Biology subjects

Davidson, R. J.

Publications and source records attributed to Davidson, R. J..

14 recordsLinked to original sources

bioRxiv2025

Volumetric Differences of Thalamic Nuclei are Associated with Post-Trauma Psychopathology

Previous investigations of whole thalamus and thalamic nuclei volumes in post-trauma psychopathology have been sparse, limited in scope, and yielded inconsistent results. To address this, volumetric estimates of whole thalamus and thalamic nuclei were obtained from structural brain MRI scans from 2,058 participants across 20 worldwide sites in the ENIGMA PTSD working group. Thalamic volumes were compared between trauma-exposed participants with posttraumatic stress disorder (PTSD) (n=238), major depressive disorder (MDD) (n=184), comorbid PTSD+MDD (n=618), and trauma-exposed control participants (n=1,018). PTSD and MDD symptom severity, PTSD symptom clusters, and childhood trauma were similarly examined for associations with thalamic volume. Participants with PTSD only compared to controls had smaller thalamic nuclei volumes in sensorimotor nuclei, including the parafascicular (Pf), ventral anterior magnocellular (VAmc), medial pulvinar (PuM), and anterior pulvinar (PuA) nuclei of the thalamus. MDD only and comorbid PTSD+MDD participants exhibited smaller mediodorsal thalamus volumes compared to controls. Overall PTSD and MDD symptom severity negatively correlated with the volume of the mediodorsal thalamus. A significant interaction between PTSD and MDD severity was found, such that MDD severity was positively associated with thalamic volume only among individuals with high PTSD severity. Avoidance and hyperarousal symptoms of PTSD were positively associated with thalamic volume, while re- experiencing and negative mood/cognition symptoms were negatively associated with thalamic volume. Childhood physical and emotional abuse were positively and negatively associated with thalamic volume, respectively. Whole thalamus volume and volumes of the sensorimotor and limbic thalamus may play an important role in the development of PTSD and MDD in the aftermath of trauma exposure. The interaction between PTSD and MDD symptoms and contrasting effects across PTSD symptom clusters and types of childhood adversity suggests multiple neurobiological mechanisms are involved in shaping thalamic volume post-trauma.

neuroscience↗

bioRxiv2024

Connectome predictive modeling of trait mindfulness

IntroductionTrait mindfulness refers to ones disposition or tendency to pay attention to their experiences in the present moment, in a non-judgmental and accepting way. Trait mindfulness has been robustly associated with positive mental health outcomes, but its neural underpinnings are poorly understood. Prior resting-state fMRI studies have associated trait mindfulness with within- and between-network connectivity of the default-mode (DMN), fronto-parietal (FPN), and salience networks. However, it is unclear how generalizable the findings are, how they relate to different components of trait mindfulness, and how other networks and brain areas may be involved. MethodsTo address these gaps, we conducted the largest resting-state fMRI study of trait mindfulness to-date, consisting of a pre-registered connectome predictive modeling analysis in 367 adults across three samples collected at different sites. ResultsIn the model-training dataset, we did not find connections that predicted overall trait mindfulness, but we identified neural models of two mindfulness subscales, Acting with Awareness and Non-judging. Models included both positive networks (sets of pairwise connections that positively predicted mindfulness with increasing connectivity) and negative networks, which showed the inverse relationship. The Acting with Awareness and Non-judging positive network models showed distinct network representations involving FPN and DMN, respectively. The negative network models, which overlapped significantly across subscales, involved connections across the whole brain with prominent involvement of somatomotor, visual and DMN networks. Only the negative networks generalized to predict subscale scores out-of-sample, and not across both test datasets. Predictions from both models were also negatively correlated with predictions from a well-established mind-wandering connectome model. ConclusionsWe present preliminary neural evidence for a generalizable connectivity models of trait mindfulness based on specific affective and cognitive facets. However, the incomplete generalization of the models across all sites and scanners, limited stability of the models, as well as the substantial overlap between the models, underscores the difficulty of finding robust brain markers of mindfulness facets.

neuroscience↗

bioRxiv2024

Intracranial substrates of meditation-induced neuromodulation in amygdala and hippocampus

Meditation is an accessible mental practice associated with emotional regulation and well-being. Loving-kindness meditation (LKM), a specific sub-type of meditative practice, involves focusing ones attention on thoughts of well-being for oneself and others. Meditation has been proven to be beneficial in a variety of settings, including therapeutical applications, but the neural activity underlying meditative practices and their positive effects are not well understood. In particular, its been difficult to understand the contribution of deep limbic structures given the difficulty of studying neural activity directly in the human brain. Here, we leverage a unique patient population, epilepsy patients chronically implanted with responsive neurostimulation device that allow chronic, invasive electrophysiology recording to investigate the physiological correlates of loving-kindness meditation in the amygdala and hippocampus of novice meditators. We find that LKM-associated changes in physiological activity specific to periodic, but not aperiodic, features of neural activity. LKM was associated with an increase in {gamma} (30-55 Hz) power and an alternation in the duration of {beta} (13-30 Hz) and {gamma} oscillatory bursts in both the amygdala and hippocampus, two regions associated with mood disorders. These findings reveals the nature of LKM-induced modulation of limbic activity in first-time meditators. SIGNIFICANCE STATEMENTWe leverage rare chronic, invasive electrophysiology recordings while participants engage in loving-kindness meditation to demonstrate that meditation induces neural changes in beta and {gamma} activity in the amygdala and hippocampus of novice meditators. These results build on previous findings in experienced meditators and reveal meditations potential for noninvasive neuromodulation of neural mechanisms associated with emotional regulation and mood disorders.

neuroscience↗

bioRxiv2024

multimedia: Multimodal Mediation Analysis of Microbiome Data

Mediation analysis has emerged as a versatile tool for answering mechanistic questions in microbiome research because it provides a statistical framework for attributing treatment effects to alternative causal pathways. Using a series of linked regressions, this analysis quantifies how complementary data relate to one another and respond to treatments. Despite these advances, existing softwares rigid assumptions often result in users viewing mediation analysis as a black box. We designed the multimedia R package to make advanced mediation analysis techniques accessible, ensuring that statistical components are interpretable and adaptable. The package provides a uniform interface to direct and indirect effect estimation, synthetic null hypothesis testing, bootstrap confidence interval construction, and sensitivity analysis, enabling experimentation with various mediator and outcome models while maintaining a simple overall workflow. The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. We illustrate the package through two case studies. The first re-analyzes a study of the microbiome and metabolome of Inflammatory Bowel Disease patients, uncovering potential mechanistic interactions between the microbiome and disease-associated metabolites, not found in the original study. The second analyzes new data about the influence of mindfulness practice on the microbiome. The mediation analysis highlights shifts in taxa previously associated with depression that cannot be explained indirectly by diet or sleep behaviors alone. A gallery of examples and further documentation can be found at https://go.wisc.edu/830110. IMPORTANCEMicrobiome studies routinely gather complementary data to capture different aspects of a microbiomes response to a change, such as the introduction of a therapeutic. Mediation analysis clarifies the extent to which responses occur sequentially via mediators, thereby supporting causal, rather than purely descriptive, interpretation. multimedia is a modular R package with close ties to the wider microbiome software ecosystem that makes statistically rigorous, flexible mediation analysis easily accessible, setting the stage for precise and causally informed microbiome engineering.

bioinformatics↗

bioRxiv2023

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual alpha peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of alpha oscillations and the relative balance of excitatory:inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection timepoints. Post-hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging. Significance StatementMeasures of intrinsic brain function at rest assessed noninvasively from the scalp using electroencephalography (EEG) show promise as predictors of cognitive decline in humans. Using data from 235 participants from the Midlife in the United States (MIDUS) longitudinal study, we found two resting EEG markers (individual peak alpha frequency and aperiodic exponent) interacted to predict cognitive decline over a span of 10 years. Follow-up analyses revealed that "mismatched" markers (i.e., high in one and low in the other) predicted greater cognitive decline compared to "matching" markers. Because of the low cost and ease of collecting EEG data at rest, the current research provides evidence for possible scalable clinical applications for identifying individuals at risk for accelerated cognitive decline.

