Tal Kenet, Ph.D. - US grants

[unreadable] DESCRIPTION (provided by applicant): The language and communication deficits that, in spite of the heterogeneous phenotype, by definition affect nearly autistic individuals to some degree, are perhaps the most devastating features of autism. We hypothesize that language impairments in autism are driven by early stage auditory cortical processing abnormalities and reduced connectivity between early auditory cortex and language areas. Indeed, several groups now show abnormal processing of auditory information in primary auditory cortex, and recent studies also point toward reduced functional connectivity in the autistic cortex. Another challenge of autism research is to establish links between functional response parameters, and anatomical and behavioral/clinical measures. The objectives of the proposed project are therefore (a) to characterize auditory processing deficits in autism, with an emphasis on local temporal dynamics in early auditory cortex and in language areas; (b) to study coherence of activity across brain regions that process auditory information, both within and across hemispheres, as a measure of functional connectivity; (c) to investigate the correlations between the extent of functional auditory processing impairments, and clinically determined language skills, as well as severity of the autism; (d) to coregister functional MEG data with multimodal anatomical MRI data in order to correlate tissue and functional abnormalities. Such levels of integration are necessary to establish quantitative biomarkers for early diagnosis, and to facilitate the design of specialized, well- targeted therapeutic interventions. To achieve these goals, auditory processing dynamics in healthy and autistic children ages 7 to 10 will be recorded using MEG, and mapped onto anatomical MRI data. Expected outcomes are (1) characterization of temporal dynamics of responses in auditory cortical areas in both populations (2) investigation of the hypothesis claiming reduced functional connectivity in autism with anatomical correlates, and (3) establishing whether cortical level impairments indeed lie at the origin of clinical observations of language and communication deficits that characterize autism, through correlations between functional and anatomical imaging measures, and behaviorally derived neurocognitive profiles. PUBLIC HEALTH RELEVANCE: Autism is a complex and devastating disorder which is diagnosed through a triad of behaviors and currently has full spectrum prevalence of about 1 in 175 children. In addition to affecting the individuals afflicted with the disorder, autism also affects not only the individual's family, but also the schools and communities, as the estimated cost of educating a child with autism in the public school system ranges from $50,000 to $80,000 per year, approximately 5 times the cost of educating a healthy child. This project aims to integrate functional and neuroanatomical studies of the neural substrates of autism, to better our understanding of the brain dysfunctions that cause autism, which in turn could contribute to the development of more effective treatments and, perhaps, approaches to allow for biological, rather than behavioral, and thus earlier, diagnosis. [unreadable] [unreadable] [unreadable]

DESCRIPTION (provided by applicant): Over the past decade, the hypothesis that autism spectrum disorder (ASD) is a disorder of reduced long-range and increased local functional connectivity has been gaining traction. To date, however, there is no evidence of increased local functional connectivity in ASD. If, furthermore, ASD is indeed a disorder of functional connectivity, then similar abnormalities ought to manifest in both the social communication (core/defining) and the non-social communication (non-core/non-defining) domains of deficits. Whether this is the case, however, has never been systematically tested in one group of participants. The objectives of the current proposal are to determine the nature of local and long-range functional connectivity abnormalities in ASD, the relationship between them, whether they are manifested similarly in both the core social and non-core non-social domains of ASD deficits, and their correlations with behavioral and structural measures. Our central hypotheses are that ASD is in fact a disorder of connectivity, and that reduced long-range and reduced, not increased, local functional connectivity are distributed, cortex-wide features of ASD, manifested in all domains of deficits. We further hypothesize that local and long-range functional connectivity are reduced proportionally to one another in ASD. These hypotheses will be tested by investigating local and long-range functional connectivity in the social communication (Aim 1) and non-social (Aim 2) domains of deficits of ASD, and their correlation with the ASD phenotype (Aim 3). The proposed studies will take advantage of MEG's high spatial and temporal resolutions to examine functional connectivity in 45 ASD children, ages 8-12, and 45 matched typically developing children, as they perform tasks that tap into core social communication (face perception and speech processing) and non-core (executive control and auditory processing) domains of ASD deficits. The proposed research is conceptually innovative because our hypothesis challenges the prevailing notion that local functional connectivity is increased in ASD, and further proposes a direct correlation between the reductions in local and long-range functional connectivity. Furthermore, it addresses multiple domains of deficits simultaneously in the same participants. The proposed research is analytically innovative because it uses novel analytical tools to study phase-amplitude cross-frequency coupling (a measure of local functional connectivity) non-invasively in cortical space. The proposed research is significant because (a) it will examine the nature of both local and long-range functional connectivity abnormalities in multiple domains of deficits simultaneously, (b) it will determine whether local and long-range functional connectivity abnormalities are directly correlated in ASD, (c) it will provide a novel approach for non-invasively measuring local functional connectivity in cortical space, (d) it will set the stage for developing novel functiona-connectivity based neurophysiological biomarkers for ASD, which have potential applications for treatment and early diagnosis.

