Functional magnetic resonance imaging (fMRI) in conscious animals is evolving as a critical tool for neuroscientists. The present study explored the effectiveness of an acclimation procedure in minimizing the stress experienced by the animal as assessed by alterations in physiological parameters including heart rate, respiratory rate, and serum corticosterone levels. Results confirm that as the stress of the protocol is minimized, there is a significant decrease in head movements and enhancement in data quality. The feasibility of improving the quality of fMRI data acquired in alert rats by utilizing a relatively simple technique is presented.

ObjectivesTo evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, subdural, epidural, and subarachnoid) in non-contrast head CT.MethodsA total of 2836 subjects (ICH/normal, 1836/1000) from three institutions were included in this ethically approved retrospective study, with a total of 76,621 slices from non-contrast head CT scans. ICH and its five subtypes were annotated by three independent experienced radiologists, with majority voting as reference standard for both the subject level and the slice level. Ninety percent of data was used for training and validation, and the rest 10% for final evaluation. A joint CNN-RNN classification framework was proposed, with the flexibility to train when subject-level or slice-level labels are available. The predictions were compared with the interpretations from three junior radiology trainees and an additional senior radiologist.ResultsIt took our algorithm less than 30 s on average to process a 3D CT scan. For the two-type classification task (predicting bleeding or not), our algorithm achieved excellent values (≥ 0.98) across all reporting metrics on the subject level. For the five-type classification task (predicting five subtypes), our algorithm achieved > 0.8 AUC across all subtypes. The performance of our algorithm was generally superior to the average performance of the junior radiology trainees for both two-type and five-type classification tasks.ConclusionsThe proposed method was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.Key Points • A 3D joint CNN-RNN deep learning framework was developed for ICH detection and subtype classification, which has the flexibility to train with either subject-level labels or slice-level labels. • This deep learning framework is fast and accurate at detecting ICH and its subtypes. • The performance of the automated algorithm was superior to the average performance of three junior radiology trainees in this work, suggesting its potential to reduce initial misinterpretations. Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06163-2) contains supplementary material, which is available to authorized users.

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A review of smoking cessation studies revealed that pharmacotherapy was effective in China. More RCTs of TCM approaches and physician advice are needed with long-term follow-up assessments and biochemical verification of self-reported abstinence before these approaches are adopted as evidence-based smoking cessation interventions in China.

Our observation that male rats innate fear response differed with hormonal status, as well as the higher prevalence of fear and anxiety disorders in human females led to the current investigation of the impact of phases of the estrous cycle on innate fear responding. Female rats in different phases of the cycle were exposed to an innate fear-inducing stimulus (2,5-dihydro- 2,4,5-trimethylthiazoline, TMT odor) and monitored for changes in behavior and brain activation. Behavioral data showed freezing responses to TMT were significantly enhanced during estrus as compared to other phases of the cycle. This data was supported by significant increases in pixel intensity in cortical and sub-cortical regions in estrus compared to proestrus and diestrus. Imaging results demonstrated significant increases in brain activation in the somatosensory and insular cortices when comparing estrus to diestrus. There were significant increases in neural activity in the BNST and septum in estrus as compared to proestrus. Additionally, the hippocampus, hypothalamus, olfactory system, and cingulate cortex show significant increases in the estrus phase when compared to both diestrus and proestrus. Taken together, these results suggest that the female's hormonal status may be correlated with alterations in both neuronal and behavioral indices of fear.

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