Dataset Information

Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults.

ABSTRACT: Part B of the Trail Making Test (TMT-B) is widely used as a quick and easy to administer measure of executive dysfunction. The current study investigated the relationships between TMT-B performance, brain volumes, cortical thickness and white matter water diffusion characteristics in a large sample of older participants, before and after controlling for processing speed. Four hundred and eleven healthy, community-dwelling older adults who were all born in 1936 were assessed on TMT-B, 5 tests of processing speed, and provided contemporaneous structural and diffusion MRI data. Significant relationships were found between slower TMT-B completion times and thinner cortex in the frontal, temporal and inferior parietal regions as well as the Sylvian fissure/insula. Slower TMT-B completion time was also significantly associated with poorer white matter microstructure of the left anterior thalamic radiation, and the right uncinate fasciculus. The majority of these associations were markedly attenuated when additionally controlling for processing speed. These data suggest that individual differences in processing speed contribute to the associations between TMT-B completion time and the grey and white matter structure of older adults.

SUBMITTER: MacPherson SE

PROVIDER: S-EPMC5637162 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults.

MacPherson Sarah E SE Cox Simon R SR Dickie David A DA Karama Sherif S Starr John M JM Evans Alan C AC Bastin Mark E ME Wardlaw Joanna M JM Deary Ian J IJ

Cortex; a journal devoted to the study of the nervous system and behavior 20170809

Part B of the Trail Making Test (TMT-B) is widely used as a quick and easy to administer measure of executive dysfunction. The current study investigated the relationships between TMT-B performance, brain volumes, cortical thickness and white matter water diffusion characteristics in a large sample of older participants, before and after controlling for processing speed. Four hundred and eleven healthy, community-dwelling older adults who were all born in 1936 were assessed on TMT-B, 5 tests of ...[more]

PMID: 28865241

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Project description:The clinical applications of the association of cortical thickness and white matter fiber with freezing of gait (FoG) are limited in patients with Parkinson's disease (PD). In this retrospective study, using white matter fiber from diffusion-weighted imaging and cortical thickness from structural-weighted imaging of magnetic resonance imaging, we investigated whether a machine learning-based model can help assess the risk of FoG at the individual level in patients with PD. Data from the Parkinson's Disease Progression Marker Initiative database were used as the discovery cohort, whereas those from the Fujian Medical University Union Hospital Parkinson's Disease database were used as the external validation cohort. Clinical variables, white matter fiber, and cortical thickness were selected by random forest regression. The selected features were used to train the support vector machine(SVM) learning models. The median area under the receiver operating characteristic curve (AUC) was calculated. Model performance was validated using the external validation cohort. In the discovery cohort, 25 patients with PD were defined as FoG converters (15 men, mean age 62.1 years), whereas 60 were defined as FoG nonconverters (38 men, mean age 58.5 years). In the external validation cohort, 18 patients with PD were defined as FoG converters (8 men, mean age 66.9 years), whereas 37 were defined as FoG nonconverters (21 men, mean age 65.1 years). In the discovery cohort, the model trained with clinical variables, cortical thickness, and white matter fiber exhibited better performance (AUC, 0.67-0.88). More importantly, SVM-radial kernel models trained using random over-sampling examples, incorporating white matter fiber, cortical thickness, and clinical variables exhibited better performance (AUC, 0.88). This model trained using the above mentioned features was successfully validated in an external validation cohort (AUC, 0.91). Furthermore, the following minimal feature sets that were used: fractional anisotropy value and mean diffusivity value for right thalamic radiation, age at baseline, and cortical thickness for left precentral gyrus and right dorsal posterior cingulate gyrus. Therefore, machine learning-based models using white matter fiber and cortical thickness can help predict the risk of FoG conversion at the individual level in patients with PD, with improved performance when combined with clinical variables.

Dataset Information

Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults.

Publications

Processing speed and the relationship between Trail Making Test-B performance, cortical thinning and white matter microstructure in older adults.

Similar Datasets

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