Neural Dynamics of Autistic Repetitive Behaviors and Fragile X Syndrome: Basal Ganglia Movement Gating and mGluR-Modulated Adaptively Timed Learning

The correlation between brain structure characteristics and emotion regulation ability in children at high risk of autism spectrum disorder

As indicated by longitudinal observation, autism has difficulty controlling emotions to a certain extent in early childhood, and most children's emotional and behavioral problems are further aggravated with the growth of age. This study aimed at exploring the correlation between white matter and white matter fiber bundle connectivity characteristics and their emotional regulation ability in children with autism using machine learning methods, which can lay an empirical basis for early clinical intervention of autism. Fifty-five high risk of autism spectrum disorder (HR-ASD) children and 52 typical development (TD) children were selected to complete the skull 3D-T1 structure and diffusion tensor imaging (DTI). The emotional regulation ability of the two groups was compared using the still-face paradigm (SFP). The classification and regression models of white matter characteristics and white matter fiber bundle connections of emotion regulation ability in the HR-ASD group were built based on the machine learning method. The volume of the right amygdala (R2 = 0.245) and the volume of the right hippocampus (R2 = 0.197) affected constructive emotion regulation strategies. FA (R2 = 0.32) and MD (R2 = 0.34) had the predictive effect on self-stimulating behaviour. White matter fiber bundle connection predicted constructive regulation strategies (positive edging R2 = 0.333, negative edging R2 = 0.334) and mother-seeking behaviors (positive edging R2 = 0.667, negative edging R2 = 0.363). The emotional regulation ability of HR-ASD children is significantly correlated with the connections of multiple white matter fiber bundles, which is a potential neuro-biomarker of emotional regulation ability.

Spinophilin Limits Metabotropic Glutamate Receptor 5 Scaffolding to the Postsynaptic Density and Cell Type Specifically Mediates Excessive Grooming

BACKGROUND

Grooming dysfunction is a hallmark of the obsessive-compulsive spectrum disorder trichotillomania. Numerous preclinical studies have utilized SAPAP3-deficient mice for understanding the neurobiology of repetitive grooming, suggesting that excessive grooming is caused by increased metabotropic glutamate receptor 5 (mGluR5) activity in striatal direct- and indirect-pathway medium spiny neurons (MSNs). However, the MSN subtype-specific signaling mechanisms that mediate mGluR5-dependent adaptations underlying excessive grooming are not fully understood. Here, we investigated the MSN subtype-specific roles of the striatal signaling hub protein spinophilin in mediating repetitive motor dysfunction associated with mGluR5 function.

METHODS

Quantitative proteomics and immunoblotting were utilized to identify how spinophilin impacts mGluR5 phosphorylation and protein interaction changes. Plasticity and repetitive motor dysfunction associated with mGluR5 action were measured using our novel conditional spinophilin mouse model in which spinophilin was knocked out from striatal direct-pathway MSNs and/or indirect-pathway MSNs.

RESULTS

Loss of spinophilin only in indirect-pathway MSNs decreased performance of a novel motor repertoire, but loss of spinophilin in either MSN subtype abrogated striatal plasticity associated with mGluR5 function and prevented excessive grooming caused by SAPAP3 knockout mice or treatment with the mGluR5-specific positive allosteric modulator VU0360172 without impacting locomotion-relevant behavior. Biochemically, we determined that the spinophilin-mGluR5 interaction correlates with grooming behavior and that loss of spinophilin shifts mGluR5 interactions from lipid raft-associated proteins toward postsynaptic density proteins implicated in psychiatric disorders.

CONCLUSIONS

These results identify spinophilin as a novel striatal signaling hub molecule in MSNs that cell subtype specifically mediates behavioral, functional, and molecular adaptations associated with repetitive motor dysfunction in psychiatric disorders.

Disrupted functional connectivity between the nucleus accumbens and posterior cingulate cortex in autism spectrum disorder

OBJECTIVES

Dysfunctions in the basal ganglia have been repeatedly found in autism spectrum disorder (ASD). The nucleus accumbens (NAcc) is known for its central role in social functions and also in its abnormality in ASD. In this study, whole-brain functional connectivity of the NAcc was examined to isolate brain regions that are differently connected to the NAcc in autism, using resting-state functional MRI (rs-fMRI) data.

