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Zou A, Ji J, Lei M, Liu J, Song Y. Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7871-7883. [PMID: 36399590 DOI: 10.1109/tnnls.2022.3221617] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.
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Xu K, Wang J, Liu G, Yan J, Chang M, Jiang L, Zhang J. Altered dynamic effective connectivity of the default mode network in type 2 diabetes. Front Neurol 2024; 14:1324988. [PMID: 38288329 PMCID: PMC10822894 DOI: 10.3389/fneur.2023.1324988] [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: 10/30/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
Introduction Altered functional connectivity of resting-state functional magnetic resonance imaging (rs-fMRI) within default mode network (DMN) regions has been verified to be closely associated with cognitive decline in patients with Type 2 diabetes mellitus (T2DM), but most studies neglected the fluctuations of brain activities-the dynamic effective connectivity (DEC) within DMN of T2DM is still unknown. Methods For the current investigation, 40 healthy controls (HC) and 36 T2DM patients have been recruited as participants. To examine the variation of DEC between T2DM and HC, we utilized the methodologies of independent components analysis (ICA) and multivariate granger causality analysis (mGCA). Results We found altered DEC within DMN only show decrease in state 1. In addition, the causal information flow of diabetic patients major affected areas which are closely associated with food craving and metabolic regulation, and T2DM patients stayed longer in low activity level and exhibited decreased transition rate between states. Moreover, these changes related negatively with the MoCA scores and positively with HbA1C level. Conclusion Our study may offer a fresh perspective on brain dynamic activities to understand the mechanisms underlying T2DM-related cognitive deficits.
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Affiliation(s)
- Kun Xu
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou University Second Hospital, Lanzhou, China
| | - Jiahao Yan
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Miao Chang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Linzhen Jiang
- Second Clinical School, Lanzhou University, Lanzhou, China
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou University Second Hospital, Lanzhou, China
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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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Sendi MS, Zendehrouh E, Fu Z, Liu J, Du Y, Mormino E, Salat DH, Calhoun VD, Miller RL. Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease. Brain Connect 2023; 13:334-343. [PMID: 34102870 PMCID: PMC10442683 DOI: 10.1089/brain.2020.0847] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | | | - David H. Salat
- Harvard Medical School, Cambridge, Massachusetts, USA
- Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
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Dang C, Wang Y, Li Q, Lu Y. Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers. PSYCHORADIOLOGY 2023; 3:kkad009. [PMID: 38666112 PMCID: PMC11003434 DOI: 10.1093/psyrad/kkad009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 04/28/2024]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.
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Affiliation(s)
- Chun Dang
- Department of Periodical Press, West China Hospital, Sichuan University, Chengdu 610000, China
| | - Yanchao Wang
- Department of Neurology, Chifeng University of Affiliated Hospital, Chifeng 024000, China
| | - Qian Li
- Department of Neurology, the Second Affiliated Hospital of Harbin Medical University, Harbin 150081, China
| | - Yaoheng Lu
- Department of General Surgery, Chengdu Integrated Traditional Chinese Medicine and Western Medicine Hospital, Chengdu 610000, China
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Ji J, Zou A, Liu J, Yang C, Zhang X, Song Y. A Survey on Brain Effective Connectivity Network Learning. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1879-1899. [PMID: 34469315 DOI: 10.1109/tnnls.2021.3106299] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Human brain effective connectivity characterizes the causal effects of neural activities among different brain regions. Studies of brain effective connectivity networks (ECNs) for different populations contribute significantly to the understanding of the pathological mechanism associated with neuropsychiatric diseases and facilitate finding new brain network imaging markers for the early diagnosis and evaluation for the treatment of cerebral diseases. A deeper understanding of brain ECNs also greatly promotes brain-inspired artificial intelligence (AI) research in the context of brain-like neural networks and machine learning. Thus, how to picture and grasp deeper features of brain ECNs from functional magnetic resonance imaging (fMRI) data is currently an important and active research area of the human brain connectome. In this survey, we first show some typical applications and analyze existing challenging problems in learning brain ECNs from fMRI data. Second, we give a taxonomy of ECN learning methods from the perspective of computational science and describe some representative methods in each category. Third, we summarize commonly used evaluation metrics and conduct a performance comparison of several typical algorithms both on simulated and real datasets. Finally, we present the prospects and references for researchers engaged in learning ECNs.
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Contreras JA, Fujisaki K, Ortega N, Barisano G, Sagare A, Pappas I, Chui H, Ringman JM, Joe EB, Zlokovic B, Toga AW, Pa J. Decreased functional connectivity is associated with increased levels of Cerebral Spinal Fluid soluble-PDGFRβ, a marker of blood brain barrier breakdown, in older adults. RESEARCH SQUARE 2023:rs.3.rs-2644974. [PMID: 36945439 PMCID: PMC10029080 DOI: 10.21203/rs.3.rs-2644974/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
Resting-state functional connectivity (FC) is suggested to be cross-sectionally associated with both vascular burden and Alzheimer's disease (AD) pathology. For instance, studies in pre-clinical AD subjects have shown increases of cerebral spinal fluid soluble platelet-derived growth factor receptor-β (CSF sPDGFRβ, a marker of BBB breakdown) but have not demonstrated if this vascular impairment affects neuronal dysfunction. It's possible that increased levels of sPDGFRβ in the CSF may correlate with impaired FC in metabolically demanding brain regions (i.e. Default Mode Network, DMN). Our study aimed to investigate the relationship between these two markers in older individuals that were cognitively normal and had cognitive impairment. Eighty-nine older adults without dementia from the University of Southern California were selected from a larger cohort. Region of interest (ROI) to ROI analyses were conducted using DMN seed regions. Linear regression models measured significant associations between BOLD FC strength among seed-target regions and sPDGFRβ values, while covarying for age and sex. Comparison of a composite ROI created by averaging FC values between seed and all target regions among cognitively normal and impaired individuals was also examined. Using CSF sPDGFRβ as a biomarker of BBB breakdown, we report that increased breakdown correlated with decreased functional connectivity in DMN areas, specifically the PCC while the hippocampus exhibited an interaction effect using CDR score. We conclude that BBB breakdown as measured by CSF sPDGFRβ affects neural networks resulting in decreased functional connections that leads to cognitive dysfunction.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Judy Pa
- University of California, San Diego
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8
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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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Effective Connectivity Evaluation of Resting-State Brain Networks in Alzheimer's Disease, Amnestic Mild Cognitive Impairment, and Normal Aging: An Exploratory Study. Brain Sci 2023; 13:brainsci13020265. [PMID: 36831808 PMCID: PMC9954618 DOI: 10.3390/brainsci13020265] [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: 12/24/2022] [Revised: 01/27/2023] [Accepted: 02/01/2023] [Indexed: 02/09/2023] Open
Abstract
(1) Background: Alzheimer's disease (AD) is a neurodegenerative disease with a high prevalence. Despite the cognitive tests to diagnose AD, there are pitfalls in early diagnosis. Brain deposition of pathological markers of AD can affect the direction and intensity of the signaling. The study of effective connectivity allows the evaluation of intensity flow and signaling pathways in functional regions, even in the early stage, known as amnestic mild cognitive impairment (aMCI). (2) Methods: 16 aMCI, 13 AD, and 14 normal subjects were scanned using resting-state fMRI and T1-weighted protocols. After data pre-processing, the signal of the predefined nodes was extracted, and spectral dynamic causal modeling analysis (spDCM) was constructed. Afterward, the mean and standard deviation of the Jacobin matrix of each subject describing effective connectivity was calculated and compared. (3) Results: The maps of effective connectivity in the brain networks of the three groups were different, and the direction and strength of the causal effect with the progression of the disease showed substantial changes. (4) Conclusions: Impaired information flow in the resting-state networks of the aMCI and AD groups was found versus normal groups. Effective connectivity can serve as a potential marker of Alzheimer's pathophysiology, even in the early stages of the disease.
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Li H, Huang Z, Gao Z, Zhu W, Li Y, Zhou S, Li X, Yu Y. Sex Difference in General Cognition Associated with Coupling of Whole-brain Functional Connectivity Strength to Cerebral Blood Flow Changes During Alzheimer's Disease Progression. Neuroscience 2023; 509:187-200. [PMID: 36496188 DOI: 10.1016/j.neuroscience.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/28/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Alzheimer's disease (AD) is a progressive age-related neurodegenerative disorder that results in irreversible cognitive impairments. Nonetheless, there are numerous sex-dependent differences in clinical course. We examined potential contributions of neurovascular coupling deficits to sex differences in AD progression. T1-weighted three-dimensional structural magnetic resonance images, functional blood oxygen level dependent and arterial spin labeling images were acquired from 50 AD patients (28 females), 52 amnesic mild cognitive impairment patients (31 females), and 59 healthy controls (36 females). Short- and long-range functional connectivity strength (FCS) and cerebral blood flow (CBF) values were calculated for all participants. Then, the CBF/FCS coupling ratio, which represented the amount of blood supply per unit of connectivity strength, was calculated for each voxel. Two-way ANOVA was performed to identify group × sex interactions and main effects of group. Correlation analysis was used to assess associations between CBF/FCS ratios and Mini-Mental State Examination (MMSE). There were significant group × sex interaction effects on short-range coupling ratios of right middle temporal gyrus, left angular gyrus, left inferior orbital frontal gyrus, and left superior frontal gyrus as well as on the long-range coupling ratios of right middle temporal gyrus, left precuneus, left posterior cingulate cortex, and left angular gyrus. There were significant negative correlations between MMSE scores and CBF/FCS ratios for all regions with significant group × sex interactions among female patients, while positive correlations were found among male patients. Our results demonstrate significant sex differences in neurovascular coupling mechanisms associated with cognitive function during the course of AD.
