Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits.

BACKGROUND: Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD. METHODS: Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects. Linked independent component analysis (LICA) and correlation analysis were applied to identify associations between local neuroimaging patterns across modalities (including gray matter volume, white matter integrity, and spontaneous functional activity) and clinical factors. RESULTS: LICA identified eight multimodal neuroimaging patterns related to phenotypic variations, including three related to symptoms and diagnosis. One imaging pattern (IC9) that included both the amplitude of low-frequency fluctuation measure of spontaneous functional activity and white matter integrity measures correlated negatively with OCD diagnosis and diagnostic scales. Two imaging patterns (IC10 and IC27) correlated with compulsion symptoms: IC10 included primarily anatomical measures and IC27 included primarily functional measures. In addition, we identified imaging patterns associated with age, gender, and emotional expression across subjects. CONCLUSIONS: We established that data fusion techniques can identify local multimodal neuroimaging patterns associated with OCD phenotypes. The results inform our understanding of the neurobiological underpinnings of compulsive behaviors and OCD diagnosis.

Social visual preference mediates the effect of cortical thickness on symptom severity in children with autism spectrum disorder.

Background: Evidence suggests that there is a robust relationship between altered neuroanatomy and autistic symptoms in individuals with autism spectrum disorder (ASD). Social visual preference, which is regulated by specific brain regions, is also related to symptom severity. However, there were a few studies explored the potential relationships among brain structure, symptom severity, and social visual preference. Methods: The current study investigated relationships among brain structure, social visual preference, and symptom severity in 43 children with ASD and 26 typically developing (TD) children (aged 2-6 years). Results: Significant differences were found in social visual preference and cortical morphometry between the two groups. Decreased percentage of fixation time in digital social images (%DSI) was negatively related to not only the thickness of the left fusiform gyrus (FG) and right insula, but also the Calibrated Severity Scores for the Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA-CSS). Mediation analysis showed that %DSI partially mediated the relationship between neuroanatomical alterations (specifically, thickness of the left FG and right insula) and symptom severity. Conclusion: These findings offer initial evidence that atypical neuroanatomical alterations may not only result in direct effects on symptom severity but also lead to indirect effects on symptom severity through social visual preference. This finding enhances our understanding of the multiple neural mechanisms implicated in ASD.

Impairment of arbitration between model-based and model-free reinforcement learning in obsessive-compulsive disorder.

Introduction: Obsessive-compulsive disorder (OCD) is characterized by an imbalance between goal-directed and habitual learning systems in behavioral control, but it is unclear whether these impairments are due to a single system abnormality of the goal-directed system or due to an impairment in a separate arbitration mechanism that selects which system controls behavior at each point in time. Methods: A total of 30 OCD patients and 120 healthy controls performed a 2-choice, 3-stage Markov decision-making paradigm. Reinforcement learning models were used to estimate goal-directed learning (as model-based reinforcement learning) and habitual learning (as model-free reinforcement learning). In general, 29 high Obsessive-Compulsive Inventory-Revised (OCI-R) score controls, 31 low OCI-R score controls, and all 30 OCD patients were selected for the analysis. Results: Obsessive-compulsive disorder (OCD) patients showed less appropriate strategy choices than controls regardless of whether the OCI-R scores in the control subjects were high (p = 0.012) or low (p < 0.001), specifically showing a greater model-free strategy use in task conditions where the model-based strategy was optimal. Furthermore, OCD patients (p = 0.001) and control subjects with high OCI-R scores (H-OCI-R; p = 0.009) both showed greater system switching rather than consistent strategy use in task conditions where model-free use was optimal. Conclusion: These findings indicated an impaired arbitration mechanism for flexible adaptation to environmental demands in both OCD patients and healthy individuals reporting high OCI-R scores.

Dynamic multi-site graph convolutional network for autism spectrum disorder identification.

Multi-site learning has attracted increasing interests in autism spectrum disorder (ASD) identification tasks by its efficacy on capturing data heterogeneity of neuroimaging taken from different medical sites. However, existing multi-site graph convolutional network (MSGCN) often ignores the correlations between different sites, and may obtain suboptimal identification results. Moreover, current feature extraction methods characterizing temporal variations of functional magnetic resonance imaging (fMRI) signals require the time series to be of the same length and cannot be directly applied to multi-site fMRI datasets. To address these problems, we propose a dual graph based dynamic multi-site graph convolutional network (DG-DMSGCN) for multi-site ASD identification. First, a sliding-window dual-graph convolutional network (SW-DGCN) is introduced for feature extraction, simultaneously capturing temporal and spatial features of fMRI data with different series lengths. Then we aggregate the features extracted from multiple medical sites through a novel dynamic multi-site graph convolutional network (DMSGCN), which effectively considers the correlations between different sites and is beneficial to improve identification performance. We evaluate the proposed DG-DMSGCN on public ABIDE I dataset containing data from 17 medical sites. The promising results obtained by our framework outperforms the state-of-the-art methods with increase in identification accuracy, indicating that it has a potential clinical prospect for practical ASD diagnosis. Our codes are available on https://github.com/Junling-Du/DG-DMSGCN.

