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Parker DM, Adams JN, Kim S, McMillan L, Yassa MA. NODDI-derived measures of microstructural integrity in medial temporal lobe white matter pathways are associated with Alzheimer's disease pathology and cognitive outcomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.11.561946. [PMID: 37905117 PMCID: PMC10614746 DOI: 10.1101/2023.10.11.561946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
INTRODUCTION Diffusion tensor imaging has been used to assess white matter (WM) changes in the early stages of Alzheimer's disease (AD). However, the tensor model is necessarily limited by its assumptions. Neurite Orientation Dispersion and Density Imaging (NODDI) can offer insights into microstructural features of WM change. We assessed whether NODDI more sensitively detects AD-related changes in medial temporal lobe WM than traditional tensor metrics. METHODS Standard diffusion and NODDI metrics were calculated for medial temporal WM tracts from 199 older adults drawn from ADNI3 who also received PET to measure pathology and neuropsychological testing. RESULTS NODDI measures in medial temporal tracts were more strongly correlated to cognitive performance and pathology than standard measures. The combination of NODDI and standard metrics exhibited the strongest prediction of cognitive performance in random forest analyses. CONCLUSIONS NODDI metrics offer additional insights into contributions of WM degeneration to cognitive outcomes in the aging brain.
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Affiliation(s)
- Dana M. Parker
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine
| | - Jenna N. Adams
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine
| | - Soyun Kim
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine
| | - Liv McMillan
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine
| | - Michael A. Yassa
- Department of Neurobiology and Behavior and Center for the Neurobiology of Learning and Memory, University of California, Irvine
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Age-related assessment of diffusion parameters in specific brain tracts correlated with cortical thinning. Neurol Sci 2020; 42:1799-1809. [PMID: 32886260 DOI: 10.1007/s10072-020-04688-9] [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: 02/10/2020] [Accepted: 08/11/2020] [Indexed: 10/23/2022]
Abstract
The aging process is associated with many brain structural alterations. These changes are not associated with neuronal loss but can be due to cortical structural changes that may be related to white matter (WM) structural alterations. In this study, we evaluated age-related changes in WM and gray matter (GM) parameters and how they correlate for specific brain tracts in a cohort of 158 healthy individuals, aged between 18 and 83 years old. In the tract-cortical analysis, cortical regions connected by tracts demonstrated similar thinning patterns for the majority of tracts. Additionally, a significant relationship was found between mean cortical thinning rate with fractional anisotropy (FA) and mean diffusivity (MD) alteration rates. For all tracts, age was the main effect controlling diffusion parameter alterations. We found no direct correlations between cortical thickness and FA or MD, except for in the fornix, for which the subcallosal gyrus thickness was significantly correlated to FA and MD (p < 0.05 FDR corrected). Our findings lead to the conclusion that alterations in the WM diffusion parameters are explained by the aging process, also associated with cortical thickness changes. Also, the alteration rates of the structural parameters are correlated to the different brain tracts in the aging process.
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Madan CR, Kensinger EA. Predicting age from cortical structure across the lifespan. Eur J Neurosci 2018; 47:399-416. [PMID: 29359873 PMCID: PMC5835209 DOI: 10.1111/ejn.13835] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2017] [Revised: 01/12/2018] [Accepted: 01/15/2018] [Indexed: 01/22/2023]
Abstract
Despite interindividual differences in cortical structure, cross-sectional and longitudinal studies have demonstrated a large degree of population-level consistency in age-related differences in brain morphology. This study assessed how accurately an individual's age could be predicted by estimates of cortical morphology, comparing a variety of structural measures, including thickness, gyrification and fractal dimensionality. Structural measures were calculated across up to seven different parcellation approaches, ranging from one region to 1000 regions. The age prediction framework was trained using morphological measures obtained from T1-weighted MRI volumes collected from multiple sites, yielding a training dataset of 1056 healthy adults, aged 18-97. Age predictions were calculated using a machine-learning approach that incorporated nonlinear differences over the lifespan. In two independent, held-out test samples, age predictions had a median error of 6-7 years. Age predictions were best when using a combination of cortical metrics, both thickness and fractal dimensionality. Overall, the results reveal that age-related differences in brain structure are systematic enough to enable reliable age prediction based on metrics of cortical morphology.
