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Longitudinal studies have become more common in the past years due to their superiority over cross-sectional samples. In light of the ongoing replication crisis, the factors that may introduce variability in resting-state networks have been widely debated. This publication aimed to address the potential sources of variability, namely, time of day, sex, and age, in longitudinal studies within individual resting-state fMRI data. DCM was used to analyze the fMRI time series, extracting EC connectivitymeasures and parameters that define the BOLD signal. In addition, a two-way ANOVA was used to assess the change in EC and parameters that define the BOLD signal between data collection waves. The results indicate that time of day and gender have significant model evidence for the parameters that define the BOLD signal but not EC. From the ANOVA analysis, findings indicate that there was a significant change in the two nodes of the DMN and their connections with the fronto-parietal network. Overall, these findings suggest that in addition to age and gender, which are commonly accounted for in the fMRI data collection, studies should note the time of day, possibly treating it as a covariate in longitudinal samples. [ABSTRACT FROM AUTHOR]

Introduction: The extinction of fear memories is an important component in regulating defensive behaviors, contributing toward adaptive processes essential for survival. The cerebellar medial nucleus (MCN) has bidirectional connections with the ventrolateral periaqueductal gray (vlPAG) and is implicated in the regulation of multiple aspects of fear, such as conditioned fear learning and the expression of defensive motor outputs. However, it is unclear how communication between the MCN and vlPAG changes during conditioned fear extinction. Methods: We use dynamic causal models (DCMs) to infer effective connectivity between the MCN and vlPAG during auditory cue-conditioned fear retrieval and extinction in the rat. DCMs determine causal relationships between neuronal sources by using neurobiologically motivated models to reproduce the dynamics of post-synaptic potentials generated by synaptic connections within and between brain regions. Auditory event related potentials (ERPs) during the conditioned tone offset were recorded simultaneously from MCN and vlPAG and then modeled to identify changes in the strength of the synaptic inputs between these brain areas and the relationship to freezing behavior across extinction trials. The DCMs were structured to model evoked responses to best represent conditioned tone offset ERPs and were adapted to represent PAG and cerebellar circuitry. Results: With the use of Parametric Empirical Bayesian (PEB) analysis we found that the strength of the information flow, mediated through enhanced synaptic efficacy from MCN to vlPAG was inversely related to freezing during extinction, i.e., communication from MCN to vlPAG increased with extinction. Discussion: The results are consistent with the cerebellum contributing to predictive processes that underpin fear extinction. [ABSTRACT FROM AUTHOR]

Longitudinal studies have become more common in the past years due to their superiority over cross-sectional samples. In light of the ongoing replication crisis, the factors that may introduce variability in resting-state networks have been widely debated. This publication aimed to address the potential sources of variability, namely, time of day, sex, and age, in longitudinal studies within individual resting-state fMRI data. DCM was used to analyze the fMRI time series, extracting EC connectivity measures and parameters that define the BOLD signal. In addition, a two-way ANOVA was used to assess the change in EC and parameters that define the BOLD signal between data collection waves. The results indicate that time of day and gender have significant model evidence for the parameters that define the BOLD signal but not EC. From the ANOVA analysis, findings indicate that there was a significant change in the two nodes of the DMN and their connections with the fronto-parietal network. Overall, these findings suggest that in addition to age and gender, which are commonly accounted for in the fMRI data collection, studies should note the time of day, possibly treating it as a covariate in longitudinal samples.

IntroductionThe extinction of fear memories is an important component in regulating defensive behaviors, contributing toward adaptive processes essential for survival. The cerebellar medial nucleus (MCN) has bidirectional connections with the ventrolateral periaqueductal gray (vlPAG) and is implicated in the regulation of multiple aspects of fear, such as conditioned fear learning and the expression of defensive motor outputs. However, it is unclear how communication between the MCN and vlPAG changes during conditioned fear extinction.MethodsWe use dynamic causal models (DCMs) to infer effective connectivity between the MCN and vlPAG during auditory cue-conditioned fear retrieval and extinction in the rat. DCMs determine causal relationships between neuronal sources by using neurobiologically motivated models to reproduce the dynamics of post-synaptic potentials generated by synaptic connections within and between brain regions. Auditory event related potentials (ERPs) during the conditioned tone offset were recorded simultaneously from MCN and vlPAG and then modeled to identify changes in the strength of the synaptic inputs between these brain areas and the relationship to freezing behavior across extinction trials. The DCMs were structured to model evoked responses to best represent conditioned tone offset ERPs and were adapted to represent PAG and cerebellar circuitry.ResultsWith the use of Parametric Empirical Bayesian (PEB) analysis we found that the strength of the information flow, mediated through enhanced synaptic efficacy from MCN to vlPAG was inversely related to freezing during extinction, i.e., communication from MCN to vlPAG increased with extinction.DiscussionThe results are consistent with the cerebellum contributing to predictive processes that underpin fear extinction.