neuroscience↗

bioRxiv2022

Multimodal Imaging-Based Classification of PTSD Using Data-Driven Computational Approaches: A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BackgroundCurrent clinical assessments of Posttraumatic stress disorder (PTSD) rely solely on subjective symptoms and experiences reported by the patient, rather than objective biomarkers of the illness. Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. Here we aimed to classify individuals with PTSD versus controls using heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. MethodsWe analyzed brain MRI data from 3,527 structural-MRI; 2,502 resting state-fMRI; and 1,953 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls (TEHC and HC) using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. ResultsWe found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history across all three modalities (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. ConclusionOur findings highlight the promise offered by machine learning methods for the diagnosis of patients with PTSD. The utility of brain biomarkers across three MRI modalities and the contribution of DVAE models for improving generalizability offers new insights into neural mechanisms involved in PTSD. Significance Classifying PTSD from trauma-unexposed healthy controls (HC) using three imaging modalities performed well ([~]75% AUC), but performance suffered markedly when classifying PTSD from trauma-exposed healthy controls (TEHC) using three imaging modalities ([~]60% AUC). Using deep learning for feature reduction (denoising variational auto-encoder; DVAE) dramatically reduced the number of features with no concomitant performance degradation. Utilizing denoising variational autoencoder (DVAE) models improves generalizability across heterogeneous multi-site data compared with the traditional machine learning frameworks

neuroscience↗

bioRxiv2022

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies. Author summaryThe ability to detect and adapt to changes in the environment is an essential feature for survival of living beings. Two main theories have been proposed to explain how the brain performs such an automatic task in the auditory domain. The first one, adaptation, emphasizes the ability of auditory cortical and sub-cortical neurons to attenuate their response to repeated stimuli, which renders the brain more sensitive to deviations from expected sensory inputs. The second one, Bayesian learning, further involves higher-level cortical regions which would update their predictions about incoming stimuli, depending on their performance at predicting previous ones. These two views may not be mutually exclusive, but few experimental works compared them directly. We used computational models inspired from both accounts to assess which view may provide a better fit of two independent electrophysiological datasets from similar auditory experiments. Evidence from a large sample of 82 human subjects provided a complex picture, with adaptation processes seemingly dominating the early phase of auditory brain response, and Bayesian learning processes appearing later on. Our results converge with other recent works in animals and points to the necessary reconciliation of those two theories for a better understanding of auditory perception and statistical learning.

neuroscience↗

bioRxiv2021

Long-term meditation training is associated with enhanced attention and stronger posterior cingulate - rostrolateral prefrontal cortex resting connectivity

Mindfulness meditation has been shown to increase resting state functional connectivity (rsFC) between the posterior cingulate cortex (PCC) and dorsolateral prefrontal cortex (DLPFC), which is thought to reflect improvements in attention to the present moment. However, prior research in long-term meditation practitioners lacked quantitative measures of attention that would provide a more direct behavioral correlate and interpretational anchor for PCC-DLPFC connectivity and was inherently limited by small sample sizes. Moreover, whether mindfulness meditation primarily impacts brain function locally, or impacts the dynamics of large-scale brain networks, remained unclear. Here, we sought to replicate and extend prior findings of increased PCC - DLPFC rsFC in a sample of 40 long-term meditators (average practice= 3759 hours) who also completed a behavioral assay of attention. In addition, we tested a network-based framework of changes in inter-regional connectivity by examining network-level connectivity. We found that meditators had stronger PCC-rostrolateral prefrontal cortex (PFC) rsFC, lower connector hub strength across the default mode network (DMN) relative to other functional networks, and better attention to task, compared to 124 meditation-naive controls. Orienting attention positively correlated with PCC- rostrolateral PFC connectivity, and negatively correlated with DMN connector hub strength. These findings provide novel evidence that posterior cingulate - rostrolateral PFC rsFC may support attention orienting, consistent with a role for rostrolateral PFC in meta-cognitive background awareness that is a core component of mindfulness meditation training. Our results further demonstrate that long-term mindfulness meditation may improve attention and strengthen the underlying brain networks.

