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.

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.

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.

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.

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.

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.

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.

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.

Clinical features and outcomes between African American and Caucasian patients with Takotsubo Syndrome.

BACKGROUND: Takotsubo syndrome (TS) is an acute, reversible form of heart failure, often mimicking an acute coronary syndrome (ACS). Data regarding racial differences in TS are inconsistent. The aim is to assess clinical features associated with unfavorable in-hospital outcomes between African American (AA) and Caucasian (CAU) patients. METHODS: A retrospective electronic health record query identified 44 AA patients and 110 CAU patients with a diagnosis of TS. Our primary outcome was a composite of death, stroke, and cardiogenic shock during hospitalization. Variables associated with an increased risk of the primary composite outcomes were included in a logistic regression model. RESULTS: Compared to CAU patients, AA patients were a more comorbid population, and presented a higher prevalence of history of illicit drug use (27.3% vs. 13.6% P=0.044). There were no significant differences regarding in-hospital complication rates between AA and CAU patients. In the logistic regression model, infection was associated with greater risk of developing the primary outcome in AA patients (OR=7.26 [95% CI 1.22-43.17], P=0.029), whereas angina was a protective factor (OR=0.11 [95% CI 0.02-0.65], P=0.015). In CAU patients, severely depressed ejection fraction and worse peak creatinine during hospitalization increased risk of developing the primary outcome (OR=5.88 95% CI [2.01-17.17], P<0.001 and OR=1.64 [95% CI 1.15-2.58], P=0.031, respectively). Meanwhile, emotional stressors were protective (OR=0.16 [95% CI 0.03-0.88], P=0.004). CONCLUSIONS: Despite experiencing the same rate of in-hospital complications, the clinical profiles of AA patients are distinct from CAU patients admitted for TS, and clinical variables correlated with worse in-hospital outcomes also differ by race.

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.

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.

Altered Effective Connectivity of Central Autonomic Network in Response to Negative Facial Expression in Adults With Cannabis Use Disorder.

BACKGROUND: Cannabis use is associated with an increased risk of stress-related adverse cardiovascular events. Because brain regions of the central autonomic network largely overlap with brain regions related to the neural response to emotion and stress, the central autonomic network may mediate the autonomic response to negative emotional stimuli. We aimed to obtain evidence to determine whether neural connectivity of the central autonomic network is altered in individuals with cannabis use disorder (CUD) when they are exposed to negative emotional stimuli. METHODS: Effective (directional) connectivity (EC) analysis using dynamic causal modeling was applied to functional magnetic resonance imaging data acquired from 23 subjects with CUD and 23 control subjects of the Human Connectome Project while they performed an emotional face-matching task with interleaving periods of negative-face (fearful/angry) and neutral-shape stimuli. The EC difference (modulatory change) was measured during the negative-face trials relative to the neutral-shape trials. RESULTS: The CUD group was similar to the control group in nonimaging measures and brain activations but showed greater modulatory changes in left amygdala to hypothalamus EC (positively associated with Perceived Stress Scale score), right amygdala to bilateral fusiform gyri ECs (positively associated with Perceived Stress Scale score), and left ventrolateral prefrontal cortex to bilateral fusiform gyri ECs (negatively associated with Perceived Stress Scale score). CONCLUSIONS: Left amygdala to hypothalamus EC and right amygdala to bilateral fusiform gyri ECs are possibly part of circuits underlying the risk of individuals with CUD to stress-related disorders. Correspondingly, left ventrolateral prefrontal cortex to bilateral fusiform gyri ECs are possibly part of circuits reflecting a protective mechanism.

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.

Cingulo-hippocampal effective connectivity positively correlates with drug-cue attentional bias in opioid use disorder.

Individuals with opioid use disorder (OUD) often relapse when exposed to opioid-related cues. Previous functional magnetic resonance imaging (fMRI) studies have identified neuronal corticolimbic changes related to drug cue reactivity in OUD. However, the corresponding manner in which brain regions interact is still unclear. Effective (directional) connectivity was analyzed using dynamic causal modeling of fMRI data acquired from 27 OUD participants (13 with OUD and 14 with OUD and cocaine use disorder [OUD+CUD]), while performing an opioid-word Stroop task. Participants were shown opioid and neutral words presented in different colors and were instructed to indicate word color but ignore word meaning. The effects of opioid words relative to neutral words on effective connectivity and on behavioral reaction time were defined as modulatory change and attentional bias, respectively. For all the 27 participants, left anterior cingulate cortex (ACC) to right hippocampus effective connectivity exhibited the largest modulatory change, which was positively correlated with attentional bias. The findings for the ACC to hippocampus EC were consistent across OUD and CUD found in a previous study.

