Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum

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.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Tic-Related Obsessive-Compulsive and Eating Disorders in Dandy-Walker Variant: A Case Report and Systematic Reappraisal of Psychiatric Profiles

Dandy-Walker complex (DWC) consists of a continuum of brain malformations involving the posterior fossa, often leading to psychiatric manifestations during adulthood. We discussed the case of a young woman with Dandy-Walker variant (DWV) and a comorbid complex neuropsychiatric presentation, who was diagnosed with an eating disorder, obsessive-compulsive disorder, and a tic disorder. Afterwards, we conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant systematic review reappraising all evidence of psychiatric outcomes in adults with DWC. Overall, 34 studies were eligible for data extraction, comprising 36 patients. Psychiatric profiles were more common among young adult males, with DWC lesions, especially DWV subtype, being often discovered incidentally after admission to mental health inpatient facilities. Most patients were diagnosed with psychosis and bipolar disorder, often comorbid with cognitive impairment. Psychotropic polypharmacy was frequently prescribed, generally leading to complete recovery. Evidence from our case report and systematic review indicates the importance of monitoring long-term psychiatric sequelae among adult patients with DWC malformations.

A Systematic Review of Direct Outputs from the Cerebellum to the Brainstem and Diencephalon in Mammals

The cerebellum is involved in many motor, autonomic and cognitive functions, and new tasks that have a cerebellar contribution are discovered on a regular basis. Simultaneously, our insight into the functional compartmentalization of the cerebellum has markedly improved. Additionally, studies on cerebellar output pathways have seen a renaissance due to the development of viral tracing techniques. To create an overview of the current state of our understanding of cerebellar efferents, we undertook a systematic review of all studies on monosynaptic projections from the cerebellum to the brainstem and the diencephalon in mammals. This revealed that important projections from the cerebellum, to the motor nuclei, cerebral cortex, and basal ganglia, are predominantly di- or polysynaptic, rather than monosynaptic. Strikingly, most target areas receive cerebellar input from all three cerebellar nuclei, showing a convergence of cerebellar information at the output level. Overall, there appeared to be a large level of agreement between studies on different species as well as on the use of different types of neural tracers, making the emerging picture of the cerebellar output areas a solid one. Finally, we discuss how this cerebellar output network is affected by a range of diseases and syndromes, with also non-cerebellar diseases having impact on cerebellar output areas.

Comprehensive exploration of multi-modal and multi-branch imaging markers for autism diagnosis and interpretation: insights from an advanced deep learning model

Autism spectrum disorder is a complex neurodevelopmental condition with diverse genetic and brain involvement. Despite magnetic resonance imaging advances, autism spectrum disorder diagnosis and understanding its neurogenetic factors remain challenging. We propose a dual-branch graph neural network that effectively extracts and fuses features from bimodalities, achieving 73.9% diagnostic accuracy. To explain the mechanism distinguishing autism spectrum disorder from healthy controls, we establish a perturbation model for brain imaging markers and perform a neuro-transcriptomic joint analysis using partial least squares regression and enrichment to identify potential genetic biomarkers. The perturbation model identifies brain imaging markers related to structural magnetic resonance imaging in the frontal, temporal, parietal, and occipital lobes, while functional magnetic resonance imaging markers primarily reside in the frontal, temporal, occipital lobes, and cerebellum. The neuro-transcriptomic joint analysis highlights genes associated with biological processes, such as "presynapse," "behavior," and "modulation of chemical synaptic transmission" in autism spectrum disorder's brain development. Different magnetic resonance imaging modalities offer complementary information for autism spectrum disorder diagnosis. Our dual-branch graph neural network achieves high accuracy and identifies abnormal brain regions and the neuro-transcriptomic analysis uncovers important genetic biomarkers. Overall, our study presents an effective approach for assisting in autism spectrum disorder diagnosis and identifying genetic biomarkers, showing potential for enhancing the diagnosis and treatment of this condition.

Both stronger and weaker cerebro-cerebellar functional connectivity patterns during processing of spoken sentences in autism spectrum disorder

Cerebellar differences have long been documented in autism spectrum disorder (ASD), yet the extent to which such differences might impact language processing in ASD remains unknown. To investigate this, we recorded brain activity with magnetoencephalography (MEG) while ASD and age-matched typically developing (TD) children passively processed spoken meaningful English and meaningless Jabberwocky sentences. Using a novel source localization approach that allows higher resolution MEG source localization of cerebellar activity, we found that, unlike TD children, ASD children showed no difference between evoked responses to meaningful versus meaningless sentences in right cerebellar lobule VI. ASD children also had atypically weak functional connectivity in the meaningful versus meaningless speech condition between right cerebellar lobule VI and several left-hemisphere sensorimotor and language regions in later time windows. In contrast, ASD children had atypically strong functional connectivity for in the meaningful versus meaningless speech condition between right cerebellar lobule VI and primary auditory cortical areas in an earlier time window. The atypical functional connectivity patterns in ASD correlated with ASD severity and the ability to inhibit involuntary attention. These findings align with a model where cerebro-cerebellar speech processing mechanisms in ASD are impacted by aberrant stimulus-driven attention, which could result from atypical temporal information and predictions of auditory sensory events by right cerebellar lobule VI.

Disrupted network interactions serve as a neural marker of dyslexia

Dyslexia, a frequent learning disorder, is characterized by severe impairments in reading and writing and hypoactivation in reading regions in the left hemisphere. Despite decades of research, it remains unclear to date if observed behavioural deficits are caused by aberrant network interactions during reading and whether differences in functional activation and connectivity are directly related to reading performance. Here we provide a comprehensive characterization of reading-related brain connectivity in adults with and without dyslexia. We find disrupted functional coupling between hypoactive reading regions, especially between the left temporo-parietal and occipito-temporal cortices, and an extensive functional disruption of the right cerebellum in adults with dyslexia. Network analyses suggest that individuals with dyslexia process written stimuli via a dorsal decoding route and show stronger reading-related interaction with the right cerebellum. Moreover, increased connectivity within networks is linked to worse reading performance in dyslexia. Collectively, our results provide strong evidence for aberrant task-related connectivity as a neural marker for dyslexia that directly impacts behavioural performance. The observed differences in activation and connectivity suggest that one effective way to alleviate reading problems in dyslexia is through modulating interactions within the reading network with neurostimulation methods.

Speech-in-noise perception in autistic adolescents with and without early language delay

Speech-in-noise perception seems aberrant in individuals with autism spectrum disorder (ASD). Potential aggravating factors are the level of linguistic skills and impairments in auditory temporal processing. Here, we investigated autistic adolescents with and without language delay as compared to non-autistic peers, and we assessed speech perception in steady-state noise, temporally modulated noise, and concurrent speech. We found that autistic adolescents with intact language capabilities and not those with language delay performed worse than NT peers on words-in-stationary-noise perception. For the perception of sentences in stationary noise, we did not observe significant group differences, although autistic adolescents with language delay tend to perform worse in comparison to their TD peers. We also found evidence for a robust deficit in speech-in-concurrent-speech processing in ASD independent of language ability, as well as an association between early language delay in ASD and inadequate temporal speech processing. We propose that reduced voice stream segregation and inadequate social attentional orienting in ASD result in disproportional informational masking of the speech signal. These findings indicate a speech-in-speech processing deficit in autistic adolescents with broad implications for the quality of social communication.

