Functional Connectivity Between the Temporoparietal Cortex and Cerebellum in Autism Spectrum Disorder

A Functional Atlas of the Cerebellum Based on NeuroSynth Task Coordinates

Although the human cerebellum has a surface that is about 80% of that of the cerebral cortex and has about four times as many neurons, its functional organization is still very much uncharted. Despite recent attempts to provide resting-state and task-based parcellations of the cerebellum, these two approaches lead to large discrepancies. This article describes a comprehensive task-based functional parcellation of the human cerebellum based on a large-scale functional database, NeuroSynth, involving an unprecedented diversity of tasks, which were reliably associated with ontological key terms referring to psychological functions. Involving over 44,500 participants from this database, we present a parcellation that exhibits replicability with earlier resting-state parcellations across cerebellar and neocortical structures. The functional parcellation of the cerebellum confirms the major networks revealed in prior work, including sensorimotor, directed (dorsal) attention, divided (ventral) attention, executive control, mentalizing (default mode) networks, tiny patches of a limbic network, and also a unilateral language network (but not the visual network), and the association of these networks with underlying ontological key terms confirms their major functionality. The networks are revealed at locations that are roughly similar to prior resting-state cerebellar parcellations, although they are less symmetric and more fragmented across the two hemispheres. This functional parcellation of the human cerebellum and associated key terms can provide a useful guide in designing studies to test specific functional hypotheses and provide a reference for interpreting the results.

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.

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.

Effective cerebello-cerebral connectivity during implicit and explicit social belief sequence learning using dynamic causal modeling

To study social sequence learning, earlier functional magnetic resonance imaging (fMRI) studies investigated the neural correlates of a novel Belief Serial Reaction Time task in which participants learned sequences of beliefs held by protagonists. The results demonstrated the involvement of the mentalizing network in the posterior cerebellum and cerebral areas (e.g. temporoparietal junction, precuneus and temporal pole) during implicit and explicit social sequence learning. However, little is known about the neural functional interaction between these areas during this task. Dynamic causal modeling analyses for both implicit and explicit belief sequence learning revealed that the posterior cerebellar Crus I & II were effectively connected to cerebral mentalizing areas, especially the bilateral temporoparietal junction, via closed loops (i.e. bidirectional functional connections that initiate and terminate at the same cerebellar and cerebral areas). There were more closed loops during implicit than explicit learning, which may indicate that the posterior cerebellum may be more involved in implicitly learning sequential social information. Our analysis supports the general view that the posterior cerebellum receives incoming signals from critical mentalizing areas in the cerebrum to identify sequences of social actions and then sends signals back to the same cortical mentalizing areas to better prepare for others' social actions and one's responses to it.

A narrative sequencing and mentalizing training for adults with autism: A pilot study

Adults diagnosed with autism experience difficulties with understanding the mental states of others, or themselves (mentalizing) and with adequately sequencing personal stories (narrative coherence). Given that the posterior cerebellum is implicated in both skills, as well as in the etiology of autism, we developed a narrative sequencing and mentalizing training for autistic adults. Participants with an official autism diagnosis were randomly assigned to a Training group (n = 17) or a waiting-list Control group (n = 15). The Training group took part in six weekly sessions in groups of three participants lasting each about 60 min. During training, participants had to (re)tell stories from the perspective of the original storyteller and answer questions that required mentalizing. We found significant improvements in mentalizing about others’ beliefs and in narrative coherence for the Training group compared to the Control group immediately after the training compared to before the training. Almost all participants from the Training group expressed beneficial effects of the training on their mood and half of the participants reported positive effects on their self-confidence in social situations. All participants recommended the current training to others. Results are discussed in light of cerebellar theories on sequencing of social actions during mentalizing. Further improvements to the program are suggested. Our results highlight the potential clinical utility of adopting a neuroscience-informed approach to developing novel therapeutic interventions for autistic populations.

Cerebellum and Emotion in Social Behavior

Accumulating evidence suggests that the cerebellum plays a crucial role not only in the motor and cognitive domains but also in emotions and social behavior. In the present chapter, after a general introduction on the significance of the emotional components of social behavior, we describe recent efforts to understand the contributions of the cerebellum in social cognition focusing on the emotional and affective aspects. Specifically, starting from the description of the cerebello-cortical networks subtending the social-affective domains, we illustrate the most recent findings on the social cerebellum and the possible functional mechanisms by which the cerebellum modulate social-affective behavior. Finally, we discuss the possible consequences of cerebellar dysfunction in the social-affective domain, focusing on those neurological and psychopathological conditions in which emotional and social behavior difficulties have been described as being associated with cerebellar structural or functional alterations.

