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Anticipating the behaviour of moving objects in the physical environment is essential for a wide range of daily actions. This ability is thought to rely on mental simulations and has been shown to involve frontoparietal and early visual areas. Yet, the connectivity patterns between these regions during intuitive physical inference remain largely unknown. In this study, participants underwent fMRI while performing a task requiring them to infer the parabolic trajectory of an occluded ball falling under Newtonian physics, and a control task. Building on our previous research showing that when solving the physical inference task, early visual areas encode task-specific and perception-like information about the inferred trajectory, the present study aimed to (i) identify regions that are functionally coupled with early visual areas during the physical inference task, and (ii) investigate changes in effective connectivity within this network of regions. We found that early visual areas are functionally connected to a set of parietal and premotor regions when inferring occluded trajectories. Using dynamic causal modelling, we show that predicting occluded trajectories is associated with changes in effective connectivity within a parieto-premotor network, which may drive internally generated early visual activity in a top-down fashion. These findings offer new insights into the interaction between early visual and frontoparietal regions during physical inference, contributing to our understanding of the neural mechanisms underlying the ability to predict physical outcomes. [ABSTRACT FROM AUTHOR]

This study assesses the reliability of resting‐state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance‐based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting‐state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between‐subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within‐subject between‐session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first‐level DCMs, we compare model evidence associated with the covariance among subject‐specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within‐subject, within‐session, and between‐epochs; (ii) within‐subject between‐session; and (iii) within‐site between‐subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance‐based DCMs for resting‐state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders. [ABSTRACT FROM AUTHOR]

Bo Zhang,1,2 Minmin Guo,2 Ting Dong,2 Huajuan Yang,2 Qiujuan Zhang,2 Quanxin Yang,2 Xiaoqian Zhou,2 Cuiping Mao,2 Ming Zhang1 1Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China; 2Department of Medical Imaging, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, People’s Republic of ChinaCorrespondence: Ming Zhang, Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710061, People’s Republic of China, Email zmmri@163.comPurpose: Chronic low back pain (cLBP) is a recurring and intractable disease that is often accompanied by emotional and cognitive disorders such as depression and anxiety. The nucleus accumbens (NAc) plays an important role in mediating emotional and cognitive processes and analgesia. This study investigated the resting-state functional connectivity (rsFC) and effective connectivity (EC) of NAc and its subregions in cLBP.Methods: Thirty-four cLBP patients and 34 age- and sex-matched healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Seed-based rsFC and Dynamic Causal Modelling (DCM) were used to examine the alteration of the rsFC and EC of the NAc.Results: Our results showed that the cLBP group had increased rsFC of the bilateral NAc-left superior frontal cortex (SFC), orbital frontal cortex (OFC), left angular gyrus, the left NAc-bilateral middle temporal gyrus, as well as decreased rsFC of left NAc-left supramarginal gyrus, right precentral gyrus, left cerebellum, brainstem (medulla oblongata), and right insula pathways compared with the HC; the results of the subregions were largely consistent with the whole NAc. In addition, the rsFC of the left NAc-left SFC was negatively correlated with Hamilton’s Depression Scale (HAMD) scores (r = − 0.402, p = 0.018), and the rsFC of left NAc-OFC was positively correlated with present pain intensity scores (r = 0.406, p = 0.017) in the cLBP group. DCM showed that the cLBP group showed significantly increased EC from the left cerebellum to the right NAc (p = 0.012) as compared with HC.Conclusion: Overall, our findings demonstrate aberrant rsFC and EC between NAc and regions that are associated with emotional regulation and cognitive processing in individuals with cLBP, underscoring the pivotal roles of emotion and cognition in cLBP.Keywords: low back pain, nucleus accumbens, resting-state functional connectivity, dynamic causal modelling

The triple‐network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple‐network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange‐I datasets. Spatial reference‐based independent component analysis was used to identify the anterior and posterior part of default‐mode network (aDMN and pDMN), salience network (SN), and bilateral executive‐control network (ECN) from resting‐state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple‐network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self‐inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self‐inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple‐networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments. [ABSTRACT FROM AUTHOR]

An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation–inhibition (E–I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E–I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition. [ABSTRACT FROM AUTHOR]

Abstract Background Microdosing psychedelics is a phenomenon with claimed cognitive benefits that are relatively untested clinically. Pre-clinically, psychedelics have demonstrated enhancing effects on neuroplasticity, which cannot be measured directly in humans, but may be indexed by non-invasive electroencephalography (EEG) paradigms. This study used a visual long-term potentiation (LTP) EEG paradigm to test the effects of microdosed lysergic acid diethylamide (LSD) on neural plasticity, both acutely while on the drug and cumulatively after microdosing every third day for six weeks. Healthy adult males (n = 80) completed the visual LTP paradigm at baseline, 2.5 h following a dose of 10 µg of LSD or inactive placebo, and 6 weeks later after taking 14 repeated microdoses. Visually induced LTP was used as indirect index of neural plasticity. Surface level event-related potential (ERPs) based analyses are presented alongside dynamic causal modelling of the source localised data using a generative thalamocortical model (TCM) of visual cortex to elucidate underlying synaptic circuitry. Results Event-related potential (ERP) analyses of N1b and P2 components did not show evidence of changes in visually induced LTP by LSD either acutely or after 6 weeks of regular dosing. However modelling the complete timecourse of the ERP with the TCM demonstrated changes in laminar connectivity in primary visual cortex. This primarily included changes to self-gain and inhibitory input parameters acutely. Layer 2/3 to layer 5 excitatory connectivity was also different between LSD and placebo groups. After regular dosing only excitatory input from layer 2/3 into layer 5 and inhibitory input into layer 4 were different between groups. Conclusions Without modulation of the ERPs it is difficult to relate the findings to other studies visually inducing LTP. It also indicates the classic peak analysis may not be sensitive enough to demonstrate evidence for changes in LTP plasticity in humans at such low doses. The TCM provides a more sensitive approach to assessing changes to plasticity as differences in plasticity mediated laminar connectivity were found between the LSD and placebo groups. Trial registration: ANZCTR registration number ACTRN12621000436875; Registered 16/04/2021 https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381476 .

