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Sultana OF, Bandaru M, Islam MA, Reddy PH. Unraveling the complexity of human brain: Structure, function in healthy and disease states. Ageing Res Rev 2024; 100:102414. [PMID: 39002647 PMCID: PMC11384519 DOI: 10.1016/j.arr.2024.102414] [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: 05/23/2024] [Revised: 06/29/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024]
Abstract
The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.
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Affiliation(s)
- Omme Fatema Sultana
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Madhuri Bandaru
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - Md Ariful Islam
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Sciences, Texas Tech University, Lubbock, TX 79409, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Neurology, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA 5. Department of Public Health, Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Speech, Language, and Hearing Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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Kang X, Wang D, Lin J, Yao H, Zhao K, Song C, Chen P, Qu Y, Yang H, Zhang Z, Zhou B, Han T, Liao Z, Chen Y, Lu J, Yu C, Wang P, Zhang X, Li M, Zhang X, Jiang T, Zhou Y, Liu B, Han Y, Liu Y. Convergent Neuroimaging and Molecular Signatures in Mild Cognitive Impairment and Alzheimer's Disease: A Data-Driven Meta-Analysis with N = 3,118. Neurosci Bull 2024; 40:1274-1286. [PMID: 38824231 PMCID: PMC11365916 DOI: 10.1007/s12264-024-01218-x] [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: 08/26/2023] [Accepted: 11/24/2023] [Indexed: 06/03/2024] Open
Abstract
The current study aimed to evaluate the susceptibility to regional brain atrophy and its biological mechanism in Alzheimer's disease (AD). We conducted data-driven meta-analyses to combine 3,118 structural magnetic resonance images from three datasets to obtain robust atrophy patterns. Then we introduced a set of radiogenomic analyses to investigate the biological basis of the atrophy patterns in AD. Our results showed that the hippocampus and amygdala exhibit the most severe atrophy, followed by the temporal, frontal, and occipital lobes in mild cognitive impairment (MCI) and AD. The extent of atrophy in MCI was less severe than that in AD. A series of biological processes related to the glutamate signaling pathway, cellular stress response, and synapse structure and function were investigated through gene set enrichment analysis. Our study contributes to understanding the manifestations of atrophy and a deeper understanding of the pathophysiological processes that contribute to atrophy, providing new insight for further clinical research on AD.
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Affiliation(s)
- Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Jiaji Lin
- Department of Neurology, the Second Affiliated Hospital of Air Force Medical University, Xi'an, 710032, China
- Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, 250063, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yida Qu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Zengqiang Zhang
- Branch of Chinese, PLA General Hospital, Sanya, 572013, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, 310014, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300070, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, Yunnan, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China
| | - Tianzi Jiang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300222, China
| | - Bing Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, 100875, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, 100053, China.
- National Clinical Research Center for Geriatric Disorders, Beijing, 100053, China.
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, 100053, China.
| | - Yong Liu
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100191, China.
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Frings L, Blazhenets G, Brumberg J, Rau A, Urbach H, Meyer PT. Deformation-based morphometry applied to FDG PET data reveals hippocampal atrophy in Alzheimer's disease. Sci Rep 2024; 14:20030. [PMID: 39198541 PMCID: PMC11358471 DOI: 10.1038/s41598-024-70380-z] [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: 02/06/2024] [Accepted: 08/16/2024] [Indexed: 09/01/2024] Open
Abstract
Cerebral atrophy is a key finding in patients with dementia and usually determined on MRI. We tested whether cerebral atrophy can be imaged with FDG PET by applying deformation-based morphometry (DBM). We retrospectively identified 26 patients with a biomarker-supported clinical diagnosis of Alzheimer's disease (AD) who had received FDG PET on a fully-digital PET/CT system and structural MRI and compared them to 13 healthy elderly controls (HEC). We performed DBM with FDG PET data (FDG-DBM). As a reference standard for determining atrophy we used voxel-based morphometry of MRI data (MRI-VBM). For conventional analysis of hypometabolism, scaled FDG PET scans (reference: brain parenchyma) were compared between groups. Receiver operating characteristic (ROC) analyses were performed. ROI read-outs were tested for associations with cognitive test performance. FDG-DBM showed abnormalities in AD mainly in the bilateral hippocampi. Similarly, MRI-VBM showed hippocampal atrophy. By contrast, conventional FDG PET analysis revealed reduced bilateral temporo-parietal FDG uptake (all p < 0.05, FWE-corrected). FDG-DBM measures of the hippocampus significantly separated AD from HEC with an AUC of 0.81; MRI-VBM achieved an AUC of 0.87; the difference between the two ROC curves was not significant (p = 0.40). Whereas FDG uptake of the hippocampus did not separate AD from HEC, FDG uptake of the Landau Meta-ROI achieved an AUC of 0.88. Verbal memory was significantly associated with FDG-DBM measures of the hippocampus (p = 0.009), but not of the Landau Meta-ROI (p > 0.1). The opposite held true for conventional FDG uptake (p > 0.1 and p = 0.001, respectively). Hippocampal atrophy in AD can be detected by applying DBM to clinical, fully-digital FDG PET. It correlates with cognitive performance and might constitute a biomarker of neurodegeneration that is complementary to conventional FDG PET analysis of regional hypometabolism.
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Affiliation(s)
- Lars Frings
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Joachim Brumberg
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp T Meyer
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Mu X, Cui C, Liao J, Wu Z, Hu L. Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics. EJNMMI REPORTS 2024; 8:19. [PMID: 38945980 PMCID: PMC11214937 DOI: 10.1186/s41824-024-00206-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 04/22/2024] [Indexed: 07/02/2024]
Abstract
BACKGROUND This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD). METHODS In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression. RESULTS Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD. CONCLUSION Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.
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Affiliation(s)
- Xuxu Mu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Caozhe Cui
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Jue Liao
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Zhifang Wu
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China
| | - Lingzhi Hu
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, Shanxi, People's Republic of China.
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Nance RM, Fohner AE, McClelland RL, Redline S, Nick Bryan R, Desiderio L, Habes M, Longstreth WT, Schwab RJ, Wiemken AS, Heckbert SR. The Association of Upper Airway Anatomy with Brain Structure: The Multi-Ethnic Study of Atherosclerosis. Brain Imaging Behav 2024; 18:510-518. [PMID: 38194040 PMCID: PMC11222025 DOI: 10.1007/s11682-023-00843-w] [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] [Accepted: 12/21/2023] [Indexed: 01/10/2024]
Abstract
Sleep apnea, affecting an estimated 1 in 4 American adults, has been reported to be associated with both brain structural abnormality and impaired cognitive function. Obstructive sleep apnea is known to be affected by upper airway anatomy. To better understand the contribution of upper airway anatomy to pathways linking sleep apnea with impaired cognitive function, we investigated the association of upper airway anatomy with structural brain abnormalities. Based in the Multi-Ethnic Study of Atherosclerosis, a longitudinal cohort study of community-dwelling adults, a comprehensive sleep study and an MRI of the upper airway and brain were performed on 578 participants. Machine learning models were used to select from 74 upper airway measures those measures most associated with selected regional brain volumes and white matter hyperintensity volume. Linear regression assessed associations between the selected upper airway measures, sleep measures, and brain structure. Maxillary divergence was positively associated with hippocampus volume, and mandible length was negatively associated with total white and gray matter volume. Both coefficients were small (coefficients per standard deviation 0.063 mL, p = 0.04, and - 7.0 mL, p < 0.001 respectively), and not affected by adjustment for sleep study measures. Self-reported snoring >2 times per week was associated with larger hippocampus volume (coefficient 0.164 mL, p = 0.007), and higher percentage of time in the N3 sleep stage was associated with larger total white and gray matter volume (4.8 mL, p = 0.004). Despite associations of two upper airway anatomy measures with brain volume, the evidence did not suggest that these upper airway and brain structure associations were acting primarily through the pathway of sleep disturbance.
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Affiliation(s)
- Robin M Nance
- University of Washington, Seattle, WA, USA.
- , 325 9th Ave, Box 359931, Seattle, WA, 98104, USA.
| | - Alison E Fohner
- Department of Epidemiology & Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | | | - Susan Redline
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - W T Longstreth
- Departments of Neurology and Epidemiology, University of Washington, Seattle, WA, USA
| | - Richard J Schwab
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew S Wiemken
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Li Q, Lv X, Qian Q, Liao K, Du X. Neuroticism polygenic risk predicts conversion from mild cognitive impairment to Alzheimer's disease by impairing inferior parietal surface area. Hum Brain Mapp 2024; 45:e26709. [PMID: 38746977 PMCID: PMC11094517 DOI: 10.1002/hbm.26709] [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: 05/13/2023] [Revised: 03/19/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
The high prevalence of conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD) makes early prevention of AD extremely critical. Neuroticism, a heritable personality trait associated with mental health, has been considered a risk factor for conversion from aMCI to AD. However, whether the neuroticism genetic risk could predict the conversion of aMCI and its underlying neural mechanisms is unclear. Neuroticism polygenic risk score (N-PRS) was calculated in 278 aMCI patients with qualified genomic and neuroimaging data from ADNI. After 1-year follow-up, N-PRS in patients of aMCI-converted group was significantly greater than those in aMCI-stable group. Logistic and Cox survival regression revealed that N-PRS could significantly predict the early-stage conversion risk from aMCI to AD. These results were well replicated in an internal dataset and an independent external dataset of 933 aMCI patients from the UK Biobank. One sample Mendelian randomization analyses confirmed a potentially causal association from higher N-PRS to lower inferior parietal surface area to higher conversion risk of aMCI patients. These analyses indicated that neuroticism genetic risk may increase the conversion risk from aMCI to AD by impairing the inferior parietal structure.
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Affiliation(s)
- Qiaojun Li
- College of Information EngineeringTianjin University of CommerceTianjinChina
| | - Xingping Lv
- College of SciencesTianjin University of CommerceTianjinChina
| | - Qian Qian
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Kun Liao
- College of SciencesTianjin University of CommerceTianjinChina
| | - Xin Du
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
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Hu H, Wang L, Abdul S, Tang X, Feng Q, Mu Y, Ge X, Liao Z, Ding Z. Frequency-dependent alterations in functional connectivity in patients with Alzheimer's Disease spectrum disorders. Front Aging Neurosci 2024; 16:1375836. [PMID: 38605859 PMCID: PMC11007178 DOI: 10.3389/fnagi.2024.1375836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/04/2024] [Indexed: 04/13/2024] Open
Abstract
Background In the spectrum of Alzheimer's Disease (AD) and related disorders, the resting-state functional magnetic resonance imaging (rs-fMRI) signals within the cerebral cortex may exhibit distinct characteristics across various frequency ranges. Nevertheless, this hypothesis has not yet been substantiated within the broader context of whole-brain functional connectivity. This study aims to explore potential modifications in degree centrality (DC) and voxel-mirrored homotopic connectivity (VMHC) among individuals with amnestic mild cognitive impairment (aMCI) and AD, while assessing whether these alterations differ across distinct frequency bands. Methods This investigation encompassed a total of 53 AD patients, 40 aMCI patients, and 40 healthy controls (HCs). DC and VMHC values were computed within three distinct frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz) for the three respective groups. To discern differences among these groups, ANOVA and subsequent post hoc two-sample t-tests were employed. Cognitive function assessment utilized the mini-mental state examination (MMSE) and Montreal Cognitive Assessment (MoCA). Pearson correlation analysis was applied to investigate the associations between MMSE and MoCA scores with DC and VMHC. Results Significant variations in degree centrality (DC) were observed among different groups across diverse frequency bands. The most notable differences were identified in the bilateral caudate nucleus (CN), bilateral medial superior frontal gyrus (mSFG), bilateral Lobule VIII of the cerebellar hemisphere (Lobule VIII), left precuneus (PCu), right Lobule VI of the cerebellar hemisphere (Lobule VI), and right Lobule IV and V of the cerebellar hemisphere (Lobule IV, V). Likewise, disparities in voxel-mirrored homotopic connectivity (VMHC) among groups were predominantly localized to the posterior cingulate gyrus (PCG) and Crus II of the cerebellar hemisphere (Crus II). Across the three frequency bands, the brain regions exhibiting significant differences in various parameters were most abundant in the slow-5 frequency band. Conclusion This study enhances our understanding of the pathological and physiological mechanisms associated with AD continuum. Moreover, it underscores the importance of researchers considering various frequency bands in their investigations of brain function.
