Cerebellar Volume in Autism: Literature Meta-analysis and Analysis of the Autism Brain Imaging Data Exchange Cohort

Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults

Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.

Neurodevelopmental trajectories of cerebellar grey matter associated with verbal abilities in males with autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition frequently associated with structural cerebellar abnormalities. Whether cerebellar grey matter volumes (GMV) are linked to verbal impairments remains controversial. Here, the association between cerebellar GMV and verbal abilities in ASD was examined across the lifespan. Lobular segmentation of the cerebellum was performed on structural MRI scans from the ABIDE I dataset in male individuals with ASD (N=144, age: 8.5-64.0 years) and neurotypical controls (N=188; age: 8.0-56.2 years). Stepwise linear mixed effects modeling including group (ASD vs. neurotypical controls), lobule-wise GMV, and age was performed to identify cerebellar lobules which best predicted verbal abilities as measured by verbal IQ (VIQ). An age-specific association between VIQ and GMV of bilateral Crus II was found in ASD relative to neurotypical controls. In children with ASD, higher VIQ was associated with larger GMV of left Crus II but smaller GMV of right Crus II. By contrast, in adults with ASD, higher VIQ was associated with smaller GMV of left Crus II and larger GMV of right Crus II. These findings indicate that relative to the contralateral hemisphere, an initial reliance on the language-nonspecific left cerebellar hemisphere is offset by more typical right-lateralization in adulthood.

Cerebellar phenotypes in germline PTEN mutation carriers

PTEN hamartoma tumour syndrome (PHTS) comprises different hereditary conditions caused by germline PTEN mutations, predisposing to the development of multiple hamartomas in many body tissues and also increasing the risk of some types of cancer. Cerebellar involvement in PHTS patients has been long known due to the development of a pathognomonic cerebellar hamartoma (known as dysplastic gangliocytoma of the cerebellum or Lhermitte-Duclos disease). Recently, a crucial role of the cerebellum has been highlighted in the pathogenesis of autism spectrum disorders, now recognised as a phenotype expressed in a variable percentage of PHTS children. In addition, rare PTEN variants are indeed identified in medulloblastoma as well, even if they are less frequent than other germline gene mutations. The importance of PTEN and its downstream signalling enzymatic pathways, PI3K/AKT/mTOR, has been studied at different levels in both human clinical settings and animal models, not only leading to a better understanding of the pathogenesis of different disorders but, most importantly, to identify potential targets for specific therapies. In particular, PTEN integrity makes an important contribution to the normal development of tissue architecture in the nervous system, including the cerebellum. Thus, in patients with PTEN germline mutations, the cerebellum is an affected organ that is increasingly recognised in different disorders, whereas, in animal models, cerebellar Pten loss causes a variety of functional and histological alterations. In this review, we summarise the range of cerebellar involvement observed in PHTS and its relationships with germline PTEN mutations, along with the phenotypes expressed by murine models with PTEN deficiency in cerebellar tissue.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

Self-supervised learning with application for infant cerebellum segmentation and analysis

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

Cerebellar contributions to a brainwide network for flexible behavior in mice

The cerebellum regulates nonmotor behavior, but the routes of influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task through a network of diencephalic and neocortical structures, and in flexibility of free behavior. After chemogenetic inhibition of lobule VI vermis or hemispheric crus I Purkinje cells, mice could learn a water Y-maze but were impaired in ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of whole-body movement revealed deficiencies in across-day behavioral habituation to an open-field environment. Taken together, these experiments reveal brainwide systems for cerebellar influence that affect multiple flexible responses.

Understanding the relationship between cerebellar structure and social abilities

BACKGROUND

The cerebellum contains more than 50% of all neurons in the brain and is involved in a broad range of cognitive functions, including social communication and social cognition. Inconsistent atypicalities in the cerebellum have been reported in individuals with autism compared to controls suggesting the limits of categorical case control comparisons. Alternatively, investigating how clinical dimensions are related to neuroanatomical features, in line with the Research Domain Criteria approach, might be more relevant. We hypothesized that the volume of the "cognitive" lobules of the cerebellum would be associated with social difficulties.

METHODS

We analyzed structural MRI data from a large pediatric and transdiagnostic sample (Healthy Brain Network). We performed cerebellar parcellation with a well-validated automated segmentation pipeline (CERES). We studied how social communication abilities-assessed with the social component of the Social Responsiveness Scale (SRS)-were associated with the cerebellar structure, using linear mixed models and canonical correlation analysis.

RESULTS

In 850 children and teenagers (mean age 10.8 ± 3 years; range 5-18 years), we found a significant association between the cerebellum, IQ and social communication performance in our canonical correlation model.

LIMITATIONS

Cerebellar parcellation relies on anatomical boundaries, which does not overlap with functional anatomy. The SRS was originally designed to identify social impairments associated with autism spectrum disorders.

CONCLUSION

Our results unravel a complex relationship between cerebellar structure, social performance and IQ and provide support for the involvement of the cerebellum in social and cognitive processes.

