Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity

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.

Dysregulated Salience Network Control over Default-Mode and Central-Executive Networks in Schizophrenia Revealed Using Stochastic Dynamical Causal Modeling

Introduction: Neuroimaging studies suggest that the human brain consists of intrinsically organized, large-scale neural networks. Among these networks, the interplay among the default-mode network (DMN), salience network (SN), and central-executive network (CEN) has been widely used to understand the functional interaction patterns in health and disease. This triple network model suggests that the SN causally controls over the DMN and CEN in healthy individuals. This interaction is often referred to as SN's dynamic regulating mechanism. However, such interactions are not well understood in individuals with schizophrenia. Methods: In this study, we leveraged resting-state functional magnetic resonance imaging data from schizophrenia (n = 67) and healthy controls (n = 81) and evaluated the directional functional interactions among DMN, SN, and CEN using stochastic dynamical causal modeling methodology. Results: In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10-8). In schizophrenia, however, our analyses revealed a disrupted SN-based controlling mechanism over the DMN and CEN (Mann-Whitney U test; p < 10-16). Conclusions: These results indicate that the disrupted controlling mechanism of SN over the other two neural networks may be a candidate neuroimaging phenotype in schizophrenia.

Neural Mechanisms of Resting-State Networks and the Amygdala Underlying the Cognitive and Emotional Effects of Psilocybin

BACKGROUND

Serotonergic psychedelics, such as psilocybin, alter perceptual and cognitive systems that are functionally integrated with the amygdala. These changes can alter cognition and emotions that are hypothesized to contribute to their therapeutic utility. However, the neural mechanisms of cognitive and subcortical systems altered by psychedelics are not well understood.

METHODS

We used resting-state functional magnetic resonance images collected during a randomized, double-blind, placebo-controlled clinical trial of 24 healthy adults under 0.2 mg/kg psilocybin to estimate the directed (i.e., effective) changes between the amygdala and 3 large-scale resting-state networks involved in cognition. These networks are the default mode network, the salience network, and the central executive network.

RESULTS

We found a pattern of decreased top-down effective connectivity from these resting-state networks to the amygdala. Effective connectivity decreased within the default mode network and salience network but increased within the central executive network. These changes in effective connectivity were statistically associated with behavioral measures of altered cognition and emotion under the influence of psilocybin.

CONCLUSIONS

Our findings suggest that temporary amygdala signal attenuation is associated with mechanistic changes to resting-state network connectivity. These changes are significant for altered cognition and perception and suggest targets for research investigating the efficacy of psychedelic therapy for internalizing psychiatric disorders. More broadly, our study suggests the value of quantifying the brain's hierarchical organization using effective connectivity to identify important mechanisms for basic cognitive function and how they are integrated to give rise to subjective experiences.