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.

The aging process is accompanied by changes in the brain’s cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework’s ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer’s disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

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.

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R2 = 0.88; support vector regression, MAE = 4.42 years, R2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

No abstract

Migraine is commonly comorbid with insomnia; both disorders are linked to functional disturbance of the default mode network (DMN). Evidence suggests that DMN could be segregated into multiple subnetworks with specific roles that underline different cognitive processes. However, the relative contributions of DMN subnetworks in the comorbidity of migraine and insomnia remain largely unknown. This study sought to identify altered functional connectivity (FC) profiles of DMN subnetworks in the comorbidity of migraine and insomnia. Direct group comparisons with healthy controls, followed by conjunction analyses, were used to identify shared FC alterations of DMN subnetworks. The shared FC changes of the DMN subnetworks in the migraine and insomnia groups were identified in the dorsomedial prefrontal and posteromedial cortex subnetworks. These shared FC changes were primarily associated with motor and somatosensory systems, and consistently found in patients with comorbid migraine and insomnia. Additionally, the magnitude of FC between the posteromedial cortex and postcentral gyrus correlated with insomnia duration in patients with comorbid migraine and insomnia. Our findings point to specific FC alterations of the DMN subnetwork in migraine and insomnia. The shared patterns of FC disturbance may be associated with the underlying mechanisms of the comorbidity of the two disorders.

The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease (CSVD) are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI datasets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age-gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of CSVD. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in CSVD. Global and regional (400 cortical, 14 subcortical, and 28 cerebellum regions-of-interest) brain-age prediction models were constructed using gray-matter features from MRI of 1482 healthy individuals (age: 18-92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the CSVD group. The CSVD group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-CSVD group. Global brain-age-gap was significantly higher in the CSVD (3.71 ± 7.60 years) than that in non-CSVD (-0.43 ± 9.47 years) group (p = 0.003, η2 = 0.012). There were 82 cerebral cortical, 3 subcortical, and 4 cerebellar regions showing significantly different brain-age-gap between the CSVD and non-CSVD groups. Global brain-age-gap failed to mediate the relationship between CSVD and any of the cognitive-domains. In 89 regions with increased brain-age-gap in the CSVD group, seven regional brain-age-gaps were able to show significant mediation effects in CSVD-related cognitive impairment (we set the statistical significance p < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age-gap explained poorer global cognitive performance in CSVD. We demonstrated the interconnections between CSVD and brain age. Strategic brain aging, i.e. advanced brain-aging in critical regions, may be involved in the pathophysiology of CSVD-related cognitive impairment. Regional rather than global brain-age-gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with CSVD.