Preserved navigation abilities and spatio-temporal memory in individuals with autism spectrum disorder

Cerebellar abnormalities have been reported in autism spectrum disorder (ASD). Beyond its role in hallmark features of ASD, the cerebellum and its connectivity with forebrain structures also play a role in navigation. However, the current understanding of navigation abilities in ASD is equivocal, as is the impact of the disorder on the functional anatomy of the cerebellum. In the present study, we investigated the navigation behavior of a population of ASD and typically developing (TD) adults related to their brain anatomy as assessed by structural and functional MRI at rest. We used the Starmaze task, which permits assessing and distinguishing two complex navigation behaviors, one based on allocentric learning and the other on egocentric learning of a route with multiple decision points. Compared to TD controls, individuals with ASD showed similar exploration, learning, and strategy performance and preference. In addition, there was no difference in the structural or functional anatomy of the cerebellar circuits involved in navigation between the two groups. The findings of our work suggest that navigation abilities, spatio-temporal memory, and their underlying circuits are preserved in individuals with ASD.

Altered cortical gyrification, sulcal depth, and fractal dimension in the autism spectrum disorder comorbid attention-deficit/hyperactivity disorder than the autism spectrum disorder

Autism spectrum disorder (ASD) frequently occurs accompanied by attention-deficit/hyperactivity disorder (ADHD), which catches increasing attention. The comorbid diagnosis of ASD with ADHD (ASD + ADHD) is permitted in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). However, compared to autism spectrum disorder without other symptoms (ASD-only), the special neural underpinnings in ASD+ADHD remain unclear. Therefore, this study aimed to uncover the differences in cortical complexity between ASD + ADHD and ASD-only patients. A total of 114 ASD participants (i.e. containing 40 ASD + ADHD and 74 ASD-only participants) with T1-weighted magnetic resonance images were collected from the Autism Brain Imaging Data Exchange II. Afterward, a surface-based morphometry method was carried out to compare the cortical complexity (i.e. gyrification index, fractal dimension, and sulcal depth) between the ASD + ADHD and ASD-only cohorts. Results showed the increased fractal dimension in the right fusiform gyrus of the ASD + ADHD cohort in comparison to the ASD-only cohort. Moreover, the ASD + ADHD cohort exhibited increased sulcal depth in the left middle temporal gyrus/inferior temporal gyrus and right middle temporal gyrus compared to the ASD-only cohort. Last but not least, the increased gyrification index in the insula/postcentral gyrus was observed in the ASD + ADHD cohort in comparison to the ASD-only cohort. Overall, the present study contributes to the delineation of particular structural abnormalities in ASD + ADHD than ASD-only, enriching the evidence of the combined phenotype of ASD + ADHD.

Relationship between gray matter structure and age in children and adolescents with high-functioning autism spectrum disorder

Objective

The present study used magnetic resonance imaging to investigate the difference in the relationship between gray matter structure and age in children and adolescents with autism spectrum disorder (ASD) and typically developing (TD) subjects.

Methods

After screening T1 structural images from the Autism Brain Imaging Data Exchange (ABIDE) database, 111 children and adolescents (7-18 years old) with high-functioning ASD and 151 TD subjects matched for age, sex and full IQ were included in the current study. By using the voxel-based morphological analysis method, gray matter volume/density (GMV/GMD) maps were obtained for each participant. Then, a multiple regression analysis was performed for ASD and TD groups, respectively to estimate the relationship between GMV/GMD and age with gender, education, site, and IQ scores as covariates. Furthermore, a z-test was used to compare such relationship difference between the groups.

Results

Results showed that compared with TD, the GMD of ASD showed stronger positive correlations with age in the prefrontal cortex, and a stronger negative correlation in the left inferior parietal lobule, and a weaker positive correlation in the right inferior parietal lobule. The GMV of ASD displayed stronger positive correlations with age in the prefrontal cortex and cerebellum.

Conclusion

These findings may provide evidence to support that the brain structure abnormalities underlying ASD during childhood and adolescence may differ from each other.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection.

Methods

Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections.

Results

Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections.

Conclusions

We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

Deficits in Cerebellum-Dependent Learning and Cerebellar Morphology in Male and Female BTBR Autism Model Mice

Recently, there has been increased interest in the role of the cerebellum in autism spectrum disorder (ASD). To better understand the pathophysiological role of the cerebellum in ASD, it is necessary to have a variety of mouse models that have face validity for cerebellar disruption in humans. Here, we add to the literature on the cerebellum in mouse models of autism with the characterization of the cerebellum in the idiopathic BTBR T + Itpr3tf/J (BTBR) inbred mouse strain, which has behavioral phenotypes that are reminiscent of ASD in patients. When we examined both male and female BTBR mice in comparison to C57BL/6J (C57) controls, we noted that both sexes of BTBR mice showed motor coordination deficits characteristic of cerebellar dysfunction, but only the male mice showed differences in delay eyeblink conditioning, a cerebellum-dependent learning task that is known to be disrupted in ASD patients. Both male and female BTBR mice showed considerable expansion of, and abnormal foliation in, the cerebellum vermis—including a significant expansion of specific lobules in the anterior cerebellum. In addition, we found a slight but significant decrease in Purkinje cell density in both male and female BTBR mice, irrespective of the lobule. Finally, there was a marked reduction of Purkinje cell dendritic spine density in both male and female BTBR mice. These findings suggest that, for the most part, the BTBR mouse model phenocopies many of the characteristics of the subpopulation of ASD patients that have a hypertrophic cerebellum. We discuss the significance of strain differences in the cerebellum as well as the importance of this first effort to identify both similarities and differences between male and female BTBR mice with regard to the cerebellum.