Thalamo-cortical and cerebello-cortical functional connectivity in development

The thalamus is a critical relay center for neural pathways involving sensory, motor, and cognitive functions, including cortico-striato-thalamo-cortical and cortico-ponto-cerebello-thalamo-cortical loops. Despite the importance of these circuits, their development has been understudied. One way to investigate these pathways in human development in vivo is with functional connectivity MRI, yet few studies have examined thalamo-cortical and cerebello-cortical functional connectivity in development. Here, we used resting-state functional connectivity to measure functional connectivity in the thalamus and cerebellum with previously defined cortical functional networks in 2 separate data sets of children (7-12 years old) and adults (19-40 years old). In both data sets, we found stronger functional connectivity between the ventral thalamus and the somatomotor face cortical functional network in children compared with adults, extending previous cortico-striatal functional connectivity findings. In addition, there was more cortical network integration (i.e. strongest functional connectivity with multiple networks) in the thalamus in children than in adults. We found no developmental differences in cerebello-cortical functional connectivity. Together, these results suggest different maturation patterns in cortico-striato-thalamo-cortical and cortico-ponto-cerebellar-thalamo-cortical pathways.

Different levels of visuospatial abilities linked to differential brain correlates underlying visual mental segmentation processes in autism

The neural underpinnings of enhanced locally oriented visual processing that are specific to autistics with a Wechsler's Block Design (BD) peak are largely unknown. Here, we investigated the brain correlates underlying visual segmentation associated with the well-established autistic superior visuospatial abilities in distinct subgroups using functional magnetic resonance imaging. This study included 31 male autistic adults (15 with (AUTp) and 16 without (AUTnp) a BD peak) and 28 male adults with typical development (TYP). Participants completed a computerized adapted BD task with models having low and high perceptual cohesiveness (PC). Despite similar behavioral performances, AUTp and AUTnp showed generally higher occipital activation compared with TYP participants. Compared with both AUTnp and TYP participants, the AUTp group showed enhanced task-related functional connectivity within posterior visuoperceptual regions and decreased functional connectivity between frontal and occipital-temporal regions. A diminished modulation in frontal and parietal regions in response to increased PC was also found in AUTp participants, suggesting heavier reliance on low-level processing of global figures. This study demonstrates that enhanced visual functioning is specific to a cognitive phenotypic subgroup of autistics with superior visuospatial abilities and reinforces the need to address autistic heterogeneity by good cognitive characterization of samples in future studies.

A neuro-computational social learning framework to facilitate transdiagnostic classification and treatment across psychiatric disorders

Social deficits are among the core and most striking psychiatric symptoms, present in most psychiatric disorders. Here, we introduce a novel social learning framework, which consists of neuro-computational models that combine reinforcement learning with various types of social knowledge structures. We outline how this social learning framework can help specify and quantify social psychopathology across disorders and provide an overview of the brain regions that may be involved in this type of social learning. We highlight how this framework can specify commonalities and differences in the social psychopathology of individuals with autism spectrum disorder (ASD), personality disorders (PD), and major depressive disorder (MDD) and improve treatments on an individual basis. We conjecture that individuals with psychiatric disorders rely on rigid social knowledge representations when learning about others, albeit the nature of their rigidity and the behavioral consequences can greatly differ. While non-clinical cohorts tend to efficiently adapt social knowledge representations to relevant environmental constraints, psychiatric cohorts may rigidly stick to their preconceived notions or overly coarse knowledge representations during learning.

Absence of associations with prefrontal cortex and cerebellum may link to early language and social deficits in preschool children with ASD

Introduction

Autism spectrum disorder (ASD) is a complex developmental disorder, characterized by language and social deficits that begin to appear in the first years of life. Research in preschool children with ASD has consistently reported increased global brain volume and abnormal cortical patterns, and the brain structure abnormalities have also been found to be clinically and behaviorally relevant. However, little is known regarding the associations between brain structure abnormalities and early language and social deficits in preschool children with ASD.

Methods

In this study, we collected magnetic resonance imaging (MRI) data from a cohort of Chinese preschool children with and without ASD (24 ASD/20 non-ASD) aged 12-52 months, explored group differences in brain gray matter (GM) volume, and examined associations between regional GM volume and early language and social abilities in these two groups, separately.

Results

We observed significantly greater global GM volume in children with ASD as compared to those without ASD, but there were no regional GM volume differences between these two groups. For children without ASD, GM volume in bilateral prefrontal cortex and cerebellum was significantly correlated with language scores; GM volume in bilateral prefrontal cortex was significantly correlated with social scores. No significant correlations were found in children with ASD.

Discussion

Our data demonstrate correlations of regional GM volume with early language and social abilities in preschool children without ASD, and the absence of these associations appear to underlie language and social deficits in children with ASD. These findings provide novel evidence for the neuroanatomical basis associated with language and social abilities in preschool children with and without ASD, which promotes a better understanding of early deficits in language and social functions in ASD.

Amplitude of low frequency fluctuations during resting state fMRI in autistic children

Resting state fMRI (rs-fMRI) provides an excellent platform for examining the amplitude of low frequency fluctuations (ALFF) and fractional amplitude of low frequency fluctuations (fALFF), which are key indices of brain functioning. However, ALFF and fALFF have been used only sporadically to study autism. rs-fMRI data from 69 children (40 autistic, mean age = 8.47 ± 2.20 years; age range: 5.2 to 13.2; and 29 non-autistic, mean age = 9.02 ± 1.97 years; age range 5.9 to 12.9) were obtained from the Autism Brain Imaging Data Exchange (ABIDE II). ALFF and fALFF were measured using CONN connectivity toolbox and SPM12, at whole-brain & network-levels. A two-sampled t-test and a 2 Group (autistic, non-autistic) × 7 Networks ANOVA were conducted to test group differences in ALFF and fALFF. The whole-brain analysis identified significantly reduced ALFF values for autistic participants in left parietal opercular cortex, precuneus, and right insula. At the network level, there was a significant effect of diagnostic group and brain network on ALFF values, and only significant effect of network, not group, on fALFF values. Regression analyses indicated a significant effect of age on ALFF values of certain networks in autistic participants. Such intrinsically different network-level responses in autistic participants may have implications for task-level recruitment and synchronization of brain areas, which may in turn impact optimal cognitive functioning. Moreover, differences in low frequency fluctuations of key networks, such as the DMN and SN, may underlie alterations in brain responses in autism that are frequently reported in the literature.

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.

