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Moisseinen N, Ahveninen L, Martínez‐Molina N, Sairanen V, Melkas S, Kleber B, Sihvonen AJ, Särkämö T. Choir singing is associated with enhanced structural connectivity across the adult lifespan. Hum Brain Mapp 2024; 45:e26705. [PMID: 38716698 PMCID: PMC11077432 DOI: 10.1002/hbm.26705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 03/06/2024] [Accepted: 04/21/2024] [Indexed: 05/12/2024] Open
Abstract
The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.
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Affiliation(s)
- Nella Moisseinen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Lotta Ahveninen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
| | - Noelia Martínez‐Molina
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversity Pompeu FabraBarcelonaSpain
| | - Viljami Sairanen
- Department of RadiologyKanta‐Häme Central HospitalHämeenlinnaFinland
- Baby Brain Activity Center, Children's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Susanna Melkas
- Clinical Neurosciences, NeurologyUniversity of HelsinkiHelsinkiFinland
| | - Boris Kleber
- Center for Music in the Brain, Department of Clinical MedicineAarhus University and The Royal Academy of Music Aarhus/AalborgAarhusDenmark
| | - Aleksi J. Sihvonen
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
- Centre for Clinical Research, School of Health and Rehabilitation SciencesUniversity of QueenslandBrisbaneAustralia
- Department of NeurologyHelsinki University HospitalHelsinkiFinland
| | - Teppo Särkämö
- Cognitive Brain Research Unit, Centre of Excellence in Music, Mind, Body and the Brain, Department of Psychology and Logopedics, Faculty of MedicineUniversity of HelsinkiHelsinkiFinland
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Tayebi M, Kwon E, Maller J, McGeown J, Scadeng M, Qiao M, Wang A, Nielsen P, Fernandez J, Holdsworth S, Shim V. Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts. Brain Commun 2024; 6:fcae027. [PMID: 38638147 PMCID: PMC11024816 DOI: 10.1093/braincomms/fcae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 10/06/2023] [Accepted: 02/07/2024] [Indexed: 04/20/2024] Open
Abstract
Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.
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Affiliation(s)
- Maryam Tayebi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Eryn Kwon
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | | | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Miriam Scadeng
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Miao Qiao
- Department of Computer Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Poul Nielsen
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Samantha Holdsworth
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
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Mu S, Lu W, Yu G, Zheng L, Qiu J. Deep learning-based grading of white matter hyperintensities enables identification of potential markers in multi-sequence MRI data. Comput Methods Programs Biomed 2024; 243:107904. [PMID: 37924768 DOI: 10.1016/j.cmpb.2023.107904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 10/06/2023] [Accepted: 10/27/2023] [Indexed: 11/06/2023]
Abstract
BACKGROUND White matter hyperintensities (WMHs) are widely-seen in the aging population, which are associated with cerebrovascular risk factors and age-related cognitive decline. At present, structural atrophy and functional alterations coexisted with WMHs lacks comprehensive investigation. This study developed a WMHs risk prediction model to evaluate WHMs according to Fazekas scales, and to locate potential regions with high risks across the entire brain. METHODS We developed a WMHs risk prediction model, which consisted of the following steps: T2 fluid attenuated inversion recovery (T2-FLAIR) image of each participant was firstly segmented into 1000 tiles with the size of 32 × 32 × 1, features from the tiles were extracted using the ResNet18-based feature extractor, and then a 1D convolutional neural network (CNN) was used to score all tiles based on the extracted features. Finally, a multi-layer perceptron (MLP) was constructed to predict the Fazekas scales based on the tile scores. The proposed model was trained using T2-FLAIR images, we selected tiles with abnormal scores in the test set after prediction, and evaluated their corresponding gray matter (GM) volume, white matter (WM) volume, fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF) via longitudinal and multi-sequence Magnetic Resonance Imaging (MRI) data analysis. RESULTS The proposed WMHs risk prediction model could accurately predict the Fazekas ratings based on the tile scores from T2-FLAIR MRI images with accuracy of 0.656, 0.621 in training data set and test set, respectively. The longitudinal MRI validation revealed that most of the high-risk tiles predicted by the WMHs risk prediction model in the baseline images had WMHs in the corresponding positions in the longitudinal images. The validation on multi-sequence MRI demonstrated that WMHs were associated with GM and WM atrophies, WM micro-structural and perfusion alterations in high-risk tiles, and multi-modal MRI measures of most high-risk tiles showed significant associations with Mini Mental State Examination (MMSE) score. CONCLUSION Our proposed WMHs risk prediction model can not only accurately evaluate WMH severities according to Fazekas scales, but can also uncover potential markers of WMHs across modalities. The WMHs risk prediction model has the potential to be used for the early detection of WMH-related alterations in the entire brain and WMH-induced cognitive decline.
