Showing papers in "NeuroImage in 2015"

TL;DR: The results show that ICA-AROMA effectively reduces motion-induced signal variations in fMRI data, is applicable across datasets without requiring classifier re-training, and preserves the temporal characteristics of the f MRI data.

TL;DR: The purpose of this review is to communicate and synthesize recent findings related to motion artifact in resting state fMRI, and to highlight gaps in current knowledge and avenues for future research.

TL;DR: The results show that the atlas and companion segmentation method can segment T1 and T2 images, as well as their combination, replicate findings on mild cognitive impairment based on high-resolution T2 data, and can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy.

TL;DR: This paper proposes to use deep convolutional neural networks (CNNs) for segmenting isointense stage brain tissues using multi-modality MR images, and compared the performance of the approach with that of the commonly used segmentation methods on a set of manually segmented isointENSE stage brain images.

TL;DR: The fundamental theory from signal processing is provided to address parameter choices when estimating and interpreting dynFC and shows how spurious fluctuations in dynFC can arise due to the estimation method when the window length is shorter than the largest wavelength present in both signals.

TL;DR: The results presented in this study demonstrate the potential of the suggested approach for early AD diagnosis and an important role of MRI in the MCI-to-AD conversion prediction.

TL;DR: It is concluded that further progress in the cognitive and clinical neuroscience of spontaneous thought will therefore require a re-balancing of the authors' view of the contributions of various regions and networks throughout the brain, and beyond the DMN.

TL;DR: How various anatomical features systematically shape the electric field distribution in the brain during tDCS is shown, namely the thicknesses of the cerebrospinal fluid and the skull, the gyral depth and the distance to the anode and cathode.

TL;DR: SIFT2 is presented, which provides a more logically direct and computationally efficient solution to the streamlines connectivity quantification problem: by determining an appropriate cross-sectional area multiplier for each streamline rather than removing streamlines altogether, biologically accurate measures of fibre connectivity are obtained whilst making use of the complete streamlines reconstruction.

TL;DR: It is demonstrated that a slight enhancement of the non-linearity of the network nodes is sufficient to broaden the repertoire of possible network behaviors, leading to modes of fluctuations, reminiscent of some of the most frequently observed Resting State Networks.

TL;DR: An alternative parcellation of the orbitofrontal cortex is described for the automated anatomical labeling atlas of Tzourio-Mazoyer et al. (2002) and is available as a toolbox for SPM at http://www.gin.fr/AAL2.

TL;DR: A novel toolbox that facilitates both reconstruction of the lead electrode trajectory and the contact placement is introduced and allows for a precise and fast reconstruction of DBS contacts by proposing a semi-automated procedure.

TL;DR: Results demonstrate that ICA-AROMA is an effective strategy for removing motion-related artifacts from rfMRI data and preserves the temporal non-artifactual time-series characteristics and limits the loss in tDoF, thereby increasing statistical power at both the subject- and the between-subject analysis level.

TL;DR: The results suggest that SPM12 TIV estimates are an acceptable substitute for labour-intensive manual estimates even in the challenging context of multiple centres and the presence of neurodegenerative pathology.

TL;DR: It is shown that volume and thickness estimates of the cortical gray matter are biased by head motion with an average apparent volume loss of roughly 0.7%/mm/min of subject motion, suggesting that reported morphometric effects of movement disorders or other conditions with increased motion tendency may need to be revisited.

TL;DR: This work presents a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and shows how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms.

TL;DR: A symmetric orthogonalisation method is proposed to correct for artificial correlations between a set of multiple regions of interest (ROIs) and enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs.

TL;DR: A grand challenge to objectively compare algorithms based on a clinically representative multi-center data set of three diagnostic groups, finding the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity.

TL;DR: The combined examination of the 21 protocols in the same dataset suggests possible strategies towards developing a harmonized subfield segmentation protocol and facilitates comparison between published studies.

TL;DR: It is demonstrated that the MST is insensitive to alterations in connection strength or link density, and the behavior of MST and conventional network-characteristics for simulated regular and scale-free networks that were gradually rewired to random networks were explored.

TL;DR: Low-demand movies like Inscapes may represent a useful intermediate condition between task-free rest and typical narrative movies while still improving participant compliance, and mean functional connectivity of somatomotor, visual and ventral attention networks differed significantly across various analyses.

TL;DR: A novel fMRI paradigm that independently manipulates both empathy and ToM and allows separation of affective and cognitive routes to understanding others is presented and may benefit future clinical, developmental, and intervention studies on identifying selective impairments and improvement in specific components of social cognition.

TL;DR: The mathematical framework of the computational model at the core of the tool The Virtual Brain, designed to simulate collective whole brain dynamics by virtualizing brain structure and function, is described, allowing simultaneous outputs of a number of experimental modalities.

TL;DR: A unique combination of magnetization transfer, diffusion imaging and histology is presented, providing a novel method for in vivo magnetic resonance imaging of the axon volume fraction and the myelin g-ratio.

TL;DR: With a fixed set of parameters for the initial susceptibility estimation and subsequent streaking artifact estimation and removal, the method provides an unbiased estimate of tissue susceptibility with negligible streaking artifacts, as compared to multi-orientation QSM reconstruction.

TL;DR: A novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE), which uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology.

TL;DR: It is demonstrated that non-stationary fluctuations in functional connectivity can in theory be detected with much shorter window lengths (e.g. 40s), while maintaining nominal control of false positives.

TL;DR: Overall, network measures were found to be unstable across absolute thresholds, and caution should be used when applying thresholds to functional connectivity data and when interpreting results from binary graph models.

TL;DR: It is suggested that the μFA and OP may complement FA by independently quantifying the microscopic anisotropy and the level of orientation coherence, which is a major limitation to the use of FA as a biomarker for "tissue integrity" in regions of complex microarchitecture.

TL;DR: Spectral DCM was not only more accurate but also more sensitive to group differences and a comparative evaluation was performed using real resting state fMRI data to study the functional integration within default mode network using spectral and stochastic DCMs.