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Kurumada C, Rivera R, Allen P, Bennetto L. Perception and adaptation of receptive prosody in autistic adolescents. Sci Rep 2024; 14:16409. [PMID: 39013983 PMCID: PMC11252140 DOI: 10.1038/s41598-024-66569-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024] Open
Abstract
A fundamental aspect of language processing is inferring others' minds from subtle variations in speech. The same word or sentence can often convey different meanings depending on its tempo, timing, and intonation-features often referred to as prosody. Although autistic children and adults are known to experience difficulty in making such inferences, the science remains unclear as to why. We hypothesize that detail-oriented perception in autism may interfere with the inference process if it lacks the adaptivity required to cope with the variability ubiquitous in human speech. Using a novel prosodic continuum that shifts the sentence meaning gradiently from a statement (e.g., "It's raining") to a question (e.g., "It's raining?"), we have investigated the perception and adaptation of receptive prosody in autistic adolescents and two groups of non-autistic controls. Autistic adolescents showed attenuated adaptivity in categorizing prosody, whereas they were equivalent to controls in terms of discrimination accuracy. Combined with recent findings in segmental (e.g., phoneme) recognition, the current results provide the basis for an emerging research framework for attenuated flexibility and reduced influence of contextual feedback as a possible source of deficits that hinder linguistic and social communication in autism.
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Affiliation(s)
- Chigusa Kurumada
- Brain and Cognitive Sciences, University of Rochester, Rochester, 14627, USA.
| | - Rachel Rivera
- Psychology, University of Rochester, Rochester, 14627, USA
| | - Paul Allen
- Psychology, University of Rochester, Rochester, 14627, USA
- Otolaryngology, University of Rochester Medical Center, Rochester, 14642, USA
| | - Loisa Bennetto
- Psychology, University of Rochester, Rochester, 14627, USA
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Liu M, Zhang H, Liu M, Chen D, Zhuang Z, Wang X, Zhang L, Peng D, Wang Q. Randomizing Human Brain Function Representation for Brain Disease Diagnosis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2537-2546. [PMID: 38376975 DOI: 10.1109/tmi.2024.3368064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.
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Noel JP, Balzani E, Acerbi L, Benson J, Savin C, Angelaki DE. A common computational and neural anomaly across mouse models of autism. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593232. [PMID: 38766250 PMCID: PMC11100696 DOI: 10.1101/2024.05.08.593232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1 , Cntnap2 , and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals' long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.
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Sapey-Triomphe LA, Sanchez G, Hénaff MA, Sonié S, Schmitz C, Mattout J. Disentangling sensory precision and prior expectation of change in autism during tactile discrimination. NPJ SCIENCE OF LEARNING 2023; 8:54. [PMID: 38057355 DOI: 10.1038/s41539-023-00207-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Predictive coding theories suggest that core symptoms in autism spectrum disorders (ASD) may stem from atypical mechanisms of perceptual inference (i.e., inferring the hidden causes of sensations). Specifically, there would be an imbalance in the precision or weight ascribed to sensory inputs relative to prior expectations. Using three tactile behavioral tasks and computational modeling, we specifically targeted the implicit dynamics of sensory adaptation and perceptual learning in ASD. Participants were neurotypical and autistic adults without intellectual disability. In Experiment I, tactile detection thresholds and adaptation effects were measured to assess sensory precision. Experiments II and III relied on two-alternative forced choice tasks designed to elicit a time-order effect, where prior knowledge biases perceptual decisions. Our results suggest a subtler explanation than a simple imbalance in the prior/sensory weights, having to do with the dynamic nature of perception, that is the adjustment of precision weights to context. Compared to neurotypicals, autistic adults showed no difference in average performance and sensory sensitivity. Both groups managed to implicitly learn and adjust a prior that biased their perception. However, depending on the context, autistic participants showed no, normal or slower adaptation, a phenomenon that computational modeling of trial-to-trial responses helped us to associate with a higher expectation for sameness in ASD, and to dissociate from another observed robust difference in terms of response bias. These results point to atypical perceptual learning rather than altered perceptual inference per se, calling for further empirical and computational studies to refine the current predictive coding theories of ASD.
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Affiliation(s)
- Laurie-Anne Sapey-Triomphe
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France.
| | - Gaëtan Sanchez
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France
| | - Marie-Anne Hénaff
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France
| | - Sandrine Sonié
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France
- Centre de Ressource Autisme Rhône-Alpes, Centre Hospitalier Le Vinatier, Bron, France
- Hôpital Saint-Jean-de-Dieu, Lyon, France
| | - Christina Schmitz
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France
| | - Jérémie Mattout
- Université Claude Bernard Lyon 1, CNRS UMR5292, INSERM U1028, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, COPHY, F-69500, Bron, France
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