Showing papers in "NeuroImage in 2017"

TL;DR: This review aims to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that the authors see as most promising for the future developments of the field.

TL;DR: It is highlighted that there is not a single “right” way to process resting state data that reveals the “true” nature of the brain, and different processing approaches likely reveal complementary insights about the brain's functional organisation.

TL;DR: A systematic evaluation of 14 participant‐level confound regression methods for functional connectivity highlights the heterogeneous efficacy of existing methods, and suggests that different confounding regression strategies may be appropriate in the context of specific scientific goals.

TL;DR: There is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders, however, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper.

TL;DR: An auto‐context version of the VoxResNet is proposed by combining the low‐level image appearance features, implicit shape information, and high‐level context together for further improving the segmentation performance, and achieved the best performance in the 2013 MICCAI MRBrainS challenge.

TL;DR: It is shown that the DTI measurements are highly site‐specific, highlighting the need of correcting for site effects before performing downstream statistical analyses, and that ComBat, a popular batch‐effect correction tool used in genomics, performs best at modeling and removing the unwanted inter‐site variability in FA and MD maps.

TL;DR: Age predictions can be accurately generated on raw T1‐MRI data, substantially reducing computation time for novel data, bringing the process closer to giving real‐time information on brain health in clinical settings.

TL;DR: These results indicate that simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts, and group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy.

TL;DR: T theory and experiments outline that the popular “leave‐one‐out” strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred, and it can be favorable to use sane defaults, in particular for non‐sparse decoders.

TL;DR: An overview of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi‐stable, suggests quasi‐simultaneity of activity among the nodes of large‐scale networks.

TL;DR: The feasibility of inter‐site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi‐site autism dataset is demonstrated.

TL;DR: The Cam-CAN Stage 2 repository contains multi-modal (MRI, MEG, and cognitive-behavioural) data from a large, cross-sectional adult lifespan (18–87 years old) population-based sample, providing a depth of neurocognitive phenotyping that is currently unparalleled, enabling integrative analyses of age-related changes in brain structure, brain function, and cognition.

TL;DR: Quicksilver as mentioned in this paper predicts the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDFMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization.

TL;DR: The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression, and a detailed account of AD classification challenges is provided.

TL;DR: The importance of signal denoising as an essential step in the analysis pipeline of task‐based and resting state fMRI studies is summarized and practical recommendations regarding the optimization of the preprocessing pipeline are indicated.

TL;DR: The BrainNetCNN framework is applied to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm and outperforms a fully connected neural‐network with the same number of model parameters on both phantoms with focal and diffuse injury patterns.

TL;DR: Although predominantly peppered with examples from human neuroimaging, it is hoped that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure.

TL;DR: This work maps the spatial and temporal properties of the global signal, individually, in 1000+ fMRI scans from 8 sites to demonstrate the need for methods capable of isolating and removing global artifactual variance while preserving putative “neural” variance.

TL;DR: The FBM method is presented as an integral piece within a comprehensive fixel‐based analysis framework to investigate measures of fibre density, fibre‐bundle morphology (cross‐section), and a combined measure of fibredensity and cross‐section.

TL;DR: In this article, the authors raise awareness on error bars of cross-validation, which are often underestimated and propose solutions to increase sample size, tackling possible increases in heterogeneity of the data.

TL;DR: A computational model is developed to show that E:I changes can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and provides evidence thatE:I ratio may be inferred from electrophysics recordings at many spatial scales, ranging from the local field potential to surface electrocorticography.

TL;DR: A practical “how‐to” guide to help determine whether single‐subject fMRI independent components (ICs) characterise structured noise or not and how the data quality, data type and preprocessing can influence the characteristics of ICs.

TL;DR: It is shown across both empirical and artificial patient‐control datasets that lower levels of overall FC in either the patient or control group will most often lead to differences in network efficiency and clustering, suggesting that differences in FC across subjects will be artificially inflated or translated into differences innetwork organization.

TL;DR: A composite atlas based on manual segmentations of a multimodal high resolution brain template, histology and structural connectivity is presented that can be used to segment DBS targets in single subjects, yielding more accurate results compared to priorly published atlases.

TL;DR: It is found that multimodal data improves brain‐based age prediction, resulting in a mean absolute prediction error of 4.29 years, and the discrepancy between predicted age and chronological age captures cognitive impairment.

TL;DR: The Spinal Cord Toolbox is introduced, a comprehensive software dedicated to the processing of spinal cord MRI data that is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution.

TL;DR: Recent advances in the understanding of how age affects the authors' brain's intrinsic organization are discus, and a perspective on potential challenges and future directions of the field is shared.

TL;DR: This work is the first to study subcortical structure segmentation on such large‐scale and heterogeneous data and yielded segmentations that are highly consistent with a standard atlas‐based approach, while running in a fraction of the time needed by atlas-based methods and avoiding registration/normalization steps.

TL;DR: DeepNAT is an end‐to‐end learning‐based approach to brain segmentation that jointly learns an abstract feature representation and a multi‐class classification and the results show that DeepNAT compares favorably to state‐of‐the‐art methods.

TL;DR: The data indicate that whole‐brain t Tau PET measures might be adequate biomarkers to detect AD‐related tau pathology, however, regional measures covering AD‐vulnerable regions may increase sensitivity to early tau PET signal, atrophy and memory decline.