Individual differences in rate of acquiring stable neural representations of tasks in fMRI.

Task-related functional magnetic resonance imaging (fMRI) is a widely-used tool for studying the neural processing correlates of human behavior in both healthy and clinical populations. There is growing interest in mapping individual differences in fMRI task behavior and neural responses. By utilizing neuroadaptive task designs accounting for such individual differences, task durations can be personalized to potentially optimize neuroimaging study outcomes (e.g., classification of task-related brain states). To test this hypothesis, we first retrospectively tracked the volume-by-volume changes of beta weights generated from general linear models (GLM) for 67 adult subjects performing a stop-signal task (SST). We then modeled the convergence of the volume-by-volume changes of beta weights according to their exponential decay (ED) in units of half-life. Our results showed significant differences in beta weight convergence estimates of optimal stopping times (OSTs) between go following successful stop trials and failed stop trials for both cocaine dependent (CD) and control group (Con), and between go following successful stop trials and go following failed stop trials for Con group. Further, we implemented support vector machine (SVM) classification for 67 CD/Con labeled subjects and compared the classification accuracies of fMRI-based features derived from (1) the full fMRI task versus (2) the fMRI task truncated to multiples of the unit of half-life. Among the computed binary classification accuracies, two types of task durations based on 2 half-lives significantly outperformed the accuracies using fully acquired trials, supporting this length as the OST for the SST. In conclusion, we demonstrate the potential of a neuroadaptive task design that can be widely applied to personalizing other task-based fMRI experiments in either dynamic real-time fMRI applications or within fMRI preprocessing pipelines

Implicit emotion regulation in adolescent girls: An exploratory investigation of Hidden Markov Modeling and its neural correlates.

Numerous data demonstrate that distracting emotional stimuli cause behavioral slowing (i.e. emotional conflict) and that behavior dynamically adapts to such distractors. However, the cognitive and neural mechanisms that mediate these behavioral findings are poorly understood. Several theoretical models have been developed that attempt to explain these phenomena, but these models have not been directly tested on human behavior nor compared. A potential tool to overcome this limitation is Hidden Markov Modeling (HMM), which is a computational approach to modeling indirectly observed systems. Here, we administered an emotional Stroop task to a sample of healthy adolescent girls (N = 24) during fMRI and used HMM to implement theoretical behavioral models. We then compared the model fits and tested for neural representations of the hidden states of the most supported model. We found that a modified variant of the model posited by Mathews et al. (1998) was most concordant with observed behavior and that brain activity was related to the model-based hidden states. Particularly, while the valences of the stimuli themselves were encoded primarily in the ventral visual cortex, the model-based detection of threatening targets was associated with increased activity in the bilateral anterior insula, while task effort (i.e. adaptation) was associated with reduction in the activity of these areas. These findings suggest that emotional target detection and adaptation are accomplished partly through increases and decreases, respectively, in the perceived immediate relevance of threatening cues and also demonstrate the efficacy of using HMM to apply theoretical models to human behavior

Smartphone intervention to optimize medication-assisted treatment outcomes for opioid use disorder: study protocol for a randomized controlled trial

Abstract Background Opioids accounted for 75% of drug overdoses in the USA in 2020, with rural states particularly impacted by the opioid crisis. While medication-assisted treatment (MAT) with Suboxone remains one of the more efficacious treatments for opioid use disorder (OUD), approximately 40% of people receiving Suboxone for outpatient MAT for OUD (MOUD) relapse within the first 6 months of treatment. We developed the smartphone app-based intervention OptiMAT as an adjunctive intervention to improve MOUD outcomes. The aims of this study are to (1) evaluate the efficacy of adjunctive OptiMAT use in reducing opioid misuse among people receiving MOUD and (2) evaluate the role of specific OptiMAT features in reducing opioid misuse, including the use of GPS-driven just-in-time intervention. Methods We will conduct a two-arm, single-blind, randomized controlled trial of adults receiving outpatient MOUD in the greater Little Rock AR area. Participants are English-speaking adults ages 18 or older recently enrolled in outpatient MOUD at one of our participating study clinics. Participants will be allocated via 1:1 randomized block design to (1) MOUD with adjunctive use of OptiMAT (MOUD+OptiMAT) or (2) MOUD without OptiMAT (MOUD-only). Our blinded research statistician will evaluate differences between the two groups in opioid misuse (as determined by quantitative urinalysis conducted by clinical lab staff blinded to group membership) during the 6-months following study enrolment. Secondary analyses will evaluate if OptiMAT-usage patterns within the MOUD+OptiMAT group predict opioid misuse or continued abstinence. Discussion This study will test if adjunctive use of OptiMAT improve MOUD outcomes. Study findings could lead to expansion of OptiMAT into rural clinical settings, and the identification of OptiMAT features which best predict positive clinical outcome could lead to refinement of this and similar smartphone app-based interventions. Trial registration ClinicalTrials.gov identifier: NCT05336188 , registered March 21, 2022

Neural encoding of the detection of the target stimulus.

<p>Shown are areas that were differentially responsive to the detection of threatening versus neutral target stimuli, indicating a neural correlate of the distractor detector. Positive values (orange) indicate that a region was activated in response to the detection of a threatening target and/or deactivated by the detection of a neutral target. Likewise, the negative values (blue) follow the inverse of this relationship.</p

Neural encoding of reaction time.

<p>Results are from a multiple regression amplitude-modulated deconvolution and depict areas where activity scaled significantly with reaction time. Positive values (orange) indicate that greater activity was associated with longer reaction times. Likewise, negative values (blue) are areas where activity was inversely related to reaction time.</p

Schematic depiction of the 5 models implemented.

<p>The Cohen et al. (1990) and Mathews et al. (1998) models do not contain between-trial relationships (Fig 1A and 1B). The remaining model do, as depicted by the relationships between trial n-1 and trial n. (N = neutral, T = threat-related, F = face stimulus, S = scene stimulus, Cog = cognitive control, Neg = Negative emotion).</p

Neural encoding of threat evaluation.

<p>Shown are areas that were differentially responsive to high threat evaluation versus low threat evaluation, indicating a neural correlate of the threat evaluation node. Positive values (orange) indicate that a region was activated in response to high threat evaluation and/or deactivated by low threat evaluation.</p

Full description of significant clusters.

<p>All clusters reaching statistical significance at a whole brain corrected <i>p</i> < 0.05 (<i>t</i> > = 2.807, cluster threshold 23, k = 23). Coordinates are based on the MNI templates.</p

Neural encoding of task effort.

<p>Shown are areas that were differentially responsive to high task effort versus low task effort, indicating a neural correlate of the task effort node. Negative values (blue) indicate that a region was deactivated in response to high task effort and/or activated by low task effort.</p

Model fit comparisons.

<p>Lesser values indicate better model fit. According to all criteria the modified Mathews et al. (1998) model fit best to the behavioral data. These values do not provide a statistical comparison between the model fits and has no variance, but they do provide a relative likelihood that one model is not actually better than the next. According to this value, the likelihood that the modified Mathews model is not actually best is 1.8 x 10⁻⁶⁴.</p