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Affiliation(s)
- Hui Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Ziang Huang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Ziwen Gao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Wanqiu Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Yuqing Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
| | - Xiaoshu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.
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Huang J, Jung JY, Nam CS. Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study. Front Hum Neurosci 2022; 16:1060936. [PMID: 36590062 PMCID: PMC9797690 DOI: 10.3389/fnhum.2022.1060936] [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: 10/03/2022] [Accepted: 11/24/2022] [Indexed: 12/23/2022] Open
Abstract
Introduction Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression. Methods We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores. Results The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores. Discussion Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.
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Affiliation(s)
- Jiali Huang
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States
| | - Jae-Yoon Jung
- Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, South Korea,Department of Big Data Analytics, Kyung Hee University, Yongin-si, South Korea
| | - Chang S. Nam
- Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, United States,Department of Industrial and Management Systems Engineering, Kyung Hee University, Yongin-si, South Korea,*Correspondence: Chang S. Nam
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Kazemi-Harikandei SZ, Shobeiri P, Salmani Jelodar MR, Tavangar SM. Effective connectivity in individuals with Alzheimer's disease and mild cognitive impairment: A systematic review. NEUROSCIENCE INFORMATICS 2022; 2:100104. [DOI: 10.1016/j.neuri.2022.100104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2023]
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González-López M, Gonzalez-Moreira E, Areces-González A, Paz-Linares D, Fernández T. Who's driving? The default mode network in healthy elderly individuals at risk of cognitive decline. Front Neurol 2022; 13:1009574. [DOI: 10.3389/fneur.2022.1009574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 11/08/2022] [Indexed: 12/02/2022] Open
Abstract
IntroductionAge is the main risk factor for the development of neurocognitive disorders, with Alzheimer's disease being the most common. Its physiopathological features may develop decades before the onset of clinical symptoms. Quantitative electroencephalography (qEEG) is a promising and cost-effective tool for the prediction of cognitive decline in healthy older individuals that exhibit an excess of theta activity. The aim of the present study was to evaluate the feasibility of brain connectivity variable resolution electromagnetic tomography (BC-VARETA), a novel source localization algorithm, as a potential tool to assess brain connectivity with 19-channel recordings, which are common in clinical practice.MethodsWe explored differences in terms of functional connectivity among the nodes of the default mode network between two groups of healthy older participants, one of which exhibited an EEG marker of risk for cognitive decline.ResultsThe risk group exhibited increased levels of delta, theta, and beta functional connectivity among nodes of the default mode network, as well as reversed directionality patterns of connectivity among nodes in every frequency band when compared to the control group.DiscussionWe propose that an ongoing pathological process may be underway in healthy elderly individuals with excess theta activity in their EEGs, which is further evidenced by changes in their connectivity patterns. BC-VARETA implemented on 19-channels EEG recordings appears to be a promising tool to detect dysfunctions at the connectivity level in clinical settings.
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Liu J, Ji J, Xun G, Zhang A. Inferring Effective Connectivity Networks From fMRI Time Series With a Temporal Entropy-Score. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:5993-6006. [PMID: 33886478 DOI: 10.1109/tnnls.2021.3072149] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Inferring brain-effective connectivity networks from neuroimaging data has become a very hot topic in neuroinformatics and bioinformatics. In recent years, the search methods based on Bayesian network score have been greatly developed and become an emerging method for inferring effective connectivity. However, the previous score functions ignore the temporal information from functional magnetic resonance imaging (fMRI) series data and may not be able to determine all orientations in some cases. In this article, we propose a novel score function for inferring effective connectivity from fMRI data based on the conditional entropy and transfer entropy (TE) between brain regions. The new score employs the TE to capture the temporal information and can effectively infer connection directions between brain regions. Experimental results on both simulated and real-world data demonstrate the efficacy of our proposed score function.
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A New Method on Construction of Brain Effective Connectivity Based on Functional Magnetic Resonance Imaging. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4542106. [PMID: 35419076 PMCID: PMC9001109 DOI: 10.1155/2022/4542106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 03/10/2022] [Indexed: 11/17/2022]
Abstract
Most of the existing methods about the causal relationship based on functional magnetic resonance imaging (fMRI) data are either the hypothesis-driven methods or based on a linear model, which can result in the deviation for detecting the original brain activity. Therefore, it is necessary to develop a new method for detecting the effective connectivity (EC) of the brain activity by the nonlinear calculation. In this study, we firstly proposed a new technology evaluating effective connectivity of the human brain based on back-propagation neural network with nonlinear model, named EC-BP. Next, we simulated four time series for assessing the feasibility and accuracy of EC-BP compared to Granger causality analysis (GCA). Finally, the proposed EC-BP was applied to the brain fMRI from 60 healthy subjects. The results from the four simulated time series showed that the proposed EC-BP can detect the originally causal relationship, consistent with the actual causality. However, the GCA can not find nonlinear causality. Based on the analysis of the fMRI data from the healthy participants, EC-BP and GCA showed the huge differences in the top 50 connections in descending order of EC. EC-BP showed all ECs related to hippocampus and parahippocampus, whereas GCA showed most ECs related to the paracentral lobule, caudate, putamen, and pallidum, which represents the brain regions with most frequent information passing measured by different methods. The proposed EC-BP method can provide supplementary information to GCA, which will promote more comprehensive detection and evaluation of brain EC.
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Luo Y, Qiao M, Liang Y, Chen C, Zeng L, Wang L, Wu W. Functional Brain Connectivity in Mild Cognitive Impairment With Sleep Disorders: A Study Based on Resting-State Functional Magnetic Resonance Imaging. Front Aging Neurosci 2022; 14:812664. [PMID: 35360208 PMCID: PMC8960737 DOI: 10.3389/fnagi.2022.812664] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/25/2022] [Indexed: 01/07/2023] Open
Abstract
Purpose To investigate the effect of sleep disorder (SD) on the changes of brain network dysfunction in mild cognitive impairment (MCI), we compared network connectivity patterns among MCI, SD, and comorbid MCI and sleep disorders (MCI-SD) patients using resting state functional magnetic resonance imaging (RS-fMRI). Patients and Methods A total of 60 participants were included in this study, 20 each with MCI, SD, or MCI-SD. And all participants underwent structural and functional MRI scanning. The default-mode network (DMN) was extracted by independent component analysis (ICA), and regional functional connectivity strengths were calculated and compared among groups. Results Compared to MCI patients, The DMN of MCI-SD patients demonstrated weaker functional connectivity with left middle frontal gyrus, right superior marginal gyrus, but stronger connectivity with the left parahippocampus, left precuneus and left middle temporal gyrus. Compared to the SD group, MCI-SD patients demonstrated weaker functional connectivity with right transverse temporal gyrus (Heschl’s gyrus), right precentral gyrus, and left insula, but stronger connectivity with posterior cerebellum, right middle occipital gyrus, and left precuneus. Conclusion Patients with MCI-SD show unique changes in brain network connectivity patterns compared to MCI or SD alone, likely reflecting a broader functional disconnection and the need to recruit more brain regions for functional compensation.
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Affiliation(s)
- Yuxi Luo
- Department of Geriatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Mengyuan Qiao
- Department of Geriatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yuqing Liang
- Department of Geriatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Chongli Chen
- Department of Geriatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lichuan Zeng
- Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lin Wang
- Health Management Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
| | - Wenbin Wu
- Department of Geriatrics, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China
- *Correspondence: Wenbin Wu, , orcid.org/0000-0001-8784-6137
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17
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Modulation of the brain's core-self network by self-appraisal processes. Neuroimage 2022; 251:118980. [PMID: 35143976 DOI: 10.1016/j.neuroimage.2022.118980] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/06/2022] [Accepted: 02/06/2022] [Indexed: 11/22/2022] Open
Abstract
The 'core' regions of the default mode network (DMN) - the medial prefrontal cortex (MPFC), the posterior cingulate cortex (PCC), and inferior parietal lobules (IPL) - show consistent involvement across mental states that involve self-oriented processing. Precisely how these regions interact in support of such processes remains an important unanswered question. In the current functional magnetic resonance imaging (fMRI) study, we examined dynamic interactions of the 'core-self' DMN regions during two forms of self-referential cognition: direct self-appraisal (thinking about oneself) and reflected self-appraisal (thinking about oneself from a third-person perspective). One-hundred and eleven participants completed our dual self-appraisal task during fMRI, and general linear models were used to characterize common and distinct neural responses to these conditions. Informed by these results, we then applied dynamic causal modelling to examine causal interactions among the 'core-self' regions, and how they were specifically modulated under the influence of direct and reflected self-appraisal. As a primary observation, this network modelling revealed a distinct inhibitory influence of the left IPL on the PCC during reflected compared to direct self-appraisal, which was accompanied by evidence of greater activation in both regions during the reflected self-appraisal condition. We suggest that the greater engagement posterior DMN regions during reflected self-appraisal is a function of the higher-order processing needed for this form of self-appraisal, with the left IPL supporting abstract self-related processes including episodic memory retrieval and shifts of perspective. Overall, we show that core DMN regions interact in functionally unique ways in support of self-referential processes, even when these processes are inter-related. Further characterization of DMN functional interactions across self-related mental states is likely to inform a deeper understanding of how this brain network orchestrates the self.
