Annals of translational medicine · 2022
Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson's disease.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Mo Jiajie, Yang Bowen, Wang Xiu, Zhang Jianguo, Hu Wenhan, Zhang Chao, Zhang Kai
Аннотация
BACKGROUND: Surface-based cortical morphological patterns provide insight into the neural mechanisms of Parkinson's disease (PD). Explorations of the relationship between these patterns and the clinical assessment and treatment effects could be used to inform early intervention and treatment planning.
METHODS: We recruited 78 PD patients who underwent presurgical evaluation and 55 healthy controls. We assessed neocortical sulcal depth, gyrification index, and fractal dimension and applied a general linear model using the multivariate Hotelling's t-test to determine the joint effect of surface-based shape abnormalities in PD. The relationship between the neuroimaging pattern and clinical assessment was investigated using a multivariate linear regression model. A machine learning model based on surfaced-based features was used to predict responses to medication and deep brain stimulation (DBS).
RESULTS: The surface-based neuroimaging pattern of PD included decreases in morphological metrics in the gyrus (left: F=4.32; right: F=4.13), insular lobe (left: F=4.87; right: F=4.53), paracentral lobe (left: F=4.01; right: F=4.26), left posterior cingulate cortex (F=4.48), and left occipital lobe (F=4.27, P<0.01). This pattern was significantly associated with cognitive performance and motor symptoms (P<0.01). The machine learning model using morphological metrics was able to predict the drug response in the tremor score (R=-0.34, P<0.01) and postural instability and gait disorders score (R=0.24, P=0.04).
CONCLUSIONS: We identified the surface-based neuroimaging pattern associated with PD and explored its association with clinical assessment. Our findings suggest that these morphological indicators have potential value in informing personalized medicine and patient management.
METHODS: We recruited 78 PD patients who underwent presurgical evaluation and 55 healthy controls. We assessed neocortical sulcal depth, gyrification index, and fractal dimension and applied a general linear model using the multivariate Hotelling's t-test to determine the joint effect of surface-based shape abnormalities in PD. The relationship between the neuroimaging pattern and clinical assessment was investigated using a multivariate linear regression model. A machine learning model based on surfaced-based features was used to predict responses to medication and deep brain stimulation (DBS).
RESULTS: The surface-based neuroimaging pattern of PD included decreases in morphological metrics in the gyrus (left: F=4.32; right: F=4.13), insular lobe (left: F=4.87; right: F=4.53), paracentral lobe (left: F=4.01; right: F=4.26), left posterior cingulate cortex (F=4.48), and left occipital lobe (F=4.27, P<0.01). This pattern was significantly associated with cognitive performance and motor symptoms (P<0.01). The machine learning model using morphological metrics was able to predict the drug response in the tremor score (R=-0.34, P<0.01) and postural instability and gait disorders score (R=0.24, P=0.04).
CONCLUSIONS: We identified the surface-based neuroimaging pattern associated with PD and explored its association with clinical assessment. Our findings suggest that these morphological indicators have potential value in informing personalized medicine and patient management.