INTRODUCTION: Different facets of divergent thinking (DT) are associated with connectivity between different cerebral areas. However, the causal interactions between the key DT nodes have yet to be explored. It is hypothesised that with creativity stimulation, changes in effective connectivity among regions will be observed. MATERIALS AND METHODS: By using control (n=26) and experimental (n=24) participants, this study aimed to investigate the effective connectivity between brain areas associated with divergent thinking that accentuate fluency, flexibility, and originality. The experimental participants attended a two-day creativity stimulation, followed by three task-based fMRI sessions for all participants, which included basic use (BU) identification, alternative use (AU) generation and unusual use (UU) determination tasks. Dynamic causal modelling (DCM) was used to determine the most optimal causal model representing the most possible modulatory influence on the connections between medial prefrontal cortices (mPFC), inferior frontal gyrus (IFG), and inferior parietal lobule (IPL). RESULTS: The experimental participants scored higher fluency and flexibility than the controls (p<0.05). At neuronal level, the control group showed similar intrinsic connections receiving modulatory influence for AU and UU tasks, while the experimental group preferred a different perturbation of connection between both tasks. These intergroup differences may be caused by different thinking strategies involving semantic and episodic memory retrieval, and integration of remotely associated ideas to construct new combination among the experimental participants. CONCLUSION: Different DT demands may influence the effective connectivity between mPFC, IFG and IPL especially among individuals with higher DT abilities, especially in fluency and flexibility versus originality. [ABSTRACT FROM AUTHOR]

Previous studies have suggested that the Papez circuit may be involved in the cognitive impairment observed after hearing loss in presbycusis patients, yet relatively little is known about the pattern of changes in effective connectivity within the circuit. The aim of this study was to investigate abnormal alterations in resting‐state effective connectivity within the Papez circuit and their association with cognitive decline in presbycusis patients. The spectral dynamic causal modelling (spDCM) approach was used for resting‐state effective connectivity analysis in 61 presbycusis patients and 52 healthy controls (HCs) within the Papez circuit. The hippocampus (HPC), mamillary body (MB), anterior thalamic nuclei (ATN), anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), entorhinal cortex (ERC), subiculum (Sub) and parahippocampal gyrus (PHG) were selected as the regions of interest (ROIs). The fully connected model difference in effective connectivity between the two groups was assessed, and the correlation between effective connectivity alteration and cognitive scale was analysed. We found that presbycusis patients demonstrated decreased effective connectivity from MB, PCC, and Sub to ACC relative to HCs, whereas higher effective connectivity strength was shown from HPC to MB, from ATN to PHG and from PHG to Sub. The effective connectivity from PHG to Sub was significantly negatively correlated with the complex figure test (CFT)‐delay score (rho = −0.259, p = 0.044). The results support and reinforce the role of abnormal effective connectivity within the Papez circuit in the pathophysiology of presbycusis‐related cognitive impairment and reveal its potential as a novel imaging marker. [ABSTRACT FROM AUTHOR]

Introduction: The combined oral contraceptive (COC) pill is often employed to address physical and neurological symptoms in menstrual cycle-related disorders by suppressing shifts in endogenous gonadal hormone fluctuations. Symptom persistence, especially in the lead up to the hormone-free interval (HFI), suggests an underlying neurobiological mechanism of preserved cycling. Our study utilised a non-invasive method of visually inducing long-term potentiation (LTP) to index changes in neural plasticity in the absence of hormonal fluctuations. Methods: Visually induced LTP was recorded using electroencephalography in 24 healthy female COC users across three sessions: days 3 and 21 during active hormone pills, and day 24 during the HFI. The Daily Record of the Severity of Problems (DRSP) questionnaire tracked premenstrual symptoms. Dynamic causal modelling (DCM) was used to elucidate the neural connectivity and receptor activity changes associated with LTP across different days of COC. Results: Visually induced LTP was greater on day 21 than day 3 (p = 0.011) and was localised to the P2 visually evoked potential. There was no effect of the HFI (day 24) on LTP. DCM of differences between days 3 and 21 showed changes to inhibitory interneuronal gating of LTP in cortical layer VI. The DRSP only showed a significant increase in symptoms in the HFI, meaning the LTP result appeared more sensitive to cyclicity. Conclusions: This study provides objective evidence of preserved cyclicity in COC users through enhanced LTP on day 21 compared to day 3 of a 28-day COC regimen, indicating that relatively higher excitation in the brain despite peripheral gonadal suppression may underlie and exacerbate menstrual cycle-related disorders. [ABSTRACT FROM AUTHOR]

