Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing

Brain-Charting Autism and Attention-Deficit/Hyperactivity Disorder Reveals Distinct and Overlapping Neurobiology

BACKGROUND

Autism and attention-deficit/hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology that is still poorly understood. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together and sex differences are often overlooked. Population modeling, often referred to as normative modeling, provides a unified framework for studying age-specific and sex-specific divergences in brain development.

METHODS

Here, we used population modeling and a large, multisite neuroimaging dataset (N = 4255 after quality control) to characterize cortical anatomy associated with autism and ADHD, benchmarked against models of average brain development based on a sample of more than 75,000 individuals. We also examined sex and age differences and relationship with autistic traits and explored the co-occurrence of autism and ADHD.

RESULTS

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume that was localized to the superior temporal cortex, whereas individuals with ADHD showed more global increases in cortical thickness but lower cortical volume and surface area across much of the cortex. The co-occurring autism+ADHD group showed a unique pattern of widespread increases in cortical thickness and certain decreases in surface area. We also found that sex modulated the neuroanatomy of autism but not ADHD, and there was an age-by-diagnosis interaction for ADHD only.

CONCLUSIONS

These results indicate distinct cortical differences in autism and ADHD that are differentially affected by age and sex as well as potentially unique patterns related to their co-occurrence.

Identifying Distinct Developmental Patterns of Brain Complexity in Autism: A Cross-Sectional Cohort Analysis Using the Autism Brain Imaging Data Exchange

AIM

Autistic traits exhibit neurodiversity with varying behaviors across developmental stages. Brain complexity theory, illustrating the dynamics of neural activity, may elucidate the evolution of autistic traits over time. Our study explored the patterns of brain complexity in autistic individuals from childhood to adulthood.

METHODS

We analyzed functional magnetic resonance imaging data from 1087 autistic participants and neurotypical controls aged 6 to 30 years within the ABIDE I (Autism Brain Imaging Data Exchange) data set. Sample entropy was calculated to measure brain complexity among 90 brain regions, utilizing an automated anatomical labeling template for voxel parcellation. Participants were grouped using sliding age windows with partial overlaps. We assessed the average brain complexity of the entire brain and brain regions for both groups across age categories. Cluster analysis was conducted using generalized association plots to identify brain regions with similar developmental complexity trajectories. Finally, the relationship between brain region complexity and autistic traits was examined.

RESULTS

Autistic individuals may tend toward higher whole-brain complexity during adolescence and lower complexity during childhood and adulthood, indicating possible distinct developmental trajectories. However, these results do not remain after Bonferroni correction. Two clusters of brain regions were identified, each with unique patterns of complexity changes over time. Correlations between brain region complexity, age, and autistic traits were also identified.

CONCLUSION

The study revealed brain complexity trajectories in autistic individuals, providing insight into the neurodiversity of autism and suggesting that age-related changes in brain complexity could be a potential neurodevelopmental marker for the dynamic nature of autism.

Cortical Gyrification Is Associated With the Clinical Phenotype in Tuberous Sclerosis Complex

BACKGROUND

Tuberous sclerosis complex (TSC) is characterized by cortical tubers, determining cortical disarrangement and consequently drug-resistant epilepsy, intellectual disability, and TSC-associated neuropsychiatric disorders (TAND).

AIM OF THE STUDY

To establish whether gyrification index (GI), a software-based neuroradiological parameter, could be associated with the severity of phenotype in TSC, identifying the cortical regions that are more associated with the severity of the main clinical manifestations.

METHODS

This was a retrospective cross-sectional study. Magnetic resonance imaging was acquired on a 1.5-T scanner. CAT12 toolbox was used for the estimation of GI. Data analysis was performed with Jamovi. The level of significance was set to P < 0.05 for all tests.

RESULTS

Forty-five patients with TSC and 42 healthy controls were included. Patients with TSC were characterized by higher total GI (P = 0.002) compared with healthy controls. Among patients with TSC, a higher total GI was associated with impaired neurological examination (P = 0.039), epilepsy (P = 0.017), intellectual disability (P = 0.013), TAND (P = 0.013), and higher number of cortical tubers (P < 0.001). An increased local GI in specific cortical areas was associated with TAND and autism spectrum disorders.

CONCLUSIONS

GI is a software-based neuroradiological parameter that could represent a reliable overall prognostic marker in TSC. Local GI can be used to identify phenotype-specific gyrification patterns allowing an early characterization of patients with TSC.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology

Background

Autism and attention deficit hyperactivity disorder (ADHD) are heterogeneous neurodevelopmental conditions with complex underlying neurobiology. Despite overlapping presentation and sex-biased prevalence, autism and ADHD are rarely studied together, and sex differences are often overlooked. Normative modelling provides a unified framework for studying age-specific and sex-specific divergences in neurodivergent brain development.

Methods

Here we use normative modelling and a large, multi-site neuroimaging dataset to characterise cortical anatomy associated with autism and ADHD, benchmarked against models of typical brain development based on a sample of over 75,000 individuals. We also examined sex and age differences, relationship with autistic traits, and explored the co-occurrence of autism and ADHD (autism+ADHD).

