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AbstractChronic pain affects more than 50 million Americans. Treatments remain inadequate, in large part, because the pathophysiological mechanisms underlying the development of chronic pain remain poorly understood. Pain biomarkers could potentially identify and measure biological pathways and phenotypical expressions that are altered by pain, provide insight into biological treatment targets, and help identify at-risk patients who might benefit from early intervention. Biomarkers are used to diagnose, track, and treat other diseases, but no validated clinical biomarkers exist yet for chronic pain. To address this problem, the National Institutes of Health Common Fund launched the Acute to Chronic Pain Signatures (A2CPS) program to evaluate candidate biomarkers, develop them into biosignatures, and discover novel biomarkers for chronification of pain after surgery. This article discusses candidate biomarkers identified by A2CPS for evaluation, including genomic, proteomic, metabolomic, lipidomic, neuroimaging, psychophysical, psychological, and behavioral measures. Acute to Chronic Pain Signatures will provide the most comprehensive investigation of biomarkers for the transition to chronic postsurgical pain undertaken to date. Data and analytic resources generatedby A2CPS will be shared with the scientific community in hopes that other investigators will extract valuable insights beyond A2CPS's initial findings. This article will review the identified biomarkers and rationale for including them, the current state of the science on biomarkers of the transition from acute to chronic pain, gaps in the literature, and how A2CPS will address these gaps.

Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or "omics," quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.

Purpose: To develop and demonstrate a fast 3D fMRI acquisition technique with high spatial resolution over a reduced FOV, named k‐t 3D reduced FOV imaging (3D‐rFOVI). Methods: Based on 3D gradient‐echo EPI, k‐t 3D‐rFOVI used a 2D RF pulse to reduce the FOV in the in‐plane phase‐encoding direction, boosting spatial resolution without increasing echo train length. For image acceleration, full sampling was applied in the central k‐space region along the through‐slab direction (kz) for all time frames, while randomized undersampling was used in outer kz regions at different time frames. Images were acquired at 3T and reconstructed using a method based on partial separability. fMRI detection sensitivity of k‐t 3D‐rFOVI was quantitively analyzed with simulation data. Human visual fMRI experiments were performed to evaluate k‐t 3D‐rFOVI and compare it with a commercial multiband EPI sequence. Results: The simulation data showed that k‐t 3D‐rFOVI can detect 100% of fMRI activations with an acceleration factor (R) of 2 and ˜80% with R = 6. In the human fMRI data acquired with 1.5‐mm spatial resolution and 800‐ms volume TR (TRvol), k‐t 3D‐rFOVI with R = 4 detected 46% more activated voxels in the visual cortex than the multiband EPI. Additional fMRI experiments showed that k‐t 3D‐rFOVI can achieve TRvol of 480 ms with R = 6, while reliably detecting visual activation. Conclusions: k‐t 3D‐rFOVI can simultaneously achieve a high spatial resolution (1.5‐mm isotropically) and short TRvol (480‐ms) at 3T. It offers a robust acquisition technique for fast fMRI studies over a focused brain volume. [ABSTRACT FROM AUTHOR]

Resting-state brain activity has been investigated extensively using BOLD contrast. However, BOLD signal represents the combined effects of multiple physiological processes and its spatial localization is less accurate than that of cerebral blood flow and volume (CBF and CBF, respectively). In this study, we demonstrate that resting-state brain activity can be reliably detected by spontaneous fluctuations of CBV-weighted signal using whole-brain gradient and spin echo (GRASE) based vascular space occupancy (VASO) imaging. Specifically, using independent component analysis, intrinsic brain networks, including default mode, salience, executive control, visual, auditory, and sensorimotor networks were revealed robustly by the VASO technique. We further demonstrate that task-evoked VASO signal aligned well with expected gray matter areas, while blood-oxygenation level dependent (BOLD) signal extended outside of these areas probably due to their different spatial specificity. The improved spatial localization of VASO is consistent with previous studies using animal models. Moreover, we showed that the 3D-GRASE VASO images had reduced susceptibility-induced signal voiding, compared to the BOLD technique. This is attributed to the fact that VASO does not require T2* weighting, thus the acquisition can use a shorter TE and can employ spin-echo scheme. Consequently VASO-based functional connectivity signals were well preserved in brain regions that tend to suffer from signal loss and geometric distortion in BOLD, such as orbital prefrontal cortex. Our study suggests that 3D-GRASE VASO imaging, with its improved spatial specificity and less sensitivity to susceptibility artifacts, may have advantages in resting-state fMRI studies.

