Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections

Altered EEG Microstates Dynamics in Individuals with Subthreshold Depression When Generating Negative Future Events

Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.

Modulation of the brain's core-self network by self-appraisal processes

The 'core' regions of the default mode network (DMN) - the medial prefrontal cortex (MPFC), the posterior cingulate cortex (PCC), and inferior parietal lobules (IPL) - show consistent involvement across mental states that involve self-oriented processing. Precisely how these regions interact in support of such processes remains an important unanswered question. In the current functional magnetic resonance imaging (fMRI) study, we examined dynamic interactions of the 'core-self' DMN regions during two forms of self-referential cognition: direct self-appraisal (thinking about oneself) and reflected self-appraisal (thinking about oneself from a third-person perspective). One-hundred and eleven participants completed our dual self-appraisal task during fMRI, and general linear models were used to characterize common and distinct neural responses to these conditions. Informed by these results, we then applied dynamic causal modelling to examine causal interactions among the 'core-self' regions, and how they were specifically modulated under the influence of direct and reflected self-appraisal. As a primary observation, this network modelling revealed a distinct inhibitory influence of the left IPL on the PCC during reflected compared to direct self-appraisal, which was accompanied by evidence of greater activation in both regions during the reflected self-appraisal condition. We suggest that the greater engagement posterior DMN regions during reflected self-appraisal is a function of the higher-order processing needed for this form of self-appraisal, with the left IPL supporting abstract self-related processes including episodic memory retrieval and shifts of perspective. Overall, we show that core DMN regions interact in functionally unique ways in support of self-referential processes, even when these processes are inter-related. Further characterization of DMN functional interactions across self-related mental states is likely to inform a deeper understanding of how this brain network orchestrates the self.

Effective Connectivity of the Hippocampus Can Differentiate Patients with Schizophrenia from Healthy Controls: A Spectral DCM Approach

We applied spectral dynamic causal modelling (Friston et al. in Neuroimage 94:396–407. 10.1016/j.neuroimage.2013.12.009, 2014) to analyze the effective connectivity differences between the nodes of three resting state networks (i.e. default mode network, salience network and dorsal attention network) in a dataset of 31 male healthy controls (HC) and 25 male patients with a diagnosis of schizophrenia (SZ). Patients showed increased directed connectivity from the left hippocampus (LHC) to the: dorsal anterior cingulate cortex (DACC), right anterior insula (RAI), left frontal eye fields and the bilateral inferior parietal sulcus (LIPS & RIPS), as well as increased connectivity from the right hippocampus (RHC) to the: bilateral anterior insula (LAI & RAI), right frontal eye fields and RIPS. In SZ, negative symptoms predicted the connectivity strengths from the LHC to: the DACC, the left inferior parietal sulcus (LIPAR) and the RHC, while positive symptoms predicted the connectivity strengths from the LHC to the LIPAR and from the RHC to the LHC. These results reinforce the crucial role of hippocampus dysconnectivity in SZ pathology and its potential as a biomarker of disease severity.

Effects of an Allostatic Closed-Loop Neurotechnology (HIRREM) on Brain Functional Connectivity Laterality in Military-Related Traumatic Stress

BACKGROUND AND PURPOSE

Brain asymmetries are reported in posttraumatic stress disorder, but many aspects of laterality and traumatic stress remain underexplored. This study explores lateralization changes in resting state brain network functional connectivity in a cohort with symptoms of military‐related traumatic stress, associated with use of a closed‐loop neurotechnology, HIRREM.

METHODS

Eighteen participants (17 males, mean age 41 years [SD = 7]) received 19.5 (1.1) HIRREM sessions over 12 days. Whole brain resting magnetic resonance imaging was done pre‐ and post‐HIRREM. Laterality of functional connectivity was assessed on a whole brain basis, and in six predefined networks or regions. Laterality of connectivity within networks or regions was assessed separately from laterality of connections between networks or regions.

RESULTS

Before HIRREM, significant laterality effects of connection type (ipsilateral for either side, or contralateral in either direction) were observed for the whole brain, within networks or regions, and between networks or regions. Post‐HIRREM, there were significant changes for within‐network or within‐region analysis in the motor network, and changes for between‐network or between‐region analyses for the salience network and the motor cortex.

CONCLUSIONS

Among military service members and Veterans with symptoms of traumatic stress, asymmetries of network and brain region connectivity patterns were identified prior to usage of HIRREM. A variety of changes in lateralized patterns of brain connectivity were identified postintervention. These laterality findings may inform future studies of brain connectivity in traumatic stress disorders, with potential to point to mechanisms of action for successful intervention.

