Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis.

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Relationship between central autonomic effective connectivity and heart rate variability: A Resting-state fMRI dynamic causal modeling study.

The central autonomic network (CAN) serves as a regulatory hub with top-down regulatory control and integration of bottom-up physiological feedback via the autonomic nervous system. Heart rate variability (HRV)-the time variance of the heart's beat-to-beat intervals-is an index of the CAN's affective and behavioral regulatory capacity. Although neural functional connectivities that are associated with HRV and CAN have been well studied, no published report to date has studied effective (directional) connectivities (EC) that are associated with HRV and CAN. Better understanding of neural EC in the brain has the potential to improve our understanding of how the CAN sub-regions regulate HRV. To begin to address this knowledge gap, we employed resting-state functional magnetic resonance imaging and dynamic causal modeling (DCM) with parametric empirical Bayes analyses in 34 healthy adults (19 females; mean age= 32.68 years [SD= 14.09], age range 18-68 years) to examine the bottom-up and top-down neural circuits associated with HRV. Throughout the whole brain, we identified 12 regions associated with HRV. DCM analyses revealed that the ECs from the right amygdala to the anterior cingulate cortex and to the ventrolateral prefrontal cortex had a negative linear relationship with HRV and a positive linear relationship with heart rate. These findings suggest that ECs from the amygdala to the prefrontal cortex may represent a neural circuit associated with regulation of cardiodynamics.

Inside the mindful moment: The effects of brief mindfulness practice on large-scale network organization and intimate partner aggression.

Mindfulness can produce neuroplastic changes that support adaptive cognitive and emotional functioning. Recently interest in single-exercise mindfulness instruction has grown considerably because of the advent of mobile health technology. Accordingly, the current study sought to extend neural models of mindfulness by investigating transient states of mindfulness during single-dose exposure to focused attention meditation. Specifically, we examined the ability of a brief mindfulness induction to attenuate intimate partner aggression via adaptive changes to intrinsic functional brain networks. We employed a dual-regression approach to examine a large-scale functional network organization in 50 intimate partner dyads (total n = 100) while they received either mindfulness (n = 50) or relaxation (n = 50) instruction. Mindfulness instruction reduced coherence within the Default Mode Network and increased functional connectivity within the Frontoparietal Control and Salience Networks. Additionally, mindfulness decoupled primary visual and attention-linked networks. Yet, this induction was unable to elicit changes in subsequent intimate partner aggression, and such aggression was broadly unassociated with any of our network indices. These findings suggest that minimal doses of focused attention-based mindfulness can promote transient changes in large-scale brain networks that have uncertain implications for aggressive behavior.

Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging.

Geostatistical modeling for continuous point-referenced data has extensively been applied to neuroimaging because it produces efficient and valid statistical inference. However, diffusion tensor imaging (DTI), a neuroimaging technique characterizing the brain's anatomical structure, produces a positive-definite (p.d.) matrix for each voxel. Currently, only a few geostatistical models for p.d. matrices have been proposed because introducing spatial dependence among p.d. matrices properly is challenging. In this paper, we use the spatial Wishart process, a spatial stochastic process (random field), where each p.d. matrix-variate random variable marginally follows a Wishart distribution, and spatial dependence between random matrices is induced by latent Gaussian processes. This process is valid on an uncountable collection of spatial locations and is almost-surely continuous, leading to a reasonable way of modeling spatial dependence. Motivated by a DTI data set of cocaine users, we propose a spatial matrix-variate regression model based on the spatial Wishart process. A problematic issue is that the spatial Wishart process has no closed-form density function. Hence, we propose an approximation method to obtain a feasible Cholesky decomposition model, which we show to be asymptotically equivalent to the spatial Wishart process model. A local likelihood approximation method is also applied to achieve fast computation. The simulation studies and real data application demonstrate that the Cholesky decomposition process model produces reliable inference and improved performance, compared to other methods.

Altered effective connectivity of the reward network during an incentive-processing task in adults with alcohol use disorder.

