A critical role of brain network architecture in a continuum model of autism spectrum disorders spanning from healthy individuals with genetic liability to individuals with ASD.

Studies have shown cortical alterations in individuals with autism spectrum disorders (ASD) as well as in individuals with high polygenic risk for ASD. An important addition to the study of altered cortical anatomy is the investigation of the underlying brain network architecture that may reveal brain-wide mechanisms in ASD and in polygenic risk for ASD. Such an approach has been proven useful in other psychiatric disorders by revealing that brain network architecture shapes (to an extent) the disorder-related cortical alterations. This study uses data from a clinical dataset-560 male subjects (266 individuals with ASD and 294 healthy individuals, CTL, mean age at 17.2 years) from the Autism Brain Imaging Data Exchange database, and data of 391 healthy individuals (207 males, mean age at 12.1 years) from the Pediatric Imaging, Neurocognition and Genetics database. ASD-related cortical alterations (group difference, ASD-CTL, in cortical thickness) and cortical correlates of polygenic risk for ASD were assessed, and then statistically compared with structural connectome-based network measures (such as hubs) using spin permutation tests. Next, we investigated whether polygenic risk for ASD could be predicted by network architecture by building machine-learning based prediction models, and whether the top predictors of the model were identified as disease epicenters of ASD. We observed that ASD-related cortical alterations as well as cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. We also observed that age progression of ASD-related cortical alterations and cortical correlates of polygenic risk for ASD implicated cortical hubs more strongly than non-hub regions. Further investigation revealed that structural connectomes predicted polygenic risk for ASD (r = 0.30, p < 0.0001), and two brain regions (the left inferior parietal and left suparmarginal) with top predictive connections were identified as disease epicenters of ASD. Our study highlights a critical role of network architecture in a continuum model of ASD spanning from healthy individuals with genetic risk to individuals with ASD. Our study also highlights the strength of investigating polygenic risk scores in addition to multi-modal neuroimaging measures to better understand the interplay between genetic risk and brain alterations associated with ASD.

Enhancing UAV Detection in Surveillance Camera Videos through Spatiotemporal Information and Optical Flow.

The growing intelligence and prevalence of drones have led to an increase in their disorderly and illicit usage, posing substantial risks to aviation and public safety. This paper focuses on addressing the issue of drone detection through surveillance cameras. Drone targets in images possess distinctive characteristics, including small size, weak energy, low contrast, and limited and varying features, rendering precise detection a challenging task. To overcome these challenges, we propose a novel detection method that extends the input of YOLOv5s to a continuous sequence of images and inter-frame optical flow, emulating the visual mechanisms employed by humans. By incorporating the image sequence as input, our model can leverage both temporal and spatial information, extracting more features of small and weak targets through the integration of spatiotemporal data. This integration augments the accuracy and robustness of drone detection. Furthermore, the inclusion of optical flow enables the model to directly perceive the motion information of drone targets across consecutive frames, enhancing its ability to extract and utilize features from dynamic objects. Comparative experiments demonstrate that our proposed method of extended input significantly enhances the network's capability to detect small moving targets, showcasing competitive performance in terms of accuracy and speed. Specifically, our method achieves a final average precision of 86.87%, representing a noteworthy 11.49% improvement over the baseline, and the speed remains above 30 frames per second. Additionally, our approach is adaptable to other detection models with different backbones, providing valuable insights for domains such as Urban Air Mobility and autonomous driving.

WeBrain: A web-based brainformatics platform of computational ecosystem for EEG big data analysis.

The current evolution of 'cloud neuroscience' leads to more efforts with the large-scale EEG applications, by using EEG pipelines to handle the rapidly accumulating EEG data. However, there are a few specific cloud platforms that seek to address the cloud computational challenges of EEG big data analysis to benefit the EEG community. In response to the challenges, a WeBrain cloud platform (https://webrain.uestc.edu.cn/) is designed as a web-based brainformatics platform and computational ecosystem to enable large-scale EEG data storage, exploration and analysis using cloud high-performance computing (HPC) facilities. WeBrain connects researchers from different fields to EEG and multimodal tools that have become the norm in the field and the cloud processing power required to handle those large EEG datasets. This platform provides an easy-to-use system for novice users (even no computer programming skills) and provides satisfactory maintainability, sustainability and flexibility for IT administrators and tool developers. A range of resources are also available on https://webrain.uestc.edu.cn/, including documents, manuals, example datasets related to WeBrain, and collected links to open EEG datasets and tools. It is not necessary for users or administrators to install any software or system, and all that is needed is a modern web browser, which reduces the technical expertise required to use or manage WeBrain. The WeBrain platform is sponsored and driven by the China-Canada-Cuba international brain cooperation project (CCC-Axis, http://ccc-axis.org/), and we hope that WeBrain will be a promising cloud brainformatics platform for exploring brain information in large-scale EEG applications in the EEG community.

