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Khalilullah KMI, Agcaoglu O, Sui J, Adali T, Duda M, Calhoun VD. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease. Hum Brain Mapp 2023; 44:5167-5179. [PMID: 37605825 PMCID: PMC10502647 DOI: 10.1002/hbm.26456] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 07/11/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.
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Affiliation(s)
- K. M. Ibrahim Khalilullah
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Oktay Agcaoglu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jing Sui
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Tülay Adali
- Department of Electrical and Computer EngineeringUniversity of MarylandBaltimoreMarylandUSA
| | - Marlena Duda
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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2
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Marawi T, Ainsworth NJ, Zhukovsky P, Rashidi-Ranjbar N, Rajji TK, Tartaglia MC, Voineskos AN, Mulsant BH. Brain-cognition relationships in late-life depression: a systematic review of structural magnetic resonance imaging studies. Transl Psychiatry 2023; 13:284. [PMID: 37598228 PMCID: PMC10439902 DOI: 10.1038/s41398-023-02584-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/21/2023] Open
Abstract
BACKGROUND Most patients with late-life depression (LLD) have cognitive impairment, and at least one-third meet diagnostic criteria for mild cognitive impairment (MCI), a prodrome to Alzheimer's dementia (AD) and other neurodegenerative diseases. However, the mechanisms linking LLD and MCI, and brain alterations underlying impaired cognition in LLD and LLD + MCI remain poorly understood. METHODS To address this knowledge gap, we conducted a systematic review of studies of brain-cognition relationships in LLD or LLD + MCI to identify circuits underlying impaired cognition in LLD or LLD + MCI. We searched MEDLINE, PsycINFO, EMBASE, and Web of Science databases from inception through February 13, 2023. We included studies that assessed cognition in patients with LLD or LLD + MCI and acquired: (1) T1-weighted imaging (T1) measuring gray matter volumes or thickness; or (2) diffusion-weighted imaging (DWI) assessing white matter integrity. Due to the heterogeneity in studies, we only conducted a descriptive synthesis. RESULTS Our search identified 51 articles, resulting in 33 T1 studies, 17 DWI studies, and 1 study analyzing both T1 and DWI. Despite limitations, reviewed studies suggest that lower thickness or volume in the frontal and temporal regions and widespread lower white matter integrity are associated with impaired cognition in LLD. Lower white matter integrity in the posterior cingulate region (precuneus and corpus callosum sub-regions) was more associated with impairment executive function and processing speed than with memory. CONCLUSION Future studies should analyze larger samples of participants with various degrees of cognitive impairment and go beyond univariate statistical models to assess reliable brain-cognition relationships in LLD.
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Affiliation(s)
- Tulip Marawi
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Nicholas J Ainsworth
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Neda Rashidi-Ranjbar
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, Toronto, ON, Canada
| | - Tarek K Rajji
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Department of Neurology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Benoit H Mulsant
- Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada.
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Song J. Amygdala activity and amygdala-hippocampus connectivity: Metabolic diseases, dementia, and neuropsychiatric issues. Biomed Pharmacother 2023; 162:114647. [PMID: 37011482 DOI: 10.1016/j.biopha.2023.114647] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 04/04/2023] Open
Abstract
With rapid aging of the population worldwide, the number of people with dementia is dramatically increasing. Some studies have emphasized that metabolic syndrome, which includes obesity and diabetes, leads to increased risks of dementia and cognitive decline. Factors such as insulin resistance, hyperglycemia, high blood pressure, dyslipidemia, and central obesity in metabolic syndrome are associated with synaptic failure, neuroinflammation, and imbalanced neurotransmitter levels, leading to the progression of dementia. Due to the positive correlation between diabetes and dementia, some studies have called it "type 3 diabetes". Recently, the number of patients with cognitive decline due to metabolic imbalances has considerably increased. In addition, recent studies have reported that neuropsychiatric issues such as anxiety, depressive behavior, and impaired attention are common factors in patients with metabolic disease and those with dementia. In the central nervous system (CNS), the amygdala is a central region that regulates emotional memory, mood disorders, anxiety, attention, and cognitive function. The connectivity of the amygdala with other brain regions, such as the hippocampus, and the activity of the amygdala contribute to diverse neuropathological and neuropsychiatric issues. Thus, this review summarizes the significant consequences of the critical roles of amygdala connectivity in both metabolic syndromes and dementia. Further studies on amygdala function in metabolic imbalance-related dementia are needed to treat neuropsychiatric problems in patients with this type of dementia.
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Affiliation(s)
- Juhyun Song
- Department of Anatomy, Chonnam National University Medical School, Hwasun 58128, Jeollanam-do, Republic of Korea.