Cerebellar Atypicalities in Autism?

BACKGROUND

The cerebellum contains more than 50% of the brain's neurons and is involved in social cognition. Cerebellar anatomical atypicalities have repeatedly been reported in individuals with autism. However, studies have yielded inconsistent findings, likely because of a lack of statistical power, and did not capture the clinical and neuroanatomical diversity of autism. Our aim was to better understand cerebellar anatomy and its diversity in autism.

METHODS

We studied cerebellar gray matter morphology in 274 individuals with autism and 219 control subjects of a multicenter European cohort, EU-AIMS LEAP (European Autism Interventions-A Multicentre Study for Developing New Medications; Longitudinal European Autism Project). To ensure the robustness of our results, we conducted lobular parcellation of the cerebellum with 2 different pipelines in addition to voxel-based morphometry. We performed statistical analyses with linear, multivariate (including normative modeling), and meta-analytic approaches to capture the diversity of cerebellar anatomy in individuals with autism and control subjects. Finally, we performed a dimensional analysis of cerebellar anatomy in an independent cohort of 352 individuals with autism-related symptoms.

RESULTS

We did not find any significant difference in the cerebellum when comparing individuals with autism and control subjects using linear models. In addition, there were no significant deviations in our normative models in the cerebellum in individuals with autism. Finally, we found no evidence of cerebellar atypicalities related to age, IQ, sex, or social functioning in individuals with autism.

CONCLUSIONS

Despite positive results published in the last decade from relatively small samples, our results suggest that there is no striking difference in cerebellar anatomy of individuals with autism.

Identification of shared and differentiating genetic architecture for autism spectrum disorder, attention-deficit hyperactivity disorder and case subgroups

Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable neurodevelopmental conditions, with considerable overlap in their genetic etiology. We dissected their shared and distinct genetic etiology by cross-disorder analyses of large datasets. We identified seven loci shared by the disorders and five loci differentiating them. All five differentiating loci showed opposite allelic directions in the two disorders and significant associations with other traits, including educational attainment, neuroticism and regional brain volume. Integration with brain transcriptome data enabled us to identify and prioritize several significantly associated genes. The shared genomic fraction contributing to both disorders was strongly correlated with other psychiatric phenotypes, whereas the differentiating portion was correlated most strongly with cognitive traits. Additional analyses revealed that individuals diagnosed with both ASD and ADHD were double-loaded with genetic predispositions for both disorders and showed distinctive patterns of genetic association with other traits compared with the ASD-only and ADHD-only subgroups. These results provide insights into the biological foundation of the development of one or both conditions and of the factors driving psychopathology discriminatively toward either ADHD or ASD.

The atypical functional connectivity of Broca's area at multiple frequency bands in autism spectrum disorder

As a developmental disorder, autism spectrum disorder (ASD) has drawn much attention due to its severe impacts on one's language capacity. Broca's area, an important brain region of the language network, is largely involved in language-related functions. Using the Autism Brain Image Data Exchange (ABIDE) dataset, a mega-analysis was performed involving a total of 1454 participants (including 618 individuals with ASD and 836 healthy controls (HCs). To detect the neural pathophysiological mechanism of ASD from the perspective of language, we conducted a functional connectivity (FC) analysis with Broca's area as the seed in multiple frequency bands (conventional: 0.01-0.08 Hz; slow-4: 0.027-0.073 Hz; slow-5: 0.01-0.027 Hz). We found that compared with HC, ASD patients demonstrated increased FC in the left thalamus, left precuneus, left anterior cingulate and paracingulate gyri, and left medial orbital of the superior frontal gyrus in the conventional frequency band (0.01-0.08 Hz). The results of the slow-5 frequency band (0.01-0.027 Hz) presented increased FC values of the left precuneus, left medial orbital of the superior frontal gyrus, right medial orbital of the superior frontal gyrus and right thalamus. No significant cluster was detected in the slow-4 frequency band (0.027-0.073 Hz). In conclusion, the abnormal functional connectivity in patients with ASD has frequency-specific properties. Furthermore, the slow-5 frequency band (0.01-0.027 Hz) mainly contributed to the findings of the conventional frequency band (0.01-0.08 Hz). The current study might shed new light on the neural pathophysiological mechanism of language impairments in people with ASD.

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 - far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

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.

Cerebellar demyelination and neurodegeneration associated with mTORC1 hyperactivity may contribute to the developmental onset of autism-like neurobehavioral phenotype in a rat model

