Increased hippocampal volumes in adults with high functioning autism spectrum disorder and an IQ>100: A manual morphometric study

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children

BACKGROUND

Hippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies.

METHODS

A retrospective study from Hainan Children's Hospital database (2020-2023) included ASD patients and matched controls. We classified ASD participants based on severity, dividing all subjects into four groups: normal, mild, moderate, and severe. High-resolution T1-weighted MRI images were analyzed for hippocampal subregion segmentation and volume calculations using Freesurfer. Texture features were extracted via the Gray-Level Co-occurrence Matrix. The Receiver Operating Characteristic curve was used to evaluate seven random forest predictive models constructed from volume, subregion, and texture features, as well as their combinations following feature selection.

RESULTS

The study included 114 ASD patients (98 boys, 2-8 years; 16 girls, 2-6 years; 17 mild, 57 moderate, 40 severe) and 111 healthy controls (HCs). No significant differences in volumes were found between ASD patients and HCs (adjusted P-value >0.05). The seven random forest models showed that single volume and texture features performed poorly for ASD classification; however, integrating various feature types improved AUC values. Further selection of texture, subregion, and volume features enhanced AUC performance across normal and varying severity categories, demonstrating the potential value of specific subregions and integrated features in ASD diagnosis.

CONCLUSION

Random forest models revealed that hippocampal volume, texture features, and subregion characteristics are crucial for diagnosing and assessing the severity of ASD. Integrating selected texture and subregion features optimized diagnostic efficacy, while combining texture, subregion, and volume features further improved severity grading effectiveness.

Resolving autism spectrum disorder (ASD) through brain topologies using fMRI dataset with multi-layer perceptron (MLP)

Autism is a neurodevelopmental disorder that manifests in individuals during childhood and has enduring consequences for their social interactions and communication. The prediction of Autism Spectrum Disorder (ASD) in individuals based on the differences in brain networks and activities have been studied extensively in the recent past, however, with lower accuracies. Therefore in this research, identification at the early stage through computer-aided algorithms to differentiate between ASD and TD patients is proposed. In order to identify features, a Multi-Layer Perceptron (MLP) model is developed which utilizes logistic regression on characteristics extracted from connectivity matrices of subjects derived from fMRI images. The features that significantly contribute to the classification of individuals as having Autism Spectrum Disorder (ASD) or typically developing (TD) are identified by the logistic regression model. To enhance emphasis on essential attributes, an AND operation is integrated. This involves selecting features demonstrating statistical significance across diverse logistic regression analyses conducted on various random distributions. The iterative approach contributes to a comprehensive understanding of relevant features for accurate classification. By implementing this methodology, the estimation of feature importance became more dependable, and the potential for overfitting is moderated through the evaluation of model performance on various subsets of data. It is observed from the experimentation that the highly correlated Left Lateral Occipital Cortex and Right Lateral Occipital Cortex ROIs are only found in ASD. Also, it is noticed that the highly correlated Left Cerebellum Tonsil and Right Cerebellum Tonsil are only found in TD participants. Among the MLP classifier, a recall of 82.61 % is achieved followed by Logistic Regression with an accuracy of 72.46 %. MLP also stands out with a commendable accuracy of 83.57 % and AUC of 0.978.

Research progress on hippocampal neurogenesis in autism spectrum disorder

Autism spectrum disorder (ASD) is a group of severe neurodevelopmental disorders with unclear etiology and significant heterogeneity that is emerging as a global public health concern. Increasing research suggests the involvement of hippocampal neurogenesis defects in the onset and development of ASD, drawing increasing amounts of attention to hippocampal neurogenesis issues in ASD. In this paper, we analyze relevant international studies on hippocampal neurogenesis in ASD, discuss the role of neurobiology in the pathogenesis of ASD, and explore the potential of improving hippocampal neurogenesis as a therapeutic approach for ASD. This review aims to provide new treatment perspectives and theoretical foundations for clinical practice.

