An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy
PurposeFunctional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on rest...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2024.1468246/full |
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| author | Zhengping Pu Zhengping Pu Hongna Huang Man Li Hongyan Li Xiaoyan Shen Qingfeng Wu Qin Ni Yong Lin Donghong Cui |
| author_facet | Zhengping Pu Zhengping Pu Hongna Huang Man Li Hongyan Li Xiaoyan Shen Qingfeng Wu Qin Ni Yong Lin Donghong Cui |
| author_sort | Zhengping Pu |
| collection | DOAJ |
| description | PurposeFunctional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.MethodsFunctional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals. Differences in FC were analyzed among the groups. FC strength and neuropsychological test scores were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95% confidence interval (CI) values.ResultsStatistical analysis revealed a trend toward compensatory enhanced prefrontal FC in SCD and MCI individuals. The models showed a satisfactory ability to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 94.9% for MCI vs. NC, 79.4% for MCI vs. SCD, and 77.0% for SCD vs. NC were achieved, and the highest AUC values were 97.5% (95% CI: 95.0%–100.0%) for MCI vs. NC, 83.7% (95% CI: 77.5%–89.8%) for MCI vs. SCD, and 80.6% (95% CI: 72.7%–88.4%) for SCD vs. NC.ConclusionThe developed screening method based on resting-state prefrontal FC measured by fNIRS and machine learning may help predict early-stage cognitive impairment. |
| format | Article |
| id | doaj-art-f8d2e5d5cb6141749264340318f8b29e |
| institution | Kabale University |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-f8d2e5d5cb6141749264340318f8b29e2025-01-08T06:12:04ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-01-011610.3389/fnagi.2024.14682461468246An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopyZhengping Pu0Zhengping Pu1Hongna Huang2Man Li3Hongyan Li4Xiaoyan Shen5Qingfeng Wu6Qin Ni7Yong Lin8Donghong Cui9Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaDepartment of Psychogeriatrics, Kangci Hospital of Jiaxing, Tongxiang, Zhejiang, ChinaShanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaPurposeFunctional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.MethodsFunctional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals. Differences in FC were analyzed among the groups. FC strength and neuropsychological test scores were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95% confidence interval (CI) values.ResultsStatistical analysis revealed a trend toward compensatory enhanced prefrontal FC in SCD and MCI individuals. The models showed a satisfactory ability to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 94.9% for MCI vs. NC, 79.4% for MCI vs. SCD, and 77.0% for SCD vs. NC were achieved, and the highest AUC values were 97.5% (95% CI: 95.0%–100.0%) for MCI vs. NC, 83.7% (95% CI: 77.5%–89.8%) for MCI vs. SCD, and 80.6% (95% CI: 72.7%–88.4%) for SCD vs. NC.ConclusionThe developed screening method based on resting-state prefrontal FC measured by fNIRS and machine learning may help predict early-stage cognitive impairment.https://www.frontiersin.org/articles/10.3389/fnagi.2024.1468246/fullsubjective cognitive declinemild cognitive impairmentfunctional near-infrared spectroscopymachine learningprefrontal cortexresting-state functional connectivity |
| spellingShingle | Zhengping Pu Zhengping Pu Hongna Huang Man Li Hongyan Li Xiaoyan Shen Qingfeng Wu Qin Ni Yong Lin Donghong Cui An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy Frontiers in Aging Neuroscience subjective cognitive decline mild cognitive impairment functional near-infrared spectroscopy machine learning prefrontal cortex resting-state functional connectivity |
| title | An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy |
| title_full | An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy |
| title_fullStr | An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy |
| title_full_unstemmed | An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy |
| title_short | An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy |
| title_sort | exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting state prefrontal functional connectivity assessed by functional near infrared spectroscopy |
| topic | subjective cognitive decline mild cognitive impairment functional near-infrared spectroscopy machine learning prefrontal cortex resting-state functional connectivity |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2024.1468246/full |
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