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4
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Khalilullah KMI, Agcaoglu O, Sui J, Adali T, Duda M, Calhoun VD. Multimodal fusion of multiple rest fMRI networks and MRI gray matter via multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.28.530458. [PMID: 36909478 PMCID: PMC10002680 DOI: 10.1101/2023.02.28.530458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/07/2023]
Abstract
In this paper we focus on estimating the joint relationship between structural MRI (sMRI) gray matter (GM) and multiple functional MRI (fMRI) intrinsic connectivity networks (ICN) using a novel approach called multi-link joint independent component analysis (ml-jICA). The proposed model offers several improvements over the existing joint independent component analysis (jICA) model. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for Alzheimer's disease versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to gray matter components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.
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5
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Du Y, Yu J, Liu M, Qiu Q, Fang Y, Zhao L, Wei W, Wang J, Lin X, Yan F, Li X. The relationship between depressive symptoms and cognitive function in Alzheimer's disease: The mediating effect of amygdala functional connectivity and radiomic features. J Affect Disord 2023; 330:101-109. [PMID: 36863470 DOI: 10.1016/j.jad.2023.02.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND Depressive symptoms are common in Alzheimer's disease (AD) and are associated with cognitive function. Amygdala functional connectivity (FC) and radiomic features related to depression and cognition. However, studies have yet to explore the neural mechanisms underlying these associations. METHODS We enrolled eighty-two AD patients with depressive symptoms (ADD) and 85 healthy controls (HCs) in this study. We compared amygdala FC using the seed-based approach between ADD patients and HCs. The least absolute shrinkage and selection operator (LASSO) was used to select amygdala radiomic features. A support vector machine (SVM) model was constructed based on the identified radiomic features to distinguish ADD from HCs. We used mediation analyses to explore the mediating effects of amygdala radiomic features and amygdala FC on cognition. RESULTS We found that ADD patients showed decreased amygdala FC with posterior cingulate cortex, middle frontal gyrus (MFG), and parahippocampal gyrus involved in the default mode network compared to HCs. The area under the receiver operating characteristic curve (AUC) of the amygdala radiomic model was 0.95 for ADD patients and HCs. Notably, the mediation model demonstrated that amygdala FC with the MFG and amygdala-based radiomic features mediated the relationship between depressive symptoms and cognitive function in AD. LIMITATIONS This study is a cross-sectional study and lacks longitudinal data. CONCLUSION Our findings may not only expand existing biological knowledge of the relationship between cognition and depressive symptoms in AD from the perspective of brain function and structure but also may ultimately provide potential targets for personalized treatment strategies.
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Affiliation(s)
- Yang Du
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Manhua Liu
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Qiu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yuan Fang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lu Zhao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wenjing Wei
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jinghua Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiang Lin
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Feng Yan
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xia Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
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6
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Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics. Brain Sci 2022; 13:brainsci13010008. [PMID: 36671990 PMCID: PMC9856687 DOI: 10.3390/brainsci13010008] [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: 11/21/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.
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7
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Park CH, Kim BR, Park HK, Lim SM, Kim E, Jeong JH, Kim GH. Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging. Cereb Cortex 2021; 32:4183-4190. [PMID: 34969093 DOI: 10.1093/cercor/bhab474] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 01/12/2023] Open
Abstract
Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology underlying successful aging. We aimed to investigate the unique patterns of functional brain connectome of superagers and develop predictive models to differentiate superagers from typical agers based on machine learning methods. We obtained resting-state functional magnetic resonance imaging (rsfMRI) data and cognitive measures from 32 superagers and 58 typical agers. The accuracies of three machine learning methods including the linear support vector machine classifier (SV), the random forest classifier (RF), and the logistic regression classifier (LR) in predicting superagers were comparable (SV = 0.944, RF = 0.944, LR = 0.944); however, RF achieved the highest area under the curve (AUC; 0.979). An ensemble learning method combining the three classifiers achieved the highest AUC (0.986). The most discriminative nodes for predicting superagers encompassed areas in the precuneus; posterior cingulate gyrus; insular cortex; and superior, middle, and inferior frontal gyrus, which were located in default, salient, and multiple-demand networks. Thus, rsfMRI data can provide high accuracy for predicting superagers, thereby capturing and describing the unique characteristics of their functional brain connectome.