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18
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Zhao L, Zeng W, Shi Y, Nie W. Dynamic effective connectivity network based on change points detection. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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19
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Ghirga S, Chiodo L, Marrocchio R, Orlandi JG, Loppini A. Inferring Excitatory and Inhibitory Connections in Neuronal Networks. ENTROPY 2021; 23:e23091185. [PMID: 34573810 PMCID: PMC8465838 DOI: 10.3390/e23091185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/16/2022]
Abstract
The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.
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Affiliation(s)
- Silvia Ghirga
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
| | - Letizia Chiodo
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
| | - Riccardo Marrocchio
- Institute of Sound and Vibration Research, Highfield Campus, University of Southampton, Southampton SO17 1BJ, UK;
| | | | - Alessandro Loppini
- Center for Life Nano- & Neuro-Science, Istituto Italiano di Tecnologia (IIT), Viale Regina Elena 291, 00161 Roma, Italy;
- Engineering Department, Campus Bio-Medico University of Rome, Via Álvaro del Portillo 21, 00154 Roma, Italy;
- Correspondence:
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20
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Chen YC, Yong W, Xing C, Feng Y, Haidari NA, Xu JJ, Gu JP, Yin X, Wu Y. Directed functional connectivity of the hippocampus in patients with presbycusis. Brain Imaging Behav 2021; 14:917-926. [PMID: 31270776 DOI: 10.1007/s11682-019-00162-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Presbycusis, associated with a diminished quality of life characterized by bilateral sensorineural hearing loss at high frequencies, has become an increasingly critical public health problem. This study aimed to identify directed functional connectivity (FC) of the hippocampus in patients with presbycusis and to explore the causes if the directed functional connections of the hippocampus were disrupted. Presbycusis patients (n = 32) and age-, sex-, and education-matched healthy controls (n = 40) were included in this study. The seed regions of bilateral hippocampus were selected to identify directed FC in patients with presbycusis using Granger causality analysis (GCA) approach. Correlation analyses were conducted to detect the associations of disrupted directed FC of hippocampus with clinical measures of presbycusis. Compared to healthy controls, decreased directed FC between inferior parietal lobule, insula, right supplementary motor area, middle temporal gyrus and hippocampus were detected in presbycusis patients. Furthermore, a negative correlation between TMB score and the decline of directed FC from left inferior parietal lobule to left hippocampus (r = -0.423, p = 0.025) and from right inferior parietal lobule to right hippocampus (r = -0.516, p = 0.005) were also observed. The decreased directed functional connections of the hippocampus were detected in patients with presbycusis, which was associated with specific cognitive performance. This study mainly emphasizes the crucial role of hippocampus in presbycusis and will enhance our understanding of the neuropathological mechanisms of presbycusis.
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Affiliation(s)
- Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Wei Yong
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Chunhua Xing
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Nasir Ahmad Haidari
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Jian-Ping Gu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
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Sun M, Xie H, Tang Y. Directed Network Defects in Alzheimer's Disease Using Granger Causality and Graph Theory. Curr Alzheimer Res 2020; 17:939-947. [PMID: 33327911 DOI: 10.2174/1567205017666201215140625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 09/19/2020] [Accepted: 11/17/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Few works studied the directed whole-brain interaction between different brain regions of Alzheimer's disease (AD). Here, we investigated the whole-brain effective connectivity and studied the graph metrics associated with AD. METHODS Large-scale Granger causality analysis was conducted to explore abnormal whole-brain effective connectivity of patients with AD. Moreover, graph-theoretical metrics including smallworldness, assortativity, and hierarchy, were computed from the effective connectivity network. Statistical analysis identified the aberrant network properties of AD subjects when compared against healthy controls. RESULTS Decreased small-worldness, and increased characteristic path length, disassortativity, and hierarchy were found in AD subjects. CONCLUSION This work sheds insight into the underlying neuropathological mechanism of the brain network of AD individuals such as less efficient information transmission and reduced resilience to a random or targeted attack.
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Affiliation(s)
- Man Sun
- School of Computer Science and Engineering, Central South University, Changsha, 410008 Hunan, China
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, 94305, United States
| | - Yan Tang
- School of Computer Science and Engineering, Central South University, Changsha, 410008 Hunan, China
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22
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Qi H, Hu Y, Lv Y, Wang P. Primarily Disrupted Default Subsystems Cause Impairments in Inter-system Interactions and a Higher Regulatory Burden in Alzheimer's Disease. Front Aging Neurosci 2020; 12:593648. [PMID: 33262699 PMCID: PMC7686542 DOI: 10.3389/fnagi.2020.593648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/26/2020] [Indexed: 12/03/2022] Open
Abstract
Background: Intrinsically organized large-scale brain networks and their interactions support complex cognitive function. Investigations suggest that the default network (DN) is the earliest disrupted network and that the frontoparietal control network (FPCN) and dorsal attention network (DAN) are subsequently impaired in Alzheimer's disease (AD). These large-scale networks comprise different subsystems (DN: medial temporal lobe (MTL), dorsomedial prefrontal cortex (DM) subsystems and a Core; FPCN: FPCNA and FPCNB). Our previous research has indicated that different DN subsystems are not equally damaged in AD. However, changes in the patterns of interactions among these large-scale network subsystems and the underlying cause of the alterations in AD remain unclear. We hypothesized that disrupted DN subsystems cause specific impairments in inter-system interactions and a higher regulatory burden for the FPCNA. Method: To test this hypothesis, Granger causality analysis (GCA) was performed to explore effective functional connectivity (FC) pattern of these networks. The regional information flow strength (IFS) was calculated and compared across groups to explore changes in the subsystems and their inter-system interactions and the relationship between them. To investigate specific inter-system changes, we summed the inter-system IFS and performed correlation analyses of the bidirectional inter-system IFS, which was compared across groups. Additionally, correlation analyses of dynamic effective FC patterns were performed to reveal alterations in the temporal co-evolution of sets of inter-subsystem interactions. Furthermore, we used partial correlation analysis to quantify the FPCN's regulatory effects. Finally, we applied a support vector machine (SVM) linear classifier to probe which network most effectively discriminated patients from controls. Results: Compared with controls, AD patients showed a decreased intra-DN regional IFS, which was significantly related to the inter-network's IFS. The IFS between the DN subsystems and FPCN subsystems/DAN decreased. Critically, the correlation values of the decreased bidirectional IFS between the DN subsystems and FPCNA diminished. Additionally, the Core and DM play pivotal roles in disordered temporal co-evolution. Furthermore, the FPCNA showed enhanced regulation of the Core. Finally, the MTL subsystem and Core were effective at discriminating patients from controls. Conclusion: The predominantly disrupted DN subsystems caused impaired inter-system interactions and created a higher regulatory burden for the FPCNA.
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Affiliation(s)
- Huihui Qi
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingru Lv
- Department of Imaging, Huashan Hospital, Fudan University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital Affiliated With Tongji University, Shanghai, China
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23
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Meeker KL, Ances BM, Gordon BA, Rudolph CW, Luckett P, Balota DA, Morris JC, Fagan AM, Benzinger TL, Waring JD. Cerebrospinal fluid Aβ42 moderates the relationship between brain functional network dynamics and cognitive intraindividual variability. Neurobiol Aging 2020; 98:116-123. [PMID: 33264709 DOI: 10.1016/j.neurobiolaging.2020.10.027] [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: 05/21/2020] [Revised: 10/26/2020] [Accepted: 10/28/2020] [Indexed: 11/16/2022]
Abstract
As Alzheimer's disease (AD) pathology accumulates, resting-state functional connectivity (rs-fc) within and between brain networks decreases, and fluctuations in cognitive performance known as intraindividual variability (IIV) increase. Here, we assessed the relationship between IIV and anticorrelations in rs-fc between the default mode network (DMN)-dorsal attention network (DAN) in cognitively normal older adults and symptomatic AD participants. We also evaluated the relationship between cerebrospinal fluid (CSF) biomarkers of AD (amyloid-beta [Aβ42] and tau) and IIV-anticorrelation in rs-fc. We observed that cognitive IIV and anticorrelations between DMN × DAN were higher in individuals with AD compared with cognitively normal participants. As DMN × DAN relationship became more positive, cognitive IIV increased, indicating that stronger anticorrelations between networks support more consistent cognitive performance. Moderation analyses indicated that continuous CSF Aβ42, but not CSF total tau, moderated the relationship between cognitive IIV and DMN × DAN, collectively demonstrating that greater amyloid burden and alterations in functional network dynamics are associated with cognitive changes seen in AD. These findings are valuable, as they suggest that amyloid affects cognitive functioning during the early stages of AD.
