Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI→

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Much recent attention has been paid to quantifying anatomic and functional neuroimaging on the individual subject level. For optimal individual subject characterization, specific acquisition and analysis features need to be identified that maximize interindividual variability while concomitantly minimizing intra-subject variability. We delineate the effect of various acquisition parameters (length of acquisition, sampling frequency) and analysis methods (time course extraction, region of interest parcellation, and thresholding of connectivity-derived network graphs) on characterizing individual subject differentiation. We utilize a non-parametric statistical metric that quantifies the degree to which a parameter set allows this individual subject differentiation by both maximizing interindividual variance and minimizing intra-individual variance. We apply this metric to analysis of four publicly available test-retest resting-state fMRI (rs-fMRI) data sets. We find that for the question of maximizing individual differentiation, (i) for increasing sampling, there is a relative tradeoff between increased sampling frequency and increased acquisition time; (ii) for the sizes of the interrogated data sets, only 3-4 min of acquisition time was sufficient to maximally differentiate each subject with an algorithm that utilized no a priori information regarding subject identification; and (iii) brain regions that most contribute to this individual subject characterization lie in the default mode, attention, and executive control networks. These findings may guide optimal rs-fMRI experiment design and may elucidate the neural bases for subject-to-subject differences

 

Overview of molecular fMRI→

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Comprehensive analysis of brain function depends on understanding the dynamics of diverse neural signaling processes over large tissue volumes in intact animals and humans. Most existing approaches to measuring brain signaling suffer from limited tissue penetration, poor resolution, or lack of specificity for well-defined neural events. Here we discuss a new brain activity mapping method that overcomes some of these problems by combining MRI with contrast agents sensitive to neural signaling. The goal of this “molecular fMRI” approach is to permit noninvasive whole-brain neuroimaging with specificity and resolution approaching current optical neuroimaging methods. In this article, we describe the context and need for molecular fMRI as well as the state of the technology today. We explain how major types of MRI probes work and how they can be sensitized to neurobiological processes, such as neurotransmitter release, calcium signaling, and gene expression changes. We comment both on past work in the field and on challenges and promising avenues for future development.

 

Trade-off between angular and spatial resolutions in in vivo fiber tractography→

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Tractography is becoming an increasingly popular method to reconstruct white matter connections in vivo. The diffusion MRI data that tractography is based on requires a high angular resolution to resolve crossing fibers whereas high spatial resolution is required to distinguish kissing from crossing fibers. However, scan time increases with increasing spatial and angular resolutions, which can become infeasible in clinical settings. Here we investigated the trade-off between spatial and angular resolutions to determine which of these factors is most worth investing scan time in. We created a unique diffusion MRI dataset with 1.0 mm isotropic resolution and a high angular resolution (100 directions) using an advanced 3D diffusion-weighted multi-slab EPI acquisition. This dataset was reconstructed to create subsets of lower angular (75, 50, and 25 directions) and lower spatial (1.5, 2.0, and 2.5 mm) resolution. Using all subsets, we investigated the effects of angular and spatial resolutions in three fiber bundles—the corticospinal tract, arcuate fasciculus and corpus callosum—by analyzing the volumetric bundle overlap and anatomical correspondence between tracts. Our results indicate that the subsets of 25 and 50 directions provided inferior tract reconstructions compared with the datasets with 75 and 100 directions. Datasets with spatial resolutions of 1.0, 1.5, and 2.0 mm were comparable, while the lowest resolution (2.5 mm) datasets had discernible inferior quality. In conclusion, we found that angular resolution appeared to be more influential than spatial resolution in improving tractography results. Spatial resolutions higher than 2.0 mm only appear to benefit multi-fiber tractography methods if this is not at the cost of decreased angular resolution.

