Crawley, WA 6009, Australia
| | - Hanadi Tarawneh
- Ear Science Institute Australia, Subiaco, WA 6008, Australia; (H.Y.T.); (H.K.M.); (A.P.); (H.T.)
- Ear Sciences Centre, School of Surgery, University of Western Australia, Crawley, WA 6009, Australia
| | - Dona M. P. Jayakody
- Ear Science Institute Australia, Subiaco, WA 6008, Australia; (H.Y.T.); (H.K.M.); (A.P.); (H.T.)
- Ear Sciences Centre, School of Surgery, University of Western Australia, Crawley, WA 6009, Australia
- WA Centre for Health and Aging, University of Western Australia, Crawley, WA 6009, Australia
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31
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Addressing the Missing Data Challenge in Multi-Modal Datasets for the Diagnosis of Alzheimer’s Disease. J Neurosci Methods 2022; 375:109582. [DOI: 10.1016/j.jneumeth.2022.109582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022]
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32
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Vignando M, Ffytche D, Lewis SJG, Lee PH, Chung SJ, Weil RS, Hu MT, Mackay CE, Griffanti L, Pins D, Dujardin K, Jardri R, Taylor JP, Firbank M, McAlonan G, Mak HKF, Ho SL, Mehta MA. Mapping brain structural differences and neuroreceptor correlates in Parkinson's disease visual hallucinations. Nat Commun 2022; 13:519. [PMID: 35082285 PMCID: PMC8791961 DOI: 10.1038/s41467-022-28087-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 12/14/2021] [Indexed: 12/16/2022] Open
Abstract
Parkinson's psychosis (PDP) describes a spectrum of symptoms that may arise in Parkinson's disease (PD) including visual hallucinations (VH). Imaging studies investigating the neural correlates of PDP have been inconsistent in their findings, due to differences in study design and limitations of scale. Here we use empirical Bayes harmonisation to pool together structural imaging data from multiple research groups into a large-scale mega-analysis, allowing us to identify cortical regions and networks involved in VH and their relation to receptor binding. Differences of morphometrics analysed show a wider cortical involvement underlying VH than previously recognised, including primary visual cortex and surrounding regions, and the hippocampus, independent of its role in cognitive decline. Structural covariance analyses point to the involvement of the attentional control networks in PD-VH, while associations with receptor density maps suggest neurotransmitter loss may be linked to the cortical changes.
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Affiliation(s)
- Miriam Vignando
- Department of Neuroimaging, King's College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, SE5 8AF, UK.
| | - Dominic Ffytche
- Department of Old Age Psychiatry, King's College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, SE5 8AF, UK
| | - Simon J G Lewis
- ForeFront Parkinson's Disease Research Clinic, Brain and Mind Centre, School of Medical Sciences, University of Sydney, Camperdown, NSW, Australia
| | - Phil Hyu Lee
- Yonsei University College of Medicine, Seoul, South Korea
| | | | - Rimona S Weil
- Dementia Research Centre, University College London, 8-11 Queen Square, London, WC1M 3BG, UK
- Wellcome Centre for Neuroimaging, University College London, London, UK
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, Oxford, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Clare E Mackay
- Oxford Parkinson's Disease Centre, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ludovica Griffanti
- Oxford Parkinson's Disease Centre, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Delphine Pins
- Univ. Lille, Inserm, CHU Lille, U1172 - Centre Lille Neuroscience & Cognition, 59000, Lille, France
| | - Kathy Dujardin
- Univ. Lille, Inserm, CHU Lille, U1172 - Centre Lille Neuroscience & Cognition, 59000, Lille, France
| | - Renaud Jardri
- Univ. Lille, Inserm, CHU Lille, U1172 - Centre Lille Neuroscience & Cognition, 59000, Lille, France
| | - John-Paul Taylor
- Newcastle University, Translational and Clinical Research Institute, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle Upon Tyne, NE4 5PL, UK
| | - Michael Firbank
- Newcastle University, Translational and Clinical Research Institute, Biomedical Research Building, Campus for Ageing and Vitality, Newcastle Upon Tyne, NE4 5PL, UK
| | - Grainne McAlonan
- King's College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, SE5 8AF, UK
| | - Henry K F Mak
- Division of Neurology, Dept of Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Shu Leong Ho
- Division of Neurology, Dept of Medicine, LKS Faculty of Medicine, University of Hong Kong, Hong Kong, Hong Kong
| | - Mitul A Mehta
- Department of Neuroimaging, King's College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London, SE5 8AF, UK
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Canal-Garcia A, Gómez-Ruiz E, Mijalkov M, Chang YW, Volpe G, Pereira JB. Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data. Cereb Cortex 2022; 32:3501-3515. [PMID: 35059722 PMCID: PMC9376877 DOI: 10.1093/cercor/bhab429] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 11/25/2022] Open
Abstract
The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.
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Affiliation(s)
- Anna Canal-Garcia
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
| | | | - Mite Mijalkov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - Yu-Wei Chang
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Joana B Pereira
- Address correspondence to Department of NVS, Division of Clinical Geriatrics, NEO seventh floor, Blickagången 16, 141 52 Huddinge, Sweden. ; Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf
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Wisse LEM, Xie L, Das SR, De Flores R, Hansson O, Habes M, Doshi J, Davatzikos C, Yushkevich PA, Wolk DA. Tau pathology mediates age effects on medial temporal lobe structure. Neurobiol Aging 2022; 109:135-144. [PMID: 34740075 PMCID: PMC8800343 DOI: 10.1016/j.neurobiolaging.2021.09.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/14/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023]
Abstract
Hippocampal atrophy is endemic in 'normal aging' but it is unclear what factors drive age-related changes in medial temporal lobe (MTL) structural measures. We investigated cross-sectional (n = 191) and longitudinal (n = 164) MTL atrophy patterns in cognitively normal older adults from ADNI-GO/2 with no to low cerebral β-amyloid and assessed whether white matter hyperintensities (WMHs) and cerebrospinal fluid (CSF) phospho tau (p-tau) levels can explain age-related changes in the MTL. Age was significantly associated with hippocampal volumes and Brodmann Area (BA) 35 thickness, regions affected early by neurofibrillary tangle pathology, in the cross-sectional analysis and with anterior and/or posterior hippocampus, entorhinal cortex and BA35 in the longitudinal analysis. CSF p-tau was significantly associated with hippocampal volumes and atrophy rates. Mediation analyses showed that CSF p-tau levels partially mediated age effects on hippocampal atrophy rates. No significant associations were observed for WMHs. These findings point toward a role of tau pathology, potentially reflecting Primary Age-Related Tauopathy, in age-related MTL structural changes and suggests a potential role for tau-targeted interventions in age-associated neurodegeneration and memory decline.
