Connectome-based models can predict processing speed in older adults
Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-12-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381192030776X |
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| author | Mengxia Gao Clive H.Y. Wong Huiyuan Huang Robin Shao Ruiwang Huang Chetwyn C.H. Chan Tatia M.C. Lee |
| author_facet | Mengxia Gao Clive H.Y. Wong Huiyuan Huang Robin Shao Ruiwang Huang Chetwyn C.H. Chan Tatia M.C. Lee |
| author_sort | Mengxia Gao |
| collection | DOAJ |
| description | Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process. |
| first_indexed | 2024-12-14T07:33:54Z |
| format | Article |
| id | doaj.art-04659af96b6a4e93866cbbf929963f39 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-14T07:33:54Z |
| publishDate | 2020-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-04659af96b6a4e93866cbbf929963f392022-12-21T23:11:16ZengElsevierNeuroImage1095-95722020-12-01223117290Connectome-based models can predict processing speed in older adultsMengxia Gao0Clive H.Y. Wong1Huiyuan Huang2Robin Shao3Ruiwang Huang4Chetwyn C.H. Chan5Tatia M.C. Lee6The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, ChinaThe State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, ChinaSchool of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, ChinaThe State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, ChinaSchool of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China; Corresponding authors.Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hum Hom, Hong Kong; Corresponding authors.The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong, China; Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Guangzhou, China; Corresponding authors.Decrement in processing speed (PS) is a primary cognitive morbidity in clinical populations and could significantly influence other cognitive functions, such as attention and memory. Verifying the usefulness of connectome-based models for predicting neurocognitive abilities has significant translational implications on clinical and aging research. In this study, we verified that resting-state functional connectivity could be used to predict PS in 99 older adults by using connectome-based predictive modeling (CPM). We identified two distinct connectome patterns across the whole brain: the fast-PS and slow-PS networks. Relative to the slow-PS network, the fast-PS network showed more within-network connectivity in the motor and visual networks and less between-network connectivity in the motor-visual, motor-subcortical/cerebellum and motor-frontoparietal networks. We further verified that the connectivity patterns for prediction of PS were also useful for predicting attention and memory in the same sample. To test the generalizability and specificity of the connectome-based predictive models, we applied these two connectome models to an independent sample of three age groups (101 younger adults, 103 middle-aged adults and 91 older adults) and confirmed these models could specifically be generalized to predict PS of the older adults, but not the younger and middle-aged adults. Taking all the findings together, the identified connectome-based predictive models are strong for predicting PS in older adults. The application of CPM to predict neurocognitive abilities can complement conventional neurocognitive assessments, bring significant clinical benefits to patient management and aid the clinical diagnoses, prognoses and management of people undergoing the aging process.http://www.sciencedirect.com/science/article/pii/S105381192030776XConnectome-based predictive modelsFunctional connectivityProcessing speedResting-stateOlder adults |
| spellingShingle | Mengxia Gao Clive H.Y. Wong Huiyuan Huang Robin Shao Ruiwang Huang Chetwyn C.H. Chan Tatia M.C. Lee Connectome-based models can predict processing speed in older adults NeuroImage Connectome-based predictive models Functional connectivity Processing speed Resting-state Older adults |
| title | Connectome-based models can predict processing speed in older adults |
| title_full | Connectome-based models can predict processing speed in older adults |
| title_fullStr | Connectome-based models can predict processing speed in older adults |
| title_full_unstemmed | Connectome-based models can predict processing speed in older adults |
| title_short | Connectome-based models can predict processing speed in older adults |
| title_sort | connectome based models can predict processing speed in older adults |
| topic | Connectome-based predictive models Functional connectivity Processing speed Resting-state Older adults |
| url | http://www.sciencedirect.com/science/article/pii/S105381192030776X |
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