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Affiliation(s)
- Hanjun Hu
- The Fourth Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Luoyu Wang
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Sammad Abdul
- International Education College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Xue Tang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, China
| | - Qi Feng
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Yuzhu Mu
- The Fourth Clinical College, Zhejiang Chinese Medical University, Hangzhou, China
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Xiuhong Ge
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People’s Hospital/People’s Hospital of Hangzhou Medical College, Hangzhou, China
| | - Zhongxiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou, China
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8
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Tang X, Guo Z, Chen G, Sun S, Xiao S, Chen P, Tang G, Huang L, Wang Y. A Multimodal Meta-Analytical Evidence of Functional and Structural Brain Abnormalities Across Alzheimer's Disease Spectrum. Ageing Res Rev 2024; 95:102240. [PMID: 38395200 DOI: 10.1016/j.arr.2024.102240] [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: 01/05/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Numerous neuroimaging studies have reported that Alzheimer's disease (AD) spectrum have been linked to alterations in intrinsic functional activity and cortical thickness (CT) of some brain areas. However, the findings have been inconsistent and the correlation with the transcriptional profile and neurotransmitter systems remain largely unknown. METHODS We conducted a meta-analysis to identify multimodal differences in the amplitude of low-frequency fluctuation (ALFF)/fractional ALFF (fALFF) and CT in patients with AD and preclinical AD compared to healthy controls (HCs), using the Seed-based d Mapping with Permutation of Subject Images software. Transcriptional data were retrieved from the Allen Human Brain Atlas. The atlas-based nuclear imaging-derived neurotransmitter maps were investigated by JuSpace toolbox. RESULTS We included 26 ALFF/fALFF studies comprising 884 patients with AD and 1,020 controls, along with 52 studies comprising 2,046 patients with preclinical AD and 2,336 controls. For CT, we included 11 studies comprising 353 patients with AD and 330 controls. Overall, compared to HCs, patients with AD showed decreased ALFF/fALFF in the bilateral posterior cingulate gyrus (PCC)/precuneus and right angular gyrus, as well as increased ALFF/fALFF in the bilateral parahippocampal gyrus (PHG). Patients with peclinical AD showed decreased ALFF/fALFF in the left precuneus. Additionally, patients with AD displayed decreased CT in the bilateral PHG, left PCC, bilateral orbitofrontal cortex, sensorimotor areas and temporal lobe. Furthermore, gene sets related to brain structural and functional changes in AD and preclincal AD were enriched for G protein-coupled receptor signaling pathway, ion gated channel activity, and components of biological membrane. Functional and structural alterations in AD and preclinical AD were spatially associated with dopaminergic, serotonergic, and GABAergic neurotransmitter systems. CONCLUSIONS The multimodal meta-analysis demonstrated that patients with AD exhibited convergent functional and structural alterations in the PCC/precuneus and PHG, as well as cortical thinning in the primary sensory and motor areas. Furthermore, patients with preclinical AD showed reduced functional activity in the precuneus. AD and preclinical AD showed genetic modulations/neurotransmitter deficits of brain functional and structural impairments. These findings may provide new insights into the pathophysiology of the AD spectrum.
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Affiliation(s)
- Xinyue Tang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Zixuan Guo
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Shilin Sun
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Shu Xiao
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Pan Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Guixian Tang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China.
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Wang Y, Li Q, Yao L, He N, Tang Y, Chen L, Long F, Chen Y, Kemp GJ, Lui S, Li F. Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2024; 34:bhae094. [PMID: 38521993 DOI: 10.1093/cercor/bhae094] [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: 10/02/2023] [Revised: 02/19/2024] [Accepted: 02/21/2024] [Indexed: 03/25/2024] Open
Abstract
Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.
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Affiliation(s)
- Yaxuan Wang
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Qian Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Li Yao
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Ning He
- Department of Psychiatry, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Yingying Tang
- Department of Neurology, West China Hospital of Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan, P.R. China
| | - Lizhou Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fenghua Long
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Yufei Chen
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Graham J Kemp
- Institute of Life Course and Medical Sciences, University of Liverpool, 6 West Derby Street, Liverpool L7 8TX, United Kingdom
| | - Su Lui
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
| | - Fei Li
- Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular lmaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Wuhou District, Chengdu 610041, Sichuan Province, P.R. China
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10
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Shah SN, Dounavi ME, Malhotra PA, Lawlor B, Naci L, Koychev I, Ritchie CW, Ritchie K, O’Brien JT. Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study. Brain Commun 2024; 6:fcae046. [PMID: 38444908 PMCID: PMC10914447 DOI: 10.1093/braincomms/fcae046] [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: 07/26/2023] [Revised: 12/31/2023] [Accepted: 02/13/2024] [Indexed: 03/07/2024] Open
Abstract
A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.
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Affiliation(s)
- Sita N Shah
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Maria-Eleni Dounavi
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
| | - Paresh A Malhotra
- Department of Brain Sciences, Imperial College London, London W12 0NN, UK
- UK Dementia Research Institute Care Research and Technology Centre, Imperial College London and the University of Surrey, London SW7 2AZ, UK
| | - Brian Lawlor
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin D02 PX31, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin D02 X9W9, Ireland
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin D02 PX31, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin D02 X9W9, Ireland
| | - Ivan Koychev
- Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK
| | - Craig W Ritchie
- Centre for Dementia Prevention, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Karen Ritchie
- Institute de Neurosciences de Montpellier, INSERM, Montpellier 34093, France
| | - John T O’Brien
- Department of Psychiatry, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK
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11
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Andriuta D, Wiener E, Perron A, Ouin E, Masmoudi I, Thibaut W, Martin J, Roussel M, Constans JM, Aarabi A, Godefroy O. Neuroimaging determinants of cognitive impairment in the memory clinic: how important is the vascular burden? J Neurol 2024; 271:504-518. [PMID: 37777991 DOI: 10.1007/s00415-023-12009-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 09/14/2023] [Accepted: 09/15/2023] [Indexed: 10/03/2023]
Abstract
While neurodegenerative and vascular neurocognitive disorder (NCD) often co-occur, the contribution of vascular lesions, especially stroke lesions identified on MRI, to global cognition in a real-life memory clinic population remains unclear. The main objective of this retrospective study was to determine NCD neuroimaging correlates: the GM atrophy pattern and vascular lesions (especially stroke lesion localization by voxel-based lesion-symptom mapping, VLSM) in a memory clinic. We included 336 patients with mild or major NCD who underwent cerebral MRI and a neuropsychological assessment. The GM atrophy pattern (obtained by voxel-based morphometry, VBM) and the stroke lesion localization (obtained by VLSM) associated with G5 z-score (a global cognitive score), were included as independent variables with other neuroimaging and clinical indices in a stepwise linear regression model. The mean age was 70.3 years and the mean MMSE score 21.3. On MRI, 75 patients had at least one stroke lesion. The G 5 z-score was associated with GM density in the pattern selected by the VBM analysis (R2 variation = 0.166, p < 0.001) and the presence of a stroke lesion in the region selected by the VSLM analysis (mainly in the right frontal region; R2 variation = 0.018, p = 0.008). The interaction between the two factors was insignificant (p = 0.374). In conclusion, in this first study combining VBM and VLSM analysis in a memory clinic, global cognition was associated with a specific GM atrophy pattern and the presence of a stroke lesion mainly in the right frontal region.
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Affiliation(s)
- Daniela Andriuta
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France.
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France.
| | - Emmanuel Wiener
- Department of Neurology, Versailles - Le Chesnay Medical Center, Le Chesnay-Rocquencourt, France
| | - Alexandre Perron
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
| | - Elisa Ouin
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
| | - Ines Masmoudi
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
| | - William Thibaut
- Department of Neurology, La Reunion University Medical Center, Site South Saint-Pierre, Saint-Pierre, La Reunion, France
| | - Jeanne Martin
- Department of Neurology, Bretagne Atlantique Medical Center, Vannes, France
| | - Martine Roussel
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
| | - Jean-Marc Constans
- Department of Radiology, Amiens University Medical Center, Amiens, France
| | - Ardalan Aarabi
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
| | - Olivier Godefroy
- Department of Neurology, Amiens University Medical Center, Amiens University Hospital, 80054, Amiens, France
- Laboratoire de Neurosciences Fonctionnelles Et Pathologies (UR UPJV 4559), Jules Verne University of Picardy, Amiens, France
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12
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Zhong S, Lou J, Ma K, Shu Z, Chen L, Li C, Ye Q, Zhou L, Shen Y, Ye X, Zhang J. Disentangling in-vivo microstructural changes of white and gray matter in mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis. Brain Imaging Behav 2023; 17:764-777. [PMID: 37752311 DOI: 10.1007/s11682-023-00805-2] [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] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
The microstructural characteristics of white and gray matter in mild cognitive impairment (MCI) and the early-stage of Alzheimer's disease (AD) remain unclear. This study aimed to systematically identify the microstructural damages of MCI/AD in studies using neurite orientation dispersion and density imaging (NODDI), and explore their correlations with cognitive performance. Multiple databases were searched for eligible studies. The 10 eligible NODDI studies were finally included. Patients with MCI/AD showed overall significant reductions in neurite density index (NDI) of specific white matter structures in bilateral hemispheres (left hemisphere: -0.40 [-0.53, -0.27], P < 0.001; right: -0.33 [-0.47, -0.19], P < 0.001), involving the bilateral superior longitudinal fasciculus (SLF), uncinate fasciculus (UF), the left posterior thalamic radiation (PTR), and the left cingulum. White matter regions exhibited significant increased orientation dispersion index (ODI) (left: 0.25 [0.02, 0.48], P < 0.05; right: 0.27 [0.07, 0.46], P < 0.05), including the left cingulum, the right UF, and the bilateral parahippocampal cingulum (PHC), and PTR. Additionally, the ODI of gray matter showed significant reduction in bilateral hippocampi (left: -0.97 [-1.42, -0.51], P < 0.001; right: -0.90 [-1.35, -0.45], P < 0.001). The cognitive performance in MCI/AD was significantly associated with NDI (r = 0.50, P < 0.001). Our findings highlight the microstructural changes in MCI/AD were characterized by decreased fiber orientation dispersion in the hippocampus, and decreased neurite density and increased fiber orientation dispersion in specific white matter tracts, including the cingulum, UF, and PTR. Moreover, the decreased NDI may indicate the declined cognitive level of MCI/AD patients.
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Affiliation(s)
- Shuchang Zhong
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jingjing Lou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ke Ma
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Lin Chen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Chao Li
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Qing Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liang Zhou
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Ye Shen
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangming Ye
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Jie Zhang
- Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
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13
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Yue J, Han SW, Liu X, Wang S, Zhao WW, Cai LN, Cao DN, Mah JZ, Hou Y, Cui X, Wang Y, Chen L, Li A, Li XL, Yang G, Zhang Q. Functional brain activity in patients with amnestic mild cognitive impairment: an rs-fMRI study. Front Neurol 2023; 14:1244696. [PMID: 37674874 PMCID: PMC10477362 DOI: 10.3389/fneur.2023.1244696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/07/2023] [Indexed: 09/08/2023] Open
Abstract
Background Amnestic mild cognitive impairment (aMCI) is an early stage of Alzheimer's disease (AD). Regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) are employed to explore spontaneous brain function in patients with aMCI. This study applied ALFF and ReHo indicators to analyze the neural mechanism of aMCI by resting-state functional magnetic resonance imaging (rs-fMRI). Methods Twenty-six patients with aMCI were included and assigned to the aMCI group. The other 26 healthy subjects were included as a healthy control (HC) group. Rs-fMRI was performed for all participants in both groups. Between-group comparisons of demographic data and neuropsychological scores were analyzed using SPSS 25.0. Functional imaging data were analyzed using DPARSF and SPM12 software based on MATLAB 2017a. Gender, age, and years of education were used as covariates to obtain ALFF and ReHo indices. Results Compared with HC group, ALFF decreased in the left fusiform gyrus, left superior temporal gyrus, and increased in the left cerebellum 8, left inferior temporal gyrus, left superior frontal gyrus (BA11), and right inferior temporal gyrus (BA20) in the aMCI group (p < 0.05, FWE correction). In addition, ReHo decreased in the right middle temporal gyrus and right anterior cuneiform lobe, while it increased in the left middle temporal gyrus, left inferior temporal gyrus, cerebellar vermis, right parahippocampal gyrus, left caudate nucleus, right thalamus, and left superior frontal gyrus (BA6) (p < 0.05, FWE correction). In the aMCI group, the ALFF of the left superior frontal gyrus was negatively correlated with Montreal Cognitive Assessment (MoCA) score (r = -0.437, p = 0.026), and the ALFF of the left superior temporal gyrus was positively correlated with the MoCA score (r = 0.550, p = 0.004). The ReHo of the right hippocampus was negatively correlated with the Mini-Mental State Examination (MMSE) score (r = -0.434, p = 0.027), and the ReHo of the right middle temporal gyrus was positively correlated with MMSE score (r = 0.392, p = 0.048). Conclusion Functional changes in multiple brain regions rather than in a single brain region have been observed in patients with aMCI. The abnormal activity of multiple specific brain regions may be a manifestation of impaired central function in patients with aMCI.
