Increasing the Analytical Accessibility of Multishell and Diffusion Spectrum Imaging Data Using Generalized Q-Sampling Conversion

Many diffusion MRI researchers, including the Human Connectome Project (HCP), acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets are not readily accessible to high angular resolution diffusion imaging (HARDI) analysis methods that are popular in connectomics analysis. Here we introduce a scheme conversion approach that transforms multishell and DSI data into their corresponding HARDI representations, thereby empowering HARDI-based analytical methods to make use of data acquired using non-HARDI approaches. This method was evaluated on both phantom and in-vivo human data sets by acquiring multishell, DSI, and HARDI data simultaneously, and comparing the converted HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the phantom shows that the converted HARDI from DSI and multishell data strongly predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study shows that the converted HARDI can be reconstructed by constrained spherical deconvolution, and the fiber orientation distributions are consistent with those from the original HARDI. We further illustrate that our scheme conversion method can be applied to HCP data, and the converted HARDI do not appear to sacrifice angular resolution. Thus this novel approach can benefit all HARDI-based analysis approaches, allowing greater analytical accessibility to non-HARDI data, including data from the HCP

Binding During Sequence Learning Does Not Alter Cortical Representations of Individual Actions

Copyright © 2019 the authors. As a sequence of movements is learned, serially ordered actions get bound together into sets to reduce computational complexity during planning and execution. Here, we investigated how actions become naturally bound over the course of learning and how this learning affects cortical representations of individual actions. Across 5 weeks of practice, neurologically healthy human subjects learned either a complex 32-item sequence of finger movements (trained group, n = 9; 3 female) or randomly ordered actions (control group, n = 9; 3 female). Over the course of practice, responses during sequence production in the trained group became temporally correlated, consistent with responses being bound together under a common command. These behavioral changes, however, did not coincide with plasticity in the multivariate representations of individual finger movements, assessed using fMRI, at any level of the cortical motor hierarchy. This suggests that the representations of individual actions remain stable, even as the execution of those same actions become bound together in the context of producing a well learned sequence.SIGNIFICANCE STATEMENT Extended practice on motor sequences results in highly stereotyped movement patterns that bind successive movements together. This binding is critical for skilled motor performance, yet it is not currently understood how it is achieved in the brain. We examined how binding altered the patterns of activity associated with individual movements that make up the sequence. We found that fine finger control during sequence production involved correlated activity throughout multiple motor regions; however, we found no evidence for plasticity of the representations of elementary movements. This suggests that binding is associated with plasticity at a more abstract level of the motor hierarchy

Cerebral blood flow links insulin resistance and baroreflex sensitivity

Insulin resistance confers risk for diabetes mellitus and associates with a reduced capacity of the arterial baroreflex to regulate blood pressure. Importantly, several brain regions that comprise the central autonomic network, which controls the baroreflex, are also sensitive to the neuromodulatory effects of insulin. However, it is unknown whether peripheral insulin resistance relates to activity within central autonomic network regions, which may in turn relate to reduced baroreflex regulation. Accordingly, we tested whether resting cerebral blood flow within central autonomic regions statistically mediated the relationship between insulin resistance and an indirect indicator of baroreflex regulation; namely, baroreflex sensitivity. Subjects were 92 community-dwelling adults free of confounding medical illnesses (48 men, 30-50 years old) who completed protocols to assess fasting insulin and glucose levels, resting baroreflex sensitivity, and resting cerebral blood flow. Baroreflex sensitivity was quantified by measuring the magnitude of spontaneous and sequential associations between beat-by-beat systolic blood pressure and heart rate changes. Individuals with greater insulin resistance, as measured by the homeostatic model assessment, exhibited reduced baroreflex sensitivity (b = -0.16, p < .05). Moreover, the relationship between insulin resistance and baroreflex sensitivity was statistically mediated by cerebral blood flow in central autonomic regions, including the insula and cingulate cortex (mediation coefficients < -0.06, p-values < .01). Activity within the central autonomic network may link insulin resistance to reduced baroreflex sensitivity. Our observations may help to characterize the neural pathways by which insulin resistance, and possibly diabetes mellitus, relates to adverse cardiovascular outcomes. © 2013 Ryan et al

