CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol.

Intra-V1 functional networks and classification of observed stimuli.

Introduction: Previous studies suggest that co-fluctuations in neural activity within V1 (measured with fMRI) carry information about observed stimuli, potentially reflecting various cognitive mechanisms. This study explores the neural sources shaping this information by using different fMRI preprocessing methods. The common response to stimuli shared by all individuals can be emphasized by using inter-subject correlations or de-emphasized by deconvolving the fMRI with hemodynamic response functions (HRFs) before calculating the correlations.

Familiarity Processing through Faces and Names: Insights from Multivoxel Pattern Analysis.

The way our brain processes personal familiarity is still debatable. We used searchlight multivoxel pattern analysis (MVPA) to identify areas where local fMRI patterns could contribute to familiarity detection for both faces and name categories. Significantly, we identified cortical areas in frontal, temporal, cingulate, and insular areas, where it is possible to accurately cross-classify familiar stimuli from one category using a classifier trained with the stimulus from the other (i.

Relationship between gait parameters and cognitive indexes in adult aging.

Purpose: This study aimed to identify the most effective summary cognitive index predicted from spatio-temporal gait features (STGF) extracted from gait patterns.

Methods: The study involved 125 participants, including 40 young (mean age: 27.65 years, 50% women), and 85 older adults (mean age: 73.

Stable Sparse Classifiers predict cognitive impairment from gait patterns.

Background: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.

Purposes: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and healthy older adults (YA vs.

Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials.

Antecedent: The event-related potential (ERP) components P300 and mismatch negativity (MMN) have been linked to cognitive deficits in patients with schizophrenia. The diagnosis of schizophrenia could be improved by applying machine learning procedures to these objective neurophysiological biomarkers. Several studies have attempted to achieve this goal, but no study has examined Multiple Kernel Learning (MKL) classifiers.

Text segmentation ability predicts future reading efficiency in Spanish-speaking children.

Can the ability to parse unspaced texts (measured by a Text Segmentation Task, TST) index and predict reading efficiency in Spanish-speaking children? A sample of 1112 children (1st to 6th grade) was assessed. Additionally, two subsamples (51 children of 4th-5th grades and 71 children of 1st grade) were followed up. Our results indicate that the TST: a) reflects the acquisition of reading over primary school grades; b) reflects the teacher's judgment about the child's reading development; c) accurately predicts oral reading efficiency one and four years later year, in the former case even after removing the contributions of the IQ and oral reading speed.

Objects seen as scenes: Neural circuitry for attending whole or parts.

Depending on our goals, we pay attention to the global shape of an object or to the local shape of its parts, since it's difficult to do both at once. This typically effortless process can be impaired in disease. However, it is not clear which cortical regions carry the information needed to constrain shape processing to a chosen global/local level.

Recurrent Activation of Neural Circuits during Attention to Global and Local Visual Information.

The attentional selection of different hierarchical level within compound (Navon) figures has been studied with event related potentials (ERPs), by controlling the ERPs obtained during attention to the global or the local echelon. These studies, using the canonical Navon figures, have produced contradictory results, with doubts regarding the scalp distribution of the effects. Moreover, the evidence about the temporal evolution of the processing of these two levels is not clear.

Fast Gaussian Naïve Bayes for searchlight classification analysis.

The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across all searchlights, which entails large computational costs especially when testing the statistical significance of the accuracies with permutation methods. In this article, a new implementation of the Gaussian Naive Bayes classifier is presented (henceforth massive-GNB).

Automated discrimination of brain pathological state attending to complex structural brain network properties: the shiverer mutant mouse case.

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.