Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification
Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC ne...
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| Vydáno v: | Human brain mapping Ročník 38; číslo 10; s. 5019 - 5034 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
John Wiley & Sons, Inc
01.10.2017
John Wiley and Sons Inc |
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| ISSN: | 1065-9471, 1097-0193, 1097-0193 |
| On-line přístup: | Získat plný text |
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| Abstract | Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co‐variations of the blood oxygenation level‐dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS‐fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor‐based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS‐fMRI data. Moreover, a sliding window approach is further used to partition the voxel‐wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS‐fMRI data alone. Hum Brain Mapp 38:5019–5034, 2017. © 2017 Wiley Periodicals, Inc. |
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| AbstractList | Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc. Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co‐variations of the blood oxygenation level‐dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS‐fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor‐based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS‐fMRI data. Moreover, a sliding window approach is further used to partition the voxel‐wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS‐fMRI data alone. Hum Brain Mapp 38:5019–5034, 2017 . © 2017 Wiley Periodicals, Inc. Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc. |
| Author | Shen, Celina Zhang, Han Zhang, Lichi Lee, Seong‐Whan Shen, Dinggang Chen, Xiaobo |
| AuthorAffiliation | 1 Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill North Carolina 2 Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea |
| AuthorAffiliation_xml | – name: 1 Department of Radiology and BRIC University of North Carolina at Chapel Hill Chapel Hill North Carolina – name: 2 Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea |
| Author_xml | – sequence: 1 givenname: Xiaobo orcidid: 0000-0001-9940-1637 surname: Chen fullname: Chen, Xiaobo organization: University of North Carolina at Chapel Hill – sequence: 2 givenname: Han surname: Zhang fullname: Zhang, Han organization: University of North Carolina at Chapel Hill – sequence: 3 givenname: Lichi surname: Zhang fullname: Zhang, Lichi organization: University of North Carolina at Chapel Hill – sequence: 4 givenname: Celina surname: Shen fullname: Shen, Celina organization: University of North Carolina at Chapel Hill – sequence: 5 givenname: Seong‐Whan surname: Lee fullname: Lee, Seong‐Whan organization: Korea University – sequence: 6 givenname: Dinggang surname: Shen fullname: Shen, Dinggang email: dgshen@med.unc.edu organization: Korea University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28665045$$D View this record in MEDLINE/PubMed |
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| ISSN | 1065-9471 1097-0193 |
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| Issue | 10 |
| Keywords | mild cognitive impairment functional connectivity functional correlation tensor resting-state fMRI Alzheimer's disease |
| Language | English |
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| Notes | Xiaobo Chen and Han Zhang are co‐first authors. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
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| PublicationDate | October 2017 |
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| PublicationTitle | Human brain mapping |
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| Publisher | John Wiley & Sons, Inc John Wiley and Sons Inc |
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| Snippet | Brain functional connectivity (FC) extracted from resting‐state fMRI (RS‐fMRI) has become a popular approach for diagnosing various neurodegenerative diseases,... Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases,... |
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| SubjectTerms | Alzheimer's disease Brain Brain - diagnostic imaging Brain - physiopathology Brain Mapping Cerebrovascular Circulation - physiology Classification Cognitive ability Cognitive Dysfunction - classification Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - physiopathology Diagnosis Diagnosis, Computer-Assisted - methods Diffusion Tensor Imaging Feature extraction functional connectivity functional correlation tensor Functional magnetic resonance imaging Gray Matter - diagnostic imaging Gray Matter - physiopathology Humans Learning algorithms Machine Learning Magnetic Resonance Imaging - methods Mathematical analysis mild cognitive impairment Neural networks Neural Pathways - diagnostic imaging Neural Pathways - physiopathology Neurodegenerative diseases Neuroimaging Neurological diseases Oxygen - blood Oxygenation resting‐state fMRI Segments Sensitivity and Specificity Substantia alba Substantia grisea Tensors White Matter - diagnostic imaging White Matter - physiopathology |
| Title | Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification |
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