Effective connectivity in autism

Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury

In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.

Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment

Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.

Central Causation of Autism/ASDs via Excessive [Ca2+]i Impacting Six Mechanisms Controlling Synaptogenesis during the Perinatal Period: The Role of Electromagnetic Fields and Chemicals and the NO/ONOO(-) Cycle, as Well as Specific Mutations

The roles of perinatal development, intracellular calcium [Ca2+]i, and synaptogenesis disruption are not novel in the autism/ASD literature. The focus on six mechanisms controlling synaptogenesis, each regulated by [Ca2+]i, and each aberrant in ASDs is novel. The model presented here predicts that autism epidemic causation involves central roles of both electromagnetic fields (EMFs) and chemicals. EMFs act via voltage-gated calcium channel (VGCC) activation and [Ca2+]i elevation. A total of 15 autism-implicated chemical classes each act to produce [Ca2+]i elevation, 12 acting via NMDA receptor activation, and three acting via other mechanisms. The chronic nature of ASDs is explained via NO/ONOO(-) vicious cycle elevation and MeCP2 epigenetic dysfunction. Genetic causation often also involves [Ca2+]i elevation or other impacts on synaptogenesis. The literature examining each of these steps is systematically examined and found to be consistent with predictions. Approaches that may be sed for ASD prevention or treatment are discussed in connection with this special issue: The current situation and prospects for children with ASDs. Such approaches include EMF, chemical avoidance, and using nutrients and other agents to raise the levels of Nrf2. An enriched environment, vitamin D, magnesium, and omega-3s in fish oil may also be helpful.

Highly logical and non-emotional decisions in both risky and social contexts: understanding decision making in autism spectrum disorder through computational modeling

In risky contexts, autism spectrum disorder (ASD) individuals exhibit more logical consistency and non-emotional decisions than do typical adults (TAs). This way of deciding could be also prevailing in social contexts, leading to maladaptive decisions. This evidence is scarce and inconsistent, and further research is needed. Recent developments in computational modeling allow analysis of decisional subcomponents that could provide valuable information to understand the decision-making and help address inconsistencies. Twenty-seven individuals with ASD and 25 TAs were submitted to a framing-task and the ultimatum game (UG). The Rescorla-Wagner computational model was used to analyze UG decisions. Results showed that in the UG, the ASD group exhibited a higher utilitarianism, characterized by lower aversion to unfairness and higher acceptance of offers. Moreover, this way of deciding was predicted by the higher economic rationality found in the framing task, where people with ASD did not manifest emotional biases such as framing effect. These results could suggest an atypical decision making, highly logical and non-emotional, as a robust feature of ASD.

Secondary analysis: Graph analysis of brain connectivity network in autism spectrum disorder

Background

Autism spectrum disorder is a neurodevelopmental condition in which impaired connectivity of the brain network. The functional magnetic resonance imaging (fMRI) technique can provide information on the early diagnosis of autism by evaluating communication patterns in the brain. The present study aimed to assess functional connectivity (FC) variations in autism patients.

Materials and Methods

Resting-state fMRI data were obtained from the "ABIDE" website. These data include 294 autism patients with a mean (standard deviation) age of 16.49 (7.63) and 312 healthy individuals with a mean (standard deviation) age of 15.98 (6.31). In this study, changes in communication patterns across different brain regions in autism patients were investigated using graph-based models.

Results

The FC cluster of 17 regions in the brain, such as the hippocampus, cuneus, and inferior temporal, was different between the patient and healthy groups. Based on connectivity analysis of pair regions, 36 of the 136 correlations in the cluster were significantly different between the two groups. The middle temporal gyrus had more communication than the other regions. The largest difference between groups was - 0.112, which corresponding to the right middle temporal and right thalamus regions.

Conclusion

The findings of this study revealed functional relationship alterations in patients with autism compared to healthy individuals, indicating the disease's effects on the brain connectivity network.

Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference

BACKGROUND

Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies.

METHODS

Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD.

RESULTS

We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network.

CONCLUSIONS

The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.