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Affiliation(s)
- Jinhuan Yue
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Vitality University, Hayward, CA, United States
| | - Sheng-wang Han
- Department of Third Rehabilitation Medicine, Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiao Liu
- Department of Pediatrics, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | | | - Li-na Cai
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Dan-na Cao
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jeffrey Zhongxue Mah
- Department of Acupuncture and Moxibustion, Vitality University, Hayward, CA, United States
| | - Yu Hou
- Department of Gynecology, Harbin Traditional Chinese Medicine Hospital, Harbin, China
| | - Xuan Cui
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yang Wang
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Li Chen
- Confucius Institute for TCM, London South Bank University, London, United Kingdom
| | - Ang Li
- Sanofi-Aventis China Investment Co., Ltd., Beijing, China
| | - Xiao-ling Li
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH, United States
| | - Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
- Department of Acupuncture and Moxibustion, Heilongjiang University of Chinese Medicine, Harbin, China
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14
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Seyedsalehi A, Warrier V, Bethlehem RAI, Perry BI, Burgess S, Murray GK. Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis. Brain 2023; 146:2059-2074. [PMID: 36310536 PMCID: PMC10151197 DOI: 10.1093/brain/awac392] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/01/2022] [Accepted: 09/19/2022] [Indexed: 11/13/2022] Open
Abstract
Higher educational attainment is observationally associated with lower risk of Alzheimer's disease. However, the biological mechanisms underpinning this association remain unclear. The protective effect of education on Alzheimer's disease may be mediated via increased brain reserve. We used two-sample Mendelian randomization to explore putative causal relationships between educational attainment, structural brain reserve as proxied by MRI phenotypes and Alzheimer's disease. Summary statistics were obtained from genome-wide association studies of educational attainment (n = 1 131 881), late-onset Alzheimer's disease (35 274 cases, 59 163 controls) and 15 measures of grey or white matter macro- or micro-structure derived from structural or diffusion MRI (nmax = 33 211). We conducted univariable Mendelian randomization analyses to investigate bidirectional associations between (i) educational attainment and Alzheimer's disease; (ii) educational attainment and imaging-derived phenotypes; and (iii) imaging-derived phenotypes and Alzheimer's disease. Multivariable Mendelian randomization was used to assess whether brain structure phenotypes mediated the effect of education on Alzheimer's disease risk. Genetically proxied educational attainment was inversely associated with Alzheimer's disease (odds ratio per standard deviation increase in genetically predicted years of schooling = 0.70, 95% confidence interval 0.60, 0.80). There were positive associations between genetically predicted educational attainment and four cortical metrics (standard deviation units change in imaging phenotype per one standard deviation increase in genetically predicted years of schooling): surface area 0.30 (95% confidence interval 0.20, 0.40); volume 0.29 (95% confidence interval 0.20, 0.37); intrinsic curvature 0.18 (95% confidence interval 0.11, 0.25); local gyrification index 0.21 (95% confidence interval 0.11, 0.31)]; and inverse associations with cortical intracellular volume fraction [-0.09 (95% confidence interval -0.15, -0.03)] and white matter hyperintensities volume [-0.14 (95% confidence interval -0.23, -0.05)]. Genetically proxied levels of surface area, cortical volume and intrinsic curvature were positively associated with educational attainment [standard deviation units change in years of schooling per one standard deviation increase in respective genetically predicted imaging phenotype: 0.13 (95% confidence interval 0.10, 0.16); 0.15 (95% confidence interval 0.11, 0.19) and 0.12 (95% confidence interval 0.04, 0.19)]. We found no evidence of associations between genetically predicted imaging-derived phenotypes and Alzheimer's disease. The inverse association of genetically predicted educational attainment with Alzheimer's disease did not attenuate after adjusting for imaging-derived phenotypes in multivariable analyses. Our results provide support for a protective causal effect of educational attainment on Alzheimer's disease risk, as well as potential bidirectional causal relationships between education and brain macro- and micro-structure. However, we did not find evidence that these structural markers affect risk of Alzheimer's disease. The protective effect of education on Alzheimer's disease may be mediated via other measures of brain reserve not included in the present study, or by alternative mechanisms.
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Affiliation(s)
- Aida Seyedsalehi
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
- Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford OX3 7JX, UK
| | - Varun Warrier
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Benjamin I Perry
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0BB, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- CAMEO, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge CB4 1PX, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane 4072, Australia
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15
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Kahhale I, Buser NJ, Madan CR, Hanson JL. Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer. Brain Inform 2023; 10:9. [PMID: 37029203 PMCID: PMC10082143 DOI: 10.1186/s40708-023-00189-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here, we assessed the spatial and numerical reliability for newly deployed automated segmentation of hippocampal subfields and amygdala nuclei in FreeSurfer 7. In a sample of participants with repeated structural imaging scans (N = 928), we found numerical reliability (as assessed by intraclass correlations, ICCs) was reasonable. Approximately 95% of hippocampal subfields had "excellent" numerical reliability (ICCs ≥ 0.90), while only 67% of amygdala subnuclei met this same threshold. In terms of spatial reliability, 58% of hippocampal subfields and 44% of amygdala subnuclei had Dice coefficients ≥ 0.70. Notably, multiple regions had poor numerical and/or spatial reliability. We also examined correlations between spatial reliability and person-level factors (e.g., participant age; T1 image quality). Both sex and image scan quality were related to variations in spatial reliability metrics. Examined collectively, our work suggests caution should be exercised for a few hippocampal subfields and amygdala nuclei with more variable reliability.
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16
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Zhang X, Hao Y, Zhang J, Ji Y, Zou S, Zhao S, Xie S, Du L. A multi-task SCCA method for brain imaging genetics and its application in neurodegenerative diseases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 232:107450. [PMID: 36905750 DOI: 10.1016/j.cmpb.2023.107450] [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: 06/09/2022] [Revised: 02/24/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVES In brain imaging genetics, multi-task sparse canonical correlation analysis (MTSCCA) is effective to study the bi-multivariate associations between genetic variations such as single nucleotide polymorphisms (SNPs) and multi-modal imaging quantitative traits (QTs). However, most existing MTSCCA methods are neither supervised nor capable of distinguishing the shared patterns of multi-modal imaging QTs from the specific patterns. METHODS A new diagnosis-guided MTSCCA (DDG-MTSCCA) with parameter decomposition and graph-guided pairwise group lasso penalty was proposed. Specifically, the multi-tasking modeling paradigm enables us to comprehensively identify risk genetic loci by jointly incorporating multi-modal imaging QTs. The regression sub-task was raised to guide the selection of diagnosis-related imaging QTs. To reveal the diverse genetic mechanisms, the parameter decomposition and different constraints were utilized to facilitate the identification of modality-consistent and -specific genotypic variations. Besides, a network constraint was added to find out meaningful brain networks. The proposed method was applied to synthetic data and two real neuroimaging data sets respectively from Alzheimer's disease neuroimaging initiative (ADNI) and Parkinson's progression marker initiative (PPMI) databases. RESULTS Compared with the competitive methods, the proposed method exhibited higher or comparable canonical correlation coefficients (CCCs) and better feature selection results. In particular, in the simulation study, DDG-MTSCCA showed the best anti-noise ability and achieved the highest average hit rate, about 25% higher than MTSCCA. On the real data of Alzheimer's disease (AD) and Parkinson's disease (PD), our method obtained the highest average testing CCCs, about 40% ∼ 50% higher than MTSCCA. Especially, our method could select more comprehensive feature subsets, and the top five SNPs and imaging QTs were all disease-related. The ablation experimental results also demonstrated the significance of each component in the model, i.e., the diagnosis guidance, parameter decomposition, and network constraint. CONCLUSIONS These results on simulated data, ADNI and PPMI cohorts suggested the effectiveness and generalizability of our method in identifying meaningful disease-related markers. DDG-MTSCCA could be a powerful tool in brain imaging genetics, worthy of in-depth study.
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Affiliation(s)
- Xin Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Yipeng Hao
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Jin Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Yanuo Ji
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Shihong Zou
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Songyun Xie
- School of Electronics and Information, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China
| | - Lei Du
- School of Automation, Northwestern Polytechnical University, Xi'an, Shannxi 710072, China.
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17
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Liu F, Wang H, Liang SN, Jin Z, Wei S, Li X. MPS-FFA: A multiplane and multiscale feature fusion attention network for Alzheimer's disease prediction with structural MRI. Comput Biol Med 2023; 157:106790. [PMID: 36958239 DOI: 10.1016/j.compbiomed.2023.106790] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 02/13/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023]
Abstract
Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
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Affiliation(s)
- Fei Liu
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
| | - Huabin Wang
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
| | - Shiuan-Ni Liang
- School of Engineering, Monash University Malaysia, Kuala Lumpur, Malaysia
| | - Zhe Jin
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Shicheng Wei
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China
| | - Xuejun Li
- Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China
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18
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Heinzinger N, Maass A, Berron D, Yakupov R, Peters O, Fiebach J, Villringer K, Preis L, Priller J, Spruth EJ, Altenstein S, Schneider A, Fliessbach K, Wiltfang J, Bartels C, Jessen F, Maier F, Glanz W, Buerger K, Janowitz D, Perneczky R, Rauchmann BS, Teipel S, Killimann I, Göerß D, Laske C, Munk MH, Spottke A, Roy N, Heneka MT, Brosseron F, Dobisch L, Ewers M, Dechent P, Haynes JD, Scheffler K, Wolfsgruber S, Kleineidam L, Schmid M, Berger M, Düzel E, Ziegler G. Exploring the ATN classification system using brain morphology. Alzheimers Res Ther 2023; 15:50. [PMID: 36915139 PMCID: PMC10009950 DOI: 10.1186/s13195-023-01185-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/08/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort. METHODS We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups. RESULTS The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead. CONCLUSION Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy. TRIAL REGISTRATION DRKS00007966, 04/05/2015, retrospectively registered.
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Affiliation(s)
- Nils Heinzinger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. .,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany.