Maladaptive Laterality in Cortical Networks Related to Social Communication in Autism Spectrum Disorder

Neuroimaging studies of individuals with autism spectrum disorders (ASDs) consistently find an aberrant pattern of reduced laterality in brain networks that support functions related to social communication and language. However, it is unclear how the underlying functional organization of these brain networks is altered in ASD individuals. We tested four models of reduced laterality in a social communication network in 70 ASD individuals (14 females) and a control group of the same number of tightly matched typically developing (TD) individuals (19 females) using high-quality resting-state fMRI data and a method of measuring patterns of functional laterality across the brain. We found that a functionally defined social communication network exhibited the typical pattern of left laterality in both groups, whereas there was a significant increase in within- relative to across-hemisphere connectivity of homotopic regions in the right hemisphere in ASD individuals. Furthermore, greater within- relative to across-hemisphere connectivity in the left hemisphere was positively correlated with a measure of verbal ability in both groups, whereas greater within- relative to across-hemisphere connectivity in the right hemisphere in ASD, but not TD, individuals was negatively correlated with the same verbal measure. Crucially, these differences in patterns of laterality were not found in two other functional networks and were specifically correlated to a measure of verbal ability but not metrics of other core components of the ASD phenotype. These results suggest that previous reports of reduced laterality in social communication regions in ASD is because of the two hemispheres functioning more independently than seen in TD individuals, with the atypical right-hemisphere network component being maladaptive.SIGNIFICANCE STATEMENT A consistent neuroimaging finding in individuals with ASD is an aberrant pattern of reduced laterality of the brain networks that support functions related to social communication and language. We tested four models of reduced laterality in a social communication network in ASD individuals and a TD control group using high-quality resting-state fMRI data. Our results suggest that reduced laterality of social communication regions in ASD may be because of the two hemispheres functioning more independently than seen in TD individuals, with atypically greater within- than across-hemisphere connectivity in the right hemisphere being maladaptive.

Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders

INTRODUCTION

Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear.

OBJECTS

The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state.

MATERIALS AND METHODS

The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics.

RESULTS

For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores.

CONCLUSION

Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.

Asymmetric information flow in brain networks supporting expressive language in childhood

Low-beta (13-23 Hz) event-related desynchrony (ERD), a neural signature of expressive language, lateralizes from bilateral to left hemisphere in development. In contrast, low-beta event-related synchrony (ERS), thought to reflect inhibition, lateralizes from bilateral to the right hemisphere across development. Using whole-brain directed connectivity analyses, we aimed to characterize hemispheric and regional contributions to expressive language, in childhood. We studied 80 children and adolescents, 4 to less than 19 years of age, performing covert auditory verb generation in magnetoencephalography. Outdegree, indegree, and betweenness centrality were used to differentiate regions acting as drivers, receivers, and bridging hubs, respectively. The number of suprathreshold connections significantly increased with age for delta band (p < .01). Delta outflow was mapped to left inferior frontal gyrus (IFG), while regions of right hemisphere, including right IFG, showed significant inflow. The right parietal cortex showed significant ERS, but without corresponding outdegree or indegree. Betweenness mapped to midline cortical and subcortical structures. Results suggest Broca's area develops a driving role in the language network, while Broca's homologue receives information without necessarily propagating it. Subcortical and midline hubs act as intrahemispheric relays. Findings suggest that Broca's homologue is inhibited during expressive language, in development.

Cerebellar Tubers in Tuberous Sclerosis Complex Patients: New Imaging Characteristics and the Relationship with Cerebral Tubers

Objective The imaging characteristics, evolution, and clinical features of cerebellar tubers in tuberous sclerosis complex (TSC) patients have not been well described. The purpose of this study is to investigate the imaging characteristics of cerebellar tubers, including their dynamic changes, and to evaluate the relationship with cerebral tubers in TSC patients.

Materials and Methods Two observers retrospectively reviewed 75 consecutive TSC patients to identify cerebellar tubers and to evaluate their imaging characteristics, including location, presence of retraction change, calcification, contrast enhancement, and the presence of an associated vascular anomaly, as well as dynamic changes in these characteristics. The number of cerebral tubers was compared between TSC patients with and without cerebellar tubers.

Results Twenty-five TSC patients with 28 cerebellar tubers were identified. All cerebellar tubers occurred within the lateral portions of the cerebellar hemispheres. Thirteen cerebellar tubers demonstrated calcification. Ten cerebellar tubers showed contrast enhancement, half of which demonstrated a zebra-like appearance. A vascular anomaly was associated with 12 tubers, one of which subsequently developed parenchymal hemorrhage. Fifteen cerebellar tubers demonstrated complex dynamic changes in size and contrast enhancement. Patients with cerebellar tubers had more cerebral tubers (p = 0.001).

Conclusion Cerebellar tubers demonstrate a specific distribution, suggesting a possible influence on higher brain function. The presence of an associated vascular anomaly may be an important imaging characteristic. Cerebellar tubers may be associated with a more severe manifestation of TSC, given their association with increased numbers of cerebral tubers. These findings may provide insights into the pathogenesis and clinical manifestations of cerebellar tubers in TSC patients.

Cognitive theories of autism based on the interactions between brain functional networks

Cognitive functions are directly related to interactions between the brain's functional networks. This functional organization changes in the autism spectrum disorder (ASD). However, the heterogeneous nature of autism brings inconsistency in the findings, and specific pattern of changes based on the cognitive theories of ASD still requires to be well-understood. In this study, we hypothesized that the theory of mind (ToM), and the weak central coherence theory must follow an alteration pattern in the network level of functional interactions. The main aim is to understand this pattern by evaluating interactions between all the brain functional networks. Moreover, the association between the significantly altered interactions and cognitive dysfunctions in autism is also investigated. We used resting-state fMRI data of 106 subjects (5–14 years, 46 ASD: five female, 60 HC: 18 female) to define the brain functional networks. Functional networks were calculated by applying four parcellation masks and their interactions were estimated using Pearson's correlation between pairs of them. Subsequently, for each mask, a graph was formed based on the connectome of interactions. Then, the local and global parameters of the graph were calculated. Finally, statistical analysis was performed using a two-sample t-test to highlight the significant differences between autistic and healthy control groups. Our corrected results show significant changes in the interaction of default mode, sensorimotor, visuospatial, visual, and language networks with other functional networks that can support the main cognitive theories of autism. We hope this finding sheds light on a better understanding of the neural underpinning of autism.

Cerebellar and basal ganglia motor network predicts trait depression and hyperactivity

In the human brain, the cerebellum (CB) and basal ganglia (BG) are implicated in cognition-, emotion-, and motor-related cortical processes and are highly interconnected, both to cortical regions via separate, trans-thalamic pathways and to each other via subcortical disynaptic pathways. We previously demonstrated a distinction between cognitive and motor CB-BG networks (CCBN, MCBN, respectively) as it relates to cortical network integration in healthy young adults, suggesting the subcortical networks separately support cortical networks. The CB and BG are also implicated in the pathophysiology of schizophrenia, Parkinson's, and compulsive behavior; thus, integration within subcortical CB-BG networks may be related to transdiagnostic symptomology. Here, we asked whether CCBN or MCBN integration predicted Achenbach Self-Report scores for anxiety, depression, intrusive thoughts, hyperactivity and inactivity, and cognitive performance in a community sample of young adults. We computed global efficiency for each CB-BG network and 7 canonical resting-state networks for all right-handed participants in the Human Connectome Project 1200 release with a complete set of preprocessed resting-state functional MRI data (N = 783). We used multivariate regression to control for substance abuse and age, and permutation testing with exchangeability blocks to control for family relationships. MCBN integration negatively predicted depression and hyperactivity, and positively predicted cortical network integration. CCBN integration predicted cortical network integration (except for the emotional network) and marginally predicted a positive relationship with hyperactivity, indicating a potential dichotomy between cognitive and motor CB-BG networks and hyperactivity. These results highlight the importance of CB-BG interactions as they relate to motivation and symptoms of depression.

