Locally low-rank denoising in transform domains.

Purpose: To develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and Methods: LLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. Results: For both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. Conclusion: A transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.

Exploring the Utility of a Functional Magnetic Resonance Imaging (fMRI) Cannabis Cue-Reactivity Paradigm in Treatment Seeking Adults with Cannabis Use Disorder.

Introduction: Functional magnetic resonance imaging (fMRI) studies examining cue-reactivity in cannabis use disorder (CUD) to date have either involved non-treatment seeking participants or been small. We addressed this gap by administering an fMRI cue-reactivity task to CUD participants entering two separate clinical trials. Methods: Treatment-seeking participants with moderate or severe CUD had behavioral craving measured at baseline via the Marijuana Craving Questionnaire (MCQ-SF). They additionally completed a visual cannabis cue-reactivity paradigm during fMRI following 24-hours of abstinence from cannabis. During fMRI, the Blood Oxygen Level Dependent (BOLD) signal was acquired while participants viewed cannabis-images or matched-neutral-images. BOLD responses were correlated with the MCQ-SF using a General Linear Model. Results: N=65 participants (32% female; mean age 30.4±9.9SD) averaged 46.3±15.5SD on the MCQ-SF. When contrasting cannabis-images vs. matched-neutral-images, participants showed greater BOLD response in bilateral ventromedial prefrontal, dorsolateral prefrontal, anterior cingulate, and visual cortices, as well as the striatum. Similarly, there was stronger task-based functional-connectivity (tbFC) between the medial prefrontal cortex and both the amygdala and the visual cortex. There were no significant differences in either activation or tbFC between studies or between sexes. Craving negatively correlated with BOLD response in the left ventral striatum (R 2 =-0.25; p =0.01). Conclusions: We found that, among two separate treatment-seeking CUD groups, cannabis cue-reactivity was evidenced by greater activation and tbFC in regions related to executive function and reward processing, and craving was negatively associated with cue-reactivity in the ventral striatum. Future directions include examining if pharmacological, neuromodulatory, or psychosocial interventions can alter corticostriatal cue-reactivity.

Evaluating increases in sensitivity from NORDIC for diverse fMRI acquisition strategies.

As the neuroimaging field moves towards detecting smaller effects at higher spatial resolutions, and faster sampling rates, there is increased attention given to the deleterious contribution of unstructured, thermal noise. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, for suppressing thermal noise using datasets acquired with various field strengths, voxel sizes, sampling rates, and task designs. Following minimal preprocessing, statistical activation (t-values) of NORDIC processed data was compared to the results obtained with alternative denoising methods. Additionally, we examined the consistency of the estimates of task responses at the single-voxel, single run level, using a finite impulse response (FIR) model. To examine the potential impact on effective image resolution, the overall smoothness of the data processed with different methods was estimated. Finally, to determine if NORDIC alters or removes temporal information important for modeling responses, we employed an exhaustive leave-p-out cross validation approach, using FIR task responses to predict held out timeseries, quantified using R2. After NORDIC, the t-values are increased, an improvement comparable to what could be achieved by 1.5 voxels smoothing, and task events are clearly visible and have less cross-run error. These advantages are achieved with smoothness estimates increasing by less than 4%, while 1.5 voxel smoothing is associated with increases of over 140%. Cross-validated R2s based on the FIR models show that NORDIC is not measurably distorting the temporal structure of the data under this approach and is the best predictor of non-denoised time courses. The results demonstrate that analyzing 1 run of data after NORDIC produces results equivalent to using 2 to 3 original runs and that NORDIC performs equally well across a diverse array of functional imaging protocols. Significance Statement: For functional neuroimaging, the increasing availability of higher field strengths and ever higher spatiotemporal resolutions has led to concomitant increase in concerns about the deleterious effects of thermal noise. Historically this noise source was suppressed using methods that reduce spatial precision such as image blurring or averaging over a large number of trials or sessions, which necessitates large data collection efforts. Here, we critically evaluate the performance of a recently developed reconstruction method, termed NORDIC, which suppresses thermal noise. Across datasets varying in field strength, voxel sizes, sampling rates, and task designs, NORDIC produces substantial gains in data quality. Both conventional t-statistics derived from general linear models and coefficients of determination for predicting unseen data are improved. These gains match or even exceed those associated with 1 voxel Full Width Half Max image smoothing, however, even such small amounts of smoothing are associated with a 52% reduction in estimates of spatial precision, whereas the measurable difference in spatial precision is less than 4% following NORDIC.