BACKGROUND: Abnormalities of reward sensitivity and impulsivity are known to be correlated with each other and alcohol use disorder (AUD) risk, but the underlying aberrant neural circuitry involved is not clearly defined. We sought to extend the current knowledge of AUD pathophysiology by studying incentive processing in persons with AUD using functional neuroimaging data. METHODS: We utilized functional MRI data from the Human Connectome Project Database obtained during performance of a number-guessing incentive-processing task with win, loss, and neutral feedback conditions in 78 participants with either DSM-IV alcohol abuse or dependence (combined as the AUD group) and 78 age- and sex-matched control (CON) participants. Within a network consisting of anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), insula, ventral striatum, and dorsal striatum (DS) in the right hemisphere, we performed dynamic causal modeling analysis to test group-level differences (AUD vs. CON) in effective directional connectivity (EC) as modulated by "win" and "loss" conditions. We used linear regression analyses to characterize the relations between each EC outcome and measures of cumulative alcohol exposure and impulsivity. RESULTS: During wins, AUD participants had lower ECs from ACC to the other four nodes, greater ECs from insula to the other four nodes, greater ECs from DLPFC to the other four nodes, and greater DS to DS self-connection EC than CON participants. In the total sample, EC from the insula to the DLPFC (insula â†’ DLPFC) during wins was positively correlated with both impulsivity (as measured by the delay-discounting task) and cumulative alcohol exposure. The DS to DS self-connection EC during wins was positively correlated with impulsivity. Many of the altered ECs from the ACC and insula to other nodes were correlated with cumulative alcohol exposure. CONCLUSIONS: Individuals with AUD have disrupted EC in both instrumentally driven and automatized corticostriatal reward circuits during non-alcohol reward feedback. These results point to disrupted corticostriatal EC in both "top-down" and "bottom-up" pathways among individuals with AUD.

Cannabis Use as a Risk Factor for Takotsubo (Stress) Cardiomyopathy: Exploring the Evidence from Brain-Heart Link.

PURPOSE OF REVIEW: Recently, an association between cannabis use and Takotsubo (stress) cardiomyopathy (TTC) has been shown. With the current trend of legalization of cannabis, it is important to understand brain effects of cannabis use that could lead to cardiac disease, such as TTC. Here we review recent brain imaging studies in order to search for the evidence supporting the association between cannabis use, stress, and TTC. RECENT FINDINGS: There exist brain imaging studies showing similar findings across TTC, stress, and cannabis use. These similar findings are mainly centered on a key central autonomic network region amygdala, i.e., amygdala hyperactivity/hyperconnectivity when exposed to challenge, stress, or negative stimuli. This similarity supports a close association among cannabis use, stress, and TTC. Amygdala-centered neuronal circuits could underlie cannabis use as risk factor to TTC. Based on the findings, several directions for future studies are proposed.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence.

Neuroimaging and genetic studies provide distinct and complementary information about the structural and biological aspects of a disease. Integrating the two sources of data facilitates the investigation of the links between genetic variability and brain mechanisms among different individuals for various medical disorders. This article presents a general statistical framework for integrative Bayesian analysis of neuroimaging-genetic (iBANG) data, which is motivated by a neuroimaging-genetic study in cocaine dependence. Statistical inference necessitated the integration of spatially dependent voxel-level measurements with various patient-level genetic and demographic characteristics under an appropriate probability model to account for the multiple inherent sources of variation. Our framework uses Bayesian model averaging to integrate genetic information into the analysis of voxel-wise neuroimaging data, accounting for spatial correlations in the voxels. Using multiplicity controls based on the false discovery rate, we delineate voxels associated with genetic and demographic features that may impact diffusion as measured by fractional anisotropy (FA) obtained from DTI images. We demonstrate the benefits of accounting for model uncertainties in both model fit and prediction. Our results suggest that cocaine consumption is associated with FA reduction in most white matter regions of interest in the brain. Additionally, gene polymorphisms associated with GABAergic, serotonergic and dopaminergic neurotransmitters and receptors were associated with FA.

Prevalence of traumatic brain injury in cocaine-dependent research volunteers.

BACKGROUND: There is a high prevalence of traumatic brain injury (TBI) among those with substance dependence. However, TBI often remains undiagnosed in these individuals, due to lack of routine screening in substance use treatment settings or due to overlap in some of the cognitive sequelae (eg impulsivity, disinhibition) of TBI and cocaine dependence. METHODS: The prevalence of self-reported mild to moderate TBI in a group of cocaine-dependent (n = 95) and a group of healthy volunteers (n = 75) enrolled at the same facility was assessed. Additionally, the relationship between TBI and clinically relevant correlates, including impulsivity, cocaine use history, and treatment outcome in the cocaine-dependent group was also examined. RESULTS: A higher proportion of individuals with cocaine dependence (29.5%) reported having suffered a TBI in their lifetime compared to controls (8%) on a Closed Head Injury scale. Among cocaine users, the average age of sustaining TBI was significantly lower than the age of initiating cocaine use. Presence of TBI was not associated with higher impulsivity on the Barratt Impulsiveness Scale-11 or self-reported years of cocaine use. No differences were noted on treatment outcome for cocaine dependence as measured by treatment effectiveness scores (TES) between cocaine users with TBI and their non-TBI counterparts. CONCLUSIONS: These results are the first to highlight the high prevalence of TBI among individuals with cocaine dependence. This study underscores the possible role of TBI history as a risk factor for onset of cocaine use, however, more research is needed to determine the impact of co-morbid TBI as a complicating factor in the substance abuse treatment setting.

Stochastic dynamic causal modeling of working memory connections in cocaine dependence.

Although reduced working memory brain activation has been reported in several brain regions of cocaine-dependent subjects compared with controls, very little is known about whether there is altered connectivity of working memory pathways in cocaine dependence. This study addresses this issue by using functional magnetic resonance imaging-based stochastic dynamic causal modeling (DCM) analysis to study the effective connectivity of 19 cocaine-dependent subjects and 14 healthy controls while performing a working memory task. Stochastic DCM is an advanced method that has recently been implemented in SPM8 that can obtain improved estimates, relative to deterministic DCM, of hidden neuronal causes before convolution with the hemodynamic response. Thus, stochastic DCM may be less influenced by the confounding effects of variations in blood oxygen level-dependent response caused by disease or drugs. Based on the significant regional activation common to both groups and consistent with previous working memory activation studies, seven regions of interest were chosen as nodes for DCM analyses. Bayesian family level inference, Bayesian model selection analyses, and Bayesian model averaging (BMA) were conducted. BMA showed that the cocaine-dependent subjects had large differences compared with the control subjects in the strengths of prefrontal-striatal modulatory (B matrix) DCM parameters. These findings are consistent with altered cortical-striatal networks that may be related to reduced dopamine function in cocaine dependence. As far as we are aware, this is the first between-group DCM study using stochastic methodology.

Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals.

Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests temporal characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to functional connectivity analysis. However, existing studies of OUD analyze BOLD signals using measures computed across all time points. This study, for the first time in the literature, employs data-driven machine learning (ML) modeling of rs-fMRI BOLD features representing multiple time points to identify region(s) of interest that differentiate OUD subjects from healthy controls (HC). Following the triple network model, we obtain rs-fMRI BOLD features from the default mode network (DMN), salience network (SN), and executive control network (ECN) for 31 OUD and 45 HC subjects. Then, we use the Boruta ML algorithm to identify statistically significant BOLD features that differentiate OUD from HC, identifying the DMN as the most salient functional network for OUD. Furthermore, we conduct brain activity mapping, showing heightened neural activity within the DMN for OUD. We perform 5-fold cross-validation classification (OUD vs. HC) experiments to study the discriminative power of functional network features with and without fusing demographic features. The DMN shows the most discriminative power, achieving mean AUC and F1 scores of 80.91% and 73.97%, respectively, when fusing BOLD and demographic features. Follow-up Boruta analysis using BOLD features extracted from the medial prefrontal cortex, posterior cingulate cortex, and left and right temporoparietal junctions reveals significant features for all four functional hubs within the DMN.

Executive control network resting state fMRI functional and effective connectivity and delay discounting in cocaine dependent subjects compared to healthy controls.

