Dynamic Causal Modeling Analysis Research Articles

Effects of exogenous testosterone application on network connectivity within emotion regulation systems

Studies with steroid hormones underlined the vital role of testosterone on social-emotional processing. However, there is still a lack of studies investigating whether testosterone modulates network connectivity during resting-state. Here, we tested how the exogenous application of testosterone would affect functional connectivity between regions implicated in emotion regulation. In total, 96 male participants underwent resting-state fMRI scanning. Before the measurement, half of the subjects received 5 g TestimTM gel (containing 50 mg testosterone) and the other half a corresponding amount of placebo gel. Seeds for the connectivity analysis were meta-analytically defined. First, all regions associated with emotion regulation were chosen via Neurosynth (data driven). Among those, specific seeds were selected and categorized based on the neural model of emotion regulation by Etkin and colleagues (Etkin et al., 2015) (theory-guided). Resting-state connectivity analysis revealed decreased connectivity between the right DLPFC and the right amygdala as well as between the VMPFC and the left IPL for the testosterone group compared to the placebo group. A complementary dynamic causal modeling (DCM) analysis on findings from the resting-state connectivity analysis underlined a bidirectional coupling which was decreased close to zero by testosterone administration. Our results demonstrate that testosterone administration disrupts resting-state connectivity within fronto-subcortical and fronto-parietal circuits. The findings suggest that even without a specific task (e.g. challenge, reward processing) testosterone modulates brain networks important for social-emotional processing.

Acupuncture Induces Reduction in Limbic-Cortical Feedback of a Neuralgia Rat Model: A Dynamic Causal Modeling Study.

Background Neuropathic pain after brachial plexus avulsion remained prevalent and intractable currently. However, the neuroimaging study about neural mechanisms or etiology was limited and blurred. Objective This study is aimed at investigating the effect of electroacupuncture on effective connectivity and neural response in corticolimbic circuitries during implicit processing of nociceptive stimulus in rats with brachial plexus pain. Methods An fMRI scan was performed in a total of 16 rats with brachial plexus pain, which was equally distributed into the model group and the electroacupuncture group. The analysis of task-dependent data determined pain-related activation in each group. Based on those results, several regions including AMY, S1, and h were recruited as ROI in dynamic causal modeling (DCM) analysis comparing evidence for different neuronal hypotheses describing the propagation of noxious stimuli in regions of interest and horizontal comparison of effective connections between the model and electroacupuncture groups. Results In both groups, DCM revealed that noxious stimuli were most likely driven by the somatosensory cortex, with bidirectional propagation with the hypothalamus and amygdala and the interactions in them. Also, the 3-month intervention of acupuncture reduced effective connections of h-S1 and AMY-S1. Conclusions We showed an evidence that a full connection model within the brain network of brachial plexus pain and electroacupuncture intervention reduces effective connectivity from h and AMY to S1. Our study for the first time explored the relationship of involved brain regions with dynamic causal modeling. It provided novel evidence for the feature of the organization of the cortical-limbic network and the alteration caused by acupuncture.

Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach.

We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. The particular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.

Functional neural changes and altered cortical-subcortical connectivity associated with recovery from Internet gaming disorder.

Background and aimsAlthough studies have suggested that individuals with Internet gaming disorder (IGD) may have impairments in cognitive functioning, the nature of the relationship is unclear given that the information is typically derived from cross-sectional studies.MethodsIndividuals with active IGD (n = 154) and those individuals no longer meeting criteria (n = 29) after 1 year were examined longitudinally using functional magnetic resonance imaging during performance of cue-craving tasks. Subjective responses and neural correlates were contrasted at study onset and at 1 year.ResultsSubjects’ craving responses to gaming cues decreased significantly at 1 year relative to study onset. Decreased brain responses in the anterior cingulate cortex (ACC) and lentiform nucleus were observed at 1 year relative to onset. Significant positive correlations were observed between changes in brain activities in the lentiform nucleus and changes in self-reported cravings. Dynamic causal modeling analysis showed increased ACC–lentiform connectivity at 1 year relative to study onset.ConclusionsAfter recovery from IGD, individuals appear less sensitive to gaming cues. This recovery may involve increased ACC-related control over lentiform-related motivations in the control over cravings. The extent to which cortical control over subcortical motivations may be targeted in treatments for IGD should be examined further.