METHODS

In the initial dataset, 68 individuals with ASD (13.13 ± 2.41 years old) and 77 typically developing individuals (14.79 ± 3.57 years old) were compared.

RESULTS

The precuneus cortex, lingual gyrus, thalamus, dorsal striatum, anterior cingulate cortex and posterior cingulate cortex showed weaker connectivity to the NAcc in the group with ASD. Among these regions, dysconnectivity of the posterior cingulate cortex (PCC) was replicated in the replication dataset, with 78 individuals with ASD and 105 without ASD.

CONCLUSIONS

This study suggests that the dysconnectivity between the NAcc and PCC may account for social dysfunctions in ASD.

A Neural Model of Intrinsic and Extrinsic Hippocampal Theta Rhythms: Anatomy, Neurophysiology, and Function

This article describes a neural model of the anatomy, neurophysiology, and functions of intrinsic and extrinsic theta rhythms in the brains of multiple species. Topics include how theta rhythms were discovered; how theta rhythms organize brain information processing into temporal series of spatial patterns; how distinct theta rhythms occur within area CA1 of the hippocampus and between the septum and area CA3 of the hippocampus; what functions theta rhythms carry out in different brain regions, notably CA1-supported functions like learning, recognition, and memory that involve visual, cognitive, and emotional processes; how spatial navigation, adaptively timed learning, and category learning interact with hippocampal theta rhythms; how parallel cortical streams through the lateral entorhinal cortex (LEC) and the medial entorhinal cortex (MEC) represent the end-points of the What cortical stream for perception and cognition and the Where cortical stream for spatial representation and action; how the neuromodulator acetylcholine interacts with the septo-hippocampal theta rhythm and modulates category learning; what functions are carried out by other brain rhythms, such as gamma and beta oscillations; and how gamma and beta oscillations interact with theta rhythms. Multiple experimental facts about theta rhythms are unified and functionally explained by this theoretical synthesis.

Impaired Thiol/Disulfide Homeostasis in Children Diagnosed with Autism: A Case-Control Study

Although genetic factors occupy an important place in the development of autism spectrum disorder (ASD), oxidative stress and exposure to environmental toxicants have also been linked to the condition. The aim of this study was to examine dynamic thiol/disulfide homeostasis in children diagnosed with ASD. Forty-eight children aged 3-12 years diagnosed with ASD and 40 age- and sex-matched healthy children were included in the study. A sociodemographic data form was completed for all the cases, and the Childhood Autism Rating Scale (CARS) was applied to the patients. Thiol/disulfide parameters in serum were measured in all cases and compared between the two groups. Mean native thiol, total thiol concentrations (μmol/L), and median reduced thiol ratios were significantly lower in the ASD group than in the control group (p = 0.001 for all). Median disulfide concentrations (μmol/L), redox potential, and median oxidized thiol ratios were significantly higher in the ASD group than in the control group (p = 0.001, p = 0.001, and p = 0.001, respectively). ROC analysis revealed that area under the curve (AUC) values with "excellent discriminatory potential," for native thiol, total thiol, the reduced thiol ration, the oxidized thiol ratio, and redox potential and with "acceptable discriminatory potential" for disulfide were significantly capable of differentiating individuals with ASD from healthy individuals. No correlation was determined between the severity of autism and laboratory parameters. Impaired dynamic thiol/disulfide homeostasis was observed in children with ASD, suggesting that dynamic thiol/disulfide homeostasis in serum may be of diagnostic value in autism.

High KEAP1, NRF2 and Low HO-1 Serum Levels in Children with Autism

Introduction

The purpose of our study was to investigate heme oxygenase-1 (HO-1), nuclear factor erythroid-2-related factor 2 (NRF2), and kelch-like ECH-associated protein 1 (KEAP1) levels in children with autism spectrum disorder (ASD) and to reveal their association with the severity of autism.