A network perspective on cognitive function and obsessive-compulsive related symptoms.

BACKGROUND: The relationship between cognitive function and psychopathological symptoms has been an important research field in recent years. Previous studies have typically applied case-control designs to explore differences in certain cognitive variables. Multivariate analyses are needed to deepen our understanding of the intercorrelations among cognitive and symptom phenotypes in OCD. METHODS: The present study used network analysis to construct networks of cognitive variables and OCD-related symptoms in patients with OCD and healthy controls (N = 226), aiming to explore the relationship among numerous cognitive function variables and OCD-related symptoms in detail and compare the network features between the two groups. RESULTS: In the network of cognitive function and OCD-related symptoms, nodes representing IQ, letter/number span test, accuracy of task switching test and obsession were much important in the network in terms of their larger strengths and edges. By constructing the networks of these two groups respectively, there was a strong similarity except that the symptom's network in healthy group had a higher degree of overall connectivity. LIMITATIONS: Due to the small sample size, the stability of the network cannot be guaranteed. Due to the cross-sectional nature of the data, we were unable to determine how the cognitive-symptom network would change with disease deterioration or treatment. CONCLUSIONS: The present study highlights the important role of variables such as obsession and IQ from a network perspective. These results deepen our understanding of the multivariate relationship between cognitive dysfunction and OCD symptoms, and may promote the prediction and diagnosis of OCD.

Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder.

Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.

Obsessive-compulsive disorder is characterized by decreased Pavlovian influence on instrumental behavior.

Obsessive-compulsive disorder (OCD) is characterized by uncontrollable repetitive actions thought to rely on abnormalities within fundamental instrumental learning systems. We investigated cognitive and computational mechanisms underlying Pavlovian biases on instrumental behavior in both clinical OCD patients and healthy controls using a Pavlovian-Instrumental Transfer (PIT) task. PIT is typically evidenced by increased responding in the presence of a positive (previously rewarded) Pavlovian cue, and reduced responding in the presence of a negative cue. Thirty OCD patients and thirty-one healthy controls completed the Pavlovian Instrumental Transfer test, which included instrumental training, Pavlovian training for positive, negative and neutral cues, and a PIT phase in which participants performed the instrumental task in the presence of the Pavlovian cues. Modified Rescorla-Wagner models were fitted to trial-by-trial data of participants to estimate underlying computational mechanism and quantify individual differences during training and transfer stages. Bayesian hierarchical methods were used to estimate free parameters and compare the models. Behavioral and computational results indicated a weaker Pavlovian influence on instrumental behavior in OCD patients than in HC, especially for negative Pavlovian cues. Our results contrast with the increased PIT effects reported for another set of disorders characterized by compulsivity, substance use disorders, in which PIT is enhanced. A possible reason for the reduced PIT in OCD may be impairment in using the contextual information provided by the cues to appropriately adjust behavior, especially when inhibiting responding when a negative cue is present. This study provides deeper insight into our understanding of deficits in OCD from the perspective of Pavlovian influences on instrumental behavior and may have implications for OCD treatment modalities focused on reducing compulsive behaviors.

Imbalance between the caudate and putamen connectivity in obsessive-compulsive disorder.

BACKGROUND: Compulsive behaviors in obsessive-compulsive disorder (OCD) have been suggested to result from an imbalance in cortico-striatal connectivity. However, the nature of this impairment, the relative involvement of different striatal areas, their imbalance in genetically related but unimpaired individuals, and their relationship with cognitive dysfunction in OCD patients, remain unknown. METHODS: In the current study, striatal (i.e., caudate and putamen) whole-brain connectivity was computed in a sample of OCD patients (OCD, n = 62), unaffected first-degree relatives (UFDR, n = 53) and healthy controls (HC, n = 73) by ROI-based resting-state functional magnetic resonance imaging (rs-fMRI). A behavioral task switch paradigm outside of the scanner was also performed to measure cognitive flexibility in OCD patients. RESULTS: There were significantly increased strengths (Z-transformed Pearson correlation coefficient) in caudate connectivity in OCD patients. A significant correlation between the two types of connectivity strengths in the relevant regions was observed only in the OCD patient group. Furthermore, the caudate connectivity of patients was negatively associated with their task-switch performance. CONCLUSIONS: The imbalance between the caudate and putamen connectivity, arising from the abnormal increase of caudate activity, may serve as a clinical characteristic for obsessive-compulsive disorder.