Altered resting state dynamic functional connectivity of amygdala subregions in patients with autism spectrum disorder: A multi-site fMRI study

BACKGROUND

Autism spectrum disorder (ASD) is associated with altered brain connectivity. Previous studies have focused on the static functional connectivity pattern from amygdala subregions in ASD while ignoring its dynamics. Considering that dynamic functional connectivity (dFC) can provide different perspectives, the present study aims to investigate the dFC pattern of the amygdala subregions in ASD patients.

METHODS

Data of 618 ASD patients and 836 typical controls (TCs) of 30 sites were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. The sliding window approach was applied to conduct seed-based dFC analysis. The seed regions were bilateral basolateral (BLA) and centromedial-superficial amygdala (CSA). A two-sample t-test was done at each site. Image-based meta-analysis (IBMA) based on the results from all sites was performed. Correlation analysis was conducted between the dFC values and the clinical scores.

RESULTS

The ASD patients showed lower dFC between the left BLA and the bilateral inferior temporal (ITG)/left superior frontal gyrus, between the right BLA and right ITG/right thalamus/left superior temporal gyrus, and between the right CSA and middle temporal gyrus. The ASD patients showed higher dFC between the left BLA and temporal lobe/right supramarginal gyrus, between the right BLA and left calcarine gyrus, and between the left CSA and left calcarine gyrus. Correlation analysis revealed that the symptom severity was positively correlated with the dFC between the bilateral BLA and ITG in ASD.

CONCLUSIONS

Abnormal dFC of the specific amygdala subregions may provide new insights into the pathological mechanisms of ASD.

Atypical cerebellar functional connectivity at 9 months of age predicts delayed socio-communicative profiles in infants at high and low risk for autism

BACKGROUND

While the cerebellum is traditionally known for its role in sensorimotor control, emerging research shows that particular subregions, such as right Crus I (RCrusI), support language and social processing. Indeed, cerebellar atypicalities are commonly reported in autism spectrum disorder (ASD), a neurodevelopmental disorder characterized by socio-communicative impairments. However, the cerebellum's contribution to early socio-communicative development remains virtually unknown.

METHODS

Here, we characterized functional connectivity within cerebro-cerebellar networks implicated in language/social functions in 9-month-old infants who exhibit distinct 3-year socio-communicative developmental profiles. We employed a data-driven clustering approach to stratify our sample of infants at high (n = 82) and low (n = 37) familial risk for ASD into three cohorts-Delayed, Late-Blooming, and Typical-who showed unique socio-communicative trajectories. We then compared the cohorts on indices of language and social development. Seed-based functional connectivity analyses with RCrusI were conducted on infants with fMRI data (n = 66). Cohorts were compared on connectivity estimates from a-priori regions, selected on the basis of reported coactivation with RCrusI during language/social tasks.

RESULTS

The three trajectory-based cohorts broadly differed in social communication development, as evidenced by robust differences on numerous indices of language and social skills. Importantly, at 9 months, the cohorts showed striking differences in cerebro-cerebellar circuits implicated in language/social functions. For all regions examined, the Delayed cohort exhibited significantly weaker RCrusI connectivity compared to both the Late-Blooming and Typical cohorts, with no significant differences between the latter cohorts.

CONCLUSIONS

We show that hypoconnectivity within distinct cerebro-cerebellar networks in infancy predicts altered socio-communicative development before delays overtly manifest, which may be relevant for early detection and intervention. As the cerebellum is implicated in prediction, our findings point to probabilistic learning as a potential intermediary mechanism that may be disrupted in infancy, cascading into alterations in social communication.

The Altered Pattern of the Functional Connectome Related to Pathological Biomarkers in Individuals for Autism Spectrum Disorder Identification

Objective

Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by the development of multiple symptoms, with incidences rapidly increasing worldwide. An important step in the early diagnosis of ASD is to identify informative biomarkers. Currently, the use of functional brain network (FBN) is deemed important for extracting data on brain imaging biomarkers. Unfortunately, most existing studies have reported the utilization of the information from the connection to train the classifier; such an approach ignores the topological information and, in turn, limits its performance. Thus, effective utilization of the FBN provides insights for improving the diagnostic performance.

Methods

We propose the combination of the information derived from both FBN and its corresponding graph theory measurements to identify and distinguish ASD from normal controls (NCs). Specifically, a multi-kernel support vector machine (MK-SVM) was used to combine multiple types of information.

Results

The experimental results illustrate that the combination of information from multiple connectome features (i.e., functional connections and graph measurements) can provide a superior identification performance with an area under the receiver operating characteristic curve (ROC) of 0.9191 and an accuracy of 82.60%. Furthermore, the graph theoretical analysis illustrates that the significant nodal graph measurements and consensus connections exists mostly in the salience network (SN), default mode network (DMN), attention network, frontoparietal network, and social network.

Conclusion

This work provides insights into potential neuroimaging biomarkers that may be used for the diagnosis of ASD and offers a new perspective for the exploration of the brain pathophysiology of ASD through machine learning.

Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI⁺) involved in cerebellar neurodevelopment and a negative gene set (GCI^-) related to neurotransmission. A more interesting finding is that GCI^- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

The Cerebellar Involvement in Autism Spectrum Disorders: From the Social Brain to Mouse Models

Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders that include a variety of forms and clinical phenotypes. This heterogeneity complicates the clinical and experimental approaches to ASD etiology and pathophysiology. To date, a unifying theory of these diseases is still missing. Nevertheless, the intense work of researchers and clinicians in the last decades has identified some ASD hallmarks and the primary brain areas involved. Not surprisingly, the areas that are part of the so-called “social brain”, and those strictly connected to them, were found to be crucial, such as the prefrontal cortex, amygdala, hippocampus, limbic system, and dopaminergic pathways. With the recent acknowledgment of the cerebellar contribution to cognitive functions and the social brain, its involvement in ASD has become unmistakable, though its extent is still to be elucidated. In most cases, significant advances were made possible by recent technological developments in structural/functional assessment of the human brain and by using mouse models of ASD. Mouse models are an invaluable tool to get insights into the molecular and cellular counterparts of the disease, acting on the specific genetic background generating ASD-like phenotype. Given the multifaceted nature of ASD and related studies, it is often difficult to navigate the literature and limit the huge content to specific questions. This review fulfills the need for an organized, clear, and state-of-the-art perspective on cerebellar involvement in ASD, from its connections to the social brain areas (which are the primary sites of ASD impairments) to the use of monogenic mouse models.

