Introduction The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding ...of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. Methods We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition ( n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. Results We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” ( https://nrdg.github.io/tractoscope ). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window ...method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses.
Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies ...suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.
Transcranial magnetic stimulation (TMS)-evoked potentials (TEPs), recorded using electroencephalography (TMS-EEG), offer a powerful tool for measuring causal interactions in the human brain. However, ...the test-retest reliability of TEPs, critical to their use in clinical biomarker and interventional studies, remains poorly understood.
We quantified TEP reliability to: (i) determine the minimal TEP amplitude change which significantly exceeds that associated with simply re-testing, (ii) locate the most reliable scalp regions of interest (ROIs) and TEP peaks, and (iii) determine the minimal number of TEP pulses for achieving reliability.
TEPs resulting from stimulation of the left dorsolateral prefrontal cortex were collected on two separate days in sixteen healthy participants. TEP peak amplitudes were compared between alternating trials, split-halves of the same run, two runs five minutes apart and two runs on separate days. Reliability was quantified using concordance correlation coefficient (CCC) and smallest detectable change (SDC).
Substantial concordance was achieved in prefrontal electrodes at 40 and 60 ms, centroparietal and left parietal ROIs at 100 ms, and central electrodes at 200 ms. Minimum SDC was found in the same regions and peaks, particularly for the peaks at 100 and 200 ms. CCC, but not SDC, reached optimal values by 60–100 pulses per run with saturation beyond this number, while SDC continued to improve with increased pulse numbers.
TEPs were robust and reliable, requiring a relatively small number of trials to achieve stability, and are thus well suited as outcomes in clinical biomarker or interventional studies.
•Measured test-retest reliability of TMS-EEG evoked potentials (TEPs).•TEPs achieved substantial concordance when measurements are repeated days apart.•Measurement error is low enough for future group biomarker identification.•Maximum concordance occurs in the N100 and P200 peaks in distinct scalp regions.•Optimal concordance is achieved with 60–100 trials per run.
Electrophysiological resting state functional connectivity using high density electroencephalography (hdEEG) is gaining momentum. The increased resolution offered by hdEEG, usually either 128 or 256 ...channels, permits source localization of EEG signals on the cortical surface. However, the number of methodological options for the acquisition and analysis of resting state hdEEG is extremely large. These include acquisition duration, eyes open/closed, channel density, source localization methods, and functional connectivity metric.
We undertake an extensive examination of the test-retest reliability and methodological agreement of all these options for regional measures of functional connectivity.
Power envelope connectivity shows larger test-retest reliability than imaginary coherence across all bands. While channel density doesn’t strongly impact reliability or agreement, source localization methods produce systematically different functional connectivity, highlighting an important obstacle for replicating results in the literature. Most importantly, reliability and agreement often plateaus at or after 6 minutes of acquisition, well beyond the typical duration of 3 minutes. Finally, our study demonstrates that resting EEG can be as or more reliable than resting fMRI acquired in the same individuals.
The competitive reliability and agreement of power envelope connectivity greatly increases our confidence in measuring resting state connectivity using EEG and its capacity to find individual differences.
•EEG Power envelope connectivity is more reliable than imaginary coherence.•EEG channel density doesn’t strongly impact reliability or agreement.•EEG source localization methods produce systematically different connectivity.•EEG reliability and agreement plateaus at or after 6 min of acquisition.•Resting-state EEG can be as or more reliable than resting fMRI.
Objective:A major challenge in understanding and treating posttraumatic stress disorder (PTSD) is its clinical heterogeneity, which is likely determined by various neurobiological perturbations. This ...heterogeneity likely also reduces the effectiveness of standard group comparison approaches. The authors tested whether a statistical approach aimed at identifying individual-level neuroimaging abnormalities that are more prevalent in case subjects than in control subjects could reveal new clinically meaningful insights into the heterogeneity of PTSD.Methods:Resting-state functional MRI data were recorded from 87 unmedicated PTSD case subjects and 105 war zone–exposed healthy control subjects. Abnormalities were modeled using tolerance intervals, which referenced the distribution of healthy control subjects as the “normative population.” Out-of-norm functional connectivity values were examined for enrichment in cases and then used in a clustering analysis to identify biologically defined PTSD subgroups based on their abnormality profiles.Results:The authors identified two subgroups among PTSD cases, each with a distinct pattern of functional connectivity abnormalities with respect to healthy control subjects. Subgroups differed clinically on levels of reexperiencing symptoms and improved case-control discriminability and were detectable using independently recorded resting-state EEG data.Conclusions:The results provide proof of concept for the utility of abnormality-based approaches for studying heterogeneity within clinical populations. Such approaches, applied not only to neuroimaging data, may allow detection of subpopulations with distinct biological signatures so that further clinical and mechanistic investigations can be focused on more biologically homogeneous subgroups.
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