The habenula has been implicated in the pathogenesis of pain and analgesia, while evidence concerning its function in chronic low back pain (cLBP) is sparse. This study aims to investigate the resting‐state functional connectivity (rsFC) and effective connectivity of the habenula in 52 patients with cLBP and 52 healthy controls (HCs) and assess the feasibility of distinguishing cLBP from HCs based on connectivity by machine learning methods. Our results indicated significantly enhanced rsFC of the habenula‐left superior frontal cortex (SFC), habenula‐right thalamus, and habenula‐bilateral insular pathways as well as decreased rsFC of the habenula‐pons pathway in cLBP patients compared to HCs. Dynamic causal modelling revealed significantly enhanced effective connectivity from the right thalamus to right habenula in cLBP patients compared with HCs. RsFC of the habenula‐SFC was positively correlated with pain intensities and Hamilton Depression scores in the cLBP group. RsFC of the habenula‐right insula was negatively correlated with pain duration in the cLBP group. Additionally, the combination of the rsFC of the habenula‐SFC, habenula‐thalamus, and habenula‐pons pathways could reliably distinguish cLBP patients from HCs with an accuracy of 75.9% by support vector machine, which was validated in an independent cohort (N = 68, accuracy = 68.8%, p =.001). Linear regression and random forest could also distinguish cLBP and HCs in the independent cohort (accuracy = 73.9 and 55.9%, respectively). Overall, these findings provide evidence that cLBP may be associated with abnormal rsFC and effective connectivity of the habenula, and highlight the promise of machine learning in chronic pain discrimination. [ABSTRACT FROM AUTHOR]

This paper investigates how brain regions are connected while we try to hold and maintain verbal information briefly in our minds (verbal working memory). We examined this in both adults and children, and how reading difficulties (dyslexia) affect this process using a functional Magnetic Resonance Imaging (fMRI). fMRI is a non-invasive brain scan that can show changes in brain activity during a task. We examined the connectivity of these brain networks between 35 young adults and compared them to brain networks seen in 10 typically developing children. We also examined how the network connections are different in 10 children with dyslexia. The findings of this study may provide insights to understand dyslexia better. It can help improve our ability to diagnose the condition, and develop precise ways to help children struggling with reading. [ABSTRACT FROM AUTHOR]

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We present a hierarchical empirical Bayesian framework for testing hypotheses about neurotransmitters’ concertation as empirical prior for synaptic physiology using ultra-high field magnetic resonance spectroscopy (7T-MRS) and magnetoencephalography data (MEG). A first level dynamic causal modelling of cortical microcircuits is used to infer the connectivity parameters of a generative model of individuals’ neurophysiological observations. At the second level, individuals’ 7T-MRS estimates of regional neurotransmitter concentration supply empirical priors on synaptic connectivity. We compare the group-wise evidence for alternative empirical priors, defined by monotonic functions of spectroscopic estimates, on subsets of synaptic connections. For efficiency and reproducibility, we used Bayesian model reduction (BMR), parametric empirical Bayes and variational Bayesian inversion. In particular, we used Bayesian model reduction to compare alternative model evidence of how spectroscopic neurotransmitter measures inform estimates of synaptic connectivity. This identifies the subset of synaptic connections that are influenced by individual differences in neurotransmitter levels, as measured by 7T-MRS. We demonstrate the method using resting-state MEG (i.e., task-free recording) and 7T-MRS data from healthy adults. Our results confirm the hypotheses that GABA concentration influences local recurrent inhibitory intrinsic connectivity in deep and superficial cortical layers, while glutamate influences the excitatory connections between superficial and deep layers and connections from superficial to inhibitory interneurons. Using within-subject split-sampling of the MEG dataset (i.e., validation by means of a held-out dataset), we show that model comparison for hypothesis testing can be highly reliable. The method is suitable for applications with magnetoencephalography or electroencephalography, and is well-suited to reveal the mechanisms of neurological and psychiatric disorders, including responses to psychopharmacological interventions.

Core regions of the salience network (SN), including the anterior insula (aINS) and dorsal anterior cingulate cortex (dACC), coordinate rapid adaptive changes in attentional and autonomic processes in response to negative emotional events. In doing so, the SN incorporates bottom-up signals from subcortical brain regions, such as the amygdala and periaqueductal gray (PAG). However, the precise influence of these subcortical regions is not well understood. Using ultra-high field 7-Tesla functional magnetic resonance imaging, this study investigated the bottom-up interactions of the amygdala and PAG with the SN during negative emotional salience processing. Thirty-seven healthy participants completed an emotional oddball paradigm designed to elicit a salient negative emotional response via the presentation of random, task-irrelevant negative emotional images. Negative emotional processing was associated with prominent activation in the SN, spanning the amygdala, PAG, aINS, and dACC. Consistent with previous research, analysis using dynamic causal modelling revealed an excitatory influence from the amygdala to the aINS, dACC, and PAG. In contrast, the PAG showed an inhibitory influence on amygdala, aINS and dACC activity. Our findings suggest that the amygdala may amplify the processing of negative emotional stimuli in the SN to enable upstream access to attentional resources. In comparison, the inhibitory influence of the PAG possibly reflects its involvement in modulating sympathetic-parasympathetic autonomic arousal mediated by the SN. This PAG-mediated effect may be driven by amygdala input and facilitate bottom-up processing of negative emotional stimuli. Overall, our results show that the amygdala and PAG modulate divergent functions of the SN during negative emotional processing.

