Showing papers in "NeuroImage in 2019"

TL;DR: A high-level overview of the features of the MRtrix3 framework and general-purpose image processing applications provided with the software is provided.

TL;DR: The ICLabel classifier improves upon existing methods by improving the accuracy of the computed label estimates and by enhancing its computational efficiency by outperforms or performs comparably to the previous best publicly available automated IC component classification method for all measured IC categories.

TL;DR: This work represents a multi‐institutional collaborative effort to develop a comprehensive, open source pipeline for DBS imaging and connectomics, which has already empowered several studies, and may facilitate a variety of future studies in the field.

TL;DR: The baseline neuroimaging processing and subject-level analysis methods used by the Adolescent Brain Cognitive Development Study are described to be a resource of unprecedented scale and depth for studying typical and atypical development.

TL;DR: It is suggested that studies using the AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field of brain structure and function.

TL;DR: This whole‐brain network atlas – released as an open resource for the neuroscience community – places all brain structures across both cortex and subcortex into a single large‐scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.

TL;DR: This study represents the first meta-analysis and systematic review investigating test-retest reliability of edge-level functional connectivity and suggests there is room for improvement, but care should be taken to avoid promoting reliability at the expense of validity.

TL;DR: Ten simple rules to help researchers apply predictive modeling to connectivity data are offered and it is hoped these ten rules will increase the use of predictive models with neuroimaging data.

TL;DR: This work systematically study the prediction performances of models in 6 different cohorts and a total of 2000 individuals, encompassing neuro‐degenerative (Alzheimer's, Post‐traumatic stress disorder), neuro‐psychiatric (Schizophrenia, Autism), drug impact (Cannabis use) clinical settings and psychological trait (fluid intelligence).

TL;DR: This work evaluates different estimation methods on simulated and real data, and finds the strongest correlations of corrected brain age delta with 5,792 non-imaging variables and also with 2,641 multimodal brain imaging-derived phenotypes with data from 19,000 participants in UK Biobank.

TL;DR: The results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures.

TL;DR: The overall study protocol is provided, including approaches for subject recruitment, strategies for imaging typically developing children 0–5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures.

TL;DR: The findings stress the need to include a peripheral multisensory control stimulation in the design of TMS‐EEG studies to enable a dissociation between truly transcranial and non‐transcranial components of TEPs.

TL;DR: This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (effective connectivity) and provides a tutorial style explanation of the underlying theory and assumptions.

TL;DR: A novel deep learning architecture, based on dual learning and an ad hoc layer for 3D separable convolutions, which aims at identifying MCI patients who have a high likelihood of developing AD within 3 years and is the highest performance achieved so far using similar datasets.

TL;DR: QuickNAT, a fully convolutional, densely connected neural network that segments a MRI brain scan in 20 s, is introduced and achieves superior segmentation accuracy and reliability in comparison to state‐of‐the‐art methods, while being orders of magnitude faster.

TL;DR: General functional connectivity (GFC) is presented as a method for leveraging shared features across resting‐state and task fMRI and it is demonstrated that GFC offers better test‐retest reliability than intrinsic connectivity estimated from the same amount of resting‐ state data alone.

TL;DR: The 3D Validation of Tractography with Experimental MRI (3D‐VoTEM) challenge results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

TL;DR: The more similar two people's interpretations of the abstract shapes animation were, the more similar were their neural responses in regions of the default mode network (DMN) and fronto‐parietal network, suggesting a network of high‐level regions that are sensitive to subtle individual differences in narrative interpretation during naturalistic conditions, but also resilient to large differences in the modality of the narrative.

TL;DR: An automated diffusion MRI QC framework for single subject and group studies is introduced, based on a comprehensive, non‐parametric approach for movement and distortion correction: FSL EDDY, which allows for a rich set of QC metrics that are both sensitive and specific to different types of artefacts.

TL;DR: Findings are starting to reveal DMN RSFC as a potential biomarker for predicting clinical outcomes in SUD and identify the DMN as a promising target for the treatment of addiction.

TL;DR: SLANTbrainSeg as discussed by the authors proposed the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation.

TL;DR: The rationale for the study design and sufficient details of the resource for scientists to plan future analyses of the HCP‐A data are provided, which will enable in‐depth studies of typical brain aging.

TL;DR: The physical basis for the signal equation for OPMs is established and the equations defining the bounds on OPM performance are re-derive, leading to a direct upper bound on the gain change due to cross-talk for a multi-channel OPM system.

TL;DR: This guide steps through an exemplar fMRI analysis in detail, reviewing the current implementation of DCM and demonstrating recent developments in group-level connectivity analysis, and moving from subject-level to group- level modelling using the Parametric Empirical Bayes framework.

TL;DR: The spectral exponent of the resting EEG discriminated states in which consciousness was present from states where consciousness was reduced or abolished, corroborating its interpretation as a marker of the presence of consciousness.

TL;DR: An implementation of PSI for seed-based d mapping (SDM) method, which additionally benefits from the use of effect sizes, random-effects models, Freedman-Lane-based permutations and threshold-free cluster enhancement (TFCE) statistics, among others.

TL;DR: Findings are summarized that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI‐based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.

TL;DR: Alternative formulations of Morlet wavelets in time and in frequency are presented that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (expressed as full-width at half-maximum).