Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Microstructural asymmetry in the human cortex

The human cerebral cortex shows hemispheric asymmetry, yet the microstructural basis of this asymmetry remains incompletely understood. Here, we probe layer-specific microstructural asymmetry using one post-mortem male brain. Overall, anterior and posterior regions show leftward and rightward asymmetry respectively, but this pattern varies across cortical layers. A similar anterior-posterior pattern is observed using in vivo Human Connectome Project (N = 1101) T1w/T2w microstructural data, with average cortical asymmetry showing the strongest similarity with post-mortem-based asymmetry of layer III. Moreover, microstructural asymmetry is found to be heritable, varies as a function of age and sex, and corresponds to intrinsic functional asymmetry. We also observe a differential association of language and markers of mental health with microstructural asymmetry patterns at the individual level, illustrating a functional divergence between inferior-superior and anterior-posterior microstructural axes, possibly anchored in development. Last, we could show concordant evidence with alternative in vivo microstructural measures: magnetization transfer (N = 286) and quantitative T1 (N = 50). Together, our study highlights microstructural asymmetry in the human cortex and its functional and behavioral relevance.

Superior temporal sulcus folding, functional network connectivity, and autistic-like traits in a non-clinical population

BACKGROUND

Autistic-like traits (ALT) are prevalent across the general population and might be linked to some facets of a broader autism spectrum disorder (ASD) phenotype. Recent studies suggest an association of these traits with both genetic and brain structural markers in non-autistic individuals, showing similar spatial location of findings observed in ASD and thus suggesting a potential neurobiological continuum.

METHODS

In this study, we first tested an association of ALTs (assessed with the AQ questionnaire) with cortical complexity, a cortical surface marker of early neurodevelopment, and then the association with disrupted functional connectivity. We analysed structural T1-weighted and resting-state functional MRI scans in 250 psychiatrically healthy individuals without a history of early developmental disorders, in a first step using the CAT12 toolbox for cortical complexity analysis and in a second step we used regional cortical complexity findings to apply the CONN toolbox for seed-based functional connectivity analysis.

RESULTS

Our findings show a significant negative correlation of both AQ total and AQ attention switching subscores with left superior temporal sulcus (STS) cortical folding complexity, with the former being significantly correlated with STS to left lateral occipital cortex connectivity, while the latter showed significant positive correlation of STS to left inferior/middle frontal gyrus connectivity (n = 233; all p < 0.05, FWE cluster-level corrected). Additional analyses also revealed a significant correlation of AQ attention to detail subscores with STS to left lateral occipital cortex connectivity.

LIMITATIONS

Phenotyping might affect association results (e.g. choice of inventories); in addition, our study was limited to subclinical expressions of autistic-like traits.

CONCLUSIONS

Our findings provide further evidence for biological correlates of ALT even in the absence of clinical ASD, while establishing a link between structural variation of early developmental origin and functional connectivity.

Preterm Birth Alters the Regional Development and Structural Covariance of Cerebellum at Term-Equivalent Age

Preterm birth is associated with increased risk for a spectrum of neurodevelopmental disabilities. The cerebellum is implicated in a wide range of cognitive functions extending beyond sensorimotor control and plays an increasingly recognized role in brain development. Morphometric studies based on volume analyses have revealed impaired cerebellar development in preterm infants. However, the structural covariance between the cerebellum and cerebral cortex has not been studied during the neonatal period, and the extent to which structural covariance is affected by preterm birth remains unknown. In this study, using the structural MR images of 52 preterm infants scanned at term-equivalent age and 312 full-term controls from the Developing Human Connectome Project, we compared volumetric growth, local cerebellum shape development and cerebello-cerebral structural covariance between the two groups. We found that although there was no significant difference in the overall volume measurements between preterm and full-term infants, the shape measurements were different. Compared with the control infants, preterm infants had significantly larger thickness in the vermis and lower thickness in the lateral portions of the bilateral cerebral hemispheres. The structural covariance between the cerebellum and frontal and parietal lobes was significantly greater in preterm infants than in full-term controls. The findings in this study suggested that cerebellar development and cerebello-cerebral structural covariance may be affected by premature birth.

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.