ABSTRACT / PROJECT SUMMARY Functional connectivity in the brains of individuals with autism spectrum disorders (ASD) has emerged as an important marker of neural abnormalities associated with the disorder. However, despite hundreds of studies on the topic, the specific nature of functional connectivity abnormalities that characterize the disorder remains unresolved and no unifying framework has emerged to describe it. Constructing a consistent model of the functional connectivity abnormalities that underlie ASD is absolutely essential for advancing our understanding of the neural etiology of the disorder. While the commonly accepted model is one where long- range functional connectivity is decreased in ASD while local functional connectivity is increased, many studies have shown increased or normal long-range functional connectivity in ASD and the evidence supporting the hypothesis that local functional connectivity is increased remains scant and indirect. To date, the vast majority of studies of functional connectivity in ASD have been carried out using fMRI, a technique that relies on the hemodynamic response and thus has a temporal resolution of <1Hz. It is well known, however, that functional connectivity is usually mediated by much faster frequency bands, commonly divided into five fundamental frequency bands: delta (1-2 Hz), theta (3-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), and gamma (31-80 Hz). There is also recent evidence that these frequency bands mediate functional connectivity with preferred directionality. Based on our own preliminary data and current studies, we propose to test the hypothesis that ASD is characterized by increased long-range bottom-up (feedforward) functional connectivity, alongside decreased long-range top-down (feedback ) functional connectivity, and that the gamma and beta frequency bands, respectively, mediate these functional connectivity abnormalities. Here, we propose to test our hypothesis by obtaining MEG (magnetoencephalography) data from two spatial attention paradigms, visual and auditory, optimized for assessing bottom-up versus top-down functional connectivity, in 50 TD and 60 ASD individuals, ages 14-17. Specifically, we propose the following aims: (1) Test the hypothesis that bottom-up functional connectivity is abnormally increased in ASD in the auditory and visual domains, and this is manifested primarily in the gamma frequency band. (2) Test the hypothesis that top- down functional connectivity is abnormally reduced in ASD in the auditory and visual domains, and this is manifested primarily in the beta frequency band. (3) Test the hypothesis that neurophysiological functional connectivity measures derived using MEG will be predictive of ASD severity, diagnosis, and behavioral features, using robust correlations, canonical correlations, and machine learning techniques. We expect that the results of this study will lead to a substantially more detailed, comprehensive, and mechanistically motivated framework for the wide range of functional connectivity abnormalities observed in ASD.

ABSTRACT / SUMMARY There is currently a clear need for objective brain based outcome measures for autism spectrum disorders (ASD). Outcome measures are measures that are used before a clinical or intervention trial begins, to get a baseline measure, and are then repeated at the end of the trial, to determine whether changes have occurred as a result of the intervention. Currently, the vast majority of outcome measures used in ASD treatment trials are based on behavioral measures, usually parental, and are therefore subjective by nature. Even in double blind trials, there is still risk of contamination of the measures from a placebo effect. Therefore, there is a clear need for objective outcome measures in the field. One such class of potential outcome measures is brain-based. Since the most oft-found clinical lab equipment is a simple 10-20 EEG system, the ideal brain-based outcome measures for ASD would use such a system. However, the vast majority of known biomarkers for ASD, that would in theory have outcome measure potential, are derived using equipment that is far too complex and expensive to replicate in clinical settings, and using paradigms that require a setup and analyses methods that are too complex to carry out clinically on a large number of subjects with high inter-subject variability. Here, we propose a novel approach to this problem. We propose to translate three biomarkers for ASD identified in our lab or by other groups using magnetoencephalography (MEG), to clinical EEG. The study will focus on MEG-based biomarkers already known to be associated with ASD diagnosis and severity, one in the auditory domain, one in the tactile domain, and one using a relatively novel analysis of resting state data. The translation to clinical EEG would be judiciously carried out, by combining MEG and EEG data collection, and then using a data driven approach to validate the results. We will also develop a user-friendly toolbox as part of the process, to standardize data analysis and make the process easily portable and uniform across multiple sites. The initial development will be carried out using 10 typically developing participants, and 10 ASD participants, using high density simultaneous EEG/MEG data collection. The results will then be validated using 15 new participants per group, and simultaneous ?clinical EEG? (i.e. minimal sensor locations) and high-density MEG data collection. This proof of concept proposal will take place entirely onsite, and will form the basis for future offsite studies across multiple clinical settings, ideally in conjunction with ongoing clinical trials.