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Affiliation(s)
- LEM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - L Xie
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - SR Das
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - R De Flores
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen, France
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - M Habes
- Biggs Alzheimer’s Institute, UT Health, San Antonio, USA
| | - J Doshi
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - C Davatzikos
- Section of Biomedical Image Analysis, University of Pennsylvania, Philadelphia, PA, USA
| | - PA Yushkevich
- Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - DA Wolk
- Penn Memory Center, Department of Neurology, University of Pennsylvania, Philadelphia, USA
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35
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Frangou S, Modabbernia A, Williams SCR, Papachristou E, Doucet GE, Agartz I, Aghajani M, Akudjedu TN, Albajes‐Eizagirre A, Alnæs D, Alpert KI, Andersson M, Andreasen NC, Andreassen OA, Asherson P, Banaschewski T, Bargallo N, Baumeister S, Baur‐Streubel R, Bertolino A, Bonvino A, Boomsma DI, Borgwardt S, Bourque J, Brandeis D, Breier A, Brodaty H, Brouwer RM, Buitelaar JK, Busatto GF, Buckner RL, Calhoun V, Canales‐Rodríguez EJ, Cannon DM, Caseras X, Castellanos FX, Cervenka S, Chaim‐Avancini TM, Ching CRK, Chubar V, Clark VP, Conrod P, Conzelmann A, Crespo‐Facorro B, Crivello F, Crone EA, Dale AM, Dannlowski U, Davey C, de Geus EJC, de Haan L, de Zubicaray GI, den Braber A, Dickie EW, Di Giorgio A, Doan NT, Dørum ES, Ehrlich S, Erk S, Espeseth T, Fatouros‐Bergman H, Fisher SE, Fouche J, Franke B, Frodl T, Fuentes‐Claramonte P, Glahn DC, Gotlib IH, Grabe H, Grimm O, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Gur RE, Gur RC, Hahn T, Harrison BJ, Hartman CA, Hatton SN, Heinz A, Heslenfeld DJ, Hibar DP, Hickie IB, Ho B, Hoekstra PJ, Hohmann S, Holmes AJ, Hoogman M, Hosten N, Howells FM, Hulshoff Pol HE, Huyser C, Jahanshad N, James A, Jernigan TL, Jiang J, Jönsson EG, Joska JA, Kahn R, Kalnin A, Kanai R, Klein M, Klyushnik TP, Koenders L, Koops S, Krämer B, Kuntsi J, Lagopoulos J, Lázaro L, Lebedeva I, Lee WH, Lesch K, Lochner C, Machielsen MWJ, Maingault S, Martin NG, Martínez‐Zalacaín I, Mataix‐Cols D, Mazoyer B, McDonald C, McDonald BC, McIntosh AM, McMahon KL, McPhilemy G, Meinert S, Menchón JM, Medland SE, Meyer‐Lindenberg A, Naaijen J, Najt P, Nakao T, Nordvik JE, Nyberg L, Oosterlaan J, de la Foz VO, Paloyelis Y, Pauli P, Pergola G, Pomarol‐Clotet E, Portella MJ, Potkin SG, Radua J, Reif A, Rinker DA, Roffman JL, Rosa PGP, Sacchet MD, Sachdev PS, Salvador R, Sánchez‐Juan P, Sarró S, Satterthwaite TD, Saykin AJ, Serpa MH, Schmaal L, Schnell K, Schumann G, Sim K, Smoller JW, Sommer I, Soriano‐Mas C, Stein DJ, Strike LT, Swagerman SC, Tamnes CK, Temmingh HS, Thomopoulos SI, Tomyshev AS, Tordesillas‐Gutiérrez D, Trollor JN, Turner JA, Uhlmann A, van den Heuvel OA, van den Meer D, van der Wee NJA, van Haren NEM, van 't Ent D, van Erp TGM, Veer IM, Veltman DJ, Voineskos A, Völzke H, Walter H, Walton E, Wang L, Wang Y, Wassink TH, Weber B, Wen W, West JD, Westlye LT, Whalley H, Wierenga LM, Wittfeld K, Wolf DH, Worker A, Wright MJ, Yang K, Yoncheva Y, Zanetti MV, Ziegler GC, Thompson PM, Dima D. Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years. Hum Brain Mapp 2022; 43:431-451. [PMID: 33595143 PMCID: PMC8675431 DOI: 10.1002/hbm.25364] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/02/2021] [Accepted: 01/21/2021] [Indexed: 12/25/2022] Open
Abstract
Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
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Affiliation(s)
- Sophia Frangou
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverCanada
| | | | - Steven C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Efstathios Papachristou
- Psychology and Human DevelopmentInstitute of Education, University College LondonLondonUnited Kingdom
| | - Gaelle E. Doucet
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
| | - Moji Aghajani
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
- Section Forensic Family & Youth CareInstitute of Education & Child StudiesLeiden UniversityNetherlands
| | - Theophilus N. Akudjedu
- Institute of Medical Imaging and Visualisation, Department of Medical Science and Public Health, Faculty of Health and Social SciencesBournemouth UniversityPooleUnited Kingdom
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Anton Albajes‐Eizagirre
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and AddictionInstitute of Clinical Medicine, University of OsloOsloNorway
| | | | - Micael Andersson
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
| | - Nancy C. Andreasen
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Philip Asherson
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Nuria Bargallo
- Imaging Diagnostic CentreHospital Clinic, Barcelona University ClinicBarcelonaSpain
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
| | - Sarah Baumeister
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Ramona Baur‐Streubel
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWürzburgGermany
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Aurora Bonvino
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Dorret I. Boomsma
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Stefan Borgwardt
- Department of Psychiatry & PsychotherapyUniversity of LübeckLübeckGermany
| | - Josiane Bourque
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Daniel Brandeis
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Alan Breier
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Rachel M. Brouwer
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Jan K. Buitelaar
- Donders Center of Medical NeurosciencesRadboud UniversityNijmegenNetherlands
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Geraldo F. Busatto
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Randy L. Buckner
- Department of Psychology, Center for Brain ScienceHarvard UniversityCambridgeMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Vincent Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory University, USA Neurology, Radiology, Psychiatry and Biomedical Engineering, Emory UniversityAtlantaGeorgiaUSA
| | - Erick J. Canales‐Rodríguez
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Xavier Caseras
- MRC Centre for Neuropsychiatric Genetics and GenomicsCardiff UniversityCardiffUnited Kingdom
| | | | - Simon Cervenka