Methods

This study measured serum HO-1, KEAP1, and NRF2 levels in 43 patients with ASD (aged 3-12 years) and in 41 age- and gender-matched healthy controls. ASD severity was rated using the Childhood Autism Rating Scale (CARS). HO-1, KEAP1, and NRF2 levels were determined in the biochemistry laboratory using the ELISA technique.

Results

HO-1 levels were significantly lower in patients aged 3-12 years compared to controls aged 3-12, while KEAP1 and NRF2 levels were significantly higher (p=0.020, p<0.001, and p=0.017, respectively). No correlation was determined between ASD severity on the basis of total CARS scores and HO-1, KEAP1 or NRF2 (p>0.05).

Conclusion

This study suggests that oxidative stress is higher in children with ASD and that HO-1 levels are insufficient to achieve oxidative balance.

A Path Toward Explainable AI and Autonomous Adaptive Intelligence: Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action

Biological neural network models whereby brains make minds help to understand autonomous adaptive intelligence. This article summarizes why the dynamics and emergent properties of such models for perception, cognition, emotion, and action are explainable, and thus amenable to being confidently implemented in large-scale applications. Key to their explainability is how these models combine fast activations, or short-term memory (STM) traces, and learned weights, or long-term memory (LTM) traces. Visual and auditory perceptual models have explainable conscious STM representations of visual surfaces and auditory streams in surface-shroud resonances and stream-shroud resonances, respectively. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. Even if it makes some accurate classifications, they are not explainable and thus cannot be used with confidence. Deep Learning shares these problems with the back propagation algorithm, whose computational problems due to non-local weight transport during mismatch learning were described in the 1980s. Deep Learning became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning. ART is a self-organizing production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning and only locally computable quantities, to rapidly classify large non-stationary databases without experiencing catastrophic forgetting. ART classifications and predictions are explainable using the attended critical feature patterns in STM on which they build. The LTM adaptive weights of the fuzzy ARTMAP algorithm induce fuzzy IF-THEN rules that explain what feature combinations predict successful outcomes. ART has been successfully used in multiple large-scale real world applications, including remote sensing, medical database prediction, and social media data clustering. Also explainable are the MOTIVATOR model of reinforcement learning and cognitive-emotional interactions, and the VITE, DIRECT, DIVA, and SOVEREIGN models for reaching, speech production, spatial navigation, and autonomous adaptive intelligence. These biological models exemplify complementary computing, and use local laws for match learning and mismatch learning that avoid the problems of Deep Learning.

Gut-Amygdala Interactions in Autism Spectrum Disorders: Developmental Roles via regulating Mitochondria, Exosomes, Immunity and microRNAs

BACKGROUND

Autism Spectrum Disorders (ASD) have long been conceived as developmental disorder. A growing body of data highlights a role for alterations in the gut in the pathoetiology and/or pathophysiology of ASD. Recent work shows alterations in the gut microbiome to have a significant impact on amygdala development in infancy, suggesting that the alterations in the gut microbiome may act to modulate not only amygdala development but how the amygdala modulates the development of the frontal cortex and other brain regions.

METHODS

This article reviews wide bodies of data pertaining to the developmental roles of the maternal and foetal gut and immune systems in the regulation of offspring brain development.

RESULTS

A number of processes seem to be important in mediating how genetic, epigenetic and environmental factors interact in early development to regulate such gut-mediated changes in the amygdala, wider brain functioning and inter-area connectivity, including via regulation of microRNA (miR)-451, 14-3-3 proteins, cytochrome P450 (CYP)1B1 and the melatonergic pathways. As well as a decrease in the activity of monoamine oxidase, heightened levels of in miR-451 and CYP1B1, coupled to decreased 14-3-3 act to inhibit the synthesis of N-acetylserotonin and melatonin, contributing to the hyperserotonemia that is often evident in ASD, with consequences for mitochondria functioning and the content of released exosomes. These same factors are likely to play a role in regulating placental changes that underpin the association of ASD with preeclampsia and other perinatal risk factors, including exposure to heavy metals and air pollutants. Such alterations in placental and gut processes act to change the amygdala-driven biological underpinnings of affect-cognitive and affect-sensory interactions in the brain.

