Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment
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| Titel: | Machine learning analysis reveals aberrant dynamic changes in amplitude of low-frequency fluctuations among patients with retinal detachment |
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| Autoren: | Yu Ji, Yuan-yuan Wang, Qi Cheng, Wen-wen Fu, Shui-qin Huang, Pei-pei Zhong, Xiao-lin Chen, Ben-liang Shu, Bin Wei, Qin-yi Huang, Xiao-rong Wu |
| Quelle: | Front Neurosci Frontiers in Neuroscience, Vol 17 (2023) |
| Verlagsinformationen: | Frontiers Media SA, 2023. |
| Publikationsjahr: | 2023 |
| Schlagwörter: | k-means clustering method, retinal detachment, dynamic amplitude of low-frequency fluctuation, resting-state functional magnetic resonance imaging, brain region, Neurosciences. Biological psychiatry. Neuropsychiatry, sliding window, 3. Good health, RC321-571, Neuroscience |
| Beschreibung: | BackgroundThere is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients.AimThis study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach.MethodsWe investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier.ResultsRD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxel-level p p ConclusionOur findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis. |
| Publikationsart: | Article Other literature type |
| ISSN: | 1662-453X |
| DOI: | 10.3389/fnins.2023.1227081 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/37547140 https://doaj.org/article/9cc40a46195e426e8c2ae6fd5f427f2c |
| Rights: | CC BY |
| Dokumentencode: | edsair.doi.dedup.....beab0e4cb1cd5035d7f8a219d0f2179f |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | BackgroundThere is increasing evidence that patients with retinal detachment (RD) have aberrant brain activity. However, neuroimaging investigations remain focused on static changes in brain activity among RD patients. There is limited knowledge regarding the characteristics of dynamic brain activity in RD patients.AimThis study evaluated changes in dynamic brain activity among RD patients, using a dynamic amplitude of low-frequency fluctuation (dALFF), k-means clustering method and support vector machine (SVM) classification approach.MethodsWe investigated inter-group disparities of dALFF indices under three different time window sizes using resting-state functional magnetic resonance imaging (rs-fMRI) data from 23 RD patients and 24 demographically matched healthy controls (HCs). The k-means clustering method was performed to analyze specific dALFF states and related temporal properties. Additionally, we selected altered dALFF values under three distinct conditions as classification features for distinguishing RD patients from HCs using an SVM classifier.ResultsRD patients exhibited dynamic changes in local intrinsic indicators of brain activity. Compared with HCs, RD patients displayed increased dALFF in the bilateral middle frontal gyrus, left putamen (Putamen_L), left superior occipital gyrus (Occipital_Sup_L), left middle occipital gyrus (Occipital_Mid_L), right calcarine (Calcarine_R), right middle temporal gyrus (Temporal_Mid_R), and right inferior frontal gyrus (Frontal_Inf_Tri_R). Additionally, RD patients showed significantly decreased dALFF values in the right superior parietal gyrus (Parietal_Sup_R) and right paracentral lobule (Paracentral_Lobule_R) [two-tailed, voxel-level p p ConclusionOur findings offer important insights concerning the neuropathology that underlies RD and provide robust evidence that dALFF, a local indicator of brain activity, may be useful for clinical diagnosis. |
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| ISSN: | 1662453X |
| DOI: | 10.3389/fnins.2023.1227081 |