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      Alzheimer’s Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study

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          Abstract

          Background

          Alzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart.

          Method

          We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM.

          Results

          All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks.

          Conclusion

          Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.

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          Most cited references67

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Tracking whole-brain connectivity dynamics in the resting state.

            Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.
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              Alzheimer's disease.

              Alzheimer's disease is a chronic illness with long preclinical and prodromal phases (20 years) and an average clinical duration of 8-10 years. The disease has an estimated prevalence of 10-30% in the population >65 years of age with an incidence of 1-3%. Most patients with Alzheimer's disease (>95%) have the sporadic form, which is characterized by a late onset (80-90 years of age), and is the consequence of the failure to clear the amyloid-β (Aβ) peptide from the interstices of the brain. A large number of genetic risk factors for sporadic disease have been identified. A small proportion of patients (<1%) have inherited mutations in genes that affect processing of Aβ and develop the disease at a much younger age (mean age of ∼45 years). Detection of the accumulation of Aβ is now possible in preclinical and prodromal phases using cerebrospinal fluid biomarkers and PET. Several approved drugs ameliorate some of the symptoms of Alzheimer's disease, but no current interventions can modify the underlying disease mechanisms. Management is focused on the support of the social networks surrounding the patient and the treatment of any co-morbid illnesses, such as cerebrovascular disease.
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                Author and article information

                Contributors
                Journal
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                1662-5110
                21 January 2021
                2020
                : 14
                : 593263
                Affiliations
                [1] 1Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University , Atlanta, GA, United States
                [2] 2School of Electrical and Computer Engineering, Georgia Institute of Technology , Atlanta, GA, United States
                [3] 3Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University , Atlanta, GA, United States
                [4] 4Department of Computer Science, Georgia State University , Atlanta, GA, United States
                [5] 5School of Computer and Information Technology, Shanxi University , Taiyuan, China
                [6] 6School of Medicine, Stanford University , Palo Alto, CA, United States
                [7] 7Department of Neurology and Neurological Sciences, School of Medicine, Stanford University , Stanford, CA, United States
                [8] 8Harvard Medical School , Cambridge, MA, United States
                [9] 9Massachusetts General Hospital , Boston, MA, United States
                Author notes

                Edited by: Alessandro Gozzi, Italian Institute of Technology (IIT), Italy

                Reviewed by: Hannes Almgren, Ghent University, Belgium; Shella Keilholz, Emory University, United States; Aldo Cordova-Palomera, Stanford University, United States

                *Correspondence: Vince D. Calhoun, vcalhoun@ 123456gsu.edu
                Mohammad S. E. Sendi, mseslampanah@ 123456gatech.edu
                Article
                10.3389/fncir.2020.593263
                7859281
                33551754
                df3ba0d8-e345-4ea1-8a0d-e87cb5bd6a90
                Copyright © 2021 Sendi, Zendehrouh, Miller, Fu, Du, Liu, Mormino, Salat and Calhoun.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 10 August 2020
                : 15 December 2020
                Page count
                Figures: 6, Tables: 3, Equations: 5, References: 66, Pages: 15, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                alzheimer’s disease,resting state fmr imaging,hidden markov model,longitudinal study,dynamic functional network connectivity

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