DLPFC stimulation alters working memory related activations and performance: An interleaved TMS-fMRI study.

BACKGROUND: Findings from correlative neuroimaging studies link increased frontoparietal network (FPN) activation and default mode network (DMN) deactivation to enhanced high cognitive demand processing. To causally investigate FPN-DMN contributions to high cognitive demand processing, the current interleaved TMS-fMRI study simultaneously manipulated and indexed neural activity while tracking cognitive performance during high and low cognitive load conditions. METHODS: Twenty participants completed an n-back task consisting of four conditions (0-back, 0-backTMS, 2-back, 2-backTMS) while undergoing interleaved TMS-fMRI. During TMS concurrent with n-back blocks, TMS single pulses were delivered to the left DLPFC at 100% motor-threshold every 2.4s. RESULTS: TMS delivered during high cognitive load strengthened cognitive processing. FPN node activations and DMN node deactivations were increased in the high versus low cognitive load TMS condition. Contrary to our hypothesis, TMS did not increase high load related activation in FPN nodes. However, as hypothesized, increased DMN node deactivations emerged as a function of TMS during high load (right angular gyrus) and from interactions between cognitive load and TMS (right middle temporal gyrus). Load and TMS combined to dampen activation within the DMN at trend level (p = .058). Deactivation in a dorsomedial DMN node was associated with TMS driven improvements in high load cognitive processing. CONCLUSIONS: Exogenous perturbation of the DLPFC via single pulse TMS amplified DMN node deactivations and enhanced high cognitive demand processing. Neurobehavioral findings linking these effects hint at a promising, albeit preliminary, cognitive control substrate requiring replication in higher-powered studies that use control stimulation.

Neurophysiologic Effects of Transcutaneous Auricular Vagus Nerve Stimulation (taVNS) via Electrical Stimulation of the Tragus: A Concurrent taVNS/fMRI Study and Review.

(Appeared originally in Brain Stimulation 2018; 11:492-500) Reprinted with permission from Elsevier.

Sonication of the Anterior Thalamus With MRI-Guided Transcranial Focused Ultrasound (tFUS) Alters Pain Thresholds in Healthy Adults: A Double-Blind, Sham-Controlled Study.

(Appeared originally in Brain Stimulation 2020; 13:1805-1812) Reprinted with permission from Elsevier.

A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction.

High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are used in large studies that involve ultrahigh field fMRI, such as the Human Connectome Project. However, for even higher acceleration rates, these methods cannot be reliably utilized due to aliasing and noise artifacts. Deep learning (DL) reconstruction techniques have recently gained substantial interest for improving highly-accelerated MRI. Supervised learning of DL reconstructions generally requires fully-sampled training datasets, which is not available for high-resolution fMRI studies. To tackle this challenge, self-supervised learning has been proposed for training of DL reconstruction with only undersampled datasets, showing similar performance to supervised learning. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that our self-supervised DL reconstruction produce high-quality images at this 20-fold acceleration, substantially improving on existing methods, while showing similar functional precision and temporal effects in the subsequent analysis compared to a standard 10-fold accelerated acquisition.

Statistical power or more precise insights into neuro-temporal dynamics? Assessing the benefits of rapid temporal sampling in fMRI.