Fullerenols Ameliorate Social Deficiency and Rescue Cognitive Dysfunction of BTBR T+Itpr3tf/J Autistic-Like Mice

Background

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interaction and communication and can cause stereotypic behavior. Fullerenols, a type of carbon nanomaterial known for its neuroprotective properties, have not yet been studied for their potential in treating ASD. We aimed to investigate its role in improving autistic behaviors in BTBR T+Itpr3tf/J (BTBR) mice and its underlying mechanism, which could provide reliable clues for future ASD treatments.

Methods

Our research involved treating C57BL/6J (C57) and BTBR mice with either 0.9% NaCl or fullerenols (10 mg/kg) daily for one week at seven weeks of age. We then conducted ASD-related behavioral tests in the eighth week and used RNA-seq to screen for vital pathways in the mouse hippocampus. Additionally, we used real-time quantitative PCR (RT-qPCR) to verify related pathway genes and evaluated the number of stem cells in the hippocampal dentate gyrus (DG) by Immunofluorescence staining.

Results

Our findings revealed that fullerenols treatment significantly improved the related ASD-like behaviors of BTBR mice, manifested by enhanced social ability and improved cognitive deficits. Immunofluorescence results showed that fullerenols treatment increased the number of DCX+ and SOX2+/GFAP+ cells in the DG region of BTBR mice, indicating an expanded neural progenitor cell (NPC) pool of BTBR mice. RNA-seq analysis of the mouse hippocampus showed that VEGFA was involved in the rescued hippocampal neurogenesis by fullerenols treatment.

Conclusion

In conclusion, our findings suggest that fullerenols treatment improves ASD-like behavior in BTBR mice by upregulating VEGFA, making nanoparticle- fullerenols a promising drug for ASD treatment.

Autism-associated brain differences can be observed in utero using MRI

Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.

Abnormal Brain Structure Is Associated with Social and Communication Deficits in Children with Autism Spectrum Disorder: A Voxel-Based Morphometry Analysis

Structural magnetic resonance imaging (sMRI) studies have shown abnormalities in the brain structure of ASD patients, but the relationship between structural changes and social communication problems is still unclear. This study aims to explore the structural mechanisms of clinical dysfunction in the brain of ASD children through voxel-based morphometry (VBM). After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 98 children aged 8-12 years old with ASD were matched with 105 children aged 8-12 years old with typical development (TD). Firstly, this study compared the differences in gray matter volume (GMV) between the two groups. Then, this study evaluated the relationship between GMV and the subtotal score of communications and social interaction on the Autism Diagnostic Observation Schedule (ADOS) in ASD children. Research has found that abnormal brain structures in ASD include the midbrain, pontine, bilateral hippocampus, left parahippocampal gyrus, left superior temporal gyrus, left temporal pole, left middle temporal gyrus and left superior occipital gyrus. In addition, in ASD children, the subtotal score of communications and social interaction on the ADOS were only significantly positively correlated with GMV in the left hippocampus, left superior temporal gyrus and left middle temporal gyrus. In summary, the gray matter structure of ASD children is abnormal, and different clinical dysfunction in ASD children is related to structural abnormalities in specific regions.

Larger left hippocampal presubiculum is associated with lower risk of antisocial behavior in healthy adults with childhood conduct history

Conduct Disorder (CD) is defined as aggressive, antisocial, and rule-breaking behavior during childhood. It is a major risk factor for developing antisocial personality disorder (ASPD) in adulthood. However, nearly half the CDs do not develop ASPD. Identification of reversion factors seems crucial for proper interventions. We identified 40 subjects with childhood history of CD (CC) and 1166 control subjects (HC) from Human Connectome Project. Their psychiatric, emotional, impulsivity, and personality traits were extracted. An emotion recognition task-fMRI analysis was done. We also did subregion analysis of hippocampus and amygdala in 35 CC and 69 demographically matched HCs. CC subjects scored significantly higher in antisocial-related evaluations. No differences in task-fMRI activation of amygdala and hippocampus were observed. CCs had larger subfields of the left hippocampus: presubiculum, CA3, CA4, and dentate gyrus. Further, an interaction model revealed a significant presubiculum volume × group association with antisocial, aggression, and agreeableness scores. Our study shows that healthy young adults with a prior history of CD still exhibit some forms of antisocial-like behavior with larger left hippocampal subfields, including presubiculum that also explains the variability in antisocial behavior. These larger left hippocampal subfield volumes may play a protective role against CD to ASPD conversion.