The cerebellum hosts more than half of all neurons of the human brain, with their organized activity playing a key role in coordinating motor functions. Cerebellar activity has also been implicated in the control of speech, communication, and social behavior, which are compromised in autism spectrum disorders (ASD). Despite major research advances, there is a shortage of mechanistic data relating cellular and molecular changes in the cerebellum to autistic behavior. We studied the impact of tuberous sclerosis complex 2 haploinsufficiency (Tsc2+/-) with downstream mTORC1 hyperactivity on cerebellar morphology and cellular organization in 1, 9, and 18 m.o. Eker rats, to determine possible structural correlates of an autism-like behavioural phenotype in this model. We report a greater developmental expansion of the cerebellar vermis, owing to enlarged white matter and thickened molecular layer. Histochemical and immunofluorescence data suggest age-related demyelination of central tract of the vermis, as evident from reduced level of myelin-basic protein in the arbora vitae. We also observed a higher number of astrocytes in Tsc2+/- rats of older age while the number of Purkinje cells (PCs) in these animals was lower than in wild-type controls. Unlike astrocytes and PCs, Bergmann glia remained unaltered at all ages in both genotypes, while the number of microglia was higher in Tsc2+/- rats of older age. The convergent evidence for a variety of age-dependent cellular changes in the cerebellum of rats associated with mTORC1 hyperactivity, thus, predicts an array of functional impairments, which may contribute to the developmental onset of an autism-like behavioral phenotype in this model. LAY SUMMARY: This study elucidates the impact of constitutive mTORC1 hyperactivity on cerebellar morphology and cellular organization in a rat model of autism and epilepsy. It describes age-dependent degeneration of Purkinje neurons, with demyelination of central tract as well as activation of microglia, and discusses the implications of these changes for neuro-behavioral phenotypes. The described changes provide new indications for the putative mechanisms underlying cerebellar impairments with their age-related onset, which may contribute to the pathobiology of autism, epilepsy, and related disorders.

Lessons Learned From Neuroimaging Studies of Copy Number Variants: A Systematic Review

Pathogenic copy number variants (CNVs) and aneuploidies alter gene dosage and are associated with neurodevelopmental psychiatric disorders such as autism spectrum disorder and schizophrenia. Brain mechanisms mediating genetic risk for neurodevelopmental psychiatric disorders remain largely unknown, but there is a rapid increase in morphometry studies of CNVs using T1-weighted structural magnetic resonance imaging. Studies have been conducted one mutation at a time, leaving the field with a complex catalog of brain alterations linked to different genomic loci. Our aim was to provide a systematic review of neuroimaging phenotypes across CNVs associated with developmental psychiatric disorders including autism and schizophrenia. We included 76 structural magnetic resonance imaging studies on 20 CNVs at the 15q11.2, 22q11.2, 1q21.1 distal, 16p11.2 distal and proximal, 7q11.23, 15q11-q13, and 22q13.33 (SHANK3) genomic loci as well as aneuploidies of chromosomes X, Y, and 21. Moderate to large effect sizes on global and regional brain morphometry are observed across all genomic loci, which is in line with levels of symptom severity reported for these variants. This is in stark contrast with the much milder neuroimaging effects observed in idiopathic psychiatric disorders. Data also suggest that CNVs have independent effects on global versus regional measures as well as on cortical surface versus thickness. Findings highlight a broad diversity of regional morphometry patterns across genomic loci. This heterogeneity of brain patterns provides insight into the weak effects reported in magnetic resonance imaging studies of cognitive dimension and psychiatric conditions. Neuroimaging studies across many more variants will be required to understand links between gene function and brain morphometry.

Unique Morphometric Features of the Cerebellum and Cerebellocerebral Structural Correlation Between Autism Spectrum Disorder and Schizophrenia

Background

Although cerebellar morphological involvement has been increasingly recognized in autism spectrum disorder (ASD) and schizophrenia (SZ), the extent to which there are morphological differences between them has not been definitively quantified. Furthermore, although previous studies have demonstrated increased anatomical cerebellocerebral correlations in both conditions, differences between their associations have not been well characterized.

Methods

We compared cerebellar volume between males with ASD (n = 31), males with SZ (n = 28), and typically developing males (n = 49). A total of 31 cerebellar subregions were investigated with the cerebellum segmented into their constituent lobules, in gray matter (GM) and white matter (WM) separately. Additionally, structural correlations with the contralateral cerebrum were analyzed for each cerebellar lobule.

Results

We found significantly larger WM volume in the bilateral lobules VI and Crus I in the ASD group than in other groups. While WM or GM volumes of these right lobules had positive associations with ASD symptoms, there was a negative association between GM volume of the right Crus I and SZ symptoms. We further observed, in the ASD group specifically, significant correlations between WM of the right lobule VI and WM of the left frontal pole (r = 0.67) and between GM of the right lobule VI and the left caudate (r = 0.60).

Conclusions

Our findings support evidence that cerebellar morphology is involved in ASD and SZ with different mechanisms. Furthermore, this study showed that these biological differences require consideration when determining diagnostic criteria and treatment for these disorders.

Brain morphology, autistic traits, and polygenic risk for autism: A population-based neuroimaging study

Autism spectrum disorders (ASD) are associated with widespread brain alterations. Previous research in our group linked autistic traits with altered gyrification, but without pronounced differences in cortical thickness. Herein, we aim to replicate and extend these findings using a larger and older sample. Additionally, we examined whether (a) brain correlates of autistic traits were associated with polygenic risk scores (PRS) for ASD, and (b) autistic traits are related with brain morphological changes over time in a subset of children with longitudinal data available. The sample included 2400 children from the Generation R cohort. Autistic traits were measured using the Social Responsiveness Scale (SRS) at age 6 years. Gyrification, cortical thickness, surface area, and global morphological measures were obtained from high-resolution structural MRI scans at ages 9-to-12 years. We performed multiple linear regression analyses on a vertex-wise level. Corresponding regions of interest were tested for association with PRS. Results showed that autistic traits were related to (a) lower gyrification in the lateral occipital and the superior and inferior parietal lobes, (b) lower cortical thickness in the superior frontal region, and (c) lower surface area in inferior temporal and rostral middle frontal regions. PRS for ASD and longitudinal analyses showed significant associations that did not survive correction for multiple testing. Our findings support stability in the relationship between higher autistic symptoms and lower gyrification and smaller surface areas in school-aged children. These relationships remained when excluding ASD cases, providing neurobiological evidence for the extension of autistic traits into the general population. LAY SUMMARY: We found that school-aged children with higher levels of autistic traits had smaller total brain volume, cerebellum, cortical thickness, and surface area. Further, we also found differences in the folding patterns of the brain (gyrification). Overall, genetic susceptibility for autism spectrum disorders was not related to these brain regions suggesting that other factors could be involved in their origin. These results remained significant when excluding children with a diagnosis of ASD, providing support for the extension of the relationship between autistic traits and brain findings into the general population.

Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen's d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

The neuropathology of autism: A systematic review of post-mortem studies of autism and related disorders

Post-mortem studies allow for the direct investigation of brain tissue in those with autism and related disorders. Several review articles have focused on aspects of post-mortem abnormalities but none has brought together the entire post-mortem literature. Here, we systematically review the evidence from post-mortem studies of autism, and of related disorders that present with autistic features. The literature consists of a small body of studies with small sample sizes, but several remarkably consistent findings are evident. Cortical layering is largely undisturbed, but there are consistent reductions in minicolumn numbers and aberrant myelination. Transcriptomics repeatedly implicate abberant synaptic, metabolic, proliferation, apoptosis and immune pathways. Sufficient replicated evidence is available to implicate non-coding RNA, aberrant epigenetic profiles, GABAergic, glutamatergic and glial dysfunction in autism pathogenesis. Overall, the cerebellum and frontal cortex are most consistently implicated, sometimes revealing distinct region-specific alterations. The literature on related disorders such as Rett syndrome, Fragile X and copy number variations (CNVs) predisposing to autism is particularly small and inconclusive. Larger studies, matched for gender, developmental stage, co-morbidities and drug treatment are required.

Are Vermal Lobules VI-VII Smaller in Autism Spectrum Disorder?

Cerebellar volume, in particular vermal lobule areas VI-VII, have been extensively researched in individuals with autism spectrum disorder (ASD), although findings are often unclear. The aim of the present study is to consolidate all existing cerebellar and age data of individuals with ASD, and compare this data to typically developing (TD) controls. Raw data, or the means and standard deviations of cerebellar volume and age, were obtained from 17 studies (N_Cerebellum: 421 ASD and 370 TD participants; N_VI-VII: 506 ASD and 290 TD participants). Total cerebellar volume, or VI-VII area, was plotted against age and lines of fit of ASD and TD data were compared. Mean differences in cerebellar volume and VI-VII area between participants with ASD and TD participants were then compared via ANCOVA analyses. Findings revealed multiple differences in VI-VII area between participants with ASD and TD participants below 18 years of age. Additionally, cerebellar volume was greater in males with ASD than TD males between 2 and 4 years. In the present study, cerebellar volume and VI-VII area show different rates of change across age for those with autism compared with those without. These morphological differences provide a neurobiological justification to investigate related behavioural correlates.

Association between spectral electroencephalography power and autism risk and diagnosis in early development

Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant-sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi-site dataset with 432 participants, including 222 at high-risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3-36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed. LAY SUMMARY: Infants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.

Motor Abnormalities in Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Are Associated With Regional Grey Matter Volumes

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are associated with motor impairments, with some children holding a comorbid diagnosis of Developmental Coordination Disorder (DCD). However, DCD is underdiagnosed in these populations and the volume abnormalities that contribute to explaining these motor impairments are poorly understood. In this study, motor abilities as measured by the Developmental Coordination Disorder Questionnaire (DCDQ) were compared between children with ADHD, children with ASD and typically developing (TD) children, aged 8–12 years old. Additionally, the association between the DCDQ scores (general coordination, fine motor/handwriting, control during movement, total) and regional volume abnormalities were explored in 6 regions of interest (pre-central gyrus, post-central gyrus, inferior parietal cortex, superior frontal gyrus, middle frontal gyrus, medial frontal gyrus), within each group and across all participants. Children with ASD and children with ADHD showed impaired motor abilities in all the DCDQ-derived scores compared to TD children. Additionally, most children with ASD or ADHD had an indication or suspicion of DCD. Within the ASD group, coordination abilities were associated with the volume of the right medial frontal gyrus, and within the ADHD group, the total DCDQ score was associated with the volume of the right superior frontal gyrus. This study underlines the importance of routinely checking motor abilities in populations with ASD or ADHD in clinical practise and contributes to the understanding of structural abnormalities subtending motor impairments in these disorders.

Disorder-specific brain volumetric abnormalities in Attention-Deficit/Hyperactivity Disorder relative to Autism Spectrum Disorder

The overlap/distinctiveness between Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) has been increasingly investigated in recent years, particularly since the DSM-5 allows the dual diagnosis of ASD and ADHD, but the underlying brain mechanisms remain unclear. Although both disorders are associated with brain volumetric abnormalities, it is necessary to unfold the shared and specific volume abnormalities that could contribute to explain the similarities and differences in the clinical and neurocognitive profiles between ADHD and ASD. In this voxel-based morphometry (VBM) study, regional grey matter volumes (GMV) were compared between 22 children with ADHD, 18 children with ASD and 17 typically developing (TD) children aged 8 to 12 years old, controlling for age and total intracranial volume. When compared to TD children or children with ASD, children with ADHD had a larger left precuneus, and a smaller right thalamus, suggesting that these brain abnormalities are specific to ADHD relative to ASD. Overall, this study contributes to the delineation of disorder-specific structural abnormalities in ADHD and ASD.

