Faculty Bibliography

The neural mechanisms underlying conscious recognition remain unclear, particularly the roles played by the prefrontal cortex, deactivated brain areas and subcortical regions. We investigated neural activity during conscious object recognition using 7 Tesla fMRI while human participants viewed object images presented at liminal contrasts. Here, we show both recognized and unrecognized images recruit widely distributed cortical and subcortical regions; however, recognized images elicit enhanced activation of visual, frontoparietal, and subcortical networks and stronger deactivation of the default-mode network. For recognized images, object category information can be decoded from all of the involved cortical networks but not from subcortical regions. Phase-scrambled images trigger strong involvement of inferior frontal junction, anterior cingulate cortex and default-mode network, implicating these regions in inferential processing under increased uncertainty. Our results indicate that content-specific activity in both activated and deactivated cortical networks and non-content-specific subcortical activity support conscious recognition.

PURPOSE/OBJECTIVE:The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). RESULTS:Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. DISCUSSION AND CONCLUSION/CONCLUSIONS:While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.

The pathological cascade of tissue damage in mild traumatic brain injury is set forth by a perturbation in ionic homeostasis. However, whether this class of injury can be detected in vivo and serve as a surrogate marker of clinical outcome is unknown. We employ sodium MRI to test the hypotheses that regional and global total sodium concentrations: (i) are higher in patients than in controls and (ii) correlate with clinical presentation and neuropsychological function. Given the novelty of sodium imaging in traumatic brain injury, effect sizes from (i), and correlation types and strength from (ii), were compared to those obtained using standard diffusion imaging metrics. Twenty-seven patients (20 female, age 35.9 ± 12.2 years) within 2 months after injury and 19 controls were scanned with proton and sodium MRI at 3 Tesla. Total sodium concentration, fractional anisotropy and apparent diffusion coefficient were obtained with voxel averaging across 12 grey and white matter regions. Linear regression was used to obtain global grey and white matter total sodium concentrations. Patient outcome was assessed with global functioning, symptom profiles and neuropsychological function assessments. In the regional analysis, there were no statistically significant differences between patients and controls in apparent diffusion coefficient, while differences in sodium concentration and fractional anisotropy were found only in single regions. However, for each of the 12 regions, sodium concentration effect sizes were uni-directional, due to lower mean sodium concentration in patients compared to controls. Consequently, linear regression analysis found statistically significant lower global grey and white matter sodium concentrations in patients compared to controls. The strongest correlation with outcome was between global grey matter sodium concentration and the composite z-score from the neuropsychological testing. In conclusion, both sodium concentration and diffusion showed poor utility in differentiating patients from controls, and weak correlations with clinical presentation, when using a region-based approach. In contrast, sodium linear regression, capitalizing on partial volume correction and high sensitivity to global changes, revealed high effect sizes and associations with patient outcome. This suggests that well-recognized sodium imbalances in traumatic brain injury are (i) detectable non-invasively; (ii) non-focal; (iii) occur even when the antecedent injury is clinically mild. Finally, in contrast to our principle hypothesis, patients' sodium concentrations were lower than controls, indicating that the biological effect of traumatic brain injury on the sodium homeostasis may differ from that in other neurological disorders. Note: This figure has been annotated.

INTRODUCTION/BACKGROUND:Connectome analysis of the human brain's structural and functional architecture provides a unique opportunity to understand the organization of the brain's functional architecture. In previous studies, connectome fingerprinting using brain functional connectivity profiles as an individualized trait was able to predict an individual's neurocognitive performance from the Human Connectome Project (HCP) neurocognitive datasets. MATERIALS AND METHODS/METHODS:In the present study, we extend connectome fingerprinting from functional connectivity (FC) to structural connectivity (SC), identifying multiple relationships between behavioral traits and brain connectivity. Higher-order neurocognitive tasks were found to have a weaker association with structural connectivity than its functional connectivity counterparts. RESULTS:Neurocognitive tasks with a higher sensory footprint were, however, found to have a stronger association with structural connectivity than their functional connectivity counterparts. Language behavioral measurements had a particularly stronger correlation, especially between performance on the picture language test (Pic Vocab) and both FC (r = .28, p < .003) and SC (r = 0.27, p < .00077). CONCLUSIONS:At the neural level, we found that the pattern of structural brain connectivity related to high-level language performance is consistent with the language white matter regions identified in presurgical mapping. We illustrate how this approach can be used to generalize the connectome fingerprinting framework to structural connectivity and how this can help understand the connections between cognitive behavior and the white matter connectome of the brain.