A Brain Phenotype for Stressor‐Evoked Blood Pressure Reactivity

Background Individuals who exhibit large‐magnitude blood pressure (BP) reactions to acute psychological stressors are at risk for hypertension and premature death by cardiovascular disease. This study tested whether a multivariate pattern of stressor‐evoked brain activity could reliably predict individual differences in BP reactivity, providing novel evidence for a candidate neurophysiological source of stress‐related cardiovascular risk. Methods and Results Community‐dwelling adults (N=310; 30–51 years; 153 women) underwent functional magnetic resonance imaging with concurrent BP monitoring while completing a standardized battery of stressor tasks. Across individuals, the battery evoked an increase systolic and diastolic BP relative to a nonstressor baseline period (M ∆systolic BP/∆diastolic BP=4.3/1.9 mm Hg [95% confidence interval=3.7–5.0/1.4–2.3 mm Hg]). Using cross‐validation and machine learning approaches, including dimensionality reduction and linear shrinkage models, a multivariate pattern of stressor‐evoked functional magnetic resonance imaging activity was identified in a training subsample (N=206). This multivariate pattern reliably predicted both systolic BP (r=0.32; P<0.005) and diastolic BP (r=0.25; P<0.01) reactivity in an independent subsample used for testing and replication (N=104). Brain areas encompassed by the pattern that were strongly predictive included those implicated in psychological stressor processing and cardiovascular responding through autonomic pathways, including the medial prefrontal cortex, anterior cingulate cortex, and insula. Conclusions A novel multivariate pattern of stressor‐evoked brain activity may comprise a phenotype that partly accounts for individual differences in BP reactivity, a stress‐related cardiovascular risk factor

Remote Infrared Imaging of the Space Shuttle During Hypersonic Flight: HYTHIRM Mission Operations and Coordination

The Hypersonic Thermodynamic Infrared Measurements (HYTHIRM) project has been responsible for obtaining spatially resolved, scientifically calibrated in-flight thermal imagery of the Space Shuttle Orbiter during reentry. Starting with STS-119 in March of 2009 and continuing through to the majority of final flights of the Space Shuttle, the HYTHIRM team has to date deployed during seven Shuttle missions with a mix of airborne and ground based imaging platforms. Each deployment of the HYTHIRM team has resulted in obtaining imagery suitable for processing and comparison with computational models and wind tunnel data at Mach numbers ranging from over 18 to under Mach 5. This paper will discuss the detailed mission planning and coordination with the NASA Johnson Space Center Mission Control Center that the HYTHIRM team undergoes to prepare for and execute each mission

Caudate nucleus volume mediates the link between cardiorespiratory fitness and cognitive flexibility in older adults

The basal ganglia play a central role in regulating the response selection abilities that are critical formental flexibility. In neocortical areas, higher cardiorespiratory fitness levels are associated with increased gray matter volume, and these volumetric differences mediate enhanced cognitive performance in a variety of tasks. Here we examine whether cardiorespiratory fitness correlates with the volume of the subcortical nuclei that make up the basal ganglia and whether this relationship predicts cognitive flexibility in older adults. Structural MRI was used to determine the volume of the basal ganglia nuclei in a group of older, neurologically healthy individuals (mean age 66 years, N = 179).Measures of cardiorespiratory fitness (VO2max), cognitive flexibility (task switching), and attentional control (flanker task) were also collected. Higher fitness levels were correlated with higher accuracy rates in the Task Switching paradigm. In addition, the volume of the caudate nucleus, putamen, and globus pallidus positively correlated with Task Switching accuracy.Nested regression modeling revealed that caudate nucleus volume was a significantmediator of the relationship between cardiorespiratory fitness, and task switching performance. These findings indicate that higher cardiorespiratory fitness predicts better cognitive flexibility in older adults through greater grey matter volume in the dorsal striatum

Deterministic diffusion fiber tracking improved by quantitative anisotropy

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T 1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics. © 2013 Yeh et al

Diffusion Capillary Phantom vs. Human Data: Outcomes for Reconstruction Methods Depend on Evaluation Medium

Purpose: Diffusion MRI provides a non-invasive way of estimating structural connectivity in the brain. Many studies have used diffusion phantoms as benchmarks to assess the performance of different tractography reconstruction algorithms and assumed that the results can be applied to in vivo studies. Here we examined whether quality metrics derived from a common, publically available, diffusion phantom can reliably predict tractography performance in human white matter tissue. Material and Methods: We compared estimates of fiber length and fiber crossing among a simple tensor model (diffusion tensor imaging), a more complicated model (ball-and-sticks) and model-free (diffusion spectrum imaging, generalized q-sampling imaging) reconstruction methods using a capillary phantom and in vivo human data (N=14). Results: Our analysis showed that evaluation outcomes differ depending on whether they were obtained from phantom or human data. Specifically, the diffusion phantom favored a more complicated model over a simple tensor model or model-free methods for resolving crossing fibers. On the other hand, the human studies showed the opposite pattern of results, with the model-free methods being more advantageous than model-based methods or simple tensor models. This performance difference was consistent across several metrics, including estimating fiber length and resolving fiber crossings in established white matter pathways. Conclusions: These findings indicate that the construction of current capillary diffusion phantoms tends to favor complicated reconstruction models over a simple tensor model or model-free methods, whereas the in vivo data tends to produce opposite results. This brings into question the previous phantom-based evaluation approaches and suggests that a more realistic phantom or simulation is necessary to accurately predict the relative performance of different tractography reconstruction methods. Acronyms: BSM: ball-and-sticks model; dODF diffusion orientation distribution function; DSI: diffusion spectrum imaging; DTI: diffusion tensor imaging; GQI: generalized q-sampling imaging; ODF: orientation distribution function

Converting Multi-shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in-vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7º at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions