Dynamics and concordance alterations of regional brain function indices in vestibular migraine: a resting-state fMRI study.

BACKGROUND: Prior MRI studies on vestibular migraine (VM) have revealed abnormalities in static regional intrinsic brain activity (iBA) and dynamic functional connectivity between brain regions or networks. However, the temporal variation and concordance of regional iBA measures remain to be explored. METHODS: 57 VM patients during the interictal period were compared to 88 healthy controls (HC) in this resting-state functional magnetic resonance imaging (fMRI) study. The dynamics and concordance of regional iBA indices, including amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), were examined by utilizing sliding time-window analysis. Partial correlation analyses were performed between clinical parameters and resting-state fMRI indices in brain areas showing significant group differences. RESULTS: The VM group showed increased ALFF and ReHo dynamics, as well as increased temporal concordance between ALFF and ReHo in the bilateral paracentral lobule and supplementary motor area relative to the HC group. We also found decreased ReHo dynamics in the right temporal pole, and decreased ALFF dynamics in the right cerebellum posterior lobe, bilateral angular gyrus and middle occipital gyrus (MOG) in the VM group compared with the HC group. Moreover, a positive correlation was observed between ALFF dynamics in the left MOG and vertigo disease duration across all VM patients. CONCLUSION: Temporal dynamics and concordance of regional iBA indices were altered in the motor cortex, cerebellum, occipital and temporoparietal cortex, which may contribute to disrupted multisensory processing and vestibular control in patients with VM. ALFF dynamics in the left MOG may be useful biomarker for evaluating vertigo burden in this disorder.

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis.

BACKGROUND: Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. METHODS: A total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. RESULTS: Schizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. CONCLUSIONS: The static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.