The diagnosis of various disorders is hindered by the lack of an imaging technique that reveals the architecture of living tissue at the fine resolution of the associated pathological processes. Indeed, even the most powerful imaging techniques such as MRI can only resolve or visualize biological tissue down to the scale of a cubic millimetre. However, MRI may be able to reveal what happens on a much finer scale, as it is sensitive to the random thermal motion of water molecules and, more importantly, their interactions with surrounding cells constituting the microstructure of the tissue. The gap between being sensitive and specific is bridged by the development of a tissue model that decomposes the MRI signal into components that probe relevant features of the underlying microstructure, typically affected by pathology. Hence, biophysical modelling is potentially a diagnostic tool that allows scientists to identify problems that arise in the unexplored depths of our organs, driving forward treatment and understanding of disease progression. In this chapter, we will introduce the main concepts of multiparametric modelling, lay out a general framework of multi-compartmental models, and discuss limitations and challenges.
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Objectives: To demonstrate the development and use of an acute imaging protocol for the metabolic assessment of tissue viability during acute stroke.
Method(s): The DAWN and DEFUSE 3 trials (1,2) have demonstrated that there is much to gain from the use of physiologically based guidelines to extend the use of mechanical recanalization. Literature reports provide strong data supporting the use of brain tissue sodium concentration (TSC) as a biomarker for identifying physiologically non-viable tissue during evolving brain ischemia (3,4). Testing this hypothesis in vivo, in humans, have been previously hampered by acquisition times that were long for routine clinical use. Recent developments in MRI data acquisition and hardware make it possible to acquire the data to provide the aforementioned assessment in under 5 minutes at a level of signal-to-noise ratio (SNR) and spatial resolution compatible with physiologically driven MRI scans such as diffusion weighted imaging and perfusion imaging. This was achieved using an Ultra-Short-Echo Time sequence with optimal acquisition throughput (TPI, TE/TR 0.3/100 ms, p 0.2). Signal excitation/reception was performed using a patient-friendly double-tuned (¹H/²³Na) birdcage coil (Quality Electrodynamics Inc., Mayfield Heights, Ohio). The protocol was implemented on a MAGNETOM Skyra 3 Tesla scanner at NYU's Tisch hospital. The scanner is located adjacent (20 feet) to the neuro interventional suite where patients are recanalized. Subject's anesthesia was maintained (FabiusMRI, DraegerInc., Telford, PA) and physiological status continuously monitored using MRI-compatible equipment (Expression MR400, Phillips Healthcare, Andover, MA).
Result(s): After phantom validation and healthy volunteer studies to determine the quantitative performance of the data acquisition techniques the protocol was used on post-endovascular thrombectomy subjects (n 3), immediately upon procedure completion and under its own IRB approved protocol. During these studies, the use of the proposed methodology was found to be compatible with the clinical care of the subjects. Specifically, performing the required scans was not found to interfere with the subject's post-recanalization care. Tissue sodium concentration data were, likewise, found to meet the required levels of SNR to provide the quantitative assessment mentioned above. A representative data set from one of these sessions is shown in figure 1. This mechanically-recanalized patient had an area of non-salvaged tissue in the left parietal lobe that is clearly depicted on the ²³Na MRI scan. The TSC in this area was 76 mM at the time of the scan. (Figure presented)
Conclusion(s): This work demonstrates that state-of-the-art MRI methodology can be used to provide a clinically viable imaging protocol for evaluating the use of sodium MRI as a quantitative biomarker for identifying physiologically viable tissue during evolving brain ischemia

Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.

A mechanistic understanding of the pathology of psychiatric disorders has been hampered by extensive heterogeneity in biology, symptoms, and behavior within diagnostic categories that are defined subjectively. We investigated whether leveraging individual differences in information-processing impairments in patients with post-traumatic stress disorder (PTSD) could reveal phenotypes within the disorder. We found that a subgroup of patients with PTSD from two independent cohorts displayed both aberrant functional connectivity within the ventral attention network (VAN) as revealed by functional magnetic resonance imaging (fMRI) neuroimaging and impaired verbal memory on a word list learning task. This combined phenotype was not associated with differences in symptoms or comorbidities, but nonetheless could be used to predict a poor response to psychotherapy, the best-validated treatment for PTSD. Using concurrent focal noninvasive transcranial magnetic stimulation and electroencephalography, we then identified alterations in neural signal flow in the VAN that were evoked by direct stimulation of that network. These alterations were associated with individual differences in functional fMRI connectivity within the VAN. Our findings define specific neurobiological mechanisms in a subgroup of patients with PTSD that could contribute to the poor response to psychotherapy.

The original version of this article, published on 04 June 2018, unfortunately contained a mistake.