Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years

Altered gray matter structural covariance networks in young adults with obesity

BACKGROUND

Overwhelming evidence showed that obesity was associated with abnormal brain functional networks. However, the changes of structural covariance networks (SCNs) based on cortical thickness (CT) and cortical surface area (CSA) in obesity is still unclear.

METHODS

In this study, 243 young adults with obesity and matched 243 lean individuals were enrolled from the Human Connectome Project Release S1200 dataset. All participants underwent magnetic resonance imaging scans following clinical and neuropsychological assessments. SCNs matrices were constructed by Brain Connectivity Toolbox based on both CT and CSA. Nonparametric permutation tests were adopted to examine group differences of these matrices.

RESULTS

Young adults with obesity exhibited lower CSA of left entorhinal cortex, but higher CT of both left rostral anterior cingulate cortex and right superior parietal lobule, as well as lower CT of left temporal pole. While in terms of global network measures, there were no significant group differences; in terms of nodal network measures, young adults with obesity exhibited alterations in widespread brain regions including left posterior cingulate cortex, bilateral superior frontal gyrus, left entorhinal cortex and right insula.

CONCLUSIONS

Young adults with obesity exhibited abnormal nodal network measures in widespread brain regions involved in default mode network, central executive network and salience network. These findings indicate the adverse effects of obesity on young adults might be associated with the altered triple network.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

Objective

Explore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).

Materials and methods

Utilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.

Results

In Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group's brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.

Conclusion

This study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature

Background

A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities.

Methods

Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023.

Results

In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis.

Conclusions

Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Relationship between autism and brain cortex surface area: genetic correlation and a two-sample Mendelian randomization study

BACKGROUND

Alterations in surface area (SA) in specific regions of the cortex have been reported in many individuals with autism spectrum disorder (ASD), however, the genetic background between ASD and SA is still unclear. This study estimated the genetic correlation and causal effect of ASD and cortical SA.

METHODS

Summarized data of genome-wide association studies (GWAS) were separately downloaded from the Psychiatric Genomics Consortium (18,381 cases of ASD, and 27,969 controls) and the Enhancing Neuroimaging Genetics through Meta-Analysis Consortium (33,992 participants of Europeans). We used Linkage disequilibrium score regression (LDSC) and Heritability Estimation from Summary Statistics (HESS) to calculate the heritability of each trait. As for the genetic correlation between ASD and SA, LDSC was used for global correlation and HESS was used to examine the local genetic covariance further. We used three Mendelian randomization (MR) methods, Inverse-variance weighted, MR-Egger, and weighted median to estimate the causal relationship.

RESULTS

LDSC observed a nominal significant genetic correlation (rg = 0.1229, P-value = 0.0346) between ASD and SA of the rostral anterior cingulate gyrus whereas analysis through HESS did not reveal any significant loci having genetic covariance. Based on MR results, statistically meaningful estimations were found in the following areas, postcentral cortex (β (SE) = 21.82 (7.84) mm, 95% CI: 6.46 to 37.19 mm, PIVW = 5.38 × 10- 3, PFDR = 3.09 × 10- 2), posterior cingulate gyrus (β (SE) = 6.23 (2.69) mm, 95% CI: 0.96 to 11.49 mm, PIVW = 2.05 × 10- 2, PFDR = 4.26 × 10- 2), supramarginal gyrus (β (SE) = 19.25 (8.43) mm, 95% CI: 29.29 to 35.77 mm, PIVW = 2.24 × 10- 2, PFDR = 4.31 × 10- 2).

CONCLUSION

Our results provided genetic evidence to support the opinion that individuals with ASD tend to develop differences in cortical SA of special areas. The findings contributed to understanding the genetic relationship between ASD and cortical SA.

Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.

Association of GABRG3, GABRB3, HTR2A Gene Variants with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a neurodevelopmental and neurobehavioral disorder characterized by impaired social communication, repetitive and restricted patterns of behavior, activity, or interest, and altered emotional processing. Reported prevalence is 4 times higher in men and it has increased in recent years. Immunological, environmental, epigenetic, and genetic factors play a role in the pathophysiology of autism. Many neurochemical pathways and neuroanatomical events are effective in determining the disease. It is still unclear how the main symptoms of autism occur because of this complex and heterogeneous situation. In this study, we focused on gamma amino butyric acid (GABA) and serotonin, which are thought to contribute to the etiology of autism; it is aimed to elucidate the mechanism of the disease by investigating variant changes in the GABA receptor subunit genes GABRB3, GABRG3 and the HTR2A gene, which encodes one of the serotonin receptors. 200 patients with ASD between the ages of 3-9 and 100 healthy volunteers were included in the study. Genomic DNA isolation was performed from peripheral blood samples taken from volunteers. Genotyping was performed using the RFLP method with PCR specific for specific variants. Data were analyzed with SPSS v25.0 program. According to the data obtained in our study; In terms of HTR2A (rs6313 T102C) genotypes, the homozygous C genotype carrying frequency in the patient group and the homozygous T genotype carrying frequency in the GABRG3 (rs140679 C/T) genotypes were found to be significantly higher in the patient group compared to the control group (*p: 0.0001, p: 0.0001). It was determined that the frequency of individuals with homozygous genotype was significantly higher in the patient group compared to the control group and having homozygous genotypes increased the disease risk approximately 1.8 times. In terms of GABRB3 (rs2081648 T/C) genotypes, it was determined that there was no statistically significant difference in the frequency of carrying homozygous C genotype in the patient group compared to the control group (p: 0.36). According to the results of our study, we think that the HTR2A (rs6313 T102C) polymorphism is effective in modulating the empathic and autistic characteristics of individuals, and that the HTR2A (rs6313 T102C) polymorphism is more distributed in the post-synaptic membranes in individuals with a higher number of C alleles. We believe that this situation can be attributed to the spontaneous stimulatory distribution of the HTR2A gene in the postsynaptic membranes because of T102C transformation. In genetically based autism cases, carrying the point mutation in the rs6313 variant of the HTR2A gene and the C allele and the point mutation in the rs140679 variant of the GABRG3 gene and accordingly carrying the T allele provide a predisposition to the disease.

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.