Distinct effects of the basal ganglia and cerebellum on the thalamocortical pathway in idiopathic generalized epilepsy.

The aberrant thalamocortical pathways of epilepsy have been detected recently, while its underlying effects on epilepsy are still not well understood. Exploring pathoglytic changes in two important thalamocortical pathways, that is, the basal ganglia (BG)-thalamocortical and the cerebellum-thalamocortical pathways, in people with idiopathic generalized epilepsy (IGE), could deepen our understanding on the pathological mechanism of this disease. These two pathways were reconstructed and investigated in this study by combining diffusion and functional MRI. Both pathways showed connectivity changes with the perception and cognition systems in patients. Consistent functional connectivity (FC) changes were observed mainly in perception regions, revealing the aberrant integration of sensorimotor and visual information in IGE. The pathway-specific FC alterations in high-order regions give neuroimaging evidence of the neural mechanisms of cognitive impairment and epileptic activities in IGE. Abnormal functional and structural integration of cerebellum, basal ganglia and thalamus could result in an imbalance of inhibition and excitability in brain systems of IGE. This study located the regulated cortical regions of BG and cerebellum which been affected in IGE, established possible links between the neuroimaging findings and epileptic symptoms, and enriched the understanding of the regulatory effects of BG and cerebellum on epilepsy.

Objects seen as scenes: Neural circuitry for attending whole or parts.

Depending on our goals, we pay attention to the global shape of an object or to the local shape of its parts, since it's difficult to do both at once. This typically effortless process can be impaired in disease. However, it is not clear which cortical regions carry the information needed to constrain shape processing to a chosen global/local level. Here, novel stimuli were used to dissociate functional MRI responses to global and local shapes. This allowed identification of cortical regions containing information about level (independent from shape). Crucially, these regions overlapped part of the cortical network implicated in scene processing. As expected, shape information (independent of level) was mainly located in category-selective areas specialized for object- and face-processing. Regions with the same informational profile were strongly linked (as measured by functional connectivity), but were weak when the profiles diverged. Specifically, in the ventral-temporal-cortex (VTC) regions favoring level and shape were consistently separated by the mid-fusiform sulcus (MFS). These regions also had limited crosstalk despite their spatial proximity, thus defining two functional pathways within VTC. We hypothesize that object hierarchical level is processed by neural circuitry that also analyses spatial layout in scenes, contributing to the control of the spatial-scale used for shape recognition. Use of level information tolerant to shape changes could guide whole/part attentional selection but facilitate illusory shape/level conjunctions under impoverished vision.

Evaluation of functional connectivity in subdivisions of the thalamus in schizophrenia.

BACKGROUND: Previous studies in schizophrenia revealed abnormalities in the cortico-cerebellar-thalamo-cortical circuit (CCTCC) pathway, suggesting the necessity for defining thalamic subdivisions in understanding alterations of brain connectivity.AimsTo parcellate the thalamus into several subdivisions using a data-driven method, and to evaluate the role of each subdivision in the alterations of CCTCC functional connectivity in patients with schizophrenia. METHOD: There were 54 patients with schizophrenia and 42 healthy controls included in this study. First, the thalamic structural and functional connections computed, based on diffusion magnetic resonance imaging (MRI, white matter tractography) and resting-state functional MRI, were clustered to parcellate thalamus. Next, functional connectivity of each thalamus subdivision was investigated, and the alterations in thalamic functional connectivity for patients with schizophrenia were inspected. RESULTS: Based on the data-driven parcellation method, six thalamic subdivisions were defined. Loss of connectivity was observed between several thalamic subdivisions (superior-anterior, ventromedial and dorsolateral part of the thalamus) and the sensorimotor system, anterior cingulate cortex and cerebellum in patients with schizophrenia. A gradual pattern of dysconnectivity was observed across the thalamic subdivisions. Additionally, the altered connectivity negatively correlated with symptom scores and duration of illness in individuals with schizophrenia. CONCLUSIONS: The findings of the study revealed a wide range of thalamic functional dysconnectivity in the CCTCC pathway, increasing our understanding of the relationship between the CCTCC pathway and symptoms associated with schizophrenia, and further indicating a potential alteration pattern in the thalamic nuclei in people with schizophrenia.Declaration of interestNone.

Progressive Reduction in Gray Matter in Patients with Schizophrenia Assessed with MR Imaging by Using Causal Network Analysis.

