The NM template combines some of the main advantages of histology-based atlases (e.g. information about the cytoarchitectural structure) with features more commonly associated with MRI-based templates (isotropic nature of the dataset, and probabilistic analyses). The underlying workflow may be found useful in the future development of 3D brain atlases that incorporate information about the variability of areas in species for which it may be impractical to ensure homogeneity of the sample in terms of age, sex and genetic background.A number of computational techniques have been lately devised to image the Ensemble Average Propagator (EAP) within the white matter of the brain, propelled by the deployment of multi-shell acquisition protocols and databases approaches like Mean Apparent Propagator Imaging (MAP-MRI) and its Laplacian-regularized version (MAPL) aim at describing the low frequency spectrum of the EAP (limited by the maximum b-value acquired) and afterwards computing scalar indices that embed useful descriptions of the white matter, e. g. the Return-to-Origin, Plane, or Axis Probabilities (RTOP, RTPP, RTAP). These methods resort to a non-parametric, bandwidth limited representation of the EAP that implies fitting a set of 3-D basis functions in a large-scale optimization problem. We propose a semi-parametric approach inspired by signal theory the EAP is approximated as the spherical convolution of a Micro-Structure adaptive Gaussian kernel with a non-parametric orientation histogram, which aims at representing the low-frequency response of an ensemble of coherent sets of fiber bundles at the white matter. This way, the optimization involves just the 2 to 3 parameters that describe the kernel, making our approach far more efficient than the related state of the art. We devise dual Fourier domains Integral Transforms to analytically compute RTxP-like scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The so-called MiSFIT is both time efficient (a typical multi-shell data set can be processed in roughly one minute) and accurate it provides estimates of widely validated indices like RTOP, RTPP, and RTAP comparable to MAPL for a wide variety of white matter configurations.While learning from mistakes is a lifelong process, the rate at which an individual makes errors on any given task decreases through late adolescence. Previous fMRI adult work indicates that several control brain networks are reliably active when participants make errors across multiple tasks. Less is known about the consistency and localization of error processing in the child brain because previous research has used single tasks. The current analysis pooled data across three studies to examine error-related task activation (two tasks per study, three tasks in total) for a group of 232 children aged 8-17 years. We found that, consistent with the adult literature, the majority of applied cingulo-opercular brain regions, including medial superior frontal cortex, dorsal anterior cingulate, and bilateral anterior insula, showed consistent error processing engagement in children across multiple tasks. Error-related activity in many of these cingulo-opercular regions correlated with task performance. However, unlike in the adult literature, we found a lack of error-related activation across tasks in dorsolateral frontal areas, and we also did not find any task-consistent relations with age in these regions. Our findings suggest that the task-general error processing signal in the developing brain is fairly robust and similar to adults, with the exception of lateral frontal cortex.Integrating visual information for motor output is an essential process of visually-guided motor control. The brainstem is known to be a major center involved in the integration of sensory information for motor output, however, limitations of functional imaging in humans have impaired our knowledge about the individual roles of sub-nuclei within the brainstem. Thus, the bulk of our knowledge surrounding the function of the brainstem is based on anatomical and behavioral studies in non-human primates, cats, and rodents, despite studies demonstrating differences in the organization of visuomotor processing between mammals. fMRI studies in humans have examined activity related to visually-guided motor tasks, however, few have done so while controlling for both force without visual feedback activity and visual stimuli without force activity. Of the studies that have controlled for both conditions, none have reported brainstem activity. Here, we employed a novel fMRI paradigm focused on the brainstem and cerebellum to systematically investigate the hypothesis that the pons and midbrain are critical for the integration of visual information for motor control. Visuomotor activity during visually-guided pinch-grip force was measured while controlling for force without visual feedback activity and visual stimuli without force activity in healthy adults. Using physiological noise correction and multiple task repetitions, we demonstrated that visuomotor activity occurs in the inferior portion of the basilar pons and the midbrain. These findings provide direct evidence in humans that the pons and midbrain support the integration of visual information for motor control. We also determined the effect of physiological noise and task repetitions on the visuomotor signal that will be useful in future studies of neurodegenerative diseases affecting the brainstem.We describe a novel method for robust identification of common brain networks and their corresponding temporal dynamics across subjects from asynchronous functional MRI (fMRI) using tensor decomposition. We first temporally align asynchronous fMRI data using the orthogonal BrainSync transform, allowing us to study common brain networks across sessions and subjects. We then map the synchronized fMRI data into a 3D tensor (vertices × time × subject/session). Finally, we apply Nesterov-accelerated adaptive moment estimation (Nadam) within a scalable and robust sequential Canonical Polyadic (CP) decomposition framework to identify a low rank tensor approximation to the data. As a result of CP tensor decomposition, we successfully identified twelve known brain networks with their corresponding temporal dynamics from 40 subjects using the Human Connectome Project's language task fMRI data without any prior information regarding the specific task designs. https://www.selleckchem.com/products/gdc-0077.html Seven of these networks show distinct subjects' responses to the language task with differing temporal dynamics; two show sub-components of the default mode network that exhibit deactivation during the tasks; the remaining three components reflect non-task-related activities.