Functional magnetic resonance imaging (fMRI), a non-invasive and widely used human neuroimaging method, is most known for its spatial precision. However, there is a growing interest in its temporal sensitivity. This is despite the temporal blurring of neuronal events by the blood oxygen level dependent (BOLD) signal, the peak of which lags neuronal firing by 4-6 seconds. Given this, the goal of this review is to answer a seemingly simple question - "What are the benefits of increased temporal sampling for fMRI?". To answer this, we have combined fMRI data collected at multiple temporal scales, from 323 to 1000 milliseconds, with a review of both historical and contemporary temporal literature. After a brief discussion of technological developments that have rekindled interest in temporal research, we next consider the potential statistical and methodological benefits. Most importantly, we explore how fast fMRI can uncover previously unobserved neuro-temporal dynamics - effects that are entirely missed when sampling at conventional 1 to 2 second rates. With the intrinsic link between space and time in fMRI, this temporal renaissance also delivers improvements in spatial precision. Far from producing only statistical gains, the array of benefits suggest that the continued temporal work is worth the effort.

Lowering the thermal noise barrier in functional brain mapping with magnetic resonance imaging.

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.

Evaluating a novel MR-compatible foot pedal device for unipedal and bipedal motion: Test-retest reliability of evoked brain activity.

The purpose of this study was to develop and evaluate a new, open-source MR-compatible device capable of assessing unipedal and bipedal lower extremity movement with minimal head motion and high test-retest reliability. To evaluate the prototype, 20 healthy adults participated in two magnetic resonance imaging (MRI) visits, separated by 2-6 months, in which they performed a visually guided dorsiflexion/plantar flexion task with their left foot, right foot, and alternating feet. Dependent measures included: evoked blood oxygen level-dependent (BOLD) signal in the motor network, head movement associated with dorsiflexion/plantar flexion, the test-retest reliability of these measurements. Left and right unipedal movement led to a significant increase in BOLD signal compared to rest in the medial portion of the right and left primary motor cortex (respectively), and the ipsilateral cerebellum (FWE corrected, p < .001). Average head motion was 0.10 ± 0.02 mm. The test-retest reliability was high for the functional MRI data (intraclass correlation coefficients [ICCs]: >0.75) and the angular displacement of the ankle joint (ICC: 0.842). This bipedal device can robustly isolate activity in the motor network during alternating plantarflexion and dorsiflexion with minimal head movement, while providing high test-retest reliability. Ultimately, these data and open-source building instructions will provide a new, economical tool for investigators interested in evaluating brain function resulting from lower extremity movement.

Clarifying the role of higher-level cortices in resolving perceptual ambiguity using ultra high field fMRI.

The brain is organized into distinct, flexible networks. Within these networks, cognitive variables such as attention can modulate sensory representations in accordance with moment-to-moment behavioral requirements. These modulations can be studied by varying task demands; however, the tasks employed are often incongruent with the postulated functions of a sensory system, limiting the characterization of the system in relation to natural behaviors. Here we combine domain-specific task manipulations and ultra-high field fMRI to study the nature of top-down modulations. We exploited faces, a visual category underpinned by a complex cortical network, and instructed participants to perform either a stimulus-relevant/domain-specific or a stimulus-irrelevant task in the scanner. We found that 1. perceptual ambiguity (i.e. difficulty of achieving a stable percept) is encoded in top-down modulations from higher-level cortices; 2. the right inferior-temporal lobe is active under challenging conditions and uniquely encodes trial-by-trial variability in face perception.

NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing.

Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.

Sonication of the anterior thalamus with MRI-Guided transcranial focused ultrasound (tFUS) alters pain thresholds in healthy adults: A double-blind, sham-controlled study.