Dysfunction of goal-directed control in patients with depression and nonsuicidal self-injury.

BACKGROUND: Non-suicidal self-injury (NSSI) is a common problem associated with dangerous outcomes. Dysfunction of goal-directed behavioral control may contribute to NSSI. To test this, we used a novel experimental paradigm (Pavlovian-to-Instrumental Transfer, PIT) to test whether patients with NSSI utilize Pavlovian conditioned stimuli (CSs) during goal-directed control of ongoing behavior. METHODS: Thirty-five depressed patients with NSSI (D-NSSI) and thirty-four healthy controls performed a PIT task. We measured the influence of positive and negative background CSs on instrumental responses for rewards. RESULTS: The results showed that D-NSSI performed significantly lower PIT than controls, and PIT measures were negatively correlated with NSSI frequency. Furthermore, in a subset of patients exhibiting high levels of compulsivity, PIT positively moderated the relationship between compulsivity and NSSI frequency. CONCLUSIONS: These results indicate that D-NSSI patients have difficulties in using different CSs to control ongoing behavior in a goal-directed manner, and the dysfunction of goal-directed control may contribute to NSSI.

Impacts of the psychological stress response on nonsuicidal self-injury behavior in students during the COVID-19 epidemic in China: the mediating role of sleep disorders.

BACKGROUND: The sudden outbreak of COVID-19 had a great impact on the physical and mental health of people all over the world, especially for students whose physical and mental development was not yet mature. In order to understand the physical and mental conditions of students during the epidemic period and provide a theoretical basis for coping with psychological problems in public health emergencies, this study explored the mediating role of sleep disorders in the effect of the psychological stress response (PSR) on non-suicidal self-injury (NSSI), along with the moderating role of emotional management ability (EMA). METHODS: The SRQ-20, Pittsburgh Sleep Quality Index, NSSI Behavior Questionnaire, and Emotional Management Questionnaire were used to investigate the mental health of Chinese students in April 10-20 (Time point 1, T1) and May 20-30 (Time point 2, T2), 2020. A total of 1,955 students (Mage = 19.64 years, 51.4% male) were examined at T1 and 342 students (Mage = 20.06 years, 48.2% male) were reassessed at T2. RESULTS: Overall, the detection rate of PSR and NSSI were 17.60% (n = 344) and 24.90% (n = 486) respectively in the T1 sample, and were 16.37% (n = 56) and 25.44% (n = 87), in the T2 sample. We also found that sleep disorders played a mediating role in the effect of PSR on NSSI in the T1 and T2 samples. In addition, EMA was shown to regulate the effect of PSR on sleep disorders and the effect of sleep disorders on NSSI in the T1 samples. CONCLUSION: We found that PSR resulting from public health emergency might lead to NSSI behaviors in individuals. PSR may also cause sleep disorders, which can bring about NSSI. However, these effects were also moderated by the EMA. This research expands our understanding of PSR and NSSI in students during the pandemic.

Diagnosis of obsessive-compulsive disorder via spatial similarity-aware learning and fused deep polynomial network.

Obsessive-compulsive disorder (OCD) is a type of hereditary mental illness, which seriously affect the normal life of the patients. Sparse learning has been widely used in detecting brain diseases objectively by removing redundant information and retaining monitor valuable biological characteristics from the brain functional connectivity network (BFCN). However, most existing methods ignore the relationship between brain regions in each subject. To solve this problem, this paper proposes a spatial similarity-aware learning (SSL) model to build BFCNs. Specifically, we embrace the spatial relationship between adjacent or bilaterally symmetric brain regions via a smoothing regularization term in the model. We develop a novel fused deep polynomial network (FDPN) model to further learn the powerful information and attempt to solve the problem of curse of dimensionality using BFCN features. In the FDPN model, we stack a multi-layer deep polynomial network (DPN) and integrate the features from multiple output layers via the weighting mechanism. In this way, the FDPN method not only can identify the high-level informative features of BFCN but also can solve the problem of curse of dimensionality. A novel framework is proposed to detect OCD and unaffected first-degree relatives (UFDRs), which combines deep learning and traditional machine learning methods. We validate our algorithm in the resting-state functional magnetic resonance imaging (rs-fMRI) dataset collected by the local hospital and achieve promising performance.

