Collaborating Authors


Adolescents with autism may engage neural control systems differently, study finds: Researchers used brain scans to measure proactive and reactive executive control


Executive control difficulties are common in individuals with autism and are associated with challenges completing tasks and managing time. The study, published in Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, sought to tease out whether these difficulties represent a disruption in proactive executive control (engaged and maintained before a cognitively demanding event) or in reactive executive control (engaged as the event occurs). Using functional magnetic resonance imaging (fMRI), the researchers took brain scans of 141 adolescents and young adults ages 12-22 (64 with autism, 77 neurotypical controls) enrolled in the Cognitive Control in Autism Study. During the scan, the participants completed a task that required them to adapt their behavior. They were shown a green or red cue, followed by a white arrow (probe) pointing left or right.

Identifying autism blood biomarkers with machine learning


The UT Southwestern team has used machine learning tools to analyse hundreds of proteins that has led to the identification of nine serum proteins that predict the disorder. The researchers hope this will help develop more effective therapies for ASD sooner. The study has been published in the journal PLOS ONE. Early diagnosis of ASD is vital to make a difference to the lives of young children living with ASD who are typically not diagnosed until the age of four, says Dwight German, PhD, professor of psychiatry at UT Southwestern and senior author of the study. To date, blood-based biomarkers such as neurotransmitters, cytokines, and markers of mitochondrial dysfunction, oxidative stress, and impaired methylation, have been investigated.

AI-Augmented Behavior Analysis for Children with Developmental Disabilities: Building Towards Precision Treatment Artificial Intelligence

Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision-making using Artificial Intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

Helpful apps for neurodiverse kids and students


Finding apps that are entertaining or helpful can be difficult – what a friend loves may not work for you. This challenge is acute for people who are neurodiverse. Most apps aren't designed for people like them, and wading through the options is time consuming. Neurodiversity covers a broad range of brain differences that, for some students, can mean they need extra support for executive functioning, social skills, or communication. Those who are twice exceptional because they are both gifted and have a learning disorder can also benefit from opportunities to extend their passions and find new ones.

Developing emotion recognition for video conference software to support people with autism Artificial Intelligence

We develop an emotion recognition software for the use with a video conference software for autistic individuals which are unable to recognize emotions properly. It can get an image out of the video stream, detect the emotion in it with the help of a neural network and display the prediction to the user. The network is trained on facial landmark features. The software is fully modular to support adaption to different video conference software, programming languages and implementations.