Genetic architecture of brain morphology and overlap with neuropsychiatric traits

Uncovering the genetic architectures of brain morphology offers valuable insights into brain development and disease. Genetic association studies of brain morphological phenotypes have discovered thousands of loci. However, interpretation of these loci presents a significant challenge. One potential solution is exploring the genetic overlap between brain morphology and disorders, which can improve our understanding of their complex relationships, ultimately aiding in clinical applications. In this review, we examine current evidence on the genetic associations between brain morphology and neuropsychiatric traits. We discuss the impact of these associations on the diagnosis, prediction, and treatment of neuropsychiatric diseases, along with suggestions for future research directions.

Clinical implications of brain asymmetries

No two human brains are alike, and with the rise of precision medicine in neurology, we are seeing an increased emphasis on understanding the individual variability in brain structure and function that renders every brain unique. Functional and structural brain asymmetries are a fundamental principle of brain organization, and recent research suggests substantial individual variability in these asymmetries that needs to be considered in clinical practice. In this Review, we provide an overview of brain asymmetries, variations in such asymmetries and their relevance in the clinical context. We review recent findings on brain asymmetries in neuropsychiatric and neurodevelopmental disorders, as well as in specific learning disabilities, with an emphasis on large-scale database studies and meta-analyses. We also highlight the relevance of asymmetries for disease symptom onset in neurodegenerative diseases and their implications for lateralized treatments, including brain stimulation. We conclude that alterations in brain asymmetry are not sufficiently specific to act as diagnostic biomarkers but can serve as meaningful symptom or treatment response biomarkers in certain contexts. On the basis of these insights, we provide several recommendations for neurological clinical practice.

Discriminant analysis using MRI asymmetry indices and cognitive scores of women with temporal lobe epilepsy or schizophrenia

PURPOSE

This study aims to assess the diagnostic power of brain asymmetry indices and neuropsychological tests for differentiating mesial temporal lobe epilepsy (MTLE) and schizophrenia (SCZ).

METHODS

We studied a total of 39 women including 13 MTLE, 13 SCZ, and 13 healthy individuals (HC). A neuropsychological test battery (NPT) was administered and scored by an experienced neuropsychologist, and NeuroQuant (CorTechs Labs Inc., San Diego, California) software was used to calculate brain asymmetry indices (ASI) for 71 different anatomical regions of all participants based on their 3D T1 MR imaging scans.

RESULTS

Asymmetry indices measured from 10 regions showed statistically significant differences between the three groups. In this study, a multi-class linear discriminant analysis (LDA) model was built based on a total of fifteen variables composed of the most five significantly informative NPT scores and ten significant asymmetry indices, and the model achieved an accuracy of 87.2%. In pairwise classification, the accuracy for distinguishing MTLE from either SCZ or HC was 94.8%, while the accuracy for distinguishing SCZ from either MTLE or HC was 92.3%.

CONCLUSION

The ability to differentiate MTLE from SCZ using neuroradiological and neuropsychological biomarkers, even within a limited patient cohort, could make a substantial contribution to research in larger patient groups using different machine learning techniques.

Noninvasive brain stimulation in autism: review and outlook for personalized interventions in adult patients

Noninvasive brain stimulation (NIBS) has been increasingly investigated during the last decade as a treatment option for persons with autism spectrum disorder (ASD). Yet, previous studies did not reach a consensus on a superior treatment protocol or stimulation target. Persons with ASD often suffer from social isolation and high rates of unemployment, arising from difficulties in social interaction. ASD involves multiple neural systems involved in perception, language, and cognition, and the underlying brain networks of these functional domains have been well documented. Aiming to provide an overview of NIBS effects when targeting these neural systems in late adolescent and adult ASD, we conducted a systematic search of the literature starting at 631 non-duplicate publications, leading to six studies corresponding with inclusion and exclusion criteria. We discuss these studies regarding their treatment rationale and the accordingly chosen methodological setup. The results of these studies vary, while methodological advances may allow to explain some of the variability. Based on these insights, we discuss strategies for future clinical trials to personalize the selection of brain stimulation targets taking into account intersubject variability of brain anatomy as well as function.