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Affiliation(s)
- Si Mu
- College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai'an, Shandong, 271000, China
| | - Weizhao Lu
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, 271000, China
| | - Guanghui Yu
- Department of Radiology, the Second Affiliated Hospital of Shandong First Medical University, Tai'an, Shandong, 271000, China
| | - Lei Zheng
- Department of Radiology, Rushan Hospital of Chinese Medicine, Rushan, Shandong, 264500, China.
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medicine Sciences, Tai'an, Shandong, 271000, China; Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China.
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Noroozian M, Kormi-Nouri R, Nyberg L, Persson J. Hippocampal and motor regions contribute to memory benefits after enacted encoding: cross-sectional and longitudinal evidence. Cereb Cortex 2023; 33:3080-3097. [PMID: 35802485 DOI: 10.1093/cercor/bhac262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
The neurobiological underpinnings of action-related episodic memory and how enactment contributes to efficient memory encoding are not well understood. We examine whether individual differences in level (n = 338) and 5-year change (n = 248) in the ability to benefit from motor involvement during memory encoding are related to gray matter (GM) volume, white matter (WM) integrity, and dopamine-regulating genes in a population-based cohort (age range = 25-80 years). A latent profile analysis identified 2 groups with similar performance on verbal encoding but with marked differences in the ability to benefit from motor involvement during memory encoding. Impaired ability to benefit from enactment was paired with smaller HC, parahippocampal, and putamen volume along with lower WM microstructure in the fornix. Individuals with reduced ability to benefit from encoding enactment over 5 years were characterized by reduced HC and motor cortex GM volume along with reduced WM microstructure in several WM tracts. Moreover, the proportion of catechol-O-methyltransferase-Val-carriers differed significantly between classes identified from the latent-profile analysis. These results provide converging evidence that individuals with low or declining ability to benefit from motor involvement during memory encoding are characterized by low and reduced GM volume in regions critical for memory and motor functions along with altered WM microstructure.
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Affiliation(s)
- Maryam Noroozian
- Department of Psychiatry, School of Medicine, South Kargar Str., Tehran 13185/1741, Iran
| | - Reza Kormi-Nouri
- School of Law, Psychology and Social Work, Örebro University, Fakultetsgatan 1, Örebro 702 81, Sweden
| | - Lars Nyberg
- Department of Radiation Sciences, Radiology, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
- Department of Integrative Medical Biology, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
- Umeå Center for Functional Brain Imaging, Umeå University, Universitetstorget 4, Umeå 901 87, Sweden
| | - Jonas Persson
- School of Law, Psychology and Social Work, Center for Lifespan Developmental Research (LEADER), Örebro University, Fakultetsgatan 1, Örebro 702 81, Sweden
- Aging Research Center (ARC), Stockholm University and Karolinska Institute, Tomtebodavägen 18A, Solna 171 65, Sweden
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Haddad SMH, Scott CJM, Ozzoude M, Berezuk C, Holmes M, Adamo S, Ramirez J, Arnott SR, Nanayakkara ND, Binns M, Beaton D, Lou W, Sunderland K, Sujanthan S, Lawrence J, Kwan D, Tan B, Casaubon L, Mandzia J, Sahlas D, Saposnik G, Hassan A, Levine B, McLaughlin P, Orange JB, Roberts A, Troyer A, Black SE, Dowlatshahi D, Strother SC, Swartz RH, Symons S, Montero-Odasso M, ONDRI Investigators, Bartha R. Comparison of Diffusion Tensor Imaging Metrics in Normal-Appearing White Matter to Cerebrovascular Lesions and Correlation with Cerebrovascular Disease Risk Factors and Severity. Int J Biomed Imaging 2022; 2022:5860364. [PMID: 36313789 PMCID: PMC9616672 DOI: 10.1155/2022/5860364] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 04/21/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2023] Open
Abstract
Alterations in tissue microstructure in normal-appearing white matter (NAWM), specifically measured by diffusion tensor imaging (DTI) fractional anisotropy (FA), have been associated with cognitive outcomes following stroke. The purpose of this study was to comprehensively compare conventional DTI measures of tissue microstructure in NAWM to diverse vascular brain lesions in people with cerebrovascular disease (CVD) and to examine associations between FA in NAWM and cerebrovascular risk factors. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were measured in cerebral tissues and cerebrovascular anomalies from 152 people with CVD participating in the Ontario Neurodegenerative Disease Research Initiative (ONDRI). Ten cerebral tissue types were segmented including NAWM, and vascular lesions including stroke, periventricular and deep white matter hyperintensities, periventricular and deep lacunar infarcts, and perivascular spaces (PVS) using T1-weighted, proton density-weighted, T2-weighted, and fluid attenuated inversion recovery MRI scans. Mean DTI metrics were measured in each tissue region using a previously developed DTI processing pipeline and compared between tissues using multivariate analysis of covariance. Associations between FA in NAWM and several CVD risk factors were also examined. DTI metrics in vascular lesions differed significantly from healthy tissue. Specifically, all tissue types had significantly different MD values, while FA was also found to be different in most tissue types. FA in NAWM was inversely related to hypertension and modified Rankin scale (mRS). This study demonstrated the differences between conventional DTI metrics, FA, MD, AD, and RD, in cerebral vascular lesions and healthy tissue types. Therefore, incorporating DTI to characterize the integrity of the tissue microstructure could help to define the extent and severity of various brain vascular anomalies. The association between FA within NAWM and clinical evaluation of hypertension and disability provides further evidence that white matter microstructural integrity is impacted by cerebrovascular function.
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Affiliation(s)
- Seyyed M. H. Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Miracle Ozzoude
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | | | - Melissa Holmes
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Sabrina Adamo
- Clinical Neurosciences, University of Toronto, Toronto, Canada
| | - Joel Ramirez
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Nuwan D. Nanayakkara
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Malcolm Binns
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Wendy Lou
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Kelly Sunderland
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - Jane Lawrence
- Thunder Bay Regional Health Research Institute, Thunder Bay, Canada
| | | | - Brian Tan
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Leanne Casaubon
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Jennifer Mandzia
- Department of Medicine, Division of Neurology, University of Western Ontario, London, Canada
| | - Demetrios Sahlas
- Department of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Canada
| | | | - Ayman Hassan
- Thunder Bay Regional Research Institute, Thunder Bay, Canada
| | - Brian Levine
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | | | - J. B. Orange
- School of Communication Sciences and Disorders, Western University, London, Canada
| | - Angela Roberts
- Roxelyn and Richard Pepper Department of Communication Sciences and Disorder, Northwestern University, Evanston, USA
| | - Angela Troyer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
| | - Sandra E. Black
- L.C. Campbell Cognitive Neurology Research Unit, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, Ontario, Canada
- Department of Medicine (Neurology), Sunnybrook Health Sciences Centre and University of Toronto, Toronto, Canada
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | | | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Richard H. Swartz
- Sunnybrook Health Sciences Centre, University of Toronto, Stroke Research Program, Toronto, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Parkwood Hospital, St. Joseph's Health Care London, London, Canada
| | - ONDRI Investigators
- Ontario Neurodegenerative Disease Initiative, Ontario Brain Institute, Toronto, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
- Department of Medical Biophysics, University of Western Ontario, London, Canada
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Frank D, Garo-Pascual M, Velasquez PAR, Frades B, Peled N, Zhang L, Strange BA. Brain structure and episodic learning rate in cognitively healthy ageing. Neuroimage 2022; 263:119630. [PMID: 36113738 DOI: 10.1016/j.neuroimage.2022.119630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/08/2022] [Accepted: 09/12/2022] [Indexed: 10/31/2022] Open
Abstract
Memory normally declines with ageing and these age-related cognitive changes are associated with changes in brain structure. Episodic memory retrieval has been widely studied during ageing, whereas learning has received less attention. Here we examined the neural correlates of episodic learning rate in ageing. Our study sample consisted of 982 cognitively healthy female and male older participants from the Vallecas Project cohort, without a clinical diagnosis of mild cognitive impairment or dementia. The learning rate across the three consecutive recall trials of the verbal memory task (Free and Cued Selective Reminding Test) recall trials was used as a predictor of grey matter (GM) using voxel-based morphometry, and WM microstructure using tract-based spatial statistics on fractional anisotropy (FA) and mean diffusivity (MD) measures. Immediate Recall improved by 1.4 items per trial on average, and this episodic learning rate was faster in women and negatively associated with age. Structurally, hippocampal and anterior thalamic GM volume correlated positively with learning rate. Learning also correlated with the integrity of WM microstructure (high FA and low MD) in an extensive network of tracts including bilateral anterior thalamic radiation, fornix, and long-range tracts. These results suggest that episodic learning rate is associated with key anatomical structures for memory functioning, motivating further exploration of the differential diagnostic properties between episodic learning rate and retrieval in ageing.