Cortical signatures of auditory object binding in children with autism spectrum disorder are anomalous in concordance with behavior and diagnosis

Organizing sensory information into coherent perceptual objects is fundamental to everyday perception and communication. In the visual domain, indirect evidence from cortical responses suggests that children with autism spectrum disorder (ASD) have anomalous figure-ground segregation. While auditory processing abnormalities are common in ASD, especially in environments with multiple sound sources, to date, the question of scene segregation in ASD has not been directly investigated in audition. Using magnetoencephalography, we measured cortical responses to unattended (passively experienced) auditory stimuli while parametrically manipulating the degree of temporal coherence that facilitates auditory figure-ground segregation. Results from 21 children with ASD (aged 7-17 years) and 26 age- and IQ-matched typically developing children provide evidence that children with ASD show anomalous growth of cortical neural responses with increasing temporal coherence of the auditory figure. The documented neurophysiological abnormalities did not depend on age, and were reflected both in the response evoked by changes in temporal coherence of the auditory scene and in the associated induced gamma rhythms. Furthermore, the individual neural measures were predictive of diagnosis (83% accuracy) and also correlated with behavioral measures of ASD severity and auditory processing abnormalities. These findings offer new insight into the neural mechanisms underlying auditory perceptual deficits and sensory overload in ASD, and suggest that temporal-coherence-based auditory scene analysis and suprathreshold processing of coherent auditory objects may be atypical in ASD.

Resting-state functional MRI language network connectivity differences in patients with brain tumors: exploration of the cerebellum and contralesional hemisphere

Brain tumors can have far-reaching impacts on functional networks. Language processing is typically lateralized to the left hemisphere, but also involves the right hemisphere and cerebellum. This resting-state functional MRI study investigated the proximal and distal effects of left-hemispheric brain tumors on language network connectivity in the ipsilesional and contralesional hemispheres. Separate language resting-state networks were generated from seeding in ipsilesional (left) and contralesional (right) Broca's Area for 29 patients with left-hemispheric brain tumors and 13 controls. Inclusion criteria for all subjects included language left-dominance based on task-based functional MRI. Functional connectivity was analyzed in each network to the respective Wernicke's Area and contralateral cerebellum. Patients were assessed for language deficits prior to scanning. Compared to controls, patients exhibited decreased connectivity in the ipsilesional and contralesional hemispheres between the Broca's Area and Wernicke's Area homologs (mean connectivity for patients/controls: left 0.51/0.59, p < 0.002; right 0.52/0.59, p < 0.0002). No differences in mean connectivity to the contralateral cerebellum were observed between groups (p > 0.09). Crossed cerebro-cerebellar connectivity was correlated in controls (rho = 0.59, p < 0.05), patients without language deficits (rho = 0.74, p < 0.0002), and patients with high-grade gliomas (rho = 0.78, p < 0.0002), but not in patients with language deficits or low-grade gliomas (p > 0.l). These findings demonstrate that brain tumors impact the language network in the contralesional hemisphere and cerebellum, which may reflect neurological deficits and lesion-induced cortical reorganization.

Cerebellar Contributions to Social Cognition in ASD: A Predictive Processing Framework

Functional, structural, and cytoarchitectural differences in the cerebellum are consistently reported in Autism Spectrum Disorders (ASD). Despite this, the mechanisms governing cerebellar contributions to ASD, particularly within the sociocognitive domain, are not well understood. Recently, it has been suggested that several core features of ASD may be associated with challenges creating and using prior expectations or predictions to rapidly adapt to changing stimuli or situations, also known as adaptive prediction. Importantly, neuroimaging, clinical, and animal work find that the cerebellum supports adaptive prediction in both motor and non-motor domains. Perturbations to the cerebellum via injury or neuromodulation have been associated with impairments in predictive skills. Here, we review evidence for a cerebellar role in social cognition and adaptive prediction across individuals with and without ASD.

Cortico-Cerebellar Hyper-Connections and Reduced Purkinje Cells Behind Abnormal Eyeblink Conditioning in a Computational Model of Autism Spectrum Disorder

Empirical evidence suggests that children with autism spectrum disorder (ASD) show abnormal behavior during delay eyeblink conditioning. They show a higher conditioned response learning rate and earlier peak latency of the conditioned response signal. The neuronal mechanisms underlying this autistic behavioral phenotype are still unclear. Here, we use a physiologically constrained spiking neuron model of the cerebellar-cortical system to investigate which features are critical to explaining atypical learning in ASD. Significantly, the computer simulations run with the model suggest that the higher conditioned responses learning rate mainly depends on the reduced number of Purkinje cells. In contrast, the earlier peak latency mainly depends on the hyper-connections of the cerebellum with sensory and motor cortex. Notably, the model has been validated by reproducing the behavioral data collected from studies with real children. Overall, this article is a starting point to understanding the link between the behavioral and neurobiological basis in ASD learning. At the end of the paper, we discuss how this knowledge could be critical for devising new treatments.

Altered resting-state neural networks in children and adolescents with functional neurological disorder

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FND in children commonly involves presentation with multiple neurological symptoms.

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Children with FND show wide-ranging connectivity changes in resting-state neural networks.

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Aberrant neural-networks changes are greater in children whose FND includes functional seizures.

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Subjective distress, autonomic arousal, and HPA dysregulation contribute to network changes.

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Children with FND (vs controls) report more subjective distress and more ACEs across the lifespan.

Objectives

Previous studies with adults suggest that aberrant communication between neural networks underpins functional neurological disorder (FND). The current study adopts a data-driven approach to investigate the extent that functional resting-state networks are disrupted in a pediatric mixed-FND cohort.

Methods

31 children with mixed FND and 33 age- and sex-matched healthy controls completed resting-state fMRI scans. Whole-brain independent component analysis (pFWE < 0.05) was then used to identify group differences in resting-state connectivity. Self-report measures included the Depression, Anxiety and Stress Scale (DASS-21) and Early Life Stress Questionnaire (ELSQ). Resting-state heart rate (HR) and cortisol-awakening response (CAR) were available in a subset.

Results

Children with FND showed wide-ranging connectivity changes in eight independent components corresponding to eight resting-state neural networks: language networks (IC6 and IC1), visual network, frontoparietal network, salience network, dorsal attention network, cerebellar network, and sensorimotor network. Children whose clinical presentation included functional seizures (vs children with other FND symptoms) showed greater connectivity decreases in the frontoparietal and dorsal attentional networks. Subjective distress (total DASS score), autonomic arousal (indexed by HR), and HPA dysregulation (attenuated/reversed CAR) contributed to changes in neural network connectivity. Children with FND (vs controls) reported more subjective distress (total DASS score) and more adverse childhood experiences (ACEs) across their lifespan.

Conclusions

Children with FND demonstrate changes in resting-state connectivity. Identified network alterations underpin a broad range of functions typically disrupted in children with FND. This study complements the adult literature by suggesting that FND in children and adolescents emerges in the context of their lived experience and that it reflects aberrant communication across neural networks.