Dynamic Causal Modelling suggests impaired effective connectivity in patients with schizophrenia spectrum disorders during gesture-speech integration

Integrating visual and auditory information during gesture-speech integration (GSI) is important for successful social communication, which is often impaired in schizophrenia. Several studies suggested the posterior superior temporal sulcus (pSTS) to be a relevant multisensory integration site. However, intact STS activation patterns were often reported in patients. Thus, here we used Dynamic Causal Modelling (DCM) to analyze whether information processing in schizophrenia spectrum disorders (SSD) is impaired during GSI on network level.We investigated GSI in three different samples. First, we replicated a recently published connectivity model for GSI in a healthy subject group (n = 19). Second, we investigated differences between patients with SSD and a matched healthy control group (n = 17 each). Participants were presented videos of an actor performing intrinsically meaningful gestures accompanied by spoken sentences in German or Russian, or just telling a German sentence without gestures.Across all groups, fMRI analyses revealed similar activation patterns, and DCM analyses resulted in the same winning model for GSI. This finding directly replicates previous results. However, patients revealed significantly reduced connectivity in the verbal pathway (from left middle temporal gyrus (MTG) to left STS). The clinical significance of this connection is supported by its correlations with the severity of concretism and a subscale of negative symptoms (SANS).Our model confirms the importance of the pSTS as integration site during audio-visual integration. Patients showed generally intact connectivity during GSI, but revealed impaired information transfer via the verbal pathway. This might be the basis of interpersonal communication problems in patients with SSD.

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.

Connectivity between the cerebrum and cerebellum during social and non-social sequencing using dynamic causal modelling

This analysis explores the effective connectivity of the cerebellum with the cerebral cortex during the generation of correct sequences of social and non-social events, using dynamic causal modelling (DCM). Our hypothesis is that during human evolution, the cerebellum’s function evolved from a mere coordinator of fluent sequences of motions and actions, to an interpreter of action sequences without overt movements that are important for social understanding. This requires efficient neural communication between the cerebellum and cerebral cortex. In a functional magnetic resonance imaging (fMRI) study, participants generated the correct chronological order of (non-)social events, including stories involving mechanical and social scripts, and true or false beliefs. Across all stories, a DCM analysis of these data revealed, as predicted, bidirectional (closed-loop) connections linking the bilateral posterior cerebellum with the bilateral temporo-parietal junction (TPJ) associated with behavior understanding, and this connectivity pattern was almost entirely significant. There was also a unidirectional connection from the right posterior cerebellum to the precuneus, but no direct connections with the dorsomedial prefrontal cortex (dmPFC). Moreover, all connections emanating from the bilateral posterior cerebellum were negative, indicative of some kind of error signal. Within the cerebral cortex, there were unidirectional connections from the bilateral TPJ to the dmPFC, as well as bidirectional connections between the precuneus and dmPFC, and between the bilateral TPJ. These results confirm that the effective connectivity between the posterior cerebellum and mentalizing areas in the cerebral cortex play a critical role in the understanding and construction of the correct order of social and non-social action sequences.

Atypical Amygdala-Neocortex Interaction During Dynamic Facial Expression Processing in Autism Spectrum Disorder.