Utilising TMS-EEG to Assess the Response to Cerebellar-Brain Inhibition

Cerebellar-brain inhibition (CBI) is a transcranial magnetic stimulation (TMS) paradigm indexing excitability of cerebellar projections to motor cortex (M1). Stimulation involved with CBI is often considered to be uncomfortable, and alternative ways to index connectivity between cerebellum and the cortex would be valuable. We therefore sought to assess the utility of electroencephalography in conjunction with TMS (combined TMS-EEG) to record the response to CBI. A total of 33 volunteers (25.7 ± 4.9 years, 20 females) participated across three experiments. These investigated EEG responses to CBI induced with a figure-of-eight (F8; experiment 1) or double cone (DC; experiment 2) conditioning coil over cerebellum, in addition to multisensory sham stimulation (experiment 3). Both F8 and DC coils suppressed early TMS-evoked EEG potentials (TEPs) produced by TMS to M1 (P < 0.05). Furthermore, the TEP produced by CBI stimulation was related to the motor inhibitory response to CBI recorded in a hand muscle (P < 0.05), but only when using the DC coil. Multisensory sham stimulation failed to modify the M1 TEP. Cerebellar conditioning produced changes in the M1 TEP that were not apparent following sham stimulation, and that were related to the motor inhibitory effects of CBI. Our findings therefore suggest that it is possible to index the response to CBI using TMS-EEG. In addition, while both F8 and DC coils appear to recruit cerebellar projections, the nature of these may be different.

Globally elevated excitation-inhibition ratio in children with autism spectrum disorder and below-average intelligence

Background

Altered neuronal excitation–inhibition (E–I) balance is strongly implicated in ASD. However, it is not known whether the direction and degree of changes in the E–I ratio in individuals with ASD correlates with intellectual disability often associated with this developmental disorder. The spectral slope of the aperiodic 1/f activity reflects the E–I balance at the scale of large neuronal populations and may uncover its putative alternations in individuals with ASD with and without intellectual disability.

Methods

Herein, we used magnetoencephalography (MEG) to test whether the 1/f slope would differentiate ASD children with average and below–average (< 85) IQ. MEG was recorded at rest with eyes open/closed in 49 boys with ASD aged 6–15 years with IQ ranging from 54 to 128, and in 49 age-matched typically developing (TD) boys. The cortical source activity was estimated using the beamformer approach and individual brain models. We then extracted the 1/f slope by fitting a linear function to the log–log-scale power spectra in the high-frequency range.

Results

The global 1/f slope averaged over all cortical sources demonstrated high rank-order stability between the two conditions. Consistent with previous research, it was steeper in the eyes-closed than in the eyes-open condition and flattened with age. Regardless of condition, children with ASD and below-average IQ had flatter slopes than either TD or ASD children with average or above-average IQ. These group differences could not be explained by differences in signal-to-noise ratio or periodic (alpha and beta) activity.

Limitations

Further research is needed to find out whether the observed changes in E–I ratios are characteristic of children with below-average IQ of other diagnostic groups.

Conclusions

The atypically flattened spectral slope of aperiodic activity in children with ASD and below-average IQ suggests a shift of the global E–I balance toward hyper-excitation. The spectral slope can provide an accessible noninvasive biomarker of the E–I ratio for making objective judgments about treatment effectiveness in people with ASD and comorbid intellectual disability.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-022-00498-2.

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.