Sex differences of language abilities of preschool children with autism spectrum disorder and their anatomical correlation with Broca and Wernicke areas

OBJECTIVE

People with autism spectrum disorder (ASD) often have language difficulties. This study focuses on whether there are sex differences in language ability in children with ASD and aims to analyze whether such differences may arise from developmental imbalances in the anatomical structures of Broca and Wernicke areas.

METHODS

The language development quotient (DQ) scores of Gesell Developmental Scale (GDS) and the scores of language communication of Childhood Autism Rating Scale (CARS) were used to judge the language ability, and the FREESURFER software extracted the anatomical structures of Broca and Wernicke areas on 3DT1 sequences. We analyzed the correlation between the anatomical structure of Broca/Wernicke areas and language abilities assessments.

RESULTS

The study initially included 44 cases of ASD, with 36 males (81.8 %) and 8 females (18.2%), and the age range was 24-72 months. Males have better language abilities than females. Specifically, the GDS verbal DQ of males was significantly higher than that of females (56.50 ± 18.02 vs. 29.23 ± 6.67, p < 0.001). Broca thickness-L was positively correlated with verbal DQ scores in GDS (r = 0.382, p = 0.011) and lower than grade 2 and 3 on the CARS verbal communication grade 4 (5.76 ± 0.17 vs. 6.21 ± 0.30 and 6.11 ± 0.35), with statistically significant differences between groups (p < 0.05).

CONCLUSION

There were sex differences in the language abilities of preschoolers with ASD, which may be due to an imbalance development of certain structures in Broca and Wernicke areas, especially Broca area.

Cerebellar Transcranial Direct Current Stimulation in Children with Autism Spectrum Disorder: A Pilot Study on Efficacy, Feasibility, Safety, and Unexpected Outcomes in Tic Disorder and Epilepsy

Patients with autism spectrum disorder (ASD) display distinctive neurophysiological characteristics associated with significant cognitive, emotional, and behavioral symptoms. Transcranial direct current stimulation (tDCS) applied to the frontal or temporoparietal lobes has demonstrated potential to reduce the severity of ASD-related symptoms. Recently, the cerebellum has been identified as a brain area involved in ASD pathophysiology. In this open-label pilot study, seven ASD patients aged between 9 and 13 years underwent 20 daily sessions of 20 min cathodal stimulation of the right cerebellar lobe. At the end of the treatment, the Aberrant Behavior Checklist (ABC) scores showed a 25% mean reduction in global severity of symptoms, with a more pronounced reduction in the “social withdrawal and lethargy” (−35%), “hyperactivity and noncompliance” (−26%), and “irritability, agitation, and crying” (−25%) subscales. Minor and no improvement were observed in the “stereotypic behavior” (−18%) and “inappropriate speech” (−0%) subscales, respectively. Improvements were not detected in the two patients who were taking psychotropic drugs during the study. Clinical response showed a symptom-specific time course. Quality of sleep and mood improved earlier than hyperactivity and social withdrawal. The treatment was generally accepted by patients and well tolerated. No serious adverse events were reported. Stimulation also appeared to markedly reduce the severity of tics in a patient with comorbid tic disorder and led to the disappearance of a frontal epileptogenic focus in another patient with a history of seizures. In conclusion, cerebellar tDCS is safe, feasible, and potentially effective in the treatment of ASD symptoms among children. Strategies to improve recruitment and retention are discussed.

Elucidating the structural-functional connectome of language in glioma-induced aphasia using nTMS and DTI

Glioma‐induced aphasia (GIA) is frequently observed in patients with newly diagnosed gliomas. Previous studies showed an impact of gliomas not only on local brain regions but also on the functionality and structure of brain networks. The current study used navigated transcranial magnetic stimulation (nTMS) to localize language‐related regions and to explore language function at the network level in combination with connectome analysis. Thirty glioma patients without aphasia (NA) and 30 patients with GIA were prospectively enrolled. Tumors were located in the vicinity of arcuate fasciculus‐related cortical and subcortical regions. The visualized ratio (VR) of each tract was calculated based on their respective fractional anisotropy (FA) and maximal FA. Using a thresholding method of each tract at 25% VR and 50% VR, DTI‐based tractography was performed to construct structural brain networks for graph‐based connectome analysis, containing functional data acquired by nTMS. The average degree of left hemispheric networks (M_left) was higher in the NA group than in the GIA group for both VR thresholds. Differences of global and local efficiency between 25% and 50% VR thresholds were significantly lower in the NA group than in the GIA group. Aphasia levels correlated with connectome properties in M_left and networks based on positive nTMS mapping regions (M_pos). A more substantial relation to language performance was found in M_pos and M_left compared to the network of negative mapping regions (M_neg). Gliomas causing deterioration of language are related to various cerebral networks. In NA patients, mainly M_neg was impacted, while M_pos was impacted in GIA patients.

Adaptive Prediction for Social Contexts: The Cerebellar Contribution to Typical and Atypical Social Behaviors

Social interactions involve processes ranging from face recognition to understanding others' intentions. To guide appropriate behavior in a given context, social interactions rely on accurately predicting the outcomes of one's actions and the thoughts of others. Because social interactions are inherently dynamic, these predictions must be continuously adapted. The neural correlates of social processing have largely focused on emotion, mentalizing, and reward networks, without integration of systems involved in prediction. The cerebellum forms predictive models to calibrate movements and adapt them to changing situations, and cerebellar predictive modeling is thought to extend to nonmotor behaviors. Primary cerebellar dysfunction can produce social deficits, and atypical cerebellar structure and function are reported in autism, which is characterized by social communication challenges and atypical predictive processing. We examine the evidence that cerebellar-mediated predictions and adaptation play important roles in social processes and argue that disruptions in these processes contribute to autism.

Subtyping Autism Spectrum Disorder via Joint Modeling of Clinical and Connectomic Profiles

BACKGROUND

Autism Spectrum Disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional MRI studies, however the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes.

METHODS

Canonical correlation analysis was performed on two-hundred and ten ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine classifier was used to verify that subtypes were comprised of subjects with distinct clinical and connectivity features.

RESULTS

Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased IQ scores and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe ADI-R and SRS symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class support vector machine using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%).

CONCLUSION

The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.

Cerebellar Dysfunction in Autism Spectrum Disorders: Deriving Mechanistic Insights from an Internal Model Framework

Autism spectrum disorders (ASD) are highly prevalent neurodevelopmental disorders; however, the neurobiological mechanisms underlying disordered behavior in ASD remain poorly understood. Notably, individuals with ASD have demonstrated difficulties generating implicitly derived behavioral predictions and adaptations. Although many brain regions are involved in these processes, the cerebellum contributes an outsized role to these behavioral functions. Consistent with this prominent role, cerebellar dysfunction has been increasingly implicated in ASD. In this review, we will utilize the foundational, theoretical contributions of the late neuroscientist Masao Ito to establish an internal model framework for the cerebellar contribution to ASD-relevant behavioral predictions and adaptations. Additionally, we will also explore and then apply his key experimental contributions towards an improved, mechanistic understanding of the contribution of cerebellar dysfunction to ASD.