Resting state functional magnetic resonance imaging (fMRI) has been used to study functional connectivity of brain networks in addictions. However, most studies to-date have focused on the default mode network (DMN) with fewer studies assessing the executive control network (ECN) and salience network (SN), despite well-documented cognitive executive behavioral deficits in addictions. The present study assessed the functional and effective connectivity of the ECN, DMN, and SN in cocaine dependent subjects (CD) (n = 22) compared to healthy control subjects (HC) (n = 22) matched on age and education. This study also investigated the relationship between impulsivity measured by delay discounting and functional and effective connectivity of the ECN, DMN, and SN. The Left ECN (LECN), Right ECN (RECN), DMN, and SN functional networks were identified using FSL MELODIC independent component analysis. Functional connectivity differences between CD and HC were assessed using FSL Dual Regression analysis and FSLNets. Effective connectivity differences between CD and HC were measured using the Parametric Empirical Bayes module of Dynamic Causal Modeling. The relationship between delay discounting and functional and effective connectivity were examined using regression analyses. Dynamic causal modeling (DCM) analysis showed strong evidence (posterior probability > 0.95) for CD to have greater effective connectivity than HC in the RECN to LECN pathway when tobacco use was included as a factor in the model. DCM analysis showed strong evidence for a positive association between delay discounting and effective connectivity for the RECN to LECN pathway and for the DMN to DMN self-connection. There was strong evidence for a negative association between delay discounting and effective connectivity for the DMN to RECN pathway and for the SN to DMN pathway. Results also showed strong evidence for a negative association between delay discounting and effective connectivity for the RECN to SN pathway in CD but a positive association in HC. These novel findings provide preliminary support that RECN effective connectivity may differ between CD and HC after controlling for tobacco use. RECN effective connectivity may also relate to tobacco use and impulsivity as measured by delay discounting.

Chronic Cocaine Use and White Matter Coherence: A Diffusion Tensor Imaging Study.

OBJECTIVE: Chronic substance use and its effects on brain function and structure has long been of interest to clinicians and researchers. Prior cross-sectional comparisons of diffusion tensor imaging (DTI) metrics have suggested deleterious effects of chronic substance use (i.e., cocaine use) on white matter coherence. However, it is unclear how these effects may replicate across geographic regions when examined with similar technologies. In this study, we sought to conduct a replication of previous work in this area and determine whether there are any patterns of persistent differences in white matter microstructure between individuals with a history of cocaine use disorder (CocUD, according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition) and healthy controls. METHOD: A total of 46 participants (21 healthy controls, 25 chronic cocaine users) were recruited from the Richmond, Virginia metropolitan area. Information regarding past and current substance use was collected from all participants. Participants also completed structural and DTI scans. RESULTS: Consistent with previous DTI studies, significant differences were found between fractional anisotropy (FA) and axial diffusivity (AD) CocUD and controls, with CocUD showing lower FA and AD in the right inferior and superior longitudinal fasciculus, the genu, body, and splenium of the corpus callosum, and the anterior, posterior, and superior corona radiata, among several other regions. These differences were not significant for other diffusivity metrics. Lifetime alcohol consumption was greater in the CocUD group, but lifetime alcohol consumption did not show a significant linear relationship with any of the DTI metrics in within-group regression analyses. CONCLUSIONS: These data align with previously reported declines in white matter coherence in chronic cocaine users. However, it is less clear whether comorbid alcohol consumption results in an additive deleterious effect on white matter microstructure.

Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling.

Previous neuroimaging studies have shown that working memory load has marked effects on regional neural activation. However, the mechanism through which working memory load modulates brain connectivity is still unclear. In this study, this issue was addressed using dynamic causal modeling (DCM) based on functional magnetic resonance imaging (fMRI) data. Eighteen normal healthy subjects were scanned while they performed a working memory task with variable memory load, as parameterized by two levels of memory delay and three levels of digit load (number of digits presented in each visual stimulus). Eight regions of interest, i.e., bilateral middle frontal gyrus (MFG), anterior cingulate cortex (ACC), inferior frontal cortex (IFC), and posterior parietal cortex (PPC), were chosen for DCM analyses. Analysis of the behavioral data during the fMRI scan revealed that accuracy decreased as digit load increased. Bayesian inference on model structure indicated that a bilinear DCM in which memory delay was the driving input to bilateral PPC and in which digit load modulated several parieto-frontal connections was the optimal model. Analysis of model parameters showed that higher digit load enhanced connection from L PPC to L IFC, and lower digit load inhibited connection from R PPC to L ACC. These findings suggest that working memory load modulates brain connectivity in a parieto-frontal network, and may reflect altered neuronal processes, e.g., information processing or error monitoring, with the change in working memory load. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc.