The role of cerebellum in the child neuropsychological functioning

This chapter proposes a review of neuropsychologic and behavior findings in pediatric pathologies of the cerebellum, including cerebellar malformations, pediatric ataxias, cerebellar tumors, and other acquired cerebellar injuries during childhood. The chapter also contains reviews of the cerebellar mutism/posterior fossa syndrome, reported cognitive associations with the development of the cerebellum in typically developing children and subjects born preterm, and the role of the cerebellum in neurodevelopmental disorders such as autism spectrum disorders and developmental dyslexia. Cognitive findings in pediatric cerebellar disorders are considered in the context of known cerebellocerebral connections, internal cellular organization of the cerebellum, the idea of a universal cerebellar transform and computational internal models, and the role of the cerebellum in specific cognitive and motor functions, such as working memory, language, timing, or control of eye movements. The chapter closes with a discussion of the strengths and weaknesses of the cognitive affective syndrome as it has been described in children and some conclusions and perspectives.

Brief Report: Neuroimaging Endophenotypes of Social Robotic Applications in Autism Spectrum Disorder

A plethora of neuroimaging studies have focused on the discovery of potential neuroendophenotypes useful to understand the etiopathogenesis of autism and predict treatment response. Social robotics has recently been proposed as an effective tool to strengthen the current treatments in children with autism. However, the high clinical heterogeneity characterizing this disorder might interfere with behavioral effects. Neuroimaging is set to overcome these limitations by capturing the level of heterogeneity. Here, we provide a preliminary evaluation of the neural basis of social robotics and how extracting neural hallmarks useful to design more effective behavioral applications. Despite the endophenotype-oriented neuroimaging research approach is in its relative infancy, this preliminary evidence encourages innovation to address its current limitations.

A preliminary study of atypical cortical change ability of dynamic whole-brain functional connectivity in autism spectrum disorder

OBJECTIVES

Designing new objectively diagnostic methods of autism spectrum disorder (ASD) are burning questions. Dynamic functional connectivity (DFC) methodology based on fMRI data are an effective lever to investigate changeability evolution of signal synchronization in macroscopic neural activity patterns.

METHODS

Embracing the network dynamics concepts, this paper introduces changeability index (C-score)which is focused on time-varying aspects of FCs, and develops a new framework for researching the roots of ASD brains at resting states in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in C-scores of between atypical and typical DFCs of 30 healthy controls (HCs) and 48 ASD patients.

RESULTS

The abnormities of edge C-scores are found across widespread brain cortex in ASD brains. For whole brain regional C-scores of ASD patients, orbitofrontal middle cortex L, inferior triangular frontal gyrus L, middle occipital gyrus L, postcentral gyrus L, supramarginal L, supramarginal R, cerebellum 8 L, and cerebellum 10 Rare endowed with significantly different C-scores.At brain subsystems level, C-scores in left hemisphere, right hemisphere, top hemisphere, bottom hemisphere, frontal lobe, parietal lobe, occipital lobe, cerebellum sub systems are abnormal in ASD patients.

CONCLUSIONS

The ASD brains have whole-brain abnormity on widespread regions. Through the strict evidence-based study, it was found that the changeability index (C-score) is a meaningful biological marker to explore cortical activity in ASD.

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.

A heuristic information cluster search approach for precise functional brain mapping

Detection of the relevant brain regions for characterizing the distinction between cognitive conditions is one of the most sought after objectives in neuroimaging research. A popular approach for achieving this goal is the multivariate pattern analysis which is currently conducted through a number of approaches such as the popular searchlight procedure. This is due to several advantages such as being automatic and flexible with regards to size of the search region. However, these approaches suffer from a number of limitations which can lead to misidentification of truly informative regions which in turn results in imprecise information maps. These limitations mainly stem from several factors such as the fact that the information value of the search spheres are assigned to the voxel at the center of them (in case of searchlight), the requirement for manual tuning of parameters such as searchlight radius and shape, and high complexity and low interpretability in commonly used machine learning-based approaches. Other drawbacks include overlooking the structure and interactions within the regions, and the disadvantages of using certain regularization techniques in analysis of datasets with characteristics of common functional magnetic resonance imaging data. In this article, we propose a fully data-driven maximum relevance minimum redundancy search algorithm for detecting precise information value of the clusters within brain regions while alleviating the above-mentioned limitations. Moreover, in order to make the proposed method faster, we propose an efficient algorithmic implementation. We evaluate and compare the proposed algorithm with the searchlight procedure as well as least absolute shrinkage and selection operator regularization-based mapping approach using both real and synthetic datasets. The analysis results of the proposed approach demonstrate higher information detection precision and map specificity compared to the benchmark approaches.

Brain volumes in relation to loneliness and social competence in preadolescents born very preterm

INTRODUCTION

The aim of the present study was to assess how regional brain volumes associate with self-experienced social and emotional loneliness and social competence in very preterm and term-born preadolescents.

