%0 Conference Paper %B Conference on Cognitive Computational Neuroscience %D 2019 %T Are topographic deep convolutional neural networks better models of the ventral visual stream? %A K.M. Jozwik %A Lee, H. %A Nancy Kanwisher %A James J. DiCarlo %B Conference on Cognitive Computational Neuroscience %G eng %0 Conference Paper %B The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 %D 2019 %T Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge %A K.M. Jozwik %A Lee, M. %A Marques, T. %A Martin Schrimpf %A Pouya Bashivan %B The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 %C MIT, Cambridge MA %8 8/14/2019 %G eng %U https://www.biorxiv.org/content/10.1101/689844v2.full %R 10.1101/689844 %0 Conference Paper %B BioRxiv %D 2019 %T To find better neural network models of human vision, find better neural network models of primate vision %A K.M. Jozwik %A Martin Schrimpf %A Nancy Kanwisher %A James J. DiCarlo %X

Specific deep artificial neural networks (ANNs) are the current best models of ventral visual processing and object recognition behavior in monkeys. We here explore whether models of non-human primate vision generalize to visual processing in the human primate brain. Specifically, we asked if model match to monkey IT is a predictor of model match to human IT, even when scoring those matches on different images. We found that the model match to monkey IT is a positive predictor of the model match to human IT (R = 0.36), and that this approach outperforms the current standard predictor of model accuracy on ImageNet. This suggests a more powerful approach for pre-selecting models as hypotheses of human brain processing.

%B BioRxiv %G eng %U https://www.biorxiv.org/content/10.1101/688390v1.full %0 Conference Paper %B BioRxiv %D 2018 %T Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons %A Adhya, D. %A Swarup, V. %A Nowosaid, P. %A Shum, C. %A K.M. Jozwik %A McAlonan, G. %A Mendez, M.A. %A Horder, J. %A Murphy, D. %A Geschwind, D.H %A Price, J. %A Carroll, J. %A Srivastava, D.P. %A Baron-Cohen, S. %X

Autism Spectrum Conditions (henceforth, autism) are a diverse set of neurodevelopmental phenotypes with a complex genetic basis. Idiopathic autism, characterized by a diagnosis of autism not caused by a known genetic variant, is associated with hundreds of rare and common genetic variants each of small effect. Functional genomics analyses of post mortem brain tissue have identified convergent atypical gene correlation networks in idiopathic autism. However, post mortem tissue is difficult to obtain and is susceptible to unknown confounding factors related to the cause of death and to storage conditions. To circumvent these limitations, we created induced pluripotent stem cells (iPSCs) from hair follicles of idiopathic autistic individuals and made iPSC-derived neurons, to investigate its usefulness as a substitute for post mortem brain tissue. Plucking hair follicles is a relatively painless and ethical procedure, and hair samples can be obtained from anyone. Functional genomics analyses were used as a replicable analysis pipeline to assess efficacy of iPSC-derived neurons. Gene expression networks, previously identified in adult autism brains, were atypical in the iPSC autism neural cultures in this study. These included those associated with neuronal maturation, synaptic maturation, immune response and inflammation, and gene regulatory mechanisms. In addition, GABRA4, HTR7, ROBO1 and SLITRK5 were atypically expressed among genes previously associated with autism. A drawback of this study was its small sample size, reflecting practical challenges in generating iPSCs from patient cohorts. We conclude that, using rigorous functional genomics analyses, atypical molecular processes seen in the adult autistic postmortem brain can be modelled in hair follicle iPSC-derived neurons. There is thus potential for scaling up of autism transcriptome studies using an iPSC-based model system.

%B BioRxiv %8 6/19/2018 %G eng %R 10.1101/349415