CONCLUSION

Such a perspective readily incorporates previously disparate bodies of data in ASD, including the role of the mu-opioid receptor, dopamine signaling and dopamine receptors, as well as the changes occurring to oxytocin and taurine levels. This has a number of treatment implications, the most readily applicable being the utilization of sodium butyrate and melatonin.

The resonant brain: How attentive conscious seeing regulates action sequences that interact with attentive cognitive learning, recognition, and prediction

This article describes mechanistic links that exist in advanced brains between processes that regulate conscious attention, seeing, and knowing, and those that regulate looking and reaching. These mechanistic links arise from basic properties of brain design principles such as complementary computing, hierarchical resolution of uncertainty, and adaptive resonance. These principles require conscious states to mark perceptual and cognitive representations that are complete, context sensitive, and stable enough to control effective actions. Surface-shroud resonances support conscious seeing and action, whereas feature-category resonances support learning, recognition, and prediction of invariant object categories. Feedback interactions between cortical areas such as peristriate visual cortical areas V2, V3A, and V4, and the lateral intraparietal area (LIP) and inferior parietal sulcus (IPS) of the posterior parietal cortex (PPC) control sequences of saccadic eye movements that foveate salient features of attended objects and thereby drive invariant object category learning. Learned categories can, in turn, prime the objects and features that are attended and searched. These interactions coordinate processes of spatial and object attention, figure-ground separation, predictive remapping, invariant object category learning, and visual search. They create a foundation for learning to control motor-equivalent arm movement sequences, and for storing these sequences in cognitive working memories that can trigger the learning of cognitive plans with which to read out skilled movement sequences. Cognitive-emotional interactions that are regulated by reinforcement learning can then help to select the plans that control actions most likely to acquire valued goal objects in different situations. Many interdisciplinary psychological and neurobiological data about conscious and unconscious behaviors in normal individuals and clinical patients have been explained in terms of these concepts and mechanisms.

The Embodied Brain of SOVEREIGN2: From Space-Variant Conscious Percepts During Visual Search and Navigation to Learning Invariant Object Categories and Cognitive-Emotional Plans for Acquiring Valued Goals

This article develops a model of how reactive and planned behaviors interact in real time. Controllers for both animals and animats need reactive mechanisms for exploration, and learned plans to efficiently reach goal objects once an environment becomes familiar. The SOVEREIGN model embodied these capabilities, and was tested in a 3D virtual reality environment. Neural models have characterized important adaptive and intelligent processes that were not included in SOVEREIGN. A major research program is summarized herein by which to consistently incorporate them into an enhanced model called SOVEREIGN2. Key new perceptual, cognitive, cognitive-emotional, and navigational processes require feedback networks which regulate resonant brain states that support conscious experiences of seeing, feeling, and knowing. Also included are computationally complementary processes of the mammalian neocortical What and Where processing streams, and homologous mechanisms for spatial navigation and arm movement control. These include: Unpredictably moving targets are tracked using coordinated smooth pursuit and saccadic movements. Estimates of target and present position are computed in the Where stream, and can activate approach movements. Motion cues can elicit orienting movements to bring new targets into view. Cumulative movement estimates are derived from visual and vestibular cues. Arbitrary navigational routes are incrementally learned as a labeled graph of angles turned and distances traveled between turns. Noisy and incomplete visual sensor data are transformed into representations of visual form and motion. Invariant recognition categories are learned in the What stream. Sequences of invariant object categories are stored in a cognitive working memory, whereas sequences of movement positions and directions are stored in a spatial working memory. Stored sequences trigger learning of cognitive and spatial/motor sequence categories or plans, also called list chunks, which control planned decisions and movements toward valued goal objects. Predictively successful list chunk combinations are selectively enhanced or suppressed via reinforcement learning and incentive motivational learning. Expected vs. unexpected event disconfirmations regulate these enhancement and suppressive processes. Adaptively timed learning enables attention and action to match task constraints. Social cognitive joint attention enables imitation learning of skills by learners who observe teachers from different spatial vantage points.

Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices

BACKGROUND

The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions.

METHODS

This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions.

RESULTS

Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.

CONCLUSION

This article hereby proposes a unified neural theory of prefrontal cortex and its functions.