Surface-based morphological patterns associated with neuropsychological performance, symptom severity, and treatment response in Parkinson's disease

Abstract

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.

Keywords: Parkinson’s disease (PD); neuroimaging patterns; neurophysiological performance; symptom severity; treatment response.

Figure 1

Surface-based neuroimaging pattern of Parkinson’s disease (PD). (A) Univariate analysis showed regions of widespread decreased sulcal depth, gyrification index, and fractal dimension in PD patients compared to healthy controls. Only significant clusters (P<0.01) are shown, and the vertex-wise Student’s t value is indicated by the color bar. (B) Multivariate analysis assessed the joint distribution of changes to sulcal depth, gyrification index, and fractal dimension, finding significant group differences in the bilateral precentral gyrus, insular lobe, paracentral lobe, left posterior cingulate cortex, and left occipital lobe. Only significant clusters (P<0.01) are shown, and the Hotelling’s F value is indicated by the color bar. The statistical model was corrected for cortical thickness and demographic information.

Figure 2

The association between the neuroimaging pattern and clinical assessment measures. Regression maps identified associations between the neuroimaging pattern and clinical assessment measures. (A) Differences in the right frontal lobe were significantly associated with MMSE score. (B) Differences in the right insular lobe were significantly associated with the UPDRS-III total score, left insular lobe with the Tremor score, and bilateral insular and left PCC with the PIGD score. Only significant clusters (P<0.01) are shown, Hotelling’s F value is indicated using the color bar. Arrows indicate the significant clusters that are too small to easily see. The statistical model corrected for cortical thickness and demographic information. MMSE, mini-mental state examination; UPDRS, unified Parkinson’s disease rating scale; PCC, posterior cingulate cortex; PIGD, postural instability and gait disorders.

Figure 3

Prediction of drug response. The neuroimaging pattern successfully predicted the response to medication in the form of the Tremor score and the PIGD score. The X-axis represents the actual value, while the Y-axis shows the predictive value, and the line represents the slope. *: statistical significance. PIGD, postural instability and gait disorders.