Functional and Neuropathological Evidence for a Role of the Brainstem in Autism

The brainstem includes many nuclei and fiber tracts that mediate a wide range of functions. Data from two parallel approaches to the study of autistic spectrum disorder (ASD) implicate many brainstem structures. The first approach is to identify the functions affected in ASD and then trace the neural systems mediating those functions. While not included as core symptoms, three areas of function are frequently impaired in ASD: (1) Motor control both of the limbs and body and the control of eye movements; (2) Sensory information processing in vestibular and auditory systems; (3) Control of affect. There are critical brainstem nuclei mediating each of those functions. There are many nuclei critical for eye movement control including the superior colliculus. Vestibular information is first processed in the four nuclei of the vestibular nuclear complex. Auditory information is relayed to the dorsal and ventral cochlear nuclei and subsequently processed in multiple other brainstem nuclei. Critical structures in affect regulation are the brainstem sources of serotonin and norepinephrine, the raphe nuclei and the locus ceruleus. The second approach is the analysis of abnormalities from direct study of ASD brains. The structure most commonly identified as abnormal in neuropathological studies is the cerebellum. It is classically a major component of the motor system, critical for coordination. It has also been implicated in cognitive and language functions, among the core symptoms of ASD. This structure works very closely with the cerebral cortex; the cortex and the cerebellum show parallel enlargement over evolution. The cerebellum receives input from cortex via relays in the pontine nuclei. In addition, climbing fiber input to cerebellum comes from the inferior olive of the medulla. Mossy fiber input comes from the arcuate nucleus of the medulla as well as the pontine nuclei. The cerebellum projects to several brainstem nuclei including the vestibular nuclear complex and the red nucleus. There are thus multiple brainstem nuclei distributed at all levels of the brainstem, medulla, pons, and midbrain, that participate in functions affected in ASD. There is direct evidence that the cerebellum may be abnormal in ASD. The evidence strongly indicates that analysis of these structures could add to our understanding of the neural basis of ASD.

Three-Dimensional Heterogeneity of Cerebellar Interposed Nucleus-Recipient Zones in the Thalamic Nuclei

The cerebellum is conceptualized as a processor of complex movements and is also endowed with roles in cognitive and emotional behaviors. Although the axons of deep cerebellar nuclei are known to project to primary thalamic nuclei, macroscopic investigation of the characteristics of these projections, such as the spatial distribution of recipient zones, is lacking. Here, we studied the output of the cerebellar interposed nucleus (IpN) to the ventrolateral (VL) and centrolateral (CL) thalamic nuclei using electrophysiological recording in vivo and trans-synaptic viral tracing. We found that IpN stimulation induced mono-synaptic evoked potentials (EPs) in the VL but not the CL region. Furthermore, both the EPs induced by the IpN and the innervation of IpN projections displayed substantial heterogeneity across the VL region in three-dimensional space. These findings indicate that the recipient zones of IpN inputs vary between and within thalamic nuclei and may differentially control thalamo-cortical networks.

What Is Social about Autism? The Role of Allostasis-Driven Learning

Scientific research on neuro-cognitive mechanisms of autism often focuses on circuits that support social functioning. However, autism is a heterogeneous developmental variation in multiple domains, including social communication, but also language, cognition, and sensory-motor control. This suggests that the underlying mechanisms of autism share a domain-general foundation that impacts all of these processes. In this Perspective Review, we propose that autism is not a social deficit that results from an atypical "social brain". Instead, typical social development relies on learning. In social animals, infants depend on their caregivers for survival, which makes social information vitally salient. The infant must learn to socially interact in order to survive and develop, and the most prominent learning in early life is crafted by social interactions. Therefore, the most prominent outcome of a learning variation is atypical social development. To support the hypothesis that autism results from a variation in learning, we first review evidence from neuroscience and developmental science, demonstrating that typical social development depends on two domain-general processes that determine learning: (a) motivation, guided by allostatic regulation of the internal milieu; and (b) multi-modal associations, determined by the statistical regularities of the external milieu. These two processes are basic ingredients of typical development because they determine allostasis-driven learning of the social environment. We then review evidence showing that allostasis and learning are affected among individuals with autism, both neurally and behaviorally. We conclude by proposing a novel domain-general framework that emphasizes allostasis-driven learning as a key process underlying autism. Guided by allostasis, humans learn to become social, therefore, the atypical social profile seen in autism can reflect a domain-general variation in allostasis-driven learning. This domain-general view raises novel research questions in both basic and clinical research and points to targets for clinical intervention that can lower the age of diagnosis and improve the well-being of individuals with autism.

Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.

Multivariate Neural Connectivity Patterns in Early Infancy Predict Later Autism Symptoms

BACKGROUND

Functional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Functional disruption during infancy could provide earlier markers of ASD, thus providing a crucial opportunity to improve developmental outcomes. Using a whole-brain multivariate approach, we asked whether electroencephalography measures of neural connectivity at 3 months of age predict autism symptoms at 18 months.