Sex differences of the triple network model in children with autism: A resting-state fMRI investigation of effective connectivity

Autism spectrum disorder (ASD) has a pronounced male predominance, but the underlying neurobiological basis of this sex bias remains unclear. Gender incoherence (GI) theory suggests that ASD is more neurally androgynous than same-sex controls. Given its central role, altered structures and functions, and sex-dependent network differences in ASD, the triple network model, including the central executive network (CEN), default mode network (DMN), and salience network (SN), has emerged as a candidate for characterizing this sex difference. Here, we measured the sex-related effective connectivity (EC) differences within and between these three networks in 72 children with ASD (36 females, 8-14 years) and 72 typically developing controls (TCs) (36 females, 8-14 years) from 5 sites of the Autism Brain Imaging Data Exchange repositories using a 2 × 2 analysis of covariance factorial design. We also assessed brain-behavior relationships and the effects of age on EC. We found significant diagnosis-by-sex interactions on EC: females with ASD had significantly higher EC than their male counterparts within the DMN and between the SN and CEN. The interaction pattern supported the GI theory by showing that the higher EC observed in females with ASD reflected a shift towards the higher level of EC displayed in male TCs (neural masculinization), and the lower EC seen in males with ASD reflected a shift towards the lower level of EC displayed in female TCs (neural feminization). We also found significant brain-behavior correlations and significant effects of age on EC.

Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment

Neuroscientific studies aim to find an accurate and reliable brain Effective Connectome (EC). Although current EC discovery methods have contributed to our understanding of brain organization, their performances are severely constrained by the short sample size and poor temporal resolution of fMRI data, and high dimensionality of the brain connectome. By leveraging the DTI data as prior knowledge, we introduce two Bayesian causal discovery frameworks -the Bayesian GOLEM (BGOLEM) and Bayesian FGES (BFGES) methods- that offer significantly more accurate and reliable ECs and address the shortcomings of the existing causal discovery methods in discovering ECs based on only fMRI data. Moreover, to numerically assess the improvement in the accuracy of ECs with our method on empirical data, we introduce the Pseudo False Discovery Rate (PFDR) as a new computational accuracy metric for causal discovery in the brain. Through a series of simulation studies on synthetic and hybrid data (combining DTI from the Human Connectome Project (HCP) subjects and synthetic fMRI), we demonstrate the effectiveness of our proposed methods and the reliability of the introduced metric in discovering ECs. By employing the PFDR metric, we show that our Bayesian methods lead to significantly more accurate results compared to the traditional methods when applied to the Human Connectome Project (HCP) data. Additionally, we measure the reproducibility of discovered ECs using the Rogers-Tanimoto index for test-retest data and show that our Bayesian methods provide significantly more reliable ECs than traditional methods. Overall, our study's numerical and visual results highlight the potential for these frameworks to significantly advance our understanding of brain functionality.

Reduced brain connectivity along the autism spectrum controlled for familial confounding by co-twin design

Previous studies on brain connectivity correlates of autism have often focused on selective connections and yielded inconsistent results. By applying global fiber tracking and utilizing a within-twin pair design, we aimed to contribute to a more unbiased picture of white matter connectivity in association with clinical autism and autistic traits. Eighty-seven twin pairs (n = 174; 55% monozygotic; 24 with clinical autism) underwent diffusion tensor imaging. Linear regressions assessed within-twin pair associations between structural brain connectivity of anatomically defined brain regions and both clinical autism and autistic traits. These were explicitly adjusted for IQ, other neurodevelopmental/psychiatric conditions and multiple testing, and implicitly for biological sex, age, and all genetic and environmental factors shared by twins. Both clinical autism and autistic traits were associated with reductions in structural connectivity. Twins fulfilling diagnostic criteria for clinical autism had decreased brainstem-cuneus connectivity compared to their co-twins without clinical autism. Further, twins with higher autistic traits had decreased connectivity of the left hippocampus with the left fusiform and parahippocampal areas. These associations were also significant in dizygotic twins alone. Reduced brainstem-cuneus connectivity might point towards alterations in low-level visual processing in clinical autism while higher autistic traits seemed to be more associated with reduced connectivity in networks involving the hippocampus and the fusiform gyrus, crucial especially for processing of faces and other (higher order) visual processing. The observed associations were likely influenced by both genes and environment.