Altered brain connectivity between regions of the reading network has been associated with reading difficulties. However, it remains unclear whether connectivity differences between children with dyslexia (DYS) and those with typical reading skills (TR) are specific to reading impairments or to reading experience. In this functional MRI study, 132 children (M = 10.06 y, SD = 1.46) performed a phonological lexical decision task. We aimed to disentangle (1) disorder-specific from (2) experience-related differences in effective connectivity and to (3) characterize the development of DYS and TR. We applied dynamic causal modeling to age-matched (ndys = 25, nTR = 35) and reading-level-matched (ndys = 25, nTR = 22) groups. Developmental effects were assessed in beginning and advanced readers (TR: nbeg = 48, nadv = 35, DYS: nbeg = 24, nadv = 25). We show that altered feedback connectivity between the inferior parietal lobule and the visual word form area (VWFA) during print processing can be specifically attributed to reading impairments, because these alterations were found in DYS compared to both the age-matched and reading-level-matched TR. In contrast, feedforward connectivity from the VWFA to parietal and frontal regions characterized experience in TR and increased with age and reading skill. These directed connectivity findings pinpoint disorder-specific and experience-dependent alterations in the brain's reading network.

Stimulus repetition normally causes reduced neural activity in brain regions that process that stimulus. Some theories claim that this “repetition suppression” reflects local mechanisms such as neuronal fatigue or sharpening within a region, whereas other theories claim that it results from changed connectivity between regions, following changes in synchrony or top-down predictions. In this study, we applied dynamic causal modeling (DCM) on a public fMRI dataset involving repeated presentations of faces and scrambled faces to test whether repetition affected local (self-connections) and/or between-region connectivity in left and right early visual cortex (EVC), occipital face area (OFA) and fusiform face area (FFA). Face “perception” (faces versus scrambled faces) modulated nearly all connections, within and between regions, including direct connections from EVC to FFA, supporting a non-hierarchical view of face processing. Face “recognition” (familiar versus unfamiliar faces) modulated connections between EVC and OFA/FFA, particularly in the left hemisphere. Most importantly, immediate and delayed repetition of stimuli were also best captured by modulations of connections between EVC and OFA/FFA, but not self-connections of OFA/FFA, consistent with synchronization or predictive coding theories, though also possibly reflecting local mechanisms like synaptic depression.

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data. [ABSTRACT FROM AUTHOR]

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Neuroimaging has revealed numerous atypical functional connectivity of default mode network (DMN) dedicated to social communications (SC) in autism spectrum disorder (ASD), yet their nature and directionality remain unclear. Here, preschoolers with autism received physical intervention from a 12-week mini-basketball training program (12W-MBTP). Therefore, the directionality and nature of regional interactions within the DMN after the intervention are evaluated while assessing the impact of an intervention on SC. Based on the results of independent component analysis (ICA), we applied spectral dynamic causal modeling (DCM) for participants aged 3–6 years (experimental group, N = 17, control group, N = 14) to characterize the longitudinal changes following intervention in intrinsic and extrinsic effective connectivity (EC) between core regions of the DMN. Then, we analyzed the correlation between the changes in EC and SRS-2 scores to establish symptom-based validation. We found that after the 12W-MBTP intervention, the SRS-2 score of preschoolers with ASD in the experimental group was decreased. Concurrently, the inhibitory directional connections were observed between the core regions of the DMN, including increased self-inhibition in the medial prefrontal cortex (mPFC), and the changes of EC in mPFC were significantly correlated with change in the social responsiveness scale-2 (SRS-2) score. These new findings shed light on DMN as a potential intervention target, as the inhibitory information transmission between its core regions may play a positive role in improving SC behavior in preschoolers with ASD, which may be a reliable neuroimaging biomarker for future studies. Clinical Trial Registration: This study registered with the Chinese Clinical Trial Registry (ChiCTR1900024973) on August 05, 2019. [ABSTRACT FROM AUTHOR]

Motor recovery after stroke relies on functional reorganization of the motor network, which is commonly assessed via functional magnetic resonance imaging (fMRI)‐based resting‐state functional connectivity (rsFC) or task‐related effective connectivity (trEC). Measures of either connectivity mode have been shown to successfully explain motor impairment post‐stroke, posing the question whether motor impairment is more closely reflected by rsFC or trEC. Moreover, highly similar changes in ipsilesional and interhemispheric motor network connectivity have been reported for both rsFC and trEC after stroke, suggesting that altered rsFC and trEC may capture similar aspects of information integration in the motor network reflecting principle, state‐independent mechanisms of network reorganization rather than state‐specific compensation strategies. To address this question, we conducted the first direct comparison of rsFC and trEC in a sample of early subacute stroke patients (n = 26, included on average 7.3 days post‐stroke). We found that both rsFC and trEC explained motor impairment across patients, stressing the clinical potential of fMRI‐based connectivity. Importantly, intrahemispheric connectivity between ipsilesional M1 and premotor areas depended on the activation state, whereas interhemispheric connectivity between homologs was state‐independent. From a mechanistic perspective, our results may thus arise from two distinct aspects of motor network plasticity: task‐specific compensation within the ipsilesional hemisphere and a more fundamental form of reorganization between hemispheres. [ABSTRACT FROM AUTHOR]

Neuroimaging has revealed numerous atypical functional connectivity of default mode network (DMN) dedicated to social communications (SC) in autism spectrum disorder (ASD), yet their nature and directionality remain unclear. Here, preschoolers with autism received physical intervention from a 12-week mini-basketball training program (12W-MBTP). Therefore, the directionality and nature of regional interactions within the DMN after the intervention are evaluated while assessing the impact of an intervention on SC. Based on the results of independent component analysis (ICA), we applied spectral dynamic causal modeling (DCM) for participants aged 3–6 years (experimental group, N = 17, control group, N = 14) to characterize the longitudinal changes following intervention in intrinsic and extrinsic effective connectivity (EC) between core regions of the DMN. Then, we analyzed the correlation between the changes in EC and SRS-2 scores to establish symptom-based validation. We found that after the 12W-MBTP intervention, the SRS-2 score of preschoolers with ASD in the experimental group was decreased. Concurrently, the inhibitory directional connections were observed between the core regions of the DMN, including increased self-inhibition in the medial prefrontal cortex (mPFC), and the changes of EC in mPFC were significantly correlated with change in the social responsiveness scale-2 (SRS-2) score. These new findings shed light on DMN as a potential intervention target, as the inhibitory information transmission between its core regions may play a positive role in improving SC behavior in preschoolers with ASD, which may be a reliable neuroimaging biomarker for future studies.Clinical Trial Registration: This study registered with the Chinese Clinical Trial Registry (ChiCTR1900024973) on August 05, 2019.