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - David Berron
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Renat Yakupov
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Jochen Fiebach
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
| | - Lukas Preis
- Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany.,Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany.,University of Edinburgh and UK DRI, Edinburgh, UK
| | - Eike Jacob Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany.,Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
| | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany.,Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany.,Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany.,Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Franziska Maier
- Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Katharina Buerger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Robert Perneczky
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany.,Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Stefan Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Ingo Killimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany.,Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Doreen Göerß
- Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Matthias H Munk
- German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.,Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurology, University of Bonn, Bonn, Germany
| | - Nina Roy
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Michael T Heneka
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Frederic Brosseron
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Michael Ewers
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Peter Dechent
- MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, Göttingen, Germany
| | - John Dylan Haynes
- Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Berlin, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
| | - Luca Kleineidam
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Matthias Schmid
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Moritz Berger
- Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
| | - Gabriel Ziegler
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany.,Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
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Xiao Y, Wang J, Huang K, Gao L, Yao S. Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease. Front Aging Neurosci 2023; 14:1064667. [PMID: 36688148 PMCID: PMC9853893 DOI: 10.3389/fnagi.2022.1064667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. Objective This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. Materials We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. Results The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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20
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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment. Clin Neuroradiol 2022; 33:445-453. [DOI: 10.1007/s00062-022-01226-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022]
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21
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Álvarez-San Millán A, Iglesias J, Gutkin A, Olivares EI. Progressive attenuation of visual global precedence across healthy aging and Alzheimer's disease. Front Aging Neurosci 2022; 14:893818. [PMID: 36204552 PMCID: PMC9530062 DOI: 10.3389/fnagi.2022.893818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/17/2022] [Indexed: 11/24/2022] Open
Abstract
In the perception of Navon hierarchical stimuli (e.g., large letters made up of small letters), young adults identify large letters faster than small ones (known as 'global advantage') and identify more slowly small letters when they form a different (or incongruent) large letter (known as 'unidirectional global interference'). Since some global/local perceptual alterations might be occurring with aging, we investigated whether these effects vary across healthy aging and Alzheimer's disease (AD). Here, the Navon letter task was administered to 26 healthy elderly (HE), 21 adults with mild cognitive impairment (MCI), and 26 adults with AD. The same task was administered 1 year later, and different neuropsychological variables were incorporated into the analyses. The cross-sectional study revealed no global advantage but did reveal both global and local interferences in all groups when response times were analyzed. Regarding discrimination sensitivity, HE showed unidirectional global interference, while AD displayed better discrimination of local than global letters in the incongruent condition, which denotes less interference by global distractors than by local ones. The longitudinal study revealed that 1 year later the participants with MCI showed a slowdown in inhibiting local distractors in the global task, revealing a certain bias toward focus in their attention on small stimuli. The elders with AD reflected a generalized slowing of their responses with a clear bias toward local analysis of stimuli, also suggested by their better discrimination in the incongruent local task at the second moment of assessment. Furthermore, all response timing measures in the Navon task were correlated with several neuropsychological indexes of highly sensitive neuropsychological tests, suggesting that performance in this task may also have a potential diagnostic value for differentiating typical from atypical cognitive aging. All these results support the need for a multidomain approach to define neuropsychological markers of progression toward AD, including visual perceptual organization evaluated via measures of performance quality.
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Affiliation(s)
- Andrea Álvarez-San Millán
- Department of Psychology, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Madrid, Spain
| | - Jaime Iglesias
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
| | - Anahí Gutkin
- Department of Social Psychology and Methodology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
| | - Ela I. Olivares
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
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22
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Liu C, Lee SH, Loewenstein DA, Galvin JE, Camargo CJ, Alperin N. Poor sleep accelerates hippocampal and posterior cingulate volume loss in cognitively normal healthy older adults. J Sleep Res 2022; 31:e13538. [PMID: 34927298 PMCID: PMC10731580 DOI: 10.1111/jsr.13538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 11/12/2021] [Accepted: 12/03/2021] [Indexed: 01/05/2023]
Abstract
Poor sleep quality is a known risk factor for Alzheimer's disease. This longitudinal imaging study aimed to determine the acceleration in the rates of tissue loss in cognitively critical brain regions due to poor sleep in healthy elderly individuals. Cognitively-normal healthy individuals, aged ≥60 years, reported Pittsburgh Sleep Quality Index (PSQI) and underwent baseline and 2-year follow-up magnetic resonance imaging brain scans. The links between self-reported sleep quality, rates of tissue loss in cognitively-critical brain regions, and white matter hyperintensity load were assessed. A total of 48 subjects were classified into normal (n = 23; PSQI score <5) and poor sleepers (n = 25; PSQI score ≥5). The two groups were not significantly different in terms of age, gender, years of education, ethnicity, handedness, body mass index, and cognitive performance. Compared to normal sleepers, poor sleepers exhibited much faster rates of volume loss, over threefold in the right hippocampus and fivefold in the right posterior cingulate over 2 years. In contrast, there were no significant differences in the rates of volume loss in the cerebral and cerebellar grey and white matter between the two groups. Rates of volume loss in the right posterior cingulate were negatively associated with global PSQI scores. Poor sleep significantly accelerates volume loss in the right hippocampus and the right posterior cingulate cortex. These findings demonstrate that self-reported sleep quality explains inter-individual differences in the rates of volume loss in cognitively-critical brain regions in healthy older adults and provide a strong impetus to offer sleep interventions to cognitively normal older adults who are poor sleepers.
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Affiliation(s)
- Che Liu
- Department of Radiology, University of Miami Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
| | - Sang H. Lee
- Department of Radiology, University of Miami Miller School of Medicine, University of Miami, Miami, FL, USA
| | - David A. Loewenstein
- Department of Psychiatry and Behavioral Sciences, University of Miami Miller School of Medicine, Miami, FL, USA
| | - James E. Galvin
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Christian J. Camargo
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Noam Alperin
- Department of Radiology, University of Miami Miller School of Medicine, University of Miami, Miami, FL, USA
- Department of Biomedical Engineering, University of Miami, Miami, FL, USA
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23
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Zhang M, Huang X, Li B, Shang H, Yang J. Gray Matter Structural and Functional Alterations in Idiopathic Blepharospasm: A Multimodal Meta-Analysis of VBM and Functional Neuroimaging Studies. Front Neurol 2022; 13:889714. [PMID: 35734475 PMCID: PMC9207395 DOI: 10.3389/fneur.2022.889714] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 05/16/2022] [Indexed: 12/03/2022] Open
Abstract
Background Neuroimaging studies have shown gray matter structural and functional alterations in patients with idiopathic blepharospasm (iBSP) but with variations. Here we aimed to investigate the specific and common neurostructural/functional abnormalities in patients with iBSP. Methods A systematic literature search from PubMed, Web of Science and Embase was conducted to identify relevant publications. We conducted separate meta-analysis for whole-brain voxel-based morphometry (VBM) studies and for functional imaging studies, and a multimodal meta-analysis across VBM and functional studies in iBSP, using anisotropic effect size-based signed differential mapping. Results The structural database comprised 129 patients with iBSP and 144 healthy controls whilst the functional database included 183 patients with iBSP and 253 healthy controls. The meta-analysis of VBM studies showed increased gray matter in bilateral precentral and postcentral gyri, right supplementary motor area and bilateral paracentral lobules, while decreased gray matter in right superior and inferior parietal gyri, left inferior parietal gyrus, left inferior temporal gyrus, left fusiform gyrus and parahippocampal gyrus. The meta-analysis of functional studies revealed hyperactivity in right dorsolateral superior frontal gyrus, left thalamus and right fusiform gyrus, while hypoactivity in left temporal pole, left insula, left precentral gyrus, bilateral precuneus and paracentral lobules, right supplementary motor area and middle frontal gyrus. The multimodal meta-analysis identified conjoint anatomic and functional changes in left precentral gyrus, bilateral supplementary motor areas and paracentral lobules, right inferior occipital gyrus and fusiform gyrus. Conclusions The patterns of conjoint and dissociated gray matter alterations identified in the meta-analysis may enhance our understanding of the pathophysiological mechanisms underlying iBSP.
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Li Q, Lv X, Jin F, Liao K, Gao L, Xu J. Associations of Polygenic Risk Score for Late-Onset Alzheimer's Disease With Biomarkers. Front Aging Neurosci 2022; 14:849443. [PMID: 35493930 PMCID: PMC9047857 DOI: 10.3389/fnagi.2022.849443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Late-onset Alzheimer's disease (LOAD) is a common irreversible neurodegenerative disease with heterogeneous genetic characteristics. Identifying the biological biomarkers with the potential to predict the conversion from normal controls to LOAD is clinically important for early interventions of LOAD and clinical treatment. The polygenic risk score for LOAD (AD-PRS) has been reported the potential possibility for reliably identifying individuals with risk of developing LOAD recently. To investigate the external phenotype changes resulting from LOAD and the underlying etiology, we summarize the comprehensive associations of AD-PRS with multiple biomarkers, including neuroimaging, cerebrospinal fluid and plasma biomarkers, cardiovascular risk factors, cognitive behavior, and mental health. This systematic review helps improve the understanding of the biomarkers with potential predictive value for LOAD and further optimizing the prediction and accurate treatment of LOAD.
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Affiliation(s)
- Qiaojun Li
- School of Information Engineering, Tianjin University of Commerce, Tianjin, China
| | - Xingping Lv
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Fei Jin
- Department of Molecular Imaging, Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Kun Liao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Liyuan Gao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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25
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Volume, density, and thickness brain abnormalities in mild cognitive impairment: an ALE meta-analysis controlling for age and education. Brain Imaging Behav 2022; 16:2335-2352. [PMID: 35416608 DOI: 10.1007/s11682-022-00659-0] [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] [Accepted: 03/06/2022] [Indexed: 11/02/2022]
Abstract
Prior meta-analyses have provided important information regarding which brain areas are structurally compromised in individuals with mild cognitive impairment (MCI). These studies have not however separated volume, density, and thickness, controlled for important demographic influences, considered null findings, or recognized studies indicating increased brain volumes in MCI individuals. Furthermore, there is a question as to whether deficits extend into cortical regions, and also into the thalamus. This study aims to address these issues using activation likelihood estimation (ALE) analyses with a sample size more than twice that of prior meta-analyses. A total of 71 studies were identified and entered into the ALE analysis which consisted of 2262 with MCI and 1902 healthy controls. Three major clusters were identified showing decreased gray matter volume in the MCI group compared to controls, with the most salient decreases being in the hippocampus, parahippocampal gyrus, and the amygdala. Reduced thalamic volume was also observed, but to a lesser extent. Density was reduced in the left hippocampus, while thickness was reduced in the uncus. No significant cluster emerged from an ALE meta-analysis of studies finding volume increases in MCI individuals. While the MCI group was significantly older and less educated than controls, controlling for these factors still resulted in significant, albeit attenuated findings. These results support hippocampal and parahippocampal deficits in MCI, and further highlight the amygdala, thalamus, and uncus as other areas to be considered in future MCI studies.
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26
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Wang W, Kong W, Wang S, Wei K. Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis. J Mol Neurosci 2022; 72:841-865. [PMID: 35080765 DOI: 10.1007/s12031-021-01963-y] [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: 08/31/2021] [Accepted: 12/29/2021] [Indexed: 12/01/2022]
Abstract
Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.
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Affiliation(s)
- Wenbo Wang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
| | - Wei Kong
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China.
| | - Shuaiqun Wang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
| | - Kai Wei
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China
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27
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Huang X, Lin J, Shang H, Yang J. Voxel-based meta-analysis of gray matter abnormalities in idiopathic dystonia. J Neurol 2022; 269:2862-2873. [PMID: 35013788 DOI: 10.1007/s00415-022-10961-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/03/2022] [Accepted: 01/04/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Neuroimaging studies have reported gray matter changes in patients with idiopathic dystonia but with considerable variations. Here, we aimed to investigate the convergence of dystonia-related gray matter changes across studies. METHODS The whole brain voxel-based morphometry studies comparing idiopathic dystonia and healthy controls were systematically searched in the PubMed, Web of Science and Embase. Meta-analysis of gray matter changes was performed using the anisotropic effect size-based signed differential mapping. RESULTS Twenty-eight studies comparing 701 idiopathic dystonia patients and 712 healthy controls were included in the meta-analysis. Compared to healthy controls, idiopathic dystonia patients showed increased gray matter in bilateral precentral and postcentral gyri, bilateral putamen and pallidum, right insula, and left supramarginal gyrus, while decreased gray matter in bilateral temporal poles, bilateral supplementary motor areas, right angular gyrus, inferior parietal gyrus and precuneus, left insula and inferior frontal gyrus. These findings remained robust in the jackknife sensitivity analysis, and no significant heterogeneity was detected. Subgroup analyses of different phenotypes of dystonia were performed to further confirm the above findings. CONCLUSION The meta-analysis showed that consistent widespread gray matter abnormalities were shared in different subtypes of idiopathic dystonia and were not restricted to the corticostriatal circuits.