16p11.2 deletion mice exhibit compromised fronto-temporal connectivity, GABAergic dysfunction, and enhanced attentional ability

Autism spectrum disorders are more common in males, and have a substantial genetic component. Chromosomal 16p11.2 deletions in particular carry strong genetic risk for autism, yet their neurobiological impact is poorly characterised, particularly at the integrated systems level. Here we show that mice reproducing this deletion (16p11.2 DEL mice) have reduced GABAergic interneuron gene expression (decreased parvalbumin mRNA in orbitofrontal cortex, and male-specific decreases in Gad67 mRNA in parietal and insular cortex and medial septum). Metabolic activity was increased in medial septum, and in its efferent targets: mammillary body and (males only) subiculum. Functional connectivity was altered between orbitofrontal, insular and auditory cortex, and between septum and hippocampus/subiculum. Consistent with this circuit dysfunction, 16p11.2 DEL mice showed reduced prepulse inhibition, but enhanced performance in the continuous performance test of attentional ability. Level 1 autistic individuals show similarly heightened performance in the equivalent human test, also associated with parietal, insular-orbitofrontal and septo-subicular dysfunction. The data implicate cortical and septal GABAergic dysfunction, and resulting connectivity changes, as the cause of pre-attentional and attentional changes in autism.

Logical decisions after a psychosocial stressor: The late phase of acute stress reduces loss aversion

Loss aversion, the principle that losses have a greater impact on decision-making than gains, can be modulated by stress. Most findings reported that stress reduces loss aversion, in line with the alignment hypothesis. Yet, decision-making was always assessed at the early stages of the stress response. Instead, the latter phase of the stress response enhances the salience-network and then, it could amplify the salience of losses, thereby increasing loss aversion. To our knowledge, it has never been studied how the latter stress response influences loss aversion and our aim is to fill this gap. 92 participants were divided into experimental and control group. The first one was exposed to the Trier Social Stress Test, and controls viewed a match-length distractor video. Both groups performed a mixed gamble task to measure loss aversion through a Bayesian-computational model. During and after the stressor, experimental group exhibited signs of both physiological and psychological stress which indicated that stress induction was effective. However, rather than increasing, loss aversion of stressed participants was lower. These results constitute a new evidence of stress influencing loss aversion and are discussed within the alignment hypothesis, according to which stress aligns sensitivity to gains and losses.

Later stages of acute stress impair reinforcement-learning and feedback sensitivity in decision making

Whereas the effects of the early stages of acute stress seem to improve learning and increase loss aversion in decision making, in later stages, the opposite has been found, an impairment in decision making probably due to higher reward-attraction, as the STARS approach suggests. This study aims to investigate the effects of the later stages of acute stress on decision making and its underlying processes using a computational model. We hypothesized that stress would affect underlying cognitive strategies during decision making. Ninety-five participants were randomly distributed into two groups, experimental (N = 46) and control (N = 49). A virtual version of The Trier Social Stress Test (TSST) was used as a laboratory stressor. After 20minutes, decision making was assessed by using the Iowa Gambling Task (IGT). The Value-Plus-Preservation (VPP) RL computational model was used to extract decision-making components. As expected, the stressed participants showed deficits in IGT performance on reinforcement-learning and feedback sensitivity. However, there was no gains attraction. These results are discussed by considering that decision making in later stages of acute stress could be based on impairments in prefrontal cortex functioning.

Effect of regional intrinsic activity following two kinds of theta burst stimulation on precuneus

Theta burst stimulation (TBS) has been widely used in the treatment of mental disorders, but the cerebral functional difference between intermittent TBS (iTBS) and continuous TBS (cTBS) after one single session of stimulation is not clear. Here we applied resting-state functional magnetic resonance imaging (RS-FMRI) to evaluate the alterations in intrinsic brain activity after iTBS and cTBS in the precuneus. We recruited 32 healthy young adults and performed a single session each of iTBS and cTBS at a 1-week interval. RS-fMRI was collected at baseline before and immediately after the stimulation. Parameters for regional brain activity (ALFF/fALFF/ReHo) and functional connectivity (FC) with the stimulated site of the precuneus after iTBS and cTBS were calculated and compared between each stimulation using a paired t-test. Correlation analysis among those parameters was calculated to explore whether changes in functional connectivity were associated with local spontaneous activity. After iTBS stimulation, fALFF increased in the bilateral precuneus, while fALFF decreased in the bilateral middle temporal gyrus. Reductions in precuneus FC were found in the bilateral cuneus, superior occipital gyrus, superior temporal gyrus, precentral gyrus, and postcentral gyrus, which correlated with regional activity. After cTBS, fALFF decreased in the bilateral insula, and precuneus FC was decreased in the bilateral inferior occipital gyrus and increased in the thalamus. In the current study, we observed that one session of iTBS or cTBS could cause inhibitory effects in remote brain regions, but only iTBS caused significant local activation in the target region.

