Showing papers in "NeuroImage in 2021"

TL;DR: A multimodal semantic control network was delineated with formal meta-analyses and a large extent of posterior temporal cortex was implicated and no parietal regions were implicated.

TL;DR: This work describes a set of possible confounds (including nonlinear effects and interactions that researchers may wish to consider for their studies using UK Biobank data, and studies the extent to which each of these confounds affects the data and the spurious correlations that may arise if they are not controlled.

TL;DR: In this paper, the authors comprehensively assess the performance of ten published null frameworks in statistical analyses of neuroimaging data and find that naive null models do not preserve spatial autocorrelation consistently yield elevated false positive rates and unrealistically liberal statistical estimates.

TL;DR: In this paper, the age trajectories in healthy individuals aged 18 to 94 years (57.12% women) were investigated using linear mixed effect models and machine learning based brain age prediction.

TL;DR: Fuzzy-based analysis (FBA) as mentioned in this paper has been widely used in the neuroimaging community to extract micro-structural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes.

TL;DR: In this article, a systematic review and meta-analysis of studies that validate microstructural imaging by combining it with myelin histology was performed, and the authors found meta-analytic evidence for correlations between various histology metrics and markers from different MRI modalities, including fractional anisotropy, radial diffusivity, macromolecular pool, magnetization transfer ratio, susceptibility and longitudinal relaxation rate.

TL;DR: In this paper, the authors show that compared to the resting state, functional connectivity measured during naturalistic viewing (i.e., movie watching) yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion.

TL;DR: Increased small-worldness of brain networks predicts LSD-induced ego-dissolution and untethers functional connectivity from the constraint of structural connectivity, which bridge pharmacodynamics, subjective experience and brain dynamics.

TL;DR: Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects.

TL;DR: The role of DBS electrode connectivity profiles as a promising method to investigate DBS effects and to potentially guide DBS programming is supported and results hint at the fact that patient-specific connectivity may bear the potential of estimating slightly more variance when compared to group connectomes.

TL;DR: A 3D CNN architecture to predict chronological age, using a training dataset of 12,802 T1-weighted MRI images and a further 6,885 images for testing, and it is shown that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting the clinical relevance.

TL;DR: The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community as mentioned in this paper.

TL;DR: This resource makes it possible for researchers to employ standardized ERP paradigms in their research, apply carefully designed analysis pipelines and use a priori selected parameters for data processing, rigorously assess the quality of their data, and test new analytic techniques with standardized data from a wide range of paradigm.

TL;DR: Wang et al. as mentioned in this paper established modified index for diffusion tensor image analysis along the perivascular space (mALPS-index), which was calculated on diffusion tensors image (DTI), compared it with the classical detection of glymphatic clearance function calculated on Glymphatic MRI after intrathecal administration of gadolinium, and analyzed the relationship between CSVD imaging markers and mALPSindex in CSVD patients from the CIRCLE study (ClinicalTrials.gov ID: NCT03542734) (study 2).

TL;DR: Overall, MRI-based myelin imaging methods show a fairly good correlation with histology and a good reproducibility, however, the amount of validation data is too limited and the variability in performance between studies is too large to select the optimal MRI method for myelin quantification yet.

TL;DR: Evidence is presented for a new approach to studying DBS’ mechanisms of action, focused on measuring objective cognitive/emotional deficits that underpin these and many other mental disorders, and it is suggested that a focus on cognition could lead to reliable network biomarkers at an electrophysiologic level.

TL;DR: In this paper, a deep learning based training scheme was proposed to reduce the influence of the scanner on network predictions, which can be used for regression, classification and segmentation tasks with two different network architectures.

TL;DR: Together, the results provide insight into the spatiotemporal dynamics of TUTs and the neural markers that may facilitate their detection and information contained in dynamic interactions between large-scale cortical networks was predictive of transient changes in attentional focus above other modalities.

TL;DR: The results establish that the spontaneous activity's intrinsic neural timescale is related to the neural capacity that specifically supports sensory rather than motor information processing in the healthy brain.

TL;DR: Using movies and narratives as naturalistic stimuli in human neuroimaging studies has yielded significant advances in understanding of cognitive and emotional functions, including discovering a cortical hierarchy of temporal receptive windows, which supports processing of dynamic information that accumulates over several time scales.

TL;DR: The overall connectivity increased in strength from childhood to adolescence and became spatially similar to that in the young adult group (N=61, 18-28 years of age), indicating an enhanced capability in parallel information communication within the brain system.

TL;DR: Overall, the findings suggest that susceptibility in the deep and inferior gray nuclei, particularly the pallidum and putamen, may be a marker of cognitive decline, amyloid deposition, and off-target binding of the tau ligand.

TL;DR: In this paper, the authors proposed to account for intrinsic uncertainty through a heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference, and integrate the two to quantify predictive uncertainty over the output image.

TL;DR: In this paper, a wearable 50-channel OPM-MEG system enables characterisation of the electrophysiological connectome, and the connectivity in the resting state and during a visuo-motor task, using both OPM and a 275-channel cryogenic MEG device.

TL;DR: The proposed framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.

TL;DR: In this article, the locus coeruleus (LC) in dementia and Parkinson's disease was identified in vivo using non-invasive magnetic resonance imaging techniques which have potential as biomarkers for detecting and monitoring disease progression.

TL;DR: A new deep learning framework is developed to predict an individual's brain function from their structural connectome, achieving prediction accuracies that substantially exceeded state-of-the-art biophysical models and suggesting that structure-function coupling in human brain networks is substantially tighter than previously suggested.

TL;DR: This work constitutes a first demonstration that an interdisciplinary, real-world, crowdsourcing neuroscience approach may provide a promising method to collect large, rich datasets pertaining to real-life face-to-face interactions and is a demonstration of how the general public can participate and engage in the scientific process outside of the laboratory.

TL;DR: The NIMH Macaque Template (NMT) v2 as discussed by the authors is a high-resolution population average anatomical template and associated neuroimaging resources, providing researchers with a standard space for macaque neuro-imaging.

TL;DR: In this paper, a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients is presented.