Showing papers in "NeuroImage in 2016"

TL;DR: The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes.

TL;DR: A post-processing technique for fast denoising of diffusion-weighted MR images is introduced and it is demonstrated that the technique suppresses local signal fluctuations that solely originate from thermal noise rather than from other sources such as anatomical detail.

TL;DR: A non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion is presented, indicating high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.

TL;DR: It is shown that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations, and it is found that most of the functional connections are in fact dynamic.

TL;DR: This paper addressed two pressing questions related to ALE meta-analysis, and showed as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative.

TL;DR: The robustness of Bayesian model reduction to violations of the Laplace assumption in dynamic causal modelling is illustrated and how its recursive application can facilitate both classical and Bayesian inference about group differences is considered.

TL;DR: A 3D convolutional deep learning architecture to address shortcomings of existing methods, not limited to non-enhanced T1w images, and may prove useful for large-scale studies and clinical trials.

TL;DR: In this paper, the authors compare the reliability of three classes of dissimilarity measures: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance, using simulations and four real functional magnetic resonance imaging (fMRI) datasets.

TL;DR: It is shown that accounting for cranial/brain size affects models of regional brain development, particularly with respect to sex differences, and some best practices for statistical control of cranial volume and brain size in future studies are suggested.

TL;DR: The extent to which many popular measures of stationary connectivity are suitable for use in resting-state MEG is investigated, localising magnetic sources with a scalar beamformer and finding the most consistent methods for stationary connectivity estimation are simple amplitude envelope correlation and partial correlation measures.

TL;DR: This study examined self-referential and resting-state processes to clarify the extent to which DMN activity was common and distinct between the conditions, and examined the neural network properties of the identified 'core-self' DMN regions-in medial prefrontal cortex, posterior cingulate cortex, and inferior parietal lobule-using dynamic causal modeling.

TL;DR: The proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns.

TL;DR: The findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies.

TL;DR: The normative values of the new biomarkers for a large cohort of healthy young adults are established, which may then support clinical diagnostics in patients, and it is shown that the microscopic diffusion indices offer direct sensitivity to pathological tissue alterations.

TL;DR: This work proposes a method that can simultaneously identify and describe fast transient multiregion dynamics in terms of their temporal, spectral and spatial properties, and demonstrates that this can be used to identify short-lived transient brain states with distinct power and functional connectivity properties in an MEG data set collected during a volitional motor task.

TL;DR: This paper proposes a framework for automatic classification of schizophrenia, bipolar and healthy subjects based on their static and dynamic FNC features, and compares cross-validated classification performance between static andynamic FNC.

TL;DR: It is suggested that pMTG may play a crucial role within a large-scale network that allows the integration of automatic retrieval in the default mode network with executively-demanding goal-oriented cognition, and that this could support the ability to understand actions and non-dominant semantic associations, allowing semantic retrieval to be ‘shaped’ to suit a task or context.

TL;DR: The Philadelphia Neurodevelopmental Cohort is a large-scale study of child development that combines neuroimaging, diverse clinical and cognitive phenotypes, and genomics and is evolving to be a significant resource for the broader neuroscience community for studies of normal and abnormal neurodevelopment.

TL;DR: A microstructure model, the diffusion tensor distribution (DTD) model, is proposed, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors.

TL;DR: A conceptual framework is provided, emphasizing principal strategies and highlighting promising future directions to exploit the benefits of combining NTBS with neuroimaging or electrophysiology to close the loop between measuring and modulating brain activity by means of closed-loop brain state-dependent NTBS.

TL;DR: The authors explored a family of generative models of the human connectome that yield synthetic networks designed according to different wiring rules combining geometric and a broad range of topological factors and found that a combination of geometric constraints with a homophilic attachment mechanism can create synthetic networks that closely match many topological characteristics of individual human connectomes, including features that were not included in the optimization of the generative model itself.

TL;DR: It is argued that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity, due to FPs occurring more prevalently between network modules rather than within them.

TL;DR: The recruiting and screening of the children are described and a brief overview of the assessments performed, the imaging methods applied, the genetic data produced, and the numbers of cases for whom different data types are available are given.

TL;DR: This paper proposes a novel methodological architecture that combines deep learning and state-space modelling, and applies it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis, and designs a Deep Auto-Encoder to discover hierarchical non-linear functional relations among regions, which transform the regional features into an embedding space, whose bases are complex functional networks.

TL;DR: The statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.

TL;DR: It is suggested that CTh follows a simple linear decline in most cortical areas by age 5, and all areas byage 8, which further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders.

TL;DR: Simulated networks with known transitions are used to examine the effects of parameters such as window length, window offset, window type, noise, filtering, and sampling rate on sliding window correlation (SWC), showing that the detection of state transitions and durations in the SWC is most strongly influenced by the window length and offset, followed by noise and filtering parameters.

TL;DR: Preliminary findings link IGF-I levels to hippocampal volume changes and putatively hippocampus-dependent memory changes that seem to occur over time independently of exercise.

TL;DR: Property of statistics used with the general linear model (GLM) and their distributions are exploited to obtain accelerations irrespective of generic software or hardware improvements and method (iv) was found the best as long as symmetric errors can be assumed.

TL;DR: The characteristics of the various noise sources as well as the mechanisms through which they affect the fMRI signal are reviewed and approaches for distinguishing signal from noise are reviewed.