Atypical reciprocal social interactions involving emotional facial expressions are a core clinical feature of autism spectrum disorder (ASD). Previous functional magnetic resonance imaging (fMRI) studies have demonstrated that some social brain regions, including subcortical (e.g., amygdala) and neocortical regions (e.g., fusiform gyrus, FG) are less activated during the processing of facial expression stimuli in individuals with ASD. However, the functional networking patterns between the subcortical and cortical regions in processing emotional facial expressions remain unclear. We investigated this issue in ASD (n = 31) and typically developing (TD; n = 31) individuals using fMRI. Participants viewed dynamic facial expressions of anger and happiness and their corresponding mosaic images. Regional brain activity analysis revealed reduced activation of several social brain regions, including the amygdala, in the ASD group compared with the TD group in response to dynamic facial expressions vs. dynamic mosaics (p < 0.05, = 0.19). Dynamic causal modeling (DCM) analyses were then used to compare models with forward, backward, and bi-directional effective connectivity between the amygdala and neocortical networks. The results revealed that: (1) the model with effective connectivity from the amygdala to the neocortex best fit the data of both groups; and (2) the same model best accounted for group differences. Coupling parameter (i.e., effective connectivity) analyses showed that the modulatory effects of dynamic facial processing were substantially weaker in the ASD group than in the TD group. These findings suggest that atypical modulation from the amygdala to the neocortex underlies impairment in social interaction involving dynamic facial expressions in individuals with ASD.

Resting-state neural activity and connectivity associated with subjective happiness

The majority of people throughout the world rate subjective happiness as the top of the important thing in life. A recent structural neuroimaging study exploring neurocognitive mechanisms underlying subjective happiness has suggested that the gray matter volume of the right precuneus is associated with Subjective Happiness Scale (SHS) scores. However, how the neural activity in this region, as well as the neural functional coupling between this and other regions, could be related to SHS scores remains unclear. To investigate these issues, we performed resting-state functional magnetic resonance imaging and analyzed the fractional amplitude of low-frequency fluctuation (fALFF) in participants, whose subjective happiness was evaluated using the SHS. Lower fALFF values in the right precuneus were associated with higher SHS scores. Furthermore, functional connectivity and spectral dynamic causal modeling analyses showed that both functional and effective connectivity of the right precuneus with the right amygdala were positively associated with SHS scores. These findings, together with other evidence on the information-processing functions of these brain regions, suggest the possibility that subjective happiness is associated with a reduction in self-referential mental processes, which are well integrated with emotional processing.

The epileptic network of Lennox-Gastaut syndrome: Cortically driven and reproducible across age.

To identify brain regions underlying interictal generalized paroxysmal fast activity (GPFA), and their causal interactions, in children and adults with Lennox-Gastaut syndrome (LGS). Concurrent scalp EEG-fMRI was performed in 2 separately analyzed patient groups with LGS: 10 children (mean age 8.9 years) scanned under isoflurane-remifentanil anesthesia and 15 older patients (mean age 31.7 years) scanned without anesthesia. Whole-brain event-related analysis determined GPFA-related activation in each group. Results were used as priors in a dynamic causal modeling (DCM) analysis comparing evidence for different neuronal hypotheses describing initiation and propagation of GPFA between cortex, thalamus, and brainstem. A total of 1,045 GPFA events were analyzed (cumulative duration 1,433 seconds). In both pediatric and older groups, activation occurred in distributed association cortical areas, as well as the thalamus and brainstem (p < 0.05, corrected for family-wise error). Activation was similar across individual patients with structural, genetic, and unknown etiologies of epilepsy, particularly in frontoparietal cortex. In both groups, DCM revealed that GPFA was most likely driven by prefrontal cortex, with propagation occurring first to the brainstem and then from brainstem to thalamus. We show reproducible evidence of a cortically driven process within the epileptic network of LGS. This network is present early (in children) and late (in older patients) in the course of the syndrome and across diverse etiologies of epilepsy, suggesting that LGS reflects shared "secondary network" involvement. A cortical-to-subcortical hierarchy is postulated whereby GPFA rapidly propagates from prefrontal cortex to the brainstem via extrapyramidal corticoreticular pathways, whereas the thalamus is engaged secondarily.