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases. [ABSTRACT FROM AUTHOR]

The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

We derive a theoretical construct that allows for the characterisation of both scalable and scale free systems within the dynamic causal modelling (DCM) framework. We define a dynamical system to be “scalable” if the same equation of motion continues to apply as the system changes in size. As an example of such a system, we simulate planetary orbits varying in size and show that our proposed methodology can be used to recover Kepler’s third law from the timeseries. In contrast, a “scale free” system is one in which there is no characteristic length scale, meaning that images of such a system are statistically unchanged at different levels of magnification. As an example of such a system, we use calcium imaging collected in murine cortex and show that the dynamical critical exponent, as defined in renormalization group theory, can be estimated in an empirical biological setting. We find that a task-relevant region of the cortex is associated with higher dynamical critical exponents in task vs. spontaneous states and vice versa for a task-irrelevant region.

We derive a theoretical construct that allows for the characterisation of both scalable and scale free systems within the dynamic causal modelling (DCM) framework. We define a dynamical system to be "scalable" if the same equation of motion continues to apply as the system changes in size. As an example of such a system, we simulate planetary orbits varying in size and show that our proposed methodology can be used to recover Kepler's third law from the timeseries. In contrast, a "scale free" system is one in which there is no characteristic length scale, meaning that images of such a system are statistically unchanged at different levels of magnification. As an example of such a system, we use calcium imaging collected in murine cortex and show that the dynamical critical exponent, as defined in renormalization group theory, can be estimated in an empirical biological setting. We find that a task-relevant region of the cortex is associated with higher dynamical critical exponents in task vs. spontaneous states and vice versa for a task-irrelevant region. [ABSTRACT FROM AUTHOR]

Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols.

Although most activities of daily life require simultaneous coordination of both proximal and distal joints, motor preparation during such movements has not been well studied. Previous results for motor preparation have focused on hand/finger movements. For simple hand/finger movements, results have found that such movements typically evoke activity primarily in the contralateral motor cortices. However, increasing the complexity of the finger movements, such as during a distal sequential finger-pressing task, leads to additional recruitment of ipsilateral resources. It has been suggested that this involvement of the ipsilateral hemisphere is critical for temporal coordination of distal joints. The goal of the current study was to examine whether increasing simultaneous coordination of multiple joints (both proximal and distal) leads to a similar increase in coupling with ipsilateral sensorimotor cortices during motor preparation compared to a simple distal movement such as hand opening. To test this possibility, 12 healthy individuals participated in a high-density EEG experiment in which they performed either hand opening or simultaneous hand opening while lifting at the shoulder on a robotic device. We quantified within- and cross-frequency cortical coupling across the sensorimotor cortex for the two tasks using dynamic causal modeling. Both hand opening and simultaneous hand opening while lifting at the shoulder elicited coupling from secondary motor areas to primary motor cortex within the contralateral hemisphere exclusively in the beta band, as well as from ipsilateral primary motor cortex. However, increasing the task complexity by combining hand opening while lifting at the shoulder also led to an increase in cross-frequency coupling within the ipsilateral hemisphere including theta, beta, and gamma frequencies, as well as a change in the coupling frequency of the interhemispheric coupling between the primary motor and premotor cortices. These findings demonstrate that increasing the demand of joint coordination between proximal and distal joints leads to increases in communication with the ipsilateral hemisphere as previously observed in distal sequential finger tasks.

Despite being a commonly used protocol to treat major depressive disorder (MDD), the underlying mechanism of repetitive transcranial magnetic stimulation (rTMS) on dorsolateral prefrontal cortex (DLPFC) remains unclear. In the current study, we investigated the resting-state fMRI data of 100 healthy subjects by exploring three overlapping functional networks associated with the psychopathologically MDD-related areas (the nucleus accumbens, amygdala, and ventromedial prefrontal cortex). Our results showed that these networks converged at the bilateral DLPFC, which suggested that rTMS over DLPFC might improve MDD by remotely modulating the MDD-related areas synergistically. Additionally, they functionally converged at the DMPFC and bilateral insula which are known to be associated with MDD. These two areas could also be potential targets for rTMS treatment. Dynamic causal modelling (DCM) and Granger causality analysis (GCA) revealed that all pairwise connections among bilateral DLPFC, DMPFC, bilateral insula, and three psychopathologically MDD-related areas contained significant causality. The DCM results also suggested that most of the functional interactions between MDD-related areas and bilateral DLPFC, DMPFC, and bilateral insula can predominantly be explained by the effective connectivity from the psychopathologically MDD-related areas to the rTMS stimulation sites. Finally, we found the conventional functional connectivity to be a more representative measure to obtain connectivity parameters compared to GCA and DCM analysis. Our research helped inspecting the convergence of the functional networks related to a psychiatry disorder. The results identified potential targets for brain stimulation treatment and contributed to the optimization of patient-specific brain stimulation protocols. [ABSTRACT FROM AUTHOR]

Recent evidence from neurophysiological and functional imaging research has demonstrated that semantically congruent sounds can modulate the identification of a degraded visual object. However, it remains unclear how different integration regions interact with each other when only a visual object was obscured. The present study aimed to elucidate the neural bases of cross-modal functional interactions in degraded visual object recognition. Naturally degraded images and semantically congruent sounds were used in our experiment. Participants were presented with three different modalities of audio-visual stimuli: auditory only (A), degraded visual only (Vd), and simultaneous auditory and degraded visual (AVd). We used conjunction analysis and the classical 'max criterion' to define three audiovisual integration cortical hubs: the visual association cortex, the superior temporal sulcus and the Heschl's gyrus. Dynamic causal modeling (DCM) was then used to infer effective connectivity between these regions. The DCM results revealed that the modulation of an auditory stimulus resulted in increased connectivity from the Heschl's gyrus to the visual association cortex and from the superior temporal sulcus to the visual association cortex. It was shown that the visual association cortex is modulated not only via feedback and top-down connections from higher-order convergence areas but also via lateral feedforward connectivity from the auditory cortex. The present findings give support to interconnected models of cross-modal information integration. [ABSTRACT FROM AUTHOR]

The view that the left cerebral hemisphere in humans "dominates" over the "subdominant" right hemisphere has been so deeply entrenched in neuropsychology that no amount of evidence seems able to overcome it. In this article, we examine inhibitory cause-and-effect connectivity among human brain structures related to different parts of the triune evolutionary stratification —archicortex, paleocortex and neocortex— in relation to early and late phases of a prolonged resting-state functional magnetic resonance imaging (fMRI) experiment. With respect to the evolutionarily youngest parts of the human cortex, the left and right frontopolar regions, we also provide data on the asymmetries in underlying molecular mechanisms, namely on the differential expression of the protein-coding genes and regulatory microRNA sequences. In both domains of research, our results contradict the established view by demonstrating a pronounced right-to-left vector of causation in the hemispheric interaction at multiple levels of brain organization. There may be several not mutually exclusive explanations for the evolutionary significance of this pattern of lateralization. One of the explanations emphasizes the computational advantage of separating the neural substrates for processing novel information ("exploration") mediated predominantly by the right hemisphere, and processing with reliance on established cognitive routines and representations ("exploitation") mediated predominantly by the left hemisphere. [ABSTRACT FROM AUTHOR]

Background. Ideas about relationships between “I”, egocentric spatial orientation and the sense of bodily “Self ” date back to work by classics of philosophy and psychology. Cognitive neuroscience has provided knowledge about brain areas involved in self-ref­erential processing, such as the rostral prefrontal, temporal and parietal cortices, often active as part of the default mode network (DMN). Objective and Method. Little is known about the contribution of inferior parietal areas to self-referential processing. Therefore, we collected observations of everyday be­havior, social communication and problem solving in patients with brain lesions local­ized either in the left inferior parietal cortex (LIPC group, n = 45) or the right inferior parietal cortex (RIPC group, n = 58). Results. A key characteristic of the LIPC group was an overestimation of task com­plexity. This led to a prolonged phase of redundant and disruptive contemplations pre­ceding task solution. In the RIPC group, we observed disorders in reflective control and voluntary regulation of behavior. Abilities for experiencing emotions, understanding mental states, and social communication were to a great extent lost. Results are inter­preted within a multilevel framework of cognitive-affective organization (velichkovsky, 2002). In particular, we highlight the role of right-hemisphere mechanisms in self-refer­ential cognition, emotional and corporeal awareness. This is consistent with recent data on a profound asymmetry in connectivity of left and right hippocampi within the DMN (Ushakov et al., 2016) Conclusion. It seems that the center of egocentric spatial representation plays a spe­cial role in accessing self-related data. Normally, the right hippocampus provides a holis­tic representation of surrounding and, thus, an easy-to-find gateway into much of what we used to call “subjective experience”. This heuristics becomes misleading in the case of right-sided brain lesions.

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, ηp2 = 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.

Cortical reorganization in response to peripheral nervous system damage is only poorly understood. In patients with complete brachial plexus avulsion and subsequent reconnection of the end of the musculocutaneous nerve to the side of a phrenic nerve, reorganization leads to a doubled arm representation in the primary motor cortex. Despite, homuncular organization being one of the most fundamental principles of the human brain, movements of the affected arm now activate 2 loci: the completely denervated arm representation and the diaphragm representation. Here, we investigate the details behind this peripherally triggered reorganization, which happens in healthy brains. fMRI effective connectivity changes within the motor network were compared between a group of patients and age matched healthy controls at 7 Tesla (6 patients and 12 healthy controls). Results show the establishment of a driving input of the denervated arm area to the diaphragm area which is now responsible for arm movements. The findings extend current knowledge about neuroplasticity in primary motor cortex: a denervated motor area may drive an auxilliary area to reroute its motor output.

Functional Electrical Stimulation (FES) has demonstrated to improve walking ability and to induce the carryover effect, long-lasting persisting improvement. Functional magnetic resonance imaging has been used to investigate effective connectivity differences and longitudinal changes in a group of chronic stroke patients that attended a FES-based rehabilitation program for foot-drop correction, distinguishing between carryover effect responders and non-responders, and in comparison with a healthy control group. Bayesian hierarchical procedures were employed, involving nonlinear models at within-subject level—dynamic causal models—and linear models at between-subjects level. Selected regions of interest were primary sensorimotor cortices (M1, S1), supplementary motor area (SMA), and angular gyrus. Our results suggest the following: (i) The ability to correctly plan the movement and integrate proprioception information might be the features to update the motor control loop, towards the carryover effect, as indicated by the reduced sensitivity to proprioception input to S1 of FES non-responders; (ii) FES-related neural plasticity supports the active inference account for motor control, as indicated by the modulation of SMA and M1 connections to S1 area; (iii) SMA has a dual role of higher order motor processing unit responsible for complex movements, and a superintendence role in suppressing standard motor plans as external conditions changes.

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, η p 2 = 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. [ABSTRACT FROM AUTHOR]

Abstract Bimanual coordination is impaired in Parkinson's disease (PD), affecting patients' quality of life. Besides dysfunction of the basal ganglia network, alterations of cortical oscillatory coupling, particularly between prefrontal and (pre-)motoric areas, are thought to underlie this impairment. Here, we studied 16 PD patients OFF and ON medication and age-matched healthy controls recording high-resolution electroencephalography (EEG) during performance of spatially coupled and uncoupled bimanual finger movements. Dynamic causal modeling (DCM) for induced responses was used to infer task-induced effective connectivity within a network comprising bilateral prefrontal cortex (PFC), lateral premotor cortex (lPM), supplementary motor area (SMA), and primary motor cortex (M1). Performing spatially coupled movements, excitatory left-hemispheric PFC to lPM coupling was significantly stronger in controls compared to unmedicated PD patients. Levodopa-induced enhancement of this connection correlated with increased movement accuracy. During performance of spatially uncoupled movements, PD patients OFF medication exhibited inhibitory connectivity from left PFC to SMA. Levodopa intake diminished these inhibitory influences and restored excitatory PFC to lPM coupling. This restoration, however, did not improve motor function. Concluding, our results indicate that lateralization of prefrontal to premotor connectivity in PD can be augmented by levodopa substitution and is of compensatory nature up to a certain extent of complexity. Highlights • Bimanual coordination is impaired in Parkinson's Disease (PD). • In PD, β-activity in left primary motor cortex (M1) induces γ-activity in right M1. • β-γ-coupling between primary motor cortices is associated with poor motor performance. • Levodopa increases left prefrontal to lateral premotor coupling in PD. • This enhancement relates to improved motor control up to a certain complexity level. [ABSTRACT FROM AUTHOR]

Abstract Autobiographical memories (AMs) are often colored by emotions experienced during an event or those arising following further appraisals. However, how affective components of memories affect the brain-wide network recruited during the recollection of AMs remains largely unknown. Here, we examined effective connectivity during the elaboration of AMs - when retrieved episodic details are integrated to form a vivid construct - in the network composed by ventromedial prefrontal cortex (vmPFC), hippocampus and amygdala, three key regions associated with memory and affective processes. Functional MRI data was collected while volunteers recollected personal events of different types of valence and emotional intensity. Using dynamic causal modeling, we characterized the connections within the triadic network, and examined how they were modulated by the emotional intensity experienced during an event, and the valence of the affect evoked when recollecting the associated memory. Results primarily indicated the existence of a vmPFC to hippocampus effective connectivity during memory elaboration. Furthermore, the strength of the connectivity increased when participants relived memories of highly emotionally arousing events or that elicited stronger positive affect. These results indicate that the vmPFC drives hippocampal activity during memory elaboration, and plays a critical role in shaping affective responses that emanate from AMs. Highlights • Volunteers recollected autobiographical memories (AMs) of different emotional intensities and valence types in the scanner. • We used dynamic causal modeling to assess effective connectivity in the vmPFC-hippocampus-amygdala network. • Results indicated that the vmPFC is a driver of hippocampal activity during the elaboration of AMs. • Moreover, the strength of the vmPFC to hippocampus connectivity increased during the elaboration of emotional AMs. • These results show that the vmPFC plays an important role during the elaboration of AMs, in particular, emotional AMs. [ABSTRACT FROM AUTHOR]

Predictive coding postulates that the brain continually predicts forthcoming sensory events based on past experiences in order to process sensory information and respond to unexpected events in a fast and efficient manner. Predictive coding models in the context of overt speech are believed to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively engaged when listening to externally-generated, temporally-predicable speech sounds. Using Electroencephalography (EEG) and Dynamic Causal Modeling (DCM) we investigated network models that are structurally connected via the arcuate fasciculus from primary auditory cortex to Wernicke’s and via Geschwind’s territory to Broca’s area. Connections between Broca’s and supplementary motor area, which are structurally connected by the frontal aslant, were also included. The results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions involved in the production and processing of auditory stimuli are also effectively connected.

Highlights • The causal connectivity among evolutionary different regions of the cortex is reported. • Resting-state data show a strong rightward bias in the regions’ interactions. • There is also a top-down bias with the only exception of the right hippocampus. • The right ventrolateral cortex seems to be a prominent source of causal influences. • Several levels of brain cognitive-affective organization are implied by these results. Abstract 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. [ABSTRACT FROM AUTHOR]

Abstract Contrary to earlier approaches that focused on the contributions of isolated brain regions to the emergence of creativity, there is now growing consensus that creative thought emerges from the interaction of multiple brain regions, often embedded within larger brain networks. Specifically, recent evidence from studies of divergent thinking suggests that kernel ideas emerge in posterior brain regions residing within the semantic system and/or the default mode network (DMN), and that the prefrontal cortex (PFC) regions within the executive control network (ECN) constrain those ideas for generating outputs that meet task demands. However, despite knowing that regions within these networks exhibit interaction, to date the direction of the relationship has not been tested directly. By applying Dynamic Causal Modeling (DCM) to fMRI data collected during a divergent thinking task, we tested the hypothesis that the PFC exerts unidirectional control over the middle temporal gyrus (MTG) and the inferior parietal lobule (IPL), vs. the hypothesis that these two sets of regions exert bidirectional control over each other (in the form of feedback loops). The data were consistent with the former model by demonstrating that the right inferior frontal gyrus (IFG) exerts unidirectional control over MTG and IPL, although the evidence was somewhat stronger in the case of the MTG than the IPL. Our findings highlight potential causal pathways that could underlie the neural bases of divergent thinking. Highlights • IFG controls brain activation in MTG during divergent thinking. • IFG also controls brain activation in MTG during recall from memory. • IFG's control over MTG activation appears to be task-invariant. • IFG exerts weaker control over IPL during divergent thinking. • IFG and IPL exert bidirectional control over one another during recall from memory. [ABSTRACT FROM AUTHOR]

Predictive coding postulates that the brain continually predicts forthcoming sensory events based on past experiences in order to process sensory information and respond to unexpected events in a fast and efficient manner. Predictive coding models in the context of overt speech are believed to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively engaged when listening to externallygenerated, temporally-predicable speech sounds. Using Electroencephalography (EEG) and Dynamic Causal Modeling (DCM) we investigated network models that are structurally connected via the arcuate fasciculus from primary auditory cortex to Wernicke's and via Geschwind's territory to Broca's area. Connections between Broca's and supplementary motor area, which are structurally connected by the frontal aslant, were also included. The results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions involved in the production and processing of auditory stimuli are also effectively connected. [ABSTRACT FROM AUTHOR]

Faces contain multifaceted information that is important for human communication. Neuroimaging studies have revealed face-specific activation in multiple brain regions, including the inferior occipital gyrus (IOG) and amygdala; it is often assumed that these regions constitute the neural network responsible for the processing of faces. However, it remains unknown whether and how these brain regions transmit information during face processing. This study investigated these questions by applying dynamic causal modeling of induced responses to human intracranial electroencephalography data recorded from the IOG and amygdala during the observation of faces, mosaics, and houses in upright and inverted orientations. Model comparisons assessing the experimental effects of upright faces versus upright houses and upright faces versus upright mosaics consistently indicated that the model having face-specific bidirectional modulatory effects between the IOG and amygdala was the most probable. The experimental effect between upright versus inverted faces also favored the model with bidirectional modulatory effects between the IOG and amygdala. The spectral profiles of modulatory effects revealed both same-frequency (e.g., gamma-gamma) and cross-frequency (e.g., theta-gamma) couplings. These results suggest that the IOG and amygdala communicate rapidly with each other using various types of oscillations for the efficient processing of faces. Hum Brain Mapp 38:4511-4524, 2017. © 2017 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Theoretical models and preceding studies have described age-related alterations in neuronal activation of frontoparietal regions in a working memory (WM) load-dependent manner. However, to date, underlying neuronal mechanisms of these WM load-dependent activation changes in aging remain poorly understood. The aim of this study was to investigate these mechanisms in terms of effective connectivity by application of dynamic causal modeling with Bayesian Model Selection. Eighteen healthy younger (age: 20–32 years) and 32 older (60–75 years) participants performed an n-back task with 3 WM load levels during functional magnetic resonance imaging (fMRI). Behavioral and conventional fMRI results replicated age group by WM load interactions. Importantly, the analysis of effective connectivity derived from dynamic causal modeling, indicated an age- and performance-related reduction in WM load-dependent modulation of connectivity from dorsolateral prefrontal cortex to inferior parietal lobule. This finding provides evidence for the proposal that age-related WM decline manifests as deficient WM load-dependent modulation of neuronal top-down control and can integrate implications from theoretical models and previous studies of functional changes in the aging brain. [ABSTRACT FROM AUTHOR]

Recent views of information processing in the (human) brain emphasize the hierarchical structure of the central nervous system, which is assumed to form the basis of a functional hierarchy. Hierarchical predictive processing refers to the notion that higher levels try to predict activity in lower areas, while lower levels transmit a prediction error up the hierarchy whenever the predictions fail. The present study aims at testing hypothetical modulatory effects of unpredictable visual motion on forward connectivities within the visual cortex. Functional magnetic resonance imaging was acquired from 35 healthy volunteers while viewing a moving ball under three different levels of predictability. In two different runs subjects were asked to attend to direction changes in the ball's motion, where a button-press was required in one of these runs only. Dynamic causal modeling was applied to a network comprising V1, V5 and posterior parietal cortex in the right hemisphere. The winning model of a Bayesian model selection indicated an enhanced strength in the forward connection from V1 to V5 with decreasing predictability for the run requiring motor response. These results support the notion of hierarchical predictive processing in the sense of an augmented bottom-up transmission of prediction error with increasing uncertainty about motion direction. This finding may be of importance for promoting our understanding of trait characteristics in psychiatric disorders, as an increased forward propagation of prediction error is assumed to underlie schizophrenia and may be observable at early stages of the disease. [ABSTRACT FROM AUTHOR]

