Showing papers in "NeuroImage in 2020"

TL;DR: A third version of the automated anatomical labeling atlas, AAL3, is provided, which adds a number of brain areas not previously defined, but of interest in many neuroimaging investigations, to the existing atlas.

TL;DR: A multi-model deep learning framework based on convolutional neural network for joint automatic hippocampal segmentation and AD classification using structural MRI data is proposed and outperforms the single-model methods and several other competing methods.

TL;DR: A meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants confirms the suspected association between rumination and DMN activation and suggests a hypothesis of how DMN regions support rumination.

TL;DR: This work proposes a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer’s anatomical segmentation including surface reconstruction and cortical parcellation.

TL;DR: A comprehensive effort is described that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity to derive age trends of brain structure through the lifespan.

TL;DR: A generative null model is presented, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation matched to SA of a target brain map that can simulate surrogate brain maps that preserve the SA of cortical, subcortical, parcellated, and dense brain maps.

TL;DR: It is demonstrated that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state, to assess connections between parcels and extract network properties.

TL;DR: The sample size of highly cited experimental fMRI studies increased at a rate of 0.74 participant/year and this rate of increase was commensurate with the median sample sizes of neuroimaging studies published in top neuroim imaging journals in 2017 and 2018.

TL;DR: This study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs, therefore, kernel regression might serve as a useful baseline algorithm for future studies.

TL;DR: A fully automatic framework for fetal brain reconstruction that consists of four stages that outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments is proposed.

TL;DR: It is argued that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

TL;DR: An argument for the primacy of naturalistic paradigms is developed, and recent developments in machine learning are pointed to as an example of the transformative power of relinquishing control.

TL;DR: A wearable OPM-MEG system with ‘whole-head’ coverage based upon commercially available OPMs is constructed, and signal detection is shown for the authors' device to be highly robust, and via application of source-space modelling, it is shown that, despite having 5 times fewer sensors, the system exhibits comparable performance to an established cryogenic MEG device.

TL;DR: In this paper, a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI) was introduced.

TL;DR: A multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline is described, reporting the reproducible identification of several large-scale resting-state networks (RSN), including a mouse default-mode network, in the majority of datasets.

TL;DR: Canonical correlation analysis is a prototypical family of methods that is useful in identifying the links between variable sets from different modalities and so is well suited to the analysis of big neuroscience datasets.

TL;DR: A new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain, demonstrating that these protocols are robust against data quality, generalisable across two species and reflect the known anatomy.

TL;DR: It is shown that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions, and it is demonstrated that utilizing a band-stop filter improves post-processing fMRI data quality.

TL;DR: This article focuses on deep brain stimulation, but shows that the same principles can be applied to other forms of neuromodulation, such as transcranial magnetic stimulation and MRI-guided focused ultrasound.

TL;DR: A winner-take-all partitioning method is applied to resting-state fMRI data to generate novel functionally-constrained ROIs in the thalamus, basal ganglia, amygdala, hippocampus, and cerebellum, and it is demonstrated that combining these ROIs with established cortical ROIs recapitulates and extends previously described functional network organization.

TL;DR: This novel atlas of critical structures mediating human brain functions based on direct electrical stimulation based on a highly-specific DES mapping during real-time neuropsychological testing is presented, a well-validated tool for the exploration of cerebral processing and for performing safe surgical interventions in eloquent areas.

TL;DR: It is proposed that the study of functional gradients across the adult lifespan could provide insights that may facilitate the development of new strategies to maintain cognitive ability across the lifespan in health and disease.

TL;DR: A function-based method for cross-species alignment is developed that enables the quantification of homologous regions between humans and rhesus macaques, even when their location is decoupled from anatomical landmarks.

TL;DR: It is shown that 1/f brain activity plays an essential role in accounting for between-person variability in cognitive speed - a relationship that can be mistaken as originating from brain oscillations using conventional power spectrum analysis.

TL;DR: With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact from both viewing paradigms, with little distortion of neural activity.

TL;DR: It is argued that the accuracies of current binary EEG pathology decoders could saturate near 90% due to the imperfect inter-rater agreement of the clinical labels, and that such decoder are already clinically useful, such as in areas where clinical EEG experts are rare.

TL;DR: Whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power and recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work.

TL;DR: It is shown that physiologically-coupled fluctuations alone can produce networks that strongly resemble previously reported resting-state networks, suggesting that, in some cases, the "physiological networks" seem to mimic the neuronal networks.

TL;DR: A systematic review of 33 rTMS studies with baseline and post-rTMS measures of fMRI resting-state functional connectivity (RSFC) finds variability across studies in stimulation parameters, studied populations, and connectivity analysis methodology, suggesting that rT MS effects tend to spread across networks.

TL;DR: In simulation studies, it is shown that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate.