scholarly journals Cerebellar modulation of the reward circuitry and social behavior

Science ◽  
2019 ◽  
Vol 363 (6424) ◽  
pp. eaav0581 ◽  
Author(s):  
Ilaria Carta ◽  
Christopher H. Chen ◽  
Amanda L. Schott ◽  
Schnaude Dorizan ◽  
Kamran Khodakhah
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Mental Disorders ◽  
Ventral Tegmental Area ◽  
Social Preference ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Reward Circuitry ◽  
The Social ◽  
The Brain

The cerebellum has been implicated in a number of nonmotor mental disorders such as autism spectrum disorder, schizophrenia, and addiction. However, its contribution to these disorders is not well understood. In mice, we found that the cerebellum sends direct excitatory projections to the ventral tegmental area (VTA), one of the brain regions that processes and encodes reward. Optogenetic activation of the cerebello-VTA projections was rewarding and, in a three-chamber social task, these projections were more active when the animal explored the social chamber. Intriguingly, activity in the cerebello-VTA pathway was required for the mice to show social preference in this task. Our data delineate a major, previously unappreciated role for the cerebellum in controlling the reward circuitry and social behavior.

scholarly journals Graph Ricci Curvatures Reveal Disease-related Changes in Autism Spectrum Disorder

2021 ◽  
Author(s):  
Pavithra Elumalai ◽  
Yasharth Yadav ◽  
Nitin Williams ◽  
Emil Saucan ◽  
Jürgen Jost ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Resting State ◽  
Neurodevelopmental Disorders ◽  
Data Exchange ◽  
Meta Analysis ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
The Brain

Autism Spectrum Disorder (ASD) is a set of neurodevelopmental disorders that pose a significant global health burden. Measures from graph theory have been used to characterise ASD-related changes in resting-state fMRI functional connectivity networks (FCNs), but recently developed geometry-inspired measures have not been applied so far. In this study, we applied geometry-inspired graph Ricci curvatures to investigate ASD-related changes in resting-state fMRI FCNs. To do this, we applied Forman-Ricci and Ollivier-Ricci curvatures to compare networks of ASD and healthy controls (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We performed these comparisons at the brain-wide level as well as at the level of individual brain regions, and further, determined the behavioral relevance of region-specific differences with Neurosynth meta-analysis decoding. We found brain-wide ASD-related differences for both Forman-Ricci and Ollivier-Ricci curvatures. For Forman-Ricci curvature, these differences were distributed across 83 of the 200 brain regions studied, and concentrated within the Default Mode, Somatomotor and Ventral Attention Network. Meta-analysis decoding identified the brain regions showing curvature differences as involved in social cognition, memory, language and movement. Notably, comparison with results from previous non-invasive stimulation (TMS/tDCS) experiments revealed that the set of brain regions showing curvature differences overlapped with the set of brain regions whose stimulation resulted in positive cognitive or behavioural outcomes in ASD patients. These results underscore the utility of geometry-inspired graph Ricci curvatures in characterising disease-related changes in ASD, and possibly, other neurodevelopmental disorders.

Sickness and the social brain: How the immune system regulates behavior across species

2021 ◽  
Author(s):  
Benjamin A. Devlin ◽  
Caroline J. Smith ◽  
Staci D. Bilbo
Keyword(s):  
Immune System ◽  
Social Behavior ◽  
Brain Regions ◽  
Behavior Changes ◽  
Social Brain ◽  
Evolutionary Origins ◽  
Immune Mediators ◽  
The Social ◽  
Sickness Behaviors ◽  
The Brain

Many instances of sickness critically involve the immune system. The immune system talks to the brain in a bi-directional loop. This discourse affords the immune system immense control, such that it can influence behavior and optimize recovery from illness. These behavioral responses to infection are called sickness behaviors and can manifest in many ways, including changes in mood, motivation, or energy. Fascinatingly, most of these changes are conserved across species, and most organisms demonstrate some form of sickness behaviors. One of the most interesting sickness behaviors, and not immediately obvious, is altered sociability. Here, we discuss how the immune system impacts social behavior, by examining the brain regions and immune mediators involved in this process. We first outline how social behavior changes in response to infection in various species. Next, we explore which brain regions control social behavior and their evolutionary origins. Finally, we describe which immune mediators establish the link between illness and social behavior, in the context of both normal development and infection. Overall, we hope to make clear the striking similarities between the mechanisms that facilitate changes in sociability in derived and ancestral vertebrate, as well as invertebrate, species.

Executive Functions in Adolescents with Autism Spectrum Disorder

2017 ◽  
pp. 67-90
Author(s):  
Yael Dai ◽  
Inge-Marie Eigsti
Keyword(s):  
Autism Spectrum Disorder ◽  
Working Memory ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Implications ◽  
Future Directions ◽  
Adolescents With Autism ◽  
Memory Flexibility ◽  
The Brain

This chapter reviews strengths and weaknesses in executive function (EF) domains, including inhibition, working memory, flexibility, fluency, and planning, in adolescents (age 13–19) with autism spectrum disorder (ASD). Given the dramatic developmental changes in the brain regions that support EF during the period of adolescence, it is critical to evaluate which EF abilities show a distinct profile during this period. As this chapter will demonstrate, youth with ASD show deficits across all domains of EF, particularly in complex tasks that include arbitrary instructions. We describe the fundamental measures for assessing skills in each domain and discuss limitations and future directions for research, as well as clinical implications of these findings for working with youth with ASD.

scholarly journals Erroneous inference based on a lack of preference within one group: autism, mice, and the Social Approach Task

2019 ◽  
Author(s):  
Kayla R. Nygaard ◽  
Susan E. Maloney ◽  
Joseph D. Dougherty
Keyword(s):  
Social Behavior ◽  
Analytical Approach ◽  
Social Preference ◽  
Original Data ◽  
Autism Spectrum ◽  
False Positives ◽  
Simple Solution ◽  
Social Approach ◽  
The Social ◽  
Significant Difference

AbstractThe Social Approach Task is commonly used to identify sociability deficits when modeling liability factors for autism spectrum disorder (ASD) in mice. It was developed to expand upon assays available to examine distinct aspects of social behavior in rodents and has become a standard component of mouse ASD-relevant phenotyping pipelines. However, there is variability in the statistical analysis and interpretation of results from this task. A common analytical approach is to conduct within-group comparisons only, and then interpret a difference in significance levels as if it were a group difference, without any direct comparison. As an efficient shorthand, we named this approach EWOCs:Erroneous Within-group Only Comparisons. Here we examined the prevalence of EWOCs and used simulations to test whether it could produce misleading inferences. Our review of Social Approach studies of high-confidence ASD genes revealed 45% of papers sampled used only this analytical approach. Through simulations, we then demonstrate how a lack of significant difference within one group often doesn’t correspond to a significant difference between groups, and show this erroneous interpretation increases the rate of false positives up to 25%. Finally, we define a simple solution: use an index, like a social preference score, with direct statistical comparisons between groups to identify significant differences. We also provide power calculations to guide sample size in future studies. Overall, elimination of EWOCs and adoption of direct comparisons should result in more accurate, reliable, and reproducible data interpretations from the Social Approach Task across ASD liability models.Lay SummaryThe Social Approach Task is widely used to assess social behavior in mice and is frequently used in studies modeling autism. However, reviewing published studies showed nearly half do not use correct comparisons to interpret the data. Using simulated and original data, we argue the correct statistical approach is a direct comparison of scores between groups. This simple solution should reduce false positives and improve consistency of results across studies.

scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study (Preprint)

2019 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. OBJECTIVE This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. METHODS We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. RESULTS On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. CONCLUSIONS Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

Social Behavior and Characteristics of Autism Spectrum Disorder in Angelman, Cornelia de Lange, and Cri du Chat Syndromes