• Age-, reading-level-matched and developmental connectivity analyses in children. • Effective connectivity was assessed in children with and without dyslexia. • Connectivity was also assessed in development of children with and without dyslexia. • Feedforward connections from the visual word form area (VWFA) increased with age. • Connectivity from the inferior parietal lobule to the VWFA was altered in dyslexia. Altered brain connectivity between regions of the reading network has been associated with reading difficulties. However, it remains unclear whether connectivity differences between children with dyslexia (DYS) and those with typical reading skills (TR) are specific to reading impairments or to reading experience. In this functional MRI study, 132 children (M = 10.06 y, SD = 1.46) performed a phonological lexical decision task. We aimed to disentangle (1) disorder-specific from (2) experience-related differences in effective connectivity and to (3) characterize the development of DYS and TR. We applied dynamic causal modeling to age-matched (n dys = 25, n TR = 35) and reading-level-matched (n dys = 25, n TR = 22) groups. Developmental effects were assessed in beginning and advanced readers (TR: n beg = 48, n adv = 35, DYS: n beg = 24, n adv = 25). We show that altered feedback connectivity between the inferior parietal lobule and the visual word form area (VWFA) during print processing can be specifically attributed to reading impairments, because these alterations were found in DYS compared to both the age-matched and reading-level-matched TR. In contrast, feedforward connectivity from the VWFA to parietal and frontal regions characterized experience in TR and increased with age and reading skill. These directed connectivity findings pinpoint disorder-specific and experience-dependent alterations in the brain's reading network. [ABSTRACT FROM AUTHOR]

Prior studies suggest that the cerebellum contributes to the prediction of action sequences as well as the detection of social violations. In this dynamic causal modeling study, we explored the effective connectivity of the cerebellum with the cerebrum in processing social action sequences. A first model aimed to explore functional cerebello-cerebral connectivity when learning trait/stereotype-implying action sequences. We found many significant bidirectional connectivities between mentalizing areas of the cerebellum and the cerebrum including the temporo-parietal junction (TPJ) and medial prefrontal cortex (mPFC). Within the cerebrum, we found significant connectivity between the right TPJ and the mPFC, and between the TPJ bilaterally. A second model aimed to investigate cerebello-cerebral connectivity when conflicting information arises. We found many significant closed loops between the cerebellum and cerebral mentalizing (e.g. dorsal mPFC) and executive control areas (e.g. medial and lateral prefrontal cortices). Additional closed loops were found within the cerebral mentalizing and executive networks. The current results confirm prior research on effective connectivity linking the cerebellum with mentalizing areas in the cerebrum for predicting social sequences, and extend it to cerebral executive areas for social violations. Overall, this study emphasizes the critical role of cerebello-cerebral connectivity in understanding social sequences., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Balance of motor network activity between the two brain hemispheres after stroke is crucial for functional recovery. Several studies have extensively studied the role of the affected brain hemisphere to better understand changes in motor network activity following stroke. Very few studies have examined the role of the unaffected brain hemisphere and confirmed the test-retest reliability of connectivity measures on unaffected hemisphere. We recorded blood oxygenation level dependent functional magnetic resonance imaging (fMRI) signals from nine stroke survivors with hemiparesis of the left or right hand. Participants performed a motor execution task with affected hand, unaffected hand, and both hands simultaneously. Participants returned for a repeat fMRI scan 1 week later. Using dynamic causal modeling (DCM), we evaluated effective connectivity among three motor areas: the primary motor area (M1), the premotor cortex (PMC) and the supplementary motor area for the affected and unaffected hemispheres separately. Five participants' manual motor ability was assessed by Fugl-Meyer Motor Assessment scores and root-mean square error of participants' tracking ability during a robot-assisted game. We found (i) that the task performance with the affected hand resulted in strengthening of the connectivity pattern for unaffected hemisphere, (ii) an identical network of the unaffected hemisphere when participants performed the task with their unaffected hand, and (iii) the pattern of directional connectivity observed in the affected hemisphere was identical for tasks using the affected hand only or both hands. Furthermore, paired t-test comparison found no significant differences in connectivity strength for any path when compared with one-week follow-up. Brain-behavior linear correlation analysis showed that the connectivity patterns in the unaffected hemisphere more accurately reflected the behavioral conditions than the connectivity patterns in the affected hemisphere. Above findings enrich our knowledge of unaffected brain hemisphere following stroke, which further strengthens our neurobiological understanding of stroke-affected brain and can help to effectively identify and apply stroke-treatments. [ABSTRACT FROM AUTHOR]

