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Cerebrovascular reactivity (CVR) is a specific indicator of autoregulatory efficiency. Studies have demonstrated that CVR depends on the baseline vascular dilation status between groups. Within the brain, there also exist spatial variations in both the resting cerebral blood flow (CBF) and CVR across different cerebral regions. However, the relationship between the regional CBF and CVR remains unclear. Hence, the primary goal of this study was to investigate the relationship between the resting CBF using pseudocontinuous arterial spin labeling (pCASL) technique and CVR using blood oxygenation level‐dependent (BOLD) technique across different cerebral regions. Eleven male and 14 female young volunteers were recruited in this study. Each subject was asked to perform the breath‐holding challenge to evaluate CVR at 3 T. The resting CBF was measured using pCASL in each subject. The relationships between CBF and CVR across the lobes were evaluated using the Spearman's rank test. The results showed that, for both sexes, the frontal lobe had the maximal resting perfusion but minimal vascular response to hypercapnia, whereas the occipital lobe had the lowest baseline CBF but maximal reactivity to hypercapnia, suggesting low and high autoregulatory efficiencies at high and low resting CBF in the brain, respectively. Sex‐related differences were observed in CBF but not in CVR. These findings may be of clinical interest in the assessment of cerebrovascular reserve and regional‐dependent vascular diseases. [ABSTRACT FROM AUTHOR]

Acupuncture is an alternative treatment for primary dysmenorrhea (PDM). However, mechanisms by which acupuncture exerts its analgesic properties are still unclear. This study aimed to explore the cerebral blood flow (CBF) response to verum and sham acupuncture treatments, and further investigate whether pre-treatment CBF is capable of assessing symptom changes after interventions. A total of 11 PDM patients in the verum group and 12 patients in the sham group participated in this study. Pain rating index (PRI), CBF, and gonadal hormone levels were acquired before and after 8-week treatments. Both verum and sham acupuncture treatments exert its analgesic effect on PDM after intervention as PRI reduced (p < 0.05). Blood gonadal levels were not significantly different after acupuncture in both groups (all p > 0.05). In the verum group, intervention-related decreases in CBF were observed in the right dorsal anterior cingulate cortex. In the sham group, regions identified as showing reductions in CBF after acupuncture included the left ventromedial prefrontal cortex, left caudate, and left insula. Patients with higher baseline CBF in the left precuneus and right hippocampus were accompanied with worse treatment response to acupuncture intervention. Mechanisms of verum and sham acupuncture treatments are dissimilar as manifested by different brain responses. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD. [ABSTRACT FROM AUTHOR]