Altered default mode network causal connectivity patterns in autism spectrum disorder revealed by Liang information flow analysis

Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo-parietal junction showed more inter-regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In-Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter-regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD.

Abnormalities of EEG Functional Connectivity and Effective Connectivity in Children with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that interferes with normal brain development. Brain connectivity may serve as a biomarker for ASD in this respect. This study enrolled a total of 179 children aged 3-10 years (90 typically developed (TD) and 89 with ASD). We used a weighted phase lag index and a directed transfer function to investigate the functional and effective connectivity in children with ASD and TD. Our findings indicated that patients with ASD had local hyper-connectivity of brain regions in functional connectivity and simultaneous significant decrease in effective connectivity across hemispheres. These connectivity abnormalities may help to find biomarkers of ASD.

Understanding the Role of Connectivity Dynamics of Resting-State Functional MRI in the Diagnosis of Autism Spectrum Disorder: A Comprehensive Study

In addition to the standard observational assessment for autism spectrum disorder (ASD), recent advancements in neuroimaging and machine learning (ML) suggest a rapid and objective alternative using brain imaging. This work presents a pipelined framework, using functional magnetic resonance imaging (fMRI) that allows not only an accurate ASD diagnosis but also the identification of the brain regions contributing to the diagnosis decision. The proposed framework includes several processing stages: preprocessing, brain parcellation, feature representation, feature selection, and ML classification. For feature representation, the proposed framework uses both a conventional feature representation and a novel dynamic connectivity representation to assist in the accurate classification of an autistic individual. Based on a large publicly available dataset, this extensive research highlights different decisions along the proposed pipeline and their impact on diagnostic accuracy. A large publicly available dataset of 884 subjects from the Autism Brain Imaging Data Exchange I (ABIDE-I) initiative is used to validate our proposed framework, achieving a global balanced accuracy of 98.8% with five-fold cross-validation and proving the potential of the proposed feature representation. As a result of this comprehensive study, we achieve state-of-the-art accuracy, confirming the benefits of the proposed feature representation and feature engineering in extracting useful information as well as the potential benefits of utilizing ML and neuroimaging in the diagnosis and understanding of autism.

Increased functional activity, bottom-up and intrinsic effective connectivity in autism

Sensory perceptual alterations such as sensory sensitivities in autism have been proposed to be caused by differences in sensory observation (Likelihood) or in forming models of the environment (Prior), which result in an increase in bottom-up information flow relative to top-down control. To investigate this conjecture, we had autistic individuals (AS) and neurotypicals (NT) perform a decision-under-uncertainty paradigm while undergoing functional magnetic resonance imaging (fMRI). There were no group differences in task performance and in Prior and Likelihood representations in brain activity. However, there were significant group differences in overall task activity, with the AS group showing significantly greater activation in the bilateral precuneus, mid-occipital gyrus, cuneus, superior frontal gyrus (SFG) and left putamen relative to the NT group. Further, when pooling the data across both groups, we found that those with higher AQ scores showed greater activity in the left cuneus and precuneus. Effective connectivity analysis using dynamic causal modelling (DCM) revealed that group differences in BOLD signals were underpinned by increased activity within sensory regions and a net increase in bottom-up connectivity from the occipital region to the precuneus and the left SFG. These findings support the hypothesis of increased bottom-up information flow in autism during sensory learning tasks.

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders

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.

Altered Effective Connectivity of the Primary Motor Cortex in Transient Ischemic Attack

Objective

This study is aimed at exploring alteration in motor-related effective connectivity in individuals with transient ischemic attack (TIA).