BACKGROUND: Transcranial focused ultrasound (tFUS) is a noninvasive brain stimulation method that may modulate deep brain structures. This study investigates whether sonication of the right anterior thalamus would modulate thermal pain thresholds in healthy individuals. METHODS: We enrolled 19 healthy individuals in this three-visit, double-blind, sham-controlled, crossover trial. Participants first underwent a structural MRI scan used solely for tFUS targeting. They then attended two identical experimental tFUS visits (counterbalanced by condition) at least one week apart. Within the MRI scanner, participants received two, 10-min sessions of either active or sham tFUS spread 10 min apart targeting the right anterior thalamus [fundamental frequency: 650 kHz, Pulse repetition frequency: 10 Hz, Pulse Width: 5 ms, Duty Cycle: 5%, Sonication Duration: 30s, Inter-Sonication Interval: 30 s, Number of Sonications: 10, ISPTA.0 995 mW/cm2, ISPTA.3 719 mW/cm2, Peak rarefactional pressure 0.72 MPa]. The primary outcome measure was quantitative sensory thresholding (QST), measuring sensory, pain, and tolerance thresholds to a thermal stimulus applied to the left forearm before and after right anterior thalamic tFUS. RESULTS: The right anterior thalamus was accurately sonicated in 17 of the 19 subjects. Thermal pain sensitivity was significantly attenuated after active tFUS. The pre-post x active-sham interaction was significant (F(1,245.95) = 4.03, p = .046). This interaction indicates that in the sham stimulation condition, thermal pain thresholds decreased 1.08 °C (SE = 0.28) pre-post session, but only decreased .51 °C (SE = 0.30) pre-post session in the active stimulation group. CONCLUSIONS: Two 10-min sessions of anterior thalamic tFUS induces antinociceptive effects in healthy individuals. Future studies should optimize the parameter space, dose and duration of this effect which may lead to multi-session tFUS interventions for pain disorders.

State-Dependent Effects of Ventromedial Prefrontal Cortex Continuous Thetaburst Stimulation on Cocaine Cue Reactivity in Chronic Cocaine Users.

Cue-induced craving is a significant barrier to obtaining abstinence from cocaine. Neuroimaging research has shown that cocaine cue exposure evokes elevated activity in a network of frontal-striatal brain regions involved in drug craving and drug seeking. Prior research from our laboratory has demonstrated that when targeted at the medial prefrontal cortex (mPFC), continuous theta burst stimulation (cTBS), an inhibitory form of non-invasive brain stimulation, can decrease drug cue-related activity in the striatum in cocaine users and alcohol users. However, it is known that there are individual differences in response to repetitive transcranial magnetic stimulation (rTMS), with some individuals being responders and others non-responders. There is some evidence that state-dependent effects influence response to rTMS, with baseline neural state predicting rTMS treatment outcomes. In this single-blind, active sham-controlled crossover study, we assess the striatum as a biomarker of treatment response by determining if baseline drug cue reactivity in the striatum influences striatal response to mPFC cTBS. The brain response to cocaine cues was measured in 19 cocaine-dependent individuals immediately before and after real and sham cTBS (110% resting motor threshold, 3600 total pulses). Group independent component analysis (ICA) revealed a prominent striatum network comprised of bilateral caudate, putamen, and nucleus accumbens, which was modulated by the cocaine cue reactivity task. Baseline drug cue reactivity in this striatal network was inversely related to change in striatum reactivity after real (vs. sham) cTBS treatment (ρ = -.79; p < .001; R 2 Adj = .58). Specifically, individuals with a high striatal response to cocaine cues at baseline had significantly attenuated striatal activity after real but not sham cTBS (t 9 = -3.76; p ≤ .005). These data demonstrate that the effects of mPFC cTBS on the neural circuitry of craving are not uniform and may depend on an individual's baseline frontal-striatal reactivity to cues. This underscores the importance of assessing individual variability as we develop brain stimulation treatments for addiction.

Sensitized brain response to acute pain in patients using prescription opiates for chronic pain: A pilot study.

