Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/067017

The Preprocessed Connectomes Project Repository of Manually Corrected Skull-stripped T1-weighted Anatomical MRI Data.

Benjamin Puccio·James P Pooley·John S Pellman·Elise C Taverna·Richard Cameron Craddock
View original source ↗Export metadata ↓

Abstract

BackgroundSkull-stripping is the procedure of removing non-brain tissue from anatomical MRI data. This procedure is necessary for calculating brain volume and for improving the quality of other image processing steps. Developing new skull-stripping algorithms and evaluating their performance requires gold standard data from a variety of different scanners and acquisition methods. We complement existing repositories with manually-corrected brain masks for 125 T1-weighted anatomical scans from the Nathan Kline Institute Enhanced Rockland Sample Neurofeedback Study.\n\nFindingsSkull-stripped images were obtained using a semi-automated procedure that involved skull-stripping the data using the brain extraction based on non local segmentation technique (BEaST) software and manually correcting the worst results. Corrected brain masks were added into the BEaST library and the procedure was reiterated until acceptable brain masks were available for all images. In total, 85 of the skull-stripped images were hand-edited and 40 were deemed to not need editing. The results are brain masks for the 125 images along with a BEaST library for automatically skull-stripping other data.\n\nConclusionSkull-stripped anatomical images from the Neurofeedback sample are available for download from the Preprocessed Connectomes Project. The resulting brain masks can be used by researchers to improve their preprocessing of the Neurofeedback data, and as training and testing data for developing new skull-stripping algorithms and evaluating the impact on other aspects of MRI preprocessing. We have illustrated the utility of these data as a reference for comparing various automatic methods and evaluated the performance of the newly created library on independent data.

Source connections

Explore related subjects

NeurosciencePreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Benjamin Puccio, James P Pooley, John S Pellman, Elise C Taverna, Richard Cameron Craddock. 2016-07-31. The Preprocessed Connectomes Project Repository of Manually Corrected Skull-stripped T1-weighted Anatomical MRI Data.. https://doi.org/10.1101/067017

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Distinct contributions of post-traumatic stress and working memory to affective and sensory dimensions of chronic pain, with pain modulation as a shared mechanism

Chronic pain is highly heterogeneous, with individuals varying substantially in symptoms. pain severity, disability, affective distress, cognitive functioning, and trauma-related symptoms. This study examined whether working memory, post-traumatic stress symptoms (PTSS), trauma exposure, and pain modulation explain distinct or shared dimensions of chronic pain variability. Individuals with chronic pain completed clinical, cognitive, trauma-related, and behavioural pain modulation measures, as well as resting-state functional magnetic resonance imaging. Multivariate regressions were used to determine whether working memory, PTSS, trauma exposure, and pain modulation independently predicted chronic pain outcomes. Principal component analysis was used to identify latent dimensions of chronic pain, and mediation analyses tested whether behavioural pain modulation explained relationships between dlPFC to vlPAG resting-state functional connectivity and clinical pain outcomes. PTSS independently predicted affective outcomes, including depression, state anxiety, and trait anxiety, whereas working memory independently predicted pain severity and pain interference. Trauma exposure was associated with greater PTSS and poorer working memory, but did not independently predict core pain outcomes after accounting for these more proximal factors. Principal component analysis identified partially distinct affective and sensory-disability dimensions, while trauma exposure loaded primarily on a separate component characterized by greater PTSS and poorer working memory. Behavioural pain modulation showed broader relationships across symptom dimensions and was associated with dlPFC to vlPAG connectivity. Exploratory mediation analyses demonstrated that pain modulation mediated relationships between dlPFC to vlPAG connectivity and both pain severity and affective distress. These findings support an integrated model where PTSS and working memory are more proximal predictors of affect and severity respectively, and trauma exposure represents a more distal vulnerability factor that predicts both. Thus, pain modulation represents a shared mechanism linking cortico-brainstem connectivity to chronic pain intensity and affect. These variables need further testing for phenotyping people with chronic pain based on their specific clinical needs.

Neuroscience↗
bioRxiv2016

Temporal and spatial localization of prediction-error signals in the visual brain

It has been suggested that the brain pre-empts changes in the visual environment through generating predictions, although real-time eletrophysiological evidence of prediction violations remains elusive. In a series of experiments we showed participants sequences of images that followed a predictable implied sequence or whose final image violated the implied sequence. Through careful design we were able to use the same final image transitions across predictable and unpredictable conditions, ensuring that any differences in neural responses were due only to preceding context and not to the images themselves. EEG and MEG recordings showed that early/mid-latency visual evoked potentials were robustly modulated by images that violated the implied sequence across a range of types of image change (expression deformations, rigid-rotations and visual field location). This modulation occurred irrespective of stimulus object category. Although the stimuli were static images, MEG source reconstruction of the early latency signal (N/M170) localised expectancy violation signals to brain areas associated with motion perception. Our findings suggest that the N/M170 can index mismatches between predicted and actual visual inputs in a system that predicts trajectories based on ongoing context. This has important implications for understanding the N/M170 and investigating how the brain represents context to generate perceptual predictions.

Neuroscience↗
bioRxiv2016

AN OSCILLATORY NETWORK MODEL OF HEAD DIRECTION, SPATIALLY PERIODIC CELLS AND PLACE CELLS USING LOCOMOTOR INPUTS

We propose a computational modeling approach that explains the formation of a range of spatial cells like head direction cells, grid cells, border cells and place cells which are believed to play a pivotal role in the spatial navigation of an animal. Most existing models insert special symmetry conditions in the models in order to obtain such symmetries in the outcome; our models do not require such symmetry assumptions. Our modeling approach is embodied in two models: a simple one (Model #1) and a more detailed version (Model #2). In Model #1, velocity input is presented to a layer of Head Direction cells, with no special topology requirements, the outputs of which are presented to a layer of Path Integration neurons. A variety of spatially periodic responses resembling grid cells, are obtained using the Principal Components of Path Integration layer. In Model #2, the input consists of the locomotor rhythms from the four legs of a virtual animal. These rhythms are integrated into the phases of a layer of oscillatory neurons, whose outputs drive a layer of Head Direction cells. The Head Direction cells in turn drive a layer of Path Integration neurons, which in turn project to two successive layers of Lateral Anti Hebbian Networks (LAHN). Cells in the first LAHN resemble grid cells (with both hexagonal and square gridness), and border cells. Cells in the second LAHN exhibit place cell behaviour and a new cell type known as corner cell. Both grid cells and place cells exhibit phase precession in 1D and 2D spaces. The models outline the neural hierarchy necessary to obtain the complete range of spatial cell responses found in the hippocampal system.

Neuroscience↗