Purpose To investigate the temporal and causal relationships of structural changes in the brain in patients with schizophrenia. Materials and Methods T1-weighted magnetic resonance (MR) images of 97 patients with schizophrenia (29 women; mean ± standard deviation age, 41 years ± 11.5; range, 16-66 years; illness duration, 16.3 years ± 10.9; range, 0-50 years) and 126 age- and sex-matched (38 years ± 14.9; range, 18-68 years; 42 women) healthy control subjects were evaluated. The causal network of structural covariance was used to assess the causal relationships of structural changes in patients with schizophrenia. This was accomplished by applying Granger causality analysis to the morphometric T1-weighted images ranked according to duration of disease. Results With greater disease duration, reduction in gray matter volume began in the thalamus and progressed to the frontal lobe, and then to the temporal and occipital cortices as well and the cerebellum (P < .00001, false discovery rate corrected). The thalamus was shown to be the primary hub of the directional network and exhibited positive causal effects on the frontal, temporal, and occipital regions as well as on the cerebellum (P < .05, false discovery rate corrected). The frontal regions, which were identified to be transitional points, projected causal effects to the occipital lobe, temporal regions, and the cerebellum and received causal effects from the thalamus (P < .05, false discovery rate corrected). Conclusion Schizophrenia shows progression of gray matter abnormalities over time, with the thalamus as the primary hub and the frontal regions as prominent nodes. © RSNA, 2018 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 5, 2018.

White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.

The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.

Action Video Game Experience Related to Altered Large-Scale White Matter Networks.

With action video games (AVGs) becoming increasingly popular worldwide, the cognitive benefits of AVG experience have attracted continuous research attention over the past two decades. Research has repeatedly shown that AVG experience can causally enhance cognitive ability and is related to neural plasticity in gray matter and functional networks in the brain. However, the relation between AVG experience and the plasticity of white matter (WM) network still remains unclear. WM network modulates the distribution of action potentials, coordinating the communication between brain regions and acting as the framework of neural networks. And various types of cognitive deficits are usually accompanied by impairments of WM networks. Thus, understanding this relation is essential in assessing the influence of AVG experience on neural plasticity and using AVG experience as an interventional tool for impairments of WM networks. Using graph theory, this study analyzed WM networks in AVG experts and amateurs. Results showed that AVG experience is related to altered WM networks in prefrontal networks, limbic system, and sensorimotor networks, which are related to cognitive control and sensorimotor functions. These results shed new light on the influence of AVG experience on the plasticity of WM networks and suggested the clinical applicability of AVG experience.

Structural and functional correlates of motor imagery BCI performance: Insights from the patterns of fronto-parietal attention network.

Motor imagery (MI)-based brain-computer interfaces (BCIs) have been widely used for rehabilitation of motor abilities and prosthesis control for patients with motor impairments. However, MI-BCI performance exhibits a wide variability across subjects, and the underlying neural mechanism remains unclear. Several studies have demonstrated that both the fronto-parietal attention network (FPAN) and MI are involved in high-level cognitive processes that are crucial for the control of BCIs. Therefore, we hypothesized that the FPAN may play an important role in MI-BCI performance. In our study, we recorded multi-modal datasets consisting of MI electroencephalography (EEG) signals, T1-weighted structural and resting-state functional MRI data for each subject. MI-BCI performance was evaluated using the common spatial pattern to extract the MI features from EEG signals. One cortical structural feature (cortical thickness (CT)) and two measurements (degree centrality (DC) and eigenvector centrality (EC)) of node centrality were derived from the structural and functional MRI data, respectively. Based on the information extracted from the EEG and MRI, a correlation analysis was used to elucidate the relationships between the FPAN and MI-BCI performance. Our results show that the DC of the right ventral intraparietal sulcus, the EC and CT of the left inferior parietal lobe, and the CT of the right dorsolateral prefrontal cortex were significantly associated with MI-BCI performance. Moreover, the receiver operating characteristic analysis and machine learning classification revealed that the EC and CT of the left IPL could effectively predict the low-aptitude BCI users from the high-aptitude BCI users with 83.3% accuracy. Those findings consistently reveal that the individuals who have efficient FPAN would perform better on MI-BCI. Our findings may deepen the understanding of individual variability in MI-BCI performance, and also may provide a new biomarker to predict individual MI-BCI performance.

Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

Spatiotemporal consistency of local neural activities: A new imaging measure for functional MRI data.