METHODS

Spontaneous electroencephalography data were collected from 65 infants with and without familial risk for ASD at 3 months of age. Neural connectivity patterns were quantified using phase coherence in the alpha range (6-12 Hz). Support vector regression analysis was used to predict ASD symptoms at age 18 months, with ASD symptoms quantified by the Toddler Module of the Autism Diagnostic Observation Schedule, Second Edition.

RESULTS

Autism Diagnostic Observation Schedule scores predicted by support vector regression algorithms trained on 3-month electroencephalography data correlated highly with Autism Diagnostic Observation Schedule scores measured at 18 months (r = .76, p = .02, root-mean-square error = 2.38). Specifically, lower frontal connectivity and higher right temporoparietal connectivity at 3 months predicted higher ASD symptoms at 18 months. The support vector regression model did not predict cognitive abilities at 18 months (r = .15, p = .36), suggesting specificity of these brain patterns to ASD.

CONCLUSIONS

Using a data-driven, unbiased analytic approach, neural connectivity across frontal and temporoparietal regions at 3 months predicted ASD symptoms at 18 months. Identifying early neural differences that precede an ASD diagnosis could promote closer monitoring of infants who show signs of neural risk and provide a crucial opportunity to mediate outcomes through early intervention.

Altered Inferior Parietal Functional Connectivity is Correlated with Praxis and Social Skill Performance in Children with Autism Spectrum Disorder

Children with autism spectrum disorder (ASD) have difficulties perceiving and producing skilled gestures, or praxis. The inferior parietal lobule (IPL) is crucial to praxis acquisition and expression, yet how IPL connectivity contributes to autism-associated impairments in praxis as well as social-communicative skill remains unclear. Using resting-state functional magnetic resonance imaging, we applied independent component analysis to test how IPL connectivity relates to praxis and social-communicative skills in children with and without ASD. Across all children (with/without ASD), praxis positively correlated with connectivity of left posterior-IPL with the left dorsal premotor cortex and with the bilateral posterior/medial parietal cortex. Praxis also correlated with connectivity of right central-IPL connectivity with the left intraparietal sulcus and medial parietal lobe. Further, in children with ASD, poorer praxis and social-communicative skills both correlated with weaker right central-IPL connectivity with the left cerebellum, posterior cingulate, and right dorsal premotor cortex. Our findings suggest that IPL connectivity is linked to praxis development, that contributions arise bilaterally, and that right IPL connectivity is associated with impaired praxis and social-communicative skills in autism. The findings underscore the potential impact of IPL connectivity and impaired skill acquisition on the development of a range of social-communicative and motor functions during childhood, including autism-associated impairments.

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.

Cerebral Cortical Activity Following Non-invasive Cerebellar Stimulation-a Systematic Review of Combined TMS and EEG Studies

The cerebellum sends dense projections to both motor and non-motor regions of the cerebral cortex via the cerebellarthalamocortical tract. The integrity of this tract is crucial for healthy motor and cognitive function. This systematic review examines research using transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) to the cerebellum with combined cortical electroencephalography (EEG) to explore the temporal features of cerebellar-cortical connectivity. A detailed discussion of the outcomes and limitations of the studies meeting review criteria is presented. Databases were searched between 1 December 2017 and 6 December 2017, with Scopus alerts current as of 23 July 2019. Of the 407 studies initially identified, 10 met review criteria. Findings suggested that cerebellar-cortical assessment is suited to combined TMS and EEG, although work is required to ensure experimental procedures are optimal for eliciting a reliable cerebellar response from stimulation. A distinct variation in methodologies and outcome measures employed across studies, and small sample sizes limited the conclusions that could be drawn regarding the electrophysiological signatures of cerebellar-cortical communication. This review highlights the need for stringent protocols and methodologies for cerebellar-cortical assessments via combined TMS and EEG. With these in place, combined TMS and EEG will provide a valuable means for exploring cerebellar connectivity with a wide range of cortical sites. Assessments have the potential to aid in the understanding of motor and cognitive function in both healthy and clinical groups, and provide insights into long-range neural communication generally.