Giftedness and atypical sexual differentiation: enhanced perceptual functioning through estrogen deficiency instead of androgen excess

Syndromic autism spectrum conditions (ASC), such as Klinefelter syndrome, also manifest hypogonadism. Compared to the popular Extreme Male Brain theory, the Enhanced Perceptual Functioning model explains the connection between ASC, savant traits, and giftedness more seamlessly, and their co-emergence with atypical sexual differentiation. Overexcitability of primary sensory inputs generates a relative enhancement of local to global processing of stimuli, hindering the abstraction of communication signals, in contrast to the extraordinary local information processing skills in some individuals. Weaker inhibitory function through gamma-aminobutyric acid type A (GABAA) receptors and the atypicality of synapse formation lead to this difference, and the formation of unique neural circuits that process external information. Additionally, deficiency in monitoring inner sensory information leads to alexithymia (inability to distinguish one's own emotions), which can be caused by hypoactivity of estrogen and oxytocin in the interoceptive neural circuits, comprising the anterior insular and cingulate gyri. These areas are also part of the Salience Network, which switches between the Central Executive Network for external tasks and the Default Mode Network for self-referential mind wandering. Exploring the possibility that estrogen deficiency since early development interrupts GABA shift, causing sensory processing atypicality, it helps to evaluate the co-occurrence of ASC with attention deficit hyperactivity disorder, dyslexia, and schizophrenia based on phenotypic and physiological bases. It also provides clues for understanding the common underpinnings of these neurodevelopmental disorders and gifted populations.

Whole-brain structural connectome asymmetry in autism

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Overview: Research on the Genetic Architecture of the Developing Cerebral Cortex in Norms and Diseases

The human brain is characterized by high cell numbers, diverse cell types with diverse functions, and intricate connectivity with an exceedingly broad surface of the cortex. Human-specific brain development was accomplished by a long timeline for maturation from the prenatal period to the third decade of life. The long timeline makes complicated architecture and circuits of human cerebral cortex possible, and it makes human brain vulnerable to intrinsic and extrinsic insults resulting in the development of variety of neuropsychiatric disorders. Unraveling the molecular and cellular processes underlying human brain development under the elaborate regulation of gene expression in a spatiotemporally specific manner, especially that of the cortex will provide a biological understanding of human cognition and behavior in health and diseases. Global research consortia and the advancing technologies in brain science including functional genomics equipped with emergent neuroinformatics such as single-cell multiomics, novel human models, and high-volume databases with high-throughput computation facilitate the biological understanding of the development of the human brain cortex. Knowing the process of interplay of the genome and the environment in cortex development will lead us to understand the human-specific cognitive function and its individual diversity. Thus, it is worthwhile to overview the recent progress in neurotechnology to foresee further understanding of the human brain and norms and diseases.

Brain structural covariance network features are robust markers of early heavy alcohol use

BACKGROUND AND AIMS

Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.

DESIGN AND SETTING

Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years).

CASES

Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.

MEASUREMENTS

Graph theory metrics of segregation and integration were used to summarize SCN.

FINDINGS

Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.

CONCLUSION

Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG

Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.

Diverging asymmetry of intrinsic functional organization in autism

Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we examined inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra- but not inter-hemispheric features were predictive of the severity of autistic traits. Our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in atypical developmental trajectories of functional organization asymmetry in autism.

Atypical hemispheric lateralization of brain function and structure in autism: a comprehensive meta-analysis study

BACKGROUND

Characteristic changes in the asymmetric nature of the human brain are associated with neurodevelopmental differences related to autism. In people with autism, these differences are thought to affect brain structure and function, although the structural and functional bases of these defects are yet to be fully characterized.

METHODS

We applied a comprehensive meta-analysis to resting-state functional and structural magnetic resonance imaging datasets from 370 people with autism and 498 non-autistic controls using seven datasets of the Autism Brain Imaging Data Exchange Project. We studied the meta-effect sizes based on standardized mean differences and standard deviations (s.d.) for lateralization of gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo). We examined the functional correlates of atypical laterality through an indirect annotation approach followed by a direct correlation analysis with symptom scores.

RESULTS

In people with autism, 85, 51, and 51% of brain regions showed a significant diagnostic effect for lateralization in GMV, fALFF, and ReHo, respectively. Among these regions, 35.7% showed overlapping differences in lateralization in GMV, fALFF, and ReHo, particularly in regions with functional annotations for language, motor, and perceptual functions. These differences were associated with clinical measures of reciprocal social interaction, communication, and repetitive behaviors. A meta-analysis based on s.d. showed that people with autism had lower variability in structural lateralization but higher variability in functional lateralization.

CONCLUSION

These findings highlight that atypical hemispheric lateralization is a consistent feature in autism across different sites and may be used as a neurobiological marker for autism.

