Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study
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 obta...
Gespeichert in:
Veröffentlicht in: | Frontiers in neural circuits 2021-01, Vol.14, p.593263-593263, Article 593263 |
---|---|
Hauptverfasser: | , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 593263 |
---|---|
container_issue | |
container_start_page | 593263 |
container_title | Frontiers in neural circuits |
container_volume | 14 |
creator | Sendi, Mohammad S. E. Zendehrouh, Elaheh Miller, Robyn L. Fu, Zening Du, Yuhui Liu, Jingyu Mormino, Elizabeth C. Salat, David H. Calhoun, Vince D. |
description | 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 sens |
doi_str_mv | 10.3389/fncir.2020.593263 |
format | Article |
fullrecord | <record><control><sourceid>proquest_webof</sourceid><recordid>TN_cdi_webofscience_primary_000614408000001CitationCount</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_caf9c928c5984009a00eb279837f6714</doaj_id><sourcerecordid>2479578006</sourcerecordid><originalsourceid>FETCH-LOGICAL-c493t-45cd3eb728bbb59979f1ccbc79931bdcddcb06d70986511bf5d55207d7b3e5743</originalsourceid><addsrcrecordid>eNqNkktv1DAUhSMEoqXwA9ggSyxAQjPYcRzHLJBGUwqVhoJ4rC0_bloPiV3spNWw4LfjzJRRywpvfGV_5_hxT1E8JXhOaSNet964OC9xiedM0LKm94pDUtfljBGC79-qD4pHKa0xrsuaVQ-LA0oZI5xVh8XvRffrAlwP8UVCxy6BSoA-x7AGM7jg0UkMPToLsVcdGgL66DqLjqEHPziFvkDbZQ4scpkc_VaSwTMYrkP8gZbB-8nnyg2bN2iBVsGfu2G0boK-5mLzuHjQqi7Bk5v5qPh-8u7b8sNs9en96XKxmplK0GFWMWMpaF42WmsmBBctMUYbLgQl2hprjca15Vg0dX6vbpllrMTcck2B8YoeFac7XxvUWl5G16u4kUE5uV0I8VyqODjTgTSqFUaUjWGiqTAWCmPQJRcN5W3NyeT1dud1OeoerMl_EVV3x_TujncX8jxcSd6w7Euywcsbgxh-jpAG2btkoOuUhzAmWVYNr0qOqwl9_g-6DmPM3zdRXDDe5KZmiuwoE0NKEdr9ZQiWU1LkNilySorcJSVrnt1-xV7xNxoZaHbANejQJuPAG9hjOJ9Lqgrn8_MgSzeoqfnLMPohS1_9v5T-AYUn3JE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2479578006</pqid></control><display><type>article</type><title>Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>PubMed Central Open Access</source><source>Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" /></source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Alma/SFX Local Collection</source><creator>Sendi, Mohammad S. E. ; Zendehrouh, Elaheh ; Miller, Robyn L. ; Fu, Zening ; Du, Yuhui ; Liu, Jingyu ; Mormino, Elizabeth C. ; Salat, David H. ; Calhoun, Vince D.</creator><creatorcontrib>Sendi, Mohammad S. E. ; Zendehrouh, Elaheh ; Miller, Robyn L. ; Fu, Zening ; Du, Yuhui ; Liu, Jingyu ; Mormino, Elizabeth C. ; Salat, David H. ; Calhoun, Vince D.</creatorcontrib><description>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.</description><identifier>ISSN: 1662-5110</identifier><identifier>EISSN: 1662-5110</identifier><identifier>DOI: 10.3389/fncir.2020.593263</identifier><identifier>PMID: 33551754</identifier><language>eng</language><publisher>LAUSANNE: Frontiers Media Sa</publisher><subject>Age ; Aged ; Aging ; Alzheimer Disease - physiopathology ; Alzheimer Disease - psychology ; Alzheimer's disease ; Brain - physiopathology ; Brain mapping ; Brain Mapping - methods ; Brain research ; Cerebellum ; Cognitive ability ; Datasets ; Dementia ; Dementia - physiopathology ; Dementia disorders ; dynamic functional network connectivity ; Female ; Functional magnetic resonance imaging ; hidden Markov model ; Humans ; Life Sciences & Biomedicine ; Longitudinal Studies ; longitudinal study ; Magnetic Resonance Imaging - methods ; Male ; Markov chains ; Middle Aged ; Nerve Net - physiopathology ; Neural networks ; Neurodegenerative diseases ; Neuroimaging ; Neuroscience ; Neurosciences ; Neurosciences & Neurology ; resting state fMR imaging ; Science & Technology ; Sensorimotor system ; Sensory integration ; Time series</subject><ispartof>Frontiers in neural circuits, 2021-01, Vol.14, p.593263-593263, Article 593263</ispartof><rights>Copyright © 2021 Sendi, Zendehrouh, Miller, Fu, Du, Liu, Mormino, Salat and Calhoun.