T1 and T2 weighted lesions and cognition in multiple Sclerosis: A systematic review and meta-analysis.

BACKGROUND: Considering the different results regarding the correlation between Magnetic Resonance Imaging (MRI) structural measures and cognitive dysfunction in patients with MS, we aimed to perform a systematic review and meta-analysis study to investigate the correlation between T1 and T2 weighted lesions and cognitive scores to find the most robust MRI markers for cognitive function in MS population. METHODS: The literature of this paper was identified through a comprehensive search of electronic datasets including PubMed, Scopus, Web of Science, and Embase in February 2022. Studies that reported the correlation between cognitive status and T1 and T2 weighted lesions in MS patients were selected. RESULTS: 21 studies with a total of 3771 MS patients with mean ages ranging from 30 to 57 years were entered into our study. Our analysis revealed that the volume of T1 lesions was significantly correlated with Symbol Digit Modality test (SDMT) (r: -0.30, 95 %CI: -0.59, -0.01) and Paced Auditory Serial-Addition Task (PASAT) scores (r: -0.23, 95 %CI: -0.36, -0.10). We investigated the correlation between T2 lesions and cognitive scores. The pooled estimates of z scores were significant for SDMT (r: -0.27, 95 %CI: -0.51, -0.03) and PASAT (r: -0.27, 95 %CI: -0.41, -0.13). CONCLUSION: In conclusion, our systematic review and meta-analysis study provides strong evidence of the correlation between T1 and T2 lesions and cognitive function in MS patients. Further research is needed to explore the potential mechanisms underlying this relationship and to develop targeted interventions to improve cognitive outcomes in MS patients.

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.

Longitudinal striatal dopamine transporter binding and cerebrospinal fluid alpha-synuclein, amyloid beta, total tau, and phosphorylated tau in Parkinson's disease.

BACKGROUND: Previous studies investigated CSF levels of α-synuclein (α-syn), amyloid-ß (Aß1-42), total tau (t-tau), and phosphorylated tau (p-tau) with clinical progression of Parkinson's disease (PD). However, there is limited data on the association between CSF biomarkers and dopamine uptake status in PD. AIM: In the current study, we aim to investigate the longitudinal association between striatal dopaminergic neuronal loss assessed by dopamine active transporter single photon emission computerized tomography (DaTSCAN) imaging with CSF α-syn, t-tau, p-tau, and Aß1-42. METHODS: A total of 413 early-stage PD patients and 187 healthy controls (HCs) from the PPMI. Striatal binding ratios (SBRs) of DaTSCAN images in caudate and putamen nuclei were calculated. We investigated the cross-sectional and longitudinal association between CSF biomarkers and dopamine uptake using partial correlation models adjusted for the effect of age, sex, and years of education over 24 months of follow-up. RESULTS: The level of CSF α-syn, Aß1-42, t-tau, and p-tau was significantly higher in HCs compared to PD groups at any time point. We found that higher CSF α-syn was associated with a higher SBR score in the left caudate at baseline (P = 0.038) and after 12 months (P = 0.012) in PD patients. Moreover, SBR scores in the left caudate and CSF Aß1-42 were positively correlated at baseline (P = 0.021), 12 months (P = 0.006), and 24 months (P = 0.014) in patients with PD. Our findings demonstrated that change in CSF Aß1-42 was positively correlated with change in SBR score in the left caudate after 24 months in the PD group (P = 0.043). CONCLUSION: We found that cross-sectional levels of α-syn and Aß1-42 could reflect the degree of dopaminergic neuron loss in the left caudate nucleus. Interestingly, longitudinal changes in CSF Aß1-42 could predict the severity of left caudal dopaminergic neuron loss throughout the disease. This suggested that Aß pathology might precede dopaminergic loss in striatal nuclei in this case left caudate and subsequently cognitive impairment in PD patients, although future studies are needed to confirm our results and expand the understanding of the pathophysiology of cognitive dysfunction in PD.

P-tau231 as a Diagnostic Biomarker for Alzheimer's Disease and Mild Cognitive Impairment: A Systematic Review and Meta-Analysis.

Objective: Some previous studies have shown that cerebrospinal fluid (CSF) levels of p-tau231 were significantly higher in patients with Alzheimer's disease (AD) compared to that in patients with mild cognitive impairment (MCI) and normal control (NC), whereas some other studies did not. Due to contradictory results, we aimed to conduct a systematic review and meta-analysis study on previous investigations to examine the potential role of CSF p-tau231 as a biomarker of AD and MCI. Method: PubMed, Scopus, and Web of Science were searched in March 2021 for studies on the CSF level of p-tau231 in AD, MCI, and NC. The statistical analysis was performed via standardized mean difference (SMD) methodology with a 95% confidence interval. Results: A total of 10 studies including 1141 subjects were included. The present study showed that CSF level of p-tau231 was significantly higher in AD patients compared to that in MCI patients (SMD = 160.94 [11.11, 310.78], P = 0.04) and NC patients (SMD = 436.21 [164.88, 707.54], P < 0.00). Moreover, comparison of MCI and NC showed a significantly higher level of CSF p-tau231 in MCI compared to NC (SMD = 341.44 [59.73, 623.14], P = 0.02). Conclusion: P-tau231 showed to be a valuable biomarker of discrimination AD, MCI, and NC based on our findings. This meta-analysis showed that the CSF p-tau231 can reliably differentiate AD patients from MCI and NC patients. Furthermore, based on our findings the level of CSF p-tau231 was significantly higher in MCI compared to NC. Therefore, p-tau231 can be added to the list of potential biomarkers for the diagnosis of AD and MCI in further studies. However, further investigations are needed to confirm our findings.

