Quantitative prediction of individual cognitive flexibility using structural MRI.
Cognitive flexibility, a core dimension of executive functions, refers to one's ability to switch between multiple tasks and sets in a quick and flexible manner. However, whether objective neuroimaging can be used to quantitatively predict cognitive flexibility at the individual level remains largel...
| Publicado en: | Brain Imaging & Behavior Vol. 13; no. 3; pp. 781 - 789 |
|---|---|
| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=136693251&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136693251 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19317557 3GSC jtl: Brain Imaging & Behavior issn: 19317557 maglogo: N pubinfo: dt: Jun2019 vid: 13 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136693251 136693251 NLM29855990 10.1007/s11682-018-9905-1 NLM29855990 136693251 ppf: 781 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Quantitative prediction of individual cognitive flexibility using structural MRI. aug: au: Zhu, Jiajia Zhu, Dao-min Zhang, Cun Wang, Yajun Yang, Ying Yu, Yongqiang affil: Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Shushan District, 230022, Hefei, China sug: subj: Executive Function Cognition Gray Matter Pathology Male Female Brain Pathology Brain Mapping Image Processing, Computer Assisted Adult Temporal Lobe Pathology Individuality Neuropsychological Tests Magnetic Resonance Imaging Methods Adult: 19-44 years Male Female ab: Cognitive flexibility, a core dimension of executive functions, refers to one's ability to switch between multiple tasks and sets in a quick and flexible manner. However, whether objective neuroimaging can be used to quantitatively predict cognitive flexibility at the individual level remains largely unexplored. High-resolution magnetic resonance imaging data of 100 healthy young participants from the Human Connectome Project (HCP) dataset were used to calculate gray matter volume (GMV). Cognitive flexibility was assessed by the Dimensional Change Card Sort Test (DCCS). Using a multivariate machine learning technique known as relevance vector regression (RVR), we examined the relationship between GMV and cognitive flexibility performance. We found that the application of RVR to GMV allowed quantitative prediction of the DCCS scores with statistically significant accuracy (correlation = 0.41, P = 0.0001; mean squared error = 73.35, P = 0.0001). Accurate prediction was mainly based on GMV in the temporal regions. In addition, a univariate approach also revealed an inverse association between DCCS scores and GMV in the temporal areas. Our findings provide preliminary support to the development of neuroimaging techniques as a useful means to inform the cognitive assessment of individuals. Furthermore, the significant contribution of temporal regions suggests the prominent role of temporal cortex morphology in individual differences in cognitive flexibility. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|