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Affiliation(s)
- Karin L Meeker
- Department of Psychology, Saint Louis University, St. Louis, MO, USA; Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
| | - Beau M Ances
- Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Cort W Rudolph
- Department of Psychology, Saint Louis University, St. Louis, MO, USA
| | - Patrick Luckett
- Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - David A Balota
- Department of Psychology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Anne M Fagan
- Department of Neurology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Tammie L Benzinger
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Jill D Waring
- Department of Psychology, Saint Louis University, St. Louis, MO, USA
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24
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Nie Y, Opoku E, Yasmin L, Song Y, Wang J, Wu S, Scarapicchia V, Gawryluk J, Wang L, Cao J, Nathoo FS. Spectral dynamic causal modelling of resting-state fMRI: an exploratory study relating effective brain connectivity in the default mode network to genetics. Stat Appl Genet Mol Biol 2020; 19:/j/sagmb.ahead-of-print/sagmb-2019-0058/sagmb-2019-0058.xml. [PMID: 32866136 DOI: 10.1515/sagmb-2019-0058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2019] [Accepted: 07/27/2020] [Indexed: 11/15/2022]
Abstract
We conduct an imaging genetics study to explore how effective brain connectivity in the default mode network (DMN) may be related to genetics within the context of Alzheimer's disease and mild cognitive impairment. We develop an analysis of longitudinal resting-state functional magnetic resonance imaging (rs-fMRI) and genetic data obtained from a sample of 111 subjects with a total of 319 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. A Dynamic Causal Model (DCM) is fit to the rs-fMRI scans to estimate effective brain connectivity within the DMN and related to a set of single nucleotide polymorphisms (SNPs) contained in an empirical disease-constrained set which is obtained out-of-sample from 663 ADNI subjects having only genome-wide data. We relate longitudinal effective brain connectivity estimated using spectral DCM to SNPs using both linear mixed effect (LME) models as well as function-on-scalar regression (FSR). In both cases we implement a parametric bootstrap for testing SNP coefficients and make comparisons with p-values obtained from asymptotic null distributions. In both networks at an initial q-value threshold of 0.1 no effects are found. We report on exploratory patterns of associations with relatively high ranks that exhibit stability to the differing assumptions made by both FSR and LME.
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Affiliation(s)
- Yunlong Nie
- Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BCV5A 1S6,Canada
| | - Eugene Opoku
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
| | - Laila Yasmin
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
| | - Yin Song
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
| | - Jie Wang
- Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BCV5A 1S6,Canada
| | - Sidi Wu
- Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BCV5A 1S6,Canada
| | - Vanessa Scarapicchia
- Department of Psychology, University of Victoria, P. O. Box 1700 STN CSC, Victoria, British Columbia, V8W 2Y2Canada
| | - Jodie Gawryluk
- Department of Psychology, University of Victoria, P. O. Box 1700 STN CSC, Victoria, British Columbia, V8W 2Y2Canada
| | - Liangliang Wang
- Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BCV5A 1S6,Canada
| | - Jiguo Cao
- Department of Statistics and Actuarial Science, Simon Fraser University, Room SC K10545 8888 University Drive, Burnaby, BCV5A 1S6,Canada
| | - Farouk S Nathoo
- Department of Mathematics and Statistics, University of Victoria, Victoria, Canada
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25
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Shi Y, Zeng W, Deng J, Nie W, Zhang Y. The Identification of Alzheimer's Disease Using Functional Connectivity Between Activity Voxels in Resting-State fMRI Data. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2020; 8:1400211. [PMID: 32355582 PMCID: PMC7186217 DOI: 10.1109/jtehm.2020.2985022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 01/04/2020] [Accepted: 03/28/2020] [Indexed: 01/06/2023]
Abstract
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease occurring in the elderly population. The effective and accurate classification of AD symptoms by using functional magnetic resonance imaging (fMRI) has a great significance for the clinical diagnosis and prediction of AD patients. Methods: Therefore, this paper proposes a new method for identifying AD patients from healthy subjects by using functional connectivities (FCs) between the activity voxels in the brain based on fMRI data analysis. Firstly, independent component analysis is used to detect the activity voxels in the fMRI signals of AD patients and healthy subjects; Secondly, the FCs between the common activity voxels of the two groups are calculated, and then the FCs with significant differences are further identified by statistical analysis between them; Finally, the classification of AD patients from healthy subjects is realized by using FCs with significant differences as the feature samples in support vector machine. Results: The results show that the proposed identification method can obtain higher classification accuracy, and the FCs between activity voxels within prefrontal lobe as well as those between prefrontal and parietal lobes play an important role in the prediction of AD patients. Furthermore, we also find that more brain regions and much more voxels in some regions are activity in AD group compared with health control group. Conclusion: It has a great potential value for the AD pathogenesis mechanism study.
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Affiliation(s)
- Yuhu Shi
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Weiming Zeng
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Jin Deng
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Weifang Nie
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
| | - Yifei Zhang
- Information Engineering CollegeShanghai Maritime UniversityShanghai201306China
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26
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Solis E, Hascup KN, Hascup ER. Alzheimer's Disease: The Link Between Amyloid-β and Neurovascular Dysfunction. J Alzheimers Dis 2020; 76:1179-1198. [PMID: 32597813 PMCID: PMC7483596 DOI: 10.3233/jad-200473] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
While prevailing evidence supports that the amyloid cascade hypothesis is a key component of Alzheimer's disease (AD) pathology, many recent studies indicate that the vascular system is also a major contributor to disease progression. Vascular dysfunction and reduced cerebral blood flow (CBF) occur prior to the accumulation and aggregation of amyloid-β (Aβ) plaques and hyperphosphorylated tau tangles. Although research has predominantly focused on the cellular processes involved with Aβ-mediated neurodegeneration, effects of Aβ on CBF and neurovascular coupling are becoming more evident. This review will describe AD vascular disturbances as they relate to Aβ, including chronic cerebral hypoperfusion, hypertension, altered neurovascular coupling, and deterioration of the blood-brain barrier. In addition, we will describe recent findings about the relationship between these vascular defects and Aβ accumulation with emphasis on in vivo studies utilizing rodent AD models.
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Affiliation(s)
- Ernesto Solis
- Department of Neurology, Neuroscience Institute, Center for Alzheimer’s Disease and Related Disorders, Southern Illinois University School of Medicine, Springfield, IL, USA
| | - Kevin N. Hascup
- Department of Neurology, Neuroscience Institute, Center for Alzheimer’s Disease and Related Disorders, Southern Illinois University School of Medicine, Springfield, IL, USA
- Department of Pharmacology, Southern Illinois University School of Medicine, Springfield, IL, USA
- Department of Medical Microbiology, Immunology, and Cell Biology, Southern Illinois University School of Medicine, Springfield, IL, USA
| | - Erin R. Hascup
- Department of Neurology, Neuroscience Institute, Center for Alzheimer’s Disease and Related Disorders, Southern Illinois University School of Medicine, Springfield, IL, USA
- Department of Pharmacology, Southern Illinois University School of Medicine, Springfield, IL, USA
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27
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Xu JJ, Cui J, Feng Y, Yong W, Chen H, Chen YC, Yin X, Wu Y. Chronic Tinnitus Exhibits Bidirectional Functional Dysconnectivity in Frontostriatal Circuit. Front Neurosci 2019; 13:1299. [PMID: 31866810 PMCID: PMC6909243 DOI: 10.3389/fnins.2019.01299] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/20/2019] [Indexed: 12/28/2022] Open
Abstract
Purpose The phantom sound of tinnitus is considered to be associated with abnormal functional coupling between the nucleus accumbens (NAc) and the prefrontal cortex, which may form a frontostriatal top-down gating system to evaluate and modulate sensory signals. Resting-state functional magnetic resonance imaging (fMRI) was used to recognize the aberrant directional connectivity of the NAc in chronic tinnitus and to ascertain the relationship between this connectivity and tinnitus characteristics. Methods Participants included chronic tinnitus patients (n = 50) and healthy controls (n = 55), matched for age, sex, education, and hearing thresholds. The hearing status of both groups was comparable. On the basis of the NAc as a seed region, a Granger causality analysis (GCA) study was conducted to investigate the directional connectivity and the relationship with tinnitus duration or distress. Results Compared with healthy controls, tinnitus patients exhibited abnormal directional connectivity between the NAc and the prefrontal cortex, principally the middle frontal gyrus (MFG), orbitofrontal cortex (OFC), and inferior frontal gyrus (IFG). Additionally, positive correlations between tinnitus handicap questionnaire (THQ) scores and increased directional connectivity from the right NAc to the left MFG (r = 0.357, p = 0.015) and from the right MFG to the left NAc (r = 0.626, p < 0.001) were observed. Furthermore, the enhanced directional connectivity from the right NAc to the right OFC was positively associated with the duration of tinnitus (r = 0.599, p < 0.001). Conclusion In concurrence with expectations, tinnitus distress was correlated with enhanced directional connectivity between the NAc and the prefrontal cortex. The current study not only helps illuminate the neural basis of the frontostriatal gating control of tinnitus sensation but also contributes to deciphering the neuropathological features of tinnitus.
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Affiliation(s)
- Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Jinluan Cui
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Wei Yong
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yuanqing Wu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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28
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Xue J, Guo H, Gao Y, Wang X, Cui H, Chen Z, Wang B, Xiang J. Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer's Disease: A Resting-State fMRI Study. Front Aging Neurosci 2019; 11:326. [PMID: 31866850 PMCID: PMC6905409 DOI: 10.3389/fnagi.2019.00326] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 11/12/2019] [Indexed: 11/29/2022] Open
Abstract
The hippocampus is generally reported as one of the regions most impacted by Alzheimer's disease (AD) and is closely associated with memory function and orientation. Undirected functional connectivity (FC) alterations occur in patients with mild cognitive impairment (MCI) and AD, and these alterations have been the subject of many studies. However, abnormal patterns of directed FC remain poorly understood. In this study, to identify changes in directed FC between the hippocampus and other brain regions, Granger causality analysis (GCA) based on voxels was applied to resting-state functional magnetic resonance imaging (rs-fMRI) data from 29 AD, 65 MCI, and 30 normal control (NC) subjects. The results showed significant differences in the patterns of directed FC among the three groups. There were fewer brain regions showing changes in directed FC with the hippocampus in the MCI group than the NC group, and these regions were mainly located in the temporal lobe, frontal lobe, and cingulate cortex. However, regarding the abnormalities in directed FC in the AD group, the number of affected voxels was greater, the size of the clusters was larger, and the distribution was wider. Most of the abnormal connections were unidirectional and showed hemispheric asymmetry. In addition, we also investigated the correlations between the abnormal directional FCs and cognitive and clinical measurement scores in the three groups and found that some of them were significantly correlated. This study revealed abnormalities in the transmission and reception of information in the hippocampus of MCI and AD patients and offer insight into the neurophysiological mechanisms underlying MCI and AD.