A serotonergic biobehavioral signature differentiates cocaine use disorder participants administered mirtazapine.

Cocaine use disorder (CUD) patients display heterogenous symptoms and unforeseeable responses to available treatment approaches, highlighting the need to identify objective, accessible biobehavioral signatures to predict clinical trial success in this population. In the present experiments, we employed a task-based behavioral and pharmacogenetic-fMRI approach to address this gap. Craving, an intense desire to take cocaine, can be evoked by exposure to cocaine-associated stimuli which can trigger relapse during attempted recovery. Attentional bias towards cocaine-associated words is linked to enhanced effective connectivity (EC) from the anterior cingulate cortex (ACC) to hippocampus in CUD participants, an observation which was replicated in a new cohort of participants in the present studies. Serotonin regulates attentional bias to cocaine and the serotonergic antagonist mirtazapine decreased activated EC associated with attentional bias, with greater effectiveness in those CUD participants carrying the wild-type 5-HT2CR gene relative to a 5-HT2CR single nucleotide polymorphism (rs6318). These data suggest that the wild-type 5-HT2CR is necessary for the efficacy of mirtazapine to decrease activated EC in CUD participants and that mirtazapine may serve as an abstinence enhancer to mitigate brain substrates of craving in response to cocaine-associated stimuli in participants with this pharmacogenetic descriptor. These results are distinctive in outlining a richer "fingerprint" of the complex neurocircuitry, behavior and pharmacogenetics profile of CUD participants which may provide insight into success of future medications development projects.

Changes occur in resting state network of motor system during 4 weeks of motor skill learning.

We tested whether the resting state functional connectivity of the motor system changed during 4 weeks of motor skill learning using functional magnetic resonance imaging (fMRI). Ten healthy volunteers learned to produce a sequential finger movement by daily practice of the task over a 4 week period. Changes in the resting state motor network were examined before training (Week 0), two weeks after the onset of training (Week 2), and immediately at the end of the training (Week 4). The resting state motor system was analyzed using group independent component analysis (ICA). Statistical Parametric Mapping (SPM) second-level analysis was conducted on independent z-maps generated by the group ICA. Three regions, namely right postcentral gyrus, and bilateral supramarginal gyri were found to be sensitive to the training duration. Specifically, the strength of resting state functional connectivity in the right postcentral gyrus and right supramarginal gyrus increased from Week 0 to Week 2, during which the behavioral performance improved significantly, and decreased from Week 2 to Week 4, during which there was no more significant improvement in behavioral performance. The strength of resting state functional connectivity in left supramarginal gyrus increased throughout the training. These results confirm changes in the resting state network during slow-learning stage of motor skill learning, and support the premise that the resting state networks play a role in improving performance.

Exploring the relationship between white matter integrity, cocaine use and GAD polymorphisms using Bayesian Model Averaging.

Past investigations utilizing diffusion tensor imaging (DTI) have demonstrated that cocaine use disorder (CUD) yields white matter changes, primarily in the corpus callosum. By applying Bayesian model averaging using multiple linear regression in DTI, we demonstrate there may exist relationships between the impaired white matter and glutamic acid decarboxylase (GAD) polymorphisms. This work explored the two-way and three-way interactions between GAD1a (SNP: rs1978340) and GAD1b (SNP: rs769390) polymorphisms and years of cocaine use (YCU). GAD1a was associated with more frontal white matter changes on its own but GAD1b was associated with more midbrain and cerebellar changes as well as a greater increase in white matter changes in the context of chronic cocaine use. The three-way interaction GAD1a|GAD1b|YCU appeared to be roughly an average of the polymorphism two-way interactions GAD1a|YCU and GAD1b|YCU. The three-way interaction demonstrated multiple regions including corpus callosum which featured fewer significant voxel changes, perhaps suggesting a small protective effect of having both polymorphisms on corpus callosum and cerebellar peduncle.

