Biology subjects

Zald, D.

Publications and source records attributed to Zald, D..

3 recordsLinked to original sources

bioRxiv2025

Loss of Effort in Chronic Low-Back Pain Patients: Motivational Anhedonia in Chronic Pain

The motivational and affective properties of chronic pain significantly impact patients lives and response to treatment but remain poorly understood. Most available phenotyping tools of chronic pain affect rely on patients self-report. Here we instead directly studied the willingness of chronic low-back pain (CLBP) patients to expend effort to win monetary rewards available for wins at different probabilities and different levels of difficulties in comparison to matched pain free controls and obtained functional brain imaging on a sub-group of our sample to link behavior to brain properties. We aimed to specifically test for a differential relationship of the functional connectivity in reward and effort related brain networks, and measures of effort in patients and pain free controls. Consistent with the hypothesis of "negative hedonic shift" in chronic pain we observed that CLBP patients are significantly less willing than pain free controls to expend effort to go for high cost/high reward choices and their reported low-back pain intensity predicted increased effort discounting. Furthermore, patients task performance was directly correlated to functional connectivity between the ventral striatum and ventro-medial prefrontal cortex, which are major nodes in the reward processing network. Patients performance was not explained by their self-reported depressive symptoms. Our results present new behavioral evidence characterizing the nature of anhedonia in chronic pain and links it directly to cortico-striatal connectivity highlighting the role of this circuitry in the pathophysiology of chronic pain.

neuroscience↗

bioRxiv2022

A neural signature of reward

Using a neurometric approach, we identify and validate a neural signature of reward encoded in a distributed pattern of brain activity using data collected from 21 different studies (N = 2,691). Our model can discriminate between receiving rewards from punishments in completely independent data with 99% accuracy and includes weights located in regions containing a high density of D2/D3 receptors. The model exhibits strong generalizability across a range of tasks probing reward, and a high degree of specificity for reward compared to non-reward constructs. We demonstrate several applications of how this model can infer psychological states of positive affect in the absence of self report. The model is sensitive to changes in brain activity following causal manipulations of homeostatic states, can uncover individual preferences for loss-aversion, and can be used to identify positive affective experiences when watching a television show. Our results suggest that there is a shared neural signature of reward elicited across these different task contexts.

neuroscience↗

bioRxiv2020

Registration-free Distortion Correction of Diffusion Weighted MRI

Diffusion magnetic resonance images may suffer from geometric distortions due to susceptibility induced off resonance fields, which cause geometric mismatch with anatomical images and ultimately affect subsequent quantification of microstructural or connectivity indices. State-of-the art diffusion distortion correction methods typically require data acquired with reverse phase encoding directions, resulting in varying magnitudes and orientations of distortion, which allow estimation of an undistorted volume. Alternatively, additional field maps acquisitions can be used along with sequence information to determine warping fields. However, not all imaging protocols include these additional scans and cannot take advantage of state-of-the art distortion correction. To avoid additional acquisitions, structural MRI (undistorted scans) can be used as registration targets for intensity driven correction. In this study, we aim to (1) enable susceptibility distortion correction with historical and/or limited diffusion datasets that do not include specific sequences for distortion correction and (2) avoid the computationally intensive registration procedure typically required for distortion correction using structural scans. To achieve these aims, we use deep learning (3D U-nets) to synthesize an undistorted b0 image that matches geometry of structural T1w images and intensity contrasts from diffusion images. Importantly, the training dataset is heterogenous, consisting of varying acquisitions of both structural and diffusion. We apply our approach to a withheld test set and show that distortions are successfully corrected after processing. We quantitatively evaluate the proposed distortion correction and intensity-based registration against state-of-the-art distortion correction (FSL topup). The results illustrate that the proposed pipeline results in b0 images that are geometrically similar to non-distorted structural images, and more closely match state-of-the-art correction with additional acquisitions. In addition, we show generalizability of the proposed approach to datasets that were not in the original training / validation / testing datasets. These datasets included varying populations, contrasts, resolutions, and magnitudes and orientations of distortion and show efficacious distortion correction. The method is available as a Singularity container, source code, and an executable trained model to facilitate evaluation.

bioengineering↗