Imbalance in functional and structural connectivity underlying goal-directed and habitual learning systems in obsessive-compulsive disorder.

An imbalance between the goal-directed and habitual learning systems has been proposed to underlie compulsivity in obsessive-compulsive disorder (OCD). In addition, the overall balance between these systems may be influenced by stress hormones. We examined the multimodal networks underlying these dual learning systems. Both functional and structural measures indicated reduced connectivity within the goal-directed subnetwork (FC: P = 0.042; SC-FN: P = 0.014) and reduced connectivity between the goal-directed and habitual subnetworks (FC: P = 0.014; SC-FA: P = 0.052), but no differences within the habitual subnetwork in patients with OCD compared with controls. Path modeling indicated that anatomical connectivity in the goal-directed subnetwork influenced compulsive symptoms (R2 = 0.41), whereas functional connectivity within the habit subnetwork and between goal-directed and habitual subnetworks influenced obsessive symptoms (R2 = 0.63). In addition, the relationship between anatomical connectivity in the goal-directed subnetwork and compulsion was moderated by the stress hormone ACTH (adrenocorticotropic hormone), such that at low levels of ACTH greater connectivity resulted in lower compulsion, but at high levels of ACTH this relationship was reversed. These results provide new insights into the neural correlates of the imbalance between dual learning systems, and their relationship with symptom dimensions in patients with OCD. It may further support the reconceptualization of OCD as "compulsive-obsessive disorder," with a greater focus on the transdiagnostic dimension of compulsivity.

Classification of Obsessive-Compulsive Disorder Using Distance Correlation on Resting-State Functional MRI Images.

Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies. Our goal was to use the multivariate pattern analysis to compare the ability of such three correlation methods to distinguish between the patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCSs), so as to find optimal correlation method. The main process includes four steps. First, the regions of interest are defined by automated anatomical labeling (AAL). Second, functional connectivity (FC) matrices are constructed by the three correlation methods. Third, the best discriminative features are selected by support vector machine recursive feature elimination (SVM-RFE) with a stratified N-fold cross-validation strategy. Finally, these discriminative features are used to train a classifier. We had a total of 128 subjects out of which 61 subjects had OCD and 67 subjects were normal. All the three correlation methods with SVM have achieved good results, among which distance correlation is the best [accuracy = 93.01%, specificity = 89.71%, sensitivity = 95.08%, and area under the receiver-operating characteristic curve (AUC) = 0.94], followed by Pearson correlation and partial correlation is the last. The most discriminative regions of the brain for distance correlation are right dorsolateral superior frontal gyrus, orbital part of left superior frontal gyrus, orbital part of right middle frontal gyrus, right anterior cingulate and paracingulate gyri, left the supplementary motor area, and right precuneus, which are the promising biomarkers of OCD.

Aberrant rich club organization in patients with obsessive-compulsive disorder and their unaffected first-degree relatives.

Recent studies suggested that the rich club organization promoting global brain communication and integration of information, may be abnormally increased in obsessive-compulsive disorder (OCD). However, the structural and functional basis of this organization is still not very clear. Given the heritability of OCD, as suggested by previous family-based studies, we hypothesize that aberrant rich club organization may be a trait marker for OCD. In the present study, 32 patients with OCD, 30 unaffected first-degree relatives (FDR) and 32 healthy controls (HC) underwent diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). We examined the structural rich club organization and its interrelationship with functional coupling. Our results showed that rich club and peripheral connection strength in patients with OCD was lower than in HC, while it was intermediate in FDR. Finally, the coupling between structural and functional connections of the rich club, was decreased in FDR but not in OCD relative to HC, which suggests a buffering mechanism of brain functions in FDR. Overall, our findings suggest that alteration of the rich club organization may reflect a vulnerability biomarker for OCD, possibly buffered by structural and functional coupling of the rich club.

Joint hub identification for brain networks by multivariate graph inference.

Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l1 -SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions. The source code can be visited by the url https://github.com/reynard-hu/mbbna.

Abnormal brain functional network dynamics in obsessive-compulsive disorder patients and their unaffected first-degree relatives.

