Biology subjects

Ekhtiari, H.

Publications and source records attributed to Ekhtiari, H..

9 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

fMRI Informed Montage Selection for Transcranial Electrical Stimulation: Frontoparietal Synchronization for Drug Cue Reactivity

BackgroundFrontoparietal network (FPN) with multiple cortical nodes is involved in executive functions. Transcranial electrical stimulation (tES) can potentially modulate interactions between these nodes using frontoparietal synchronization (FPS). Here we used fMRI and computational head models (CHMs) to inform electrode montage and dosage selection in FPS. MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Two sets of 4x1 HD electrodes with anode over F3 and F4 were simulated and spheres around maximum electric field in each hemisphere were defined as frontal seeds. Using frontal seeds, a task-based functional connectivity analysis was conducted based on a seed-to-whole brain generalized psychophysiological interaction (gPPI). Electrode placement for parietal sites was selected based on gPPI results. Task-based and resting-state connectivity were compared between fMRI-informed and classic F3-P3/F4-P4 montages. ResultsWhole-brain gPPI showed two significant clusters (left: 506 voxels P=0.006, right: 455 voxels P=0.016), located in the inferior parietal lobule under the CP5 and CP6 electrode location. Pair-wise ROI-based gPPI comparing informed (F3-CP5/F4-CP6) and classic (F3-P3/F4-P4) montages showed significant increased PPI and resting-state connectivity only in the informed montage. Cue-induced craving score was also correlated with left (F3-CP5) frontoparietal connectivity in the fMRI-informed montage. ConclusionThis study proposes an analytic pipeline to select electrode montage and dosage in dual site tES using CHMs and task-based connectivity. Stimulating F3-F4 can tap into both FPN and saliency network (SN) based on the montage selection. Using CHM and fMRI will be essential to navigating ample parameter space in the stimulation protocols for future tES studies. HighlightsO_LIWe demonstrated a methodology for montage selection in network-based tES C_LIO_LITask-based functional connectivity can inform dual-site tES montage selection C_LIO_LIHead models can help to induce balance tES dose in targeted brain regions C_LIO_LITargeting DLPFC with tES can tap into both saliency and frontoparietal networks C_LIO_LILower resting-state frontoparietal connectivity before cue exposure followed by a greater craving C_LI

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↗

bioRxiv2020

It is Never as Good the Second Time Around: Brain Areas Involved in Salience Processing Habituate During Repeated Drug Cue Exposure in Methamphetamine and Opioid Users

IntroductionThe brain response to drug-related cues is an important marker in addiction-medicine, however, the temporal dynamics of this response in repeated exposure to the cues are not well known yet. In an fMRI drug cue-reactivity task, the presence of rapid habituation or sensitization was investigated by modeling time and its interaction with condition (drug>neutral) using an initial discovery-sample. Replication of this temporal response was tested in two other clinical populations. MethodsSixty-five male participants (35.8{+/-}8.4 years-old) with methamphetamine use disorder (MUD) were recruited as the discovery-sample. A linear mixed effects model was used to identify areas with a time-by-condition interaction in the discovery-sample. Replication of these effects was tested in two other samples (29 female with MUD and 22 male with opioid use disorder). The second replication-sample was re-tested within two weeks. ResultsIn the discovery-sample, clusters within the VMPFC, amygdala and ventral striatum showed both significant condition and condition-by-time interaction with a habituation response for the drug-related cues but not neutral cues. The estimates for the main effects and interactions were generally consistent between the discovery and replication-samples across all clusters. The re-test data showed consistent lack of drug>neutral and habituation response within all selected clusters in the second cue-exposure session. ConclusionsVMPFC, amygdala and ventral striatum show a habituation in response to drug-related cues which is consistent among different clinical populations. Habituation in response in the first session of cue-exposure and lack of reactivity in the second session of exposure provide foundations for development of cue-desensitization interventions.

neuroscience↗

bioRxiv2019

LIBR Methamphetamine and Opioid Cue Database (LIBR MOCD): Development and Validation