</rights><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Copyright © 2021 Sendi, Zendehrouh, Miller, Fu, Du, Liu, Mormino, Salat and Calhoun. 2021 Sendi, Zendehrouh, Miller, Fu, Du, Liu, Mormino, Salat and Calhoun</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>true</woscitedreferencessubscribed><woscitedreferencescount>34</woscitedreferencescount><woscitedreferencesoriginalsourcerecordid>wos000614408000001</woscitedreferencesoriginalsourcerecordid><citedby>FETCH-LOGICAL-c493t-45cd3eb728bbb59979f1ccbc79931bdcddcb06d70986511bf5d55207d7b3e5743</citedby><cites>FETCH-LOGICAL-c493t-45cd3eb728bbb59979f1ccbc79931bdcddcb06d70986511bf5d55207d7b3e5743</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859281/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859281/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,865,886,2103,2115,27929,27930,39263,53796,53798</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33551754$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sendi, Mohammad S. E.</creatorcontrib><creatorcontrib>Zendehrouh, Elaheh</creatorcontrib><creatorcontrib>Miller, Robyn L.</creatorcontrib><creatorcontrib>Fu, Zening</creatorcontrib><creatorcontrib>Du, Yuhui</creatorcontrib><creatorcontrib>Liu, Jingyu</creatorcontrib><creatorcontrib>Mormino, Elizabeth C.</creatorcontrib><creatorcontrib>Salat, David H.</creatorcontrib><creatorcontrib>Calhoun, Vince D.</creatorcontrib><title>Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study</title><title>Frontiers in neural circuits</title><addtitle>FRONT NEURAL CIRCUIT</addtitle><addtitle>Front Neural Circuits</addtitle><description>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.</description><subject>Age</subject><subject>Aged</subject><subject>Aging</subject><subject>Alzheimer Disease - physiopathology</subject><subject>Alzheimer Disease - psychology</subject><subject>Alzheimer's disease</subject><subject>Brain - physiopathology</subject><subject>Brain mapping</subject><subject>Brain Mapping - methods</subject><subject>Brain research</subject><subject>Cerebellum</subject><subject>Cognitive ability</subject><subject>Datasets</subject><subject>Dementia</subject><subject>Dementia - physiopathology</subject><subject>Dementia disorders</subject><subject>dynamic functional network connectivity</subject><subject>Female</subject><subject>Functional magnetic resonance imaging</subject><subject>hidden Markov model</subject><subject>Humans</subject><subject>Life Sciences & Biomedicine</subject><subject>Longitudinal Studies</subject><subject>longitudinal study</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Markov chains</subject><subject>Middle Aged</subject><subject>Nerve Net - physiopathology</subject><subject>Neural networks</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Neuroscience</subject><subject>Neurosciences</subject><subject>Neurosciences & Neurology</subject><subject>resting state fMR imaging</subject><subject>Science & Technology</subject><subject>Sensorimotor system</subject><subject>Sensory integration</subject><subject>Time series</subject><issn>1662-5110</issn><issn>1662-5110</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNkktv1DAUhSMEoqXwA9ggSyxAQjPYcRzHLJBGUwqVhoJ4rC0_bloPiV3spNWw4LfjzJRRywpvfGV_5_hxT1E8JXhOaSNet964OC9xiedM0LKm94pDUtfljBGC79-qD4pHKa0xrsuaVQ-LA0oZI5xVh8XvRffrAlwP8UVCxy6BSoA-x7AGM7jg0UkMPToLsVcdGgL66DqLjqEHPziFvkDbZQ4scpkc_VaSwTMYrkP8gZbB-8nnyg2bN2iBVsGfu2G0boK-5mLzuHjQqi7Bk5v5qPh-8u7b8sNs9en96XKxmplK0GFWMWMpaF42WmsmBBctMUYbLgQl2hprjca15Vg0dX6vbpllrMTcck2B8YoeFac7XxvUWl5G16u4kUE5uV0I8VyqODjTgTSqFUaUjWGiqTAWCmPQJRcN5W3NyeT1dud1OeoerMl_EVV3x_TujncX8jxcSd6w7Euywcsbgxh-jpAG2btkoOuUhzAmWVYNr0qOqwl9_g-6DmPM3zdRXDDe5KZmiuwoE0NKEdr9ZQiWU1LkNilySorcJSVrnt1-xV7xNxoZaHbANejQJuPAG9hjOJ9Lqgrn8_MgSzeoqfnLMPohS1_9v5T-AYUn3JE</recordid><startdate>20210121</startdate><enddate>20210121</enddate><creator>Sendi, Mohammad S. E.</creator><creator>Zendehrouh, Elaheh</creator><creator>Miller, Robyn L.</creator><creator>Fu, Zening</creator><creator>Du, Yuhui</creator><creator>Liu, Jingyu</creator><creator>Mormino, Elizabeth C.</creator><creator>Salat, David H.</creator><creator>Calhoun, Vince D.</creator><general>Frontiers Media Sa</general><general>Frontiers Research Foundation</general><general>Frontiers Media S.A</general><scope>BLEPL</scope><scope>DTL</scope><scope>HGBXW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88I</scope><scope>8FE</scope><scope>8FH</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M2P</scope><scope>M7P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20210121</creationdate><title>Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study</title><author>Sendi, Mohammad S. E. ; Zendehrouh, Elaheh ; Miller, Robyn L. ; Fu, Zening ; Du, Yuhui ; Liu, Jingyu ; Mormino, Elizabeth C. ; Salat, David H. ; Calhoun, Vince D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c493t-45cd3eb728bbb59979f1ccbc79931bdcddcb06d70986511bf5d55207d7b3e5743</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Aged</topic><topic>Aging</topic><topic>Alzheimer Disease - physiopathology</topic><topic>Alzheimer Disease - psychology</topic><topic>Alzheimer's disease</topic><topic>Brain - physiopathology</topic><topic>Brain mapping</topic><topic>Brain Mapping - methods</topic><topic>Brain research</topic><topic>Cerebellum</topic><topic>Cognitive ability</topic><topic>Datasets</topic><topic>Dementia</topic><topic>Dementia - physiopathology</topic><topic>Dementia disorders</topic><topic>dynamic functional network connectivity</topic><topic>Female</topic><topic>Functional magnetic resonance imaging</topic><topic>hidden Markov model</topic><topic>Humans</topic><topic>Life Sciences & Biomedicine</topic><topic>Longitudinal Studies</topic><topic>longitudinal study</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>Markov chains</topic><topic>Middle Aged</topic><topic>Nerve Net - physiopathology</topic><topic>Neural networks</topic><topic>Neurodegenerative diseases</topic><topic>Neuroimaging</topic><topic>Neuroscience</topic><topic>Neurosciences</topic><topic>Neurosciences & Neurology</topic><topic>resting state fMR imaging</topic><topic>Science & Technology</topic><topic>Sensorimotor system</topic><topic>Sensory integration</topic><topic>Time series</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sendi, Mohammad S. E.