Adding cognitive connections to the cerebellum


Although the cerebellum was first described in the 17th century and its cytoarchitecture was mapped in the early 20th century, our understanding of the role of the cerebellum is rapidly changing. Initially thought to carry out simple motor control, the cerebellum is now considered to function in complex cognitive tasks. On page 1436 of this issue, Kebschull et al. ([ 1 ][1]) show that the cerebellar nuclei (CN) evolved from amniotes to humans by duplicating “subnuclei” consisting of two classes of excitatory neurons and three classes of inhibitory neurons. The excitatory cell class of the lateral nucleus that projects to the frontal cortex in mice and is affected in developmental disorders such as autism spectrum disorder (ASD) ([ 2 ][2], [ 3 ][3]) predominates in the greatly expanded human cerebellum. These studies thus provide molecular insights into emerging studies showing a role for the cerebellum in cognitive behaviors, including modulating dopaminergic reward circuits ([ 4 ][4]), language ([ 5 ][5]), and social behavior ([ 6 ][6], [ 7 ][7]). The cerebellum is the oldest cortical structure in the brain, appearing some 300 million years ago in eel-like creatures. It has an overlying cortex with two principal neurons—the granule cells (GCs) and Purkinje cells (PCs)—and deep CN. GCs receive input from the cortex, spinal cord, and brainstem and project to PCs, the sole output neuron of the cerebellar cortex. PCs in zones across the cerebellum, from the vermis (medial) to the lateral hemispheres, project to the fastigial (medial), interposed, and dentate (lateral) CN. In humans, the interposed nucleus contains the emboliform nucleus and the globose nucleus. Traditional studies have focused on the cerebellar cortex, largely ignoring the CN, which are the output region of the cerebellum. Consequently, very few molecular markers are known for CN cell types, other than those discovered in developmental studies ([ 8 ][8], [ 9 ][9]). Kebschull et al. used the markers discovered in previous developmental studies to provide molecular profiles for all of the neurons of the three CN—the fastigial (medial), interposed, and lateral (dentate) nuclei—extending developmental studies and recent profiling of the neurons of the mouse medial CN neurons ([ 10 ][10]). They also extend our understanding of the CN circuitry by showing that CN neurons project to broad areas of the cortex. This result underscores mapping studies of the past two decades, including human neuroimaging studies charting cerebellar pathways to language areas ([ 5 ][5]), and neuroanatomical tracing of cerebro-cerebellar projections in nonhuman primate cortex ([ 11 ][11]) and of fastigial CN neurons in mice ([ 10 ][10]). The work by Kebschull et al. also addresses the critical question of how new brain areas arise during evolution. In the cerebellum, the number of CN increases from one in jawless fish to two in amniotes and three in mammals. Among the three mammalian CN, the medial nucleus is phylogenetically the oldest and the lateral nucleus is the youngest. Using single-cell RNA sequencing (scRNA-seq) to cluster CN neuron types in chicken, mice, and humans, the authors show that new nuclei evolve by duplicating a subset of neurons, or subnuclei, across the three CN. Thus, each of the CN contain stereotyped subnuclei that are duplicated during evolution. The duplicated subnuclei consist of two conserved classes (A and B) of excitatory CN neurons that have high spontaneous activity rates, typical of all excitatory CN neurons ([ 12 ][12]), but distinct electrophysiological properties. In the human CN, Class-B neurons are expanded at the expense of Class-A neurons, suggesting that evolution tuned the relative abundance of excitatory cell types. Whereas neurons of the medial nuclei, which mediate motor functions, were most similar across species, Class-B excitatory neurons are expanded in the human lateral nucleus, which connect to the prefrontal cortical network and are thought to mediate higher cognitive cerebellar functions (see the figure). ![Figure][13] Cognitive functions of the cerebellum Projections of the cerebellar nuclei connect to cortical areas via the thalamus and midbrain areas. Cortical areas involved in cognitive functions and language include the network of frontal association areas, ventral orbital areas, insular cortex and ventrolateral striatum, Wernicke's area, and Broca's area. Blue areas bounded by dashed lines are out of the sagittal plane. GRAPHIC: KELLIE HOLOSKI/ SCIENCE Because neurons of the lateral nucleus project to cognitive areas in humans, the selective expansion of Class-B excitatory neurons in this nucleus is especially interesting. Analysis of the connection patterns of the Class-B neurons in lateral nuclei in mice by Kebshull et al. showed that they projected broadly to thalamic nuclei and a lateral network of frontal association areas, ventral orbital areas, and insular cortex as well as ventrolateral striatum, all of which are known to be involved in cognitive functions. This suggests that if the expanded pattern of projections that they discovered for the mouse lateral nucleus is conserved in humans, then the projections from the lateral nucleus to the cortical areas thought to mediate cognitive functions will be preferentially expanded in humans. Understanding how the activity of neurons of the CN facilitates cerebellar learning is currently a topic of great interest ([ 12 ][12], [ 13 ][14]). Input from cerebellar PCs regulates the rate and timing of CN excitatory neuron output to cortical targets. New studies are challenging the role of the cerebellum in learning. Whereas the classical view was that the cerebellum instructs changes in motor output by signaling movement errors ([ 14 ][15]), new models propose that the cerebellum uses reward prediction to reinforce learning. Indeed, calcium imaging studies in mice ([ 12 ][12]) demonstrate that climbing fibers that are connected to PCs from the olivary nucleus in the brainstem can signal reward prediction, not error prediction, that guides cerebellar learning. Recent studies also demonstrate a direct, monosynaptic pathway from the cerebellum to the ventral tegmental area (VTA) that sends dopaminergic projections to areas of the frontal cortex that regulate reward, motivation, cognition, and aversion, which control social behaviors in mice ([ 4 ][4]). This strongly implies that cerebellar circuit deficits are important for impairments of social behaviors common in developmental disorders such as ASDs. A role for the cerebellum in ASD-like behaviors is further supported by evidence that changes in the activity of projections from the lateral lobes of the cerebellar cortex to the frontal cortex affect ASD-like behaviors in mice and humans ([ 2 ][2], [ 3 ][3]). In addition, a role for the cerebellum in decision-making tasks has been demonstrated in mice ([ 15 ][16]), suggesting a potential role for the lateral cerebellum and lateral nucleus in memory in humans. The discovery of conserved subnuclei of the cerebellum that are duplicated during evolution and the unexpectedly broad range of cerebellar connections to cortical areas opens the door to a deeper understanding of how the activity of cerebellar circuits facilitates language and higher cognitive processes. 1. [↵][17]1. J. M. Kebshull et al ., Science 370, eabd5059 (2020). [OpenUrl][18][Abstract/FREE Full Text][19] 2. [↵][20]1. C. J. Stoodley et al ., Nat. Neurosci. 20, 1744 (2017). [OpenUrl][21][CrossRef][22][PubMed][23] 3. [↵][24]1. P. T. Tsai et al ., Cell Rep. 25, 357 (2018). [OpenUrl][25] 4. [↵][26]1. C. H. Carta Chen, 2. A. L. Schott, 3. S. Dorizan, 4. K. Khodakhah , Science 363, 248 (2019). [OpenUrl][27] 5. [↵][28]1. J. A. Fiez, 2. S. E. Petersen , Proc. Natl. Acad. Sci. 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4 ways tech has helped my autistic son