Methods

A total of 48 individuals with TIA and 41 age-matched and sex-matched healthy controls (HCs) were recruited for this study. The participants were scanned using MRI, and their clinical characteristics were collected. To investigate motor-related effective connectivity differences between individuals with TIA and HCs, the bilateral primary motor cortex (M1) was used as the regions of interest (ROIs) to perform a whole-brain Granger causality analysis (GCA). Furthermore, partial correlation was used to evaluate the relationship between GCA values and the clinical characteristics of individuals with TIA.

Results

Compared with HCs, individuals with TIA demonstrated alterations in the effective connectivity between M1 and widely distributed brain regions involved in motor, visual, auditory, and sensory integration. In addition, GCA values were significantly correlated with high- and low-density lipoprotein cholesterols in individuals with TIA.

Conclusion

This study provides important evidence for the alteration of motor-related effective connectivity in TIA, which reflects the abnormal information flow between different brain regions. This could help further elucidate the pathological mechanisms of motor impairment in individuals with TIA and provide a new perspective for future early diagnosis and intervention for TIA.

Stressed individuals exhibit pessimistic bursting beliefs and a lower risk preference in the balloon analogue risk task

Stress alters decision-making by usually promoting risk-taking and reward-seeking, which could be advantageous in a context where risk is rewarded, such as the Balloon Analogue Risk Task (BART). However, previous studies addressing this issue showed inconsistencies which could emerge from assessing decision-making as a single dimension. Our aim is to study through computational modelling how stress influences cognitive subprocesses of the decision-making during the BART. For this purpose, 94 healthy participants were submitted to BART, but only half were exposed to the virtual Trier Social Stress Test (TSST-VR). The Experimental-Weight Mean-Variance (EWMV) model was used to gain insight into the subprocesses involved in risk-taking during BART. Rather than reward-seeking, our results showed a pessimistic prior belief about the balloons bursting likelihood, and a lower risk preference in the stressed participants. This cautious attitude could be attributable to an alertness state promoted by stress. Yet, since risk is rewarded in BART, it could also evidence a maladaptive decision-making derived from learning difficulties and altered feedback-processing under stress.

The Atypical Effective Connectivity of Right Temporoparietal Junction in Autism Spectrum Disorder: A Multi-Site Study

Social function impairment is the core deficit of autism spectrum disorder (ASD). Although many studies have investigated ASD through a variety of neuroimaging tools, its brain mechanism of social function remains unclear due to its complex and heterogeneous symptoms. The present study aimed to use resting-state functional magnetic imaging data to explore effective connectivity between the right temporoparietal junction (RTPJ), one of the key brain regions associated with social impairment of individuals with ASD, and the whole brain to further deepen our understanding of the neuropathological mechanism of ASD. This study involved 1,454 participants from 23 sites from the Autism Brain Imaging Data Exchange (ABIDE) public dataset, which included 618 individuals with ASD and 836 with typical development (TD). First, a voxel-wise Granger causality analysis (GCA) was conducted with the RTPJ selected as the region of interest (ROI) to investigate the differences in effective connectivity between the ASD and TD groups in every site. Next, to obtain further accurate and representative results, an image-based meta-analysis was implemented to further analyze the GCA results of each site. Our results demonstrated abnormal causal connectivity between the RTPJ and the widely distributed brain regions and that the connectivity has been associated with social impairment in individuals with ASD. The current study could help to further elucidate the pathological mechanisms of ASD and provides a new perspective for future research.

One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity

In the past decade, several studies have shown that Neurofeedback (NFB) by functional magnetic resonance imaging can alter the functional coupling of targeted and non-targeted areas. However, the causal mechanisms underlying these changes remain uncertain. Here, we applied a whole-brain dynamical model to estimate Effective Connectivity (EC) profiles of resting-state data acquired before and immediately after a single-session NFB training for 17 participants who underwent motor imagery NFB training and 16 healthy controls who received sham feedback. Within-group and between-group classification analyses revealed that only for the NFB group it was possible to accurately discriminate between the 2 resting-state sessions. NFB training-related signatures were reflected in a support network of direct connections between areas involved in reward processing and implicit learning, together with regions belonging to the somatomotor, control, attention, and default mode networks, identified through a recursive-feature elimination procedure. By applying a data-driven approach to explore NFB-induced changes in spatiotemporal dynamics, we demonstrated that these regions also showed decreased switching between different brain states (i.e. metastability) only following real NFB training. Overall, our findings contribute to the understanding of NFB impact on the whole brain’s structure and function by shedding light on the direct connections between brain areas affected by NFB training.

