Biology subjects

Kuplicki, R.

Publications and source records attributed to Kuplicki, R..

16 recordsLinked to original sources

bioRxiv2026

Dose-Dependent Effects of Low-Intensity Focused Ultrasound on Human Deep White Matter Tracts

Background: Low-intensity focused ultrasound (LIFU) is emerging as a method for anatomically specific, noninvasive, and reversible neuromodulation of deep brain structures relevant to psychopathology. A key requirement for clinical translation is evidence of graded target engagement. We examined whether LIFU applied to deep white matter tracts produces dose-dependent functional connectivity changes in healthy adults. Methods: In this preregistered, double-blind, randomized study, 35 healthy adults underwent LIFU targeting (80 s, 10% duty cycle, estimated ISPPA 2.26 W/cm2, 0.5 MHz, in a {theta}-burst pattern) of individually modeled right thalamo-prefrontal white matter tracts under two dose conditions: one and three consecutive sonications. Dose-dependent changes in resting-state functional connectivity were examined during a 30-minute poststimulation period in 31 participants. Results: One sonication acutely increased right thalamic functional connectivity with the left thalamus (28 voxels; Z = 5.48, Cohen's d = 0.61), whereas three sonications decreased connectivity with the right dorsolateral prefrontal cortex (Z = -4.05, Cohen's d = -0.45). Poststimulation time-course analysis revealed a 3-LIFU-specific linear recovery trend in the left cerebellum (Z = 4.14, d = 0.48), reflecting initial suppression followed by progressive recovery across 30 minutes. Stronger thalamo-prefrontal structural connectivity was associated with condition-specific functional connectivity changes (left frontal pole; F = 14.73, {rho}2 = 0.41). Conclusions: These findings provide initial human evidence that LIFU is well tolerated and produces dose-dependent changes in functional connectivity associated with deep white matter tract stimulation, informing parameter selection for future circuit-based mechanistic and clinical studies.

neuroscience↗

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↗

bioRxiv2024

Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose an approach that simplifies gene selection using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We train a LASSO gene age model on an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC, resulting in a model with 21 genes. The L-GAGE shows higher biological aging in MDD participants than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age has a significant association with L-GAGE. This effect remains statistically significant even after adjusting for chronological age and sex. Using the 21 age genes, we find a statistically significant elevated biological age in MDD in an independent microarray gene expression dataset. We find functional enrichment of infectious disease and SARS-COV pathways using a broader feature selection of age related genes.

bioinformatics↗

bioRxiv2024

Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiotas role in NPD and its interactions with pharmacological treatments are needed.

microbiology↗

bioRxiv2023

The unique face of anxious depression: Increased sustained threat circuitry response during fear acquisition

BackgroundSensitivity to threat with dysregulation of fear learning is thought to contribute to the development of psychiatric disorders, including anxiety disorders (AD) and major depressive disorder (MDD). However, fewer studies have examined fear learning in MDD than in AD. Nearly half of individuals with MDD have an AD and the comorbid diagnosis has worse outcomes. The current study used propensity matching to examine the hypothesis that AD+MDD shows greater neural correlates of fear learning than MDD, suggesting that the co-occurrence of AD+MDD is exemplified by exaggerated defense related processes. Methods195 individuals with MDD (N = 65) or AD+MDD (N=130) were recruited from the community and completed multi-level assessments, including a Pavlovian fear learning task during functional imaging. ResultsMDD and AD+MDD showed significantly different patterns of activation for [CSplus- CSminus] in the medial amygdala ({eta}p2=0.009), anterior insula ({eta}p2=0.01), dorsolateral prefrontal cortex ({eta}p2=0.002), dorsal anterior cingulate cortex ({eta}p2=0.01), mid-cingulate cortex ({eta}p2=0.01) and posterior cingulate cortex ({eta}p2=0.02). These differences were driven by greater activation to the CS+ in late conditioning phases in ADD+MDD relative to MDD. ConclusionsAD+MDD showed a pattern of increased sustained activation in regions identified with fear learning. Effects were consistently driven by the threat condition, further suggesting fear signaling as the emergent target process. Differences emerged in regions associated with salience processing, attentional orienting/conflict, and self-relevant processing. These findings help to elucidate the fear signaling mechanisms involved in the pathophysiology of comorbid anxiety and depression, thereby highlighting promising treatment targets for this prevalent treatment group.

