Intrinsic Functional Connectivity of Dentate Nuclei in Autism Spectrum Disorder

Anteraper, Sheeba Arnold; Guell, Xavier; Taylor, Hoyt Patrick; D’Mello, Anila M.; Whitfield-Gabrieli, Susan; Joshi, Gagan

doi:10.1089/brain.2019.0692

Cited by 19 publications

(10 citation statements)

References 76 publications

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“…Accordingly, patients with ASD had decreased functional connectivity between various parts of the cerebellar cortex and nuclei and different cortical regions [476][477][478][479][480], which may suggest an important cerebellar role in the pathophysiology of ASD. Like in schizophrenia, ASD was not only associated with reduced connectivity of particular projections, but also with increased strength of others [480][481][482][483]. In conclusion, cerebellar connectivity is affected in motor and in non-motor diseases (Fig.…”

Section: Discussionmentioning

confidence: 68%

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

2022

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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.

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Section: Discussionmentioning

confidence: 68%

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

2022

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“…In our covariance model controlling for sex, age, and eTIV, we also found a negative relationship, where a quadratic term of cerebellar cortex volume best explained dentate nuclear volume values (β=879.14, se=151.2, Cohen’s f =0.31, p <0.0001) ( Fig 1b ). We focused on the dentate nuclei given previous reports of its abnormal intrinsic functional connectivity with cerebral cortex, including sensorimotor regions, in ASD 41 .…”

Section: Resultsmentioning

confidence: 99%

Structural covariation between cerebellum and cerebral cortex is atypically modulated by thalamus in autism spectrum disorder

d’Oleire Uquillas,

Sefik,

et al. 2023

Preprint

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Despite its strong interconnectivity with the cerebral cortex, the influence of the human cerebellum on neocortical structure and its role in the development of neuropsychiatric disorders is unclear. Because cerebellar damage in early postnatal life creates a high risk for autism spectrum disorder (ASD), we investigated inter-relationships in cerebello-cerebral morphology. Leveraging a large structural brain MRI dataset in neurotypical children (n=375) and those diagnosed with ASD (n=373), we examined whether volumetric variation in cerebellar structure across individuals was correlated with neocortical variation during development, modeling the thalamus as a moderating coupling factor. We found negative covariation between cerebellar cortical regions and thalamic/sensorimotor neocortical regions, and positive covariation between thalamic and sensorimotor neocortical regions. This pattern aligned with the major disynaptic path of cerebellar inhibition to thalamocortical excitation. Examining the dependence of this structural covariation on ASD diagnosis, we found that neurotypical and ASD children displayed inverted hemispheric biases. In ASD, the thalamus moderated structural associations between the left cerebellum and right sensorimotor cortex. For neurotypical children, right cerebellum and left sensorimotor cortex were coupled. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, a time during which behavioral differences were smallest between typically developing and autistic children. In addition to the sensorimotor cortex, cerebellar dentate nuclei in ASD displayed greater coupling to broad neocortical regions, and these nuclei related to cognitive function differently such that greater dentate nuclear volume was associated with greater behavioral impairment in ASD but not in controls. Graph analyses demonstrated that the cerebello-thalamocortical network was more densely and less prolifically interconnected in ASD than in typical development. Taken as a whole, our study reveals a developmental interplay between the cerebellum, thalamus, and neocortex which differs in ASD from neurotypical children. This pattern is consistent with a model of ASD in which early developmental influences of cerebellar output on brain maturation are specifically moderated by cerebello-thalamocortical pathways.