PURPOSE: To characterize the local consistency by integrating temporal and spatial information in the local region using functional magnetic resonance imaging (fMRI). MATERIALS AND METHODS: One simulation was implemented to explain the definition of FOur-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA). Then three experiments included resting state data (33 subjects), resting state reproducibility data (16 subjects), and event state data (motor execution task, 26 subjects) were designed. Finally, FOCA were respectively analyzed using statistical analysis methods, such as one-sample t-test and paired t-test, etc. RESULTS: During resting state (Experiment 1), the FOCA values (P < 0.05, family-wise error [FWE] corrected, voxel size >621 mm(3) ) were found to be distinct at the bilateral inferior frontal gyrus, middle frontal gyrus, angular gyrus, and precuneus/cuneus. In Experiment 2 (reproducibility), a high degree of consistency within subjects (correlation ≈0.8) and between subjects (correlation ≈0.6) of FOCA were obtained. Comparing event with resting state in Experiment 3, enhanced FOCA (P < 0.05, FWE-corrected, voxel size >621 mm(3) ) was observed mainly in the precentral gyrus and lingual gyrus. CONCLUSION: These findings suggest that FOCA has the potential to provide further information that will help to better understand brain function in neural imaging.

Striatum- and Cerebellum-Modulated Epileptic Networks Varying Across States with and without Interictal Epileptic Discharges.

Idiopathic generalized epilepsy (IGE) is characterized by cryptogenic etiology and the striatum and cerebellum are recognized as modulators of epileptic network. We collected simultaneous electroencephalogram and functional magnetic resonance imaging data from 145 patients with IGE, 34 of whom recorded interictal epileptic discharges (IEDs) during scanning. In states without IEDs, hierarchical connectivity was performed to search core cortical regions which might be potentially modulated by striatum and cerebellum. Node-node and edge-edge moderation models were constructed to depict direct and indirect moderation effects in states with and without IEDs. Patients showed increased hierarchical connectivity with sensorimotor cortices (SMC) and decreased connectivity with regions in the default mode network (DMN). In the state without IEDs, striatum, cerebellum, and thalamus were linked to weaken the interactions of regions in the salience network (SN) with DMN and SMC. In periods with IEDs, overall increased moderation effects on the interaction between regions in SN and DMN, and between regions in DMN and SMC were observed. The thalamus and striatum were implicated in weakening interactions between regions in SN and SMC. The striatum and cerebellum moderated the cortical interaction among DMN, SN, and SMC in alliance with the thalamus, contributing to the dysfunction in states with and without IEDs in IGE. The current work revealed state-specific modulation effects of striatum and cerebellum on thalamocortical circuits and uncovered the potential core cortical targets which might contribute to develop new clinical neuromodulation techniques.

Characterization and experimental verification of the rotating synthetic aperture optical imaging system.

The rotating synthetic aperture (RSA) optical imaging system employs a rectangular primary mirror for detection. During the imaging process, the primary mirror rotates around the center to achieve the aperture equivalent to the long side of the rectangle at different rotation angles. As a result, the system's point spread function changes over time, causing periodic time-varying characteristics in the acquired images' resolution. Moreover, due to the rectangular primary mirror, the images obtained by the RSA system are spatially asymmetric, with a lower resolution in the short side's direction than in the long side's direction. Hence, image processing techniques are necessary to enhance the image quality. To provide reference for the study of image quality improvement methods, we first characterize the imaging quality degradation mechanism of the RSA system and the time-space evolution law of the imaging process. We then establish an imaging experiment platform to simulate the dynamic imaging process of the RSA system. We quantify the RSA system's impact on image degradation using objective indexes. Subsequently, by comparing the imaging experiment results with theoretical analysis, we verify the spatially asymmetric and temporally periodic imaging characteristics of the RSA system. Lastly, we introduce image super-resolution experiments to assess the limitations of directly applying generic deep learning-based single image super-resolution methods to the images captured by the RSA system, thereby revealing the challenges involved in improving image quality for the RSA system.

Acupuncture modulates the functional connectivity among the subcortical nucleus and fronto-parietal network in adolescents with internet addiction.