Purpose: Quantitative mapping of brain perfusion, diffusion, T2*, and T1 has important applications in cerebrovascular diseases. At present, these sequences are performed separately. This study aims to develop a novel MRI technique to simultaneously estimate these parameters. Methods: This sequence to measure perfusion, diffusion, T2*, and T1 mapping with magnetic resonance fingerprinting (MRF) was based on a previously reported MRF‐arterial spin labeling (ASL) sequence, but the acquisition module was modified to include different TEs and presence/absence of bipolar diffusion‐weighting gradients. We compared parameters derived from the proposed method to those derived from reference methods (i.e., separate sequences of MRF‐ASL, conventional spin‐echo DWI, and T2* mapping). Test–retest repeatability and initial clinical application in two patients with stroke were evaluated. Results: The scan time of our proposed method was 24% shorter than the sum of the reference methods. Parametric maps obtained from the proposed method revealed excellent image quality. Their quantitative values were strongly correlated with those from reference methods and were generally in agreement with values reported in the literature. Repeatability assessment revealed that ADC, T2*, T1, and B1+ estimation was highly reliable, with voxelwise coefficient of variation (CoV) <5%. The CoV for arterial transit time and cerebral blood flow was 16% ± 3% and 25% ± 9%, respectively. The results from the two patients with stroke demonstrated that parametric maps derived from the proposed method can detect both ischemic and hemorrhagic stroke. Conclusion: The proposed method is a promising technique for multi‐parametric mapping and has potential use in patients with stroke. [ABSTRACT FROM AUTHOR]

The article discusses the ethical considerations surrounding MRI research in human subjects in the era of precision medicine. It emphasizes the importance of respecting the diversity of human subjects, including factors such as biological variables, socio-economic status, cultural heritage, and life experience. The article also highlights the need for beneficence, ensuring that research is beneficial and safe for both current and future patients. Additionally, the article addresses the concept of justice, emphasizing the importance of global collaboration and respecting cultural and regulatory differences when establishing ethical standards. The authors suggest that the International Society for Magnetic Resonance in Medicine (ISMRM) can play a significant role in promoting and facilitating ethical research in human subjects. [Extracted from the article]

Purpose: To develop a novel convolutional recurrent neural network (CRNN-DWI) and apply it to reconstruct a highly undersampled (up to six-fold) multi-b-value, multi-direction diffusion-weighted imaging (DWI) dataset. Methods: A deep neural network that combines a convolutional neural network (CNN) and recurrent neural network (RNN) was first developed by using a set of diffusion images as input. The network was then used to reconstruct a DWI dataset consisting of 14 b-values, each with three diffusion directions. For comparison, the dataset was also reconstructed with zero-padding and 3D-CNN. The experiments were performed with undersampling rates (R) of 4 and 6. Standard image quality metrics (SSIM and PSNR) were employed to provide quantitative assessments of the reconstructed image quality. Additionally, an advanced non-Gaussian diffusion model was employed to fit the reconstructed images from the different approaches, thereby generating a set of diffusion parameter maps. These diffusion parameter maps from the different approaches were then compared using SSIM as a metric. Results: Both the reconstructed diffusion images and diffusion parameter maps from CRNN-DWI were better than those from zero-padding or 3D-CNN. Specifically, the average SSIM and PSNR of CRNN-DWI were 0.750 ± 0.016 and 28.32 ± 0.69 (R = 4), and 0.675 ± 0.023 and 24.16 ± 0.77 (R = 6), respectively, both of which were substantially higher than those of zero-padding or 3D-CNN reconstructions. The diffusion parameter maps from CRNN-DWI also yielded higher SSIM values for R = 4 (>0.8) and for R = 6 (>0.7) than the other two approaches (for R = 4, <0.7, and for R = 6, <0.65). Conclusions: CRNN-DWI is a viable approach for reconstructing highly undersampled DWI data, providing opportunities to reduce the data acquisition burden. [ABSTRACT FROM AUTHOR]