When I heard the announcement of nursery and school closures brought on by the novel coronavirus last Spring, my heart sank. My son, Sidney, had just received an official diagnosis of autism, and here we were condemned to months of isolation. I knew I was in tune with Sidney and his needs, but I'm not a special needs educator. My biggest fear was that despite my best efforts, Sidney might lose some of his hard-fought language and engagement skills while suffering mentally. As the particularities of Sidney's challenges have become more clear, I've done my best to understand him and help him thrive in a world that in many ways is not set up for him.

#324: Embodied Interactions: from Robotics to Dance, with Kim Baraka


In this episode, our interviewer Lauren Klein speaks with Kim Baraka about his PhD research to enable robots to engage in social interactions, including interactions with children with Autism Spectrum Disorder. Baraka discusses how robots can plan their actions across multiple modalities when interacting with humans, and how models from psychology can inform this process. He also tells us about his passion for dance, and how dance may serve as a testbed for embodied intelligence within Human-Robot Interaction. Kim Baraka is a postdoctoral researcher in the Socially Intelligent Machines Lab at the University of Texas at Austin, and an upcoming Assistant Professor in the Department of Computer Science at Vrije Universiteit Amsterdam, where he will be part of the Social Artificial Intelligence Group. Baraka recently graduated with a dual PhD in Robotics from Carnegie Mellon University (CMU) in Pittsburgh, USA, and the Instituto Superior Técnico (IST) in Lisbon, Portugal.