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Tiffany M. Chaim‐Avancini
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Victoria Chubar
- Mind‐Body Research Group, Department of NeuroscienceKU LeuvenLeuvenBelgium
| | - Vincent P. Clark
- Department of PsychologyUniversity of New MexicoAlbuquerqueNew MexicoUSA
- Mind Research NetworkAlbuquerqueNew MexicoUSA
| | - Patricia Conrod
- Department of PsychiatryUniversité de MontréalMontrealCanada
| | - Annette Conzelmann
- Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversity of TübingenTübingenGermany
| | - Benedicto Crespo‐Facorro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- HU Virgen del Rocio, IBiSUniversity of SevillaSevillaSpain
| | - Fabrice Crivello
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Eveline A. Crone
- Erasmus School of Social and Behavioural SciencesErasmus University RotterdamRotterdamNetherlands
- Faculteit der Sociale Wetenschappen, Instituut PsychologieUniversiteit LeidenLeidenNetherlands
| | - Anders M. Dale
- Center for Multimodal Imaging and Genetics, Department of NeuroscienceUniversity of California‐San DiegoSan DiegoCaliforniaUSA
- Department of RadiologyUniversity of California‐San DiegoSan DiegoCaliforniaUSA
| | - Udo Dannlowski
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | | | - Eco J. C. de Geus
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Lieuwe de Haan
- Academisch Medisch CentrumUniversiteit van AmsterdamAmsterdamNetherlands
| | - Greig I. de Zubicaray
- Faculty of Health, Institute of Health and Biomedical InnovationQueensland University of TechnologyQueenslandAustralia
| | - Anouk den Braber
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Erin W. Dickie
- Kimel Family Translational Imaging Genetics Laboratory, Campbell Family Mental Health Research InstituteCAMHCampbellCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Annabella Di Giorgio
- Biological Psychiatry LabFondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Erlend S. Dørum
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- Sunnaas Rehabilitation Hospital HTNesoddenNorway
| | - Stefan Ehrlich
- Division of Psychological and Social Medicine and Developmental NeurosciencesTechnische Universität DresdenDresdenGermany
- Faculty of MedicineUniversitätsklinikum Carl Gustav Carus an der TU DresdenDresdenGermany
| | - Susanne Erk
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Thomas Espeseth
- Biological Psychiatry LabFondazione IRCCS Casa Sollievo della SofferenzaSan Giovanni Rotondo (FG)Italy
- Bjørknes CollegeOsloNorway
| | - Helena Fatouros‐Bergman
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Simon E. Fisher
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Language and Genetics DepartmentMax Planck Institute for PsycholinguisticsNijmegenNetherlands
| | - Jean‐Paul Fouche
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Barbara Franke
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
- Department of PsychiatryRadboud University Medical CenterNijmegenNetherlands
| | - Thomas Frodl
- Department of Psychiatry and PsychotherapyOtto von Guericke University MagdeburgMagdeburgGermany
| | - Paola Fuentes‐Claramonte
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - David C. Glahn
- Department of PsychiatryTommy Fuss Center for Neuropsychiatric Disease Research Boston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ian H. Gotlib
- Department of PsychologyStanford UniversityStanfordCaliforniaUSA
| | - Hans‐Jörgen Grabe
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Oliver Grimm
- Department for Psychiatry, Psychosomatics and PsychotherapyUniversitätsklinikum Frankfurt, Goethe UniversitatFrankfurtGermany
| | - Nynke A. Groenewold
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | | | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Patricia Gruner
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
- Learning Based Recovery CenterVA Connecticut Health SystemWest HavenConnecticutUSA
| | - Rachel E. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Children's Hospital of PhiladelphiaUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tim Hahn
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | - Ben J. Harrison
- Melbourne Neuropsychiatry CenterUniversity of MelbourneMelbourneAustralia
| | - Catharine A. Hartman
- Interdisciplinary Center Psychopathology and Emotion regulationUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sean N. Hatton
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Andreas Heinz
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Dirk J. Heslenfeld
- Departments of Experimental and Clinical PsychologyVrije Universiteit AmsterdamAmsterdamNetherlands
| | - Derrek P. Hibar
- Personalized Healthcare, Genentech, Inc.South San FranciscoCaliforniaUSA
| | - Ian B. Hickie
- Brain and Mind CentreUniversity of SydneySydneyAustralia
| | - Beng‐Choon Ho
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Pieter J. Hoekstra
- Department of PsychiatryUniversity Medical Center Groningen, University of GroningenGroningenNetherlands
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental HealthHeidelberg UniversityHeidelbergGermany
| | - Avram J. Holmes
- Department of PsychologyYale UniversityNew HavenConnecticutUSA
| | - Martine Hoogman
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
| | - Norbert Hosten
- Norbert Institute of Diagnostic Radiology and NeuroradiologyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
| | - Fleur M. Howells
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
- Neuroscience InstituteUniversity of Cape TownCape TownSouth Africa
| | | | - Chaim Huyser
- De Bascule, Academic Centre for Children and Adolescent PsychiatryAmsterdamNetherlands
| | - Neda Jahanshad
- Mind‐Body Research Group, Department of NeuroscienceKU LeuvenLeuvenBelgium
| | - Anthony James
- Department of PsychiatryOxford UniversityOxfordUnited Kingdom
| | - Terry L. Jernigan
- Center for Human Development, Departments of Cognitive Science, Psychiatry, and RadiologyUniversity of CaliforniaSan DiegoCaliforniaUSA
| | - Jiyang Jiang
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Erik G. Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - John A. Joska
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Rene Kahn
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
| | - Andrew Kalnin
- Department of RadiologyOhio State University College of MedicineColumbusOhioUSA
| | - Ryota Kanai
- Department of NeuroinformaticsAraya, Inc.TokyoJapan
| | - Marieke Klein