2013 ◽  
Vol 118 (4) ◽  
pp. 262-283 ◽  
Author(s):  
Joanna Moss ◽  
Patricia Howlin ◽  
Richard Patrick Hastings ◽  
Sarah Beaumont ◽  
Gemma M. Griffith ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Social Communication ◽  
Social Motivation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genetic Syndromes ◽  
The Social ◽  
Cornelia De Lange ◽  
Behavior Profiles

Abstract We evaluated autism spectrum disorder (ASD) characteristics and social behavior in Angelman (AS; n  =  19; mean age  = 10.35 years), Cornelia de Lange (CdLS; n  =  15; mean age  = 12.40 years), and Cri du Chat (CdCS, also known as 5 p-syndrome; n  =  19; mean age  =  8.80 years) syndromes. The proportion of individuals meeting the ASD cutoff on the Social Communication Questionnaire was significantly higher in the AS and CdLS groups than in the CdCS group (p < .01). The groups demonstrated divergent social behavior profiles during social conditions in which adult availability, adult familiarity, and social demand were manipulated. Social enjoyment was significantly heightened in AS, whereas social approaches were heightened in individuals with CdCS. Social motivation, social communication, and enjoyment were significantly lower in CdLS. The findings highlight the importance of detailed observation when evaluating ASD and social behavior in genetic syndromes.

scholarly journals From Hyposociability to Hypersociability—The Effects of PSD-95 Deficiency on the Dysfunctional Development of Social Behavior

2021 ◽  
Vol 15 ◽  
Author(s):  
Wen-Jun Gao ◽  
Nancy R. Mack
Keyword(s):  
Social Behavior ◽  
Adult Development ◽  
Ampa Receptors ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Scaffolding Protein ◽  
Excitatory Synapses ◽  
Social Brain ◽  
Functional Maturation ◽  
The Social

Abnormal social behavior, including both hypo- and hypersociability, is often observed in neurodevelopmental disorders such as autism spectrum disorders. However, the mechanisms associated with these two distinct social behavior abnormalities remain unknown. Postsynaptic density protein-95 (PSD-95) is a highly abundant scaffolding protein in the excitatory synapses and an essential regulator of synaptic maturation by binding to NMDA and AMPA receptors. The DLG4 gene encodes PSD-95, and it is a risk gene for hypersocial behavior. Interestingly, PSD-95 knockout mice exhibit hyposociability during adolescence but hypersociability in adulthood. The adolescent hyposociability is accompanied with an NMDAR hyperfunction in the medial prefrontal cortex (mPFC), an essential part of the social brain for control of sociability. The maturation of mPFC development is delayed until young adults. However, how PSD-95 deficiency affects the functional maturation of mPFC and its connection with other social brain regions remains uncharacterized. It is especially unknown how PSD-95 knockout drives the switch of social behavior from hypo- to hyper-sociability during adolescent-to-adult development. We propose an NMDAR-dependent developmental switch of hypo- to hyper-sociability. PSD-95 deficiency disrupts NMDAR-mediated synaptic connectivity of mPFC and social brain during development in an age- and pathway-specific manner. By utilizing the PSD-95 deficiency mouse, the mechanisms contributing to both hypo- and hyper-sociability can be studied in the same model. This will allow us to assess both local and long-range connectivity of mPFC and examine how they are involved in the distinct impairments in social behavior and how changes in these connections may mature over time.

scholarly journals Administration of thimerosal-containing vaccines to infant rhesus macaques does not result in autism-like behavior or neuropathology

2015 ◽  
Vol 112 (40) ◽  
pp. 12498-12503 ◽  
Author(s):  
Bharathi S. Gadad ◽  
Wenhao Li ◽  
Umar Yazdani ◽  
Stephen Grady ◽  
Trevor Johnson ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Behavior ◽  
Rhesus Macaques ◽  
Neurodevelopmental Disorder ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Mmr Vaccine ◽  
Negative Behaviors ◽  
Infant Rhesus

