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      Altered brain-wide auditory networks in a zebrafish model of fragile X syndrome

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          Abstract

          Background

          Loss or disrupted expression of the FMR1 gene causes fragile X syndrome (FXS), the most common monogenetic form of autism in humans. Although disruptions in sensory processing are core traits of FXS and autism, the neural underpinnings of these phenotypes are poorly understood. Using calcium imaging to record from the entire brain at cellular resolution, we investigated neuronal responses to visual and auditory stimuli in larval zebrafish, using fmr1 mutants to model FXS. The purpose of this study was to model the alterations of sensory networks, brain-wide and at cellular resolution, that underlie the sensory aspects of FXS and autism.

          Results

          Combining functional analyses with the neurons’ anatomical positions, we found that fmr1 −/− animals have normal responses to visual motion. However, there were several alterations in the auditory processing of fmr1 −/− animals. Auditory responses were more plentiful in hindbrain structures and in the thalamus. The thalamus, torus semicircularis, and tegmentum had clusters of neurons that responded more strongly to auditory stimuli in fmr1 −/− animals. Functional connectivity networks showed more inter-regional connectivity at lower sound intensities (a − 3 to − 6 dB shift) in fmr1 −/− larvae compared to wild type. Finally, the decoding capacities of specific components of the ascending auditory pathway were altered: the octavolateralis nucleus within the hindbrain had significantly stronger decoding of auditory amplitude while the telencephalon had weaker decoding in fmr1 −/− mutants.

          Conclusions

          We demonstrated that fmr1 −/− larvae are hypersensitive to sound, with a 3–6 dB shift in sensitivity, and identified four sub-cortical brain regions with more plentiful responses and/or greater response strengths to auditory stimuli. We also constructed an experimentally supported model of how auditory information may be processed brain-wide in fmr1 −/− larvae. Our model suggests that the early ascending auditory pathway transmits more auditory information, with less filtering and modulation, in this model of FXS.

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          Most cited references49

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          Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

          We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.
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            Salience network-based classification and prediction of symptom severity in children with autism.

            Autism spectrum disorder (ASD) affects 1 in 88 children and is characterized by a complex phenotype, including social, communicative, and sensorimotor deficits. Autism spectrum disorder has been linked with atypical connectivity across multiple brain systems, yet the nature of these differences in young children with the disorder is not well understood. To examine connectivity of large-scale brain networks and determine whether specific networks can distinguish children with ASD from typically developing (TD) children and predict symptom severity in children with ASD. Case-control study performed at Stanford University School of Medicine of 20 children 7 to 12 years old with ASD and 20 age-, sex-, and IQ-matched TD children. Between-group differences in intrinsic functional connectivity of large-scale brain networks, performance of a classifier built to discriminate children with ASD from TD children based on specific brain networks, and correlations between brain networks and core symptoms of ASD. We observed stronger functional connectivity within several large-scale brain networks in children with ASD compared with TD children. This hyperconnectivity in ASD encompassed salience, default mode, frontotemporal, motor, and visual networks. This hyperconnectivity result was replicated in an independent cohort obtained from publicly available databases. Using maps of each individual's salience network, children with ASD could be discriminated from TD children with a classification accuracy of 78%, with 75% sensitivity and 80% specificity. The salience network showed the highest classification accuracy among all networks examined, and the blood oxygen-level dependent signal in this network predicted restricted and repetitive behavior scores. The classifier discriminated ASD from TD in the independent sample with 83% accuracy, 67% sensitivity, and 100% specificity. Salience network hyperconnectivity may be a distinguishing feature in children with ASD. Quantification of brain network connectivity is a step toward developing biomarkers for objectively identifying children with ASD.
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              Imbalance of neocortical excitation and inhibition and altered UP states reflect network hyperexcitability in the mouse model of fragile X syndrome.

              Despite the pronounced neurological deficits associated with mental retardation and autism, it is unknown if altered neocortical circuit function occurs in these prevalent disorders. Here we demonstrate specific alterations in local synaptic connections, membrane excitability, and circuit activity of defined neuron types in sensory neocortex of the mouse model of Fragile X Syndrome-the Fmr1 knockout (KO). Overall, these alterations result in hyperexcitability of neocortical circuits in the Fmr1 KO. Specifically, we observe a substantial deficit in local excitatory drive ( approximately 50%) targeting fast-spiking (FS) inhibitory neurons in layer 4 of somatosensory, barrel cortex. This persists until at least 4 wk of age suggesting it may be permanent. In contrast, monosynaptic GABAergic synaptic transmission was unaffected. Overall, these changes indicate that local feedback inhibition in neocortical layer 4 is severely impaired in the Fmr1 KO mouse. An increase in the intrinsic membrane excitability of excitatory neurons may further contribute to hyperexcitability of cortical networks. In support of this idea, persistent neocortical circuit activity, or UP states, elicited by thalamic stimulation was longer in duration in the Fmr1 KO mouse. In addition, network inhibition during the UP state was less synchronous, including a 14% decrease in synchrony in the gamma frequency range (30-80 Hz). These circuit changes may be involved in sensory stimulus hypersensitivity, epilepsy, and cognitive impairment associated with Fragile X and autism.
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                Author and article information

                Contributors
                g.vanwalleghem@uq.edu.au
                ethan.scott@uq.edu.au
                Journal
                BMC Biol
                BMC Biol
                BMC Biology
                BioMed Central (London )
                1741-7007
                16 September 2020
                16 September 2020
                2020
                : 18
                : 125
                Affiliations
                [1 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Queensland Brain Institute, , The University of Queensland, ; St Lucia, Brisbane, QLD 4072 Australia
                [2 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, School of Mathematics and Physics, , The University of Queensland, ; Brisbane, 4072 Australia
                [3 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Australian Institute for Bioengineering and Nanotechnology, , The University of Queensland, ; Brisbane, QLD 4072 Australia
                Author information
                http://orcid.org/0000-0003-3150-9216
                Article
                857
                10.1186/s12915-020-00857-6
                7493858
                32938458
                22f18fa5-c3be-491d-b84e-ba13d646a39b
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 15 July 2020
                : 26 August 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000893, Simons Foundation;
                Award ID: 399432
                Award ID: 625793
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1066887
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100000923, Australian Research Council;
                Award ID: DP140102036
                Award ID: DP110103612
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Life sciences
                fragile x syndrome,autism spectrum disorder,brain/physiopathology,auditory perception,sensory systems,cgamp,calcium imaging,zebrafish,light-sheet microscopy,graph theory

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