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
- Department of Human GeneticsRadboud University Medical CenterNijmegenNetherlands
- Department of PsychiatryUniversity of California San DiegoSan DiegoCaliforniaUSA
| | | | - Laura Koenders
- Academisch Medisch CentrumUniversiteit van AmsterdamAmsterdamNetherlands
| | - Sanne Koops
- Rudolf Magnus Institute of NeuroscienceUniversity Medical Center UtrechtUtrechtNetherlands
| | - Bernd Krämer
- Section for Experimental Psychopathology and Neuroimaging, Department of General PsychiatryHeidelberg UniversityHeidelbergGermany
| | - Jonna Kuntsi
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Jim Lagopoulos
- Sunshine Coast Mind and NeuroscienceThompson Institute, University of the Sunshine CoastQueenslandAustralia
| | - Luisa Lázaro
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of Child and Adolescent Psychiatry and PsychologyHospital Clinic, University of BarcelonaBarcelonaSpain
| | - Irina Lebedeva
- Mental Health Research CenterRussian Academy of Medical SciencesMoscowRussia
| | - Won Hee Lee
- Department of PsychiatryIcahn School of Medicine at Mount SinaiNew York CityNew YorkUSA
| | - Klaus‐Peter Lesch
- Department of Psychiatry, Psychosomatics and PsychotherapyJulius‐Maximilians Universität WürzburgWürzburgGermany
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | | | - Sophie Maingault
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Nicholas G. Martin
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteQueenslandAustralia
| | - Ignacio Martínez‐Zalacaín
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - David Mataix‐Cols
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- Stockholm Health Care ServicesStockholmSweden
| | - Bernard Mazoyer
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293Université de BordeauxBordeauxFrance
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Brenna C. McDonald
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Katie L. McMahon
- School of Clinical Sciences, Institute of Health and Biomedical InnovationQueensland University of TechnologyQueenslandAustralia
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Susanne Meinert
- Department of Psychiatry and PsychotherapyUniversity of MünsterGermany
| | - José M. Menchón
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Sarah E. Medland
- Queensland Institute of Medical ResearchBerghofer Medical Research InstituteQueenslandAustralia
| | - Andreas Meyer‐Lindenberg
- Department of Psychiatry and PsychotherapyCentral Institute of Mental Health, Heidelberg UniversityHeidelbergGermany
| | - Jilly Naaijen
- Donders Centre for Cognitive NeuroimagingRadboud UniversityNijmegenNetherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenNetherlands
| | - Pablo Najt
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics and NCBES Galway Neuroscience CentreNational University of IrelandGalwayIreland
| | - Tomohiro Nakao
- Department of Clinical MedicineKyushu UniversityFukuokaJapan
| | | | - Lars Nyberg
- Department of Integrative Medical BiologyUmeå UniversityUmeåSweden
- Department of Radiation SciencesUmeå Center for Functional Brain Imaging, Umeå UniversityUmeåSweden
| | - Jaap Oosterlaan
- Department of Clinical NeuropsychologyAmsterdam University Medical Centre, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Víctor Ortiz‐García de la Foz
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryUniversity Hospital “Marques de Valdecilla”, Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM)Instituto de Salud Carlos IIIMadridSpain
| | - Yannis Paloyelis
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
| | - Paul Pauli
- Department of Psychology, Biological Psychology, Clinical Psychology and PsychotherapyUniversity of WürzburgWürzburgGermany
- Centre of Mental HealthUniversity of WürzburgWürzburgGermany
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense OrgansUniversity of Bari Aldo MoroBariItaly
| | - Edith Pomarol‐Clotet
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
| | - Maria J. Portella
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Department of PsychiatryHospital de la Santa Creu i Sant Pau, Institut d'Investigació Biomèdica Sant Pau, Universitat Autònoma de BarcelonaBarcelonaSpain
| | - Steven G. Potkin
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
| | - Joaquim Radua
- Centre for Psychiatric Research, Department of Clinical NeuroscienceKarolinska InstitutetSolnaSweden
- August Pi i Sunyer Biomedical Research Institut (IDIBAPS)BarcelonaSpain
- Department of Psychosis StudiesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUnited Kingdom
| | - Andreas Reif
- Department for Psychiatry, Psychosomatics and PsychotherapyUniversitätsklinikum Frankfurt, Goethe UniversitatFrankfurtGermany
| | - Daniel A. Rinker
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
| | - Joshua L. Roffman
- Department of PsychiatryMassachusetts General HospitalBostonMassachusettsUSA
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Matthew D. Sacchet
- Center for Depression, Anxiety, and Stress ResearchMcLean Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | | | - Pascual Sánchez‐Juan
- Department of PsychiatryUniversity Hospital “Marques de Valdecilla”, Instituto de Investigación Valdecilla (IDIVAL)SantanderSpain
- Centro de Investigacion Biomedica en Red en Enfermedades Neurodegenerativas (CIBERNED)ValderrebolloSpain
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
| | - Lianne Schmaal
- OrygenThe National Centre of Excellence in Youth Mental HealthMelbourneAustralia
- Centre for Youth Mental HealthThe University of MelbourneMelbourneAustralia
| | - Knut Schnell
- Department of Psychiatry and PsychotherapyUniversity Medical Center GöttingenGöttingenGermany
| | - Gunter Schumann
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Centre for Population Neuroscience and Precision MedicineInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUnited Kingdom
| | - Kang Sim
- Department of General PsychiatryInstitute of Mental HealthSingaporeSingapore
| | - Jordan W. Smoller
- Center for Genomic MedicineMassachusetts General HospitalBostonMassachusettsUSA
| | - Iris Sommer
- Department of Biomedical Sciences of Cells and Systems, Rijksuniversiteit GroningenUniversity Medical Center GroningenGroningenNetherlands
| | - Carles Soriano‐Mas
- Mental Health Research Networking Center (CIBERSAM)MadridSpain
- Department of PsychiatryBellvitge University Hospital‐IDIBELL, University of BarcelonaBarcelonaSpain