In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes


CRISPR targeting in vivo, especially in mammals, can be difficult and time consuming when attempting to determine the effects of a single gene. However, such studies may be required to identify pathological gene variants with effects in specific cells along a developmental trajectory. To study the function of genes implicated in autism spectrum disorders (ASDs), Jin et al. applied a gene-editing and single-cell–sequencing system, Perturb-Seq, to knock out 35 ASD candidate genes in multiple mice embryos (see the Perspective by Treutlein and Camp). This method identified networks of gene expression in neuronal and glial cells that suggest new functions in ASD-related genes. Science , this issue p. [eaaz6063][1]; see also p. [1038][2] ### INTRODUCTION Human genetic studies have revealed long lists of genes and loci associated with risk for many diseases and disorders, but to systematically evaluate their phenotypic effects remains challenging. Without any a priori knowledge, these risk genes could affect any cellular processes in any cell type or tissue, which creates an enormous search space for identifying possible downstream effects. New high-throughput approaches are needed to functionally dissect these large gene sets across a spectrum of cell types in vivo. ### RATIONALE Analysis of trio-based whole-exome sequencing has implicated a large number of de novo loss-of-function variants that contribute to autism spectrum disorder and developmental delay (ASD/ND) risk. Such de novo variants often have large effect sizes, thus providing a key entry point for mechanistic studies. We have developed in vivo Perturb-Seq to allow simultaneous assessment of the individual phenotypes of a panel of such risk genes in the context of the developing mouse brain. ### RESULTS Using CRISPR-Cas9, we introduced frameshift mutations in 35 ASD/ND risk genes in pools, within the developing mouse neocortex in utero, followed by single-cell transcriptomic analysis of perturbed cells from the early postnatal brain. We analyzed five broad cell classes—cortical projection neurons, cortical inhibitory neurons, astrocytes, oligodendrocytes, and microglia—and selected cells that had received only single perturbations. Using weighted gene correlation network analysis, we identified 14 covarying gene modules that represent transcriptional programs expressed in different classes of cortical cells. These modules included both those affecting common biological processes across multiple cell subsets and others representing cell type–specific features restricted to certain subsets. We estimated the effect size of each perturbation on each of the 14 gene modules by fitting a joint linear regression model, estimating how module gene expression in cells from each perturbation group deviated from their expression level in internal control cells. Perturbations in nine ASD/ND genes had significant effects across five modules across four cell classes, including cortical projection neurons, cortical inhibitory neurons, astrocytes, and oligodendrocytes. Some of these results were validated by using a single-perturbation model as well as a germline-modified mutant mouse model. To establish whether the perturbation-associated gene modules identified in the mouse cerebral cortex are relevant to human biology and ASD/ND pathology, we performed co-analyses of data from ASD and control human brains and human cerebral organoids. Both gene expression and gene covariation (“modularity”) of several of the gene modules identified in the mouse Perturb-Seq analysis are conserved in human brain tissue. Comparison with single-cell data from ASD patients showed overlap in both affected cell types and transcriptomic phenotypes. ### CONCLUSION In vivo Perturb-Seq can serve as a scalable tool for systems genetic studies of large gene panels to reveal their cell-intrinsic functions at single-cell resolution in complex tissues. In this work, we demonstrated the application of in vivo Perturb-Seq to ASD/ND risk genes in the developing brain. This method can be applied across diverse diseases and tissues in the intact organism. ![Figure][3] In vivo Perturb-Seq identified neuron and glia-associated effects by perturbations of risk genes implicated in ASD/ND. De novo risk genes in this study were chosen from Satterstrom et al. (2018), and co-analysis with ASD patient data at bottom right is from Velmeshev et al. (2019); full citations for both are included in the full article online. The number of disease risk genes and loci identified through human genetic studies far outstrips the capacity to systematically study their functions. We applied a scalable genetic screening approach, in vivo Perturb-Seq, to functionally evaluate 35 autism spectrum disorder/neurodevelopmental delay (ASD/ND) de novo loss-of-function risk genes. Using CRISPR-Cas9, we introduced frameshift mutations in these risk genes in pools, within the developing mouse brain in utero, followed by single-cell RNA-sequencing of perturbed cells in the postnatal brain. We identified cell type–specific and evolutionarily conserved gene modules from both neuronal and glial cell classes. Recurrent gene modules and cell types are affected across this cohort of perturbations, representing key cellular effects across sets of ASD/ND risk genes. In vivo Perturb-Seq allows us to investigate how diverse mutations affect cell types and states in the developing organism. [1]: /lookup/doi/10.1126/science.aaz6063 [2]: /lookup/doi/10.1126/science.abf3661 [3]: pending:yes

Hiking4Autism - Fundraising for Ai4Autism, organized by SJ Mufti


I like hiking and I'm going to walk from Brighton to Scotland to raise money and awareness to kick-start our smart-device to help people with autism using the power of Ai. I'm doing this for the children I know who have autism, the people in my life with autistic family members and for people who want technology that genuinely makes lives better. Artificial Intelligence, or AI for short, is a study of automated systems, designed to help us with difficult tasks. Autism refers to a range of human conditions that could affect how a person relates to, interacts with and understands the world around them. There are currently over 700,000 diagnosed with Autism in the UK (Source