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Affiliation(s)
- Xiang Huang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Junyu Lin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huifang Shang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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28
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Talwar P, Kushwaha S, Chaturvedi M, Mahajan V. Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease. Clin Neuroradiol 2021; 31:953-967. [PMID: 34297137 DOI: 10.1007/s00062-021-01057-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Accepted: 06/18/2021] [Indexed: 10/20/2022]
Abstract
Alzheimer's disease (AD) is a heterogeneous progressive neurocognitive disorder. Although different neuroimaging modalities have been used for the identification of early diagnostic and prognostic factors of AD, there is no consolidated view of the findings from the literature. Here, we aim to provide a comprehensive account of different neural correlates of cognitive dysfunction via magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI) (resting-state and task-related), positron emission tomography (PET) and magnetic resonance spectroscopy (MRS) modalities across the cognitive groups i.e., normal cognition, mild cognitive impairment (MCI), and AD. A total of 46 meta-analyses met the inclusion criteria, including relevance to MCI, and/or AD along with neuroimaging modality used with quantitative and/or functional data. Volumetric MRI identified early anatomical changes involving transentorhinal cortex, Brodmann area 28, followed by the hippocampus, which differentiated early AD from healthy subjects. A consistent pattern of disruption in the bilateral precuneus along with the medial temporal lobe and limbic system was observed in fMRI, while DTI substantiated the observed atrophic alterations in the corpus callosum among MCI and AD cases. Default mode network hypoconnectivity in bilateral precuneus (PCu)/posterior cingulate cortices (PCC) and hypometabolism/hypoperfusion in inferior parietal lobules and left PCC/PCu was evident. Molecular imaging revealed variable metabolite concentrations in PCC. In conclusion, the use of different neuroimaging modalities together may lead to identification of an early diagnostic and/or prognostic biomarker for AD.
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Affiliation(s)
- Puneet Talwar
- Department of Neurology, Institute of Human Behaviour and Allied Sciences (IHBAS), 110095, Dilshad Garden, Delhi, India.
| | - Suman Kushwaha
- Department of Neurology, Institute of Human Behaviour and Allied Sciences (IHBAS), 110095, Dilshad Garden, Delhi, India.
| | - Monali Chaturvedi
- Department of Neuroradiology, Institute of Human Behaviour and Allied Sciences (IHBAS), 110095, Dilshad Garden, Delhi, India
| | - Vidur Mahajan
- Centre for Advanced Research in Imaging, Neuroscience and Genomics (CARING), Mahajan Imaging, New Delhi, India
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29
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Du X, Zhang Y, Zhao Q, Qin W, Ma G, Fu J, Zhang Q. Effects of INSR genetic polymorphism on hippocampal volume and episodic memory in chinese type 2 diabetes. Acta Diabetol 2021; 58:1471-1480. [PMID: 34085146 DOI: 10.1007/s00592-021-01750-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 05/26/2021] [Indexed: 11/28/2022]
Abstract
AIMS We aimed to investigate the effect of the type 2 diabetes-specific insulin/IGF signaling genetic variants on the hippocampal volume and their relationships with episodic memory in Chinese patients with type 2 diabetes. METHODS Analysis of variance was used to evaluate the genotype-by-diagnosis interaction effect on hippocampal volume in Chinese participants (109 patients with type 2 diabetes, 116 healthy controls). Mediation analysis was performed to test whether the hippocampal volume would mediate the association between genotype and episodic memory in patients with type 2 diabetes. RESULTS INSR (rs8101064) exhibited a significant genotype-by-diagnosis interaction effect on the bilateral hippocampal volumes (left, P = 0.020; right, P = 0.004, PFDR < 0.05). The T allele carriers exhibited smaller bilateral hippocampal volumes than the CC homozygotes in patients with type 2 diabetes (left, P = 0.004; right, P = 0.002). Mediation analysis revealed the significant mediation effect of the left hippocampal volume on the association between INSR (rs8101064) genetic polymorphism and the short- and long-term memory scores in patients with type 2 diabetes (short-term memory: 95% CI, -2.716, -0.266; long-term memory: 95% CI, -0.823, -0.103). CONCLUSIONS Hyperglycemia exposure and INSR (rs8101064) genetic polymorphism had an interaction effect on the hippocampal volume, and the T allele of the INSR (rs8101064) may serve as a risk factor for the decreased hippocampal volume in Chinese patients with type 2 diabetes. The left hippocampal volume mediated the effect of INSR (rs8101064) genetic polymorphism on episodic memory in Chinese patients with type 2 diabetes, which provided a biological pathway for understanding how the INSR (rs8101064) genetic polymorphism affects episodic memory in type 2 diabetes.
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Affiliation(s)
- Xin Du
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Yang Zhang
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Qiuyue Zhao
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Wen Qin
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Guangyang Ma
- Department of Radiology, Tianjin Medical University Metabolic Diseases Hospital, Tianjin, 300060, China
| | - Jilian Fu
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China
| | - Quan Zhang
- Department of Medical imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin, 300052, China.
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30
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An N, Fu Y, Shi J, Guo HN, Yang ZW, Li YC, Li S, Wang Y, Yao ZJ, Hu B. Synergistic Effects of APOE and CLU May Increase the Risk of Alzheimer's Disease: Acceleration of Atrophy in the Volumes and Shapes of the Hippocampus and Amygdala. J Alzheimers Dis 2021; 80:1311-1327. [PMID: 33682707 DOI: 10.3233/jad-201162] [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: 02/07/2023]
Abstract
BACKGROUND The volume loss of the hippocampus and amygdala in non-demented individuals has been reported to increase the risk of developing Alzheimer's disease (AD). Many neuroimaging genetics studies mainly focused on the individual effects of APOE and CLU on neuroimaging to understand their neural mechanisms, whereas their synergistic effects have been rarely studied. OBJECTIVE To assess whether APOE and CLU have synergetic effects, we investigated the epistatic interaction and combined effects of the two genetic variants on morphological degeneration of hippocampus and amygdala in the non-demented elderly at baseline and 2-year follow-up. METHODS Besides the widely-used volume indicator, the surface-based morphometry method was also adopted in this study to evaluate shape alterations. RESULTS Our results showed a synergistic effect of homozygosity for the CLU risk allele C in rs11136000 and APOEɛ4 on the hippocampal and amygdalar volumes during a 2-year follow-up. Moreover, the combined effects of APOEɛ4 and CLU C were stronger than either of the individual effects in the atrophy progress of the amygdala. CONCLUSION These findings indicate that brain morphological changes are caused by more than one gene variant, which may help us to better understand the complex endogenous mechanism of AD.
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Affiliation(s)
- Na An
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yu Fu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Han-Ning Guo
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zheng-Wu Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yong-Chao Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Shan Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Yin Wang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Zhi-Jun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.,Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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31
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Gao L, Gu L, Shu H, Chen J, Zhu J, Wang B, Shi Y, Song R, Li K, Li X, Zhang H, Zhang H, Zhang Z. The reduced left hippocampal volume related to the delayed P300 latency in amnestic mild cognitive impairment. Psychol Med 2021; 51:2054-2062. [PMID: 32308167 DOI: 10.1017/s0033291720000811] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Amnestic mild cognitive impairment (aMCI) is characterized by delayed P300 latency and reduced grey matter (GM) volume, respectively. The relationship between the features in aMCI is unclear. This study was to investigate the relationship between the altered P300 latency and the GM volume in aMCI. METHODS Thirty-four aMCI and 34 well-matched normal controls (NC) were studied using electroencephalogram during a visual oddball task and scanned with MRI. Both tests were finished in the same day. RESULTS As compared with the NC group, the aMCI group exhibited delayed P300 latency in parietal cortex and reduced GM volumes in bilateral temporal pole and left hippocampus/parahippocampal gyrus. A remarkable negative correlation was found between delayed P300 latency and reduced left hippocampal volume only in the aMCI group. Interestingly, the mediating analysis found P300 latency significantly mediated the association between right supramarginal gyrus volume and information processing speed indicated by Stroop Color and Word Test A scores. CONCLUSIONS The association between delayed P300 latency and reduced left hippocampal volume in aMCI subjects suggests that reduced left hippocampal volume may be the potential structural basis of delayed P300 latency.
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Affiliation(s)
- Lijuan Gao
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Lihua Gu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Jiu Chen
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Jianli Zhu
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan453003, China
| | - Bi Wang
- Department of Radiology, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan453002, China
| | - Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Ruize Song
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
| | - Kun Li
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan453003, China
| | - Xianrui Li
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan453003, China
| | - Haisan Zhang
- Department of Radiology, Henan Mental Hospital, the Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan453002, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan453003, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu210009, China
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan453003, China
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Ding Y, Zhao K, Che T, Du K, Sun H, Liu S, Zheng Y, Li S, Liu B, Liu Y. Quantitative Radiomic Features as New Biomarkers for Alzheimer's Disease: An Amyloid PET Study. Cereb Cortex 2021; 31:3950-3961. [PMID: 33884402 DOI: 10.1093/cercor/bhab061] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/22/2021] [Indexed: 12/20/2022] Open
Abstract
Growing evidence indicates that amyloid-beta (Aβ) accumulation is one of the most common neurobiological biomarkers in Alzheimer's disease (AD). The primary aim of this study was to explore whether the radiomic features of Aβ positron emission tomography (PET) images are used as predictors and provide a neurobiological foundation for AD. The radiomics features of Aβ PET imaging of each brain region of the Brainnetome Atlas were computed for classification and prediction using a support vector machine model. The results showed that the area under the receiver operating characteristic curve (AUC) was 0.93 for distinguishing AD (N = 291) from normal control (NC; N = 334). Additionally, the AUC was 0.83 for the prediction of mild cognitive impairment (MCI) converting (N = 88) (vs. no conversion, N = 100) to AD. In the MCI and AD groups, the systemic analysis demonstrated that the classification outputs were significantly associated with clinical measures (apolipoprotein E genotype, polygenic risk scores, polygenic hazard scores, cerebrospinal fluid Aβ, and Tau, cognitive ability score, the conversion time for progressive MCI subjects and cognitive changes). These findings provide evidence that the radiomic features of Aβ PET images can serve as new biomarkers for clinical applications in AD/MCI, further providing evidence for predicting whether MCI subjects will convert to AD.
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Affiliation(s)
- Yanhui Ding
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Kun Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China.,Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tongtong Che
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang 110004, China
| | - Shu Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yuanjie Zheng
- School of Information Science and Engineering, Shandong Normal University, Ji'nan 250014, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100049, China.,Pazhou Lab, Guangzhou 510330, China.,School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
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33
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Zhang J, Liu Y, Lan K, Huang X, He Y, Yang F, Li J, Hu Q, Xu J, Yu H. Gray Matter Atrophy in Amnestic Mild Cognitive Impairment: A Voxel-Based Meta-Analysis. Front Aging Neurosci 2021; 13:627919. [PMID: 33867968 PMCID: PMC8044397 DOI: 10.3389/fnagi.2021.627919] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 03/02/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Voxel-based morphometry (VBM) has been widely used to investigate structural alterations in amnesia mild cognitive impairment (aMCI). However, inconsistent results have hindered our understanding of the exact neuropathology related to aMCI. Objectives: Our aim was to systematically review the literature reporting VBM on aMCI to elucidate consistent gray matter alterations, their functional characterization, and corresponding co-activation patterns. Methods: The PubMed, Web of Science, and EMBASE databases were searched for VBM studies on aMCI published from inception up to June 2020. Peak coordinates were extracted from clusters that showed significant gray matter differences between aMCI patients and healthy controls (HC). Meta-analysis was performed using seed-based d mapping with the permutation of subject images (SDM-PSI), a newly improved meta-analytic method. Functional characterization and task-based co-activation patterns using the BrainMap database were performed on significant clusters to explore their functional roles. Finally, VBM was performed based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to further support the findings. Results: A total of 31 studies with 681 aMCI patients and 837 HC were included in this systematic review. The aMCI group showed significant gray matter atrophy in the left amygdala and right hippocampus, which was consistent with results from the ADNI dataset. Functional characterization revealed that these regions were mainly associated with emotion, cognition, and perception. Further, meta-regression analysis demonstrated that gray matter atrophy in the left inferior frontal gyrus and the left angular gyrus was significantly associated with cognitive impairment in the aMCI group. Conclusions: The findings of gray matter atrophy in the left amygdala and right hippocampus are highly consistent and robust, and not only offer a better understanding of the underlying neuropathology but also provide accurate potential biomarkers for aMCI.