| | - Dan J. Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of PsychiatryStellenbosch UniversityStellenboschSouth Africa
| | - Lachlan T. Strike
- Queensland Brain InstituteUniversity of QueenslandQueenslandAustralia
| | | | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Psychiatric ResearchDiakonhjemmet HospitalOsloNorway
- PROMENTA Research Center, Department of PsychologyUniversity of OsloOsloNorway
| | - Henk S. Temmingh
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | | | - Diana Tordesillas‐Gutiérrez
- FIDMAG Germanes HospitalàriesBarcelonaSpain
- Neuroimaging Unit, Technological FacilitiesValdecilla Biomedical Research Institute IDIVALCantabriaSpain
| | - Julian N. Trollor
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - Jessica A. Turner
- College of Arts and SciencesGeorgia State UniversityAtlantaGeorgiaUSA
| | - Anne Uhlmann
- Department of Psychiatry and Mental HealthUniversity of Cape TownCape TownSouth Africa
| | - Odile A. van den Heuvel
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
| | - Dennis van den Meer
- Norwegian Centre for Mental Disorders Research (NORMENT)Institute of Clinical Medicine, University of OsloOsloNorway
- Division of Mental Health and AddictionInstitute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtNetherlands
| | - Nic J. A. van der Wee
- Department of PsychiatryLeiden University Medical CenterLeidenNetherlands
- Leiden Institute for Brain and CognitionLeiden University Medical CenterLeidenNetherlands
| | - Neeltje E. M. van Haren
- Department of Child and Adolescent Psychiatry/PsychologyErasmus University Medical Center, Sophia Children's HospitalRotterdamThe Netherlands
| | - Dennis van 't Ent
- Department of Biological PsychologyVrije UniversiteitAmsterdamNetherlands
| | - Theo G. M. van Erp
- Department of PsychiatryUniversity of California at IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
| | - Ilya M. Veer
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Dick J. Veltman
- Department of PsychiatryAmsterdam University Medical Centre, Vrije UniversiteitAmsterdamNetherlands
| | - Aristotle Voineskos
- Kimel Family Translational Imaging Genetics Laboratory, Campbell Family Mental Health Research InstituteCAMHCampbellCanada
- Department of PsychiatryUniversity of TorontoTorontoCanada
| | - Henry Völzke
- Institute of Community MedicineUniversity Medicine, Greifswald, University of GreifswaldGreifswaldGermany
- German Centre for Cardiovascular Research (DZHK), partner site GreifswaldGreifswaldGermany
- German Center for Diabetes Research (DZD), partner site GreifswaldGreifswaldGermany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and PsychotherapyCharité‐Universitätsmedizin BerlinBerlinGermany
| | - Esther Walton
- Department of PsychologyUniversity of BathBathUnited Kingdom
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Feinberg School of MedicineNorthwestern UniversityEvanstonIllinoisUSA
| | - Yang Wang
- Department of RadiologyMedical College of WisconsinMilwaukeeWisconsinUSA
| | - Thomas H. Wassink
- Department of Psychiatry, Carver College of MedicineThe University of IowaIowa CityIowaUSA
| | - Bernd Weber
- Institute for Experimental Epileptology and Cognition ResearchUniversity of BonnBonnGermany
| | - Wei Wen
- Centre for Healthy Brain Ageing, School of PsychiatryUniversity of New South WalesKensingtonNew South WalesAustralia
| | - John D. West
- Department of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Heather Whalley
- Division of PsychiatryUniversity of EdinburghEdinburghUnited Kingdom
| | - Lara M. Wierenga
- Developmental and Educational Psychology Unit, Institute of PsychologyLeiden UniversityLeidenNetherlands
| | - Katharina Wittfeld
- Department of Psychiatry and PsychotherapyUniversity Medicine Greifswald, University of GreifswaldGreifswaldGermany
- German Center for Neurodegenerative Diseases (DZNE)Site Rostock/GreifswaldGreifswaldGermany
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Amanda Worker
- Department of Psychiatry, Djavad Mowafaghian Centre for Brain HealthUniversity of British ColumbiaVancouverCanada
| | | | - Kun Yang
- National High Magnetic Field LaboratoryFlorida State UniversityTallahasseeFloridaUSA
| | - Yulyia Yoncheva
- Department of Child and Adolescent Psychiatry, Child Study CenterNYU Langone HealthNew York CityNew YorkUSA
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging, Departamento e Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Faculdade de MedicinaUniversidade de São PauloSão PauloBrazil
- Instituto de Ensino e PesquisaHospital Sírio‐LibanêsSão PauloBrazil
| | - Georg C. Ziegler
- Division of Molecular Psychiatry, Center of Mental HealthUniversity of WürzburgWürzburgGermany
| | | | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaMarina del ReyCaliforniaUSA
| | - Danai Dima
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUnited Kingdom
- Department of Psychology, School of Arts and Social SciencesCity University of LondonLondonUnited Kingdom
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Segen V, Ying J, Morgan E, Brandon M, Wolbers T. Path integration in normal aging and Alzheimer's disease. Trends Cogn Sci 2021; 26:142-158. [PMID: 34872838 DOI: 10.1016/j.tics.2021.11.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 12/20/2022]
Abstract
In this review we discuss converging evidence from human and rodent research demonstrating how path integration (PI) is impaired in healthy aging and Alzheimer's disease (AD), and point to the neural mechanisms that underlie these deficits. Importantly, we highlight that (i) the grid cell network in the entorhinal cortex is crucial for PI in both humans and rodents, (ii) PI deficits are present in healthy aging and are significantly more pronounced in patients with early-stage AD, (iii) compromised entorhinal grid cell computations in healthy older adults and in young adults at risk of AD are linked to PI deficits, and (iv) PI and grid cell deficits may serve as sensitive markers for pathological decline in early AD.
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Affiliation(s)
- Vladislava Segen
- Aging and Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.
| | - Johnson Ying
- Douglas Hospital Research Center, McGill University, Montreal, QC, Canada
| | - Erik Morgan
- Douglas Hospital Research Center, McGill University, Montreal, QC, Canada
| | - Mark Brandon
- Douglas Hospital Research Center, McGill University, Montreal, QC, Canada
| | - Thomas Wolbers
- Aging and Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg 39120, Germany.