Implication of Hippocampal Neurogenesis in Autism Spectrum Disorder: Pathogenesis and Therapeutic Implications

Autism spectrum disorder (ASD) is a cluster of heterogeneous neurodevelopmental conditions with atypical social communication and repetitive sensory-motor behaviors. The formation of new neurons from neural precursors in the hippocampus has been unequivocally demonstrated in the dentate gyrus of rodents and non-human primates. Accumulating evidence sheds light on how the deficits in the hippocampal neurogenesis may underlie some of the abnormal behavioral phenotypes in ASD. In this review, we describe the current evidence concerning pre-clinical and clinical studies supporting the significant role of hippocampal neurogenesis in ASD pathogenesis, discuss the possibility of improving hippocampal neurogenesis as a new strategy for treating ASD, and highlight the prospect of emerging pro-neurogenic therapies for ASD.

Subcortical Brain Morphometry Differences between Adults with Autism Spectrum Disorder and Schizophrenia

Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Subcortical structures play a significant role in cognitive and social-emotional behaviors and their abnormalities are associated with neuropsychiatric conditions. This exploratory study utilized ABIDE II/COBRE MRI and corresponding phenotypic datasets to compare subcortical volumes of adults with ASD (n = 29), SZ (n = 51) and age and gender matched neurotypicals (NT). We examined the association between subcortical volumes and select behavioral measures to determine whether core symptomatology of disorders could be explained by subcortical association patterns. We observed volume differences in ASD (viz., left pallidum, left thalamus, left accumbens, right amygdala) but not in SZ compared to their respective NT controls, reflecting morphometric changes specific to one of the disorder groups. However, left hippocampus and amygdala volumes were implicated in both disorders. A disorder-specific negative correlation (r = −0.39, p = 0.038) was found between left-amygdala and scores on the Social Responsiveness Scale (SRS) Social-Cognition in ASD, and a positive association (r = 0.29, p = 0.039) between full scale IQ (FIQ) and right caudate in SZ. Significant correlations between behavior measures and subcortical volumes were observed in NT groups (ASD-NT range; r = −0.53 to −0.52, p = 0.002 to 0.004, SZ-NT range; r = −0.41 to −0.32, p = 0.007 to 0.021) that were non-significant in the disorder groups. The overlap of subcortical volumes implicated in ASD and SZ may reflect common neurological mechanisms. Furthermore, the difference in correlation patterns between disorder and NT groups may suggest dysfunctional connectivity with cascading effects unique to each disorder and a potential role for IQ in mediating behavior and brain circuits.

Age-related Brain Morphological Alteration of Medication-naive Boys With High Functioning Autism

RATIONALE AND OBJECTIVE

To investigate age-related brain morphological changes of boys with high functioning autism (HFA).

MATERIALS AND METHODS

Forty-six medication-naive boys with HFA and 48 age-matched typically developing boys (4-12 years old) were included in this study. Structural brain images were processed with FreeSurfer to calculate the brain morphometric features including regional volume, surface area, average cortical thickness, and Gaussian curvature. General linear model was used to identify significant effects of diagnosis and age-by-diagnosis interaction. Correlations between age and the brain morphometric variables of significant clusters were explored.

RESULTS

Primarily, most of the regions with statistically significant intergroup differences were located in the temporal lobe gyri. Importantly, the volume of bilateral superior temporal gyrus (STG) and the average cortical thickness of the right STG demonstrated significantly age-related intergroup differences. Further age-stratified analysis also revealed morphological alterations of STG among subgroups of preschool and school-aged children with or without HFA.