Cognitive Brain Signatures of Youth With Early Onset and Relatives With Schizophrenia: Evidence From fMRI Meta-analyses

Deficits in cognitive function are a major characteristic of schizophrenia. Many functional magnetic resonance imaging (fMRI) studies examine brain correlates of cognitive function in adults with schizophrenia, showing altered implication of associative areas such as the prefrontal cortex and temporal cortex. fMRI studies also examine brain representation of cognitive function in adolescents with early onset schizophrenia and those at risk of the disorder, yet results are often inconsistent. We compile and analyze data from eligible fMRI studies using quantitative meta-analyses to reveal concordant brain activity associated with adolescent relatives of patients with schizophrenia and those with early onset schizophrenia. Results show similar functional hubs of brain activity (eg, precuneus) yet in opposite hemispheres and clusters in ventrolateral rather than dorsolateral prefrontal cortices. Other areas of altered implication include the middle temporal gyrus, insula, and cerebellum. We discuss the findings in reference to the protracted maturation of the prefrontal cortex and possible effects due to the medication status of the two groups.

Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence

The connectivity hub property of the hippocampus (HIP) and the medial prefrontal cortex (MPFC) is essential for their widespread involvement in cognition; however, the cooperation mechanism between them is far from clear. Herein, using resting-state functional MRI and Gaussian Bayesian network to describe the directed organizing architecture of the HIP-MPFC pathway with regions in the brain, we demonstrated that the HIP and the MPFC have central roles as the driving hub and aggregating hub, respectively. The status of the HIP and the MPFC is dominant in communications between the HIP and the default-mode network, between the HIP and core neurocognitive networks, including the default-mode, frontoparietal, and salience networks, and between brain-wide representative regions, suggesting a strong and robust central position of the two regions in regulating the dynamics of large-scale brain activity. Furthermore, we found that the directed connectivity and flow from the right HIP to the MPFC is significantly linked to fluid intelligence. Together, these results clarify the different roles of the HIP and the MPFC that jointly contribute to network dynamics and cognitive ability from a data-driven insight via the use of the directed connectivity method.

The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

The influence of global BOLD fluctuations on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting state networks (RSNs) - as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we considered four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of information gain with and without GSR. Our results showed negligible to small effects of GSR on effective connectivity within small (separately estimated) RSNs. However, although the effect sizes were small, there was substantial to conclusive evidence for an effect of GSR on connectivity parameters. For between-network connectivity, we found two important effects: the effect of GSR on between-network effective connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. In the cross-sectional (but not in the longitudinal) data, some connections showed substantial to conclusive evidence for an effect of GSR. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters characterising (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater after GSR. In conclusion, GSR is a minor concern in DCM studies; however, quantitative interpretation of between-network connections (as opposed to average between-network connectivity) and noise parameters should be treated with some caution. The Kullback-Leibler divergence of the posterior from the prior (i.e., information gain) - together with the precision of posterior estimates - might offer a useful measure to assess the appropriateness of GSR in resting state fMRI.

•

We investigated the effect of GSR on parameters estimated with DCM.

•

We found negligible to small effects of GSR in small-scale resting state networks.

•

Small effects of GSR on the net influence between networks were observed.

•

Results showed non-negligible effects of GSR on individual between-network connections.

•

The effect of GSR on noise parameters was either absent or unspecific, depending on design.

Negative affect amplifies the relation between appetitive-food-related neural responses and weight gain over three-year follow-up among adolescents

•

Interaction of negative affect and hippocampal food-image response predicts BMI gain.

•

Interaction of negative affect, vermis and precuneus food response predicts BMI gain.

•

Interaction of stress and middle occipital gyrus milkshake response predicts BMI gain.

•

Weight gain associated with restrained eating and eating-disorder related behavior.

Obesity is a major public health concern that is associated with disruption in food reward-related brain function. This study examined if negative affect and stressful events enhance the relation between the food reward-related neural response and future weight gain. Initially healthy weight adolescents (N = 135) completed fMRI paradigms in which they tasted milkshakes and viewed palatable food images, and reported on negative affect and stressful events at baseline; BMI was measured annually over 3-year follow-up. Whole-brain analyses revealed that among participants with higher negative affect, weight gain over 3-year follow-up was predicted by elevated response to appetitive versus unappetitive food images in the left hippocampus, and elevated response in the vermis and the bilateral precuneus to tastes of milkshake versus tasteless solution. Among participants who experienced more stressful events, elevated right middle occipital gyrus response to milkshakes predicted future weight gain. Profiling analyses suggested that participants with higher negative affect or more stressful events who later gained weight reported engaging in more restrained eating and eating disorder-related behaviors. Results suggest that negative affect or stressful events may amplify the relation of neural response to food and the risk for future weight gain.

Consciousness in a multilevel architecture: What causes the lateralization of effective connectivity under resting state?

Here we present our answers to a critical commentary by Elkhonon Goldberg on our recent publication (Velichkovsky et al., 2018). To avoid discussions about novelty effects in the human brain activity and memory processes, we narrowed down this response to a reanalysis of our data along the lines proposed in the commentary, namely to comparing the effective links between symmetrical brain structures during the first and the last parts of a prolonged resting-state fMRI experiment. We also tested for sex differences in our results and checked for a stability of top-down interactions during the course of experiment because learning is often expressed in the weakening of upper level control over low-level mechanisms. Our attempts to test the predictions based on the novelty hypothesis has led to mixed results suggesting that the discovered right-to-left dominance of causal connections at rest may have a deeper origin than supposed in the Goldberg's commentary.