Amygdala activation during unconscious visual processing of food

Hedonic or emotional responses to food have important positive and negative effects on human life. Behavioral studies have shown that hedonic responses to food images are elicited rapidly, even in the absence of conscious awareness of food. Although a number of previous neuroimaging studies investigated neural activity during conscious processing of food images, the neural mechanisms underlying unconscious food processing remain unknown. To investigate this issue, we measured neural activity using functional magnetic resonance imaging while participants viewed food and mosaic images presented subliminally and supraliminally. Conjunction analyses revealed that the bilateral amygdala was more strongly activated in response to food images than to mosaic images under both subliminal and supraliminal conditions. Interaction analyses revealed that the broad bilateral posterior regions, peaking at the posterior fusiform gyrus, were particularly active when participants viewed food versus mosaic images under the supraliminal compared with the subliminal condition. Dynamic causal modeling analyses supported the model in which the subcortical visual pathway from the pulvinar to the amygdala was modulated by food under the subliminal condition; in contrast, the model in which both subcortical and cortical (connecting the primary visual cortex, fusiform gyrus, and the amygdala) visual pathways were modulated by food received the most support under the supraliminal condition. These results suggest the possibility that unconscious hedonic responses to food may exert an effect through amygdala activation via the subcortical visual pathway.

Computing Social Value Conversion in the Human Brain.

Social signals play powerful roles in shaping self-oriented reward valuation and decision making. These signals activate social and valuation/decision areas, but the core computation for their integration into the self-oriented decision machinery remains unclear. Here, we study how a fundamental social signal, social value (others' reward value), is converted into self-oriented decision making in the human brain. Using behavioral analysis, modeling, and neuroimaging, we show three-stage processing of social value conversion from the offer to the effective value and then to the final decision value. First, a value of others' bonus on offer, called offered value, was encoded uniquely in the right temporoparietal junction (rTPJ) and also in the left dorsolateral prefrontal cortex (ldlPFC), which is commonly activated by offered self-bonus value. The effective value, an intermediate value representing the effective influence of the offer on the decision, was represented in the right anterior insula (rAI), and the final decision value was encoded in the medial prefrontal cortex (mPFC). Second, using psychophysiological interaction and dynamic causal modeling analyses, we demonstrated three-stage feedforward processing from the rTPJ and ldPFC to the rAI and then from rAI to the mPFC. Further, we showed that these characteristics of social conversion underlie distinct sociobehavioral phenotypes. We demonstrate that the variability in the conversion underlies the difference between prosocial and selfish subjects, as seen from the differential strength of the rAI and ldlPFC coupling to the mPFC responses, respectively. Together, these findings identified fundamental neural computation processes for social value conversion underlying complex social decision making behaviors.SIGNIFICANCE STATEMENT In daily life, we make decisions based on self-interest, but also in consideration for others' status. These social influences modulate valuation and decision signals in the brain, suggesting a fundamental process called value conversion that translates social information into self-referenced decisions. However, little is known about the conversion process and its underlying brain mechanisms. We investigated value conversion using human fMRI with computational modeling and found three essential stages in a progressive brain circuit from social to empathic and decision areas. Interestingly, the brain mechanism of conversion differed between prosocial and individualistic subjects. These findings reveal how the brain processes and merges social information into the elemental flow of self-interested decision making.

Dynamic Causal Modeling of Effective Connectivity During Anger Experience in Healthy Young Men: 7T Magnetic Resonance Imaging Study.

Little is known about how anger-associated brain regions integrate and modulate external input. Therefore, we investigated the neural connectivity architecture of anger processing using a dynamic causal modeling (DCM) approach. Thirteen subjects underwent functional magnetic resonance imaging (fMRI) while viewing anger-inducing film clips. Conventional fMRI and DCM analyses were conducted to identify a dominant connectivity model. Viewing anger-inducing film clips led to activation in the left superior temporal gyrus, left insula, and left orbitofrontal cortex (OFC). The results of a group-level comparison of eight connectivity models based on conventional fMRI findings showed superiority of the model including reciprocal effective connectivities between the left insula, left superior temporal gyrus, and left orbitofrontal gyrus and bottom-up connectivity from the left superior temporal gyrus to the left orbitofrontal gyrus. Positive coupling effects were identified for connectivities between the left superior temporal gyrus and left insula and the left superior temporal gyrus and left OFC. A negative coupling effect was identified for connectivity between the left OFC and left insula. In conclusion, we propose a model of effective connectivity associated with the anger experience based on dynamic causal modeling. The findings have implications for various psychiatric disorders related to abnormalities in anger processing.