neuroscience↗

bioRxiv2019

Perceptual metacognition of human faces is causally supported by function of the lateral prefrontal cortex

Metacognitive visual awareness--the ability to know that one is having a particular visual experience--is thought to optimally guide behavior and help us accurately navigate our complex social environments. Yet the neural underpinnings of visual metacognition continue to be the subject of vigorous debate: While prior work identified correlations between perceptual metacognitive ability and the structure and function of lateral prefrontal cortex (LPFC), evidence for a causal role of this region in promoting metacognition is controversial--in fact, some question whether the prefrontal cortex plays any role in supporting conscious visual experiences. Moreover, whether LPFC function promotes metacognitive awareness of perceptual and emotional features of complex, yet ubiquitous socio-emotional face stimuli is unknown. Using model-based analyses following a causal intervention to LPFC in humans, we demonstrate that LPFC promotes metacognitive awareness of the orientation of emotional faces--although not of their emotional expressions. Effects were specific to metacognition, as LPFC perturbation did not alter stimulus discrimination performance. Collectively, these data support the causal involvement of the prefrontal cortex in metacognitive awareness, and indicate that the role of LPFC function in metacognition encompasses perceptual experiences of naturalistic social stimuli.

neuroscience↗

bioRxiv2019

Hippocampal subfield volumes are uniquely affected in PTSD and depression: International analysis of 31 cohorts from the PGC-ENIGMA PTSD Working Group

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.\n\nMethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115; Mage=38.9{+/-}13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.\n\nResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail ({beta}=-0.13) and CA1 ({beta}=-0.09) after adjusting for covariates and multiple testing (adjusted ps (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 ({beta}=-0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls ({beta}=-0.12, q=0.012), C-PTSD-only ({beta}=-0.17, q=0.001), and Depression-only ({beta}=-0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.\n\nConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

neuroscience↗

bioRxiv2019

Optimizing the intrinsic parallel diffusivity in NODDI: an extensive empirical evaluation

PurposeNODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d||) is set to 1.7 {micro}m2*ms-1, though the effects of this assumption have not been extensively explored. This work seeks to optimize d|| by minimizing the model residuals.\n\nMethodsThe model residuals were evaluated in function of d|| over the range from 0.5 to 3.0 {micro}m2*ms-1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d|| was also explored.\n\nResultsResults show the optimum d|| is significantly lower for gray matter relative to 1.7 {micro}m2*ms-1 and to white matter. Infants showed significantly decreased optimum d|| in gray and white matter. Minor optimum d|| differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d|| resulted in significant changes to the estimated NODDI parameters.\n\nConclusionFuture implementations of NODDI would benefit from d|| optimization, particularly when investigating young populations and/or gray matter.

biophysics↗

bioRxiv2018

A common neural substrate for elevated PTSD symptoms and reduced pulse rate variability in combat-exposed veterans