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Some anecdotal reports suggest that ASD is related to exposure to ethyl mercury, in the form of the vaccine preservative, thimerosal, and/or receiving the measles, mumps, rubella (MMR) vaccine. Using infant rhesus macaques receiving thimerosal-containing vaccines (TCVs) following the recommended pediatric vaccine schedules from the 1990s and 2008, we examined behavior, and neuropathology in three brain regions found to exhibit neuropathology in postmortem ASD brains. No neuronal cellular or protein changes in the cerebellum, hippocampus, or amygdala were observed in animals following the 1990s or 2008 vaccine schedules. Analysis of social behavior in juvenile animals indicated that there were no significant differences in negative behaviors between animals in the control and experimental groups. These data indicate that administration of TCVs and/or the MMR vaccine to rhesus macaques does not result in neuropathological abnormalities, or aberrant behaviors, like those observed in ASD.

scholarly journals The Development of a Practical Artificial Intelligence Tool for Diagnosing and Evaluating Autism Spectrum Disorder: Multicenter Study

10.2196/15767 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15767 ◽  
Author(s):  
Tao Chen ◽  
Ye Chen ◽  
Mengxue Yuan ◽  
Mark Gerstein ◽  
Tingyu Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Autism Spectrum Disorder ◽  
Psychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Unknown Etiology ◽  
Healthy Controls ◽  
Anatomical Changes ◽  
The Brain

Background Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with an unknown etiology. Early diagnosis and intervention are key to improving outcomes for patients with ASD. Structural magnetic resonance imaging (sMRI) has been widely used in clinics to facilitate the diagnosis of brain diseases such as brain tumors. However, sMRI is less frequently used to investigate neurological and psychiatric disorders, such as ASD, owing to the subtle, if any, anatomical changes of the brain. Objective This study aimed to investigate the possibility of identifying structural patterns in the brain of patients with ASD as potential biomarkers in the diagnosis and evaluation of ASD in clinics. Methods We developed a novel 2-level histogram-based morphometry (HBM) classification framework in which an algorithm based on a 3D version of the histogram of oriented gradients (HOG) was used to extract features from sMRI data. We applied this framework to distinguish patients with ASD from healthy controls using 4 datasets from the second edition of the Autism Brain Imaging Data Exchange, including the ETH Zürich (ETH), NYU Langone Medical Center: Sample 1, Oregon Health and Science University, and Stanford University (SU) sites. We used a stratified 10-fold cross-validation method to evaluate the model performance, and we applied the Naive Bayes approach to identify the predictive ASD-related brain regions based on classification contributions of each HOG feature. Results On the basis of the 3D HOG feature extraction method, our proposed HBM framework achieved an area under the curve (AUC) of >0.75 in each dataset, with the highest AUC of 0.849 in the ETH site. We compared the 3D HOG algorithm with the original 2D HOG algorithm, which showed an accuracy improvement of >4% in each dataset, with the highest improvement of 14% (6/42) in the SU site. A comparison of the 3D HOG algorithm with the scale-invariant feature transform algorithm showed an AUC improvement of >18% in each dataset. Furthermore, we identified ASD-related brain regions based on the sMRI images. Some of these regions (eg, frontal gyrus, temporal gyrus, cingulate gyrus, postcentral gyrus, precuneus, caudate, and hippocampus) are known to be implicated in ASD in prior neuroimaging literature. We also identified less well-known regions that may play unrecognized roles in ASD and be worth further investigation. Conclusions Our research suggested that it is possible to identify neuroimaging biomarkers that can distinguish patients with ASD from healthy controls based on the more cost-effective sMRI images of the brain. We also demonstrated the potential of applying data-driven artificial intelligence technology in the clinical setting of neurological and psychiatric disorders, which usually harbor subtle anatomical changes in the brain that are often invisible to the human eye.

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