Increased Accuracy of Emotion Recognition in Individuals with Autism-Like Traits after Five Days of Magnetic Stimulations

Individuals with autism-like traits (ALT) belong to a subclinical group with similar social deficits as autism spectrum disorders (ASD). Their main social deficits include atypical eye contact and difficulty in understanding facial expressions, both of which are associated with an abnormality of the right posterior superior temporal sulcus (rpSTS). It is still undetermined whether it is possible to improve the social function of ALT individuals through noninvasive neural modulation. To this end, we randomly assigned ALT individuals into the real (n = 16) and sham (n = 16) stimulation groups. All subjects received five consecutive days of intermittent theta burst stimulation (iTBS) on the rpSTS. Eye tracking data and functional magnetic resonance imaging (fMRI) data were acquired on the first and sixth days. The real group showed significant improvement in emotion recognition accuracy after iTBS, but the change was not significantly larger than that in the sham group. Resting-state functional connectivity (rsFC) between the rpSTS and the left cerebellum significantly decreased in the real group than the sham group after iTBS. At baseline, rsFC in the left cerebellum was negatively correlated with emotion recognition accuracy. Our findings indicated that iTBS of the rpSTS could improve emotion perception of ALT individuals by modulating associated neural networks. This stimulation protocol could be a vital therapeutic strategy for the treatment of ASD.

Common variant of CNTNAP2 gene modulate the social performances and functional connectivity of posterior right temporoparietal junction

Social deficits are features of autism and highly heritable traits. A common variant in autism-related CNTNAP2 gene, rs2710102, has been linked with social performance, but the neural substrates are largely unknown. We investigated variations in social performance and functional connectivity (static and dynamic) in the subregions of right temporoparietal junction (RTPJ), a key node of brain social network, using resting-state magnetic resonance imaging (n = 399) by genotype at rs2710102 in healthy volunteers. Social performance was evaluated using the social domain of the Autism-Spectrum Quotient (AQ-social; n = 641) and fixation time on eye areas during an eye-tracking task (n = 32). According to previous evidence that the A-allele is the risk allele for social dysfunction, we classified participants into GG and A-allele carriers (AA/AG) groups. The A-allele carriers showed poor social performance (high AQ-social and short fixation time on eye areas) compared with the GG carriers. In the A-allele carriers, decreased stationary functional connectivity between the orbitofrontal cortex and posterior RTPJ (pRTPJ), and decreased dynamic functional connectivity (dFC) between the medial prefrontal cortex (mPFC) and pRTPJ were observed. The fixation time at eye areas positively were correlated with the pRTPJ-mPFC dFC. These findings provided insight for genetic effect on social behavior and its potential neural substrate.

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.

Altered Functional Connectivity in Children With Low-Function Autism Spectrum Disorders

Neuroimaging studies have shown that autism spectrum disorders (ASDs) may be associated with abnormalities in brain structures and functions at rest as well as during cognitive tasks. However, it remains unclear if functional connectivity (FC) of all brain neural networks is also changed in these subjects. In this study, we acquired functional magnetic resonance imaging scans from 93 children with ASD and 79 matched healthy subjects. Group independent component analysis was executed for all of the participants to estimate FC. One-sample t-tests were then performed to obtain the networks for each group. Group differences in the different brain networks were tested using two-sample t-tests. Finally, relationships between abnormal FC and clinical variables were investigated with Pearson’s correlation analysis. The results from one-sample t-tests revealed nine networks with similar spatial patterns in these two groups. When compared with the controls, children with ASD showed increased connectivity in the right dorsolateral superior frontal gyrus and left middle frontal gyrus (MFG) within the occipital pole network. Children with ASD also showed decreased connectivity in the left gyrus rectus, left middle occipital gyrus, right angular gyrus, right MFG and right inferior frontal gyrus (IFG), orbital part within the lateral visual network (LVN), the left IFG, right precuneus, and right angular gyrus within the left frontoparietal (cognition) network. Furthermore, the mean FC values within the LVN showed significant positive correlations with total score of the Childhood Autism Rating Scale. Our findings indicate that abnormal FC extensively exists within some networks in children with ASD. This abnormal FC may constitute a biomarker of ASD. Our results are an important contribution to the study of neuropathophysiological mechanisms in children with ASD.

Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi-view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint ( l_2,1 -norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group-sparse network at local level. We then linearly fuse the selected features from each individual network via a multi-kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy.

