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Abstract Physical exercise studies are generally underrepresented in young adulthood. Seventeen subjects were randomized into an intervention group (24.2 ± 3.9 years; 3 trainings/week) and 10 subjects into a passive control group (23.7 ± 4.2 years), over a duration of 6 months. Every two months, performance diagnostics, computerized spatial memory tests, and 3 Tesla magnetic resonance imaging were conducted. Here we find that the intervention group, compared to controls, showed increased cardiorespiratory fitness, spatial memory performance and subregional hippocampal volumes over time. Time-by-condition interactions occurred in right cornu ammonis 4 body and (trend only) dentate gyrus, left hippocampal tail and left subiculum. Increases in spatial memory performance correlated with hippocampal body volume changes and, subregionally, with left subicular volume changes. In conclusion, findings support earlier reports of exercise-induced subregional hippocampal volume changes. Such exercise-related plasticity may not only be of interest for young adults with clinical disorders of hippocampal function, but also for sedentary normal cohorts.

IntroductionPhysical activity influences psychological well-being. This study aimed to determine the impact of exercise intensity on psychological well-being and alterations in emotion-related brain functional connectivity (FC).MethodsTwenty young, healthy, trained athletes performed a low- and high-intensity interval exercise (LIIE and HIIE) as well as a control condition in a within-subject crossover design. Before and after each condition, Positive And Negative Affect Scale (PANAS) was assessed as well as resting-state functional MRI (rs-fMRI). Voxel-wise FC was examined for bilateral amygdala seed region to whole-brain and emotion-related anatomical regions (e.g., insula, temporal pole, precuneus). Data analyses were performed using linear mixed-effect models with fixed factors condition and time.ResultsThe PANAS Positive Affect scale showed a significant increase after LIIE and HIIE and a significant reduction in Negative Affect after the control condition. In rs-fMRI, no significant condition-by-time interactions were observed between the amygdala and whole brain. Amygdala-precuneus FC analysis showed an interaction effect, suggesting reduced post-exercise anticorrelation after the control condition, but stable, or even slightly enhanced anticorrelation for the exercise conditions, especially HIIE.DiscussionIn conclusion, both LIIE and HIIE had positive effects on mood and concomitant effects on amygdala-precuneus FC, particularly after HIIE. Although no significant correlations were found between amygdala-precuneus FC and PANAS, results should be discussed in the context of affective disorders in whom abnormal amygdala-precuneus FC has been observed.

IntroductionDopaminergic, opiod and endocannabinoid neurotransmission are thought to play an important role in the neurobiology of acute exercise and, in particular, in mediating positive affective responses and reward processes. Recent evidence indicates that changes in fractional amplitude of low-frequency fluctuations (zfALFF) in resting-state functional MRI (rs-fMRI) may reflect changes in specific neurotransmitter systems as tested by means of spatial correlation analyses.MethodsHere, we investigated this relationship at different exercise intensities in twenty young healthy trained athletes performing low-intensity (LIIE), high-intensity (HIIE) interval exercises, and a control condition on three separate days. Positive And Negative Affect Schedule (PANAS) scores and rs-fMRI were acquired before and after each of the three experimental conditions. Respective zfALFF changes were analyzed using repeated measures ANOVAs. We examined the spatial correspondence of changes in zfALFF before and after training with the available neurotransmitter maps across all voxels and additionally, hypothesis-driven, for neurotransmitter maps implicated in the neurobiology of exercise (dopaminergic, opiodic and endocannabinoid) in specific brain networks associated with “reward” and “emotion.”ResultsElevated PANAS Positive Affect was observed after LIIE and HIIE but not after the control condition. HIIE compared to the control condition resulted in differential zfALFF decreases in precuneus, temporo-occipital, midcingulate and frontal regions, thalamus, and cerebellum, whereas differential zfALFF increases were identified in hypothalamus, pituitary, and periaqueductal gray. The spatial alteration patterns in zfALFF during HIIE were positively associated with dopaminergic and μ-opioidergic receptor distributions within the ‘reward' network.DiscussionThese findings provide new insight into the neurobiology of exercise supporting the importance of reward-related neurotransmission at least during high-intensity physical activity.