Multiple sclerosis relapse after COVID-19 vaccination: A case report-based systematic review.

BACKGROUND: Concerns about vaccination increased among patients with multiple sclerosis (MS) regarding side effects, efficacy, and disease exacerbation. Recently there were reports of MS relapses after the COVID-19 vaccination, which emerged the safety concerns. Therefore, we aimed to perform a systematic review of case reports and case series studies to investigate the MS relapses after COVID-19 vaccination with most details. METHODS: We systematically searched three databases, including PubMed, Scopus, and Web of Science, in February 2022. Case reports and case series which reported relapse after COVID-19 vaccination in MS patients were eligible to include in our study. RESULTS: Seven studies were included in our systematic review after the abstract and full-text screening with a total of 29 cases. The mean duration between COVID-19 vaccination and relapse appearance was 9.48 ± 7.29 days. Among patients, 22 cases experienced relapse after their first dosage of the COVID-19 vaccine, one after the second dose, and five after the booster dose. The type of vaccine was unknown for one patient. The most common symptoms of relapses were sensory deficits (paresthesia, numbness, dysesthesia, and hypoesthesia) and weakness. CONCLUSION: Overall, the COVID-19 vaccination may trigger relapses in some MS patients, but as the infection itself can stimulate relapse, the benefit of vaccination outweighs its risk in this population, and mass vaccination against COVID-19, especially in MS patients, should be continued and encouraged.

[18F]FDG brain PET and clinical symptoms in different autoantibodies of autoimmune encephalitis: a systematic review.

INTRODUCTION: Autoimmune encephalitis (AE) is caused by the antibodies that target receptors and intracellular or surface proteins. To achieve the appropriate therapeutic results, early and proper diagnosis is still the most important issue. In this review, we provide an overview of FDG-PET imaging findings in AE patients and possible relation to different subtypes and clinical features. METHODS: PubMed, Web of Science, and Scopus were searched in August 2021 using a predefined search strategy. RESULTS: After two-step reviewing, 22 studies with a total of 332 participants were entered into our qualitative synthesis. In anti-NMDAR encephalitis, decreased activity in the occipital lobe was present, in addition, to an increase in frontal, parietal, and specifically medial temporal activity. Anti-VGKC patients showed altered metabolism in cortical and subcortical regions such as striata and cerebellum. Abnormal metabolism in patients with anti-LGI1 has been reported in diverse areas of the brain including medial temporal, hippocampus, cerebellum, and basal ganglia all of which had hypermetabolism. Hypometabolism in parietal, frontal, occipital lobes, temporal, frontal, and hippocampus was observed in AE patients with anti-GAD antibodies. CONCLUSION: Our results indicate huge diversity in metabolic patterns among different AE subtypes and it is hard to draw a firm conclusion. Moreover, the timing of imaging, seizures, and acute treatments can alter the PET patterns strongly. Further prospective investigations with specific inclusion and exclusion criteria should be carried out to identify the metabolic defect in different AE subtypes.

Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review.

BACKGROUND: In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed interesting results regarding the diagnostic efficiency of AI methods in differentiating Multiple sclerosis (MS) patients from healthy controls or other demyelinating diseases. There is a great lack of a comprehensive systematic review study on the role of AI in the diagnosis of MS. We aimed to perform a systematic review to document the performance of AI in MS diagnosis. METHODS: A systematic search was performed using four databases including PubMed, Scopus, Web of Science, and IEEE on August 2021. All original studies which focused on deep learning or AI to analyze any modalities with the purpose of diagnosing MS were included in our study. RESULTS: Finally, 38 studies were included in our systematic review after the abstract and full-text screening. A total of 5433 individuals were included, including 2924 cases of MS and 2509 healthy controls. Sensitivity and specificity were reported in 29 studies which ranged from 76.92 to 100 for sensitivity and 74 to 100 for specificity. Furthermore, 34 studies reported accuracy ranged 81 to 100. Among included studies, Magnetic Resonance Imaging (MRI) (20 studies), OCT (six studies), serum and cerebrospinal fluid markers (six studies), movement function (three studies), and other modalities such as breathing and evoked potential was used for detecting MS via AI. CONCLUSION: In conclusion, diagnosis of MS based on new markers and AI is a growing field of research with MRI images, followed by images obtained from OCT, serum and CSF biomarkers, and motor associated markers. All of these results show that with advances made in AI, the way we monitor and diagnose our MS patients can change drastically.