MATERIALS AND METHODS

Thirty-four very preterm subjects (birthweight ≤1,500 g and/or gestational age <32 weeks) without neurodevelopmental impairments and/or major brain pathologies and 31 term-born subjects underwent magnetic resonance imaging at 12 years of age. Regional brain volumes were measured using an automated image quantification tool. At 11 years of age, social and emotional loneliness were assessed with the Peer Network and Dyadic Loneliness Scale-self-report questionnaire and cooperating skills, empathy, impulsivity, and disruptiveness with the Multisource Assessment of Children's Social Competence Scale-self-report questionnaire.

RESULTS

In the very preterm group, a number of significant associations were found between smaller regional brain volumes and self-experienced emotional loneliness, more impulsivity and more disruptiveness. In the control group, brain volumes and loneliness were not associated, and brain volumes and social competence were associated with a lesser degree than in the very preterm group.

CONCLUSION

Experiences of emotional loneliness and poorer social competence appear to be more related to brain volumes in very preterm preadolescents than in those born full-term. It also appears that in very preterm preadolescents, emotional loneliness may be more reflected in brain development than social loneliness.

EEG-IP: an international infant EEG data integration platform for the study of risk and resilience in autism and related conditions

BACKGROUND

Establishing reliable predictive and diganostic biomarkers of autism would enhance early identification and facilitate targeted intervention during periods of greatest plasticity in early brain development. High impact research on biomarkers is currently limited by relatively small sample sizes and the complexity of the autism phenotype.

METHODS

EEG-IP is an International Infant EEG Data Integration Platform developed to advance biomarker discovery by enhancing the large scale integration of multi-site data. Currently, this is the largest multi-site standardized dataset of infant EEG data.

RESULTS

First, multi-site data from longitudinal cohort studies of infants at risk for autism was pooled in a common repository with 1382 EEG longitudinal recordings, linked behavioral data, from 432 infants between 3- to 36-months of age. Second, to address challenges of limited comparability across independent recordings, EEG-IP applied the Brain Imaging Data Structure (BIDS)-EEG standard, resulting in a harmonized, extendable, and integrated data state. Finally, the pooled and harmonized raw data was preprocessed using a common signal processing pipeline that maximizes signal isolation and minimizes data reduction. With EEG-IP, we produced a fully standardized data set, of the pooled, harmonized, and pre-processed EEG data from multiple sites.

CONCLUSIONS

Implementing these integrated solutions for the first time with infant data has demonstrated success and challenges in generating a standardized multi-site data state. The challenges relate to annotation of signal sources, time, and ICA analysis during pre-processing. A number of future opportunities also emerge, including validation of analytic pipelines that can replicate existing findings and/or test novel hypotheses.

Neuronal morphology alterations in autism and possible role of oxytocin

Current understanding of the neuroanatomical abnormalities in autism includes gross anatomical changes in several brain areas and microstructural alterations in neuronal cells as well. There are many controversies in the interpretation of the imaging data, evaluation of volume and size of particular brain areas, and their functional translation into a broad autism phenotype. Critical questions of neuronal pathology in autism include the concept of the reversible plasticity of morphological changes, volume alterations of brain areas, and both short- and long-term consequences of adverse events present during the brain development. At the cellular level, remodeling of the actin cytoskeleton is considered as one of the critical factors associated with the autism spectrum disorders. Alterations in the composition of the neuronal cytoskeleton, in particular abnormalities in the polymerization of actin filaments and their associated proteins underlie the functional consequences in behavior resulting in symptoms and clinical correlates of autism spectrum disorder. In the present review, a special attention is devoted to the role of oxytocin in experimental models of neurodevelopmental disorders manifesting alterations in neuronal morphology.

The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture

Like many other areas of science, experimental psychology is affected by a "replication crisis" that is causing concern in many fields of research. Approaches to tackling this crisis include better training in statistical methods, greater transparency and openness, and changes to the incentives created by funding agencies, journals, and institutions. Here, I argue that if proposed solutions are to be effective, we also need to take into account human cognitive constraints that can distort all stages of the research process, including design and execution of experiments, analysis of data, and writing up findings for publication. I focus specifically on cognitive schemata in perception and memory, confirmation bias, systematic misunderstanding of statistics, and asymmetry in moral judgements of errors of commission and omission. Finally, I consider methods that may help mitigate the effect of cognitive constraints: better training, including use of simulations to overcome statistical misunderstanding; specific programmes directed at inoculating against cognitive biases; adoption of Registered Reports to encourage more critical reflection in planning studies; and using methods such as triangulation and "pre mortem" evaluation of study design to foster a culture of dialogue and criticism.

Emerging connections between cerebellar development, behaviour and complex brain disorders

The human cerebellum has a protracted developmental timeline compared with the neocortex, expanding the window of vulnerability to neurological disorders. As the cerebellum is critical for motor behaviour, it is not surprising that most neurodevelopmental disorders share motor deficits as a common sequela. However, evidence gathered since the late 1980s suggests that the cerebellum is involved in motor and non-motor function, including cognition and emotion. More recently, evidence indicates that major neurodevelopmental disorders such as intellectual disability, autism spectrum disorder, attention-deficit hyperactivity disorder and Down syndrome have potential links to abnormal cerebellar development. Out of recent findings from clinical and preclinical studies, the concept of the 'cerebellar connectome' has emerged that can be used as a framework to link the role of cerebellar development to human behaviour, disease states and the design of better therapeutic strategies.