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Andersen E, Casteigne B, Chapman WD, Creed A, Foster F, Lapins A, Shatz R, Sawyer RP. Diagnostic biomarkers in Alzheimer’s disease. Biomark Neuropsychiatry 2021. [DOI: 10.1016/j.bionps.2021.100041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
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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: 4.7] [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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Hess JL, Radonjić NV, Patak J, Glatt SJ, Faraone SV. Autophagy, apoptosis, and neurodevelopmental genes might underlie selective brain region vulnerability in attention-deficit/hyperactivity disorder. Mol Psychiatry 2021; 26:6643-6654. [PMID: 33339955 PMCID: PMC8760041 DOI: 10.1038/s41380-020-00974-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Large-scale brain imaging studies by the ENIGMA Consortium identified structural changes associated with attention-deficit/hyperactivity disorder (ADHD). It is not clear why some brain regions are impaired and others spared by the etiological risks for ADHD. We hypothesized that spatial variation in brain cell organization and/or pathway expression levels contribute to selective brain region vulnerability (SBRV) in ADHD. In this study, we used the largest available collection of magnetic resonance imaging (MRI) results from the ADHD ENIGMA Consortium (subcortical MRI n = 3242; cortical MRI n = 4180) along with high-resolution postmortem brain microarray data from Allen Brain Atlas (donors n = 6) from 22 brain regions to investigate our SBRV hypothesis. We performed deconvolution of the bulk transcriptomic data to determine abundances of neuronal and nonneuronal cells in the brain. We assessed the relationships between gene-set expression levels, cell abundance, and standardized effect sizes representing regional changes in brain sizes in cases of ADHD. Our analysis yielded significant correlations between apoptosis, autophagy, and neurodevelopment genes with smaller brain sizes in ADHD, along with associations to regional abundances of astrocytes and oligodendrocytes. The lack of enrichment of common genetic risk variants for ADHD within implicated gene sets suggests an environmental etiology to these differences. This work provides novel mechanistic clues about SBRV in ADHD.
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Affiliation(s)
- Jonathan L Hess
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Nevena V Radonjić
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Jameson Patak
- Department of Neuroscience, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen J Glatt
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Stephen V Faraone
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA.
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Kim IB, Park SC. The Entorhinal Cortex and Adult Neurogenesis in Major Depression. Int J Mol Sci 2021; 22:11725. [PMID: 34769155 PMCID: PMC8583901 DOI: 10.3390/ijms222111725] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/26/2021] [Accepted: 10/26/2021] [Indexed: 12/16/2022] Open
Abstract
Depression is characterized by impairments in adult neurogenesis. Reduced hippocampal function, which is suggestive of neurogenesis impairments, is associated with depression-related phenotypes. As adult neurogenesis operates in an activity-dependent manner, disruption of hippocampal neurogenesis in depression may be a consequence of neural circuitry impairments. In particular, the entorhinal cortex is known to have a regulatory effect on the neural circuitry related to hippocampal function and adult neurogenesis. However, a comprehensive understanding of how disruption of the neural circuitry can lead to neurogenesis impairments in depression remains unclear with respect to the regulatory role of the entorhinal cortex. This review highlights recent findings suggesting neural circuitry-regulated neurogenesis, with a focus on the potential role of the entorhinal cortex in hippocampal neurogenesis in depression-related cognitive and emotional phenotypes. Taken together, these findings may provide a better understanding of the entorhinal cortex-regulated hippocampal neurogenesis model of depression.
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Affiliation(s)
- Il Bin Kim
- Department of Psychiatry, Hanyang University Guri Hospital, Guri 11923, Korea;
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea
| | - Seon-Cheol Park
- Department of Psychiatry, Hanyang University Guri Hospital, Guri 11923, Korea;
- Department of Psychiatry, Hanyang University College of Medicine, Seoul 04763, Korea
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Lao H, Zhang X. Regression and Classification of Alzheimers Disease Diagnosis using NMF-TDNet Features from 3D Brain MR Image. IEEE J Biomed Health Inform 2021; 26:1103-1115. [PMID: 34543210 DOI: 10.1109/jbhi.2021.3113668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
With the development of deep learning and medical imaging technology, many researchers use convolutional neural network(CNN) to obtain deep-level features of medical image in order to better classify Alzheimer's disease (AD) and predict clinical scores. The principal component analysis network (PCANet) is a lightweight deep-learning network that mainly uses principal component analysis (PCA) to generate multilevel filter banks for the centralized learning of samples and then performs binarization and generates blockwise histograms to obtain image features. However, the dimensions of the extracted PCANet features reaching tens of thousands or even hundreds of thousands, and the formation of the multilevel filter banks is sample data dependent, reducing the flexibility of PCANet. In this paper, based on the idea of PCANet, we propose a data-independent network called the nonnegative matrix factorization tensor decomposition network (NMF-TDNet), which improves the computational efficiency and solves the data dependence problem of PCANet. In this network, we use nonnegative matrix factorization (NMF) instead of PCA to create multilevel filter banks for sample learning, then uses the learning results to build a higher-order tensor and perform tensor decomposition (TD) to achieve data dimensionality reduction, producing the final image features. Finally, our method use these features as the input of the support vector machine (SVM) for AD classification diagnosis and clinical score prediction. The performance of our method is extensively evaluated on the ADNI-1, ADNI-2 and OASIS datasets. The experimental results show that NMF-TDNet can achieve data dimensionality reduction (the dimensionality of the extracted features numbers only a few hundred dimensions, far less than the hundreds of thousands required by PCANet) and the NMF-TDNet features as input achieved superior performance than using PCANet features as input.
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Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease. Brain Sci 2021; 11:brainsci11091129. [PMID: 34573151 PMCID: PMC8471837 DOI: 10.3390/brainsci11091129] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 08/12/2021] [Accepted: 08/20/2021] [Indexed: 11/17/2022] Open
Abstract
Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD.
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Iannopollo E, Garcia K. Enhanced detection of cortical atrophy in Alzheimer's disease using structural MRI with anatomically constrained longitudinal registration. Hum Brain Mapp 2021; 42:3576-3592. [PMID: 33988265 PMCID: PMC8249882 DOI: 10.1002/hbm.25455] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 04/08/2021] [Accepted: 04/11/2021] [Indexed: 12/14/2022] Open
Abstract
Cortical atrophy is a defining feature of Alzheimer's disease (AD), often detectable before symptoms arise. In surface-based analyses, studies have commonly focused on cortical thinning while overlooking the impact of loss in surface area. To capture the impact of both cortical thinning and surface area loss, we used anatomically constrained Multimodal Surface Matching (aMSM), a recently developed tool for mapping change in surface area. We examined cortical atrophy over 2 years in cognitively normal subjects and subjects with diagnoses of stable mild cognitive impairment, mild cognitive impairment that converted to AD, and AD. Magnetic resonance imaging scans were segmented and registered to a common atlas using previously described techniques (FreeSurfer and ciftify), then longitudinally registered with aMSM. Changes in cortical thickness, surface area, and volume were mapped within each diagnostic group, and groups were compared statistically. Changes in thickness and surface area detected atrophy at similar levels of significance, though regions of atrophy somewhat differed. Furthermore, we found that surface area maps offered greater consistency across scanners (3.0 vs. 1.5 T). Comparisons to the FreeSurfer longitudinal pipeline and parcellation-based (region-of-interest) analysis suggest that aMSM may allow more robust detection of atrophy, particularly in earlier disease stages and using smaller sample sizes.