CONCLUSION

The findings demonstrated abnormal age-related volume and cortical thickness atrophy of the STG in HFA children, which reflect brain development trajectories of ASD may initiate to diverge from early overgrowth in childhood period. The anatomical localization of specific brain regions would help us better understand the neurobiology alterations of HFA patients and indicate the effect of age should be carefully delineated and examined in future studies about HFA.

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

Dissecting the heterogeneous subcortical brain volume of autism spectrum disorder using community detection

Structural brain alterations in autism spectrum disorder (ASD) are heterogeneous, with limited effect sizes overall. In this study, we aimed to identify subgroups in ASD, based on neuroanatomical profiles; we hypothesized that the effect sizes for case/control differences would be increased in the newly defined subgroups. Analyzing a large data set from the ENIGMA-ASD working group (n = 2661), we applied exploratory factor analysis (EFA) to seven subcortical volumes of individuals with and without ASD to uncover the underlying organization of subcortical structures. Based on earlier findings and data availability, we focused on three age groups: boys (<=14 years), male adolescents (15-22 years), and adult men (> = 22 years). The resulting factor scores were used in a community detection (CD) analysis to cluster participants into subgroups. Three factors were found in each subsample; the factor structure in adult men differed from that in boys and male adolescents. From these factors, CD uncovered four distinct communities in boys and three communities in adolescents and adult men, irrespective of ASD diagnosis. The effect sizes for case/control comparisons were more pronounced than in the combined sample, for some communities. A significant group difference in ADOS scores between communities was observed in boys and male adolescents with ASD. We succeeded in stratifying participants into more homogeneous subgroups based on subcortical brain volumes. This stratification enhanced our ability to observe case/control differences in subcortical brain volumes in ASD, and may help to explain the heterogeneity of previous findings in ASD. LAY SUMMARY: Structural brain alterations in ASD are heterogeneous, with overall limited effect sizes. Here we aimed to identify subgroups in ASD based on neuroimaging measures. We tested whether the effect sizes for case/control differences would be increased in the newly defined subgroups. Based on neuroanatomical profiles, we succeeded in stratifying our participants into more homogeneous subgroups. The effect sizes of case/control differences were more pronounced in some subgroups than those in the whole sample.

Hippocampal Shape Maturation in Childhood and Adolescence

The hippocampus is a subcortical structure critical for learning and memory, and a thorough understanding of its neurodevelopment is important for studying these processes in health and disease. However, few studies have quantified the typical developmental trajectory of the structure in childhood and adolescence. This study examined the cross-sectional age-related changes and sex differences in hippocampal shape in a multisite, multistudy cohort of 1676 typically developing children (age 1-22 years) using a novel intrinsic brain mapping method based on Laplace-Beltrami embedding of surfaces. Significant age-related expansion was observed bilaterally and nonlinear growth was observed primarily in the right head and tail of the hippocampus. Sex differences were also observed bilaterally along the lateral and medial aspects of the surface, with females exhibiting relatively larger surface expansion than males. Additionally, the superior posterior lateral surface of the left hippocampus exhibited an age-sex interaction with females expanding faster than males. Shape analysis provides enhanced sensitivity to regional changes in hippocampal morphology over traditional volumetric approaches and allows for the localization of developmental effects. Our results further support evidence that hippocampal structures follow distinct maturational trajectories that may coincide with the development of learning and memory skills during critical periods of development.

Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder

Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry—the nonlinear scaling relationship between regional volumes and TBV—was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.

Brief Report: Memory for Self-Performed Actions in Adults with Autism Spectrum Disorder: Why Does Memory of Self Decline in ASD?

The decline in self-related memory in ASD was investigated by using encoding, forgetting, and source monitoring. Participants memorized action sentences verbally, observationally, or by enacted encoding. Then, they underwent recall, recognition, and source monitoring memory tests immediately and 1 week later. If the information were properly encoded, memory performance in the enacted encoding would be the highest (enactment effect). The result of memory tests in ASD and TD people showed that enacted encoding was superior. However, recall and source monitoring in ASD was significantly lower than in TD, which was not the case for recognition and forgetting. These results suggest that the decline in memory of self in ASD is associated with a deficit in memory reconstruction and source monitoring.