Neural Connectivity in Syntactic Movement Processing.

Linguistic theory suggests non-canonical sentences subvert the dominant agent-verb-theme order in English via displacement of sentence constituents to argument (NP-movement) or non-argument positions (wh-movement). Both processes have been associated with the left inferior frontal gyrus and posterior superior temporal gyrus, but differences in neural activity and connectivity between movement types have not been investigated. In the current study, functional magnetic resonance imaging data were acquired from 21 adult participants during an auditory sentence-picture verification task using passive and active sentences contrasted to isolate NP-movement, and object- and subject-cleft sentences contrasted to isolate wh-movement. Then, functional magnetic resonance imaging data from regions common to both movement types were entered into a dynamic causal modeling analysis to examine effective connectivity for wh-movement and NP-movement. Results showed greater left inferior frontal gyrus activation for Wh > NP-movement, but no activation for NP > Wh-movement. Both types of movement elicited activity in the opercular part of the left inferior frontal gyrus, left posterior superior temporal gyrus, and left medial superior frontal gyrus. The dynamic causal modeling analyses indicated that neither movement type significantly modulated the connection from the left inferior frontal gyrus to the left posterior superior temporal gyrus, nor vice-versa, suggesting no connectivity differences between wh- and NP-movement. These findings support the idea that increased complexity of wh-structures, compared to sentences with NP-movement, requires greater engagement of cognitive resources via increased neural activity in the left inferior frontal gyrus, but both movement types engage similar neural networks.

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

BackgroundThis 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. MethodsCollege 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. ResultsParticipants 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. ConclusionsThe 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.

Disturbed effective connectivity patterns in an intrinsic triple network model are associated with posttraumatic stress disorder

Disturbance of the triple network model was recently proposed to be associated with the occurrence of posttraumatic stress disorder (PTSD) symptoms. Based on resting-state dynamic causal modeling (rs-DCM) analysis, we investigated the neurobiological model at a neuronal level along with potential neuroimaging biomarkers for identifying individuals with PTSD. We recruited survivors of a devastating typhoon including 27 PTSD patients, 33 trauma-exposed controls (TECs), and 30 healthy controls without trauma exposure. All subjects underwent resting-state functional magnetic resonance imaging. Independent components analysis was used to identify triple networks. Detailed effective connectivity patterns were estimated by rs-DCM analysis. Spearman correlation analysis was performed on aberrant DCM parameters with clinical assessment results relevant to PTSD diagnosis. We also carried out step-wise binary logistic regression and receiver operating characteristic curve (ROC) analysis to confirm the capacity of altered effective connectivity parameters to distinguish PTSD patients. Within the executive control network, enhanced positive connectivity from the left posterior parietal cortex to the left dorsolateral prefrontal cortex was correlated with intrusion symptoms and showed good performance (area under the receiver operating characteristic curve = 0.879) in detecting PTSD patients. In the salience network, we observed a decreased causal flow from the right amygdala to the right insula and a lower transit value for the right amygdala in PTSD patients relative to TECs. Altered effective connectivity patterns in the triple network may reflect the occurrence of PTSD symptoms, providing a potential biomarker for detecting patients. Our findings shed new insight into the neural pathophysiology of PTSD.

Neural substrates of word category information as the basis of syntactic processing.

