Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome.
Hum Brain Mapp
; 43(12): 3775-3791, 2022 08 15.
Article
em En
| MEDLINE
| ID: mdl-35475571
ABSTRACT
An emerging trend is to use regression-based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome-based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI] 633 healthy older adults; Human Connectome Projects in Aging [HCP-A] 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic-Net models are more accurate and robust in connectome-based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Conectoma
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Substância Branca
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Aged
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Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article