Joint Attention and Brain Functional Connectivity in Infants and Toddlers Cerebral Cortex


Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. The emergence of joint attention (JA), the coordinated orienting of 2 people toward an object or event, occurs during the first 2 years of life, arguably the most dynamic and important period of early child development (Scaife and Bruner 1975). It is theorized that engaging in JA lays the foundation for prosocial cooperative behavior, from basic social-communicative functioning and language development (Premack 2004) to sophisticated forms of empathy (Mundy and Jarrold 2010) and theory of mind (Adolphs 2003). In fact, early exhibition of joint attention is strongly associated with later language ability (Morales et al. 2000; Mundy et al. 2007), and atypical development of the initiation of joint attention (IJA) is strongly indicative of autism spectrum disorder (ASD) (Bruinsma et al. 2004). The neural substrates underlying IJA in early childhood are poorly understood (Barak and Feng 2016), due in part to significant methodological challenges in imaging localized brain function that supports social behaviors in children during the first 2 years of life.

Transcriptome and epigenome landscape of human cortical development modeled in organoids


The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome. We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue. Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids' epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells. Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs).

Scientists Use AI To Turn Brain Signals Into Speech


A recent research study could give a voice to those who no longer have one. Scientists used electrodes and artificial intelligence to create a device that can translate brain signals into speech. This technology could help restore the ability to speak in people with brain injuries or those with neurological disorders such as epilepsy, Alzheimer disease, multiple sclerosis, Parkinson's disease and more. The new system being developed in the laboratory of Edward Chang, MD shows that it is possible to create a synthesized version of a person's voice that can be controlled by the activity of their brain's speech centers. In the future, this approach could not only restore fluent communication to individuals with a severe speech disability, the authors say, but could also reproduce some of the musicality of the human voice that conveys the speaker's emotions and personality.

An Integrated Computational Framework for Attention, Reinforcement Learning, and Working Memory

AAAI Conferences

This paper proposes a reinterpretation of selective attention as a form of control of working memory based on self-generated reward signals and model-free reinforcement learning. In addition to being simple and parsimonious, this approach systematizes a number of classic psychological constructs without calling for additional, specific mechanisms. Finally, the papers presents the results of an empirical test of this framework, and elaborates on the implications of our findings for general models of control and intelligent behavior, as well as neurobiological models of the basal ganglia.

Forget the IQ test, MRI scans could reveal how smart you REALLY are

AITopics Original Links

The IQ test has long been dismissed as an inaccurate way to discern how intelligent a person really is - but now scientists may have found a better way. Researchers say MRI scans can measure human intelligence, and define exactly what it is. This could lead to radical leaps in AI with machines programmed to think in the same way we do. Researchers say MRI scans can measure human intelligence - and define what it is. This could lead to radical leaps in AI with machines programmed to think in the same way we do.