Bruce Rosen

YNIMG Journal 2022 Journal Article

3D Echo Planar Time-resolved Imaging (3D-EPTI) for ultrafast multi-parametric quantitative MRI

• Fuyixue Wang
• Zijing Dong
• Timothy G. Reese
• Bruce Rosen
• Lawrence L. Wald
• Kawin Setsompop

Multi-parametric quantitative MRI has shown great potential to improve the sensitivity and specificity of clinical diagnosis and to enhance our understanding of complex brain processes, but suffers from long scan time especially at high spatial resolution. To address this longstanding challenge, we introduce a novel approach, termed 3D Echo Planar Time-resolved Imaging (3D-EPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI. This is achieved by exploiting the spatiotemporal correlation of MRI data at multiple timescales through new encoding strategies within and between efficient continuous readouts. Specifically, an optimized spatiotemporal CAIPI encoding within the readouts combined with a radial-block sampling strategy across the readouts enables an acceleration rate of 800 fold in the k-t space. A subspace reconstruction was employed to resolve thousands of high-quality multi-contrast images. We have demonstrated the ability of 3D-EPTI to provide robust and repeatable whole-brain simultaneous T1, T2, T2*, PD and B1 + mapping at high isotropic resolution within minutes (e. g. , 1-mm isotropic resolution in 3 minutes), and to enable submillimeter multi-parametric imaging to study detailed brain structures.

YNICL Journal 2020 Journal Article

Corrigendum to “Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain” [Neuroimage Clinical 23 (2019) 101885]

• Yiheng Tu
• Ana Ortiz
• Randy L. Gollub
• Jin Cao
• Jessica Gerber
• Courtney Lang
• Joel Park
• Georgia Wilson

The authors regret to find several errors that do not influence the main findings or conclusions.Specifically, we have found that the values of pre-and post-treatment clinical sub-scores for 'physical function' and 'sleep' in Fig. 4 contain errors.The corrected Fig. 4 is shown below: As a result, the Results section 3.3 (Page 6, right column): "Real and sham acupuncture significantly reduced PROMIS sub-scores in

YNIMG Journal 2020 Journal Article

Impaired mesocorticolimbic connectivity underlies increased pain sensitivity in chronic low back pain

• Siyi Yu
• Wen Li
• Wei Shen
• Robert R. Edwards
• Randy L. Gollub
• Georgia Wilson
• Joel Park
• Ana Ortiz

Chronic low back pain (cLBP) is a prevalent disorder. A growing body of evidence linking the pathology of the reward network to chronic pain suggests that pain sensitization may contribute to cLBP chronification via disruptions of mesocortical and mesolimbic circuits in the reward system. Resting-state (RS) functional magnetic resonance imaging (fMRI) data was acquired from 90 patients with cLBP and 74 matched pain-free controls (HCs) at baseline and after a manipulation for back pain intensification. The ventral tegmental area (VTA) was chosen as a seed region to perform RS functional connectivity (FC) analysis. Baseline rsFC of both the mesocortical (between the VTA and bilateral rostral anterior cingulate cortex (rACC)/and medial prefrontal cortex (mPFC)) and mesolimbic (between the VTA and bilateral hippocampus/parahippocampus) pathways was reduced in patients with cLBP (vs. HCs). In addition, patients exhibiting higher back pain intensity (compared to the relatively lower back pain intensity condition) also showed increases in both mesocortical and mesolimbic connectivity, implicating these pathways in pain downregulation in cLBP. Mediation analysis further isolated the mesolimbic (VTA-hippocampus/parahippocampus) dysconnectivity as a neural mechanism mediating the association between mechanical pain sensitivity (indexed by P40 pressure) and cLBP severity. In sum, the current study demonstrates deficient mesocorticolimbic connectivity in cLBP, with mesolimbic dysconnectivity potentially mediating the contribution of pain sensitization to pain chronification. These reward network dysfunctions and purportedly, dopaminergic dysregulations, may help us to identify key brain targets of neuromodulation in the treatment of cLBP.

YNICL Journal 2019 Journal Article

Corrigendum to ‘Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain’ Neuroimage Clinical, 23, 2019, 101885

• Yiheng Tu
• Ana Ortiz
• Randy L. Gollub
• Jin Cao
• Jessica Gerber
• Courtney Lang
• Joel Park
• Georgia Wilson

The authors regret to find several errors that do not influence the main findings or conclusions.Specifically, we have found that the values of pre-and post-treatment clinical sub-scores for 'physical function' and 'sleep' in Fig. 4 contain errors.The corrected Fig. 4 is shown below: As a result, the Results section 3.3 (Page 6, right column): "Real and sham acupuncture significantly reduced PROMIS sub-scores in

