Autism spectrum disorders (ASD) constitute a heterogeneous group of neurodevelopmental disorders characterized by impaired social-communication function, repetitive behaviors, and a restricted range of interests. ASD affects approximately 1 in every 88 individuals and the incidence appears to be rising. Within ASD, there exists wide phenotypic heterogeneity in adaptive function, cognitive and language abilities, and neurological comorbidities, leading many to refer to these various disorders as 'the autisms'. Therefore, two key questions emerge: (1) Despite phenotypic and genetic heterogeneity, does ASD have common neurobiological signatures? (2) Can such neurobiological signatures be studied in vivo in humans? Answers to these questions would represent a significant step forward in our understanding of ASD, as well as our ability to diagnose and treat this condition. We have recently developed a new technique for the analysis of resting state fMRI data based on mapping propagated intrinsic brain activity. When applied to fMRI data obtained in high-functioning adults with ASD, the new method appears to be more sensitive than conventional functional connectivity analysis in detecting focal brain abnormalities. The objective of this project is to apply this new technique to fMRI data collected in young, typically developing children, as well as children who develop ASD. Preliminary results indicate that our method detects changes, at the group level, in the temporal structure of intrinsic activity in 6-month-old children who subsequently develop clinical ASD. This finding is consistent with recent post-mortem pathology evidence of disrupted cortical organization in individuals with ASD very early in life, perhaps even in utero. The intimate relationships between the temporal structure of intrinsic activity, neuronal plasticity, and early brain development provide a strong theoretical basis for this investigation. Specific questions to be investigated include: (1) what is the earliest age at which patterns of propagated intrinsic activity in childre with ASD differ, at the group level, from typically developing children? (2) Are different brain regions affected by ASD at different ages? Answers to these questions may illuminate the neurobiological basis of ASD. By investigating the causes and developmental trajectory of ASD, our project is directly in line with the first two objectives of the NIMH strategic plan. We will aso explore how patterns of propagated intrinsic activity change over the course of typical early development to gain a better understanding of the neural correlates of normal developmental milestones. Characterization of normal development could inform future investigations of other neurodevelopmental disorders.
One of the important problems in the diagnosis of autism spectrums disorders (ASD) is the lack of a neurobiological signature that can be assessed non-invasively. We have developed a new technique for the analysis of resting state fMRI data that appears to identify focal brain abnormalities at the group level in children with ASD as early as 6 months of age. This project will investigate the reproducibility of our preliminary findings and study how the temporal features of intrinsic activity evolve during early development in typically developing children and children with ASD.
|Smyser, Christopher D; Snyder, Abraham Z; Shimony, Joshua S et al. (2016) Resting-State Network Complexity and Magnitude Are Reduced in Prematurely Born Infants. Cereb Cortex 26:322-33|
|Mitra, Anish; Snyder, Abraham Z; Hacker, Carl D et al. (2016) Human cortical-hippocampal dialogue in wake and slow-wave sleep. Proc Natl Acad Sci U S A 113:E6868-E6876|
|Mitra, Anish; Snyder, Abraham Z; Tagliazucchi, Enzo et al. (2015) Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep. Elife 4:|
|Mitra, Anish; Snyder, Abraham Z; Blazey, Tyler et al. (2015) Lag threads organize the brain's intrinsic activity. Proc Natl Acad Sci U S A 112:E2235-44|
|Palanca, Ben Julian A; Mitra, Anish; Larson-Prior, Linda et al. (2015) Resting-state Functional Magnetic Resonance Imaging Correlates of Sevoflurane-induced Unconsciousness. Anesthesiology 123:346-56|
|Mitra, A; Snyder, A Z; Hacker, C D et al. (2014) Lag structure in resting-state fMRI. J Neurophysiol 111:2374-91|