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Affiliation(s)
- Jinhuan Zhang
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.,Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yongfeng Liu
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Kai Lan
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Xingxian Huang
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Yuhai He
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China
| | - Fuxia Yang
- Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
| | - Jiaying Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Haibo Yu
- The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, China.,Department of Acupuncture and Moxibustion, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China
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Firbank MJ, Durcan R, O'Brien JT, Allan LM, Barker S, Ciafone J, Donaghy PC, Hamilton CA, Lawley S, Roberts G, Taylor JP, Thomas AJ. Hippocampal and insula volume in mild cognitive impairment with Lewy bodies. Parkinsonism Relat Disord 2021; 86:27-33. [PMID: 33823470 DOI: 10.1016/j.parkreldis.2021.03.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/11/2021] [Accepted: 03/11/2021] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Diagnostic criteria for prodromal dementia with Lewy bodies have recently been published. These include the use of imaging biomarkers to distinguish mild cognitive impairment with Lewy bodies (MCI-LB) from MCI due to other causes. Two potential biomarkers listed, though not formally included in the diagnostic criteria, due to insufficient evidence, are relatively preserved hippocampi, and atrophy of the insula cortex on structural brain imaging. METHODS In this report, we sought to investigate these imaging biomarkers in 105 research subjects, including well characterised groups of patients with MCI-LB (n = 38), MCI with no core features of Lewy body disease (MCI-AD; n = 36) and healthy controls (N = 31). Hippocampal and insula volumes were determined from T1 weighted structural MRI scans, using grey matter segmentation performed with SPM software. RESULTS Adjusting for age, sex and intracranial volume, there were no differences in hippocampal or insula volume between MCI-AD and MCI-LB, although in both conditions volumes were significantly reduced relative to controls. CONCLUSION Our results do not support the use of either hippocampal or insula volume to identify prodromal dementia with Lewy bodies.
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Affiliation(s)
- Michael J Firbank
- Translational and Clinical Research Institute, Newcastle University, UK.
| | - Rory Durcan
- Translational and Clinical Research Institute, Newcastle University, UK
| | | | | | - Sally Barker
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Joanna Ciafone
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Sarah Lawley
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Gemma Roberts
- Translational and Clinical Research Institute, Newcastle University, UK; Nuclear Medicine Department, Newcastle Upon Tyne Hospitals NHS Foundation Trust, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, UK
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Yang Y, Zha X, Zhang X, Ke J, Hu S, Wang X, Su Y, Hu C. Dynamics and Concordance Abnormalities Among Indices of Intrinsic Brain Activity in Individuals With Subjective Cognitive Decline: A Temporal Dynamics Resting-State Functional Magnetic Resonance Imaging Analysis. Front Aging Neurosci 2021; 12:584863. [PMID: 33568986 PMCID: PMC7868384 DOI: 10.3389/fnagi.2020.584863] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Accepted: 12/03/2020] [Indexed: 11/13/2022] Open
Abstract
Individuals with subjective cognitive decline (SCD) are more likely to develop into Alzheimer disease (AD) in the future. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown alterations of intrinsic brain activity (IBA) in SCD individuals. However, rs-fMRI studies to date have mainly focused on static characteristics of IBA, with few studies reporting dynamics- and concordance-related changes in IBA indices in SCD individuals. To investigate these aberrant changes, a temporal dynamic analysis of rs-fMRI data was conducted on 94 SCD individuals (71.07 ± 6.18 years, 60 female), 75 (74.36 ± 8.42 years, 35 female) mild cognitive impairment (MCI) patients, and 82 age-, gender-, and education-matched controls (NCs; 73.88 ± 7.40 years, 49 female) from the Alzheimer's Disease Neuroimaging Initiative database. The dynamics and concordance of the rs-fMRI indices were calculated. The results showed that SCD individuals had a lower amplitude of low-frequency fluctuations dynamics in bilateral hippocampus (HP)/parahippocampal gyrus (PHG)/fusiform gyrus (FG) and bilateral cerebellum, a lower fractional amplitude of low-frequency fluctuation dynamics in bilateral precuneus (PreCu) and paracentral lobule, and a lower regional homogeneity dynamics in bilateral cerebellum, vermis, and left FG compared with the other two groups, whereas those in MCI patients were higher (Gaussian random field-corrected, voxel-level P < 0.001, cluster-level P < 0.05). Furthermore, SCD individuals had higher concordance in bilateral HP/PHG/FG, temporal lobe, and left midcingulate cortex than NCs, but those in MCI were lower than those in NCs. No correlation between concordance values and neuropsychological scale scores was found. SCD individuals showed both dynamics and concordance-related alterations in IBA, which indicates a compensatory mechanism in SCD individuals. Temporal dynamics analysis offers a novel approach to capturing brain alterations in individuals with SCD.
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Affiliation(s)
- Yiwen Yang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Xinyi Zha
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Xiaodong Zhang
- Department of Radiology, Tianjin First Central Hospital, Tianjin, China
| | - Jun Ke
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Su Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Ximing Wang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Yunyan Su
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China
- Institute of Medical Imaging, Soochow University, Suzhou, China
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Rostampour M, Noori K, Heidari M, Fadaei R, Tahmasian M, Khazaie H, Zarei M. White matter alterations in patients with obstructive sleep apnea: a systematic review of diffusion MRI studies. Sleep Med 2020; 75:236-245. [DOI: 10.1016/j.sleep.2020.06.024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 06/17/2020] [Accepted: 06/18/2020] [Indexed: 12/25/2022]
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Chen S, Xu W, Xue C, Hu G, Ma W, Qi W, Dong L, Lin X, Chen J. Voxelwise Meta-Analysis of Gray Matter Abnormalities in Mild Cognitive Impairment and Subjective Cognitive Decline Using Activation Likelihood Estimation. J Alzheimers Dis 2020; 77:1495-1512. [PMID: 32925061 DOI: 10.3233/jad-200659] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Background: Voxel-based morphometry studies have not yielded consistent results among patients with mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Objective: Therefore, we aimed to conduct a meta-analysis of gray matter (GM) abnormalities acquired from these studies to determine their respective neuroanatomical changes. Methods: We systematically searched for voxel-based whole-brain morphometry studies that compared MCI or SCD subjects with healthy controls in PubMed, Web of Science, and EMBASE databases. We used the coordinate-based method of activation likelihood estimation to determine GM changes in SCD, MCI, and MCI sub-groups (amnestic MCI and non-amnestic MCI). Results: A total of 45 studies were included in our meta-analysis. In the MCI group, we found structural atrophy of the bilateral hippocampus, parahippocampal gyrus (PHG), amygdala, right lateral globus pallidus, right insula, and left middle temporal gyrus. The aMCI group exhibited GM atrophy in the bilateral hippocampus, PHG, and amygdala. The naMCI group presented with structural atrophy in the right putamen, right insula, right precentral gyrus, left medial/superior frontal gyrus, and left anterior cingulate. The right lingual gyrus, right cuneus, and left medial frontal gyrus were atrophic GM regions in the SCD group. Conclusion: Our meta-analysis identified unique patterns of neuroanatomical alternations in both the MCI and SCD group. Structural changes in SCD patients provide new evidence for the notion that subtle impairment of visual function, perception, and cognition may be related to early signs of cognitive impairment. In addition, our findings provide a foundation for future targeted interventions at different stages of preclinical Alzheimer’s disease.
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Affiliation(s)
- Shanshan Chen
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenwen Xu
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Guanjie Hu
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenying Ma
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Lin Dong
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xingjian Lin
- Department of Neurology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiu Chen
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, China
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Mohajer B, Abbasi N, Mohammadi E, Khazaie H, Osorio RS, Rosenzweig I, Eickhoff CR, Zarei M, Tahmasian M, Eickhoff SB. Gray matter volume and estimated brain age gap are not linked with sleep-disordered breathing. Hum Brain Mapp 2020; 41:3034-3044. [PMID: 32239749 PMCID: PMC7336142 DOI: 10.1002/hbm.24995] [Citation(s) in RCA: 18] [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: 10/02/2019] [Revised: 02/29/2020] [Accepted: 03/09/2020] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD) and sleep-disordered breathing (SDB) are prevalent conditions with a rising burden. It is suggested that SDB may contribute to cognitive decline and advanced aging. Here, we assessed the link between self-reported SDB and gray matter volume in patients with AD, mild cognitive impairment (MCI) and healthy controls (HCs). We further investigated whether SDB was associated with advanced brain aging. We included a total of 330 participants, divided based on self-reported history of SDB, and matched across diagnoses for age, sex and presence of the Apolipoprotein E4 allele, from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Gray-matter volume was measured using voxel-wise morphometry and group differences in terms of SDB, cognitive status, and their interaction were assessed. Further, using an age-prediction model fitted on gray-matter data of external datasets, we predicted study participants' age from their structural images. Cognitive decline and advanced age were associated with lower gray matter volume in various regions, particularly in the bilateral temporal lobes. Brains age was well predicted from the morphological data in HCs and, as expected, elevated in MCI and particularly in AD subjects. However, there was neither a significant difference between regional gray matter volume in any diagnostic group related to the SDB status, nor in SDB-by-cognitive status interaction. Moreover, we found no difference in estimated chronological age gap related to SDB, or by-cognitive status interaction. Contrary to our hypothesis, we were not able to find a general or a diagnostic-dependent association of SDB with either gray-matter volumetric or brain aging.
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Affiliation(s)
- Bahram Mohajer
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
- Non‐Communicable Diseases Research CenterEndocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTehranIran
| | - Nooshin Abbasi
- McConnell Brain Imaging CentreMontreal Neurological Institute, McGill UniversityMontrealQuebecCanada
| | - Esmaeil Mohammadi
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
- Non‐Communicable Diseases Research CenterEndocrinology and Metabolism Population Sciences Institute, Tehran University of Medical SciencesTehranIran
| | - Habibolah Khazaie
- Sleep Disorders Research CenterKermanshah University of Medical SciencesKermanshahIran
| | - Ricardo S. Osorio
- Department of Psychiatry, Center for Brain Health, NYU Langone Medical CenterNew YorkNew YorkUSA
- Nathan S. Kline Institute for Psychiatric ResearchNew YorkNew YorkUSA
| | - Ivana Rosenzweig
- Sleep Disorders CentreGuy's and St Thomas' Hospital, GSTT NHSLondonUK
- Sleep and Brain Plasticity Centre, Department of NeuroimagingIOPPN, King's College LondonLondonUK
| | - Claudia R. Eickhoff
- Institute of Neuroscience and Medicine (INM‐1; INM‐7), Research Center JülichJülichGermany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine UniversityDüsseldorfGermany
| | - Mojtaba Zarei
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti UniversityTehranIran
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine (INM‐1; INM‐7), Research Center JülichJülichGermany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich‐Heine UniversityDüsseldorfGermany
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Rashidi-Ranjbar N, Rajji TK, Kumar S, Herrmann N, Mah L, Flint AJ, Fischer CE, Butters MA, Pollock BG, Dickie EW, Anderson JAE, Mulsant BH, Voineskos AN. Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition. Neuropsychopharmacology 2020; 45:1567-1578. [PMID: 32422643 PMCID: PMC7360554 DOI: 10.1038/s41386-020-0715-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/15/2020] [Accepted: 05/11/2020] [Indexed: 12/11/2022]
Abstract
A history of depression is a risk factor for dementia. Despite strong epidemiologic evidence, the pathways linking depression and dementia remain unclear. We assessed structural brain alterations in white and gray matter of frontal-executive and corticolimbic circuitries in five groups of older adults putatively at-risk for developing dementia- remitted depression (MDD), non-amnestic MCI (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. We also examined two other groups: non-psychiatric ("healthy") controls (HC) and individuals with Alzheimer's dementia (AD). Magnetic resonance imaging (MRI) data were acquired on the same 3T scanner. Following quality control in these seven groups, from diffusion-weighted imaging (n = 300), we compared white matter fractional anisotropy (FA), mean diffusivity (MD), and from T1-weighted imaging (n = 333), subcortical volumes and cortical thickness in frontal-executive and corticolimbic regions of interest (ROIs). We also used exploratory graph theory analysis to compare topological properties of structural covariance networks and hub regions. We found main effects for diagnostic group in FA, MD, subcortical volume, and cortical thickness. These differences were largely due to greater deficits in the AD group and to a lesser extent aMCI compared with other groups. Graph theory analysis revealed differences in several global measures among several groups. Older individuals with remitted MDD and naMCI did not have the same white or gray matter changes in the frontal-executive and corticolimbic circuitries as those with aMCI or AD, suggesting distinct neural mechanisms in these disorders. Structural covariance global metrics suggested a potential difference in brain reserve among groups.
