Previous transcranial magnetic stimulation (TMS) studies have revealed that the activity of the primary motor cortex ipsilateral to an active hand (ipsi-M1) plays an important role in motor control. The aim of this study was to investigate whether the ipsi-M1 excitability would be influenced by goal-directed movement and laterality during unilateral finger movements. Ten healthy right-handed subjects performed four finger tapping tasks with the index finger (1) simple tapping (Tap) task, (2) Real-word task, (3) Pseudoword task, and (4) Visually guided tapping (VT) task. In the Tap task, the subject performed self-paced simple tapping on a touch screen. learn more In the real-word task, the subject tapped letters displayed on the screen one by one to create a Real-word (e.g., apple). Because the action had a specific purpose (i.e., creating a word), this task was considered to be goal-directed as compared to the Tap task. In the Pseudoword task, the subject tapped the letters to create a pseudoword (e.g., gdiok) in tor example, for post-stroke patients with severe hemiparesis.We found that SICI in the ipsi-M1 is smaller during visual illumination-guided finger movement than during the resting condition. Our finding provides basic data for designing a rehabilitation program that modulates the M1 ipsilateral to the moving limb, for example, for post-stroke patients with severe hemiparesis.Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla-Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor (CI). Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold.In vowel discrimination, commonly found discrimination patterns are directional asymmetries where discrimination is faster (or easier) if differing vowels are presented in a certain sequence compared to the reversed sequence. Different models of speech sound processing try to account for these asymmetries based on either phonetic or phonological properties. In this study, we tested and compared two of those often-discussed models, namely the Featurally Underspecified Lexicon (FUL) model (Lahiri and Reetz, 2002) and the Natural Referent Vowel (NRV) framework (Polka and Bohn, 2011). While most studies presented isolated vowels, we investigated a large stimulus set of German vowels in a more naturalistic setting within minimal pairs. We conducted an mismatch negativity (MMN) study in a passive and a reaction time study in an active oddball paradigm. In both data sets, we found directional asymmetries that can be explained by either phonological or phonetic theories. While behaviorally, the vowel discrimination was based on phonological properties, both tested models failed to explain the found neural patterns comprehensively. Therefore, we additionally examined the influence of a variety of articulatory, acoustical, and lexical factors (e.g., formant structure, intensity, duration, and frequency of occurrence) but also the influence of factors beyond the well-known (perceived loudness of vowels, degree of openness) in depth via multiple regression analyses. The analyses revealed that the perceptual factor of perceived loudness has a greater impact than considered in the literature and should be taken stronger into consideration when analyzing preattentive natural vowel processing. Focal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented. Magnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency. Neural network evaluation metrics-sensitivity, specificity, and accuracy-were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively. Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.