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Affiliation(s)
- Emily Iannopollo
- Department of Radiology and Imaging SciencesIndiana University School of MedicineEvansvilleIndianaUSA
| | - Kara Garcia
- Department of Radiology and Imaging SciencesIndiana University School of MedicineEvansvilleIndianaUSA
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Blinkouskaya Y, Weickenmeier J. Brain Shape Changes Associated With Cerebral Atrophy in Healthy Aging and Alzheimer's Disease. FRONTIERS IN MECHANICAL ENGINEERING 2021; 7:705653. [PMID: 35465618 PMCID: PMC9032518 DOI: 10.3389/fmech.2021.705653] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Both healthy and pathological brain aging are characterized by various degrees of cognitive decline that strongly correlate with morphological changes referred to as cerebral atrophy. These hallmark morphological changes include cortical thinning, white and gray matter volume loss, ventricular enlargement, and loss of gyrification all caused by a myriad of subcellular and cellular aging processes. While the biology of brain aging has been investigated extensively, the mechanics of brain aging remains vastly understudied. Here, we propose a multiphysics model that couples tissue atrophy and Alzheimer's disease biomarker progression. We adopt the multiplicative split of the deformation gradient into a shrinking and an elastic part. We model atrophy as region-specific isotropic shrinking and differentiate between a constant, tissue-dependent atrophy rate in healthy aging, and an atrophy rate in Alzheimer's disease that is proportional to the local biomarker concentration. Our finite element modeling approach delivers a computational framework to systematically study the spatiotemporal progression of cerebral atrophy and its regional effect on brain shape. We verify our results via comparison with cross-sectional medical imaging studies that reveal persistent age-related atrophy patterns. Our long-term goal is to develop a diagnostic tool able to differentiate between healthy and accelerated aging, typically observed in Alzheimer's disease and related dementias, in order to allow for earlier and more effective interventions.
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Chen W, Li S, Ma Y, Lv S, Wu F, Du J, Wu H, Wang S, Zhao Q. A simple nomogram prediction model to identify relatively young patients with mild cognitive impairment who may progress to Alzheimer's disease. J Clin Neurosci 2021; 91:62-68. [PMID: 34373060 DOI: 10.1016/j.jocn.2021.06.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 05/16/2021] [Accepted: 06/14/2021] [Indexed: 12/25/2022]
Abstract
AIM Construct a clinical predictive model based on easily accessible clinical features and imaging data to identify patients 65 years of age and younger with mild cognitive impairment(MCI) who may progress to Alzheimer's disease(AD). METHODS From the ADNI database, patients with MCI who were less than or equal to 65 years of age and who had been followed for 6-60 months were selected.We collected demographic data, neuropsychological test scale scores, and structural magnetic images of these patients. Clinical characteristics were then screened, and VBM and SBM analyses were performed using structural nuclear magnetic images to obtain imaging histology characteristics. Finally, predictive models were constructed combining the clinical and imaging histology characteristics. RESULTS The constructed nomogram has a cross-validated AUC of 0.872 in the training set and 0.867 in the verification set, and the calibration curve fits well.We also provide an online model-based forecasting tool. CONCLUSION The model has good performance and uses convenience,it should be able to provide assistance in clinical work to screen relatively young MCI patients who may progress to AD.
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Affiliation(s)
- Wenhong Chen
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Songtao Li
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yangyang Ma
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuyue Lv
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Fan Wu
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jianshi Du
- Department of Vascular Surgery, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Honglin Wu
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Shuai Wang
- Department of Gastroenterology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Qing Zhao
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
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46
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Combination of automated brain volumetry on MRI and quantitative tau deposition on THK-5351 PET to support diagnosis of Alzheimer's disease. Sci Rep 2021; 11:10343. [PMID: 33990649 PMCID: PMC8121780 DOI: 10.1038/s41598-021-89797-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 04/27/2021] [Indexed: 01/18/2023] Open
Abstract
Imaging biomarkers support the diagnosis of Alzheimer’s disease (AD). We aimed to determine whether combining automated brain volumetry on MRI and quantitative measurement of tau deposition on [18F] THK-5351 PET can aid discrimination of AD spectrum. From a prospective database in an IRB-approved multicenter study (NCT02656498), 113 subjects (32 healthy control, 55 mild cognitive impairment, and 26 Alzheimer disease) with baseline structural MRI and [18F] THK-5351 PET were included. Cortical volumes were quantified from FDA-approved software for automated volumetric MRI analysis (NeuroQuant). Standardized uptake value ratio (SUVR) was calculated from tau PET images for 6 composite FreeSurfer-derived regions-of-interests approximating in vivo Braak stage (Braak ROIs). On volumetric MRI analysis, stepwise logistic regression analyses identified the cingulate isthmus and inferior parietal lobule as significant regions in discriminating AD from HC and MCI. The combined model incorporating automated volumes of selected brain regions on MRI (cingulate isthmus, inferior parietal lobule, hippocampus) and SUVRs of Braak ROIs on [18F] THK-5351 PET showed higher performance than SUVRs of Braak ROIs on [18F] THK-5351 PET in discriminating AD from HC (0.98 vs 0.88, P = 0.033) but not in discriminating AD from MCI (0.85 vs 0.79, P = 0.178). The combined model showed comparable performance to automated volumes of selected brain regions on MRI in discriminating AD from HC (0.98 vs 0.94, P = 0.094) and MCI (0.85 vs 0.78; P = 0.065).
