Five negative symptom domains are differentially associated with resting state amplitude of low frequency fluctuations in Schizophrenia.

This study examined associations between resting-state amplitude of low frequency fluctuations (ALFF) and negative symptoms represented by total scores, second-order dimension (motivation and pleasure, expressivity), and first-order domain (anhedonia, avolition, asociality, alogia, blunted affect) factor scores in schizophrenia (n = 57). Total negative symptom scores showed positive associations with ALFF in temporal and frontal brain regions. Negative symptom domain scores showed predominantly stronger associations with regional ALFF compared to total scores, suggesting domain scores may better map to neural signatures than total scores. Improving our understanding of the neuropathology underlying negative symptoms may aid in addressing this unmet therapeutic need in schizophrenia.

A review of deep learning-based deformable medical image registration.

The alignment of images through deformable image registration is vital to clinical applications (e.g., atlas creation, image fusion, and tumor targeting in image-guided navigation systems) and is still a challenging problem. Recent progress in the field of deep learning has significantly advanced the performance of medical image registration. In this review, we present a comprehensive survey on deep learning-based deformable medical image registration methods. These methods are classified into five categories: Deep Iterative Methods, Supervised Methods, Unsupervised Methods, Weakly Supervised Methods, and Latest Methods. A detailed review of each category is provided with discussions about contributions, tasks, and inadequacies. We also provide statistical analysis for the selected papers from the point of view of image modality, the region of interest (ROI), evaluation metrics, and method categories. In addition, we summarize 33 publicly available datasets that are used for benchmarking the registration algorithms. Finally, the remaining challenges, future directions, and potential trends are discussed in our review.

The effect of stroke on micturition associated brain activity: A pilot fMRI study.

OBJECTIVE: Cerebral stroke is a unique model for studying the role of the brain in lower urinary tract (LUT) control. By its nature, stroke must change the activity of the brain to cause LUT dysfunction. The objective of this study was to describe changes in micturition-related brain activity in patients who develop LUT symptoms (LUTS) after a cerebral stroke. MATERIALS AND METHODS: Healthy controls (HC, n = 10) and patients who developed storage LUTS after a cerebral stroke (n = 7) were recruited. Functional magnetic resonance imaging was used to assess brain activity in each subject. In the task-based block design, blood-oxygen-level-dependent (BOLD) signal was detected during rest, active bladder filling, and bladder voiding. BOLD signal intensity was compared between HCs and stroke subjects during bladder filling, voiding, and voiding initiation. RESULTS: Stroke subjects exhibited higher activity in the periaqueductal gray and cerebellum during bladder filling and bladder voiding. HCs exhibited more intense activity in higher centers, such as the cingulate cortex, motor cortex, and the dorsolateral prefrontal cortex in each of the phases examined. CONCLUSIONS: Subjects with stroke-related LUTS exhibit a specific pattern of brain activity during bladder filling and voiding. There appears to be a greater reliance on primitive centers (cerebellum, midbrain) than in healthy controls during both phases of the micturition cycle. We hypothesize that these findings may reflect loss of connectivity with higher brain centers after a stroke.