neuroscience↗

bioRxiv2022

The propensity to sign-track is associated with externalizing behaviour and distinct patterns of reward-related brain activation in youth

Externalizing behaviours in childhood often predict impulse control disorders in adulthood; however, the underlying biobehavioural risk factors are incompletely understood. In animals, the propensity to signtrack, or the degree to which incentive motivational value is attributed to reward cues, is associated with externalizing-type behaviours and deficits in executive control. Using a Pavlovian conditioned approach paradigm, we quantified sign-tracking in healthy 9-12-year-olds. We also measured parent-reported externalizing behaviours and anticipatory neural activations to outcome-predicting cues using the monetary incentive delay fMRI task. Sign-tracking was associated with attentional and inhibitory control deficits and the degree of amygdala, but not cortical, activation during reward anticipation. These findings support the hypothesis that youth with a propensity to sign-track are prone to externalizing tendencies, with an over-reliance on subcortical cue-reactive brain systems. This research highlights sign-tracking as a promising experimental approach delineating the behavioural and neural circuitry of individuals at risk for externalizing disorders.

neuroscience↗

bioRxiv2021

The pitfalls of using Gaussian Process Regression for normative modeling

Normative modeling, a group of methods used to quantify an individuals deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.

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↗

bioRxiv2021

Neural indicators of human gut feelings

Understanding the neural processes governing the human gut-brain connection has been challenging due to the inaccessibility of the bodys interior. Here, we investigated neural responses to gastrointestinal sensation using a minimally invasive mechanosensory probe by quantifying brain, stomach, and perceptual responses following the ingestion of a vibrating capsule. Participants successfully perceived capsule stimulation under two vibration conditions (normal and enhanced), as evidenced by above chance accuracy scores. Perceptual accuracy improved significantly during the enhanced relative to normal stimulation, which was associated with a more rapid detection of the stimulation and reduced reaction time variability. Stomach stimulation induced early and late neural responses in parieto-occipital leads near the midline. Moreover, these gastric evoked potentials showed intensity-dependent increases in amplitude and were significantly correlated with perceptual accuracy. These findings highlight a unique form of enterically-focused sensory monitoring within the human brain, with implications for understanding gut-brain interactions in healthy and clinical populations.

neuroscience↗

bioRxiv2020

Confirmatory evidence that healthy individuals can adaptively adjust prior expectations and interoceptive precision estimates

Theoretical proposals have previously been put forward regarding the computational basis of interoception. Following on this, we recently reported using an active inference approach to 1) quantitatively simulate interoceptive computation, and 2) fit the model to behavior on a cardiac awareness task. In the present work, we attempted to replicate our previous results in an independent group of healthy participants. We provide evidence confirming our previous finding that healthy individuals adaptively adjust prior expectations and interoceptive sensory precision estimates based on task context. This offers further support for the utility of computational approaches to characterizing the dynamics of interoceptive processing.

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

Dimensions of neighborhood tracts and their associations with mental health problems

ObjectiveNeighborhood characteristics can have profound effects on resident health. The aim of this study was to use an unsupervised learning approach to reduce the multi-dimensional assessment of a neighborhood using American Community Survey (ACS) data to simplify the assessment of neighborhood influence on health.\n\nMethodMultivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011-2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2017 release.\n\nResultsA five-factor model provided the best fit for the data and the latent factors quantified the following characteristics of the census tract: (1) affluence, (2) proportion of singletons in neighborhood, (3) proportion of African-Americans in neighborhood, (4) proportion of seniors in neighborhood, and (5) proportion of noncitizens in neighborhood. African-Americans (R2 = 0.67) in neighborhood and Affluence (R2 = 0.83) were strongly associated with poor mental health.\n\nConclusionsThese findings indicate the importance of this factor model in future research focused on the relationship between neighborhood characteristics and resident health.

neuroscience↗

bioRxiv2018

A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE.

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individuals imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: 1) extract image-related features, 2) build a model on a training set that uses those features to predict an individuals age, 3) validate the model on a test dataset, producing a predicted age for each individual, 4) define the \"Brain Age Gap Estimate\" (BrainAGE) as the difference between an individuals predicted age and his/her chronological age, and 5) estimate the relationship between BrainAGE and other variables of interest, and 6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to regression to the mean. The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training=475 healthy volunteers, aged 18 - 60 years (259 female); testing=489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18 - 56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.

bioinformatics↗