The ability to use word category information (WCI) for syntactic structure building has been hypothesized to be the essence of human language faculty. The neural substrate of the ability of using the WCI for the complex syntactic hierarchical structure processing, however, is yet unknown. Therefore, we directly conducted an fMRI experiment by using a pseudo-Chinese artificial language with syntactic structures containing a center-embedded relative clause. Thirty non-Chinese native (Korean) speakers were randomly divided into two groups: one acquired WCI and WCI-based syntactic rules (the WCI group) before the scanning session, and the other did not (the non-WCI group). Both groups were required to judge the grammaticality of the testing sentences, with critical long-distance dependencies between two elements (the main verb and the relativizer). Behaviorally, the WCI group's accuracy was significantly higher and its reaction time was shorter. The scanning results showed that the left superior temporal gyrus (STG) and Broca's area were more strongly activated for the WCI group, and the dynamic causal modeling analyses revealed a distinct effective connectivity pattern for this group. Therefore, the present research, for the first time, reveals that the activation of and the functional connectivity between Broca's area and the left STG play a critical role in the ability of the rule-based use of the WCI which is crucial for complex hierarchical structure building, and might be substantially corresponding to the "labeling competence" within the linguistic framework.

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.

Unravelling the brain networks driving spike-wave discharges in genetic generalized epilepsy-common patterns and individual differences.

SummaryObjectiveGenetic generalized epilepsies (GGEs) are characterized by generalized spike‐wave discharges (GSWDs) in electroencephalography (EEG) recordings without underlying structural brain lesions. The origin of the epileptic activity remains unclear, although several studies have reported involvement of thalamus and default mode network (DMN). The aim of the current study was to investigate the networks involved in the generation and temporal evolution of GSWDs to elucidate the origin and propagation of the underlying generalized epileptic activity.MethodsWe examined 12 patients with GGE and GSWDs using EEG–functional magnetic resonance imaging (fMRI) and identified involved brain areas on the basis of a classical general linear model (GLM) analysis. The activation time courses of these areas were further investigated to reveal their temporal sequence of activations and deactivations. Dynamic causal modeling (DCM) was used to determine the generator of GSWDs in GGE.ResultsWe observed activity changes in the thalamus, DMN, dorsal attention network (DAN), salience network (SN), basal ganglia, dorsolateral prefrontal cortex, and motor cortex with supplementary motor area, however, with a certain heterogeneity between patients. Investigation of the temporal sequence of activity changes showed deactivations in the DMN and DAN and activations in the SN and thalamus preceding the onset of GSWDs on EEG by several seconds. DCM analysis indicated that the DMN gates GSWDs in GGE.SignificanceThe observed interplay between DMN, DAN, SN, and thalamus may indicate a downregulation of consciousness. The DMN seems to play a leading role as a driving force behind these changes. Overall, however, there were also clear differences in activation patterns between patients, reflecting a certain heterogeneity in this cohort of GGE patients.

Separate Neural Networks for Gains and Losses in Intertemporal Choice

An important and unresolved question is how human brain regions process information and interact with each other in intertemporal choice related to gains and losses. Using psychophysiological interaction and dynamic causal modeling analyses, we investigated the functional interactions between regions involved in the decision-making process while participants performed temporal discounting tasks in both the gains and losses domains. We found two distinct intrinsic valuation systems underlying temporal discounting in the gains and losses domains: gains were specifically evaluated in the medial regions, including the medial prefrontal and orbitofrontal cortices, and losses were evaluated in the lateral dorsolateral prefrontal cortex. In addition, immediate reward or punishment was found to modulate the functional interactions between the dorsolateral prefrontal cortex and distinct regions in both the gains and losses domains: in the gains domain, the mesolimbic regions; in the losses domain, the medial prefrontal cortex, anterior cingulate cortex, and insula. These findings suggest that intertemporal choice of gains and losses might involve distinct valuation systems, and more importantly, separate neural interactions may implement the intertemporal choices of gains and losses. These findings may provide a new biological perspective for understanding the neural mechanisms underlying intertemporal choice of gains and losses.