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.

Hemispheric asymmetries in mental disorders: evidence from rodent studies

The brain is built with hemispheric asymmetries in structure and function to enable fast neuronal processing. In neuroimaging studies, several mental disorders have been associated with altered or attenuated hemispheric asymmetries. However, the exact mechanism linking asymmetries and disorders is not known. Here, studies in animal models of mental disorders render important insights into the etiology and neuronal alterations associated with both disorders and atypical asymmetry. In this review, the current literature of animal studies in rats and mice focusing on anxiety and fear, anhedonia and despair, addiction or substance misuse, neurodegenerative disorders as well as stress exposure, and atypical hemispheric asymmetries is summarized. Results indicate overall increased right-hemispheric neuronal activity and a left-sided behavioral bias associated with symptoms of anxiety, fear, anhedonia, behavioral despair as well as stress exposure. Addiction behavior is associated with right-sided bias and transgenic models of Alzheimer's disease indicate an asymmetrical accumulation of fibrillar plaques. Most studies focused on changes in the bilateral amygdala and frontal cortex. Across studies, two crucial factors influencing atypical asymmetries arose independently of the disorder modeled: sex and developmental age. In conclusion, animal models of mental disorders demonstrate atypical hemispheric asymmetries similar to findings in patients. Particularly, increased left-sided behavior and greater right-hemispheric activity were found across models applying stress-based paradigms. However, sex- and age-dependent effects on atypical hemispheric asymmetries are present that require further investigation. Animal models enable the analysis of hemispheric changes on the molecular level which may be most effective to detect early alterations.

Menstrually-related migraine shapes the structural similarity network integration of brain

Menstrually-related migraine (MM) is a primary migraine in women of reproductive age. The underlying neural mechanism of MM was still unclear. In this study, we aimed to reveal the case-control differences in network integration and segregation for the morphometric similarity network of MM. Thirty-six patients with MM and 29 healthy females were recruited and underwent MRI scanning. The morphometric features were extracted in each region to construct the single-subject interareal cortical connection using morphometric similarity. The network topology characteristics, in terms of integration and segregation, were analyzed. Our results revealed that, in the absence of morphology differences, disrupted cortical network integration was found in MM patients compared to controls. The patients with MM showed a decreased global efficiency and increased characteristic path length compared to healthy controls. Regional efficiency analysis revealed the decreased efficiency in the left precentral gyrus and bilateral superior temporal gyrus contributed to the decreased network integration. The increased nodal degree centrality in the right pars triangularis was positively associated with the attack frequency in MM. Our results suggested MM would reorganize the morphology in the pain-related brain regions and reduce the parallel information processing capacity of the brain.

Atypical structural connectome asymmetry and associations with network communication in autism spectrum disorder

Autism spectrum disorder is a common neurodevelopmental condition showing connectome disorganization in sensory and transmodal cortices. However, alterations in the inter-hemispheric asymmetry of structural connectome are remained to be investigated. Here, we studied structural connectome asymmetry in individuals with autism using dimensionality reduction techniques and assessed its topological underpinnings by associating with network communication measures. We found that the sensory and heteromodal association regions showed significant between-group differences in inter-hemispheric asymmetry between individuals with autism and neurotypical controls. In addition, the network communication ability was particularly altered between visual and limbic areas. Our findings provide insights for understanding structural connectome alteration in autism and its topological underpinnings.Clinical Relevance- This study provides insights into the understanding of atypical macroscale structural connectome organization in individuals with autism.

Tracing the development and lifespan change of population-level structural asymmetry in the cerebral cortex