</creatorcontrib><creatorcontrib>Zendehrouh, Elaheh</creatorcontrib><creatorcontrib>Miller, Robyn L.</creatorcontrib><creatorcontrib>Fu, Zening</creatorcontrib><creatorcontrib>Du, Yuhui</creatorcontrib><creatorcontrib>Liu, Jingyu</creatorcontrib><creatorcontrib>Mormino, Elizabeth C.</creatorcontrib><creatorcontrib>Salat, David H.</creatorcontrib><creatorcontrib>Calhoun, Vince D.</creatorcontrib><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>Science Database</collection><collection>Biological Science Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Frontiers in neural circuits</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sendi, Mohammad S. E.</au><au>Zendehrouh, Elaheh</au><au>Miller, Robyn L.</au><au>Fu, Zening</au><au>Du, Yuhui</au><au>Liu, Jingyu</au><au>Mormino, Elizabeth C.</au><au>Salat, David H.</au><au>Calhoun, Vince D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study</atitle><jtitle>Frontiers in neural circuits</jtitle><stitle>FRONT NEURAL CIRCUIT</stitle><addtitle>Front Neural Circuits</addtitle><date>2021-01-21</date><risdate>2021</risdate><volume>14</volume><spage>593263</spage><epage>593263</epage><pages>593263-593263</pages><artnum>593263</artnum><issn>1662-5110</issn><eissn>1662-5110</eissn><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.</abstract><cop>LAUSANNE</cop><pub>Frontiers Media Sa</pub><pmid>33551754</pmid><doi>10.3389/fncir.2020.593263</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1662-5110 |
ispartof | Frontiers in neural circuits, 2021-01, Vol.14, p.593263-593263, Article 593263 |
issn | 1662-5110 1662-5110 |
language | eng |
recordid | cdi_webofscience_primary_000614408000001CitationCount |
source | MEDLINE; DOAJ Directory of Open Access Journals; PubMed Central Open Access; Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; EZB-FREE-00999 freely available EZB journals; PubMed Central; Alma/SFX Local Collection |
subjects | Age Aged Aging Alzheimer Disease - physiopathology Alzheimer Disease - psychology Alzheimer's disease Brain - physiopathology Brain mapping Brain Mapping - methods Brain research Cerebellum Cognitive ability Datasets Dementia Dementia - physiopathology Dementia disorders dynamic functional network connectivity Female Functional magnetic resonance imaging hidden Markov model Humans Life Sciences & Biomedicine Longitudinal Studies longitudinal study Magnetic Resonance Imaging - methods Male Markov chains Middle Aged Nerve Net - physiopathology Neural networks Neurodegenerative diseases Neuroimaging Neuroscience Neurosciences Neurosciences & Neurology resting state fMR imaging Science & Technology Sensorimotor system Sensory integration Time series |
title | Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-11T20%3A52%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Alzheimer's%20Disease%20Projection%20From%20Normal%20to%20Mild%20Dementia%20Reflected%20in%20Functional%20Network%20Connectivity:%20A%20Longitudinal%20Study&rft.jtitle=Frontiers%20in%20neural%20circuits&rft.au=Sendi,%20Mohammad%20S.%20E.&rft.date=2021-01-21&rft.volume=14&rft.spage=593263&rft.epage=593263&rft.pages=593263-593263&rft.artnum=593263&rft.issn=1662-5110&rft.eissn=1662-5110&rft_id=info:doi/10.3389/fncir.2020.593263&rft_dat=%3Cproquest_webof%3E2479578006%3C/proquest_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2479578006&rft_id=info:pmid/33551754&rft_doaj_id=oai_doaj_org_article_caf9c928c5984009a00eb279837f6714&rfr_iscdi=true |