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Post‐traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging‐based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting‐state fMRI‐based measures of functional connectivity, and amplitude of low‐frequency fluctuation (ALFF). We employed three machine‐learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross‐validation. Furthermore, we performed voxel‐wise and lobe‐wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non‐PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non‐PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR‐based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE. [ABSTRACT FROM AUTHOR]

Highlights • We studied the neural signature of positive and neutral valence with music. • We examined performance in a visual memory task in different music conditions. • Longer exposure to pleasant music led to higher spectral power and pleasantness score. • Theta power was positively correlated with pleasantness scores in the course of music. • Memory task performance during pleasant music was lower compared to neutral music. Abstract We investigated the neural correlates of pleasure induced by listening to highly pleasant and neutral musical excerpts using electroencephalography (EEG). Power spectrum analysis of EEG data showed a distinct gradual change in the power of low-frequency oscillations in response to highly pleasant, but not neutral, musical excerpts. Specifically, listening to highly pleasant music was associated with (i) relatively higher oscillatory activity in the theta band over the frontocentral (FC) area and in the alpha band over the parieto-occipital area, and (ii) a gradual increase in the oscillatory power over time. Correlation analysis between behavioral and electrophysiological data revealed that theta power over the FC electrodes was correlated with subjective assessment of pleasantness while listening to music. To study the link between attention and positive valence in our experiments, volunteers performed a delayed match-to-sample memory task while listening to the musical excerpts. The subjects' performances were significantly lower under highly pleasant conditions compared to neutral conditions. Listening to pleasant music requires higher degrees of attention, leading to the observed decline in memory performance. Gradual development of low-frequency oscillations in the frontal and posterior areas may be at least partly due to gradual recruitment of higher levels of attention over time in response to pleasurable music. [ABSTRACT FROM AUTHOR]

The presence of outliers can severely degrade learned representations and performance of deep learning methods and hence disproportionately affect the training process, leading to incorrect conclusions about the data. For example, anomaly detection using deep generative models is typically only possible when similar anomalies (or outliers) are not present in the training data. Here we focus on variational autoencoders (VAEs). While the VAE is a popular framework for anomaly detection tasks, we observe that the VAE is unable to detect outliers when the training data contains anomalies that have the same distribution as those in test data. In this paper we focus on robustness to outliers in training data in VAE settings using concepts from robust statistics. We propose a variational lower bound that leads to a robust VAE model that has the same computational complexity as the standard VAE and contains a single automatically-adjusted tuning parameter to control the degree of robustness. We present mathematical formulations for robust variational autoencoders (RVAEs) for Bernoulli, Gaussian and categorical variables. The RVAE model is based on beta-divergence rather than the standard Kullback–Leibler (KL) divergence. We demonstrate the performance of our proposed β -divergence-based autoencoder for a variety of image and categorical datasets showing improved robustness to outliers both qualitatively and quantitatively. We also illustrate the use of our robust VAE for detection of lesions in brain images, formulated as an anomaly detection task. Finally, we suggest a method to tune the hyperparameter of RVAE which makes our model completely unsupervised. [ABSTRACT FROM AUTHOR]

• No significant differences were found between pianists and non-musicians at the behavioral level. • At the neural level, we found an interaction between direction and group indicating pianists showed lower P2 amplitude in CW direction (lateral movements). • We found a complexity effect at the behavioral and neural levels. • Further research is warranted to support the far-transfer theory of piano learning to motor planning in a different task outside the music context. Psychological studies have shown that music has an impact on human cognitive function. We aimed to compare the performance and neural activity of pianists and non-musicians during a non-musical motor-planning task. In addition, we investigated the effect of task complexity on the characteristics of the behavioral and neural responses. The participants had to grasp a hexagonal knob with their right hand and rotate it 60° or 180° clockwise (CW) or counterclockwise (CCW). We examined the groups in terms of the amplitude of the P2 component in the event-related potential (at the neural level) and the planning time, grasping time, releasing time, and planning pattern for initial grip selection (at the behavioral level). At the behavioral level, we observed no significant difference between groups, while at the neural level; we found an interaction between direction and group indicating that pianists showed lower P2 amplitude in the CW directions. However, there was no significant difference between groups in the CCW direction. A significant main effect of rotation was revealed at both the neural and behavioral levels; increasing the rotation angle led to an increase in the planning time and the P2 amplitude, indicating a complexity effect. In conclusion, we observed that pianists had lower P2 amplitude in lateral movements than non-musicians; however, due to the lack of behavioral group differences, further research is warranted to support the far-transfer theory in this field. [ABSTRACT FROM AUTHOR]