Structural covariance model reveals dynamic reconfiguration of triple networks in autism spectrum disorder

Towards a Neurophenomenological Understanding of Self-Disorder in Schizophrenia Spectrum Disorders: A Systematic Review and Synthesis of Anatomical, Physiological, and Neurocognitive Findings

The concept of anomalous self-experience, also termed Self-Disorder, has attracted both clinical and research interest, as empirical studies suggest such experiences specifically aggregate in and are a core feature of schizophrenia spectrum disorders. A comprehensive neurophenomenological understanding of Self-Disorder may improve diagnostic and therapeutic practice. This systematic review aims to evaluate anatomical, physiological, and neurocognitive correlates of Self-Disorder (SD), considered a core feature of Schizophrenia Spectrum Disorders (SSDs), towards developing a neurophenomenological understanding. A search of the PubMed database retrieved 285 articles, which were evaluated for inclusion using PRISMA guidelines. Non-experimental studies, studies with no validated measure of Self-Disorder, or those with no physiological variable were excluded. In total, 21 articles were included in the review. Findings may be interpreted in the context of triple-network theory and support a core dysfunction of signal integration within two anatomical components of the Salience Network (SN), the anterior insula and dorsal anterior cingulate cortex, which may mediate connectivity across both the Default Mode Network (DMN) and Fronto-Parietal Network (FPN). We propose a theoretical Triple-Network Model of Self-Disorder characterized by increased connectivity between the Salience Network (SN) and the DMN, increased connectivity between the SN and FPN, decreased connectivity between the DMN and FPN, and increased connectivity within both the DMN and FPN. We go on to describe translational opportunities for clinical practice and provide suggestions for future research.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Autism spectrum disorder and attention-deficit/hyperactivity disorder-related symptoms in benign childhood epilepsy with centrotemporal spikes: A prospective case-control study

BACKGROUND

Benign childhood epilepsy with centrotemporal spikes (BECTS), one of the most common idiopathic epilepsy syndromes in children, has been associated with neuropsychological problems.

PURPOSE

The objective of this study was to investigate the frequency of symptoms related to comorbid neurodevelopmental disorders, the autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) in children with typical BECTS, and to identify corresponding risk factors.

METHODS

Children and adolescents with typical BECTS aged 6-16 years were included in the study period from January 1, 2017, to December 31, 2017. Children with atypical presentations of BECTS, other neurological disorders, and preexisting neuropsychiatric disorders were excluded. The ASD and ADHD were assessed by the Social Communication Questionnaire (SCQ) and the Turgay Diagnostic and Statistical Manual of Mental Disorders - 4th Edition - Disruptive Behavior Disorders Rating Scale (T-DSM-IV-S), respectively. Patients' scores were compared with those of healthy subjects. Correlation analyses were performed to evaluate the association between the age at seizure onset, the total number of seizures and the SCQ and T-DSM-IV-S scores.

RESULTS

Fifty-eight children with BECTS and 60 healthy children participated in the study. The total SCQ score, the SCQ reciprocal social interaction score, and the SCQ communication score significantly differed between children with BECTS and the control group (p = 0.001, p < 0.001, p = 0.001, respectively). The total ADHD score was significantly different between patients and controls (p < 0.001). A significant difference was observed between patients and controls in terms of the T-DSM-IV-S hyperactivity-impulsivity score and the T-DSM-IV-S inattention score (p = 0.012, p < 0.001, respectively). The age at seizure onset was significantly correlated with the total SCQ score (p = 0.03). The Spearman's correlation coefficient was 0.352 for the total SCQ score, indicating a positive association between the age at seizure onset and the total SCQ score.

CONCLUSION

Children with typical BECTS may have an increased risk of suffering from symptoms of ASD and ADHD. Children with late onset of seizures may be more likely to develop neuropsychological disturbances regarding ASD and ADHD.

Structural Covariance Networks in Children with Autism or ADHD

Background

While autism and attention-deficit/hyperactivity disorder (ADHD) are considered distinct conditions from a diagnostic perspective, clinically they share some phenotypic features and have high comorbidity. Regardless, most studies have focused on only one condition, with considerable heterogeneity in their results. Taking a dual-condition approach might help elucidate shared and distinct neural characteristics.

Method

Graph theory was used to analyse topological properties of structural covariance networks across both conditions and relative to a neurotypical (NT; n = 87) group using data from the ABIDE (autism; n = 62) and ADHD-200 datasets (ADHD; n = 69). Regional cortical thickness was used to construct the structural covariance networks. This was analysed in a theoretical framework examining potential differences in long and short-range connectivity, with a specific focus on relation between central graph measures and cortical thickness.

Results

We found convergence between autism and ADHD, where both conditions show an overall decrease in CT covariance with increased Euclidean distance between centroids compared with a NT population. The 2 conditions also show divergence. Namely, there is less modular overlap between the 2 conditions than there is between each condition and the NT group. The ADHD group also showed reduced cortical thickness and lower degree in hub regions than the autism group. Lastly, the ADHD group also showed reduced wiring costs compared with the autism groups.

Conclusions

Our results indicate a need for taking an integrated approach when considering highly comorbid conditions such as autism and ADHD. Furthermore, autism and ADHD both showed alterations in the relation between inter-regional covariance and centroid distance, where both groups show a steeper decline in covariance as a function of distance. The 2 groups also diverge on modular organization, cortical thickness of hub regions and wiring cost of the covariance network. Thus, on some network features the groups are distinct, yet on others there is convergence.