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI – namely spectral DCM – is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures – and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.

•

Across datasets, subjects’ effective connectivity patterns in the core default mode network showed hemispheric asymmetry.

•

Differences in hemispheric asymmetry was found to be a major source of between-subject variability.

•

In contrast, most subjects showed reliable within-subject hemispheric asymmetry.

•

Differences in preprocessing methods had little effect on connectivity estimates.

•

Bayesian model reduction increased the within- and between-subject consistency of connectivity patterns.

Consciousness in a multilevel architecture: Evidence from the right side of the brain

By taking into account Bruce Bridgeman's interest in an evolutionary framing of human cognition, we examine effective (cause-and-effect) connectivity among cortical structures related to different parts of the triune phylogenetic stratification: archicortex, paleocortex and neocortex. Using resting-state functional magnetic resonance imaging data from 25 healthy subjects and spectral Dynamic Causal Modeling, we report interactions among 10 symmetrical left and right brain areas. Our results testify to general rightward and top-down biases in excitatory interactions of these structures during resting state, when self-related contemplation prevails over more objectified conceptual thinking. The right hippocampus is the only structure that shows bottom-up excitatory influences extending to the frontopolar cortex. The right ventrolateral cortex also plays a prominent role as it interacts with the majority of nodes within and between evolutionary distinct brain subdivisions. These results suggest the existence of several levels of cognitive-affective organization in the human brain and their profound lateralization.

Dynamic effective connectivity in resting state fMRI

Context-sensitive and activity-dependent fluctuations in connectivity underlie functional integration in the brain and have been studied widely in terms of synaptic plasticity, learning and condition-specific (e.g., attentional) modulations of synaptic efficacy. This dynamic aspect of brain connectivity has recently attracted a lot of attention in the resting state fMRI community. To explain dynamic functional connectivity in terms of directed effective connectivity among brain regions, we introduce a novel method to identify dynamic effective connectivity using spectral dynamic causal modelling (spDCM). We used parametric empirical Bayes (PEB) to model fluctuations in directed coupling over consecutive windows of resting state fMRI time series. Hierarchical PEB can model random effects on connectivity parameters at the second (between-window) level given connectivity estimates from the first (within-window) level. In this work, we used a discrete cosine transform basis set or eigenvariates (i.e., expression of principal components) to model fluctuations in effective connectivity over windows. We evaluated the ensuing dynamic effective connectivity in terms of the consistency of baseline connectivity within default mode network (DMN), using the resting state fMRI from Human Connectome Project (HCP). To model group-level baseline and dynamic effective connectivity for DMN, we extended the PEB approach by conducting a multilevel PEB analysis of between-session and between-subject group effects. Model comparison clearly spoke to dynamic fluctuations in effective connectivity – and the dynamic functional connectivity these changes explain. Furthermore, baseline effective connectivity was consistent across independent sessions – and notably more consistent than estimates based upon conventional models. This work illustrates the advantage of hierarchical modelling with spDCM, in characterizing the dynamics of effective connectivity.

•

We describe efficient estimation of dynamics in resting state effective connectivity.

•

Spectral DCM and PEB are used to model fluctuations in neuronal coupling over time.

•

Dynamics in responses are explained in terms of its causes (effective connectivity).

•

Baseline and dynamic components of the default mode connectivity are identified.

Default mode network connectivity of fear- and anxiety-related cue and context conditioning

Classical fear conditioning is an important mechanism to adequately respond and adapt to environmental threats and has been related to the development of fear and anxiety. Both cue and context conditioning have been studied but little is known about their relation to relevant resting state networks. The default mode network (DMN) has been reported to be involved in affective learning and described as facilitating a state of readiness in responding to environmental changes. We examined resting state brain connectivity patterns of the default mode network (DMN) in 119 healthy volunteers. Specifically, we carried out correlation analyses between the DMN and skin conductance responses (SCRs) as well as arousal, valence and contingency ratings during learning. In addition, we examined the role of trait anxiety. Two different DMN patterns were identified in which stronger connectivity was linked to lower differential SCRs during fear and anxiety learning. One was related to cue conditioning and involved the amygdala and the medial prefrontal cortex, and one was associated with context conditioning and included the hippocampal formation and sensorimotor areas. These results were replicated in an independent sample. Functional connectivity of the DMN with these key regions at rest was also predictive of trait anxiety but this association could not be replicated in the second sample. We showed that DMN connectivity is differently associated with cued versus contextual learning mechanisms. Uncovering individual differences in baseline network connectivity of the DMN with these key regions might lead to a better understanding of fear and anxiety. Such findings could indeed help to identify vulnerability factors linked to network alterations at rest with dysregulation of learning processes involved in the pathophysiology of stress and anxiety disorders.