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Background Cluster headache is a disorder characterized by intermittent, severe unilateral head pain accompanied by cranial autonomic symptoms. Most cases of CH are episodic, manifesting as “in-bout” periods of frequent headache separated by month-to-year-long “out-of-bout” periods of remission. Previous imaging studies have implicated the hypothalamus and pain matrix in the pathogenesis of episodic CH. However, the pathophysiology driving the transition between in- and out-of-bout periods remains unclear. Methods The present study provides a narrative review of previous neuroimaging studies on the pathophysiology of episodic CH, addressing alterations in brain structures, metabolism, and structural and functional connectivity occurring between bout periods. Results Although the precise brain structures responsible for episodic CH are unknown, major roles are indicated for the posterior hypothalamus (especially in acute attacks), the pain neuromatrix with an emphasis on central descending pain modulation, and non-traditional pain processing networks including the occipital, cerebellar, and salience networks. These areas are potentially related to dynamic transitioning between in- and out-of-bout periods. Conclusion Recent progress in magnetic resonance imaging of episodic CH has provided additional insights into dynamic bout-associated structural and functional connectivity changes in the brain, especially in non-traditional pain processing network areas. These areas warrant future investigations as targets for neuromodulation in patients with CH. [ABSTRACT FROM AUTHOR]

Background Previous imaging studies on the pathogenesis of cluster headache (CH) have implicated the hypothalamus and multiple brain networks. However, very little is known regarding dynamic bout-associated, large-scale resting state functional network changes related to CH. Methods Resting-state functional magnetic resonance imaging data were obtained from CH patients and matched controls. Data were analyzed using independent component analysis for exploratory assessment of the changes in intrinsic brain networks and their relationship between in-bout and out-of-bout periods, as well as correlations with clinical observations. Results Compared to healthy controls, CH patients had functional connectivity (FC) changes in the temporal, frontal, salience, default mode, somatosensory, dorsal attention, and visual networks, independent of bout period. Compared to out-of-bout scans, in-bout scans showed altered FC in the frontal and dorsal attention networks. Lower frontal network FC correlated with longer duration of CH. Conclusions The present findings suggest that episodic CH with dynamic bout period shifts may involve bout-associated FC changes in multiple discrete cortical areas within networks outside traditional pain processing areas. Dynamic changes in FC in frontal and dorsal attention networks between bout periods could be important for understanding episodic CH pathophysiology. [ABSTRACT FROM AUTHOR]

Introduction: Previous studies have shown that stroke patients exhibit greater neuroimaging-derived biological "brain age" than control subjects. This difference, known as the brain age gap (BAG), is calculated by comparing the chronological age with predicted brain age and is used as an indicator of brain health and aging. However, whether stroke accelerates the process of brain aging in patients with small-volume infarcts has not been established. By utilizing longitudinal data, we aimed to investigate whether small-volume infarctions can significantly increase the BAG, indicating accelerated brain aging., Methods: A total of 123 stroke patients presenting with small-volume infarcts were included in this retrospective study. The brain age model was trained via established protocols within the field of machine learning and the structural features of the brain from our previous study. We used t -tests and regression analyses to assess longitudinal brain age changes after stroke and the associations between brain age, acute stroke severity, and poststroke outcome factors., Results: Significant brain aging occurred between the initial and 6-month follow-ups, with a mean increase in brain age of 1.04 years ( t  = 3.066, p  < 0.05). Patients under 50 years of age experienced less aging after stroke than those over 50 years of age ( p  = 0.245). Additionally, patients with a National Institute of Health Stroke Scale score >3 at admission presented more pronounced adverse effects on brain aging, even after adjusting for confounders such as chronological age, sex, and total intracranial volume ( F 1,117  = 7.339, p  = 0.008, η 2  = 0.059). There were significant differences in the proportional brain age difference at 6 months among the different functional outcome groups defined by the Barthel Index ( F 2,118  = 4.637, p  = 0.012, η 2  = 0.073)., Conclusion: Stroke accelerates the brain aging process, even in patients with relatively small-volume infarcts. This phenomenon is particularly accentuated in elderly patients, and both stroke severity and poststroke functional outcomes are closely associated with accelerated brain aging. Further studies are needed to explore the mechanisms underlying the accelerated brain aging observed in stroke patients, with a particular focus on the structural alterations and plasticity of the brain following minor strokes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Peng, Kuo, Chang, Lin and Tsai.)

Background: Multidomain interventions have demonstrable benefits for promoting healthy aging, but self-empowerment strategies to sustain long-term gains remain elusive., Objective: This study evaluated the effects of digital somatosensory dance game participation on brain imagery changes as primary outcomes and other physical and mental health measures as secondary outcomes related to healthy aging., Methods: Between August 31, 2020, and June 27, 2021, this randomized controlled trial recruited 60 eligible participants older than 55 years with no recent engagement in digital dance games. A computer-generated randomization sequence was used to allocate participants 1:1, without stratification, to an intervention group (n=30) who underwent digital somatosensory dance game training or a control group (n=30). An anonymized code masked the intervention allocations from the investigators, and individuals who assigned the interventions were not involved in analyzing the study data. The intervention entailed two 30-minute dance game sessions per week for 6 months, and the control group received healthy aging education. Primary outcomes were brain imagery changes. All variables were measured at baseline and the 6-month follow-up, and intervention effects were estimated using t tests with intention-to-treat analyses., Results: Compared with the control group, intervention participants had significantly different brain imagery in the gray matter volume (GMV) of the left putamen (estimate 0.016, 95% CI 0.008 to 0.024; P<.001), GMV of the left pallidum (estimate 0.02, 95% CI 0.006 to 0.034; P=.004), and fractional amplitude of low frequency fluctuations of the left pallidum (estimate 0.262, 95% CI 0.084 to 0.439; P=.004). Additionally, the intervention group had different imagery in the cerebellum VI GMV (estimate 0.011, 95% CI 0.003 to 0.02; P=.01). The intervention group also had improved total Montreal Cognitive Assessment scores (estimate 1.2, 95% CI 0.27 to -2.13; P<.01), quality of life (estimate 7.08, 95% CI 2.35 to 11.82; P=.004), and time spent sitting on weekdays (estimate -1.96, 95% CI -3.33 to -0.60; P=.005). Furthermore, dance performance was significantly associated with cognitive performance (P=.003), health status (P=.14), resilience (P=.007), and demoralization (P<.001)., Conclusions: Digital somatosensory dance game participation for 6 months was associated with brain imagery changes in multiple regions involving somatosensory, motor, visual, and attention functions, which were consistent with phenotypic improvements associated with healthy aging., Trial Registration: ClinicalTrials.gov NCT05411042; https://clinicaltrials.gov/study/NCT05411042., (©Heng-Hsin Tung, Chen-Yuan Kuo, Pei-Lin Lee, Chih-Wen Chang, Kun-Hsien Chou, Ching-Po Lin, Liang-Kung Chen. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.07.2024.)

Introduction: Atherosclerotic cardiovascular disease (ASCVD) is prevalent worldwide including Taiwan, however widely accepted tools to assess the risk of ASCVD are lacking in Taiwan. Machine learning models are potentially useful for risk evaluation. In this study we used two cohorts to test the feasibility of machine learning with transfer learning for developing an ASCVD risk prediction model in Taiwan., Methods: Two multi-center observational registry cohorts, T-SPARCLE and T-PPARCLE were used in this study. The variables selected were based on European, U.S. and Asian guidelines. Both registries recorded the ASCVD outcomes of the patients. Ten-fold validation and temporal validation methods were used to evaluate the performance of the binary classification analysis [prediction of major adverse cardiovascular (CV) events in one year]. Time-to-event analyses were also performed., Results: In the binary classification analysis, eXtreme Gradient Boosting (XGBoost) and random forest had the best performance, with areas under the receiver operating characteristic curve (AUC-ROC) of 0.72 (0.68-0.76) and 0.73 (0.69-0.77), respectively, although it was not significantly better than other models. Temporal validation was also performed, and the data showed significant differences in the distribution of various features and event rate. The AUC-ROC of XGBoost dropped to 0.66 (0.59-0.73), while that of random forest dropped to 0.69 (0.62-0.76) in the temporal validation method, and the performance also became numerically worse than that of the logistic regression model. In the time-to-event analysis, most models had a concordance index of around 0.70., Conclusions: Machine learning models with appropriate transfer learning may be a useful tool for the development of CV risk prediction models and may help improve patient care in the future., Competing Interests: All the authors declare no conflict of interest.

Background Previous imaging studies on the pathogenesis of cluster headache (CH) have implicated the hypothalamus and multiple brain networks. However, very little is known regarding dynamic bout-associated, large-scale resting state functional network changes related to CH. Methods Resting-state functional magnetic resonance imaging data were obtained from CH patients and matched controls. Data were analyzed using independent component analysis for exploratory assessment of the changes in intrinsic brain networks and their relationship between in-bout and out-of-bout periods, as well as correlations with clinical observations. Results Compared to healthy controls, CH patients had functional connectivity (FC) changes in the temporal, frontal, salience, default mode, somatosensory, dorsal attention, and visual networks, independent of bout period. Compared to out-of-bout scans, in-bout scans showed altered FC in the frontal and dorsal attention networks. Lower frontal network FC correlated with longer duration of CH. Conclusions The present findings suggest that episodic CH with dynamic bout period shifts may involve bout-associated FC changes in multiple discrete cortical areas within networks outside traditional pain processing areas. Dynamic changes in FC in frontal and dorsal attention networks between bout periods could be important for understanding episodic CH pathophysiology.