The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes
Background: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural fe...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2024
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| Online Access: | https://hdl.handle.net/10356/174743 |
| _version_ | 1826111013005557760 |
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| author | Vipin, Ashwati Lee, Bernett Teck Kwong Kumar, Dilip Soo, See Ann Leow, Yi Jin Ghildiyal, Smriti Lee, Faith Phemie Hui En Hilal, Saima Kandiah, Nagaendran |
| author2 | Lee Kong Chian School of Medicine (LKCMedicine) |
| author_facet | Lee Kong Chian School of Medicine (LKCMedicine) Vipin, Ashwati Lee, Bernett Teck Kwong Kumar, Dilip Soo, See Ann Leow, Yi Jin Ghildiyal, Smriti Lee, Faith Phemie Hui En Hilal, Saima Kandiah, Nagaendran |
| author_sort | Vipin, Ashwati |
| collection | NTU |
| description | Background: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. Methods: Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. Results: Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). Conclusions: Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes. |
| first_indexed | 2024-10-01T02:43:39Z |
| format | Journal Article |
| id | ntu-10356/174743 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:43:39Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1747432024-04-14T15:40:46Z The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes Vipin, Ashwati Lee, Bernett Teck Kwong Kumar, Dilip Soo, See Ann Leow, Yi Jin Ghildiyal, Smriti Lee, Faith Phemie Hui En Hilal, Saima Kandiah, Nagaendran Lee Kong Chian School of Medicine (LKCMedicine) Duke-NUS Medical School Dementia Research Centre Centre for Biomedical Informatics Medicine, Health and Life Sciences Cognitive syndromes Grey matter perfusion Background: The use of structural and perfusion brain imaging in combination with behavioural information in the prediction of cognitive syndromes using a data-driven approach remains to be explored. Here, we thus examined the contribution of brain structural and perfusion imaging and behavioural features to the existing classification of cognitive syndromes using a data-driven approach. Methods: Study participants belonged to the community-based Biomarker and Cognition Cohort Study in Singapore who underwent neuropsychological assessments, structural-functional MRI and blood biomarkers. Participants had a diagnosis of cognitively normal (CN), subjective cognitive impairment (SCI), mild cognitive impairment (MCI) and dementia. Cross-sectional structural and cerebral perfusion imaging, behavioural scale data including mild behaviour impairment checklist, Pittsburgh Sleep Quality Index and Depression, Anxiety and Stress scale data were obtained. Results: Three hundred seventy-three participants (mean age 60.7 years; 56% female sex) with complete data were included. Principal component analyses demonstrated that no single modality was informative for the classification of cognitive syndromes. However, multivariate glmnet analyses revealed a specific combination of frontal perfusion and temporo-frontal grey matter volume were key protective factors while the severity of mild behaviour impairment interest sub-domain and poor sleep quality were key at-risk factors contributing to the classification of CN, SCI, MCI and dementia (p < 0.0001). Moreover, the glmnet model showed best classification accuracy in differentiating between CN and MCI cognitive syndromes (AUC = 0.704; sensitivity = 0.698; specificity = 0.637). Conclusions: Brain structure, perfusion and behavioural features are important in the classification of cognitive syndromes and should be incorporated by clinicians and researchers. These findings illustrate the value of using multimodal data when examining syndrome severity and provide new insights into how cerebral perfusion and behavioural impairment influence classification of cognitive syndromes. Ministry of Education (MOE) Nanyang Technological University National Medical Research Council (NMRC) Published version This study received funding support from the Strategic Academic Initiative grant from the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, MOE AcRF Tier 3 Award MOE2017-T3-1-002 and National Medical Research Council, Singapore under its Clinician Scientist Award (MOH-CSAINV18nov-0007). 2024-04-09T01:17:17Z 2024-04-09T01:17:17Z 2024 Journal Article Vipin, A., Lee, B. T. K., Kumar, D., Soo, S. A., Leow, Y. J., Ghildiyal, S., Lee, F. P. H. E., Hilal, S. & Kandiah, N. (2024). The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes. Alzheimer's Research & Therapy, 16(1), 40-. https://dx.doi.org/10.1186/s13195-024-01410-1 1758-9193 https://hdl.handle.net/10356/174743 10.1186/s13195-024-01410-1 38368378 2-s2.0-85185400979 1 16 40 en MOE2017-T3-1-002 MOH-CSAINV18nov-0007 Alzheimer's Research & Therapy © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences Cognitive syndromes Grey matter perfusion Vipin, Ashwati Lee, Bernett Teck Kwong Kumar, Dilip Soo, See Ann Leow, Yi Jin Ghildiyal, Smriti Lee, Faith Phemie Hui En Hilal, Saima Kandiah, Nagaendran The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title | The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title_full | The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title_fullStr | The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title_full_unstemmed | The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title_short | The role of perfusion, grey matter volume and behavioural phenotypes in the data-driven classification of cognitive syndromes |
| title_sort | role of perfusion grey matter volume and behavioural phenotypes in the data driven classification of cognitive syndromes |
| topic | Medicine, Health and Life Sciences Cognitive syndromes Grey matter perfusion |
| url | https://hdl.handle.net/10356/174743 |
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