Phase separation and zinc-induced transition modulate synaptic distribution and association of autism-linked CTTNBP2 and SHANK3

Many synaptic proteins form biological condensates via liquid-liquid phase separation (LLPS). Synaptopathy, a key feature of autism spectrum disorders (ASD), is likely relevant to the impaired phase separation and/or transition of ASD-linked synaptic proteins. Here, we report that LLPS and zinc-induced liquid-to-gel phase transition regulate the synaptic distribution and protein-protein interaction of cortactin-binding protein 2 (CTTNBP2), an ASD-linked protein. CTTNBP2 forms self-assembled condensates through its C-terminal intrinsically disordered region and facilitates SHANK3 co-condensation at dendritic spines. Zinc binds the N-terminal coiled-coil region of CTTNBP2, promoting higher-order assemblies. Consequently, it leads to reduce CTTNBP2 mobility and enhance the stability and synaptic retention of CTTNBP2 condensates. Moreover, ASD-linked mutations alter condensate formation and synaptic retention of CTTNBP2 and impair mouse social behaviors, which are all ameliorated by zinc supplementation. Our study suggests the relevance of condensate formation and zinc-induced phase transition to the synaptic distribution and function of ASD-linked proteins.

Autism impacts synapses. This study reports that autism-linked mutations of CTTNBP2 regulate phase separation to control synaptic enrichment of that protein. A zinc-induced liquid-to-gel transition improves synaptic retention of CTTNBP2 and SHANK3.

Meditation-induced effects on whole-brain structural and effective connectivity

In the past decades, there has been a growing scientific interest in characterizing neural correlates of meditation training. Nonetheless, the mechanisms underlying meditation remain elusive. In the present work, we investigated meditation-related changes in functional dynamics and structural connectivity (SC). For this purpose, we scanned experienced meditators and control (naive) subjects using magnetic resonance imaging (MRI) to acquire structural and functional data during two conditions, resting-state and meditation (focused attention on breathing). In this way, we aimed to characterize and distinguish both short-term and long-term modifications in the brain’s structure and function. First, to analyze the fMRI data, we calculated whole-brain effective connectivity (EC) estimates, relying on a dynamical network model to replicate BOLD signals’ spatio-temporal structure, akin to functional connectivity (FC) with lagged correlations. We compared the estimated EC, FC, and SC links as features to train classifiers to predict behavioral conditions and group identity. Then, we performed a network-based analysis of anatomical connectivity. We demonstrated through a machine-learning approach that EC features were more informative than FC and SC solely. We showed that the most informative EC links that discriminated between meditators and controls involved several large-scale networks mainly within the left hemisphere. Moreover, we found that differences in the functional domain were reflected to a smaller extent in changes at the anatomical level as well. The network-based analysis of anatomical pathways revealed strengthened connectivity for meditators compared to controls between four areas in the left hemisphere belonging to the somatomotor, dorsal attention, subcortical and visual networks. Overall, the results of our whole-brain model-based approach revealed a mechanism underlying meditation by providing causal relationships at the structure-function level.

Attenuated link between the medial prefrontal cortex and the amygdala in children with autism spectrum disorder: Evidence from effective connectivity within the "social brain"

Although accumulating neuroimaging studies have reported that social behavior deficits in children with autism spectrum disorders (ASD) are commonly attributed to the dysfunction of social brain regions underlying social cognition, the dynamic interaction within the social brain network and its association with social deficits remain unclear. Here, resting-state functional magnetic resonance imaging data obtained from Autism Brain Imaging Data Exchange (I and II) were analyzed in 105 children with ASD and 102 demographically matched typically developing controls (TDCs) (age range: 7-12 years old). Term-based meta-analysis combined the prior reference and anatomical labeling were used to define the regions of interests of the social brain network, and multivariate Granger causality analysis with blind deconvolution was employed to assess the effective connectivity within the social brain network in the ASD and TDC groups. Between-group comparison revealed significantly attenuated effective connectivity from the medial prefrontal cortex (mPFC) to the bilateral amygdala in children with the ASD group compared with TDC group. In addition, raw values of the effective connectivity from the mPFC to the bilateral amygdala were used to predict social deficits in ASD. Our findings indicate the impaired mPFC-amygdala pathway and its association with social deficits in children with ASD and provide a new perspective into the neuropathology of the developing autistic brain.