 

Distortion products in auditory fMRI research: Measurements and solutions→

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Nonlinearities in the cochlea can introduce audio frequencies that are not present in the sound signal entering the ear. Known as distortion products (DPs), these added frequencies complicate the interpretation of auditory experiments. Sound production systems also introduce distortion via nonlinearities, a particular concern for fMRI research because the Sensimetrics earphones widely used for sound presentation are less linear than most high-end audio devices (due to design constraints). Here we describe the acoustic and neural effects of cochlear and earphone distortion in the context of fMRI studies of pitch perception, and discuss how their effects can be minimized with appropriate stimuli and masking noise. The amplitude of cochlear and Sensimetrics earphone DPs were measured for a large collection of harmonic stimuli to assess effects of level, frequency, and waveform amplitude. Cochlear DP amplitudes were highly sensitive to the absolute frequency of the DP, and were most prominent at frequencies below 300 Hz. Cochlear DPs could thus be effectively masked by low-frequency noise, as expected. Earphone DP amplitudes, in contrast, were highly sensitive to both stimulus and DP frequency (due to prominent resonances in the earphone's transfer function), and their levels grew more rapidly with increasing stimulus level than did cochlear DP amplitudes. As a result, earphone DP amplitudes often exceeded those of cochlear DPs. Using fMRI, we found that earphone DPs had a substantial effect on the response of pitch-sensitive cortical regions. In contrast, cochlear DPs had a small effect on cortical fMRI responses that did not reach statistical significance, consistent with their lower amplitudes. Based on these findings, we designed a set of pitch stimuli optimized for identifying pitch-responsive brain regions using fMRI. These stimuli robustly drive pitch-responsive brain regions while producing minimal cochlear and earphone distortion, and will hopefully aid fMRI researchers in avoiding distortion confounds.

 

Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI→

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While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in-scanner motion on morphological analysis of structural MRI is relatively under-studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in-scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend-level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non-overlapping sets of structural MRI scans, convergent evidence showed that in-scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non-sedated humans.

Time-of-day effects in brain volume (brain volumes are larger in the morning) (!) →

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There was a statistically significant effect of time-of-day on the BPF change in MS clinical trial datasets (− 0.180 per day, that is, 0.180% of intracranial volume, p = 0.019) as well as the ADNI dataset (− 0.438 per day, that is, 0.438% of intracranial volume, p < 0.0001), showing that the brain volume is greater in the morning. Linearly correcting the BPF values with the time-of-day reduced the required sample size to detect a 25% treatment effect (80% power and 0.05 significance level) on change in brain volume from 2 time-points over a period of 1 year by 2.6%.

Nice dipy paper: Aligning streamlines to streamline space outperforms image-based registration→

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In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter.

Test-retest reliability of dynamic causal modeling for fMRI→

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Using classical DCM (cDCM) in SPM5, we found that the test-retest reliability of DCM results was high, both concerning the model evidence (ICC = 0.94) and the model parameter estimates (median ICC = 0.47). However, when using a more recent DCM version (DCM10 in SPM8), test-retest reliability was reduced notably.

Optimization of rs-fMRI preprocessing for signal-Noise separation, test-retest reliability, group discrimination→

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We focus our analyses on the effects of common preprocessing steps, such as global signal regression (GS) (Weissenbacher et al., 2009 and Shirer et al., 2012); removal of cerebrospinal fluid (CSF) and white matter (WM) confounds (Shirer et al., 2012); noise regression of motion parameters estimated during motion correction (Friston et al., 1996, Power et al., 2012, Power et al., 2013, Satterthwaite et al., 2013 and Yan et al., 2013a); and temporal filtering at various frequency bands reported in the literature (Achard et al., 2006, Ko et al., 2011 and Guo et al., 2012).

Simultaneous brain/cervical cord fMRI reveals spinal cord involvement in motor learning→

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Impressive technical achievement.

Here, for the first time, we provide evidence for local learning-induced plasticity in intact human spinal cord through simultaneous functional magnetic resonance imaging of the brain and spinal cord during motor sequence learning. Specifically, we show learning-related modulation of activity in the C6–C8 spinal region, which is independent from that of related supraspinal sensorimotor structures. Moreover, a brain–spinal cord functional connectivity analysis demonstrates that the initial linear relationship between the spinal cord and sensorimotor cortex gradually fades away over the course of motor sequence learning, while the connectivity between spinal activity and cerebellum gains strength. T

Prospective motion correction of fMRI using optical tracking→

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The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases.

Bayesian segmentation of brainstem structures in MRI→

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Thanks to the generative nature of the scheme, the segmentation method is robust to changes in MRI contrast or acquisition hardware. Using cross validation, we show that the algorithm can segment the structures in previously unseen T1 and FLAIR scans with great accuracy (mean error under 1 mm) and robustness (no failures in 383 scans including 168 AD cases).