We utilized dynamic functional network connectivity (dFNC) analysis to compare participants with obsessive-compulsive disorder (OCD) with their unaffected first-degree relative (UFDR) and healthy controls (HC). Resting state fMRI was performed on 46 OCD, 24 UFDR, and 49 HCs, along with clinical assessments. dFNC analyses revealed two distinct connectivity states: a less frequent, integrated state characterized by the predominance of between-network connections (State I), and a more frequent, segregated state with strong within-network connections (State II). OCD patients spent more time in State II and less time in State I than HC, as measured by fractional windows and mean dwell time. Time in each state for the UFDR were intermediate between OCD patients and HC. Within the OCD group, fractional windows of time spent in State I was positively correlated with OCD symptoms (as measured by the obsessive compulsive inventory-revised [OCI-R], r = .343, p<.05, FDR correction) and time in State II was negatively correlated with symptoms (r = -.343, p<.05, FDR correction). Within each state we also examined connectivity within and between established intrinsic connectivity networks, and found that UFDR were similar to the OCD group in State I, but more similar to the HC groups in State II. The similarities between OCD and UFDR groups in temporal properties and State I connectivity indicate that these features may reflect the endophenotype for OCD. These results indicate that the temporal dynamics of functional connectivity could be a useful biomarker to identify those at risk.

Brain functional connectivity analysis based on multi-graph fusion.

In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e., a fully-connected FCN and a 1 nearest neighbor (1NN) FCN, whereas previous methods only focus on conducting FCN analysis from a single FCN. Specifically, our framework first conducts the graph fusion to produce the representation of the rs-fMRI data with high discriminative ability, and then employs the L1SVM to jointly conduct brain region selection and disease diagnosis. We further evaluate the effectiveness of the proposed framework on various data sets of the neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimers Disease (AD). The experimental results demonstrate that the proposed framework achieves the best diagnosis performance via selecting reasonable brain regions for the classification tasks, compared to state-of-the-art FCN analysis methods.

Dysfunction of the Auditory Brainstem as a Neurophysiology Subtype of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is very heterogeneous, particularly in language. Studies have suggested that language impairment is linked to auditory-brainstem dysfunction in ASD. However, not all ASD children have these deficits, which suggests potential subtypes of ASD. We classified ASD children into two subtypes according to their speech-evoked auditory brainstem response (speech-ABR) and explored the neural substrates for possible subtypes. Twenty-nine children with ASD and 25 typically developing (TD) peers were enrolled to undergo speech-ABR testing and structural magnetic resonance imaging (sMRI). There were significant differences between the ASD group and TD group in surface area, cortical volume and cortical thickness. According to speech-ABR results, ASD participants were divided into the ASD-typical (ASD-T) group and ASD-atypical (ASD-A) group. Compared with the ASD-T group, the ASD-A group had a lower score in language of the Gesell Developmental Diagnosis Scale (GDDS), increased left rostral middle frontal gyrus (lRMFG) area and decreased local gyrification index of the right superior temporal gyrus. GDDS-language and surface area of lRMFG were correlated to the wave-A amplitude in ASD. Surface area of lRMFG had an indirect effect on language performance via alteration of the wave-V amplitude. Thus, cortical deficits may impair language ability in children with ASD by causing subcortical dysfunction at preschool age. These evidences support dysfunction of the auditory brainstem as a potential subtype of ASD. Besides, this subtype-based method may be useful for various clinical applications.

White Matter Alterations of the Goal-Directed System in Patients With Obsessive-Compulsive Disorder and Their Unaffected First-Degree Relatives.

BACKGROUND: It has been postulated that the neurobiological mechanism responsible for the onset of symptoms of obsessive-compulsive disorder (OCD), especially compulsive behavior, is related to alterations of the goal-directed and habitual learning systems. However, little is known about whether changes in these learning systems co-occur with changes in the white matter structure of patients with OCD and their unaffected first-degree relatives (UFDRs). METHODS: Diffusion tensor imaging data were acquired from 32 patients with OCD (21 male), 32 UFDRs (16 male), and 32 healthy control subjects (16 male). White matter tracts in the goal-directed and habitual networks were reconstructed with seed-based probabilistic tractography. Partial least squares path modeling was used to measure the covariation between white matter connectivity, psychiatric symptoms, and cognitive flexibility. RESULTS: Patients with OCD showed reduced connectivity in the fiber tracts within the goal-directed but not within the habitual network compared with healthy control subjects. Using partial least squares path modeling, patients' symptoms were negatively associated with connectivity within the goal-directed but not within the habitual network. Cognitive flexibility was correlated negatively with caudate-dorsolateral prefrontal cortex tracts in patients with OCD. UFDRs also exhibited reduced white matter connectivity in the goal-directed network. CONCLUSIONS: These findings suggest that the balance of learning systems in OCD may be disrupted, mainly impairing white matter in the goal-directed network. Alterations of the goal-directed network could explain overt symptoms and impaired cognitive flexibility in patients with OCD. Similar alterations in the goal-directed network are present in UFDRs. The impaired goal-directed system may be an endophenotype of OCD.