Magnetic resonance-guided focused ultrasound (MRgFUS) lesioning of the ventralis intermedius nucleus (VIM) has shown promise in treating drug-refractory essential tremor (ET). It remains unknown whether focal VIM lesions by MRgFUS have broader restorative effects on information flow within the whole-brain network of ET patients. We applied an information-theoretical approach based on intrinsic ignition and the concept of transfer entropy (TE) to assess the spatiotemporal dynamics after VIM-MRgFUS. Eighteen ET patients (mean age 71.44 years) underwent repeated 3T resting-state functional magnetic resonance imaging combined with Clinical Rating Scale for Tremor (CRST) assessments one day before (T0) and one month (T1) and six months (T2) post-MRgFUS, respectively. We observed increased whole brain ignition-driven mean integration (IDMI) at T1 (p

In this study, we evaluate the influence of normalization on the performance of deep learning networks for tumor segmentation and the prediction of the pathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy. The techniques were applied to a multicenter and multimodal magnet resonance imaging data set consisting of 201 patients recorded at six centers. We implemented and investigated six different normalization methods (setting the mean and standard deviation, histogram matching, percentiles, combining percentiles and histogram matching, fixed window and an auto-encoder with adversarial loss using the imaging parameters) and evaluated their impact on four deep learning tasks: tumor segmentation, prediction of treatment outcome, and prediction of sex and age. The latter two tasks were implemented as a reference test. We trained a modified U-Net with different normalization methods in multiple configurations: on all images, images from all centers except one, and images from a single center. Our results show that normalization only plays a minor role in segmentation, with a difference in Dice of less than 0.02 between the best and worst performing networks. For the prediction of sex and treatment outcomes, the percentile method combined with histogram matching works best for all scenarios. The biggest difference in performance, depending on the normalization method, occurs for classification. In conclusion, normalization is especially important for small data sets or for generalizing to different data distributions. The deep learning method was superior to the classical methods only in a minority of cases, probably due to the limited amount of training data.

Abstract The aim of this study was to investigate central pain representations during loading of the periodontium induced by orthodontic and occlusal stress. Nineteen healthy male volunteers (25.7 ± 2.8 years) were tested on two consecutive days: after phenotyping (questionnaires) and determination of warmth (WPT) and heat (HPT) pain thresholds, functional magnetic resonance imaging was performed as event-related paradigm including 36 tooth clenchings of 3 s duration, alternating with rest periods varying between 20–30 s. The task was performed in absence (T1) and 24 h after placement of an elastic separator between the second bicuspid and the first molar on the right side of the lower jaw (T2). No significant changes in WPT and HPT were observed but pain ratings were significantly elevated at T2. Significantly elevated activation at T2, as compared to T1, was found in bilateral sensorimotor cortex, bilateral secondary sensory cortex, supplementary motor area, right rolandic operculum, and bilateral insula. Our data show for the first time in humans that periodontal stimulation, as tested by tooth clenching in the presence of an elastic separator, goes along with specific expressions of pain at behavioral and neuronal network levels. Findings supplement the existing neuroimaging literature on odontogenic pain.

Physical inactivity is documented as a health risk factor for chronic diseases, accelerated aging, and cognitive impairment. Physical exercise, on the other hand, plays an important role in healthy aging by promoting positive muscular, cardiovascular, and central nervous system adaptions. Prior studies on the effects of exercise training on cerebral perfusion have focused largely on elderly cohorts or patient cohorts, while perfusion effects of exercise training in young sedentary adults have not yet been fully assessed. Therefore, the present study examined the physiological consequence of a 6-month endurance exercise training on brain perfusion in 28 young sedentary adults randomly assigned to an intervention group (IG; regular physical exercise) or a control group (CG; without physical exercise). The IG performed an extensive running interval training three times per week over 6 months. Performance diagnostics and MRI were performed every 2 months, and training intensity was adapted individually. Brain perfusion measurements with pseudo-continuous arterial spin labeling were analyzed using the standard Oxford ASL pipeline. A significant interaction effect between group and time was found for right superior temporal gyrus (STG) perfusion, driven by an increase in the IG and a decrease in the CG. Furthermore, a significant time effect was observed in the right middle occipital region in the IG only. Perfusion increases in the right STG, in the ventral striatum, and in primary motor areas were significantly associated with increases in maximum oxygen uptake (VO2max). Overall, this study identified region-specific increases in local perfusion in a cohort of young adults that partly correlated with individual performance increases, hence, suggesting exercise dose dependency. Respective adaptations in brain perfusion are discussed in the context of physical exercise-induced vascular plasticity.

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.