Dynamic Causal Modeling of Insular, Striatal, and Prefrontal Cortex Activities During a Food-Specific Go/NoGo Task.

BACKGROUND: This study aimed to investigate the dynamic interactions among three neural systems that are implicated in substance and behavioral addictions in response to food cues in young adults. These include an impulsive system involving the striatum, a reflective system involving the prefrontal cortex, and a homeostasis sensing system involving the insular cortex. METHODS: College students (N = 45) with various levels of body mass index were recruited. Functional magnetic resonance imaging data were acquired while participants performed food-related Go/NoGo tasks, with low-calorie and high-calorie food cues. Participants were scanned under both food satiety and deprivation conditions. Dynamic causal modeling was applied to the data to examine the causal architecture of coupled or distributed dynamics among the aforementioned systems. RESULTS: Participants showed difficulty inhibiting responses to high-calorie foods as suggested by higher false alarm rate and decision bias for low-calorie food Go task. This difficulty was enhanced during the food deprivation condition. Deprivation increased neural activity of both the insula and the striatum bilaterally in response to high-calorie foods during Go trials and anterior cingulate cortex and dorsolateral prefrontal cortex activity during NoGo trials. Dynamic causal modeling analysis revealed that food deprivation modulated the communications between the insula, striatum, and dorsolateral prefrontal cortex, and the modulations were positively associated with body mass index. CONCLUSIONS: The results support tripartite views of decision making. Deprivation states, such as hunger, trigger insular activity, which modulates the balance between impulsive and reflective systems when facing tempting food cues.

Stress Cardiomyopathy Diagnosis and Treatment: JACC State-of-the-Art Review.

Stress cardiomyopathy is an acute reversible heart failure syndrome initially believed to represent a benign condition due to its self-limiting clinical course, but now recognized to be associated with a non-negligible rate of serious complications such as ventricular arrhythmias, systemic thromboembolism, and cardiogenic shock. Due to an increased awareness and recognition, the incidence of stress cardiomyopathy has been rising (15-30 cases per 100,000 per year), although the true incidence is unknown as the condition is likely underdiagnosed. Stress cardiomyopathy represents a form of neurocardiogenic myocardial stunning, and while the link between the brain and the heart is established, the exact pathophysiological mechanisms remain unclear. We herein review the proposed risk factors and triggers for the syndrome and discuss a practical approach to diagnosis and treatment of the patients with stress cardiomyopathy, highlighting potential challenges and unresolved questions.

Fronto-striatal effective connectivity of working memory in adults with cannabis use disorder.

Previous working memory (WM) studies found that relative to controls, subjects with cannabis use disorder (CUD) showed greater brain activation in some regions (e.g., left [L] and right [R] ventrolateral prefrontal cortex [VLPFC], and L dorsolateral prefrontal cortex [L-DLPFC]), and lower activation in other regions (e.g., R-DLPFC). In this study, effective connectivity (EC) analysis was applied to functional magnetic resonance imaging data acquired from 23 CUD subjects and 23 controls (two groups matched for sociodemographic factors and substance use history) while performing an n-back WM task with interleaved 2-back and 0-back periods. A 2-back minus 0-back modulator was defined to measure the modulatory changes of EC corresponding to the 2-back relative to 0-back conditions. Compared to the controls, the CUD group showed smaller modulatory change in the R-DLPFC to L-caudate pathway, and greater modulatory changes in L-DLPFC to L-caudate, R-DLPFC to R-caudate, and R-VLPFC to L-caudate pathways. Based on previous fMRI studies consistently suggesting that greater brain activations are related to a compensatory mechanism for cannabis neural effects (less regional brain activations), the smaller modulatory change in the R-DLPFC to L-caudate EC may be compensated by the larger modulatory changes in the other prefrontal-striatal ECs in the CUD individuals.