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Affiliation(s)
- Neda Rashidi-Ranjbar
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sanjeev Kumar
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Nathan Herrmann
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Linda Mah
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Baycrest Health Sciences, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Alastair J Flint
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- University Health Network, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Corinne E Fischer
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Research, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bruce G Pollock
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - John A E Anderson
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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Burke MJ, Joutsa J, Cohen AL, Soussand L, Cooke D, Burstein R, Fox MD. Mapping migraine to a common brain network. Brain 2020; 143:541-553. [PMID: 31919494 DOI: 10.1093/brain/awz405] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 10/15/2019] [Accepted: 11/11/2019] [Indexed: 11/14/2022] Open
Abstract
Inconsistent findings from migraine neuroimaging studies have limited attempts to localize migraine symptomatology. Novel brain network mapping techniques offer a new approach for linking neuroimaging findings to a common neuroanatomical substrate and localizing therapeutic targets. In this study, we attempted to determine whether neuroanatomically heterogeneous neuroimaging findings of migraine localize to a common brain network. We used meta-analytic coordinates of decreased grey matter volume in migraineurs as seed regions to generate resting state functional connectivity network maps from a normative connectome (n = 1000). Network maps were overlapped to identify common regions of connectivity across all coordinates. Specificity of our findings was evaluated using a whole-brain Bayesian spatial generalized linear mixed model and a region of interest analysis with comparison groups of chronic pain and a neurologic control (Alzheimer's disease). We found that all migraine coordinates (11/11, 100%) were negatively connected (t ≥ ±7, P < 10-6 family-wise error corrected for multiple comparisons) to a single location in left extrastriate visual cortex overlying dorsal V3 and V3A subregions. More than 90% of coordinates (10/11) were also positively connected with bilateral insula and negatively connected with the hypothalamus. Bayesian spatial generalized linear mixed model whole-brain analysis identified left V3/V3A as the area with the most specific connectivity to migraine coordinates compared to control coordinates (voxel-wise probability of ≥90%). Post hoc region of interest analyses further supported the specificity of this finding (ANOVA P = 0.02; pairwise t-tests P = 0.03 and P = 0.003, respectively). In conclusion, using coordinate-based network mapping, we show that regions of grey matter volume loss in migraineurs localize to a common brain network defined by connectivity to visual cortex V3/V3A, a region previously implicated in mechanisms of cortical spreading depression in migraine. Our findings help unify migraine neuroimaging literature and offer a migraine-specific target for neuromodulatory treatment.
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Affiliation(s)
- Matthew J Burke
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.,Neuropsychiatry Program, Department of Psychiatry, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Juho Joutsa
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Turku Brain and Mind Center, Department of Neurology, University of Turku, Turku, Finland.,Division of Clinical Neurosciences and Turku PET Center, Turku University Hospital, Turku, Finland
| | - Alexander L Cohen
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Louis Soussand
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Danielle Cooke
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Rami Burstein
- Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
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Abstract
Abstract. MoCA is a short cognitive screening tool. We examined the relationship of MoCA performance to white matter integrity, gray matter volume, and surface-based measurements at normal aging in a study in which older and younger cognitively unaffected subjects participated. The sample was split according to MoCA performance, and the data were analyzed using a general linear model (Age × MoCA). We found effects in the expected direction for all methods. The main effects on age and performance as well as interactions occurred for regions associated with aging, pathological and nonpathological. Older low-performing subjects showed structural deficits compared to older high-performing subjects. Therefore, the global index of cognitive status reflects relevant features of the brain structure.
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Affiliation(s)
- Gebhard Sammer
- Cognitive NeuroScience at the Centre for Psychiatry, University of Gießen, Germany
- Department of Psychology, University of Gießen, Germany
- Bender Institute of Neuroimaging, University of Gießen, Germany
| | - Eva Lenz
- Cognitive NeuroScience at the Centre for Psychiatry, University of Gießen, Germany
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Chagué P, Marro B, Fadili S, Houot M, Morin A, Samper-González J, Beunon P, Arrivé L, Dormont D, Dubois B, Teichmann M, Epelbaum S, Colliot O. Radiological classification of dementia from anatomical MRI assisted by machine learning-derived maps. J Neuroradiol 2020; 48:412-418. [PMID: 32407907 DOI: 10.1016/j.neurad.2020.04.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 04/25/2020] [Accepted: 04/26/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND AND PURPOSE Many artificial intelligence tools are currently being developed to assist diagnosis of dementia from magnetic resonance imaging (MRI). However, these tools have so far been difficult to integrate in the clinical routine workflow. In this work, we propose a new simple way to use them and assess their utility for improving diagnostic accuracy. MATERIALS AND METHODS We studied 34 patients with early-onset Alzheimer's disease (EOAD), 49 with late-onset AD (LOAD), 39 with frontotemporal dementia (FTD) and 24 with depression from the pre-existing cohort CLIN-AD. Support vector machine (SVM) automatic classifiers using 3D T1 MRI were trained to distinguish: LOAD vs. Depression, FTD vs. LOAD, EOAD vs. Depression, EOAD vs. FTD. We extracted SVM weight maps, which are tridimensional representations of discriminant atrophy patterns used by the classifier to take its decisions and we printed posters of these maps. Four radiologists (2 senior neuroradiologists and 2 unspecialized junior radiologists) performed a visual classification of the 4 diagnostic pairs using 3D T1 MRI. Classifications were performed twice: first with standard radiological reading and then using SVM weight maps as a guide. RESULTS Diagnostic performance was significantly improved by the use of the weight maps for the two junior radiologists in the case of FTD vs. EOAD. Improvement was over 10 points of diagnostic accuracy. CONCLUSION This tool can improve the diagnostic accuracy of junior radiologists and could be integrated in the clinical routine workflow.
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Affiliation(s)
- Pierre Chagué
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France; Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France
| | - Béatrice Marro
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Sarah Fadili
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Marion Houot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France
| | - Alexandre Morin
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Jorge Samper-González
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France
| | - Paul Beunon
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Lionel Arrivé
- Department of radiology, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Didier Dormont
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Marc Teichmann
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Stéphane Epelbaum
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France
| | - Olivier Colliot
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, 75013 Paris, France; Inria, Aramis-project team, Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, 75013 Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), 75013 Paris, France.
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Núñez C, Callén A, Lombardini F, Compta Y, Stephan-Otto C. Different Cortical Gyrification Patterns in Alzheimer's Disease and Impact on Memory Performance. Ann Neurol 2020; 88:67-80. [PMID: 32277502 DOI: 10.1002/ana.25741] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 04/03/2020] [Accepted: 04/04/2020] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The study of cortical gyrification in Alzheimer's disease (AD) could help to further understanding of the changes undergone in the brain during neurodegeneration. Here, we aimed to study brain gyrification differences between healthy controls (HC), mild cognitive impairment (MCI) patients, and AD patients, and explore how cerebral gyrification patterns were associated with memory and other cognitive functions. METHODS We applied surface-based morphometry techniques in 2 large, independent cross-sectional samples, obtained from the Alzheimer's Disease Neuroimaging Initiative project. Both samples, encompassing a total of 1,270 participants, were analyzed independently. RESULTS Unexpectedly, we found that AD patients presented a more gyrificated entorhinal cortex than HC. Conversely, the insular cortex of AD patients was hypogyrificated. A decrease in the gyrification of the insular cortex was also found in older HC participants as compared with younger HC, which argues against the specificity of this finding in AD. However, an increased degree of folding of the insular cortex was specifically associated with better memory function and semantic fluency, only in AD patients. Overall, MCI patients presented an intermediate gyrification pattern. All these findings were consistently observed in the two samples. INTERPRETATION The marked atrophy of the medial temporal lobe observed in AD patients may explain the increased folding of the entorhinal cortex. We additionally speculate regarding alternative mechanisms that may also alter its folding. The association between increased gyrification of the insular cortex and memory function, specifically observed in AD, could be suggestive of compensatory mechanisms to overcome the loss of memory function. ANN NEUROL 2020 ANN NEUROL 2020;88:67-80.
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Affiliation(s)
- Christian Núñez
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain.,Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Antonio Callén
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain.,Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Federica Lombardini
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain.,Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
| | - Yaroslau Compta
- Parkinson's Disease & Movement Disorders Unit, Neurology Service, Hospital Clínic de Barcelona & Maria de Maeztu Excellence Center Institute of Neuroscience, University of Barcelona, Barcelona, Spain.,Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Spain
| | - Christian Stephan-Otto
- Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain.,Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
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Costumero V, d'Oleire Uquillas F, Diez I, Andorrà M, Basaia S, Bueichekú E, Ortiz-Terán L, Belloch V, Escudero J, Ávila C, Sepulcre J. Distance disintegration delineates the brain connectivity failure of Alzheimer's disease. Neurobiol Aging 2020; 88:51-60. [PMID: 31941578 PMCID: PMC7085436 DOI: 10.1016/j.neurobiolaging.2019.12.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 01/03/2023]
Abstract
Alzheimer's disease (AD) is associated with brain network dysfunction. Network-based investigations of brain connectivity have mainly focused on alterations in the strength of connectivity; however, the network breakdown in AD spectrum is a complex scenario in which multiple pathways of connectivity are affected. To integrate connectivity changes that occur under AD-related conditions, here we developed a novel metric that computes the connectivity distance between cortical regions at the voxel level (or nodes). We studied 114 individuals with mild cognitive impairment, 24 with AD, and 27 healthy controls. Results showed that areas of the default mode network, salience network, and frontoparietal network display a remarkable network separation, or greater connectivity distances, from the rest of the brain. Furthermore, this greater connectivity distance was associated with lower global cognition. Overall, the investigation of AD-related changes in paths and distances of connectivity provides a novel framework for characterizing subjects with cognitive impairment; a framework that integrates the overall network topology changes of the brain and avoids biases toward unreferenced connectivity effects.
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Affiliation(s)
- Víctor Costumero
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Catalonia, Spain; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | | | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neurotechnology Laboratory, Tecnalia Health Department, Basque Country, Spain
| | - Magi Andorrà
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center of Neuroimmunology, Department of Neurology, Hospital Clinic of Barcelona, Institut d'Investigacions Biomèdiques August Pi Sunyer (IDIBAPS), University of Barcelona, Barcelona, Catalonia, Spain
| | - Silvia Basaia
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuroimaging Research Unit Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Laura Ortiz-Terán
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Joaquin Escudero
- Department of Neurology, General Hospital of Valencia, Valencia, Valencian Community, Spain
| | - César Ávila
- Neuropsychology and Functional Neuroimaging Group, Department of basic Psychology, University Jaume I, Castellón, Valencian Community, Spain
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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45
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Wang S, Yin H, Li G, Jia Y, Leng M, Meng Q, Wang C, Chen L. Detection of Mild Cognitive Impairment Based on Virtual Reality: A Scoping Review. Curr Alzheimer Res 2020; 17:126-140. [PMID: 32183674 DOI: 10.2174/1567205017666200317100421] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Revised: 12/09/2019] [Accepted: 02/04/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND To delay the decline in cognition and reduce the incidence of dementia, the precise detection of Mild Cognitive Impairment (MCI) is necessary. The application of Virtual Reality (VR) technology in this detection can overcome the shortage of traditional paper-and-pencil tests. OBJECTIVE This review aimed to summarize the research progress of the detection of MCI using VR. METHODS Eight databases from their inception to November 19, 2019, were systematically searched for studies applying VR in the detection of MCI. A thematic analysis was conducted according to the specific detection purpose and the main corresponding cognitive domains assessed were summarized; characteristics of the VR applications were also summarized. RESULTS Twenty-eight studies were finally included. The detection purposes included discrimination between healthy controls and those with MCI, discrimination between aMCI subtypes, detection of MCI patients at risk of Alzheimer's Disease (AD), and discrimination between MCI and AD. VR tasks assessing spatial memory were applicable for all detection purposes, and the assessment of combinations of memory and executive function seemed more sensitive. Executive function and intentional episodic memory could be assessed to discriminate among healthy controls, individuals with MCI and those with AD. Incidental episodic memory was effective in detecting MCI with hippocampal atrophy. The most common characteristics of the VR applications were the use of semi-immersion, joysticks or gamepad interactions and simple, one-time behavioral assessments. CONCLUSION VR applications are promising in the detection of MCI, but further research is needed for clinical use.