While monetary compensation is considered the most effective service recovery strategy, relief theory claims that humor may also be useful in service recovery situations. This study investigated the effects of humor in service recovery using dynamic causal modeling and parametric empirical Bayes analysis to identify effective connectivity (EC) patterns in the dopaminergic reward system across four conditions representing different service recovery strategies: monetary compensation and humor (MH), monetary compensation and an apology (MA), non-monetary compensation using humor (H), and non-monetary compensation using an apology (CON, the control condition). The findings support the importance of the nucleus accumbens (NAc) in the monetary compensation (MH and MA) conditions and the amygdala in the non-monetary compensation (H and CON) conditions. Monetary compensation (MH and MA) resulted in right substantia nigra (rSN) to NAc EC, suggesting the processing of recovery satisfaction associated with perceived outcome fairness. Conversely, non-monetary compensation strategies (H and CON) resulted in left substantia nigra (lSN) to amygdala EC, suggesting the processing of satisfaction related to perceived interactional fairness. The use of humor for service recovery resulted in VTA-to-lSN-to-amygdala EC during humor appreciation, while the use of apologies (CON and MA) resulted in lSN-to-amygdala and lSN-to-VTA connectivity. Surprisingly, processing satisfaction in the MH condition did not activate the amygdala during humor appreciation. Coping humor could be norm-violating for service recovery, and its effectiveness depends on multiple factors. The results suggest that monetary compensation, humorous responses, and apologies play key roles in neurological responses to service recovery strategies. • We examined service recovery strategies that used compensation, humor, and apologies. • Coping humor elicits relief and amygdala activation during service recovery. • The nucleus accumbens (NAc) plays a key role in processing monetary compensation. • Right substantia nigra (rSN) to NAc connectivity was found for monetary compensation. • Humor and apology strategies resulted in left SN (lSN) to amygdala connectivity. [ABSTRACT FROM AUTHOR]

• Repeated face images cause decreased fMRI signal in early visual cortex (EVC), occipital face area (OFA), and fusiform face area (FFA). • The repetition suppression (RS) effect can be explained by the modulation of connectivity between EVC and OFA/FFA. • The between-region modulation supports synchronization and predictive coding theories of RS. • Face perception modulates nearly all within- and between-region connectivity, including direct connections from EVC to FFA. Stimulus repetition normally causes reduced neural activity in brain regions that process that stimulus. Some theories claim that this "repetition suppression" reflects local mechanisms such as neuronal fatigue or sharpening within a region, whereas other theories claim that it results from changed connectivity between regions, following changes in synchrony or top-down predictions. In this study, we applied dynamic causal modeling (DCM) on a public fMRI dataset involving repeated presentations of faces and scrambled faces to test whether repetition affected local (self-connections) and/or between-region connectivity in left and right early visual cortex (EVC), occipital face area (OFA) and fusiform face area (FFA). Face "perception" (faces versus scrambled faces) modulated nearly all connections, within and between regions, including direct connections from EVC to FFA, supporting a non-hierarchical view of face processing. Face "recognition" (familiar versus unfamiliar faces) modulated connections between EVC and OFA/FFA, particularly in the left hemisphere. Most importantly, immediate and delayed repetition of stimuli were also best captured by modulations of connections between EVC and OFA/FFA, but not self-connections of OFA/FFA, consistent with synchronization or predictive coding theories, though also possibly reflecting local mechanisms like synaptic depression. [ABSTRACT FROM AUTHOR]

Prefrontal and parietal cortex, including the default mode network (DMN; medial prefrontal cortex (mPFC), and posterior cingulate cortex, PCC), have been implicated in addiction. Nonetheless, it remains unclear which brain regions play a crucial role in smoking addiction and the relationship among these regions. Since functional connectivity only measures correlations, addiction-related changes in effective connectivity (directed information flow) among these distributed brain regions remain largely unknown. Here we applied spectral dynamic causal modeling (spDCM) to resting state fMRI to characterize changes in effective connectivity among core regions in smoking addiction. Compared to nonsmokers, smokers had reduced effective connectivity from PCC to mPFC and from RIPL to mPFC, a higher self-inhibition within PCC and a reduction in the amplitude of endogenous neuronal fluctuations driving the mPFC. These results indicate that spDCM can differentiate the functional architectures between the two groups, and may provide insight into the brain mechanisms underlying smoking addiction. Our results also suggest that future brain-based prevention and intervention in addiction should consider the amelioration of mPFC-PCC-IPL circuits. [ABSTRACT FROM AUTHOR]

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state. [ABSTRACT FROM AUTHOR]

The functional MRI (fMRI) signal is an indirect measure of neuronal activity. In order to deconvolve the neuronal activity from the experimental fMRI data, biophysical generative models have been proposed describing the link between neuronal activity and the cerebral blood flow (the neurovascular coupling), and further the hemodynamic response and the BOLD signal equation. These generative models have been employed both for single brain area deconvolution and to infer effective connectivity in networks of multiple brain areas. In the current paper, we introduce a new fMRI model inspired by experimental observations about the physiological underpinnings of the BOLD signal and compare it with the generative models currently used in dynamic causal modeling (DCM), a widely used framework to study effective connectivity in the brain. We consider three fundamental aspects of such generative models for fMRI: (i) an adaptive two-state neuronal model that accounts for a wide repertoire of neuronal responses during and after stimulation; (ii) feedforward neurovascular coupling that links neuronal activity to blood flow; and (iii) a balloon model that can account for vascular uncoupling between the blood flow and the blood volume. Finally, we adjust the parameterization of the BOLD signal equation for different magnetic field strengths. This paper focuses on the form, motivation and phenomenology of DCMs for fMRI and the characteristics of the various models are demonstrated using simulations. These simulations emphasize a more accurate modeling of the transient BOLD responses — such as adaptive decreases to sustained inputs during stimulation and the post-stimulus undershoot. In addition, we demonstrate using experimental data that it is necessary to take into account both neuronal and vascular transients to accurately model the signal dynamics of fMRI data. By refining the models of the transient responses, we provide a more informed perspective on the underlying neuronal process and offer new ways of inferring changes in local neuronal activity and effective connectivity from fMRI. [ABSTRACT FROM AUTHOR]