Mcguireberger4271

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three p uncover a potential 'prediction signal' that reflects this fundamental learning process.Praise enhances motor performance; however, the underlying feedback pathway is unknown. Here, we hypothesized that the social evaluation feedback to the motor system is modified by the top-down effect of the social contingency valuation system, such as the anterior rostral medial prefrontal cortex (arMPFC). We developed a pseudo-interactive task that simplified a conversational student-teacher interaction and conducted a functional magnetic resonance imaging study with 33 participants (13 men, 20 women; mean age = 21.7 years; standard deviation = 2.0 years). The participant inside the scanner uttered the pseudo-English word to the English teacher outside the scanner. The teacher provided feedback of acceptance or rejection by either gestures or words, through video. As a control condition, the pseudo-word was read aloud by a computer. Approval from the teacher enhanced the participants' pleasure rate. Feedback to the participants' utterance, either rejection or acceptance, activated the arMPFC. Irrespective of the preceding utterance by self or computer, acceptance compared with rejection activated the right primary visual cortex (V1), and the reverse activated the left V1. This valence-dependent laterality of V1 activation indicates that the effect is not the domain-general modulation of visual processing. Instead, the early visual cortices are part of the valence-specific representation of the social signal. Physio-physiological interaction analysis with the seed regions in the right and left V1 and the modulator region in the arMPFC showed enhanced connectivity with the bilateral primary motor cortex. These findings indicate that the socially contingent, self-relevant signals from others act as feedback to the motor control system, and this process is mediated by the early visual cortex.Event-related potentials (ERPs) are noninvasive measures of human brain activity that index a range of sensory, cognitive, affective, and motor processes. Despite their broad application across basic and clinical research, there is little standardization of ERP paradigms and analysis protocols across studies. To address this, we created ERP CORE (Compendium of Open Resources and Experiments), a set of optimized paradigms, experiment control scripts, data processing pipelines, and sample data (N = 40 neurotypical young adults) for seven widely used ERP components N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). This resource makes it possible for researchers to 1) employ standardized ERP paradigms in their research, 2) apply carefully designed analysis pipelines and use a priori selected parameters for data processing, 3) rigorously assess the quality of their data, and 4) test new analytic techniques with standardized data from a wide range of paradigms.The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of an entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals.In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. Paxalisib The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.