YNICL Journal 2019 Journal Article

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

• Yiheng Tu
• Ana Ortiz
• Randy L. Gollub
• Jin Cao
• Jessica Gerber
• Courtney Lang
• Joel Park
• Georgia Wilson

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

YNICL Journal 2019 Journal Article

Visual network alterations in brain functional connectivity in chronic low back pain: A resting state functional connectivity and machine learning study

• Wei Shen
• Yiheng Tu
• Randy L. Gollub
• Ana Ortiz
• Vitaly Napadow
• Siyi Yu
• Georgia Wilson
• Joel Park

Chronic low back pain (cLBP) is associated with widespread functional and structural changes in the brain. This study aims to investigate the resting state functional connectivity (rsFC) changes of visual networks in cLBP patients and the feasibility of distinguishing cLBP patients from healthy controls using machine learning methods. cLBP (n = 90) and control individuals (n = 74) were enrolled and underwent resting-state BOLD fMRI scans. Primary, dorsal, and ventral visual networks derived from independent component analysis were used as regions of interest to compare resting state functional connectivity changes between the cLBP patients and healthy controls. We then applied a support vector machine classifier to distinguish the cLBP patients and control individuals. These results were further verified in a new cohort of subjects. We found that the functional connectivity between the primary visual network and the somatosensory/motor areas were significantly enhanced in cLBP patients. The rsFC between the primary visual network and S1 was negatively associated with duration of cLBP. In addition, we found that the rsFC of the visual network could achieve a classification accuracy of 79.3% in distinguishing cLBP patients from HCs, and these results were further validated in an independent cohort of subjects (accuracy = 66.7%). Our results demonstrate significant changes in the rsFC of the visual networks in cLBP patients. We speculate these alterations may represent an adaptation/self-adjustment mechanism and cross-model interaction between the visual, somatosensory, motor, attention, and salient networks in response to cLBP. Elucidating the role of the visual networks in cLBP may shed light on the pathophysiology and development of the disorder.

YNIMG Journal 2014 Journal Article

3D GABA imaging with real-time motion correction, shim update and reacquisition of adiabatic spiral MRSI

• Wolfgang Bogner
• Borjan Gagoski
• Aaron T. Hess
• Himanshu Bhat
• M. Dylan Tisdall
• Andre J.W. van der Kouwe
• Bernhard Strasser
• Małgorzata Marjańska

Gamma-aminobutyric acid (GABA) and glutamate (Glu) are the major neurotransmitters in the brain. They are crucial for the functioning of healthy brain and their alteration is a major mechanism in the pathophysiology of many neuro-psychiatric disorders. Magnetic resonance spectroscopy (MRS) is the only way to measure GABA and Glu non-invasively in vivo. GABA detection is particularly challenging and requires special MRS techniques. The most popular is MEscher–GArwood (MEGA) difference editing with single-voxel Point RESolved Spectroscopy (PRESS) localization. This technique has three major limitations: a) MEGA editing is a subtraction technique, hence is very sensitive to scanner instabilities and motion artifacts. b) PRESS is prone to localization errors at high fields (≥3T) that compromise accurate quantification. c) Single-voxel spectroscopy can (similar to a biopsy) only probe steady GABA and Glu levels in a single location at a time. To mitigate these problems, we implemented a 3D MEGA-editing MRS imaging sequence with the following three features: a) Real-time motion correction, dynamic shim updates, and selective reacquisition to eliminate subtraction artifacts due to scanner instabilities and subject motion. b) Localization by Adiabatic SElective Refocusing (LASER) to improve the localization accuracy and signal-to-noise ratio. c) K-space encoding via a weighted stack of spirals provides 3D metabolic mapping with flexible scan times. Simulations, phantom and in vivo experiments prove that our MEGA-LASER sequence enables 3D mapping of GABA+ and Glx (Glutamate+Gluatmine), by providing 1. 66 times larger signal for the 3. 02ppm multiplet of GABA+ compared to MEGA-PRESS, leading to clinically feasible scan times for 3D brain imaging. Hence, our sequence allows accurate and robust 3D-mapping of brain GABA+ and Glx levels to be performed at clinical 3T MR scanners for use in neuroscience and clinical applications.

YNIMG Journal 2014 Journal Article

Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data

• Douglas N. Greve
• Claus Svarer
• Patrick M. Fisher
• Ling Feng
• Adam E. Hansen
• William Baare
• Bruce Rosen
• Bruce Fischl