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Affiliation(s)
| | | | | | | | | | | | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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29
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Liu J, Ji J, Jia X, Zhang A. Learning Brain Effective Connectivity Network Structure Using Ant Colony Optimization Combining With Voxel Activation Information. IEEE J Biomed Health Inform 2019; 24:2028-2040. [PMID: 31603829 DOI: 10.1109/jbhi.2019.2946676] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients.
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30
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019; 13:585. [PMID: 31249501 PMCID: PMC6582769 DOI: 10.3389/fnins.2019.00585] [Citation(s) in RCA: 281] [Impact Index Per Article: 56.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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31
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Li Y, Yao Z, Yu Y, Zou Y, Fu Y, Hu B. Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography. BMC Psychiatry 2019; 19:165. [PMID: 31159754 PMCID: PMC6547610 DOI: 10.1186/s12888-019-2149-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 05/17/2019] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND Amyloid β (Aβ) and tau proteins are considered as critical factors that affect Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although many studies have conducted on these two proteins, little study has investigated the relationship between their spatial distributions. This study aims to explore the associations of spatial patterns between Aβ deposition and tau deposition in patients with MCI and normal control (NC). METHODS We used multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with MCI and NC. All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing information of 65 patients with MCI and 75 NC who both had undergone AV45 (Aβ) and AV1451 (tau) PET. To assess the spatial distribution of Aβ and tau deposition, we employed parallel independent component analysis (pICA), which enabled the joint analysis of multimodal imaging data. pICA was conducted to identify the significant difference and correlation relationship of brain networks between Aβ PET and tau PET in MCI and NC groups. RESULTS Our results revealed the strongly correlated network between Aβ PET and tau PET were colocalized with the default-mode network (DMN). Simultaneously, in comparison of the spatial distribution between Aβ PET and tau PET, it was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks. The altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients. CONCLUSIONS Findings suggested the abnormal spatial distribution regions of tau PET were correlated with the abnormal spatial distribution regions of Aβ PET, and both of which were located in DMN network. This study revealed that combining pICA with multimodal imaging data is an effective approach for distinguishing MCI patients from NC group.
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Affiliation(s)
- Yuan Li
- grid.410585.dSchool of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province 250358 People’s Republic of China
| | - Zhijun Yao
- 0000 0000 8571 0482grid.32566.34School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province China
| | - Yue Yu
- 0000 0000 8571 0482grid.32566.34School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province China
| | - Ying Zou
- 0000 0000 8571 0482grid.32566.34School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province China
| | - Yu Fu
- 0000 0000 8571 0482grid.32566.34School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province China
| | - Bin Hu
- School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province, 250358, People's Republic of China. .,School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province, China.
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32
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Li C, Li Y, Zheng L, Zhu X, Shao B, Fan G, Liu T, Wang J. Abnormal Brain Network Connectivity in a Triple-Network Model of Alzheimer’s Disease. J Alzheimers Dis 2019; 69:237-252. [DOI: 10.3233/jad-181097] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Chenxi Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Liang Zheng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Xiaoqi Zhu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Bixin Shao
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Geng Fan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Tian Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, and Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, P.R. China
- National Engineering Research Center of Health Care and Medical Devices, Xi’an, P.R. China
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33
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Liao ZL, Tan YF, Qiu YJ, Zhu JP, Chen Y, Lin SS, Wu MH, Mao YP, Hu JJ, Ding ZX, Yu EY. Interhemispheric functional connectivity for Alzheimer's disease and amnestic mild cognitive impairment based on the triple network model. J Zhejiang Univ Sci B 2019; 19:924-934. [PMID: 30507076 DOI: 10.1631/jzus.b1800381] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The purpose of this study was to explore the differences in interhemispheric functional connectivity in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) based on a triple network model consisting of the default mode network (DMN), salience network (SN), and executive control network (ECN). The technique of voxel-mirrored homotopic connectivity (VMHC) analysis was applied to explore the aberrant connectivity of all patients. The results showed that: (1) the statistically significant connections of interhemispheric brain regions included DMN-related brain regions (i.e. precuneus, calcarine, fusiform, cuneus, lingual gyrus, temporal inferior gyrus, and hippocampus), SN-related brain regions (i.e. frontoinsular cortex), and ECN-related brain regions (i.e. frontal middle gyrus and frontal inferior); (2) the precuneus and frontal middle gyrus in the AD group exhibited lower VMHC values than those in the aMCI and healthy control (HC) groups, but no significant difference was observed between the aMCI and HC groups; and (3) significant correlations were found between peak VMHC results from the precuneus and Mini Mental State Examination (MMSE) and Montreal Cognitive Scale (MOCA) scores and their factor scores in the AD, aMCI, and AD plus aMCI groups, and between the results from the frontal middle gyrus and MOCA factor scores in the aMCI group. These findings indicated that impaired interhemispheric functional connectivity was observed in AD and could be a sensitive neuroimaging biomarker for AD. More specifically, the DMN was inhibited, while the SN and ECN were excited. VMHC results were correlated with MMSE and MOCA scores, highlighting that VMHC could be a sensitive neuroimaging biomarker for AD and the progression from aMCI to AD.
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Affiliation(s)
- Zheng-Luan Liao
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
| | - Yun-Fei Tan
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
| | - Ya-Ju Qiu
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
| | - Jun-Peng Zhu
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
| | - Yan Chen
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
| | - Si-Si Lin
- Psychiatry and Mental Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Ming-Hao Wu
- Psychiatry and Mental Health, Bengbu Medical College, Bengbu 233030, China
| | - Yan-Ping Mao
- Psychiatry and Mental Health, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Jiao-Jiao Hu
- Psychiatry and Mental Health, Bengbu Medical College, Bengbu 233030, China
| | - Zhong-Xiang Ding
- Department of Radiology, Hangzhou First People's Hospital, Hangzhou 310006, China
| | - En-Yan Yu
- Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou 310014, China.,Psychiatry and Mental Health, People's Hospital of Hangzhou Medical College, Hangzhou 310014, China
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34
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Forouzannezhad P, Abbaspour A, Fang C, Cabrerizo M, Loewenstein D, Duara R, Adjouadi M. A survey on applications and analysis methods of functional magnetic resonance imaging for Alzheimer’s disease. J Neurosci Methods 2019; 317:121-140. [DOI: 10.1016/j.jneumeth.2018.12.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 12/04/2018] [Accepted: 12/17/2018] [Indexed: 12/23/2022]
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35
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Alterations in effective connectivity within the Papez circuit are correlated with insulin resistance in T2DM patients without mild cognitive impairment. Brain Imaging Behav 2019; 14:1238-1246. [DOI: 10.1007/s11682-019-00049-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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36
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Scherr M, Utz L, Tahmasian M, Pasquini L, Grothe MJ, Rauschecker JP, Grimmer T, Drzezga A, Sorg C, Riedl V. Effective connectivity in the default mode network is distinctively disrupted in Alzheimer's disease-A simultaneous resting-state FDG-PET/fMRI study. Hum Brain Mapp 2019; 42:4134-4143. [PMID: 30697878 DOI: 10.1002/hbm.24517] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 12/08/2018] [Accepted: 12/28/2018] [Indexed: 02/02/2023] Open
Abstract
A prominent finding of postmortem and molecular imaging studies on Alzheimer's disease (AD) is the accumulation of neuropathological proteins in brain regions of the default mode network (DMN). Molecular models suggest that the progression of disease proteins depends on the directionality of signaling pathways. At network level, effective connectivity (EC) reflects directionality of signaling pathways. We hypothesized a specific pattern of EC in the DMN of patients with AD, related to cognitive impairment. Metabolic connectivity mapping is a novel measure of EC identifying regions of signaling input based on neuroenergetics. We simultaneously acquired resting-state functional MRI and FDG-PET data from patients with early AD (n = 35) and healthy subjects (n = 18) on an integrated PET/MR scanner. We identified two distinct subnetworks of EC in the DMN of healthy subjects: an anterior part with bidirectional EC between hippocampus and medial prefrontal cortex and a posterior part with predominant input into medial parietal cortex. Patients had reduced input into the medial parietal system and absent input from hippocampus into medial prefrontal cortex (p < 0.05, corrected). In a multiple linear regression with unimodal imaging and EC measures (F4,25 = 5.63, p = 0.002, r2 = 0.47), we found that EC (β = 0.45, p = 0.012) was stronger associated with cognitive deficits in patients than any of the PET and fMRI measures alone. Our approach indicates specific disruptions of EC in the DMN of patients with AD and might be suitable to test molecular theories about downstream and upstream spreading of neuropathology in AD.