Brain and Language Associations in Autism Spectrum Disorder: A Scoping Review

Examining brain and behaviour associations for language in autism spectrum disorder (ASD) may bring us closer to identifying neural profiles that are unique to a subgroup of individuals with ASD identified as language impaired (e.g. ASD LI+). We conducted a scoping review to examine brain regions that are associated with language performance in ASD. Further, we examined methodological differences across studies in how language ability was characterized and what neuroimaging methods were used to explore brain regions. Seventeen studies met inclusion criteria. Brain regions specific to ASD LI+ groups were found, however inconsistencies in brain and language associations were evident across study findings. Participant age, age-appropriate language scores, and neuroimaging methods likely contributed to differences in associations found.

Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored.

METHODS

An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD.

RESULTS

Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations.

CONCLUSION

These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.

Influence of Primary Auditory Cortex in the Characterization of Autism Spectrum in Young Adults using Brain Connectivity Parameters and Deep Belief Networks: An fMRI Study

BACKGROUND

The spectrum of autism encompasses High Functioning Autism (HFA) and Low Functioning Autism (LFA). Brain mapping studies have revealed that autism individuals have overlaps in brain behavioural characteristics. Generally, high functioning individuals are known to exhibit higher intelligence and better language processing abilities. However, specific mechanisms associated with their functional capabilities are still under research.

OBJECTIVE

This work addresses the overlapping phenomenon present in autism spectrum through functional connectivity patterns along with brain connectivity parameters and distinguishes the classes using deep belief networks.

METHODS

The task-based functional Magnetic Resonance Images (fMRI) of both high and low functioning autistic groups were acquired from ABIDE database, for 58 low functioning against 43 high functioning individuals while they were involved in a defined language processing task. The language processing regions of the brain, along with Default Mode Network (DMN) have been considered for the analysis. The functional connectivity maps have been plotted through graph theory procedures. Brain connectivity parameters such as Granger Causality (GC) and Phase Slope Index (PSI) have been calculated for the individual groups. These parameters have been fed to Deep Belief Networks (DBN) to classify the subjects under consideration as either LFA or HFA.

RESULTS

Results showed increased functional connectivity in high functioning subjects. It was found that the additional interaction of the Primary Auditory Cortex lying in the temporal lobe, with other regions of interest complimented their enhanced connectivity. Results were validated using DBN measuring the classification accuracy of 85.85% for high functioning and 81.71% for the low functioning group.

CONCLUSION

Since it is known that autism involves enhanced, but imbalanced components of intelligence, the reason behind the supremacy of high functioning group in language processing and region responsible for enhanced connectivity has been recognized. Therefore, this work that suggests the effect of Primary Auditory Cortex in characterizing the dominance of language processing in high functioning young adults seems to be highly significant in discriminating different groups in autism spectrum.

Consensus Paper: Cerebellum and Social Cognition

The traditional view on the cerebellum is that it controls motor behavior. Although recent work has revealed that the cerebellum supports also nonmotor functions such as cognition and affect, only during the last 5 years it has become evident that the cerebellum also plays an important social role. This role is evident in social cognition based on interpreting goal-directed actions through the movements of individuals (social "mirroring") which is very close to its original role in motor learning, as well as in social understanding of other individuals' mental state, such as their intentions, beliefs, past behaviors, future aspirations, and personality traits (social "mentalizing"). Most of this mentalizing role is supported by the posterior cerebellum (e.g., Crus I and II). The most dominant hypothesis is that the cerebellum assists in learning and understanding social action sequences, and so facilitates social cognition by supporting optimal predictions about imminent or future social interaction and cooperation. This consensus paper brings together experts from different fields to discuss recent efforts in understanding the role of the cerebellum in social cognition, and the understanding of social behaviors and mental states by others, its effect on clinical impairments such as cerebellar ataxia and autism spectrum disorder, and how the cerebellum can become a potential target for noninvasive brain stimulation as a therapeutic intervention. We report on the most recent empirical findings and techniques for understanding and manipulating cerebellar circuits in humans. Cerebellar circuitry appears now as a key structure to elucidate social interactions.

Vestibular Functioning in Children with Neurodevelopmental Disorders Using the Functional Head Impulse Test

Several studies in children with neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASDs), reading impairment, or attention deficit/hyperactive disorder (ADHD) pointed toward a potential dysfunction of the vestibular system, specifically in its complex relationship with the cerebellum. The aim of the present study was to test the functional vestibulo-ocular reflex (VOR) responses in children with NDDs to measure functional performance of the vestibular system. The VOR is specifically involved in this stabilization of the image on the retina during rapid movements of the head. To perform this study, four groups of children with ASD, ADHD, reading impairment, and with neurotypical development (TD) were enrolled (n = 80). We performed the functional head impulse test (fHIT), which measured the percentage of correct responses by asking the child to identify an optotype briefly presented during passive head impulse in each direction of each semicircular canal plane. We observed significantly lower correct answers in children with NDDs compared with those with TD (p < 0.0001). Surprisingly, there was no significant difference between the three groups of children with NDDs. Our study fostered preliminary evidence suggesting altered efficiency of vestibular system in children with NDDs. VOR abnormalities estimated using the fHIT could be used as a proxy of NDD impairments in children, and represent a potential biomarker.

Small Semantic Networks in Individuals with Autism Spectrum Disorder Without Intellectual Impairment: A Verbal Fluency Approach

Individuals with Autism Spectrum Disorder (ASD) experience a variety of symptoms sometimes including atypicalities in language use. The study explored differences in semantic network organisation of adults with ASD without intellectual impairment. We assessed clusters and switches in verbal fluency tasks ('animals', 'human feature', 'verbs', 'r-words') via curve fitting in combination with corpus-driven analysis of semantic relatedness and evaluated socio-emotional and motor action related content. Compared to participants without ASD (n = 39), participants with ASD (n = 32) tended to produce smaller clusters, longer switches, and fewer words in semantic conditions (no p values survived Bonferroni-correction), whereas relatedness and content were similar. In ASD, semantic networks underlying cluster formation appeared comparably small without affecting strength of associations or content.

Age of Speech Onset in Autism Relates to Structural Connectivity in the Language Network

Speech onset delays (SOD) and language atypicalities are central aspects of the autism spectrum (AS), despite not being included in the categorical diagnosis of AS. Previous studies separating participants according to speech onset history have shown distinct patterns of brain organization and activation in perceptual tasks. One major white matter tract, the arcuate fasciculus (AF), connects the posterior temporal and left frontal language regions. Here, we used anatomical brain imaging to investigate the properties of the AF in adolescent and adult autistic individuals with typical levels of intelligence who differed by age of speech onset. The left AF of the AS group showed a significantly smaller volume than that of the nonautistic group. Such a reduction in volume was only present in the younger group. This result was driven by the autistic group without SOD (SOD−), despite their typical age of speech onset. The autistic group with SOD (SOD+) showed a more typical AF as adults relative to matched controls. This suggests that, along with multiple studies in AS-SOD+ individuals, atypical brain reorganization is observable in the 2 major AS subgroups and that such reorganization applies mostly to the language regions in SOD− and perceptual regions in SOD+ individuals.