IntroductionDrug cue reactivity (DCR) is widely used in experimental settings for both assessment and intervention. There is no validated database of pictorial cues available for methamphetamine and opioids.\n\nMethods360 images in three-groups (methamphetamine, opioid and neutral (control)) matched for their content (objects, hands, faces and actions) were selected in an initial development phase. 28 participants with a history of both methamphetamine and opioid use (37.1 {+/-} 8.11 years old, 12 female) with over six months of abstinence were asked to rate images for craving, valence, arousal, typicality and relatedness.\n\nResultsAll drug images were differentiated from neutral images. Drug related images received higher arousal and lower valence ratings compared to neutral images (craving (0-100) for neutral (11.5{+/-}21.9), opioid (87.7{+/-}18.5), and methamphetamine (88{+/-}18), arousal (1-9) for neutral (2.4{+/-}1.9), opioid (4.6{+/-}2.7), and methamphetamine (4.6{+/-}2.6), and valence (1-9) for neutral (4.8{+/-}1.3), opioid (4.4{+/-}1.9), and methamphetamine (4.4{+/-}1.8)). There is no difference between methamphetamine and opioid images in craving, arousal and valence. There is a significant positive relationship between the amount of time that participants spent on drug-related images and the craving they reported for the image. Every 10 points of craving were associated with an increased response time of 383millisecond. Three image sets were automatically selected for equivalent fMRI tasks (methamphetamine and opioids) from the database (tasks are available at github).\n\nConclusionLIBR MOCD provides a resource of validated images/tasks for future DCR studies. Additionally, researchers can select several sets of unique but equivalent images based-on their psychological/physical characteristics for multiple assessments/interventions.

neuroscience↗

bioRxiv2019

How Top-Down and Bottom-Up Regulation in Fronto-Amygdalar Network Changes over Time during Drug Cue-Exposure: An fMRI Study among Abstinent Heroin User

Top-down regulation is one of the major neural cores in drug-craving management and relapse prevention. The dynamic temporal behavior of top-down regulation between the dorso-lateral and ventro-medial prefrontal cortices (DLPFC and VMPFC) and amygdala during drug cue-exposure has not been studied yet. Fifteen abstinent participants with heroin use disorder were scanned using drug cue-induced craving fMRI task. Using Dynamic Causal Modelling (DCM), the winning model showed a significant reciprocal connection between the VMPFC and DLPFC while there was a one-way effect of the VMPFC on the amygdala. There is also a top-down modulation by DLPFC on the VMPFC-Amygdala connection. Craving contrast input only modulated amygdala directly. Using sliding-window for temporal evaluation, craving input to amygdala increased over time, simultaneously, DLPFC top-down modulatory effect on VMPFC-amygdala connection decreased. Temporal changes in the network connectivity during cue exposure with enhancement in craving input to amygdala and reduction in top-down modulatory effects of DLPFC, could provide us with new insights towards the dynamic nature of the cue-reactivity and failure to control its motivational consequences. Dynamic response of top-down regulatory networks during cue exposure can be considered as a new potential biomarker in the future addiction fMRI studies.

neuroscience↗

bioRxiv2016

fMRI Correlates of Reaction Time Prolongation during intentional False Responding; an inter-individual difference study

Reaction time (RT) is chiefly longer when people lie. However, the baseline speed in answering questions and the amount of RT prolongation during lying show considerable amount of inter-individual variability. In the current study, we exploited this fact to glean insights on the contribution of each lie-related brain region to hampering of response speeds when people try to be deceitful. In an event-related fMRI session, participants were interrogated by yes-no autobiographical questions and were instructed to intentionally provide false responses to a pre-selected subset of questions. Data from twenty healthy volunteers were analyzed. Baseline speed [RTtruth] and relative appended lie RT [(RTlie - RTtruth) / RTtruth] measures were calculated for each participant and were included in the group level analysis of [lie > truth] BOLD contrasts. Lying RTs were significantly longer than truth telling RTs. Lie-related increase in activity of right ventrolateral prefrontal cortex (VLPFC) and bilateral paracingulate cortex correlated with the baseline speed of participants, while the increase in activity of Left VLPFC, left lateral occipital cortex and bilateral anterior cingulate areas directly correlated with the amount of lying reaction time cost. Activity within bilateral posterior cingulate cortex and right insular cortex inversely correlated with lying RT-cost. Bilateral supplementary motor areas, internal capsule white matter and left angular gyrus showed lie-related increase in activity but did not correlate with either of behavioral measures. Provisional implications regarding the contribution of these regions to RT prolongation and their cognitive role in deceitful behavior are discussed.

neuroscience↗