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Affiliation(s)
- Martin Scherr
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neurology, Christian Doppler Medical Centre, Paracelsus Medical University Salzburg and Centre for Cognitive Neurosciences, Salzburg, Austria
| | - Lukas Utz
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Institute for Advanced Study, Technische Universität München (TUM), München, Germany
| | - Masoud Tahmasian
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Lorenzo Pasquini
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Memory and Aging Center, Department of Neurology, University of California, San Francisco, California
| | - Michel J Grothe
- Department for Clinical Research, German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Josef P Rauschecker
- Institute for Advanced Study, Technische Universität München (TUM), München, Germany.,Laboratory of Integrative Neuroscience and Cognition, Georgetown University Medical Center, Washington, District of Columbia
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany
| | | | - Christian Sorg
- Department of Psychiatry and Psychotherapy, Technische Universität München (TUM), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany
| | - Valentin Riedl
- TUM-Neuroimaging Center (TUM-NIC), Klinikum Rechts der Isar, München, Germany.,Department of Neuroradiology, Technische Universität München (TUM), München, Germany.,Department of Nuclear Medicine, Technische Universität München (TUM), München, Germany
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37
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Farahani FV, Karwowski W, Lighthall NR. Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review. Front Neurosci 2019. [PMID: 31249501 DOI: 10.3389/fnins.2019.00585/bibtex] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
Background: Analysis of the human connectome using functional magnetic resonance imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to discover the neural underpinnings of human cognition and neurological disorders. In general, brain connectivity patterns from fMRI data are classified as statistical dependencies (functional connectivity) or causal interactions (effective connectivity) among various neural units. Computational methods, especially graph theory-based methods, have recently played a significant role in understanding brain connectivity architecture. Objectives: Thanks to the emergence of graph theoretical analysis, the main purpose of the current paper is to systematically review how brain properties can emerge through the interactions of distinct neuronal units in various cognitive and neurological applications using fMRI. Moreover, this article provides an overview of the existing functional and effective connectivity methods used to construct the brain network, along with their advantages and pitfalls. Methods: In this systematic review, the databases Science Direct, Scopus, arXiv, Google Scholar, IEEE Xplore, PsycINFO, PubMed, and SpringerLink are employed for exploring the evolution of computational methods in human brain connectivity from 1990 to the present, focusing on graph theory. The Cochrane Collaboration's tool was used to assess the risk of bias in individual studies. Results: Our results show that graph theory and its implications in cognitive neuroscience have attracted the attention of researchers since 2009 (as the Human Connectome Project launched), because of their prominent capability in characterizing the behavior of complex brain systems. Although graph theoretical approach can be generally applied to either functional or effective connectivity patterns during rest or task performance, to date, most articles have focused on the resting-state functional connectivity. Conclusions: This review provides an insight into how to utilize graph theoretical measures to make neurobiological inferences regarding the mechanisms underlying human cognition and behavior as well as different brain disorders.
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Affiliation(s)
- Farzad V Farahani
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Waldemar Karwowski
- Computational Neuroergonomics Laboratory, Department of Industrial Engineering and Management Systems, University of Central Florida, Orlando, FL, United States
| | - Nichole R Lighthall
- Department of Psychology, University of Central Florida, Orlando, FL, United States
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38
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Altered functional brain networks in amnestic mild cognitive impairment: a resting-state fMRI study. Brain Imaging Behav 2018; 11:619-631. [PMID: 26972578 DOI: 10.1007/s11682-016-9539-0] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Amnestic mild cognitive impairment MCI (aMCI) has a high progression to Alzheimer's disease (AD). Recently, resting-state functional MRI (RS-fMRI) has been increasingly utilized in studying the pathogenesis of aMCI, especially in resting-state networks (RSNs). In the current study, we aimed to explore abnormal RSNs related to memory deficits in aMCI patients compared to the aged-matched healthy control group using RS-fMRI techniques. Firstly, we used ALFF (amplitude of low-frequency fluctuation) method to define the regions of interest (ROIs) which exhibited significant changes in aMCI compared with the control group. Then, we divided these ROIs into different networks in line with prior studies. The aim of this study is to explore the functional connectivity between these ROIs within networks and also to investigate the connectivity between networks. Comparing aMCI to the control group, our results showed that 1) the hippocampus (HIPP) had decreased FC with the medial prefrontal cortex (mPFC) and inferior parietal lobe (IPL), and the mPFC showed increased connectivity to IPL in the default mode network; 2) the thalamus showed decreased FC with the putamen and HIPP, and the HIPP showed increased connectivity to the putamen in the limbic system; 3) the supplementary motor area had decreased FC with the middle temporal gyrus and increased FC with the superior parietal lobe in the sensorimotor network; 4) increased connectivity between the lingual gyrus and middle occipital gyrus in the visual network; and 5) the DMN has reduced inter-network connectivities with the SMN and VN. These findings indicated that functional brain networks involved in cognition such as episodic memory, sensorimotor and visual cognition in aMCI were altered, and provided a new sight in understanding the important subtype of aMCI.
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Ochmann S, Dyrba M, Grothe MJ, Kasper E, Webel S, Hauenstein K, Teipel SJ. Does Functional Connectivity Provide a Marker for Cognitive Rehabilitation Effects in Alzheimer's Disease? An Interventional Study. J Alzheimers Dis 2018; 57:1303-1313. [PMID: 28372326 PMCID: PMC5409049 DOI: 10.3233/jad-160773] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
BACKGROUND Cognitive rehabilitation (CR) is a cognitive intervention for patients with Alzheimer's disease (AD) that aims to maintain everyday competences. The analysis of functional connectivity (FC) in resting-state functional MRI has been used to investigate the effects of cognitive interventions. OBJECTIVES We evaluated the effect of CR on the default mode network FC in a group of patients with mild AD, compared to an active control group. METHODS We performed a three-month interventional study including 16 patients with a diagnosis of AD. The intervention group (IG) consisted of eight patients, performing twelve sessions of CR. The active control group (CG) performed a standardized cognitive training. We used a seed region placed in the posterior cingulate cortex (PCC) for FC analysis, comparing scans acquired before and after the intervention. Effects were thresholded at a significance of p < 0.001 (uncorrected) and a minimal cluster size of 50 voxels. RESULTS The interaction of group by time showed a higher increase of PCC connectivity in IG compared to CG in the bilateral cerebellar cortex. CG revealed widespread, smaller clusters of higher FC increase compared with IG. Across all participants, an increase in quality of life was associated with connectivity increase over time in the bilateral precuneus. CONCLUSIONS CR showed an effect on the FC of the DMN in the IG. These effects need further study in larger samples to confirm if FC analysis may suit as a surrogate marker for the effect of cognitive interventions in AD.
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Affiliation(s)
- Sina Ochmann
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany
| | - Martin Dyrba
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany
| | - Michel J Grothe
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany
| | - Elisabeth Kasper
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany
| | - Steffi Webel
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany
| | - Karlheinz Hauenstein
- Institute of Diagnostic and Interventional Radiology, University Medicine Rostock, Rostock, Germany
| | - Stefan J Teipel
- DZNE, German Center for Neurodegenerative Diseases, Site Rostock/Greifswald, Germany.,Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
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40
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Neitzel J, Nuttall R, Sorg C. Perspectives on How Human Simultaneous Multi-Modal Imaging Adds Directionality to Spread Models of Alzheimer's Disease. Front Neurol 2018; 9:26. [PMID: 29434570 PMCID: PMC5790782 DOI: 10.3389/fneur.2018.00026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Accepted: 01/12/2018] [Indexed: 12/31/2022] Open
Abstract
Previous animal research suggests that the spread of pathological agents in Alzheimer’s disease (AD) follows the direction of signaling pathways. Specifically, tau pathology has been suggested to propagate in an infection-like mode along axons, from transentorhinal cortices to medial temporal lobe cortices and consequently to other cortical regions, while amyloid-beta (Aβ) pathology seems to spread in an activity-dependent manner among and from isocortical regions into limbic and then subcortical regions. These directed connectivity-based spread models, however, have not been tested directly in AD patients due to the lack of an in vivo method to identify directed connectivity in humans. Recently, a new method—metabolic connectivity mapping (MCM)—has been developed and validated in healthy participants that uses simultaneous FDG-PET and resting-state fMRI data acquisition to identify directed intrinsic effective connectivity (EC). To this end, postsynaptic energy consumption (FDG-PET) is used to identify regions with afferent input from other functionally connected brain regions (resting-state fMRI). Here, we discuss how this multi-modal imaging approach allows quantitative, whole-brain mapping of signaling direction in AD patients, thereby pointing out some of the advantages it offers compared to other EC methods (i.e., Granger causality, dynamic causal modeling, Bayesian networks). Most importantly, MCM provides the basis on which models of pathology spread, derived from animal studies, can be tested in AD patients. In particular, future work should investigate whether tau and Aβ in humans propagate along the trajectories of directed connectivity in order to advance our understanding of the neuropathological mechanisms causing disease progression.