Cortical properties of adults with autism spectrum disorder and an IQ>100

Previous studies on cortical volume and thickness measures in autism spectrum disorders (ASD) show inconsistent results. We acquired structural magnetic resonance images of 30 individuals with ASD and individually matched controls and extracted surface-based and deformation-based morphometry measures. All participants had an IQ>100. Neither surface-based cortical thickness nor deformation based gyrification measures differed significantly across groups. Significant decreases but no increases of the gyrification index and sulcus depth could only be observed in the ASD group before correcting for multiple comparisons. This finding suggests that possible cortical anomalies in ASD are either weak or, given the heterogeneity of findings in earlier studies, might only apply to small ASD-subgroups.

Classification of Autism Spectrum Disorder Using Random Support Vector Machine Cluster

Autism spectrum disorder (ASD) is mainly reflected in the communication and language barriers, difficulties in social communication, and it is a kind of neurological developmental disorder. Most researches have used the machine learning method to classify patients and normal controls, among which support vector machines (SVM) are widely employed. But the classification accuracy of SVM is usually low, due to the usage of a single SVM as classifier. Thus, we used multiple SVMs to classify ASD patients and typical controls (TC). Resting-state functional magnetic resonance imaging (fMRI) data of 46 TC and 61 ASD patients were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. Only 84 of 107 subjects are utilized in experiments because the translation or rotation of 7 TC and 16 ASD patients has surpassed ±2 mm or ±2°. Then the random SVM cluster was proposed to distinguish TC and ASD. The results show that this method has an excellent classification performance based on all the features. Furthermore, the accuracy based on the optimal feature set could reach to 96.15%. Abnormal brain regions could also be found, such as inferior frontal gyrus (IFG) (orbital and opercula part), hippocampus, and precuneus. It is indicated that the method of random SVM cluster may apply to the auxiliary diagnosis of ASD.

Autism As a Disorder of High Intelligence

A suite of recent studies has reported positive genetic correlations between autism risk and measures of mental ability. These findings indicate that alleles for autism overlap broadly with alleles for high intelligence, which appears paradoxical given that autism is characterized, overall, by below-average IQ. This paradox can be resolved under the hypothesis that autism etiology commonly involves enhanced, but imbalanced, components of intelligence. This hypothesis is supported by convergent evidence showing that autism and high IQ share a diverse set of convergent correlates, including large brain size, fast brain growth, increased sensory and visual-spatial abilities, enhanced synaptic functions, increased attentional focus, high socioeconomic status, more deliberative decision-making, profession and occupational interests in engineering and physical sciences, and high levels of positive assortative mating. These findings help to provide an evolutionary basis to understanding autism risk as underlain in part by dysregulation of intelligence, a core human-specific adaptation. In turn, integration of studies on intelligence with studies of autism should provide novel insights into the neurological and genetic causes of high mental abilities, with important implications for cognitive enhancement, artificial intelligence, the relationship of autism with schizophrenia, and the treatment of both autism and intellectual disability.

Neuroanatomical Alterations in High-Functioning Adults with Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental condition, affecting cognition and behavior throughout the life span. With recent advances in neuroimaging techniques and analytical approaches, a considerable effort has been directed toward identifying the neuroanatomical underpinnings of ASD. While gray-matter abnormalities have been found throughout cortical, subcortical, and cerebellar regions of affected individuals, there is currently little consistency across findings, partly due to small sample-sizes and great heterogeneity among participants in previous studies. Here, we report voxel-based morphometry of structural magnetic resonance images in a relatively large sample of high-functioning adults with ASD (n = 66) and matched typically-developing controls (n = 66) drawn from multiple studies. We found decreased gray-matter volume in posterior brain regions, including the posterior hippocampus and cuneus, as well as increased gray-matter volume in frontal brain regions, including the medial prefrontal cortex, superior and inferior frontal gyri, and middle temporal gyrus in individuals with ASD. We discuss our results in relation to findings obtained in previous studies, as well as their potential clinical implications.