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Affiliation(s)
- Shuo Wang
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Huiru Yin
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Guichen Li
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Yong Jia
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Minmin Leng
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Qiuyan Meng
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
| | - Chunyan Wang
- Senior Officials Inpatient Ward, First Hospital of Jilin University, Changchun, 130000, China
| | - Li Chen
- School of Nursing, Jilin University, 965 Xinjiang Street, Changchun, 130000, China
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Giannakopoulos P, Rodriguez C, Montandon ML, Garibotto V, Haller S, Herrmann FR. Less agreeable, better preserved? A PET amyloid and MRI study in a community-based cohort. Neurobiol Aging 2020; 89:24-31. [PMID: 32169357 DOI: 10.1016/j.neurobiolaging.2020.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 02/07/2020] [Accepted: 02/07/2020] [Indexed: 11/29/2022]
Abstract
The relationship between personality profiles and brain integrity in old age is still a matter of debate. We examined the association between Big Five factor and facet scores and MRI brain volume changes on a 54-month follow-up in 65 elderly controls with 3 neurocognitive assessments (baseline, 18 months, and 54 months), structural brain MRI (baseline and 54 months), brain amyloid PET during follow-up, and APOE genotyping. Personality was assessed with the Neuroticism Extraversion Openness Personality Inventory-Revised. Regression models were used to identify predictors of volume loss including time, age, sex, personality, amyloid load, presence of APOE ε4 allele, and cognitive evolution. Lower agreeableness factor scores (and 4 of its facets) were associated with lower volume loss in the hippocampus, entorhinal cortex, amygdala, mesial temporal lobe, and precuneus bilaterally. Higher openness factor scores (and 2 of its facets) were also associated with lower volume loss in the left hippocampus. Our findings persisted when adjusting for confounders in multivariable models. These data suggest that the combination of low agreeableness and high openness is an independent predictor of better preservation of brain volume in areas vulnerable to neurodegeneration.
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Affiliation(s)
- Panteleimon Giannakopoulos
- Department of Psychiatry, University of Geneva, Geneva, Switzerland; Medical Direction, Geneva University Hospitals, Geneva, Switzerland.
| | - Cristelle Rodriguez
- Department of Psychiatry, University of Geneva, Geneva, Switzerland; Medical Direction, Geneva University Hospitals, Geneva, Switzerland
| | - Marie-Louise Montandon
- Department of Psychiatry, University of Geneva, Geneva, Switzerland; Medical Direction, Geneva University Hospitals, Geneva, Switzerland; Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, Geneva University Hospitals, Geneva, Switzerland; Faculty of Medicine of the University of Geneva, Geneva, Switzerland
| | - Sven Haller
- Faculty of Medicine of the University of Geneva, Geneva, Switzerland; CIRD - Centre d'Imagerie Rive Droite, Geneva, Switzerland; Department of Surgical Sciences, Radiology, Uppsala University, Uppsala, Sweden
| | - François R Herrmann
- Department of Rehabilitation and Geriatrics, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
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47
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Costumero V, Marin-Marin L, Calabria M, Belloch V, Escudero J, Baquero M, Hernandez M, Ruiz de Miras J, Costa A, Parcet MA, Ávila C. A cross-sectional and longitudinal study on the protective effect of bilingualism against dementia using brain atrophy and cognitive measures. ALZHEIMERS RESEARCH & THERAPY 2020; 12:11. [PMID: 31924269 PMCID: PMC6954576 DOI: 10.1186/s13195-020-0581-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 01/05/2020] [Indexed: 01/27/2023]
Abstract
Background Evidence from previous studies suggests that bilingualism contributes to cognitive reserve because bilinguals manifest the first symptoms of Alzheimer’s disease (AD) up to 5 years later than monolinguals. Other cross-sectional studies demonstrate that bilinguals show greater amounts of brain atrophy and hypometabolism than monolinguals, despite sharing the same diagnosis and suffering from the same symptoms. However, these studies may be biased by possible pre-existing between-group differences. Methods In this study, we used global parenchymal measures of atrophy and cognitive tests to investigate the protective effect of bilingualism against dementia cross-sectionally and prospectively, using a sample of bilinguals and monolinguals in the same clinical stage and matched on sociodemographic variables. Results Our results suggest that the two groups did not differ in their cognitive status at baseline, but bilinguals had less parenchymal volume than monolinguals, especially in areas related to brain atrophy in dementia. In addition, a longitudinal prospective analysis revealed that monolinguals lost more parenchyma and had more cognitive decline than bilinguals in a mean follow-up period of 7 months. Conclusion These results provide the first prospective evidence that bilingualism may act as a neuroprotective factor against dementia and could be considered a factor in cognitive reserve.
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Affiliation(s)
- Víctor Costumero
- Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain.,Neuropsychology and Functional Neuroimaging Group, University Jaume I, Castellón, Spain.,ERI Lectura, University of Valencia, Valencia, Spain
| | - Lidon Marin-Marin
- Neuropsychology and Functional Neuroimaging Group, University Jaume I, Castellón, Spain
| | - Marco Calabria
- Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain
| | | | - Joaquín Escudero
- Department of Neurology, General Hospital of Valencia, Valencia, Spain
| | - Miguel Baquero
- Neurology Unit, University and Polytechnic Hospital La Fe, Valencia, Spain
| | - Mireia Hernandez
- Department of Cognition, Development, and Educational Psychology Section of Cognitive Processes, University of Barcelona, Barcelona, Spain.,Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain
| | | | - Albert Costa
- Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain
| | - Maria-Antònia Parcet
- Neuropsychology and Functional Neuroimaging Group, University Jaume I, Castellón, Spain
| | - César Ávila
- Neuropsychology and Functional Neuroimaging Group, University Jaume I, Castellón, Spain.
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48
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Theory of mind in Alzheimer’s disease and amnestic mild cognitive impairment: a meta-analysis. Neurol Sci 2020; 41:1027-1039. [DOI: 10.1007/s10072-019-04215-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 12/20/2019] [Indexed: 12/19/2022]
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De Simone MS, De Tollis M, Fadda L, Perri R, Caltagirone C, Carlesimo GA. Lost or unavailable? Exploring mechanisms that affect retrograde memory in mild cognitive impairment and Alzheimer's disease patients. J Neurol 2019; 267:113-124. [PMID: 31571005 DOI: 10.1007/s00415-019-09559-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 09/21/2019] [Accepted: 09/24/2019] [Indexed: 12/13/2022]
Abstract
Retrograde amnesia has been largely documented in patients with amnestic mild cognitive impairment (a-MCI) and Alzheimer's disease (AD). However, it is still not clear whether ineffectiveness in recalling past acquired information reflects loss of individual memory traces or failure to access specific stored traces. We aimed to disentangle the differential contribution of storage and retrieval processes to the pattern of retrograde amnesia in these patients. This issue was investigated in 18 a-MCI and 19 AD patients who were compared to 20 healthy controls. A novel questionnaire about public events was used; it consisted of two procedures (i.e., a free recall test and a true/false recognition test). Crucial differences emerged in the way the two groups of patients performed the experimental tasks. In fact, although both a-MCI and AD patients showed a similar pattern of impairment on the free recall test, a-MCI patients were able to normalise their performance on the recognition test, thus overcoming their deficits at the time of recall. Conversely, AD patients showed both reduced free recall ability and diminished sensitivity to benefit from recognition in recalling public events. Our findings suggest that the memory processes underlying RA were different for a-MCI and AD. Deficits in remote memory are prevalently explained by impaired retrieval abilities in a-MCI and by impaired storage in AD. This distinction between retrograde amnesia due to defective trace utilisation in a-MCI and trace storage in AD is consistent with the temporal unfolding of declining anterograde memory over the course of disease progression to AD.
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Affiliation(s)
- Maria Stefania De Simone
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy.
| | - Massimo De Tollis
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy
| | - Lucia Fadda
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy.,Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Roberta Perri
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy
| | - Carlo Caltagirone
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy.,Department of Systems Medicine, Tor Vergata University, Rome, Italy
| | - Giovanni Augusto Carlesimo
- Laboratory of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, via Ardeatina, 306, 00179, Rome, Italy.,Department of Systems Medicine, Tor Vergata University, Rome, Italy
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50
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Li J, Jin D, Li A, Liu B, Song C, Wang P, Wang D, Xu K, Yang H, Yao H, Zhou B, Bejanin A, Chetelat G, Han T, Lu J, Wang Q, Yu C, Zhang X, Zhou Y, Zhang X, Jiang T, Liu Y, Han Y. ASAF: altered spontaneous activity fingerprinting in Alzheimer's disease based on multisite fMRI. Sci Bull (Beijing) 2019; 64:998-1010. [PMID: 36659811 DOI: 10.1016/j.scib.2019.04.034] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 01/21/2023]
Abstract
Several monocentric studies have noted alterations in spontaneous brain activity in Alzheimer's disease (AD), although there is no consensus on the altered amplitude of low-frequency fluctuations in AD patients. The main aim of the present study was to identify a reliable and reproducible abnormal brain activity pattern in AD. The amplitude of local brain activity (AM), which can provide fast mapping of spontaneous brain activity across the whole brain, was evaluated based on multisite rs-fMRI data for 688 subjects (215 normal controls (NCs), 221 amnestic mild cognitive impairment (aMCI) 252 AD). Two-sample t-tests were used to detect group differences between AD patients and NCs from the same site. Differences in the AM maps were statistically analyzed via the Stouffer's meta-analysis. Consistent regions of lower spontaneous brain activity in the default mode network and increased activity in the bilateral hippocampus/parahippocampus, thalamus, caudate nucleus, orbital part of the middle frontal gyrus and left fusiform were observed in the AD patients compared with those in NCs. Significant correlations (P < 0.05, Bonferroni corrected) between the normalized amplitude index and Mini-Mental State Examination scores were found in the identified brain regions, which indicates that the altered brain activity was associated with cognitive decline in the patients. Multivariate analysis and leave-one-site-out cross-validation led to a 78.49% prediction accuracy for single-patient classification. The altered activity patterns of the identified brain regions were largely correlated with the FDG-PET results from another independent study. These results emphasized the impaired brain activity to provide a robust and reproducible imaging signature of AD.
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Affiliation(s)
- Jiachen Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ang Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan 250012, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Kaibin Xu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General Hospital, Beijing 100853, China
| | - Bo Zhou
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Alexandre Bejanin
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Gael Chetelat
- Université Normandie, Inserm, Université de Caen-Normandie, Inserm UMR-S U1237, GIP Cyceron, Caen 14000, France
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Qing Wang
- Department of Radiology, Qilu Hospital, Ji'nan 250012, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xinqing Zhang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Xi Zhang
- Institute of Geriatrics and Gerontology, Chinese PLA General Hospital, Beijing 100853, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100053, China; Beijing Institute of Geriatrics, Beijing 100053, China; National Clinical Research Center for Geriatric Disorders, Beijing 100053, China.
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