Structural connectivity from DTI to predict mild cognitive impairment in de novo Parkinson's disease.

BACKGROUND: Early detection of Parkinson's disease (PD) patients at high risk for mild cognitive impairment (MCI) can help with timely intervention. White matter structural connectivity is considered an early and sensitive indicator of neurodegenerative disease. OBJECTIVES: To investigate whether baseline white matter structural connectivity features from diffusion tensor imaging (DTI) of de novo PD patients can help predict PD-MCI conversion at an individual level using machine learning methods. METHODS: We included 90 de novo PD patients who underwent DTI and 3D T1-weighted imaging. Elastic net-based feature consensus ranking (ENFCR) was used with 1000 random training sets to select clinical and structural connectivity features. Linear discrimination analysis (LDA), support vector machine (SVM), K-nearest neighbor (KNN) and naïve Bayes (NB) classifiers were trained based on features selected more than 500 times. The area under the ROC curve (AUC), accuracy (ACC), sensitivity (SEN) and specificity (SPE) were used to evaluate model performance. RESULTS: A total of 57 PD patients were classified as PD-MCI nonconverters, and 33 PD patients were classified as PD-MCI converters. The models trained with clinical data showed moderate performance (AUC range: 0.62-0.68; ACC range: 0.63-0.77; SEN range: 0.45-0.66; SPE range: 0.64-0.84). Models trained with structural connectivity (AUC range, 0.81-0.84; ACC range, 0.75-0.86; SEN range, 0.77-0.91; SPE range, 0.71-0.88) performed similar to models that were trained with both clinical and structural connectivity data (AUC range, 0.81-0.85; ACC range, 0.74-0.85; SEN range, 0.79-0.91; SPE range, 0.70-0.89). CONCLUSIONS: Baseline white matter structural connectivity from DTI is helpful in predicting future MCI conversion in de novo PD patients.

Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson's disease.

Freezing of gait (FOG) greatly impacts the daily life of patients with Parkinson's disease (PD). However, predictors of FOG in early PD are limited. Moreover, recent neuroimaging evidence of cerebral morphological alterations in PD is heterogeneous. We aimed to develop a model that could predict the occurrence of FOG using machine learning, collaborating with clinical, laboratory, and cerebral structural imaging information of early drug-naïve PD and investigate alterations in cerebral morphology in early PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson's Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a 5-year follow-up period. We found that models trained with structural morphological features showed fair to good performance (accuracy range, 0.67-0.73). Performance improved when clinical and laboratory data was added (accuracy range, 0.71-0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69-0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the structural morphological features that were mainly distributed in the left cerebrum. Moreover, the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Overall, we found that T1WI morphometric markers helped predict future FOG occurrence in patients with early drug-naïve PD at the individual level. The OLF exhibits predominantly cortical expansion in early PD.

Disrupted Brain Structural Network Connection in de novo Parkinson's Disease With Rapid Eye Movement Sleep Behavior Disorder.

Objective: To explore alterations in white matter network topology in de novo Parkinson's disease (PD) patients with rapid eye movement sleep behavior disorder (RBD). Materials and Methods: This study included 171 de novo PD patients and 73 healthy controls (HC) recruited from the Parkinson's Progression Markers Initiative (PPMI) database. The patients were divided into two groups, PD with probable RBD (PD-pRBD, n = 74) and PD without probable RBD (PD-npRBD, N = 97), according to the RBD screening questionnaire (RBDSQ). Individual structural network of brain was constructed based on deterministic fiber tracking and analyses were performed using graph theory. Differences in global and nodal topological properties were analyzed among the three groups. After that, post hoc analyses were performed to explore further differences. Finally, correlations between significant different properties and RBDSQ scores were analyzed in PD-pRBD group. Results: All three groups presented small-world organization. PD-pRBD patients exhibited diminished global efficiency and increased shortest path length compared with PD-npRBD patients and HCs. In nodal property analyses, compared with HCs, the brain regions of the PD-pRBD group with changed nodal efficiency (Ne) were widely distributed mainly in neocortical and paralimbic regions. While compared with PD-npRBD group, only increased Ne in right insula, left middle frontal gyrus, and decreased Ne in left temporal pole were discovered. In addition, significant correlations between Ne in related brain regions and RDBSQ scores were detected in PD-pRBD patients. Conclusions: PD-pRBD patients showed disrupted topological organization of white matter in the whole brain. The altered Ne of right insula, left temporal pole and left middle frontal gyrus may play a key role in the pathogenesis of PD-RBD.

Aberrant Amplitude of Low-Frequency Fluctuations in Different Frequency Bands in Patients With Parkinson's Disease.