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Affiliation(s)
- Julia Neitzel
- Department of General and Experimental Psychology, Ludwig-Maximilians-Universität (LMU), München, Germany.,TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
| | - Rachel Nuttall
- TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
| | - Christian Sorg
- TUM-Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Neuroradiology, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany.,Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technische Universität München (TUM), München, Germany
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41
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Xu J, Yin X, Ge H, Han Y, Pang Z, Liu B, Liu S, Friston K. Heritability of the Effective Connectivity in the Resting-State Default Mode Network. Cereb Cortex 2017; 27:5626-5634. [PMID: 27913429 DOI: 10.1093/cercor/bhw332] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2023] Open
Affiliation(s)
- Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin 300350, P.R. China
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, Shandong, P.R. China
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - Xuntao Yin
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, Shandong, P.R. China
| | - Haitao Ge
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, Shandong, P.R. China
| | - Yan Han
- Department of Radiology, Affiliated Hospital of Medical College, Qingdao University, Qingdao, Shandong, P.R. China
| | - Zengchang Pang
- Department of Epidemiology, Qingdao Municipal Central for Disease Control and Prevention, Qingdao, Shandong, P.R. China
| | - Baolin Liu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin 300350, P.R. China
| | - Shuwei Liu
- Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, Shandong, P.R. China
| | - Karl Friston
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
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42
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Yao Z, Liao M, Hu T, Zhang Z, Zhao Y, Zheng F, Gutknecht J, Majoe D, Hu B, Li L. An Effective Method to Identify Adolescent Generalized Anxiety Disorder by Temporal Features of Dynamic Functional Connectivity. Front Hum Neurosci 2017; 11:492. [PMID: 29081741 PMCID: PMC5645525 DOI: 10.3389/fnhum.2017.00492] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Accepted: 09/26/2017] [Indexed: 02/02/2023] Open
Abstract
Generalized anxiety disorder (GAD) is one of common anxiety disorders in adolescents. Although adolescents with GAD are thought to be at high risk for other mental diseases, the disease-specific alterations have not been adequately explored. Recent studies have revealed the abnormal functional connectivity (FC) in adolescents with GAD. Most previous researches have investigated the static FC which ignores the fluctuations of FC over time and focused on the structures of “fear circuit”. To figure out the alterations of dynamic FC caused by GAD and the possibilities of dynamic FC as biomarkers, we propose an effective approach to identify adolescent GAD using temporal features derived from dynamic FC. In our study, the instantaneous synchronization of pairwise signals was estimated as dynamic FC. The Hurst exponent (H) and variance, indicating regularity and variable degree of a time series respectively, were calculated as temporal features of dynamic FC. By leave-one-out cross-validation (LOOCV), a relatively high accuracy of 88.46% could be achieved when H and variance of dynamic FC were combined as features. In addition, we identified the disease-related regions, including regions belonging to default mode (DM) and cerebellar networks. The results suggest that temporal features of dynamic FC could achieve a clinically acceptable diagnostic power and serve as biomarkers of adolescent GAD. Furthermore, our work could be helpful in understanding the pathophysiological mechanism of adolescent GAD.
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Affiliation(s)
- Zhijun Yao
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Mei Liao
- Mental Health Institute, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Tao Hu
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Yu Zhao
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Fang Zheng
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Jürg Gutknecht
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
| | - Dennis Majoe
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
| | - Bin Hu
- Key Laboratory of Wearable Computing of Gansu Province, Lanzhou University, Lanzhou, China
| | - Lingjiang Li
- Mental Health Institute, The Second Xiangya Hospital of Central South University, Changsha, China
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43
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Wang M, Liao Z, Mao D, Zhang Q, Li Y, Yu E, Ding Z. Application of Granger Causality Analysis of the Directed Functional Connection in Alzheimer's Disease and Mild Cognitive Impairment. J Vis Exp 2017. [PMID: 28809833 DOI: 10.3791/56015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Impaired functional connectivity in the Default Mode Network (DMN) may be involved in the progression of Alzheimer's Disease (AD). The Posterior Cingulate Cortex (PCC) is a potential imaging marker for monitoring the progression of AD. Previous studies did not focus on the functional connectivity between the PCC and nodes in regions outside the DMN, but our study is an effort to explore these overlooked functional connections. For collecting data, we used functional Magnetic Resonance Imaging (fMRI) and Granger Causality Analysis (GCA). fMRI provides a non-invasive method for studying the dynamic interactions between the different brain regions. GCA is a statistical hypothesis test for determining whether one-time series is useful in forecasting another. In simple terms, it is judged by comparing the "Known all the information on the last moment, the distribution of the probability of X at this time" and the "Known all the information on the last moment except Y, the distribution of the probability of X at this time", to determine whether there is a causal relationship between Y and X. This definition is based on the complete information source and stationary chronological sequence. The main step of this analysis is to use X and Y to establish the regression equation and draw a causal relationship by a hypothetical test. Since GCA can measure causal effects, we used it to investigate the anisotropy of the functional connectivity and explore the hub function of the PCC. Here, we screened 116 participants for MRI scanning, and after preprocessing the data obtained from neuroimaging, we used GCA to derive the causal relationship of each node. Finally, we concluded that the directed connection is significantly different between the Mild Cognitive Impairment (MCI) and AD groups, both from the PCC to the whole brain and from the whole brain to the PCC.
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Affiliation(s)
- Mei Wang
- Zhejiang Chinese Medical University
| | - Zhengluan Liao
- Department of Psychiatry, Zhejiang Provincial People's Hospital
| | - Dewang Mao
- Department of Radiology, Zhejiang Provincial People's Hospital
| | - Qi Zhang
- Zhejiang Chinese Medical University
| | - Yumei Li
- Department of Radiology, Zhejiang Provincial People's Hospital
| | - Enyan Yu
- Department of Psychiatry, Zhejiang Provincial People's Hospital
| | - Zhongxiang Ding
- Department of Radiology, Zhejiang Provincial People's Hospital;
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44
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Li Y, Yao H, Lin P, Zheng L, Li C, Zhou B, Wang P, Zhang Z, Wang L, An N, Wang J, Zhang X. Frequency-Dependent Altered Functional Connections of Default Mode Network in Alzheimer's Disease. Front Aging Neurosci 2017; 9:259. [PMID: 28824420 PMCID: PMC5540901 DOI: 10.3389/fnagi.2017.00259] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 07/20/2017] [Indexed: 11/26/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disorder associated with the progressive dysfunction of cognitive ability. Previous research has indicated that the default mode network (DMN) is closely related to cognition and is impaired in Alzheimer’s disease. Because recent studies have shown that different frequency bands represent specific physiological functions, DMN functional connectivity studies of the different frequency bands based on resting state fMRI (RS-fMRI) data may provide new insight into AD pathophysiology. In this study, we explored the functional connectivity based on well-defined DMN regions of interest (ROIs) from the five frequency bands: slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz), slow-2 (0.198–0.25 Hzs) and standard low-frequency oscillations (LFO) (0.01–0.08 Hz). We found that the altered functional connectivity patterns are mainly in the frequency band of slow-5 and slow-4 and that the decreased connections are long distance, but some relatively short connections are increased. In addition, the altered functional connections of the DMN in AD are frequency dependent and differ between the slow-5 and slow-4 bands. Mini-Mental State Examination scores were significantly correlated with the altered functional connectivity patterns in the slow-5 and slow-4 bands. These results indicate that frequency-dependent functional connectivity changes might provide potential biomarkers for AD pathophysiology.
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Affiliation(s)
- Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'an, China.,National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University BranchXi'an, China
| | - Hongxiang Yao
- Department of Radiology, Chinese PLA General HospitalBeijing, China
| | - Pan Lin
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'an, China.,National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University BranchXi'an, China
| | - Liang Zheng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'an, China.,National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University BranchXi'an, China
| | - Chenxi Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'an, China.,National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University BranchXi'an, China
| | - Bo Zhou
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General HospitalBeijing, China
| | - Pan Wang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General HospitalBeijing, China.,Department of Neurology, Tianjin Huanhu HospitalTianjin, China
| | - Zengqiang Zhang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General HospitalBeijing, China.,Hainan Branch of Chinese PLA General HospitalSanya, China
| | - Luning Wang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General HospitalBeijing, China
| | - Ningyu An
- Department of Radiology, Chinese PLA General HospitalBeijing, China
| | - Jue Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'an, China.,National Engineering Research Center of Health Care and Medical Devices, Xi'an Jiaotong University BranchXi'an, China
| | - Xi Zhang
- Department of Neurology, Institute of Geriatrics and Gerontology, Chinese PLA General HospitalBeijing, China
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45
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Zhao S, Rangaprakash D, Venkataraman A, Liang P, Deshpande G. Investigating Focal Connectivity Deficits in Alzheimer's Disease Using Directional Brain Networks Derived from Resting-State fMRI. Front Aging Neurosci 2017; 9:211. [PMID: 28729831 PMCID: PMC5498531 DOI: 10.3389/fnagi.2017.00211] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Accepted: 06/15/2017] [Indexed: 01/17/2023] Open
Abstract
Connectivity analysis of resting-state fMRI has been widely used to identify biomarkers of Alzheimer's disease (AD) based on brain network aberrations. However, it is not straightforward to interpret such connectivity results since our understanding of brain functioning relies on regional properties (activations and morphometric changes) more than connections. Further, from an interventional standpoint, it is easier to modulate the activity of regions (using brain stimulation, neurofeedback, etc.) rather than connections. Therefore, we employed a novel approach for identifying focal directed connectivity deficits in AD compared to healthy controls. In brief, we present a model of directed connectivity (using Granger causality) that characterizes the coupling among different regions in healthy controls and Alzheimer's disease. We then characterized group differences using a (between-subject) generative model of pathology, which generates latent connectivity variables that best explain the (within-subject) directed connectivity. Crucially, our generative model at the second (between-subject) level explains connectivity in terms of local or regionally specific abnormalities. This allows one to explain disconnections among multiple regions in terms of regionally specific pathology; thereby offering a target for therapeutic intervention. Two foci were identified, locus coeruleus in the brain stem and right orbitofrontal cortex. Corresponding disrupted connectivity network associated with the foci showed that the brainstem is the critical focus of disruption in AD. We further partitioned the aberrant connectomic network into four unique sub-networks, which likely leads to symptoms commonly observed in AD. Our findings suggest that fMRI studies of AD, which have been largely cortico-centric, could in future investigate the role of brain stem in AD.
