Brain structural and functional abnormalities in patients with tension-type headache: A systematic review of magnetic resonance imaging studies.

Tension-type headache (TTH) stands as the most prevalent form of headache, yet an adequate understanding of its underlying mechanisms remains elusive. This article endeavors to comprehensively review structural and functional magnetic resonance imaging (MRI) studies investigating TTH patients, to gain valuable insights into the pathophysiology of TTH, and to explore new avenues for enhanced treatment strategies. We conducted a systematic search to identify relevant articles examining brain MRI disparities between TTH individuals and headache-free controls (HFC). Fourteen studies, encompassing 312 diagnosed TTH patients, were selected for inclusion. Among these, eight studies utilized conventional MRI, one employed diffusion tensor imaging, and five implemented various functional MRI modalities. Consistent findings across these studies revealed a notable increase in white matter hyperintensity (WMH) in TTH patients. Furthermore, the potential involvement of the specific brain areas recognized to be involved in different dimensions of pain perception including cortical regions (anterior and posterior cingulate cortex, prefrontal cortex, anterior and posterior insular cortex), subcortical regions (thalamus, caudate, putamen, and parahippocampus), cerebellum in TTH pathogenesis was identified. However, no significant association was established between TTH and intracranial abnormalities or total intracranial volume. In conclusion, these findings support the hypotheses regarding the role of central mechanisms in TTH pathophysiology and offer probable brain regions implicated in these mechanisms. Due to the scarce data on the precise role of these regions in the TTH, further preclinical and clinical investigations should be done to advance our knowledge and enhance targeted therapeutic options of TTH.

Sleep profiles in epilepsy patients undergoing monotherapy and polytherapy: A comparative cross-sectional study.

OBJECTIVE: Sleep disturbances commonly reported among epilepsy patients have a reciprocal relationship with the condition; While epilepsy and anti-seizure medications (ASMs) can disrupt sleep structure, disturbed sleep can also exacerbate the frequency of seizures. This study explored subjective sleep disturbances and compared sleep profiles in patients who underwent ASM monotherapy and polytherapy. METHODS: We enrolled 176 epilepsy patients who completed a structured questionnaire containing demographic and clinical information and the Persian versions of the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), and Patient Health Questionnaire-9 (PHQ-9) to evaluate sleep quality, insomnia, excessive daytime sleepiness (EDS), and depressive symptoms, respectively. Chi-square and Mann-Whitney U tests were employed to analyze the association between variables, and logistic regression analysis was conducted to identify factors predicting sleep disturbances. RESULTS: Comparative analysis of mono/polytherapy groups revealed a significantly higher prevalence of insomnia and EDS among patients on polytherapy compared to monotherapy. However, no significant difference was found in sleep quality between the two groups. Logistic regression analysis revealed that a depressive mood serves as a robust predictor for sleep issues, whereas treatment type did not emerge as an independent predictor of sleep disturbances. CONCLUSION: Our findings suggest that an increased number of ASMs does not inherently result in a higher incidence of sleep issues. Therefore, multiple ASMs may be prescribed when necessary to achieve improved seizure control. Furthermore, this study underscores the importance of comprehensive management that addresses seizure control and treating affective symptoms in individuals with epilepsy.

Diffusion magnetic resonance imaging for treatment response prediction in schizophrenia spectrum disorders: A systematic review.

A substantial portion of schizophrenia spectrum disorder (SSD) patients exhibit resistance to antipsychotic treatments, emphasizing the need for reliable treatment response biomarkers. Previous magnetic resonance imaging (MRI) studies have identified various imaging predictors in SSD. This study focuses on evaluating the effectiveness of diffusion MRI sequences, diffusion tensor imaging (DTI) and diffusion-weighted imaging (DWI), in predicting antipsychotic response in SSD patients. A systematic search for relevant articles was conducted in PubMed, Embase, Scopus, and Web of Science on February 11, 2024. Twelve studies involving a total of 742 patients were systematically reviewed. The baseline DTI/DWI biomarkers revealed significant associations with antipsychotic treatment response. Notably a consistent negative link was found between response and baseline fractional anisotropy (FA) in fronto-temporo-limbic white matter tracts, specifically the superior longitudinal fasciculus, providing moderate-level evidence. In addition, weak-level evidence was found for the negative association between the treatment response and baseline FA in the corpus callosum, internal, and external capsule tracts. Collectively, this review demonstrated that obtaining pre-treatment brain diffusion MRI scans, particularly from white matter tracts of fronto-temporo-limbic network, can assist in delineating the treatment response trajectory in patients with SSD. However, additional larger randomized controlled trials are required to further substantiate these findings.

Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review.

Background: Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB. Methods: We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients. Results: We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors. Conclusion: ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.

Examining the validity and reliability of the Persian version of the MiND-B questionnaire in ALS patients.

BACKGROUND: In addition to affecting the nerves and muscles, amyotrophic lateral sclerosis (ALS) disease also affects the behavior and cognition of patients. In this study, we examine the validity and reliability of the Persian version of Motor Neuron Disease Behavioral instrument (MiND-B) questionnaire to investigate behavioral changes in Persian-speaking ALS patients. METHODS: Forty-six Persian-speaking patients with ALS filled out the MiND-B questionnaire. Then, the overall scores and each of the domains of this questionnaire were statistically analyzed. RESULTS: Cronbach's alpha coefficient was calculated .70 for the whole questionnaire. To check the validity of the questionnaire, the correlation of its scores with the Edinburgh Cognitive and Behavioral ALS screen (ECAS-A) questionnaire was taken, and this correlation was significant (p = .038). CONCLUSION: The findings of this study show that the Persian version of the MiND-B questionnaire has the necessary validity and reliability to investigate behavioral changes in Persian-speaking patients with ALS.