Exploratory (i. e. , voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain positron emission tomography (PET) data. These choices include the use of volume- or cortical surface-based smoothing, level of smoothing, use of voxelwise partial volume correction (PVC), and PVC masking threshold. PVC was implemented using the Muller-Gartner method with the masking out of voxels with low gray matter (GM) partial volume fraction. Dynamic PET scans of an antagonist serotonin-4 receptor radioligand ([11C]SB207145) were collected on sixteen healthy subjects using a Siemens HRRT PET scanner. Kinetic modeling was used to compute maps of non-displaceable binding potential (BPND) after preprocessing. The results showed a complicated interaction between smoothing, PVC, and masking on BPND estimates. Volume-based smoothing resulted in large bias and intersubject variance because it smears signal across tissue types. In some cases, PVC with volume smoothing paradoxically caused the estimated BPND to be less than when no PVC was used at all. When applied in the absence of PVC, cortical surface-based smoothing resulted in dramatically less bias and the least variance of the methods tested for smoothing levels 5mm and higher. When used in combination with PVC, surface-based smoothing minimized the bias without significantly increasing the variance. Surface-based smoothing resulted in 2–4 times less intersubject variance than when volume smoothing was used. This translates into more than 4 times fewer subjects needed in a group analysis to achieve similarly powered statistical tests. Surface-based smoothing has less bias and variance because it respects cortical geometry by smoothing the PET data only along the cortical ribbon and so does not contaminate the GM signal with that of white matter and cerebrospinal fluid. The use of surface-based analysis in PET should result in substantial improvements in the reliability and detectability of effects in exploratory PET analysis, with or without PVC.

YNIMG Journal 2014 Journal Article

Real-time motion- and B0-correction for LASER-localized spiral-accelerated 3D-MRSI of the brain at 3T

• Wolfgang Bogner
• Aaron T. Hess
• Borjan Gagoski
• M. Dylan Tisdall
• Andre J.W. van der Kouwe
• Siegfried Trattnig
• Bruce Rosen
• Ovidiu C. Andronesi

The full potential of magnetic resonance spectroscopic imaging (MRSI) is often limited by localization artifacts, motion-related artifacts, scanner instabilities, and long measurement times. Localized adiabatic selective refocusing (LASER) provides accurate B1-insensitive spatial excitation even at high magnetic fields. Spiral encoding accelerates MRSI acquisition, and thus, enables 3D-coverage without compromising spatial resolution. Real-time position- and shim/frequency-tracking using MR navigators correct motion- and scanner instability-related artifacts. Each of these three advanced MRI techniques provides superior MRSI data compared to commonly used methods. In this work, we integrated in a single pulse sequence these three promising approaches. Real-time correction of motion, shim, and frequency-drifts using volumetric dual-contrast echo planar imaging-based navigators were implemented in an MRSI sequence that uses low-power gradient modulated short-echo time LASER localization and time efficient spiral readouts, in order to provide fast and robust 3D-MRSI in the human brain at 3T. The proposed sequence was demonstrated to be insensitive to motion- and scanner drift-related degradations of MRSI data in both phantoms and volunteers. Motion and scanner drift artifacts were eliminated and excellent spectral quality was recovered in the presence of strong movement. Our results confirm the expected benefits of combining a spiral 3D-LASER-MRSI sequence with real-time correction. The new sequence provides accurate, fast, and robust 3D metabolic imaging of the human brain at 3T. This will further facilitate the use of 3D-MRSI for neuroscience and clinical applications.

YNIMG Journal 2009 Journal Article

An fMRI study on the interaction and dissociation between expectation of pain relief and acupuncture treatment

• Jian Kong
• Ted J. Kaptchuk
• Ginger Polich
• Irving Kirsch
• Mark Vangel
• Carolyn Zyloney
• Bruce Rosen
• Randy L. Gollub

It is well established that expectation can significantly modulate pain perception. In this study, we combined an expectancy manipulation model and fMRI to investigate how expectation can modulate acupuncture treatment. Forty-eight subjects completed the study. The analysis on two verum acupuncture groups with different expectancy levels indicates that expectancy can significantly influence acupuncture analgesia for experimental pain. Conditioning positive expectation can amplify acupuncture analgesia as detected by subjective pain sensory rating changes and objective fMRI signal changes in response to calibrated noxious stimuli. Diminished positive expectation appeared to inhibit acupuncture analgesia. This modulation effect is spatially specific, inducing analgesia exclusively in regions of the body where expectation is focused. Thus, expectation should be used as an important covariate in future studies evaluating acupuncture efficacy. In addition, we also observed dissociation between subjective reported analgesia and objective fMRI signal changes to calibrated pain in the analysis across all four groups. We hypothesize that as a peripheral-central modulation, acupuncture needle stimulation may inhibit incoming noxious stimuli; while as a top-down modulation, expectancy (placebo) may work through the emotional circuit.