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Affiliation(s)
- Sinan Zhao
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, United States
| | - D Rangaprakash
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, United States.,Department of Psychiatry and Biobehavioral Sciences, University of California, Los AngelesLos Angeles, CA, United States
| | - Archana Venkataraman
- Department of Electrical and Computer Engineering, Johns Hopkins UniversityBaltimore, MD, United States
| | - Peipeng Liang
- Department of Radiology, Xuanwu Hospital, Capital Medical UniversityBeijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijing, China.,Key Laboratory for Neurodegenerative Diseases, Ministry of EducationBeijing, China
| | - Gopikrishna Deshpande
- AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn UniversityAuburn, AL, United States.,Department of Psychology, Auburn UniversityAuburn, AL, United States.,Alabama Advanced Imaging Consortium, Auburn University and University of Alabama BirminghamAuburn, AL, United States
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46
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High-sensitivity neuroimaging biomarkers for the identification of amnestic mild cognitive impairment based on resting-state fMRI and a triple network model. Brain Imaging Behav 2017; 13:1-14. [DOI: 10.1007/s11682-017-9727-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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47
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Zhang Y, Wang L, Yang J, Yan R, Zhang J, Sang L, Li P, Liu H, Qiu M. Abnormal functional networks in resting-state of the sub-cortical chronic stroke patients with hemiplegia. Brain Res 2017; 1663:51-58. [PMID: 28214523 DOI: 10.1016/j.brainres.2017.02.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Revised: 12/31/2016] [Accepted: 02/12/2017] [Indexed: 10/20/2022]
Abstract
The aim of this study is to identify the properties of the motor network and the default-mode network (DMN) of the sub-cortical chronic stroke patients, and to study the relationship between the network connectivity and the neurological scales of the stroke patients. Twenty-eight chronic stroke patients (28-77days post-stroke) and twenty-eight healthy control subjects (HCs) were recruited. Independent component analysis (ICA) was performed to obtain the motor network and the DMN. Two sample t-tests was used to compare the differences of the motor network and the DMN between the patients and HCs. Additionally, correlations between the network connectivity and the behavioral scores of the stroke patients were studied. Compared with the HCs, the motor network connectivity of the stroke patients was significantly increased in the contralesional superior parietal lobule, but decreased in ipsilesional M1. The DMN connectivity of the stroke patients was significantly increased in the contralesional middle frontal gyrus, but decreased in bilateral precuneus, ipsilesional supramarginal and angular gyrus. Moreover, the motor network connectivity of the contralesional superior parietal lobule was positively correlated with the Fugl-Meyer assessment (FMA) score of the stroke patients. Our results showed abnormal motor network and DMN during the resting-state of the stroke patients, suggesting that resting-state network connectivity could serve as biomarkers for future stroke studies. Brain-behavior relationships could be taken into account while evaluating stroke patients.
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Affiliation(s)
- Ye Zhang
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Jun Yang
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Rubing Yan
- Department of Rehabilitation, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Linqiong Sang
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Hongliang Liu
- Department of Rehabilitation, Southwest Hospital, Third Military Medical University, Chongqing, China.
| | - Mingguo Qiu
- Department of Medical Image, College of Biomedical Engineering, Third Military Medical University, Chongqing, China.
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48
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Chen YC, Xia W, Chen H, Feng Y, Xu JJ, Gu JP, Salvi R, Yin X. Tinnitus distress is linked to enhanced resting-state functional connectivity from the limbic system to the auditory cortex. Hum Brain Mapp 2017; 38:2384-2397. [PMID: 28112466 DOI: 10.1002/hbm.23525] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 12/15/2016] [Accepted: 12/15/2016] [Indexed: 02/04/2023] Open
Abstract
The phantom sound of tinnitus is believed to be triggered by aberrant neural activity in the central auditory pathway, but since this debilitating condition is often associated with emotional distress and anxiety, these comorbidities likely arise from maladaptive functional connections to limbic structures such as the amygdala and hippocampus. To test this hypothesis, resting-state functional magnetic resonance imaging (fMRI) was used to identify aberrant effective connectivity of the amygdala and hippocampus in tinnitus patients and to determine the relationship with tinnitus characteristics. Chronic tinnitus patients (n = 26) and age-, sex-, and education-matched healthy controls (n = 23) were included. Both groups were comparable for hearing level. Granger causality analysis utilizing the amygdala and hippocampus as seed regions were used to investigate the directional connectivity and the relationship with tinnitus duration or distress. Relative to healthy controls, tinnitus patients demonstrated abnormal directional connectivity of the amygdala and hippocampus, including primary and association auditory cortex, and other non-auditory areas. Importantly, scores on the Tinnitus Handicap Questionnaires were positively correlated with increased connectivity from the left amygdala to left superior temporal gyrus (r = 0.570, P = 0.005), and from the right amygdala to right superior temporal gyrus (r = 0.487, P = 0.018). Moreover, enhanced effective connectivity from the right hippocampus to left transverse temporal gyrus was correlated with tinnitus duration (r = 0.452, P = 0.030). The results showed that tinnitus distress strongly correlates with enhanced effective connectivity that is directed from the amygdala to the auditory cortex. The longer the phantom sensation, the more likely acute tinnitus becomes permanently encoded by memory traces in the hippocampus. Hum Brain Mapp 38:2384-2397, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Wenqing Xia
- Department of Endocrinology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Huiyou Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Yuan Feng
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Jin-Jing Xu
- Department of Otolaryngology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Jian-Ping Gu
- Department of Vascular and Interventional Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
| | - Richard Salvi
- Center for Hearing and Deafness, University at Buffalo, State University of New York, Buffalo, 14214, New York
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, 210006, Nanjing, China
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49
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Zhang J, Guo Z, Liu X, Jia X, Li J, Li Y, Lv D, Chen W. Abnormal functional connectivity of the posterior cingulate cortex is associated with depressive symptoms in patients with Alzheimer's disease. Neuropsychiatr Dis Treat 2017; 13:2589-2598. [PMID: 29066900 PMCID: PMC5644530 DOI: 10.2147/ndt.s146077] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Depressive symptoms are significant and very common psychiatric complications in patients with Alzheimer's disease (AD), which can aggravate the decline in social function. However, changes in the functional connectivity (FC) of the brain in AD patients with depressive symptoms (D-AD) remain unclear. OBJECTIVE To investigate whether any differences exist in the FC of the posterior cingulate cortex (PCC) between D-AD patients and non-depressed AD patients (nD-AD). MATERIALS AND METHODS We recruited 15 D-AD patients and 17 age-, sex-, educational level-, and Mini-Mental State Examination (MMSE)-matched nD-AD patients to undergo tests using the Neuropsychiatric Inventory, Hamilton Depression Rating Scale, and 3.0T resting-state functional magnetic resonance imaging. Bilateral PCC were selected as the regions of interest and between-group differences in the PCC FC network were assessed using Student's t-test. RESULTS Compared with the nD-AD group, D-AD patients showed increased PCC FC in the right amygdala, right parahippocampus, right superior temporal pole, right middle temporal lobe, right middle temporal pole, and right hippocampus (AlphaSim correction; P<0.05). In the nD-AD group, MMSE scores were positively correlated with PCC FC in the right superior temporal pole and right hippocampus (false discovery rate corrected; P<0.05). CONCLUSION Differences were detected in PCC FC between nD-AD and D-AD patients, which may be related to depressive symptoms. Our study provides a significant enhancement to our understanding of the functional mechanisms underlying D-AD.
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Affiliation(s)
- Jiangtao Zhang
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Zhongwei Guo
- Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiaozheng Liu
- China-USA Neuroimaging Research Institute & Department of Radiology, the Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xize Jia
- Center for Cognitive Brain Disorders & Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China
| | - Jiapeng Li
- Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yaoyao Li
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
| | - Danmei Lv
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.,Key Laboratory of Medical Neurobiology of Chinese Ministry of Health, Hangzhou, Zhejiang, China
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McKenna F, Koo BB, Killiany R. Comparison of ApoE-related brain connectivity differences in early MCI and normal aging populations: an fMRI study. Brain Imaging Behav 2016; 10:970-983. [PMID: 26409470 DOI: 10.1007/s11682-015-9451-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this study, we used resting-state functional magnetic resonance imaging (rs-fMRI) scans from subjects with early mild cognitive impairment (EMCI) and control subjects to study functional network connectivity. The scans were acquired by the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used genetic data from the ADNI database to further subdivide the EMCI and control groups into genotype groups with or without the Apolipoprotein E allele e4 (APOE e4). Region of interest (ROI)-to-ROI resting-state functional connectivity was measured using Freesurfer and the Functional Connectivity Toolbox for Matlab (CONN). In our analysis, we compared whole-brain ROI connectivity strength and ROI-to-ROI functional network connectivity strength between EMCI, control and genotype subject groups. We found that the ROI network properties were disrupted in EMCI and APOE e4 carrier groups. Notably, we show that (1) EMCI disrupts functional connectivity strength in many important functionally-linked areas; (2) APOE e4 disrupts functional connectivity strength in similar areas to EMCI; and (3) the differences in functional connectivity between groups shows a multifactor contribution to functional network dysfunction along the trajectory leading to dementia.
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Affiliation(s)
- Faye McKenna
- Bioimaging Program, Boston University School of Medicine, 650 Albany Street, Boston, MA, 02118, USA.
| | - Bang-Bon Koo
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 72 East Concord Street, Boston, MA, 02118, USA
| | - Ronald Killiany
- Department of Anatomy and Neurobiology, Boston University School of Medicine, 72 East Concord Street, Boston, MA, 02118, USA.,Center for Biomedical Imaging Boston University School of Medicine, 650 Albany Street, Boston, MA, 02118, USA
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