BackgroundPrevious studies have identified reduced heart rate variability (HRV) in posttraumatic stress disorder (PTSD), which may temporally precede the onset of the disorder. A separate line of functional neuroimaging research suggests that the ventromedial prefrontal cortex (vmPFC) -- a key aspect of a descending neuromodulatory system that exerts inhibitory control over heart rate -- shows functional and structural abnormalities in PTSD. No research to date, however, has simultaneously investigated whether altered vmPFC activation is associated with reduced HRV and elevated PTSD symptoms in the same individuals.\n\nMethodsWe collected fMRI data during alternating conditions of threat of shock and safety from shock in 51 male, combat-exposed veterans with either high or low levels of PTSD symptoms. Pulse rate variability (PRV) - an HRV surrogate calculated from pulse oximetry - was assessed during a subsequent resting scan. Correlational analyses tested for hypothesized relationships between vmPFC activation, PRV, and distinct dimensions of PTSD symptomatology.\n\nResultsRe-experiencing PTSD symptoms were inversely associated with high-frequency (HF)-PRV, thought to primarily reflect parasympathetic control of heart rate, in veterans with elevated PTSD symptoms. Lower HF-PRV was associated with reduced vmPFC activation for the contrast of safety-threat in a region that also showed an inverse relationship with re-experiencing symptoms.\n\nConclusionsReduced vmPFC responses to safety vs. threat were associated with both reduced HF-PRV and increased re-experiencing symptoms. These results tie together previous observations of reduced HRV/PRV and impaired vmPFC function in PTSD and call for further research on reciprocal brain-body relationships in understanding PTSD pathophysiology.

neuroscience↗

bioRxiv2018

Perceived threat bias and reduced hippocampal volume in combat veterans

BackgroundReduced hippocampal volume is frequently observed in posttraumatic stress disorder (PTSD), but the exact psychological processes associated with these alterations remain unclear. Given the role of the hippocampus in contextual representations of threat and memory, we investigated relationships between retrospectively reported combat exposure, perceived threat, and hippocampal volume in trauma-exposed veterans.\n\nMethodsT1-weighted anatomical MRI scans were obtained from 52 male veterans with a broad range of PTSD symptoms. Hippocampal volume was estimated using automatic segmentation tools in FreeSurfer. An index of perceived threat bias was calculated, reflecting the degree of discordance between subjective perceptions of threat while deployed and self-reported combat exposure. Hippocampal volume was regressed on perceived threat bias and PTSD symptoms on the Clinician-Administered PTSD Scale (CAPS).\n\nResultsPerceived threat bias was unrelated to overall CAPS symptoms, but was positively correlated with CAPS avoidance/numbing symptoms and symptoms of depression, anxiety, and worry. The degree of perceived threat bias was inversely correlated with hippocampal volume. Hippocampal volume was also inversely related to avoidance/numbing CAPS symptoms.\n\nConclusionsThese results indicate that volume of the hippocampus, a region involved in contextual threat processing and memory, is related to recalled associations between traumatic events and accompanying subjective threat appraisals. Future research should clarify the precise temporal milieu of these effects and investigate whether individual differences in hippocampal structure and function contribute to exaggerated threat appraisal at the time of trauma, or in subsequently biased memories or appraisals of traumatic events.

neuroscience↗

bioRxiv2017

Behavioral and neural indices of affective coloring for neutral social stimuli

Emotional processing often continues beyond the presentation of emotionally evocative stimuli, which can result in affective biasing or coloring of subsequently encountered events. Here, we describe neural correlates of affective coloring and examine how individual differences in affective style impact the magnitude of affective coloring. We conducted functional magnetic resonance imaging in 117 adults who passively viewed negative, neutral, and positive pictures presented 2s prior to neutral faces. Brain responses to neutral faces were modulated by the valence of preceding pictures, with greater activation for faces following negative (vs. positive) pictures in the amygdala, dorsomedial and lateral prefrontal cortex, ventral visual cortices, posterior superior temporal sulcus, and angular gyrus. Three days after the MRI scan, participants rated their memory and liking of previously encountered neutral faces. Individuals higher in trait positive affect and emotional reappraisal rated faces as more likable when preceded by emotionally arousing (negative or positive) pictures. Additionally, greater amygdala responses to neutral faces preceded by positively valenced pictures were associated with greater memory for these faces three days later. Collectively, these results reveal individual differences in how emotions spill over onto the processing of unrelated social stimuli, resulting in persistent and affectively biased evaluations of such stimuli.

neuroscience↗