A Spatio-temporal Model for Detecting the Effect of Cocaine Use Disorder on Functional Connectivity.

Drug addiction can lead to many health-related problems and social concerns. Researchers are interested in the association between long-term drug usage and abnormal functional connectivity. Functional connectivity obtained from functional magnetic resonance imaging data promotes a variety of fundamental understandings in such association. Due to the complex correlation structure and large dimensionality, the modeling and analysis of the functional connectivity from neuroimage are challenging. By proposing a spatio-temporal model for multi-subject neuroimage data, we incorporate voxel-level spatio-temporal dependencies of whole-brain measurements to improve the accuracy of statistical inference. To tackle large-scale spatio-temporal neuroimage data, we develop a computational efficient algorithm to estimate the parameters. Our method is used to first identify functional connectivity, and then detect the effect of cocaine use disorder (CUD) on functional connectivity between different brain regions. The functional connectivity identified by our spatio-temporal model matches existing studies on brain networks, and further indicates that CUD may alter the functional connectivity in the medial orbitofrontal cortex subregions and the supplementary motor areas.

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature.

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

Preliminary Findings of Weaker Executive Control Network Resting State fMRI Functional Connectivity in Opioid Use Disorder compared to Healthy Controls.

Objective: Resting state functional magnetic resonance imaging (fMRI) functional connectivity has been used as a tool to study brain mechanisms associated with addictions. Recent research in substance use disorders has focused on three brain networks termed the default mode network (DMN), salience network (SN), and executive control network (ECN). The purpose of this study was to examine the functional connectivity of those three networks in opioid use disorder (OUD) subjects compared to healthy control subjects (HC). Methods: The present study investigated functional connectivity differences between OUD subjects compared to HC using independent component analysis. This study also examined the relationship between functional connectivity and negative urgency scores, as well as compared the functional connectivity of severe OUD to mild or moderate OUD. Results: In OUD subjects (n=25) compared to HC (n=25), a cluster in the left dorsolateral prefrontal cortex within the left ECN had significantly weaker functional connectivity. No significant differences were found between groups for the functional connectivity of the DMN, SN, or right ECN. No significant associations were found between functional connectivity and negative urgency, and no differences were found between severe OUD and mild or moderate OUD. Conclusion: These novel preliminary results suggest that ECN functional connectivity may differ between OUD and HC. This finding is consistent with previous research showing altered executive function in OUD and supports further examination of ECN functional connectivity in association with treatment response in OUD. Given our relatively small sample size (50 subjects total; 25 subjects per group), our results should be treated as preliminary for hypothesis generation, and replication will be needed in future studies.

Resting-State Directional Connectivity and Anxiety and Depression Symptoms in Adult Cannabis Users.

BACKGROUND: Anxiety and depression symptoms are common among cannabis users and could be a risk factor for cannabis use (CU) disorder. Thus, it is critical to understand the neuronal circuits underlying the associations between CU and these symptoms. Alterations in resting-state functional connectivity within and/or between the default mode network and salience network have been reported in CU, anxiety, and depressive disorders and thus could be a mechanism underlying the associations between CU disorder and anxiety/depression symptoms. METHODS: Using resting-state functional magnetic resonance imaging, effective connectivities (ECs) among 9 major nodes from the default mode network and salience network were measured using dynamic causal modeling in 2 datasets: the Human Connectome Project (28 CU participants and 28 matched non-drug-using control participants) and a local CU study (21 CU participants and 21 matched non-drug-using control participants) in separate and parallel analyses. RESULTS: Relative to the control participants, right amygdala to left amygdala, anterior cingulate cortex to left amygdala, and medial prefrontal cortex to right insula ECs were greater, and left insula to left amygdala EC was smaller in the CU group. Each of these ECs showed a reliable linear relationship with at least one of the anxiety/depression measures. Most findings on the right amygdala to left amygdala EC were common to both datasets. CONCLUSIONS: Right amygdala to left amygdala and anterior cingulate cortex to left amygdala ECs may be related to the close associations between CU and anxiety/depression symptoms. The findings on the medial prefrontal cortex to right insula and left insula to left amygdala ECs may reflect a compensatory mechanism.