Biology subjects

Soleimani, G.

Publications and source records attributed to Soleimani, G..

4 recordsLinked to original sources

bioRxiv2026

Closing the loop between brain and electrical stimulation: A proof-of-concept randomized trial of real-time fMRI-guided tACS optimization

Closed-loop transcranial alternating current stimulation (tACS) combined with real-time functional MRI (tACS-fMRI) enables adaptive optimization of noninvasive brain stimulation based on ongoing neural dynamics. We implemented one of the first randomized, double-arm, closed-loop tACS-fMRI protocols to iteratively adjust stimulation frequency and phase alignment between the right dorsolateral prefrontal cortex (F4) and right inferior parietal cortex (P4) to modulate frontoparietal functional connectivity (FFC) during a 2-back working memory task. Twenty healthy adults were randomized to an up-regulation group (n = 10), receiving parameters optimized to maintain or enhance FFC, or a down-regulation group (n = 10), receiving parameters optimized to reduce FFC using a simplex-based adaptive algorithm across two training runs. Individualized optimized parameters were applied during a subsequent test run, with resting-state fMRI acquired before and after stimulation. During the test phase, connectivity trajectories diverged across groups: FFC was preserved in the up-regulation group but declined in the down-regulation group (signed-rank p = 0.019), with a significant between-group difference confirmed by permutation testing (20,000 iterations; p = 0.043). Behavioral effects were primarily observed in the accuracy learning dynamics. Although overall mean performance was comparable, the up-regulation group demonstrated a more positive accuracy trajectory, greater within-test accuracy gain (p = 0.036), and steeper trial-wise improvement (p = 0.035). Reaction time decreased across runs in both groups, consistent with practice effects, with no significant group differences in gain; however, the test-run RT slope showed a trend toward a steeper reduction in reaction time in the up-regulation group relative to the down-regulation group (p = 0.065). Resting-state analyses revealed significant time-by-group interaction clusters in seed-based connectivity (voxel-wise p < 0.001; cluster-level FDR p < 0.05), with connectivity increases observed in the up-regulation group relative to the down-regulation group, indicating persistent network modulation following stimulation. These findings demonstrate the feasibility of closed-loop tACS-fMRI for individualized network-targeted neuromodulation and suggest that real-time connectivity optimization selectively stabilizes task-relevant connectivity and enhances accuracy learning while producing sustained effects on intrinsic brain networks.

neuroscience↗

bioRxiv2025

Machine Learning-Driven fMRI Analysis for Objective Craving Prediction

BackgroundCraving is a fundamental aspect of substance use disorder (SUD), traditionally assessed through subjective self-report measures. To develop more objective assessments, we created a brain-based marker to predict craving based on machine learning approaches using functional magnetic resonance imaging (fMRI) drug cue reactivity data from 69 participants with methamphetamine use disorders. MethodsTo predict craving intensity, our analysis demonstrated that utilizing principal component analysis (PCA) and linear regression outperformed other models in terms of Root Mean Squared Error (RMSE). Employing a 5-fold cross-validation strategy with a 20% holdout set, we established the reliability of the model. Additionally, the model successfully classified high and low craving levels and distinguished cue types (neutral vs. drug) based on fMRI data. ResultsThe model achieved an RMSE of 0.983 {+/-} 0.026 (standard deviation), with strong generalization evidenced by an out-of-sample RMSE of 0.985 and statistical significance (p < 0.026; effect size (Cliffs Delta) = 0.715; statistical power = 0.639). Key neurobiological signatures included the Parahippocampal Gyrus, Superior Temporal Gyrus, Medioventral Occipital Cortex, and Amygdala (positively associated with craving), as well as the Inferior Temporal Gyrus (negatively associated). Classification of high vs. low craving levels yielded an AUC-ROC of 0.684 {+/-} 0.084 (out-of-sample AUC-ROC = 0.714), with significant separation (p < 0.04; Cliffs Delta = 0.831). Additionally, classification of cue types (neutral vs. drug) achieved an AUC-ROC of 0.692 {+/-} 0.090 (out-of-sample AUC-ROC = 0.693), with p < 0.002, Cliffs Delta = 0.896, and statistical power = 0.800, highlighting the robustness of the model. ConclusionThese findings underscore the potential of neuroimaging and machine learning to provide objective, data-driven insights into the neural mechanisms underlying subjective experience of craving and to inform future clinical applications in SUD.

neuroscience↗

bioRxiv2021

Online Closed-Loop Real-Time tES-fMRI for Brain Modulation: Feasibility, Noise/Safety and Pilot Study

Recent studies suggest that transcranial electrical stimulation (tES) can be performed during functional magnetic resonance imaging (fMRI). The novel approach of using concurrent tES-fMRI to modulate and measure targeted brain activity/connectivity may provide unique insights into the causal interactions between the brain neural responses and psychiatric/neurologic signs and symptoms, and importantly, guide the development of new treatments. However, tES stimulation parameters to optimally influence the underlying brain activity in health and disorder may vary with respect to phase, frequency, intensity and electrodes montage. Here, we delineate how a closed-loop tES-fMRI study of frontoparietal network modulation can be designed and performed. We also discuss the challenges of running a concurrent tES-fMRI, describing how we can distinguish clinically meaningful physiological changes caused by tES from tES-related artifacts. There is a large methodological parameter space including electrode types, electrolytes, electrode montages, concurrent tES-fMRI hardware, online fMRI processing pipelines and closed-loop optimization algorithms that should be carefully selected for closed-loop tES-fMRI brain modulation. We also provide technical details on how safety and quality of tES-fMRI settings can be tested, and how these settings can be monitored during the study to ensure they do not exceed safety standards. The initial results of feasibility and applicability of closed-loop tES-fMRI are reported and potential hypotheses for the outcomes are discussed. Highlight pointsO_LIMethodological details of a closed-loop tES-fMRI study protocol are provided. C_LIO_LIThe protocol is performed successfully on a frontoparietal network without side-effects. C_LIO_LIThe temperature of electrodes in concurrent tES-fMRI remains in the safe range. C_LIO_LIProperly setup concurrent tES does not introduce MRI artifacts and noise. C_LIO_LISimplex optimizer could be used to find an optimal tES stimulation parameter. C_LI

neuroscience↗

bioRxiv2020

Group and Individual Level Variations between Symmetric and Asymmetric DLPFC Montages for tDCS over Large Scale Brain Network Nodes

Two challenges to optimizing transcranial direct current stimulation (tDCS) are selecting between, often similar, electrode montages and accounting for inter-individual differences in response. These two factors are related by how tDCS montage determines current flow through the brain considered across or within individuals. MRI-based computational head models (CHMs) predict how brain anatomy determine electric field (EF) patterns for a given tDCS montage. Because conventional tDCS produces diffuse brain current flow, stimulation outcomes may be understood as modulation of global networks. Therefore, we developed network-led, rather than region-led, approach. We specifically considered two common frontal tDCS montages that nominally target the dorsolateral prefrontal cortex; asymmetric unilateral (anode/cathode: F4/Fp1) and symmetric bilateral (F4/F3) electrode montages. CHMs of 66 participants were constructed. We showed that cathode location significantly affects EFs in the limbic network. Furthermore, using a finer parcellation of large-scale networks, we found significant differences in some of main nodes within a network, even if there is no difference at the network level. This study generally demonstrates a methodology for considering the components of large-scale networks in CHMs instead of targeting a single region and specifically provides insight into how symmetric vs asymmetric frontal tDCS may differentially modulate networks across a population.

bioengineering↗