Information Flow Pattern in Early Mild Cognitive Impairment Patients
Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI.Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with availa...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:601fb051857f4c9c92ea543a62d1af892021-11-11T05:27:26ZInformation Flow Pattern in Early Mild Cognitive Impairment Patients1664-229510.3389/fneur.2021.706631https://doaj.org/article/601fb051857f4c9c92ea543a62d1af892021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fneur.2021.706631/fullhttps://doaj.org/toc/1664-2295Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI.Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state.Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures.Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state.Haijuan HeShuang DingChunhui JiangYuanyuan WangQiaoya LuoYunling WangAlzheimer's Disease Neuroimaging InitiativeFrontiers Media S.A.articleresting state functional MRIinformation flowsupport vector classificationsupport vector regressionearly mild cognitive impairmentNeurology. Diseases of the nervous systemRC346-429ENFrontiers in Neurology, Vol 12 (2021) |
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| topic |
resting state functional MRI information flow support vector classification support vector regression early mild cognitive impairment Neurology. Diseases of the nervous system RC346-429 |
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resting state functional MRI information flow support vector classification support vector regression early mild cognitive impairment Neurology. Diseases of the nervous system RC346-429 Haijuan He Shuang Ding Chunhui Jiang Yuanyuan Wang Qiaoya Luo Yunling Wang Alzheimer's Disease Neuroimaging Initiative Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| description |
Purpose: To investigate the brain information flow pattern in patients with early mild cognitive impairment (EMCI) and explore its potential ability of differentiation and prediction for EMCI.Methods: In this study, 49 patients with EMCI and 40 age- and sex-matched healthy controls (HCs) with available resting-state functional MRI images and neurological measures [including the neuropsychological evaluation and cerebrospinal fluid (CSF) biomarkers] were included from the Alzheimer's Disease Neuroimaging Initiative. Functional MRI measures including preferred information flow direction between brain regions and preferred information flow index of each brain region parcellated by the Atlas of Intrinsic Connectivity of Homotopic Areas (AICHA) were calculated by using non-parametric multiplicative regression-Granger causality analysis (NPMR-GCA). Edge- and node-wise Student's t-test was conducted for between-group comparison. Support vector classification was performed to differentiate EMCI from HC. The least absolute shrinkage and selection operator (lasso) regression were used to evaluate the predictive ability of information flow measures for the neurological state.Results: Compared to HC, disturbed preferred information flow directions between brain regions involving default mode network (DMN), executive control network (ECN), somatomotor network (SMN), and visual network (VN) were observed in patients with EMCI. An altered preferred information flow index in several brain regions (including the thalamus, posterior cingulate, and precentral gyrus) was also observed. Classification accuracy of 80% for differentiating patients with EMCI from HC was achieved by using the preferred information flow directions. The preferred information flow directions have a good ability to predict memory and executive function, level of amyloid β, tau protein, and phosphorylated tau protein with the high Pearson's correlation coefficients (r > 0.7) between predictive and actual neurological measures.Conclusion: Patients with EMCI were presented with a disturbed brain information flow pattern, which could help clinicians to identify patients with EMCI and assess their neurological state. |
| format |
article |
| author |
Haijuan He Shuang Ding Chunhui Jiang Yuanyuan Wang Qiaoya Luo Yunling Wang Alzheimer's Disease Neuroimaging Initiative |
| author_facet |
Haijuan He Shuang Ding Chunhui Jiang Yuanyuan Wang Qiaoya Luo Yunling Wang Alzheimer's Disease Neuroimaging Initiative |
| author_sort |
Haijuan He |
| title |
Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| title_short |
Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| title_full |
Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| title_fullStr |
Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| title_full_unstemmed |
Information Flow Pattern in Early Mild Cognitive Impairment Patients |
| title_sort |
information flow pattern in early mild cognitive impairment patients |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/601fb051857f4c9c92ea543a62d1af89 |
| work_keys_str_mv |
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| _version_ |
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