Cortical asymmetry is a ubiquitous feature of brain organization that is subtly altered in some neurodevelopmental disorders, yet we lack knowledge of how its development proceeds across life in health. Achieving consensus on the precise cortical asymmetries in humans is necessary to uncover the developmental timing of asymmetry and the extent to which it arises through genetic and later influences in childhood. Here, we delineate population-level asymmetry in cortical thickness and surface area vertex-wise in seven datasets and chart asymmetry trajectories longitudinally across life (4-89 years; observations = 3937; 70% longitudinal). We find replicable asymmetry interrelationships, heritability maps, and test asymmetry associations in large-scale data. Cortical asymmetry was robust across datasets. Whereas areal asymmetry is predominantly stable across life, thickness asymmetry grows in childhood and peaks in early adulthood. Areal asymmetry is low-moderately heritable (max h2SNP ~19%) and correlates phenotypically and genetically in specific regions, indicating coordinated development of asymmetries partly through genes. In contrast, thickness asymmetry is globally interrelated across the cortex in a pattern suggesting highly left-lateralized individuals tend towards left-lateralization also in population-level right-asymmetric regions (and vice versa), and exhibits low or absent heritability. We find less areal asymmetry in the most consistently lateralized region in humans associates with subtly lower cognitive ability, and confirm small handedness and sex effects. Results suggest areal asymmetry is developmentally stable and arises early in life through genetic but mainly subject-specific stochastic effects, whereas childhood developmental growth shapes thickness asymmetry and may lead to directional variability of global thickness lateralization in the population.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Exploring the longitudinal associations of functional network connectivity and psychiatric symptom changes in youth

BACKGROUND

Functional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs.

METHODS

We adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC.

RESULTS

Small longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing.

CONCLUSION

We found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.

The relationship between alexithymia, interoception, and neural functional connectivity during facial expression processing in autism spectrum disorder

Neural processing differences of emotional facial expressions, while common in autism spectrum disorder (ASD), may be related to co-occurring alexithymia and interoceptive processing differences rather than autism per se. Here, we investigate relationships between alexithymia, interoceptive awareness of emotions, and functional connectivity during observation of facial expressions in youth (aged 8-17) with ASD (n = 28) compared to typically developing peers (TD; n = 37). Behaviorally, we found no significant differences between ASD and TD groups in interoceptive awareness of emotions, though alexithymia severity was significantly higher in the ASD group. In the ASD group, increased alexithymia was significantly correlated with lower interoceptive sensation felt during emotion. Using psycho-physiological interaction (PPI) analysis, the ASD group showed higher functional connectivity between the left ventral anterior insula and the left lateral prefrontal cortex than the TD group when viewing facial expressions. Further, alexithymia was associated with reduced left anterior insula-right precuneus connectivity and reduced right dorsal anterior insula-left ventral anterior insula connectivity when viewing facial expressions. In the ASD group, the degree of interoceptive sensation felt during emotion was positively correlated with left ventral anterior insula-right IFG connectivity when viewing facial expressions. However, across all participants, neither alexithymia nor interoceptive awareness of emotions predicted connectivity between emotion-related brain regions when viewing emotional facial expressions. To summarize, we found that in ASD compared to TD: 1) there is stronger connectivity between the insula and lateral prefrontal cortex; and 2) differences in interhemispheric and within left hemisphere connectivity between the insula and other emotion-related brain regions are related to individual differences in interoceptive processing and alexithymia. These results highlight complex relationships between alexithymia, interoception, and brain processing in ASD.

Tsc2 mutation rather than Tsc1 mutation dominantly causes a social deficit in a mouse model of tuberous sclerosis complex

BACKGROUND

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that is associated with neurological symptoms, including autism spectrum disorder. Tuberous sclerosis complex is caused by pathogenic germline mutations of either the TSC1 or TSC2 gene, but somatic mutations were identified in both genes, and the combined effects of TSC1 and TSC2 mutations have been unknown.

METHODS

The present study investigated social behaviors by the social interaction test and three-chambered sociability tests, effects of rapamycin treatment, and gene expression profiles with a gene expression microarray in Tsc1 and Tsc2 double heterozygous mutant (TscD^+/-) mice.

RESULTS

TscD^+/- mice exhibited impairments in social behaviors, and the severity of impairments was similar to Tsc2^+/- mice rather than Tsc1^+/- mice. Impairments in social behaviors were rescued by rapamycin treatment in all mutant mice. Gene expression profiles in the brain were greatly altered in TscD^+/- mice more than in Tsc1^+/- and Tsc2^+/- mice. The gene expression changes compared with wild type (WT) mice were similar between TscD^+/- and Tsc2^+/- mice, and the overlapping genes whose expression was altered in mutant mice compared with WT mice were enriched in the neoplasm- and inflammation-related canonical pathways. The "signal transducer and activator of transcription 3, interferon regulatory factor 1, interferon regulatory factor 4, interleukin-2R α chain, and interferon-γ" signaling pathway, which is initiated from signal transducer and activator of transcription 4 and PDZ and LIM domain protein 2, was associated with impairments in social behaviors in all mutant mice.

