The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In ...this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
Concussion pathophysiology in humans remains incompletely understood. Diffusion tensor imaging (DTI) has identified microstructural abnormalities in otherwise normal appearing brain tissue, using ...measures of fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). The results of prior DTI studies suggest that acute alterations in microstructure persist beyond medical clearance to return to play (RTP), but these measures lack specificity. To better understand the observed effects, this study combined DTI with neurite orientation dispersion and density imaging (NODDI), which employs a more sophisticated description of water diffusion in the brain. A total of 66 athletes were recruited, including 33 concussed athletes, scanned within 7 days after concussion and at RTP, along with 33 matched controls. Both univariate and multivariate methods identified DTI and NODDI parameters showing effects of concussion on white matter. Spatially extensive decreases in FA and increases in AD and RD were associated with reduced intra‐neurite water volume, at both the symptomatic phase of injury and RTP, indicating that effects persist beyond medical clearance. Subsequent analyses also demonstrated that concussed athletes with higher symptom burden and a longer recovery time had greater reductions in FA and increased AD, RD, along with increased neurite dispersion. This study provides the first longitudinal evaluation of concussion from acute injury to RTP using combined DTI and NODDI, significantly enhancing our understanding of the effects of concussion on white matter microstructure.
To test the hypothesis that concussion-related brain alterations seen at symptomatic injury and medical clearance to return to play (RTP) will have dissipated by 1 year after RTP.
For this ...observational study, 24 athletes with concussion were scanned longitudinally within 1 week after injury, at RTP, and 1 year after RTP. A large control cohort of 122 athletes were also scanned before the season. Each imaging session assessed global functional connectivity (Gconn) and cerebral blood flow (CBF), along with white matter fractional anisotropy (FA) and mean diffusivity (MD). The main effects of concussion on MRI parameters were evaluated at each postinjury time point. In addition, covariation was assessed between MRI parameters and clinical measures of acute symptom severity and time to RTP.
Different aspects of brain physiology showed different patterns of recovery over time. Both Gconn and FA displayed no significant effects at 1 year after RTP, whereas CBF and MD exhibited persistent long-term effects. The effects of concussion on MRI parameters were also dependent on acute symptom severity and time to RTP for all postinjury time points.
This study provides the first longitudinal evaluation of concussion focused on time of RTP and 1 year after medical clearance, using multiple different MRI measures to assess brain structure and function. These findings significantly enhance our understanding of the natural course of brain recovery after a concussion.
The trail-making test (TMT) is a popular neuropsychological test, which is used extensively to measure cognitive impairment associated with neurodegenerative disorders in older adults. Behavioural ...performance on the TMT has been investigated in older populations, but there is limited research on task-related brain activity in older adults. The current study administered a naturalistic version of the TMT to a healthy older-aged population in an MRI environment using a novel, MRI-compatible tablet. Functional MRI was conducted during task completion, allowing characterization of the brain activity associated with the TMT. Performance on the TMT was evaluated using number of errors and seconds per completion of each link. Results are reported for 36 cognitively healthy older adults between the ages of 52 and 85. Task-related activation was observed in extensive regions of the bilateral frontal, parietal, temporal and occipital lobes as well as key motor areas. Increased age was associated with reduced brain activity and worse task performance. Specifically, older age was correlated with decreased task-related activity in the bilateral occipital, temporal and parietal lobes. These results suggest that healthy older aging significantly affects brain function during the TMT, which consequently may result in performance decrements. The current study reveals the brain activation patterns underlying TMT performance in a healthy older aging population, which functions as an important, clinically-relevant control to compare to pathological aging in future investigations.
Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising ...from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, "real-time" correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.
BOLD fMRI is sensitive to blood-oxygenation changes correlated with brain function; however, it is limited by relatively weak signal and significant noise confounds. Many preprocessing algorithms ...have been developed to control noise and improve signal detection in fMRI. Although the chosen set of preprocessing and analysis steps (the "pipeline") significantly affects signal detection, pipelines are rarely quantitatively validated in the neuroimaging literature, due to complex preprocessing interactions. This paper outlines and validates an adaptive resampling framework for evaluating and optimizing preprocessing choices by optimizing data-driven metrics of task prediction and spatial reproducibility. Compared to standard "fixed" preprocessing pipelines, this optimization approach significantly improves independent validation measures of within-subject test-retest, and between-subject activation overlap, and behavioural prediction accuracy. We demonstrate that preprocessing choices function as implicit model regularizers, and that improvements due to pipeline optimization generalize across a range of simple to complex experimental tasks and analysis models. Results are shown for brief scanning sessions (<3 minutes each), demonstrating that with pipeline optimization, it is possible to obtain reliable results and brain-behaviour correlations in relatively small datasets.