Genetic Architecture Underlying Differential Resting-state Functional Connectivity of Subregions Within the Human Visual Cortex

The human visual cortex is a heterogeneous entity that has multiple subregions showing substantial variability in their functions and connections. We aimed to identify genes associated with resting-state functional connectivity (rsFC) of visual subregions using transcriptome-neuroimaging spatial correlations in discovery and validation datasets. Results showed that rsFC of eight visual subregions were associated with expression measures of eight gene sets, which were specifically expressed in brain tissue and showed the strongest correlations with visual behavioral processes. Moreover, there was a significant divergence in these gene sets and their functional features between medial and lateral visual subregions. Relative to those associated with lateral subregions, more genes associated with medial subregions were found to be enriched for neuropsychiatric diseases and more diverse biological functions and pathways, and to be specifically expressed in multiple types of neurons and immune cells and during the middle and late stages of cortical development. In addition to shared behavioral processes, lateral subregion associated genes were uniquely correlated with high-order cognition. These findings of commonalities and differences in the identified rsFC-related genes and their functional features across visual subregions may improve our understanding of the functional heterogeneity of the visual cortex from the perspective of underlying genetic architecture.

Overlapping and specific neural correlates for empathizing, affective mentalizing, and cognitive mentalizing: A coordinate-based meta-analytic study

While the discussion on the foundations of social understanding mainly revolves around the notions of empathy, affective mentalizing, and cognitive mentalizing, their degree of overlap versus specificity is still unclear. We took a meta-analytic approach to unveil the neural bases of cognitive mentalizing, affective mentalizing, and empathy, both in healthy individuals and pathological conditions characterized by social deficits such as schizophrenia and autism. We observed partially overlapping networks for cognitive and affective mentalizing in the medial prefrontal, posterior cingulate, and lateral temporal cortex, while empathy mainly engaged fronto-insular, somatosensory, and anterior cingulate cortex. Adjacent process-specific regions in the posterior lateral temporal, ventrolateral, and dorsomedial prefrontal cortex might underpin a transition from abstract representations of cognitive mental states detached from sensory facets to emotionally-charged representations of affective mental states. Altered mentalizing-related activity involved distinct sectors of the posterior lateral temporal cortex in schizophrenia and autism, while only the latter group displayed abnormal empathy related activity in the amygdala. These data might inform the design of rehabilitative treatments for social cognitive deficits.

Clustering Analysis Supports the Detection of Biological Processes Related to Autism Spectrum Disorder

Genome sequencing has identified a large number of putative autism spectrum disorder (ASD) risk genes, revealing possible disrupted biological pathways; however, the genetic and environmental underpinnings of ASD remain mostly unanswered. The presented methodology aimed to identify genetically related clusters of ASD individuals. By using the VariCarta dataset, which contains data retrieved from 13,069 people with ASD, we compared patients pairwise to build “patient similarity matrices”. Hierarchical-agglomerative-clustering and heatmapping were performed, followed by enrichment analysis (EA). We analyzed whole-genome sequencing retrieved from 2062 individuals, and isolated 11,609 genetic variants shared by at least two people. The analysis yielded three clusters, composed, respectively, by 574 (27.8%), 507 (24.6%), and 650 (31.5%) individuals. Overall, 4187 variants (36.1%) were common to the three clusters. The EA revealed that the biological processes related to the shared genetic variants were mainly involved in neuron projection guidance and morphogenesis, cell junctions, synapse assembly, and in observational, imitative, and vocal learning. The study highlighted genetic networks, which were more frequent in a sample of people with ASD, compared to the overall population. We suggest that itemizing not only single variants, but also gene networks, might support ASD etiopathology research. Future work on larger databases will have to ascertain the reproducibility of this methodology.