YNIMG Journal 2009 Journal Article

Expectancy and treatment interactions: A dissociation between acupuncture analgesia and expectancy evoked placebo analgesia

• Jian Kong
• Ted J. Kaptchuk
• Ginger Polich
• Irving Kirsch
• Mark Vangel
• Carolyn Zyloney
• Bruce Rosen
• Randy Gollub

Recent advances in placebo research have demonstrated the mind's power to alter physiology. In this study, we combined an expectancy manipulation model with both verum and sham acupuncture treatments to address: 1) how and to what extent treatment and expectancy effects — including both subjective pain intensity levels (pain sensory ratings) and objective physiological activations (fMRI) — interact; and 2) if the underlying mechanism of expectancy remains the same whether placebo treatment is given alone or in conjunction with active treatment. The results indicate that although verum acupuncture+high expectation and sham acupuncture+high expectation induced subjective reports of analgesia of equal magnitude, fMRI analysis showed that verum acupuncture produced greater fMRI signal decrease in pain related brain regions during application of calibrated heat pain stimuli on the right arm. We believe our study provides brain imaging evidence for the existence of different mechanisms underlying acupuncture analgesia and expectancy evoked placebo analgesia. Our results also suggest that the brain network involved in expectancy may vary under different treatment situations (verum and sham acupuncture treatment).

YNIMG Journal 2005 Journal Article

Crossing the blood–brain barrier: A potential application of myristoylated polyarginine for in vivo neuroimaging

• Wellington Pham
• Bing-Qiao Zhao
• Eng H. Lo
• Zdravka Medarova
• Bruce Rosen
• Anna Moore

As basic neurological research continues to reveal novel targets for therapy, the need to deliver therapeutic agents across the blood–brain barrier (BBB) becomes increasingly important. If developed, delivery modules would bring targeting molecules across the BBB to their respective active sites. In addition, it would be highly advantageous if the bioavailability of these delivered agents could be monitored over time using non-invasive imaging techniques. Here, we describe a versatile delivery module based on a myristoylated polyarginine backbone, which crosses the BBB. Incorporation of the fatty acid group was achieved using a Schotten–Bauman reaction with quantitative yield, and the peptide was further synthesized by conventional solid phase peptide synthesis (SPPS). We report for the first time the in vivo distribution of the delivery module over time into mouse brain using near-infrared (NIR) fluorescence imaging. The fluorescent cargo was detected in vivo from 24–48 h post IV injection and was further characterized in perfused brains. Immunohistochemical staining of excised brain showed that the delivery module primarily accumulated in neurons with occasional localization in astrocytes and endothelial cells. We conclude that this approach can be used for the delivery of imaging probes and potentially targeted therapeutics across the BBB.

YNIMG Journal 2001 Journal Article

BOLD sensitivity to systemic masking effects; comparison with flow-based fMRI

• Richard Hoge
• Joe B. Mandeville
• Bob Banzett
• Andrew Binks
• Bruce Rosen

YNIMG Journal 2001 Journal Article

Effects of lorazepam and scopolamine on encoding face-name associations: A pharmacologic fMRI trial

• Reisa Sperling
• Doug Greve
• Anders Dale
• Ronald Killiany
• Bruce Rosen
• Jennifer Holmes
• H.Diana Rosas
• Andrew Cocchiarella

NeurIPS Conference 1990 Conference Paper

Adaptive Range Coding

• Bruce Rosen
• James Goodwin
• Jacques Vidal

these to neuron-like processing elements. "neurons" This paper examines a class of neuron based that rely on learning systems for dynamic control adaptive range coding of sensor inputs. Sensors are assumed to provide binary coded range vectors that coarsely describe the system state. These vectors are Output input decisions generated by turn the system state, subsequently producing new affect inputs. the intervals and environment are evaluated. The neural weights as well as the ran g e b 0 u n dar i e s determining the output decisions are then altered with future Preliminary reinforcement from the promise of adapting "neural experiments show receptive learning dynamical control. The observed performance with this method exceeds that of earlier approaches. the goal of maximizing the environment.

NeurIPS Conference 1987 Conference Paper

LEARNING BY STATE RECURRENCE DETECTION

• Bruce Rosen
• James Goodwin
• Jacques Vidal

This research investigates a new technique for unsupervised learning of nonlinear control problems. The approach is applied both to Michie and Chambers BOXES algorithm and to Barto, Sutton and Anderson's extension, the ASE/ACE system, and has significantly improved the convergence rate of stochastically based learning automata. Recurrence learning is a new nonlinear reward-penalty algorithm. It exploits information found during learning trials to reinforce decisions resulting in the recurrence of nonfailing states. Recurrence learning applies positive reinforcement during the exploration of the search space, whereas in the BOXES or ASE algorithms, only negative weight reinforcement is applied, and then only on failure. Simulation results show that the added information from recurrence learning increases the learning rate. Our empirical results show that recurrence learning is faster than both basic failure driven learning and failure prediction methods. Although recurrence learning has only been tested in failure driven experiments, there are goal directed learning applications where detection of recurring oscillations may provide useful information that reduces the learning time by applying negative, instead of positive reinforcement. Detection of cycles provides a heuristic to improve the balance between evidence gathering and goal directed search.