BACKGROUND: Internet addiction (IA), recognized as a behavioral addiction, is emerging as a global public health problem. Acupuncture has been demonstrated to be effective in alleviating IA; however, the mechanism is not yet clear. To fill this knowledge gap, our study aimed to investigate the modulatory effects of acupuncture on the functional interactions among the addiction-related networks in adolescents with IA. METHODS: Thirty individuals with IA and thirty age- and sex-matched healthy control subjects (HCs) were recruited. Subjects with IA were given a 40-day acupuncture treatment, and resting-state functional magnetic resonance imaging (fMRI) data were collected before and after acupuncture sessions. HCs received no treatment and underwent one fMRI scan after enrollment. The intergroup differences in functional connectivity (FC) among the subcortical nucleus (SN) and fronto-parietal network (FPN) were compared between HCs and subjects with IA at baseline. Then, the intragroup FC differences between the pre- and post-treatment were analyzed in the IA group. A multiple linear regression model was further employed to fit the FC changes to symptom relief in the IA group. RESULTS: In comparison to HCs, subjects with IA exhibited significantly heightened FC within and between the SN and FPN at baseline. After 40 days of acupuncture treatment, the FC within the FPN and between the SN and FPN were significantly decreased in individuals with IA. Symptom improvement in subjects with IA was well fitted by the decrease in FC between the left midbrain and ventral prefrontal cortex and between the left thalamus and ventral anterior prefrontal cortex. CONCLUSION: These findings confirmed the modulatory effects of acupuncture on the aberrant functional interactions among the SN and FPN, which may partly reflect the neurophysiological mechanism of acupuncture for IA.

Efficient self-attention mechanism and structural distilling model for Alzheimer's disease diagnosis.

Structural magnetic resonance imaging (sMRI) is commonly used for the identification of Alzheimer's disease because of its keen insight into atrophy-induced changes in brain structure. Current mainstream convolutional neural network-based deep learning methods ignore the long-term dependencies between voxels; thus, it is challenging to learn the global features of sMRI data. In this study, an advanced deep learning architecture called Brain Informer (BraInf) was developed based on an efficient self-attention mechanism. The proposed model integrates representation learning, feature distilling, and classifier modeling into a unified framework. First, the proposed model uses a multihead ProbSparse self-attention block for representation learning. This self-attention mechanism selects the first ⌊lnN⌋ elements that can represent the overall features from the perspective of probability sparsity, which significantly reduces computational cost. Subsequently, a structural distilling block is proposed that applies the concept of patch merging to the distilling operation. The block reduces the size of the three-dimensional tensor and further lowers the memory cost while preserving the original data as much as possible. Thus, there was a significant improvement in the space complexity. Finally, the feature vector was projected into the classification target space for disease prediction. The effectiveness of the proposed model was validated using the Alzheimer's Disease Neuroimaging Initiative dataset. The model achieved 97.97% and 91.89% accuracy on Alzheimer's disease and mild cognitive impairment classification tasks, respectively. The experimental results also demonstrate that the proposed framework outperforms several state-of-the-art methods.

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study.

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

Neural correlates of polygenic risk score for autism spectrum disorders in general population.

Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case-control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.

Aberrant Prefrontal-Thalamic-Cerebellar Circuit in Schizophrenia and Depression: Evidence From a Possible Causal Connectivity.

Neuroimaging studies have suggested the presence of abnormalities in the prefrontal-thalamic-cerebellar circuit in schizophrenia (SCH) and depression (DEP). However, the common and distinct structural and causal connectivity abnormalities in this circuit between the two disorders are still unclear. In the current study, structural and resting-state functional magnetic resonance imaging (fMRI) data were acquired from 20 patients with SCH, 20 depressive patients and 20 healthy controls (HC). Voxel-based morphometry analysis was first used to assess gray matter volume (GMV). Granger causality analysis, seeded at regions with altered GMVs, was subsequently conducted. To discover the differences between the groups, ANCOVA and post hoc tests were performed. Then, the relationships between the structural changes, causal connectivity and clinical variables were investigated. Finally, a leave-one-out resampling method was implemented to test the consistency. Statistical analyses showed the GMV and causal connectivity changes in the prefrontal-thalamic-cerebellar circuit. Compared with HC, both SCH and DEP exhibited decreased GMV in middle frontal gyrus (MFG), and a lower GMV in MFG and medial prefrontal cortex (MPFC) in SCH than DEP. Compared with HC, both patient groups showed increased causal flow from the right cerebellum to the MPFC (common causal connectivity abnormalities). And distinct causal connectivity abnormalities (increased causal connectivity from the left thalamus to the MPFC in SCH than HC and DEP, and increased causal connectivity from the right cerebellum to the left thalamus in DEP than HC and SCH). In addition, the structural deficits in the MPFC and its causal connectivity from the cerebellum were associated with the negative symptom severity in SCH. This study found common/distinct structural deficits and aberrant causal connectivity patterns in the prefrontal-thalamic-cerebellar circuit in SCH and DEP, which may provide a potential direction for understanding the convergent and divergent psychiatric pathological mechanisms between SCH and DEP. Furthermore, concomitant structural and causal connectivity deficits in the MPFC may jointly contribute to the negative symptoms of SCH.