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Affiliation(s)
- Chang-Hyun Park
- Department of Radiology, College of Medicine, Catholic University of Korea, Seoul 06591, Korea.,Center for Neuroprosthetics and Brain Mind Institute, Swiss Federal Institute of Technology (EPFL), 1202 Geneva, Switzerland
| | - Bori R Kim
- Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul 07985, Korea.,Ewha Medical Research Institute, Ewha Womans University, Seoul 07804, Republic of Korea
| | - Hee Kyung Park
- Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul 07985, Korea
| | - Soo Mee Lim
- Department of Radiology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Korea
| | - Eunhee Kim
- Department of Radiology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul 07985, Korea
| | - Jee Hyang Jeong
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Korea
| | - Geon Ha Kim
- Department of Neurology, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul 07985, Korea
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8
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Yue Y, Jiang Y, Shen T, Pu J, Lai HY, Zhang B. ALFF and ReHo Mapping Reveals Different Functional Patterns in Early- and Late-Onset Parkinson's Disease. Front Neurosci 2020; 14:141. [PMID: 32158380 PMCID: PMC7052327 DOI: 10.3389/fnins.2020.00141] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 02/04/2020] [Indexed: 11/13/2022] Open
Abstract
Heterogeneity between late-onset Parkinson's disease (LOPD) and early-onset Parkinson's disease (EOPD) is mainly reflected in the following aspects including genetics, disease progression, drug response, clinical manifestation, and neuropathological change. Although many studies have investigated these differences in relation to clinical significance, the functional processing circuits and underlying neural mechanisms have not been entirely understood. In this study, regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF) maps were used to explore different spontaneous brain activity patterns in EOPD and LOPD patients. Abnormal synchronizations were found in the motor and emotional circuits of the EOPD group, as well as in the motor, emotional, and visual circuits of the LOPD group. EOPD patients showed functional activity change in the visual, emotional and motor circuits, and LOPD patients only showed increased functional activity in the emotional circuits. In summary, the desynchronization process in the LOPD group was relatively strengthened, and the brain areas with changed functional activity in the EOPD group were relatively widespread. The results might point out different impairments in the synchronization and functional activity for EOPD and LOPD patients.
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Affiliation(s)
- Yumei Yue
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.,Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Yasi Jiang
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.,Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ting Shen
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.,Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiali Pu
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Hsin-Yi Lai
- Department of Neurology of Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China.,Department of Neurology of the Second Affiliated Hospital, Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University, Hangzhou, China.,Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology of the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
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Belkhiria C, Vergara RC, San Martín S, Leiva A, Marcenaro B, Martinez M, Delgado C, Delano PH. Cingulate Cortex Atrophy Is Associated With Hearing Loss in Presbycusis With Cochlear Amplifier Dysfunction. Front Aging Neurosci 2019; 11:97. [PMID: 31080411 PMCID: PMC6497796 DOI: 10.3389/fnagi.2019.00097] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 04/10/2019] [Indexed: 12/14/2022] Open
Abstract
Age-related hearing loss is associated with cognitive decline and has been proposed as a risk factor for dementia. However, the mechanisms that relate hearing loss to cognitive decline remain elusive. Here, we propose that the impairment of the cochlear amplifier mechanism is associated with structural brain changes and cognitive impairment. Ninety-six subjects aged over 65 years old (63 female and 33 male) were evaluated using brain magnetic resonance imaging, neuropsychological and audiological assessments, including distortion product otoacoustic emissions as a measure of the cochlear amplifier function. All the analyses were adjusted by age, gender and education. The group with cochlear amplifier dysfunction showed greater brain atrophy in the cingulate cortex and in the parahippocampus. In addition, the atrophy of the cingulate cortex was associated with cognitive impairment in episodic and working memories and in language and visuoconstructive abilities. We conclude that the neural abnormalities observed in presbycusis subjects with cochlear amplifier dysfunction extend beyond core auditory network and are associated with cognitive decline in multiple domains. These results suggest that a cochlear amplifier dysfunction in presbycusis is an important mechanism relating hearing impairments to brain atrophy in the extended network of effortful hearing.