Results

We observed robust neuroanatomical signatures of both autism and ADHD. Overall, autistic individuals showed greater cortical thickness and volume localised to the superior temporal cortex, whereas individuals with ADHD showed more global effects of cortical thickness increases but lower cortical volume and surface area across much of the cortex. The autism+ADHD group displayed a unique pattern of widespread increases in cortical thickness, and certain decreases in surface area. We also found evidence that sex modulates the neuroanatomy of autism but not ADHD, and an age-by-diagnosis interaction for ADHD only.

Conclusions

These results indicate distinct cortical differences in autism and ADHD that are differentially impacted by age, sex, and potentially unique patterns related to their co-occurrence.

Morphological and Functional Changes of Cerebral Cortex in Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by early-onset impairments in socialization, communication, repetitive behaviors, and restricted interests. ASD exhibits considerable heterogeneity, with clinical presentations varying across individuals and age groups. The pathophysiology of ASD is hypothesized to be due to abnormal brain development influenced by a combination of genetic and environmental factors. One of the most consistent morphological parameters for assessing the abnormal brain structures in patients with ASD is cortical thickness. Studies have shown changes in the cortical thickness within the frontal, temporal, parietal, and occipital lobes of individuals with ASD. These changes in cortical thickness often correspond to specific clinical features observed in individuals with ASD. Furthermore, the aberrant brain anatomical features and cortical thickness alterations may lead to abnormal brain connectivity and synaptic structure. Additionally, ASD is associated with cortical hyperplasia in early childhood, followed by a cortical plateau and subsequent decline in later stages of development. However, research in this area has yielded contradictory findings regarding the cortical thickness across various brain regions in ASD.

Application of Multimodal MRI in the Early Diagnosis of Autism Spectrum Disorders: A Review

Autism spectrum disorder (ASD) is a neurodevelopmental disorder in children. Early diagnosis and intervention can remodel the neural structure of the brain and improve quality of life but may be inaccurate if based solely on clinical symptoms and assessment scales. Therefore, we aimed to analyze multimodal magnetic resonance imaging (MRI) data from the existing literature and review the abnormal changes in brain structural-functional networks, perfusion, neuronal metabolism, and the glymphatic system in children with ASD, which could help in early diagnosis and precise intervention. Structural MRI revealed morphological differences, abnormal developmental trajectories, and network connectivity changes in the brain at different ages. Functional MRI revealed disruption of functional networks, abnormal perfusion, and neurovascular decoupling associated with core ASD symptoms. Proton magnetic resonance spectroscopy revealed abnormal changes in the neuronal metabolites during different periods. Decreased diffusion tensor imaging signals along the perivascular space index reflected impaired glymphatic system function in children with ASD. Differences in age, subtype, degree of brain damage, and remodeling in children with ASD led to heterogeneity in research results. Multimodal MRI is expected to further assist in early and accurate clinical diagnosis of ASD through deep learning combined with genomics and artificial intelligence.

Cholinergic basal forebrain atrophy in Parkinson's disease with freezing of gait

BACKGROUND

Mounting research support that cholinergic dysfunction plays a prominent role in freezing of gait (FOG), which commonly occurs in Parkinson's disease (PD). Basal forebrain (BF), especially the cholinergic nuclei 4 (Ch4), provides the primary source of the brain cholinergic input. However, whether the degeneration of BF and its innervated cortex contribute to the pathogenesis of FOG is unknown.

OBJECTIVE

To explore the role of structural alterations of BF and its innervated cortical brain regions in the pathogenesis of PD patients with freezing.

METHODS

Magnetic resonance imaging assessments and neurological assessments were performed on 20 PD patients with FOG (PD-FOG), 20 without FOG (PD-NFOG), and 21 healthy participants. Subregion volumes of the BF were compared among groups. Local gyrification index (LGI) was computed to reveal the cortical alternations. Relationships among subregional BF volumes, LGI, and the severity of FOG were evaluated by multiple linear regression.

RESULTS

Our study discovered that, compared to PD-NFOG, PD-FOG exhibited significant Ch4 atrophy (p = 4.6 × 10^-5 ), accompanied by decreased LGI values in the left entorhinal cortex (p = 3.00 × 10^-5 ) and parahippocampal gyrus (p = 2.90 × 10^-5 ). Based on the regression analysis, Ch4 volume was negatively associated with FOG severity in PD-FOG group (β = -12.224, T = -2.556, p = 0.031).

INTERPRETATION

Our results imply that Ch4 degeneration and microstructural disorganization of its innervated cortical brain regions may play important roles in PD-FOG.

Sex-dependent structure of socioemotional salience, executive control, and default mode networks in preschool-aged children with autism

The structure of large-scale intrinsic connectivity networks is atypical in adolescents diagnosed with autism spectrum disorder (ASD or autism). However, the degree to which alterations occur in younger children, and whether these differences vary by sex, is unknown. We utilized structural magnetic resonance imaging (MRI) data from a sex- and age- matched sample of 122 autistic and 122 typically developing (TD) children (2-4 years old) to investigate differences in underlying network structure in preschool-aged autistic children within three large scale intrinsic connectivity networks implicated in ASD: the Socioemotional Salience, Executive Control, and Default Mode Networks. Utilizing structural covariance MRI (scMRI), we report network-level differences in autistic versus TD children, and further report preliminary findings of sex-dependent differences within network topology.