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“…A possible explanation for the failure of ASD in adults is due to the modulation of the DMN, as proposed by Iacoboni [ 142 ], according to which, this due to the lack of self-referential processing. Finally, in the recent study of Cherkassky and colleagues [ 143 ], using analysis of intrinsic functional correlations , results showed weaker levels in default networks in ASD participants. Also, ASD participants showed differences in the frontal-parietal network , a finding that strongly correlates with the control interactions between default networks and brain systems related to external attention.…”

Section: Methodsmentioning

confidence: 92%

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

et al. 2021

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We aim to investigate whether EEG dynamics differ in adults with ASD (Autism Spectrum Disorders) and ADHD (attention-deficit/hyperactivity disorder) compared with healthy subjects during the performance of an innovative cognitive task, Aristotle’s valid and invalid syllogisms, and how these differences correlate with brain regions and behavioral data for each subject. We recorded EEGs from 14 scalp electrodes (channels) in 21 adults with ADHD, 21 with ASD, and 21 healthy, normal subjects. The subjects were exposed in a set of innovative cognitive tasks (inducing varying cognitive loads), Aristotle’s two types of syllogism mentioned above. A set of 39 questions were given to participants related to valid–invalid syllogisms as well as a separate set of questionnaires, in order to collect a number of demographic and behavioral data, with the aim of detecting shared information with values of a feature extracted from EEG, the multiscale entropy (MSE), in the 14 channels (‘brain regions’). MSE, a nonlinear information-theoretic measure of complexity, was computed to extract a feature that quantifies the complexity of the EEG. Behavior-Partial Least Squares Correlation, PLSC, is the method to detect the correlation between two sets of data, brain, and behavioral measures. -PLSC, a variant of PLSC, was applied to build a functional connectivity of the brain regions involved in the reasoning tasks. Graph-theoretic measures were used to quantify the complexity of the functional networks. Based on the results of the analysis described in this work, a mixed 14 × 2 × 3 ANOVA showed significant main effects of group factor and brain region* syllogism factor, as well as a significant brain region* group interaction. There are significant differences between the means of MSE (complexity) values at the 14 channels of the members of the ‘pathological’ groups of participants, i.e., between ASD and ADHD, while the difference in means of MSE between both ASD and ADHD and that of the control group is not significant. In conclusion, the valid–invalid type of syllogism generates significantly different complexity values, MSE, between ASD and ADHD. The complexity of activated brain regions of ASD participants increased significantly when switching from a valid to an invalid syllogism, indicating the need for more resources to ‘face’ the task escalating difficulty in ASD subjects. This increase is not so evident in both ADHD and control. Statistically significant differences were found also in the behavioral response of ASD and ADHD, compared with those of control subjects, based on the principal brain and behavior saliences extracted by PLSC. Specifically, two behavioral measures, the emotional state and the degree of confidence of participants in answering questions in Aristotle’s valid–invalid syllogisms, and one demographic variable, age, statistically and significantly discriminate the three groups’ ASD. The seed-PLC generated functional connectivity networks for ASD, ADHD, and control, were ‘projected’ on the regions of the Default Mode Network (DMN), the ‘reference’ connectivity, of which the structural changes were found significant in distinguishing the three groups. The contribution of this work lies in the examination of the relationship between brain activity and behavioral responses of healthy and ‘pathological’ participants in the case of cognitive reasoning of the type of Aristotle’s valid and invalid syllogisms, using PLSC, a machine learning approach combined with MSE, a nonlinear method of extracting a feature based on EEGs that captures a broad spectrum of EEGs linear and nonlinear characteristics. The results seem promising in adopting this type of reasoning, in the future, after further enhancements and experimental tests, as a supplementary instrument towards examining the differences in brain activity and behavioral responses of ASD and ADHD patients. The application of the combination of these two methods, after further elaboration and testing as new and complementary to the existing ones, may be considered as a tool of analysis in helping detecting more effectively such types of disorders.

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Intrinsic Functional Connectivity of Dentate Nuclei in Autism Spectrum Disorder

Cited by 19 publications

References 76 publications

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

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

Structural covariation between cerebellum and cerebral cortex is atypically modulated by thalamus in autism spectrum disorder

Differences in Performance of ASD and ADHD Subjects Facing Cognitive Loads in an Innovative Reasoning Experiment

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