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47
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Chen N, Zhao C, Wang M, Jones JA, Liu P, Chen X, Gong G, Liu H. Linking Cortical Morphology to Interindividual Variability in Auditory Feedback Control of Vocal Production. Cereb Cortex 2021; 31:2932-2943. [PMID: 33454738 DOI: 10.1093/cercor/bhaa401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/02/2020] [Accepted: 12/14/2020] [Indexed: 11/13/2022] Open
Abstract
Speakers regulate vocal motor behaviors in a compensatory manner when perceiving errors in auditory feedback. Little is known, however, about the source of interindividual variability that exists in the degree to which speakers compensate for perceived errors. The present study included 40 young adults to investigate whether individual differences in auditory integration for vocal pitch regulation, as indexed by vocal compensations for pitch perturbations in auditory feedback, can be predicted by cortical morphology as assessed by gray-matter volume, cortical thickness, and surface area in a whole-brain manner. The results showed that greater gray-matter volume in the left inferior parietal lobule and greater cortical thickness and surface area in the left superior/middle temporal gyrus, temporal pole, inferior/superior parietal lobule, and precuneus predicted larger vocal responses. Greater cortical thickness in the right inferior frontal gyrus and superior parietal lobule and surface area in the left precuneus and cuneus were significantly correlated with smaller magnitudes of vocal responses. These findings provide the first evidence that vocal compensations for feedback errors are predicted by the structural morphology of the frontal and tempo-parietal regions, and further our understanding of the neural basis that underlies interindividual variability in auditory-motor control of vocal production.
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Affiliation(s)
- Na Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.,Department of Rehabilitation, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Chenxi Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Meng Wang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Jeffery A Jones
- Psychology Department, Laurier Centre for Cognitive Neuroscience, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Peng Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Xi Chen
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
| | - Gaolong Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.,Chinese Institute for Brain Research, Beijing 102206, China
| | - Hanjun Liu
- Department of Rehabilitation Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.,Guangdong Provincial Key Laboratory of Brain Function and Disease, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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48
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Wang X, Huang K, Yang F, Chen D, Cai S, Huang L. Association between structural brain features and gene expression by weighted gene co-expression network analysis in conversion from MCI to AD. Behav Brain Res 2021; 410:113330. [PMID: 33940051 DOI: 10.1016/j.bbr.2021.113330] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 04/16/2021] [Accepted: 04/26/2021] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease. Mild cognitive impairment (MCI) represents a state of cognitive function between normal cognition and dementia. Longitudinal studies showed that some MCI patients remained in a state of MCI, and some developed AD. The reason for these different conversions from MCI remains to be investigated. 180 MCI participants were followed for eight years. 143 MCI patients maintained the MCI state (MCI_S), and the remaining thirty-seven MCI patients were re-evaluated as having AD (MCI_AD). We obtained 1,036 structural brain characteristics and 15,481 gene expression values from the 180 MCI participants and applied weighted gene co-expression network analysis (WGCNA) to explore the relationship between structural brain features and gene expression. Regulating mediator effect analysis was employed to explore the relationships among gene expression, brain region measurements and clinical phenotypes. We found that 60 genes from the MCI_S group and 18 genes from the MCI_AD group respectively had the most significant correlations with left paracentral lobule and sulcus (L.PTS) and right subparietal sulcus (R.SubPS) thickness; CTCF, UQCR11 and WDR5B were the mutual genes between the two groups. The expression of CTCF gene and clinical score are completely mediated by L.PTS thickness, and the UQCR11 and WDR5B gene expression levels significantly regulate the mediating effect pathway. In conclusion, the factors affecting the different conversions from MCI are closely related to L.PTS thickness and the CTCF, UQCR11 and WDR5B gene expression levels. Our results add a theoretical foundation of imaging genetics for conversion from MCI to AD.
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Affiliation(s)
- Xuwen Wang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Kexin Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Fan Yang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Dihun Chen
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China
| | - Suping Cai
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
| | - Liyu Huang
- School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, 710071, PR China.
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49
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Bilateral age-related atrophy in the planum temporale is associated with vowel discrimination difficulty in healthy older adults. Hear Res 2021; 406:108252. [PMID: 33951578 DOI: 10.1016/j.heares.2021.108252] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 04/04/2021] [Accepted: 04/07/2021] [Indexed: 11/24/2022]
Abstract
In this study we investigated the association between age-related brain atrophy and behavioural as well as electrophysiological markers of vowel perception in a sample of healthy younger and older adults with normal pure-tone hearing. Twenty-three older adults and 27 younger controls discriminated a set of vowels with altered second formants embedded in consonant-vowel syllables. Additionally, mismatch negativity (MMN) responses were recorded in a separate oddball paradigm with the same set of stimuli. A structural magnet resonance scan was obtained for each participant to determine cortical architecture of the left and right planum temporale (PT). The PT was chosen for its function as a major processor of auditory cues and speech. Results suggested that older adults performed worse in vowel discrimination despite normal-for-age pure-tone hearing. In the older group, we found evidence that those with greater age-related cortical atrophy (i.e., lower cortical surface area and cortical volume) in the left and right PT also showed weaker vowel discrimination. In comparison, we found a lateralized correlation in the younger group suggesting that those with greater cortical thickness in only the left PT performed weaker in the vowel discrimination task. We did not find any associations between macroanatomical traits of the PT and MMN responses. We conclude that deficient vowel processing is not only caused by pure-tone hearing loss but is also influenced by atrophy-related changes in the ageing auditory-related cortices. Furthermore, our results suggest that auditory processing might become more bilateral across the lifespan.
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50
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Boys with autism spectrum disorder have distinct cortical folding patterns underpinning impaired self-regulation: a surface-based morphometry study. Brain Imaging Behav 2021; 14:2464-2476. [PMID: 31512098 DOI: 10.1007/s11682-019-00199-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Although impaired self-regulation (dysregulation) in autism spectrum disorder (ASD) garnered increasing awareness, the neural mechanism of dysregulation in ASD are far from conclusive. To complement our previous voxel-based morphometry findings, we estimated the cortical thickness, surface area, and local gyrification index based on the surface-based morphometry from structural MRI images in 85 ASD and 65 typically developing control (TDC) boys, aged 7-17 years. Levels of dysregulation were measured by the sum of T-scores of Attention, Aggression, and Anxiety/Depression subscales on the Child Behavior Checklist. We found both ASD and TDC shared similar relationships between dysregulation and cortical folding patterns in the left superior and inferior temporal gyri and the left premotor cortex. Significant diagnosis by dysregulation interactions in cortical folding patterns were identified over the right middle frontal and right lateral orbitofrontal regions. The statistical significance of greater local gyrification index in ASD than TDC in several brain regions disappeared when the level of dysregulation was considered. The findings of shared and distinct neural correlates underpinning dysregulation between ASD and TDC may facilitate the development of targeted interventions in the future. The present work also demonstrates that inter-subject variations in self-regulation may explain some extents of ASD-associated brain morphometric differences, likely suggesting that dysregulation is one of the yardsticks for dissecting the heterogeneity of ASD.
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