Cerebellar parcellation in schizophrenia and bipolar disorder

OBJECTIVE

The cerebellum is involved in cognitive processing and emotion control. Cerebellar alterations could explain symptoms of schizophrenia spectrum disorder (SZ) and bipolar disorder (BD). In addition, literature suggests that lithium might influence cerebellar anatomy. Our aim was to study cerebellar anatomy in SZ and BD, and investigate the effect of lithium.

METHODS

Participants from 7 centers worldwide underwent a 3T MRI. We included 182 patients with SZ, 144 patients with BD, and 322 controls. We automatically segmented the cerebellum using the CERES pipeline. All outputs were visually inspected.

RESULTS

Patients with SZ showed a smaller global cerebellar gray matter volume compared to controls, with most of the changes located to the cognitive part of the cerebellum (Crus II and lobule VIIb). This decrease was present in the subgroup of patients with recent-onset SZ. We did not find any alterations in the cerebellum in patients with BD. However, patients medicated with lithium had a larger size of the anterior cerebellum, compared to patients not treated with lithium.

CONCLUSION

Our multicenter study supports a distinct pattern of cerebellar alterations in SZ and BD.

Father-infant interactions and infant regional brain volumes: A cross-sectional MRI study

Fathers play a crucial role in their children’s socio-emotional and cognitive development. A plausible intermediate phenotype underlying this association is father’s impact on infant brain. However, research on the association between paternal caregiving and child brain biology is scarce, particularly during infancy. Thus, we used magnetic resonance imaging (MRI) to investigate the relationship between observed father–infant interactions, specifically paternal sensitivity, and regional brain volumes in a community sample of 3-to-6-month-old infants (N = 28). We controlled for maternal sensitivity and examined the moderating role of infant communication on this relationship. T2-weighted MR images were acquired from infants during natural sleep. Higher levels of paternal sensitivity were associated with smaller cerebellar volumes in infants with high communication levels. In contrast, paternal sensitivity was not associated with subcortical grey matter volumes in the whole sample, and this was similar in infants with both high and low communication levels. This preliminary study provides the first evidence for an association between father-child interactions and variation in infant brain anatomy.

A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms

Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autism is the small sample sizes of available imaging datasets with mixed sex. Thus, the majority of the investigations have involved male samples, with females somewhat overlooked. This paper deploys machine learning on partial least squares feature extraction to reveal differences in regional brain structure between individuals with autism and typically developing participants. A four-class classification problem (sex and condition) is specified, with theoretical restrictions based on the evaluation of a novel upper bound in the resubstitution estimate. These conditions were imposed on the classifier complexity and feature space dimension to assure generalizable results from the training set to test samples. Accuracies above [Formula: see text] on gray and white matter tissues estimated from voxel-based morphometry (VBM) features are obtained in a sample of equal-sized high-functioning male and female adults with and without autism ([Formula: see text], [Formula: see text]/group). The proposed learning machine revealed how autism is modulated by biological sex using a low-dimensional feature space extracted from VBM. In addition, a spatial overlap analysis on reference maps partially corroborated predictions of the “extreme male brain” theory of autism, in sexual dimorphic areas.

Autism spectrum disorder: Trends in research exploring etiopathogenesis

Autism spectrum disorder is a neurodevelopmental condition in which affected individuals have difficulties while interacting and communicating socially, and repetitive behaviors. It has a multifactorial etiology. Various risk factors, including genetic and environmental influences, have been explored while trying to understand its causation. As older evidence was suggestive of a high heritability, a majority of research focused on finding the underlying genetic causes of autism. Due to these efforts, there have been advances in the knowledge of some of the genetic factors associated with autism. But a recent trend also shows an increasing interest in exploration of various potential environmental triggers. These efforts have brought us closer to understanding the elusive disorder more so than ever before. The current review discusses the recent trends in research exploring the etiopathogenesis of autism spectrum disorder.

HITS-CLIP in various brain areas reveals new targets and new modalities of RNA binding by fragile X mental retardation protein

Fragile X syndrome (FXS), the most common form of inherited intellectual disability, is due to the functional deficiency of the fragile X mental retardation protein (FMRP), an RNA-binding protein involved in translational regulation of many messenger RNAs, playing key roles in synaptic morphology and plasticity. To date, no effective treatment for FXS is available. We searched for FMRP targets by HITS-CLIP during early development of multiple mouse brain regions (hippocampus, cortex and cerebellum) at a time of brain development when FMRP is most highly expressed and synaptogenesis reaches a peak. We identified the largest dataset of mRNA targets of FMRP available in brain and we defined their cellular origin. We confirmed the G-quadruplex containing structure as an enriched motif in FMRP RNA targets. In addition to four less represented motifs, our study points out that, in the brain, CTGKA is the prominent motif bound by FMRP, which recognizes it when not engaged in Watson–Crick pairing. All of these motifs negatively modulated the expression level of a reporter protein. While the repertoire of FMRP RNA targets in cerebellum is quite divergent, the ones of cortex and hippocampus are vastly overlapping. In these two brain regions, the Phosphodiesterase 2a (Pde2a) mRNA is a prominent target of FMRP, which modulates its translation and intracellular transport. This enzyme regulates the homeostasis of cAMP and cGMP and represents a novel and attractive therapeutic target to treat FXS.

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.