The exact mechanisms behind the effects of transcranial direct current stimulation (tDCS) at a network level are still poorly understood, with most studies to date focusing on local (cortical) effects and changes in motor-evoked potentials or BOLD signal. Here, we explored stationary and dynamic effective connectivity across the motor network at rest in two experiments where we applied tDCS over the primary motor cortex (M1-tDCS) or the cerebellum (cb-tDCS) respectively. Two cohorts of healthy volunteers (n = 21 and n = 22) received anodal, cathodal, and sham tDCS sessions (counterbalanced) during 20 min of resting-state functional magnetic resonance imaging (fMRI). We used spectral Dynamic Causal Modelling (DCM) and hierarchical Parametrical Empirical Bayes (PEB) to analyze data after (compared to a pre-tDCS baseline) and during stimulation. We also implemented a novel dynamic (sliding windows) DCM/PEB approach to model the nature of network reorganisation across time. In both experiments we found widespread effects of tDCS that extended beyond the targeted area and modulated effective connectivity between cortex, thalamus, and cerebellum. These changes were characterised by unique nonlinear temporal fingerprints across connections and polarities. Our results support growing research challenging the classic notion of anodal and cathodal tDCS as excitatory and inhibitory respectively, as well as the idea of a cumulative effect of tDCS over time. Instead, they described a rich set of changes with specific spatial and temporal patterns. Our work provides a starting point for advancing our understanding of network-level tDCS effects and may guide future work to optimise its cognitive and clinical applications.

Type 2 diabetes mellitus (T2DM) is reported to cause widespread changes in brain function, leading to cognitive impairments. Research using resting-state functional magnetic resonance imaging data already aims to understand functional changes in complex brain connectivity systems. However, no previous studies with dynamic causal modelling (DCM) tried to investigate large-scale effective connectivity in diabetes. We aimed to examine the differences in large-scale resting state networks in diabetic and obese patients using combined DCM and graph theory methodologies. With the participation of 70 subjects (43 diabetics, 27 obese), we used cross-spectra DCM to estimate connectivity between 36 regions, subdivided into seven resting networks (RSN) commonly recognized in the literature. We assessed group-wise connectivity of T2DM and obesity, as well as group differences, with parametric empirical Bayes and Bayesian model reduction techniques. We analyzed network connectivity globally, between RSNs, and regionally. We found that average connection strength was higher in T2DM globally and between RSNs, as well. On the network level, the salience network shows stronger total within-network connectivity in diabetes (8.07) than in the obese group (4.02). Regionally, we measured the most significant average decrease in the right middle temporal gyrus (-0.013 Hz) and the right inferior parietal lobule (-0.01 Hz) relative to the obese group. In comparison, connectivity increased most notably in the left anterior prefrontal cortex (0.01 Hz) and the medial dorsal thalamus (0.009 Hz). In conclusion, we find the usage of complex analysis of large-scale networks suitable for diabetes instead of focusing on specific changes in brain function. [ABSTRACT FROM AUTHOR]

Selective attention mechanisms operate across large-scale cortical networks by amplifying behaviorally relevant sensory information while suppressing interference from distractors. Although it is known that fronto-parietal regions convey information about attentional priorities, it is unclear how such cortical communication is orchestrated. Based on its unique connectivity pattern with the cortex, we hypothesized that the pulvinar, a nucleus of the thalamus, may play a key role in coordinating and modulating remote cortical activity during selective attention. By using a visual task that orthogonally manipulated top-down selection and bottom-up competition during functional MRI, we investigated the modulations induced by task-relevant (spatial cue) and task-irrelevant but salient (distractor) stimuli on functional interactions between the pulvinar, occipito-temporal cortex, and frontoparietal areas involved in selective attention. Pulvinar activity and connectivity were distinctively modulated during the co-occurrence of the cue and salient distractor stimuli, as opposed to the presence of one of these factors alone. Causal modelling analysis further indicated that the pulvinar acted by weighting excitatory signals to cortical areas, predominantly in the presence of both the cue and the distractor. These results converge to support a pivotal role of the pulvinar in integrating top-down and bottom-up signals among distributed networks when confronted with conflicting visual stimuli, and thus contributing to shape priority maps for the guidance of attention.

Introduction: Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. Objects: The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. Materials and methods: The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients’ clinical characteristics. Results: For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the “triple networks” system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. Conclusion: Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease. [ABSTRACT FROM AUTHOR]

Background: Tobacco addiction is a chronic, relapsing mental disorder characterized by compulsive tobacco seeking and smoking. Current evidence shows that tobacco addiction exerts their initial reinforcing effects by activating reward circuits in the brain, but the causal connectivity among reward circuits is still unclear. Therefore, it is vital to understand how the reward network works to lead to the compulsive smoking behaviour.Method: We applied dynamic causal modelling (DCM) to resting-state functional magnetic resonance (rs-fMRI) to characterize changes in effective connectivity (EC) among eight major hubs from reward network between 76 long-term male smokers and 55 nonsmoking volunteers (matched with age, gender and education).Results: Relative to the healthy controls, long-term smokers had stronger ECs from the right anterior insula to left ventral striatum, posterior cingulate cortex (PCC) to ventral tegmental area (VTA), PCC to left anterior insula, left anterior insula to VTA, and ventromedial prefrontal cortex (vmPFC) to PCC and weaker ECs from the VTA to left ventral striatum, right anterior insula to right ventral striatum, and anterior cingulate cortex (ACC) to right anterior insula.Conclusions: Overall, our findings revealed disrupted neural causal interactions among parts of the reward network associated with tobacco addiction, expanding the growing evidence for the potential neurobiological mechanisms of tobacco addiction. We found abnormalities within the mesocorticolimbic system and a top-down regulation disorder in the dopamine-dependent process of response inhibition and salience attribution among long-term smokers, which may facilitate the development of effective therapies in tobacco addiction. [ABSTRACT FROM AUTHOR]