Previous studies reported abnormal spontaneous neural activity in Parkinson's disease (PD) patients using resting-state functional magnetic resonance imaging (R-fMRI). However, the frequency-dependent neural activity in PD is largely unknown. Here, 35 PD patients and 35 age- and education-matched healthy controls (HCs) underwent R-fMRI scanning to investigate abnormal spontaneous neural activity of PD using the amplitude of low-frequency fluctuation (ALFF) approach within the conventional band (typical band: 0.01-0.08 Hz) and specific frequency bands (slow-5: 0.010-0.027 Hz and slow-4: 0.027-0.073 Hz). Compared with HCs, PD patients exhibited increased ALFF in the parieto-temporo-occipital regions, such as the bilateral inferior temporal gyrus/fusiform gyrus (ITG/FG) and left angular gyrus/posterior middle temporal gyrus (AG/pMTG), and displayed decreased ALFF in the left cerebellum, right precuneus, and left postcentral gyrus/supramarginal gyrus (PostC/SMG) in the typical band. PD patients showed greater increased ALFF in the left caudate/putamen, left anterior cingulate cortex/medial superior frontal gyrus (ACC/mSFG), left middle cingulate cortex (MCC), right ITG, and left hippocampus, along with greater decreased ALFF in the left pallidum in the slow-5 band, whereas greater increased ALFF in the left ITG/FG/hippocampus accompanied by greater decreased ALFF in the precentral gyrus/PostC was found in the slow-4 band (uncorrected). Additionally, the left caudate/putamen was positively correlated with levodopa equivalent daily dose (LEDD), Hoehn and Yahr (HY) stage, and disease duration. Our results suggest that PD is related to widespread abnormal brain activities and that the abnormalities of ALFF in PD are associated with specific frequency bands. Future studies should take frequency band effects into account when examining spontaneous neural activity in PD.

Aberrant Advanced Cognitive and Attention-Related Brain Networks in Parkinson's Disease with Freezing of Gait.

Background: Freezing of gait (FOG) is a disabling gait disorder influencing patients with Parkinson's disease (PD). Accumulating evidence suggests that FOG is related to the functional alterations within brain networks. We investigated the changes in brain resting-state functional connectivity (FC) in patients with PD with FOG (FOG+) and without FOG (FOG-). Methods: Resting-state functional magnetic resonance imaging (RS-fMRI) data were collected from 55 PD patients (25 FOG+ and 30 FOG-) and 26 matched healthy controls (HC). Differences in intranetwork connectivity between FOG+, FOG-, and HC individuals were explored using independent component analysis (ICA). Results: Seven resting-state networks (RSNs) with abnormalities, including motor, executive, and cognitive-related networks, were found in PD patients compared to HC. Compared to FOG- patients, FOG+ patients had increased FC in advanced cognitive and attention-related networks. In addition, the FC values of the auditory network and default mode network were positively correlated with the Gait and Falls Questionnaire (GFQ) and Freezing of Gait Questionnaire (FOGQ) scores in FOG+ patients. Conclusions: Our findings suggest that the neural basis of PD is associated with impairments of multiple functional networks. Notably, alterations of advanced cognitive and attention-related networks rather than motor networks may be related to the mechanism of FOG.

Successful aging and environmental factors in older individuals in urban and rural areas: A cross-sectional study.

OBJECTIVE: The effects of the environmental factors on successful aging (SA) are not well understood. This study aimed to assess SA and related factors in older individuals in urban and rural areas, exploring differences between groups and investigating the effects of environmental factors. METHODS: This was a cross-sectional study of 205 and 212 older people in urban and rural areas of Shandong Province, respectively, between March 2019 and September 2019. SA was measured using the Successful Aging Inventory (SAI). The environmental factors were assessed using the WHOQOL-100 scale. Univariable and multivariable analyses were performed to determine associations of different parameters with SA. RESULTS: The scores of SA and environmental factors of older individuals in urban vs. rural areas were 48.79 vs. 46.14 and 128.63 vs. 107.81, respectively (both P < 0.05). All "Environment" dimensions ("Safety and physical security", "Home environment", "Financial resources", "Health and social care", "Opportunities for acquiring new information and skills", "Participation and opportunities for leisure", and "Transport"), except "Physical environment (pollution/noise/traffic/climate)", were associated with SA (all P < 0.05). Multiple linear regression showed that psychological resilience, physical activity, self-evaluation of SA, environment, social support, and hearing status were shared factors by the urban and rural older individuals. CONCLUSION: The SA and environmental factor scores were higher in urban older individuals compared with rural ones. Environment dimensions (except "Physical environment (pollution/noise/traffic/climate)") were associated with SA.