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.

Semantic Processing in Autism Spectrum Disorders Is Associated With the Timing of Language Acquisition: A Magnetoencephalographic Study

Individuals with autism show difficulties in using sentence context to identify the correct meaning of ambiguous words, such as homonyms. In this study, the brain basis of sentence context effects on word understanding during reading was examined in autism spectrum disorder (ASD) and typical development (TD) using magnetoencephalography. The correlates of a history of developmental language delay in ASD were also investigated. Event related field responses at early (150 ms after the onset of a final word) and N400 latencies are reported for three different types of sentence final words: dominant homonyms, subordinate homonyms, and unambiguous words. Clear evidence for semantic access was found at both early and conventional N400 latencies in both TD participants and individuals with ASD with no history of language delay. By contrast, modulation of evoked activity related to semantic access was weak and not significant at early latencies in individuals with ASD with a history of language delay. The reduced sensitivity to semantic context in individuals with ASD and language delay was accompanied by strong right hemisphere lateralization at early and N400 latencies; such strong activity was not observed in TD individuals and individuals with ASD without a history of language delay at either latency. These results provide new evidence and support for differential neural mechanisms underlying semantic processing in ASD, and indicate that delayed language acquisition in ASD is associated with different lateralization and processing of language.

Altered brain language network in idiopathic peripheral facial paralysis patients with dysarthria

Background

Dysarthria is one of the common symptoms of facial paralysis (FP). This study aimed to investigate functional alterations in the brain language network in early idiopathic peripheral FP patients with dysarthria using resting-state functional magnetic resonance imaging (fMRI).

Methods

A total of 45 cases of FP (left 22, right 23) and 34 cases of healthy control (HC) were recruited into this study. The data of patients with left-side FP and matched controls (17 cases) were flipped from left to right, and the brain regions were defined as ipsilateral and contralateral regions. The FC of 16 ROIs in classical language centers and regions that may be involved in language function were calculated. After identifying the differences of FC between the two groups, the correlation analysis between altered FC and TFGS score of oral muscle movement in FP group were analyzed.

Results

The FC between bilateral language regions has a significantly decreased trend in FP group compared with HC group (P<0.05). The ipsilateral inferior frontal gyrus, superior temporal gyrus, and middle temporal gyrus exhibited significantly decreased FC with multiple brain regions. In addition, we found that thalamus and cerebellum also with a significant alteration in FC in FP patients indicating that these two regions may also be involved in the mechanism of dysarthria in FP. The correlation analysis results indicated that the decrease of FC was positively correlated with the severity of oral paralysis.

Conclusions

Idiopathic peripheral FP with dysarthria induces several FC alterations in the brain language network. The severity of oral paralysis is associated with these functional alterations.

Targeting Gamma-Related Pathophysiology in Autism Spectrum Disorder Using Transcranial Electrical Stimulation: Opportunities and Challenges

A range of scalp electroencephalogram (EEG) abnormalities correlates with the core symptoms of autism spectrum disorder (ASD). Among these are alterations of brain oscillations in the gamma-frequency EEG band in adults and children with ASD, whose origin has been linked to dysfunctions of inhibitory interneuron signaling. While therapeutic interventions aimed to modulate gamma oscillations are being tested for neuropsychiatric disorders such as schizophrenia, Alzheimer's disease, and frontotemporal dementia, the prospects for therapeutic gamma modulation in ASD have not been extensively studied. Accordingly, we discuss gamma-related alterations in the setting of ASD pathophysiology, as well as potential interventions that can enhance gamma oscillations in patients with ASD. Ultimately, we argue that transcranial electrical stimulation modalities capable of entraining gamma oscillations, and thereby potentially modulating inhibitory interneuron circuitry, are promising methods to study and mitigate gamma alterations in ASD. Autism Res 2020, 13: 1051-1071. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Brain functions are mediated by various oscillatory waves of neuronal activity, ranging in amplitude and frequency. In certain neuropsychiatric disorders, such as schizophrenia and Alzheimer's disease, reduced high-frequency oscillations in the "gamma" band have been observed, and therapeutic interventions to enhance such activity are being explored. Here, we review and comment on evidence of reduced gamma activity in ASD, arguing that modalities used in other disorders may benefit individuals with ASD as well.

EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia

The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5-1[Formula: see text]Hz), syllabic (4-8[Formula: see text]Hz) or the phoneme (12-40[Formula: see text]Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children's performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ([Formula: see text]) with children's performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca's area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.

Intrinsic Functional Connectivity of Dentate Nuclei in Autism Spectrum Disorder

Cerebellar abnormalities are commonly reported in autism spectrum disorder (ASD). Dentate nuclei (DNs) are key structures in the anatomical circuits linking the cerebellum to the extracerebellum. Previous resting-state functional connectivity (RsFc) analyses reported DN abnormalities in high-functioning ASD (HF-ASD). This study examined the RsFc of the DN in young adults with HF-ASD compared with healthy controls (HCs) with the aim to expand upon previous findings of DNs in a dataset using advanced, imaging acquisition methods that optimize spatiotemporal resolution and statistical power. Additional seed-to-voxel analyses were carried out using motor and nonmotor DN coordinates reported in previous studies as seeds. We report abnormal dentato-cerebral and dentato-cerebellar functional connectivity in ASD. Our results expand and, in part, replicate previous descriptions of DN RsFc abnormalities in this disorder and reveal correlations between DN-cerebral RsFc and ASD symptom severity.

Cerebellar Modulation of Mesolimbic Dopamine Transmission Is Functionally Asymmetrical

Cerebral and cerebellar hemispheres are known to be asymmetrical in structure and function, and previous literature supports that asymmetry extends to the neural dopamine systems. Using in vivo fixed potential amperometry with carbon fiber microelectrodes in anesthetized mice, the current study assessed hemispheric lateralization of stimulation-evoked dopamine in the nucleus accumbens (NAc) and the influence of the cerebellum in regulating this reward-associated pathway. Our results suggest that cerebellar output can modulate mesolimbic dopamine transmission, and this modulation contributes to asymmetrically lateralized dopamine release. Dopamine release did not differ between hemispheres when evoked by medial forebrain bundle (MFB) stimulation; however, dopamine release was significantly greater in the right NAc relative to the left when evoked by electrical stimulation of the cerebellar dentate nucleus (DN). Furthermore, cross-hemispheric talk between the left and right cerebellar DN does not seem to influence mesolimbic release given that lidocaine infused into the DN opposite to the stimulated DN did not alter release. These studies may provide a neurochemical mechanism for studies identifying the cerebellum as a relevant node for reward, motivational behavior, saliency, and inhibitory control. An increased understanding of the lateralization of dopaminergic systems may reveal novel targets for pharmacological interventions in neuropathology of the cerebellum and extending projections.