Successful navigation relies on the ability to identify, perceive, and correctly process the spatial structure of a scene. It is well known that visual mental imagery plays a crucial role in navigation. Indeed, cortical regions encoding navigationally relevant information are also active during mental imagery of navigational scenes. However, it remains unknown whether their intrinsic activity and connectivity reflect the individuals' ability to imagine a scene. Here, we primarily investigated the intrinsic causal interactions among scene-selective brain regions such as Parahipoccampal Place Area (PPA), Retrosplenial Complex, and Occipital Place Area (OPA) using Dynamic Causal Modelling for resting-state functional magnetic resonance data. Second, we tested whether resting-state effective connectivity parameters among scene-selective regions could reflect individual differences in mental imagery in our sample, as assessed by the self-reported Vividness of Visual Imagery Questionnaire. We found an inhibitory influence of occipito-medial on temporal regions, and an excitatory influence of more anterior on more medial and posterior brain regions. Moreover, we found that a key role in imagery is played by the connection strength from OPA to PPA, especially in the left hemisphere, since the influence of the signal between these scene-selective regions positively correlated with good mental imagery ability. Our investigation contributes to the understanding of the complexity of the causal interaction among brain regions involved in navigation and provides new insight in understanding how an essential ability, such as mental imagery, can be explained by the intrinsic fluctuation of brain signal. [ABSTRACT FROM AUTHOR]

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies.  Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings. [ABSTRACT FROM AUTHOR]

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

[Display omitted] • Amygdala contains heterogeneous nuclei presented with distinct functions. • Low back pain accompanies by negative emotion related to amygdala function. • Dynamic causal modelling can describe the connectivity direction in chronic pain states. • Greater resting-state functional connectivity of amygdala-medial prefrontal pathway was seen in chronic low back pain. • Lower effective connectivity of medial prefrontal-to-amygdala pathway was suggested in chronic low back pain. Chronic nonspecific low back pain (cNLBP) is a leading contributor to disease burden worldwide that is difficult to treat due to its nonspecific aetiology and complexity. The amygdala is a complex of structurally and functionally heterogeneous nuclei that serve as a key neural substrate for the interactions between pain and negative affective states. However, whether the functions of amygdalar subcomponents are differentially altered in cNLBP remains unknown. Little attention has focused on effective connectivity of the amygdala with the cortex in cNLBP. In this study, thirty-three patients with cNLBP and 33 healthy controls (HCs) were included. Resting-state functional connectivity (rsFC) and effective connectivity of the amygdala and its subregions were examined. Our results showed that the patient group exhibited significantly greater rsFC between the left amygdala and left dorsal medial prefrontal cortex (mPFC), which was negatively correlated with pain intensity ratings. Subregional analyses suggested a difference located at the superficial nuclei of the amygdala. Dynamic causal modelling revealed significantly lower effective connectivity from the left amygdala to the dorsal mPFC in patients with cNLBP than in HCs. Both groups exhibited stronger effective connectivity from the left amygdala to the right amygdala. In summary, these findings not only suggested altered rsFC of the amygdala-mPFC pathway in cNLBP but also implicated an abnormal direction of information processing between the amygdala and mPFC in these patients. Our results further highlight the involvement of the amygdala in the neuropathology of cNLBP. [ABSTRACT FROM AUTHOR]

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS). [ABSTRACT FROM AUTHOR]

Persistent fatigue is a major debilitating symptom in many psychiatric and neurological conditions, including stroke. Post-stroke fatigue has been linked to low corticomotor excitability. Yet, it remains elusive as to what the neuronal mechanisms are that underlie motor cortex excitability and chronic persistence of fatigue. In this cross-sectional observational study, in two experiments we examined a total of 59 non-depressed stroke survivors with minimal motoric and cognitive impairments using 'resting-state' MRI and single- and paired-pulse transcranial magnetic stimulation. In the first session of Experiment 1, we assessed resting motor thresholds-a typical measure of cortical excitability-by applying transcranial magnetic stimulation to the primary motor cortex (M1) and measuring motor-evoked potentials in the hand affected by stroke. In the second session, we measured their brain activity with resting-state MRI to assess effective connectivity interactions at rest. In Experiment 2 we examined effective inter-hemispheric connectivity in an independent sample of patients using paired-pulse transcranial magnetic stimulation. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS-7), a self-report questionnaire that has been widely applied and validated across different conditions. We used spectral dynamic causal modelling in Experiment 1 and paired-pulse transcranial magnetic stimulation in Experiment 2 to characterize how neuronal effective connectivity relates to self-reported post-stroke fatigue. In a multiple regression analysis, we used the balance in inhibitory connectivity between homologue regions in M1 as the main predictor, and have included lesioned hemisphere, resting motor threshold and levels of depression as additional predictors. Our novel index of inter-hemispheric inhibition balance was a significant predictor of post-stroke fatigue in Experiment 1 (β = 1.524, P = 7.56 × 10-5, confidence interval: 0.921 to 2.127) and in Experiment 2 (β = 0.541, P = 0.049, confidence interval: 0.002 to 1.080). In Experiment 2, depression scores and corticospinal excitability, a measure associated with subjective fatigue, also significantly accounted for variability in fatigue. We suggest that the balance in inter-hemispheric inhibitory effects between primary motor regions can explain subjective post-stroke fatigue. Findings provide novel insights into neural mechanisms that underlie persistent fatigue. [ABSTRACT FROM AUTHOR]

This paper proposes an account of neurocognitive activity without leveraging the notion of neural representation. Neural representation is a concept that results from assuming that the properties of the models used in computational cognitive neuroscience (e.g., information, representation, etc.) must literally exist the system being modelled (e.g., the brain). Computational models are important tools to test a theory about how the collected data (e.g., behavioural or neuroimaging) has been generated. While the usefulness of computational models is unquestionable, it does not follow that neurocognitive activity should literally entail the properties construed in the model (e.g., information, representation). While this is an assumption present in computationalist accounts, it is not held across the board in neuroscience. In the last section, the paper offers a dynamical account of neurocognitive activity with Dynamical Causal Modelling (DCM) that combines dynamical systems theory (DST) mathematical formalisms with the theoretical contextualisation provided by Embodied and Enactive Cognitive Science (EECS).

Background: Depression is commonly associated with fronto-amygdala dysfunction during the processing of emotional face expressions. Interactions between these regions are hypothesized to contribute to negative emotional processing biases and as such have been highlighted as potential biomarkers of treatment response. This study aimed to investigate depression associated alterations to directional connectivity and assess the utility of these parameters as predictors of treatment response. Methods: Ninety-two unmedicated adolescents and young adults (mean age 20.1; 56.5% female) with moderate-to-severe major depressive disorder and 88 healthy controls (mean age 19.8; 61.4% female) completed an implicit emotional face processing fMRI task. Patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or placebo. Using dynamic causal modelling, we examined functional relationships between six brain regions implicated in emotional face processing, comparing both patients and controls and treatment responders and non-responders. Results: Depressed patients demonstrated reduced inhibition from the dlPFC to vmPFC and reduced excitation from the dlPFC to amygdala during sad expression processing. During fearful expression processing patients showed reduced inhibition from the vmPFC to amygdala and reduced excitation from the amygdala to dlPFC. Response was associated with connectivity from the amygdala to dlPFC during sad expression processing and amygdala to vmPFC connectivity during fearful expression processing. Conclusions: Our study clarifies the nature of face processing network alterations in adolescents and young adults with depression, highlighting key interactions between the amygdala and prefrontal cortex. Moreover, these findings highlight the potential utility of these interactions in predicting treatment response. [ABSTRACT FROM AUTHOR]

Cross‐sectional studies have suggested that functional heterogeneity within the striatum in individuals with addictive behaviours may involve the transition from ventral to dorsal partitions; however, due to limitations of the cross‐sectional design, whether the contribution of this transition to addiction was confused by individual differences remains unclear, especially for internet gaming disorder (IGD). Longitudinal functional magnetic resonance imaging (fMRI) data from 22 IGD subjects and 18 healthy controls were collected at baseline and more than 6 months later. We examined the connectivity features of subregions within the striatum between these two scans. Based on the results, we further performed dynamic causal modelling to explore the directional effect between regions and used these key features for data classification in machine learning to test the replicability of the results. Compared with controls, IGD subjects exhibited decreased functional connectivity between the left dorsal striatum (putamen) and the left insula, whereas connectivity between the right ventral striatum (nucleus accumbens [Nacc]) and the left insula was relatively stable over time. An inhibitory effective connectivity from the left putamen to the right Nacc was found in IGD subjects during the follow‐up scan. Using the above features, the classification accuracy of the training model developed with the follow‐up was better than that of the model based on the initial scan. Persistent IGD status was accompanied by a switch in the locus of control within the striatum, which provided new insights into association between IGD and drug addiction. [ABSTRACT FROM AUTHOR]

This technical note introduces adiabatic dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.

The clinical syndromes caused by frontotemporal lobar degeneration are heterogeneous, including the behavioural variant frontotemporal dementia (bvFTD) and progressive supranuclear palsy. Although pathologically distinct, they share many behavioural, cognitive and physiological features, which may in part arise from common deficits of major neurotransmitters such as γ-aminobutyric acid (GABA). Here, we quantify the GABAergic impairment and its restoration with dynamic causal modelling of a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study. We analysed 17 patients with bvFTD, 15 patients with progressive supranuclear palsy, and 20 healthy age- and gender-matched controls. In addition to neuropsychological assessment and structural MRI, participants undertook two magnetoencephalography sessions using a roving auditory oddball paradigm: once on placebo and once on 10 mg of the oral GABA reuptake inhibitor tiagabine. A subgroup underwent ultrahigh-field magnetic resonance spectroscopy measurement of GABA concentration, which was reduced among patients. We identified deficits in frontotemporal processing using conductance-based biophysical models of local and global neuronal networks. The clinical relevance of this physiological deficit is indicated by the correlation between top-down connectivity from frontal to temporal cortex and clinical measures of cognitive and behavioural change. A critical validation of the biophysical modelling approach was evidence from parametric empirical Bayes analysis that GABA levels in patients, measured by spectroscopy, were related to posterior estimates of patients' GABAergic synaptic connectivity. Further evidence for the role of GABA in frontotemporal lobar degeneration came from confirmation that the effects of tiagabine on local circuits depended not only on participant group, but also on individual baseline GABA levels. Specifically, the phasic inhibition of deep cortico-cortical pyramidal neurons following tiagabine, but not placebo, was a function of GABA concentration. The study provides proof-of-concept for the potential of dynamic causal modelling to elucidate mechanisms of human neurodegenerative disease, and explains the variation in response to candidate therapies among patients. The laminar- and neurotransmitter-specific features of the modelling framework, can be used to study other treatment approaches and disorders. In the context of frontotemporal lobar degeneration, we suggest that neurophysiological restoration in selected patients, by targeting neurotransmitter deficits, could be used to bridge between clinical and preclinical models of disease, and inform the personalized selection of drugs and stratification of patients for future clinical trials. [ABSTRACT FROM AUTHOR]

In recent years, specific cortical networks have been proposed to be crucial for sustaining consciousness, including the posterior hot zone and frontoparietal resting state networks (RSN). Here, we computationally evaluate the relative contributions of three RSNs – the default mode network (DMN), the salience network (SAL), and the central executive network (CEN) – to consciousness and its loss during propofol anaesthesia. Specifically, we use dynamic causal modelling (DCM) of 10 min of high-density EEG recordings (N = 10, 4 males) obtained during behavioural responsiveness, unconsciousness and post-anaesthetic recovery to characterise differences in effective connectivity within frontal areas, the posterior ‘hot zone’, frontoparietal connections, and between-RSN connections. We estimate – for the first time – a large DCM model (LAR) of resting EEG, combining the three RSNs into a rich club of interconnectivity. Consistent with the hot zone theory, our findings demonstrate reductions in inter-RSN connectivity in the parietal cortex. Within the DMN itself, the strongest reductions are in feed-forward frontoparietal and parietal connections at the precuneus node. Within the SAL and CEN, loss of consciousness generates small increases in bidirectional connectivity. Using novel DCM leave-one-out cross-validation, we show that the most consistent out-of-sample predictions of the state of consciousness come from a key set of frontoparietal connections. This finding also generalises to unseen data collected during post-anaesthetic recovery. Our findings provide new, computational evidence for the importance of the posterior hot zone in explaining the loss of consciousness, highlighting also the distinct role of frontoparietal connectivity in underpinning conscious responsiveness, and consequently, suggest a dissociation between the mechanisms most prominently associated with explaining the contrast between conscious awareness and unconsciousness, and those maintaining consciousness.

The parieto-frontal circuit underlying grasping, which requires the serial involvement of the anterior intraparietal area (aIPs) and the ventral premotor cortex (PMv), has been recently extended enlightening the role of the dorsal premotor cortex (PMd). The supplementary motor area (SMA) has been also suggested to encode grip force for grasping actions; furthermore, both PMd and SMA are known to play a crucial role in motor imagery. Here, we aimed at assessing the dynamic couplings between left aIPs, PMv, PMd, SMA and primary motor cortex (M1) by comparing executed and imagined right-hand grasping, using Dynamic Causal Modelling (DCM) and Parametrical Empirical Bayes (PEB) analyses. 24 subjects underwent an fMRI exam (3T) during which they were asked to perform or imagine a grasping movement visually cued by photographs of commonly used objects. We tested whether the two conditions a) exert a modulatory effect on both forward and feedback couplings among our areas of interest, and b) differ in terms of strength and sign of these parameters. Results of the real condition confirmed the serial involvement of aIPs, PMv and M1. PMv also exerted a positive influence on PMd and SMA, but received an inhibitory feedback only from PMd. Our results suggest that a general motor program for grasping is planned by the aIPs-PMv circuit; then, PMd and SMA encode high-level features of the movement. During imagery, the connection strength from aIPs to PMv was weaker and the information flow stopped in PMv; thus, a less complex motor program was planned. Moreover, results suggest that SMA and PMd cooperate to prevent motor execution. In conclusion, the comparison between execution and imagery reveals that during grasping premotor areas dynamically interplay in different ways, depending on task demands.

• We leverage the idea of 'Markov blanket' as a statistical boundary to provide an analysis of partitions in neuronal systems. • We show this partition is applicable to multiple scales, from single neurons, brain regions, and brain-wide networks. • Based on the canonical micro-circuitry, our treatment has practical applications for effective connectivity. • Our proposed partition highlights the limitations of 'modular' proposals considering a single level of description. Recent characterisations of self-organising systems depend upon the presence of a 'Markov blanket': a statistical boundary that mediates the interactions between the inside and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. The notion of effective connectivity depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level of analysis. The nuance afforded by partitioning neural systems in this way highlights certain limitations of 'modular' perspectives of brain function that only consider a single level of description. [ABSTRACT FROM AUTHOR]

Antipsychotic treatment resistance affects a third of people with schizophrenia and the underlying mechanism remains unclear. We used an fMRI emotion-yoked reward learning task, allied to prefrontal cortical glutamate levels, to explain the role of cognitive control in differentiating treatment-resistant from responsive patients. We investigated how reward learning is disrupted at the network level in 21 medicated treatment-responsive and 20 medicated treatment-resistant patients with schizophrenia compared with 24 healthy controls (HC). Dynamic Causal Modelling assessed how effective connectivity between regions in a cortico-striatal-limbic network is disrupted in each patient group compared to HC. Connectivity was also examined with respect to symptoms, salience and anterior cingulate (ACC) glutamate levels measured from the same region of the ACC. We found that ACC connectivity differentiated these patient groups, with responsive patients exhibiting increased top-down connectivity from ACC to sensory regions and reduced ACC drive to the striatum, while resistant patients showed altered connectivity within the ACC itself. In these resistant patients, the ACC drive to striatum was positively correlated with their symptom severity. ACC glutamate levels were found to correlate with ACC control over sensory regions in responsive patients but not in resistant patients. We suggest a central non-dopaminergic impairment that impacts cognitive control networks in treatment-resistant schizophrenia. This impairment was associated with disrupted reward learning and could be underpinned by aberrant glutamate function. These findings should form the focus of future treatment strategies (e.g. glutamatergic targets and giving clozapine earlier) in resistant patients.

Background: Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD – as reported by several previous studies – which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals. Methods: To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups. Results: We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. Conclusion: This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

This technical note presents a dynamic causal modelling (DCM) procedure for evaluating different models of neurovascular coupling in the human brain – using combined electromagnetic (M/EEG) and functional magnetic resonance imaging (fMRI) data. This procedure compares the evidence for biologically informed models of neurovascular coupling using Bayesian model comparison. First, fMRI data are used to localise regionally specific neuronal responses. The coordinates of these responses are then used as the location priors in a DCM of electrophysiological responses elicited by the same paradigm. The ensuing estimates of model parameters are then used to generate neuronal drive functions, which model pre- or post-synaptic activity for each experimental condition. These functions form the input to a model of neurovascular coupling, whose parameters are estimated from the fMRI data. Crucially, this enables one to evaluate different models of neurovascular coupling, using Bayesian model comparison – asking, for example, whether instantaneous or delayed, pre- or post-synaptic signals mediate haemodynamic responses. We provide an illustrative application of the procedure using a single-subject auditory fMRI and MEG dataset. The code and exemplar data accompanying this technical note are available through the statistical parametric mapping (SPM) software.

This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration.

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.

Metastability is a key source of itinerant dynamics in the brain; namely, spontaneous spatiotemporal reorganization of neuronal activity. This itinerancy has been the focus of numerous dynamic functional connectivity (DFC) analyses – developed to characterize the formation and dissolution of distributed functional patterns over time, using resting state fMRI. However, aside from technical and practical controversies, these approaches cannot recover the neuronal mechanisms that underwrite itinerant (e.g., metastable) dynamics—due to their descriptive, model-free nature. We argue that effective connectivity (EC) analyses are more apt for investigating the neuronal basis of metastability. To this end, we appeal to biologically-grounded models (i.e., dynamic causal modelling, DCM) and dynamical systems theory (i.e., heteroclinic sequential dynamics) to create a probabilistic, generative model of haemodynamic fluctuations. This model generates trajectories in the parametric space of EC modes (i.e., states of connectivity) that characterize functional brain architectures. In brief, it extends an established spectral DCM, to generate functional connectivity data features that change over time. This foundational paper tries to establish the model’s face validity by simulating non-stationary fMRI time series and recovering key model parameters (i.e., transition probabilities among connectivity states and the parametric nature of these states) using variational Bayes. These data are further characterized using Bayesian model comparison (within and between subjects). Finally, we consider practical issues that attend applications and extensions of this scheme. Importantly, the scheme operates within a generic Bayesian framework – that can be adapted to study metastability and itinerant dynamics in any non-stationary time series.

This study used functional magnetic resonance imaging to explore the neural networks of pain during labor and its relief. It was hypothesized that epidural analgesia would affect the neural activities and the underlying network connectivity. Analysis using dynamic causal modelling and functional connectivity was performed to investigate the spatial activity and network connection of labor pain with and without epidural analgesia. This Institutional Review Board approved study acquired Magnetic Resonance Imaging from 15 healthy women of spontaneous normal delivery (with/without epidural analgesia = 7/8, aged 29.6 ± 2.3 and 29.3 ± 4.8 years old respectively) using a 1.5 Tesla scanner. Numerical rating score of pain was evaluated by a research nurse in the beginning of the first stage of labor and approximately 30 min after imaging examination. Six regions of interested from the activated clusters and literature were selected for dynamic causal modelling, which included primary and secondary somatosensory cortex, middle frontal gyrus, anterior cingulate cortex, insula and lentiform. Functional connectivity was calculated from selected sensory and affective regions. All analyses were performed by using software of statistical parametric mapping version 8 and CONN functional connectivity toolbox. The result showed that the experience of labor pain can lead to activations within a distributed brain network. The pain relief from epidural analgesia can be accompanied with altered functional connectivity, which was most evident in the cingulo-frontal system. The present study, therefore, provides an overview of a pain-related neural network that occur during labor and upon epidural analgesia. [ABSTRACT FROM AUTHOR]

In many decision-making situations, sub-optimal choices are increased by uncertainty. However, when wrong choices could lead to social punishment, such as blame, people might try to improve their performance by minimizing sub-optimal choices, which could be achieved by increasing the subjective cost of errors, thereby globally reducing decision noise or reducing an uncertainty-induced component of decision noise. In this functional magnetic resonance imaging (fMRI) study, 46 participants performed a choice task in which the probability of a correct choice with a given cue and the conditional probability of blame feedback (by making an incorrect choice) changed continuously. By comparing computational models of behaviour, we found that participants optimized their performance by preferentially reducing a component of decision noise associated with uncertainty. Simultaneously, expecting blame significantly deteriorated participants' mood. Model-based fMRI analyses and dynamic causal modelling indicate that the optimization mechanism based on the expectation of being blamed would be controlled by a neural circuit centred on the right medial prefrontal cortex. These results show novel behavioural and neural mechanisms regarding how humans optimize uncertain decisions under the expectation of being blamed. [ABSTRACT FROM AUTHOR]