Integration and Segregation of Default Mode Network Resting-State Functional Connectivity in Transition-Age Males with High-Functioning Autism Spectrum Disorder: A Proof-of-Concept Study

The aim of this study is to assess the resting-state functional connectivity (RsFc) profile of the default mode network (DMN) in transition-age males with autism spectrum disorder (ASD). Resting-state blood oxygen level-dependent functional magnetic resonance imaging data were acquired from adolescent and young adult males with high-functioning ASD (n = 15) and from age-, sex-, and intelligence quotient-matched healthy controls (HCs; n = 16). The DMN was examined by assessing the positive and negative RsFc correlations of an average of the literature-based conceptualized major DMN nodes (medial prefrontal cortex [mPFC], posterior cingulate cortex, bilateral angular, and inferior temporal gyrus regions). RsFc data analysis was performed using a seed-driven approach. ASD was characterized by an altered pattern of RsFc in the DMN. The ASD group exhibited a weaker pattern of intra- and extra-DMN-positive and -negative RsFc correlations, respectively. In ASD, the strength of intra-DMN coupling was significantly reduced with the mPFC and the bilateral angular gyrus regions. In addition, the polarity of the extra-DMN correlation with the right hemispheric task-positive regions of fusiform gyrus and supramarginal gyrus was reversed from typically negative to positive in the ASD group. A wide variability was observed in the presentation of the RsFc profile of the DMN in both HC and ASD groups that revealed a distinct pattern of subgrouping using pattern recognition analyses. These findings imply that the functional architecture profile of the DMN is altered in ASD with weaker than expected integration and segregation of DMN RsFc. Future studies with larger sample sizes are warranted.

Wavelet coherence-based classifier: A resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism

BACKGROUND AND OBJECTIVE

The autism spectrum disorder (ASD) diagnosis requires a long and elaborate procedure. Due to the lack of a biomarker, the procedure is subjective and is restricted to evaluating behavior. Several attempts to use functional MRI as an assisting tool (as classifier) have been reported, but they barely reach an accuracy of 80%, and have not usually been replicated or validated with independent datasets. Those attempts have used functional connectivity and structural measurements. There is, nevertheless, evidence that not the topology of networks, but their temporal dynamics is a key feature in ASD. We therefore propose a novel MRI-based ASD biomarker by analyzing temporal brain dynamics in resting-state fMRI.

METHODS

We investigate resting-state fMRI data from 2 independent datasets of adolescents: our in-house data (12 ADS, 12 controls), and the Leuven dataset (12 ASD, 18 controls, from Leuven university). Using independent component analysis we obtain relevant socio-executive resting-state networks (RSNs) and their associated time series. Upon these time series we extract wavelet coherence maps. Using these maps, we calculate our dynamics metric: time of in-phase coherence. This novel metric is then used to train classifiers for autism diagnosis. Leave-one-out cross validation is applied for performance evaluation. To assess inter-site robustness, we also train our classifiers on the in-house data, and test them on the Leuven dataset.

RESULTS

We distinguished ASD from non-ASD adolescents at 86.7% accuracy (91.7% sensitivity, 83.3% specificity). In the second experiment, using Leuven dataset, we also obtained the classification performance at 86.7% (83.3% sensitivity, and 88.9% specificity). Finally we classified the Leuven dataset, with classifiers trained with our in-house data, resulting in 80% accuracy (100% sensitivity, 66.7% specificity).

CONCLUSIONS

This study shows that change in the coherence of temporal neurodynamics is a biomarker of ASD, and wavelet coherence-based classifiers lead to robust and replicable results and could be used as an objective diagnostic tool for ASD.

Cerebellar anatomical alterations and attention to eyes in autism

The cerebellum is implicated in social cognition and is likely to be involved in the pathophysiology of autism spectrum disorder (ASD). The goal of our study was to explore cerebellar morphology in adults with ASD and its relationship to eye contact, as measured by fixation time allocated on the eye region using an eye-tracking device. Two-hundred ninety-four subjects with ASD and controls were included in our study and underwent a structural magnetic resonance imaging scan. Global segmentation and cortical parcellation of the cerebellum were performed. A sub-sample of 59 subjects underwent an eye tracking protocol in order to measure the fixation time allocated to the eye region. We did not observe any difference in global cerebellar volumes between ASD patients and controls; however, regional analyses found a decrease of the volume of the right anterior cerebellum in subjects with ASD compared to controls. There were significant correlations between fixation time on eyes and the volumes of the vermis and Crus I. Our results suggest that cerebellar morphology may be related to eye avoidance and reduced social attention. Eye tracking may be a promising neuro-anatomically based stratifying biomarker of ASD.