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Affiliation(s)
- Chama Belkhiria
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Rodrigo C Vergara
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Simón San Martín
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Alexis Leiva
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Bruno Marcenaro
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile
| | - Melissa Martinez
- Department of Neurology and Neurosurgery, Clinical Hospital of the University of Chile, Santiago, Chile
| | - Carolina Delgado
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Department of Neurology and Neurosurgery, Clinical Hospital of the University of Chile, Santiago, Chile
| | - Paul H Delano
- Department of Neuroscience, Faculty of Medicine, University of Chile, Santiago, Chile.,Biomedical Neuroscience Institute, Faculty of Medicine, University of Chile, Santiago, Chile.,Department of Otolaryngology, Clinical Hospital of the University of Chile, Santiago, Chile
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10
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Wang J, Liang Y, Chen H, Wang W, Wang Y, Liang Y, Zhang Y. Structural changes in white matter lesion patients and their correlation with cognitive impairment. Neuropsychiatr Dis Treat 2019; 15:1355-1363. [PMID: 31190839 PMCID: PMC6534061 DOI: 10.2147/ndt.s194803] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND White matter lesions (WMLs) play a role in cognitive decline and dementia. Little is known about gray matter (GM) changes in WMLs. This study aimed to investigate GM changes in WML patients. MATERIALS AND METHODS Correlations between altered structural volume and cognitive assessment scores were investigated. GM and white matter (WM) changes in 23 WML-vascular dementia (VaD) patients, 22 WML-non-dementia vascular cognitive impairment (VCIND) patients, and 23 healthy control (HC) subjects were examined. Gray matter density (GMD) was calculated by measuring local proportions of GM at thousands of homologous cortical locations. WM volume was obtained by fully automated software using voxel-based morphometry (VBM). RESULTS Widespread GMD was significantly lower in WML patients compared to control subjects in cortical and subcortical regions (p<0.05). Greatest differences were found in the bilateral anterior cingulate cortex, inferior frontal gyrus, insula, angular gyrus, caudate, precentral gyrus, and right middle temporal gyrus, right thalamus. Secondary region of interest (ROI) analysis indicated significantly greater GMD in the bilateral caudate among WML-VCIND patients (n=22) compared to HCs (p<0.05). There was a significant difference in WM volume between WML patients and control subjects (p<0.05). Greatest differences were located in the genu/body/splenium of the corpus callosum and superior corona radiata L, and posterior corona radiata L. There was a significant association between structural changes and cognitive scores (Montreal Cognitive Assessment [MoCA] score) (p<0.05). There was no significant correlation between structural changes and Mini Mental State Examination (MMSE) scores (p>0.05). CONCLUSION GMD and WM volume were changed in WMLs, and the changes were detectable. Correlation between structural changes and cognitive function was promising in understanding the pathological and physiological mechanisms of WMLs.
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Affiliation(s)
- Jinfang Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China, .,Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan 430000, China
| | - Yi Liang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan 430000, China
| | - Hongyan Chen
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China,
| | - Wanming Wang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan 430000, China
| | - Yanwen Wang
- Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan 430000, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing 100050, China
| | - Yumei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China, .,Department of Rehabilitation Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, China,
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Xiao T, Zhang S, Lee LE, Chao HH, van Dyck C, Li CSR. Exploring Age-Related Changes in Resting State Functional Connectivity of the Amygdala: From Young to Middle Adulthood. Front Aging Neurosci 2018; 10:209. [PMID: 30061823 PMCID: PMC6055042 DOI: 10.3389/fnagi.2018.00209] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 06/22/2018] [Indexed: 11/13/2022] Open
Abstract
Functional connectivities of the amygdala support emotional and cognitive processing. Life-span development of resting-state functional connectivities (rsFC) of the amygdala may underlie age-related differences in emotion regulatory mechanisms. To date, age-related changes in amygdala rsFC have been reported through adolescence but not as thoroughly for adulthood. This study investigated age-related differences in amygdala rsFC in 132 young and middle-aged adults (19–55 years). Data processing followed published routines. Overall, amygdala showed positive rsFC with the temporal, sensorimotor and ventromedial prefrontal cortex (vmPFC), insula and lentiform nucleus, and negative rsFC with visual, frontoparietal, and posterior cingulate cortex and caudate head. Amygdala rsFC with the cerebellum was positively correlated with age, and rsFCs with the dorsal medial prefrontal cortex (dmPFC) and somatomotor cortex were negatively correlated with age, at voxel p < 0.001 in combination with cluster p < 0.05 FWE. These age-dependent changes in connectivity appeared to manifest to a greater extent in men than in women, although the sex difference was only evident for the cerebellum in a slope test of age regressions (p = 0.0053). Previous studies showed amygdala interaction with the anterior cingulate cortex (ACC) and vmPFC during emotion regulation. In region of interest analysis, amygdala rsFC with the ACC and vmPFC did not show age-related changes. These findings suggest that intrinsic connectivity of the amygdala evolved from young to middle adulthood in selective brain regions, and may inform future studies of age-related emotion regulation and maladaptive development of the amygdala circuits as an etiological marker of emotional disorders.
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Affiliation(s)
- Ting Xiao
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.,Xiangya School of Medicine, Central South University, Changsha, China
| | - Sheng Zhang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States
| | - Lue-En Lee
- Department of Psychiatry, National Taiwan University, Taipei, Taiwan
| | - Herta H Chao
- Department of Medicine, Yale University School of Medicine, New Haven, CT, United States.,VA Connecticut Healthcare System, West Haven, CT, United States
| | - Christopher van Dyck
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, United States
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States.,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, United States.,Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, United States.,Beijing Huilongguan Hospital, Peking University, Beijing, China
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