Cerebrospinal fluid alpha-synuclein, amyloid beta, total tau, and phosphorylated tau in tremor-dominant Parkinson's disease.

BACKGROUND: Protein misfolding within specific brain regions is a common characteristic of neurodegenerative diseases, such as Alzheimer's disease and Parkinson's disease (PD). Therefore, a common term often used for these disorders is "proteinopathy". Currently, there has been increasing attention toward the overlap of pathogenesis between proteinopathies. AIMS: We aimed to explore the cross-sectional and longitudinal level of the CSF α-synuclein (α-syn), amyloid ßeta (Aß1-42), total tau (t-tau), and phosphorylated tau (p-tau) in PD subjects with tremor dominant (TD) and a non-tremor dominant (nonTD) subtype from the Parkinson Progression Markers Initiative (PPMI). METHODS: We enrolled 411 early-stage PD patients and 187 healthy controls (HCs) from the PPMI. We compared the level of CSF biomarkers at four time points including baseline, 6 months, 1 year, and 2 years. To investigate longitudinal changes in CSF proteins within each group, we used linear mixed models. RESULTS: The level of CSF biomarkers was significantly lower in PD patients compared to HCs at any visit. Moreover, there was no statistically significant difference in the level of CSF α-syn, Aß1-42, t-tau, and p-tau between PD-TD and PD-nonTD. Longitudinal analysis showed significant CSF α-syn reduction after one year from baseline in PD-TD patients (P = 0.047). Also, there was a significant reduction in the level of CSF Aß1-42 after two years in PD-nonTD patients but not HCs and PD-TD (P = 0.033). CONCLUSION: Our results indicate that different patterns in longitudinal changes of CSF biomarkers could be due to different pathophysiological mechanisms involved in each PD motor subtype.

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis.

BACKGROUND: The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS. METHODS: We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI). RESULTS: After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%). CONCLUSION: Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

White matter tracts associated with alexithymia and emotion regulation: A diffusion MRI study.

BACKGROUND: Alexithymia is a cognitive-affective impairment, suggested to be associated with emotion regulation. Herein, we investigated white matter (WM) tracts with Diffusion Magnetic Resonance Imaging (DMRI) connectometry approach using quantitative anisotropy (QA) tractography to discover possible associations between the emotion identification and regulation patterns and WM tracts. METHODS: DMRI data were acquired from 218 healthy subjects aging 39.15 ± 20.19 who filled the Toronto Alexithymia Scale (TAS) and emotion regulation questionnaire (ERQ) from the LEMON dataset. Connectometry analysis was applied on WM tracts in DMRI images. RESULTS: DMRI connectometry analysis revealed a significant correlation between TAS identification score and increased microstructural connectivity in WM pathways, including the body of corpus callosum (CC), bilateral fornix, and left arcuate fasciculus (AF) in males (FDR = 0.028), and corticospinal and cingulum tracts in females (FDR = 0.026). Furthermore, we found a significant positive correlation between overall TAS score and fornix properties in men (FDR = 0.026) and corticospinal tracts in women (FDR = 0.028). Middle cerebellar peduncle negatively correlated with describing emotion (FDR = 0.025) and the splenium of the CC and corticospinal tracts negatively correlated with this subscale (FDR = 0.049) in male group. However, the splenium of the CC, corticospinal tracts, and left AF positively associated with this subscale (FDR = 0.029). The splenium of the CC was negatively related to externally-oriented thinking among men (FDR = 0.038). Our results showed marginally associations between ERQ and similar WM tracts. CONCLUSION: Certain WM microstructures significantly correlate with emotion identification and regulation. These tracts are associated with specific somatosensory areas, language processing areas, and limbic area.