The Cerebellum: Adaptive Prediction for Movement and Cognition

Over the past 30 years, cumulative evidence has indicated that cerebellar function extends beyond sensorimotor control. This view has emerged from studies of neuroanatomy, neuroimaging, neuropsychology, and brain stimulation, with the results implicating the cerebellum in domains as diverse as attention, language, executive function, and social cognition. Although the literature provides sophisticated models of how the cerebellum helps refine movements, it remains unclear how the core mechanisms of these models can be applied when considering a broader conceptualization of cerebellar function. In light of recent multidisciplinary findings, we examine how two key concepts that have been suggested as general computational principles of cerebellar function- prediction and error-based learning- might be relevant in the operation of cognitive cerebro-cerebellar loops.

The cerebellum in drug craving

Craving has been considered one of the core features of addiction. It can be defined as the urge or conscious desire to use a drug elicited by the drug itself, drug-associated cues or stressors. Craving plays a major role in relapse, even after prolonged periods of abstinence, as well as in the maintenance of drug seeking in non-abstinent addicts. The circuitry of craving includes medial parts of the prefrontal cortex, ventral striatal zones, ventral tegmental area, ventral pallidum, and limbic regions. Interestingly, the cerebellum shows reciprocal loops with many of these areas. The cerebellum has been linked traditionally to motor functions but increasing evidence indicates that this part of the brain is also involved in functions related to cognition, prediction, learning, and memory. Moreover, the functional neuroimaging studies that have addressed the study of craving in humans repeatedly demonstrate cerebellar activation when craving is elicited by the presentation of drug-related cues. However, the role of cerebellar activity in these craving episodes remains unknown. Therefore, the main goal of this review is to provide a brief update on craving studies and the traditional neural basis of this phenomenon, and then discuss and propose a hypothesis for the function of the cerebellum in craving episodes.

Cortical networks involved in visual awareness independent of visual attention

It is now well established that visual attention, as measured with standard spatial attention tasks, and visual awareness, as measured by report, can be dissociated. It is possible to attend to a stimulus with no reported awareness of the stimulus. We used a behavioral paradigm in which people were aware of a stimulus in one condition and unaware of it in another condition, but the stimulus drew a similar amount of spatial attention in both conditions. The paradigm allowed us to test for brain regions active in association with awareness independent of level of attention. Participants performed the task in an MRI scanner. We looked for brain regions that were more active in the aware than the unaware trials. The largest cluster of activity was obtained in the temporoparietal junction (TPJ) bilaterally. Local independent component analysis (ICA) revealed that this activity contained three distinct, but overlapping, components: a bilateral, anterior component; a left dorsal component; and a right dorsal component. These components had brain-wide functional connectivity that partially overlapped the ventral attention network and the frontoparietal control network. In contrast, no significant activity in association with awareness was found in the banks of the intraparietal sulcus, a region connected to the dorsal attention network and traditionally associated with attention control. These results show the importance of separating awareness and attention when testing for cortical substrates. They are also consistent with a recent proposal that awareness is associated with ventral attention areas, especially in the TPJ.

Topographical Organization of Attentional, Social, and Memory Processes in the Human Temporoparietal Cortex

The temporoparietal junction (TPJ) is activated in association with a large range of functions, including social cognition, episodic memory retrieval, and attentional reorienting. An ongoing debate is whether the TPJ performs an overarching, domain-general computation, or whether functions reside in domain-specific subdivisions. We scanned subjects with fMRI during five tasks known to activate the TPJ, probing social, attentional, and memory functions, and used data-driven parcellation (independent component analysis) to isolate task-related functional processes in the bilateral TPJ. We found that one dorsal component in the right TPJ, which was connected with the frontoparietal control network, was activated in all of the tasks. Other TPJ subregions were specific for attentional reorienting, oddball target detection, or social attribution of belief. The TPJ components that participated in attentional reorienting and oddball target detection appeared spatially separated, but both were connected with the ventral attention network. The TPJ component that participated in the theory-of-mind task was part of the default-mode network. Further, we found that the BOLD response in the domain-general dorsal component had a longer latency than responses in the domain-specific components, suggesting an involvement in distinct, perhaps postperceptual, computations. These findings suggest that the TPJ performs both domain-general and domain-specific computations that reside within spatially distinct functional components.