BACKGROUND: Chronic opiate use leads to a sensitized behavioral response to acute pain, which in turn, leads to escalating doses of opiates. This study was designed to test the hypothesis that chronic opiate usage is also associated with a sensitized neurobiological response to acute pain in individuals that have used prescription opiates for 6 or more months. METHODS: Fourteen patients with non-alcoholic chronic pancreatitis that have been taking prescription opiates for 6 or more months and 14 gender matched, non-opiate using controls were enrolled. Functional neuroimaging data was acquired while participants received blocks of thermal stimulation to their wrist (individually-tailored to their pain threshold). RESULTS: Self-reported pain was significantly greater in opiate using patients (3.4 ± 3.4) than controls (0.2 ± 0.8: Brief Pain Inventory p < 0.005), however no significant difference between groups was observed in the individually-tailored pain thresholds. Opiate using patients evidenced a significantly greater response to pain than controls in two established nodes of the "Pain Matrix": somatosensory cortex (pFWE≤0.001) and anterior cingulate cortex (p ≤ 0.01). This response was positively correlated with prescribed morphine equivalent dosages (average: 133.5 ± 94.8 mg/day). CONCLUSION: The findings suggest that in chronic pancreatitis patients, a dose of opiates that normalizes their behavioral response to acute pain is associated with an amplified neural response to acute pain. Further longitudinal studies are needed to determine if this neural sensitization hastens a behavioral tolerance to opiates or the development of an opioid use disorder.

Cortical substrates of cue-reactivity in multiple substance dependent populations: transdiagnostic relevance of the medial prefrontal cortex.

Elevated drug-cue elicited brain activity is one of the most widely cited, transdiagnostically relevant traits of substance dependent populations. These populations, however, are typically studied in isolation. The goal of this study was to prospectively investigate the spatial topography of drug-cue reactivity in a large set of individuals dependent on either cocaine, alcohol, or nicotine. Functional MRI data was acquired from 156 substance dependent individuals (55 cocaine, 53 alcohol, and 48 nicotine) as they performed a standardized drug-cue exposure task. Clusters of significant activation to drug-cues relative to neutral cues ('hot spots') were isolated for each individual. K-means clustering was used to classify the spatial topography of the hotspots in the data set. The percentage of hotspots that would be reached at several distances (2-5 cm) of transcranial magnetic stimulation (TMS) were calculated. One hundred and three participants had at least one cluster of significant frontal cortex activity (66%). K-means revealed 3 distinct clusters within the medial prefrontal cortex (MPFC), left inferior frontal gyrus/insula, right premotor cortex. For the group as a whole (and for alcohol users and nicotine users independently), medial prefrontal cortex (BA 10) was the location of the greatest number of hotspots. The frontal pole was cortical location closest to the largest percentage of hotspots. While there is individual variability in the location of the cue-elicited 'hot spot' these data demonstrate that elevated BOLD signal to drug cues in the MPFC may be a transdiagnostic endophenotype of addiction which may also be a fruitful neuromodulation target.

Modulating Neural Circuits with Transcranial Magnetic Stimulation: Implications for Addiction Treatment Development.

Although the last 50 years of clinical and preclinical research have demonstrated that addiction is a brain disease, we still have no neural circuit-based treatments for substance dependence or cue reactivity at large. Now, for the first time, it appears that a noninvasive brain stimulation technique known as transcranial magnetic stimulation (TMS), which is Food and Drug Administration approved to treat depression, may be the first tool available to fill this critical void in addiction treatment development. The goals of this review are to 1) introduce TMS as a tool to induce causal change in behavior, cortical excitability, and frontal-striatal activity; 2) describe repetitive TMS (rTMS) as an interventional tool; 3) provide an overview of the studies that have evaluated rTMS as a therapeutic tool for alcohol and drug use disorders; and 4) outline a conceptual framework for target selection when designing future rTMS clinical trials in substance use disorders. The manuscript concludes with some suggestions for methodological innovation, specifically with regard to combining rTMS with pharmacotherapy as well as cognitive behavioral training paradigms. We have attempted to create a comprehensive manuscript that provides the reader with a basic set of knowledge and an introduction to the primary experimental questions that will likely drive the field of TMS treatment development forward for the next several years.