Despite being intuitive, cognitive effort has proven difficult to define quantitatively. Here, we proposed to study cognitive effort by investigating the degree to which the brain deviates from its ...default state, where brain activity is scale-invariant. Specifically, we measured such deviations by examining changes in scale-invariance of brain activity as a function of task difficulty and posited suppression of scale-invariance as a proxy for exertion of cognitive effort. While there is some fMRI evidence supporting this proposition, EEG investigations on the matter are scant, despite the EEG signal being more suitable for analysis of scale invariance (i.e., having a much broader frequency range). In the current study we validated the correspondence between scale-invariance (H) of cortical activity recorded by EEG and task load during two working memory (WM) experiments with varying set sizes. Then, we used this neural signature to disentangle cognitive effort from the number of items stored in WM within participants. Our results showed monotonic decreases in H with increased set size, even after set size exceeded WM capacity. This behavior of H contrasted with behavioral performance and an oscillatory indicator of WM load (i.e., alpha-band desynchronization), both of which showed a plateau at difficulty levels surpassing WM capacity. This is the first reported evidence for the suppression of scale-invariance in EEG due to task difficulty, and our work suggests that H suppression may be used to quantify changes in cognitive effort even when working memory load is at maximum capacity.
The presence of physiological noise in functional MRI can greatly limit the sensitivity and accuracy of BOLD signal measurements, and produce significant false positives. There are two main types of ...physiological confounds: (1) high-variance signal in non-neuronal tissues of the brain including vascular tracts, sinuses and ventricles, and (2) physiological noise components which extend into gray matter tissue. These physiological effects may also be partially coupled with stimuli (and thus the BOLD response). To address these issues, we have developed PHYCAA+, a significantly improved version of the PHYCAA algorithm (Churchill et al., 2011) that (1) down-weights the variance of voxels in probable non-neuronal tissue, and (2) identifies the multivariate physiological noise subspace in gray matter that is linked to non-neuronal tissue. This model estimates physiological noise directly from EPI data, without requiring external measures of heartbeat and respiration, or manual selection of physiological components. The PHYCAA+ model significantly improves the prediction accuracy and reproducibility of single-subject analyses, compared to PHYCAA and a number of commonly-used physiological correction algorithms. Individual subject denoising with PHYCAA+ is independently validated by showing that it consistently increased between-subject activation overlap, and minimized false-positive signal in non gray-matter loci. The results are demonstrated for both block and fast single-event task designs, applied to standard univariate and adaptive multivariate analysis models.
•We propose improved data-driven method for controlling physiological noise in fMRI.•Down-weights signal amplitude in vasculature and ventricles•Removes multivariate autocorrelated components present in neuronal tissue•Significant improvements in prediction, reproducibility of fMRI results•Minimizes false-positives in non-neuronal tissue
Concussion is associated with disrupted cerebral blood flow (CBF), although there appears to be substantial inter-individual variability in CBF response. At present, the mechanisms of variable CBF ...response remain incompletely understood, but one potential contributor is matrix metalloproteinase (MMP) expression. In more severe forms of acquired brain injury, MMP up-regulation contributes to CBF impairments via increased blood-brain barrier permeability. A similar relationship is hypothesized for concussion, where recently concussed individuals with higher MMP levels have lower CBF. To test this hypothesis, 35 concussed athletes were assessed longitudinally at early symptomatic injury (median: 5 days post-injury) and at medical clearance (median: 24 days post-injury), along with 71 athletic controls. For all athletes, plasma MMPs were measured and arterial spin labelling was used to measure CBF. Consistent with our hypothesis, higher concentrations of MMP-2 and MMP-3 were correlated with lower global CBF. The correlations between MMPs and global CBF were also significantly diminished for concussed athletes at medical clearance and for athletic controls. These results indicate an inverse relationship between plasma MMP levels and CBF that is specific to the symptomatic phase of concussion. Analyses of regional CBF further showed that correlations with MMP levels exhibited some spatial specificity, with greatest effects in occipital, parietal and temporal lobes. These findings provide new insights into the mechanisms of post-concussion cerebrovascular dysfunction.
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