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Affiliation(s)
- Darya Frank
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain.
| | - Marta Garo-Pascual
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain; Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain; PhD Program in Neuroscience, Autonoma de Madrid University, Madrid 28049, Spain.
| | - Pablo Alejandro Reyes Velasquez
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain
| | - Belén Frades
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain
| | - Noam Peled
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Linda Zhang
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Centro de Tecnología Biomédica, CTB, Universidad Politécnica de Madrid, Madrid 28223, Spain; Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, Madrid 28031, Spain.
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Hansen C, Yang Q, Ramdass K, Shafer AT, Resnick SM, Pechman KR, Gifford KA, Hohman TJ, Jefferson A, Anderson AW, Kang H, Landman BA. Aging and white matter microstructure and macrostructure: a longitudinal multi-site diffusion MRI study of 1218 participants. Brain Struct Funct 2022. [PMID: 35604444 DOI: 10.1007/s00429-022-02503-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 04/22/2022] [Indexed: 11/02/2022]
Abstract
Quantifying the microstructural and macrostructural geometrical features of the human brain's connections is necessary for understanding normal aging and disease. Here, we examine brain white matter diffusion magnetic resonance imaging data from one cross-sectional and two longitudinal data sets totaling in 1218 subjects and 2459 sessions of people aged 50-97 years. Data was drawn from well-established cohorts, including the Baltimore Longitudinal Study of Aging data set, Cambridge Centre for Ageing Neuroscience data set, and the Vanderbilt Memory & Aging Project. Quantifying 4 microstructural features and, for the first time, 11 macrostructure-based features of volume, area, and length across 120 white matter pathways, we apply linear mixed effect modeling to investigate changes in pathway-specific features over time, and document large age associations within white matter. Conventional diffusion tensor microstructure indices are the most age-sensitive measures, with positive age associations for diffusivities and negative age associations with anisotropies, with similar patterns observed across all pathways. Similarly, pathway shape measures also change with age, with negative age associations for most length, surface area, and volume-based features. A particularly novel finding of this study is that while trends were homogeneous throughout the brain for microstructure features, macrostructural features demonstrated heterogeneity across pathways, whereby several projection, thalamic, and commissural tracts exhibited more decline with age compared to association and limbic tracts. The findings from this large-scale study provide a comprehensive overview of the age-related decline in white matter and demonstrate that macrostructural features may be more sensitive to heterogeneous white matter decline. Therefore, leveraging macrostructural features may be useful for studying aging and could facilitate comparisons in a variety of diseases or abnormal conditions.
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Thomas RJ, Kim H, Maillard P, DeCarli CS, Heckman EJ, Karjadi C, Ang TFA, Au R. Digital sleep measures and white matter health in the Framingham Heart Study. Explor Med 2021; 2:253-267. [PMID: 34927164 PMCID: PMC8682916 DOI: 10.37349/emed.2021.00045] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 02/18/2021] [Indexed: 01/23/2023] Open
Abstract
AIM Impaired sleep quality and sleep oxygenation are common sleep pathologies. This study assessed the impact of these abnormalities on white matter (WM) integrity in an epidemiological cohort. METHODS The target population was the Framingham Heart Study Generation-2/Omni-1 Cohorts. Magnetic resonance imaging (diffusion tensor imaging) was used to assess WM integrity. Wearable digital devices were used to assess sleep quality: the (M1-SleepImage™ system) and the Nonin WristOx for nocturnal oxygenation. The M1 device collects trunk actigraphy and the electrocardiogram (ECG); sleep stability indices were computed using cardiopulmonary coupling using the ECG. Two nights of recording were averaged. RESULTS Stable sleep was positively associated with WM health. Actigraphic periods of wake during the sleep period were associated with increased mean diffusivity. One marker of sleep fragmentation which covaries with respiratory chemoreflex activation was associated with reduced fractional anisotropy and increased mean diffusivity. Both oxygen desaturation index and oxygen saturation time under 90% were associated with pathological directions of diffusion tensor imaging signals. Gender differences were noted across most variables, with female sex showing the larger and significant impact. CONCLUSIONS Sleep quality assessed by a novel digital analysis and sleep hypoxia was associated with WM injury, especially in women.
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Affiliation(s)
- Robert Joseph Thomas
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Hyun Kim
- Department of Anatomy & Neurobiology, and Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Pauline Maillard
- Department of Neurology, University of California Davis Health, Sacramento, CA 95817, USA
| | - Charles S. DeCarli
- Department of Neurology, University of California Davis Health, Sacramento, CA 95817, USA
| | - Eric James Heckman
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Cody Karjadi
- Department of Anatomy & Neurobiology, and Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ting Fang Alvin Ang
- Department of Anatomy & Neurobiology, and Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
| | - Rhoda Au
- Department of Anatomy & Neurobiology, and Framingham Heart Study, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology and Epidemiology, Boston University School of Medicine and Public Health, Boston, MA 02118, USA
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Bazydlo A, Zammit M, Wu M, Dean D, Johnson S, Tudorascu D, Cohen A, Cody K, Ances B, Laymon C, Klunk W, Zaman S, Handen B, Alexander A, Christian B, Hartley S. White matter microstructure associations with episodic memory in adults with Down syndrome: a tract-based spatial statistics study. J Neurodev Disord 2021; 13:17. [PMID: 33879062 PMCID: PMC8059162 DOI: 10.1186/s11689-021-09366-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 04/08/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Nearly all persons with Down syndrome will show pathology of Alzheimer's disease in their 40s. There is a critical need for studies to identify early biomarkers of these various pathological changes of Alzheimer's disease in the Down syndrome population and understand the relationship of these biomarkers to cognitive symptoms in order to inform clinical trials. Although Alzheimer's disease is often considered a disease of gray matter, white matter degeneration has been documented during the preclinical stage of Alzheimer's disease. The current study examined the association between diffusion tensor imaging (DTI) measures of white matter microstructure and episodic memory performance in 52 adults with Down syndrome. METHODS Seventy (N = 70) participants (M = 40.13, SD = 7.77 years) received baseline scans as part of the Neurodegeneration in Aging Down Syndrome (NiAD) study at two imaging facilities (36 at the University of Wisconsin-Madison [UW-Madison] and 34 at the University of Pittsburgh Medical Center [UPMC]). All participants had genetically confirmed trisomy 21. Fifty-two (N = 52) participants remained after QC. The DTI measures, fractional anisotropy (FA) and mean diffusivity (MD), were calculated for each participant. A combined measure of episodic memory was generated by summing the z-scores of (1) Free and Cued Recall test and (2) Rivermead Behavioural Memory Test for Children Picture Recognition. The DTI data were projected onto a population-derived FA skeleton and tract-based spatial statistics analysis was conducted using the FSL tool PALM to calculate Pearson's r values between FA and MD with episodic memory. RESULTS A positive correlation of episodic memory with FA and a negative correlation of episodic memory and MD in the major association white matter tracts were observed. Results were significant (p < 0.05) after correction for chronological age, imaging site, and premorbid cognitive ability. CONCLUSION These findings suggest that white matter degeneration may be implicated in early episodic memory declines prior to the onset of dementia in adults with Down syndrome. Further, our findings suggest a coupling of episodic memory and white matter microstructure independent of chronological age.
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Affiliation(s)
- Austin Bazydlo
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
| | - Matthew Zammit
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Minjie Wu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Douglas Dean
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sterling Johnson
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Dana Tudorascu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Ann Cohen
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Karly Cody
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Beau Ances
- Washington University of St. Louis, St. Louis, MO, USA
| | - Charles Laymon
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - William Klunk
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Shahid Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, University of Cambridge, Cambridge, UK
| | - Benjamin Handen
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Andrew Alexander
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Bradley Christian
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, Madison, WI, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Sigan Hartley
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- School of Human Ecology, University of Wisconsin-Madison, Madison, WI, USA
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