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Affiliation(s)
- Christopher R. Madan
- School of Psychology, University of Nottingham, Nottingham, UK
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
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Liu X, Gao X, Zhang L, Yuan Z, Zhang C, Lu W, Cui D, Zheng F, Qiu J, Xie J. Age-related changes in fiber tracts in healthy adult brains: A generalized q-sampling and connectometry study. J Magn Reson Imaging 2018; 48:369-381. [DOI: 10.1002/jmri.25949] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Accepted: 12/22/2017] [Indexed: 11/08/2022] Open
Affiliation(s)
- Xiaojing Liu
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Xiaodong Gao
- Department of Radiology; Hubei Cancer Hospital; Wu'han Hubei China
| | - Li Zhang
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Zilong Yuan
- Department of Radiology; Hubei Cancer Hospital; Wu'han Hubei China
| | - Chen Zhang
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Weizhao Lu
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Dong Cui
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
| | - Fenglian Zheng
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Jianfeng Qiu
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
- Center for Medical Engineer Technology Research; Taishan Medical University; Tai'an Shandong China
| | - Jindong Xie
- Department of Radiology; Taishan Medical University; Tai'an Shandong China
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Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications. J Int Neuropsychol Soc 2016; 22:138-63. [PMID: 26888613 DOI: 10.1017/s1355617715000995] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. METHODS We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). RESULTS Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. CONCLUSIONS Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.
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Stricker NH, Salat DH, Kuhn TP, Foley JM, Price JS, Westlye LT, Esterman MS, McGlinchey RE, Milberg WP, Leritz EC. Mild Cognitive Impairment is Associated With White Matter Integrity Changes in Late-Myelinating Regions Within the Corpus Callosum. Am J Alzheimers Dis Other Demen 2016; 31:68-75. [PMID: 25904759 PMCID: PMC4913466 DOI: 10.1177/1533317515578257] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Degenerative brain changes in Alzheimer's disease may occur in reverse order of normal brain development based on the retrogenesis model. This study tested whether evidence of reverse myelination was observed in mild cognitive impairment (MCI) using a data-driven analytic approach based on life span developmental data. Whole-brain high-resolution diffusion tensor imaging scans were obtained for 31 patients with MCI and 79 demographically matched healthy older adults. Comparisons across corpus callosum (CC) regions of interest (ROIs) showed decreased fractional anisotropy (FA) in the body but not in the genu or splenium; early-, middle-, and late-myelinating ROIs restricted to the CC revealed decreased FA in late- but not early- or middle-myelinating ROIs. Voxelwise group differences revealed areas of lower FA in MCI, but whole-brain differences were equally distributed across early-, middle-, and late-myelinating regions. Overall, results within the CC support the retrogenesis model, although caution is needed when generalizing these results beyond the CC.
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Affiliation(s)
- Nikki H Stricker
- Psychology Service, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - David H Salat
- Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA Department of Radiology, Harvard Medical School, Boston, MA, USA Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Taylor P Kuhn
- Psychology Service, VA Boston Healthcare System, Boston, MA, USA
| | - Jessica M Foley
- Psychology Service, VA Boston Healthcare System, Boston, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jenessa S Price
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA McLean Hospital, Belmont, MA, USA
| | - Lars T Westlye
- Division of Mental Health and Addiction, KG Jebsen Centre for Psychosis Research, Norwegian Centre for Mental Disorder Research (NORMENT), Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway Department of Psychology, University of Oslo, Oslo, Norway
| | - Michael S Esterman
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - Regina E McGlinchey
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, USA
| | - William P Milberg
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, USA
| | - Elizabeth C Leritz
- Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA Department of Medicine, Harvard Medical School, Boston, MA, USA
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Diffusion tensor imaging in Alzheimer's disease and affective disorders. Eur Arch Psychiatry Clin Neurosci 2014; 264:467-83. [PMID: 24595744 DOI: 10.1007/s00406-014-0496-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2013] [Accepted: 02/20/2014] [Indexed: 12/18/2022]
Abstract
The functional organization of the brain in segregated neuronal networks has become a leading paradigm in the study of brain diseases. Diffusion tensor imaging (DTI) allows testing the validity and clinical utility of this paradigm on the structural connectivity level. DTI in Alzheimer's disease (AD) suggests a selective impairment of intracortical projecting fiber tracts underlying the functional disorganization of neuronal networks supporting memory and other cognitive functions. These findings have already been tested for their utility as clinical markers of AD in large multicenter studies. Affective disorders, including major depressive disorder (MDD) and bipolar disorder (BP), show a high comorbidity with AD in geriatric populations and may even have a pathogenetic overlap with AD. DTI studies in MDD and BP are still limited to small-scale monocenter studies, revealing subtle abnormalities in cortico-subcortial networks associated with affect regulation and reward/aversion control. The clinical utility of these findings remains to be further explored. The present paper presents the methodological background of diffusion imaging, including DTI and diffusion spectrum imaging, and discusses key findings in AD and affective disorders. The results of our review strongly point toward the necessity of large-scale multicenter multimodal transnosological networks to study the structural and functional basis of neuronal disconnection underlying different neuropsychiatric diseases.
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