LIMITATIONS

It is unclear whether the signaling pathway also plays a critical role in autism spectrum disorders not caused by Tsc1 and Tsc2 mutations.

CONCLUSIONS

These findings suggest that TSC1 and TSC2 double mutations cause autistic behaviors similarly to TSC2 mutations, although significant changes in gene expression were attributable to the double mutations. These findings contribute to the knowledge of genotype-phenotype correlations in TSC and suggest that mutations in both the TSC1 and TSC2 genes act in concert to cause neurological symptoms, including autism spectrum disorder.

Proteomic-Based Approach Reveals the Involvement of Apolipoprotein A-I in Related Phenotypes of Autism Spectrum Disorder in the BTBR Mouse Model

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Abnormal lipid metabolism has been suggested to contribute to its pathogenesis. Further exploration of its underlying biochemical mechanisms is needed. In a search for reliable biomarkers for the pathophysiology of ASD, hippocampal tissues from the ASD model BTBR T+ Itpr3tf/J (BTBR) mice and C57BL/6J mice were analyzed, using four-dimensional (4D) label-free proteomic analysis and bioinformatics analysis. Differentially expressed proteins were significantly enriched in lipid metabolic pathways. Among them, apolipoprotein A-I (ApoA-I) is a hub protein and its expression was significantly higher in the BTBR mice. The investigation of protein levels (using Western blotting) also confirmed this observation. Furthermore, expressions of SphK2 and S1P in the ApoA-I pathway both increased. Using the SphK inhibitor (SKI-II), ASD core phenotype and phenotype-related protein levels of P-CREB, P-CaMKII, and GAD1 were improved, as shown via behavioral and molecular biology experiments. Moreover, by using SKI-II, we found proteins related to the development and function of neuron synapses, including ERK, caspase-3, Bax, Bcl-2, CDK5 and KCNQ2 in BTBR mice, whose levels were restored to protein levels comparable to those in the controls. Elucidating the possible mechanism of ApoA-I in ASD-associated phenotypes will provide new ideas for studies on the etiology of ASD.

Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders

Neuropsychiatric disorders are increasingly conceptualized as overlapping spectra sharing multi-level neurobiological alterations. However, whether transdiagnostic cortical alterations covary in a biologically meaningful way is currently unknown. Here, we studied co-alteration networks across six neurodevelopmental and psychiatric disorders, reflecting pathological structural covariance. In 12,024 patients and 18,969 controls from the ENIGMA consortium, we observed that co-alteration patterns followed normative connectome organization and were anchored to prefrontal and temporal disease epicenters. Manifold learning revealed frontal-to-temporal and sensory/limbic-to-occipitoparietal transdiagnostic gradients, differentiating shared illness effects on cortical thickness along these axes. The principal gradient aligned with a normative cortical thickness covariance gradient and established a transcriptomic link to cortico-cerebello-thalamic circuits. Moreover, transdiagnostic gradients segregated functional networks involved in basic sensory, attentional/perceptual, and domain-general cognitive processes, and distinguished between regional cytoarchitectonic profiles. Together, our findings indicate that shared illness effects occur in a synchronized fashion and along multiple levels of hierarchical cortical organization.

Topological analysis of brain dynamics in autism based on graph and persistent homology

Autism spectrum disorder (ASD) is a heterogeneous disorder with a rapidly growing prevalence. In recent years, the dynamic functional connectivity (DFC) technique has been used to reveal the transient connectivity behavior of ASDs' brains by clustering connectivity matrices in different states. However, the states of DFC have not been yet studied from a topological point of view. In this paper, this study was performed using global metrics of the graph and persistent homology (PH) and resting-state functional magnetic resonance imaging (fMRI) data. The PH has been recently developed in topological data analysis and deals with persistent structures of data. The structural connectivity (SC) and static FC (SFC) were also studied to know which one of the SC, SFC, and DFC could provide more discriminative topological features when comparing ASDs with typical controls (TCs). Significant discriminative features were only found in states of DFC. Moreover, the best classification performance was offered by persistent homology-based metrics and in two out of four states. In these two states, some networks of ASDs compared to TCs were more segregated and isolated (showing the disruption of network integration in ASDs). The results of this study demonstrated that topological analysis of DFC states could offer discriminative features which were not discriminative in SFC and SC. Also, PH metrics can provide a promising perspective for studying ASD and finding candidate biomarkers.

Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).