Biology subjects

Zhan, J.

Publications and source records attributed to Zhan, J..

3 recordsLinked to original sources

bioRxiv2018

Dynamic Construction of Reduced Representations in the Brain for Perceptual Decision Behavior

Current models propose that the brain uses a multi-layered architecture to reduce the high dimensional visual input to lower dimensional representations that support face, object and scene categorizations. However, understanding the brain mechanisms that support such information reduction for behavior remains challenging. We addressed the challenge using a novel information theoretic framework that quantifies the relationships between three key variables: single-trial information randomly sampled from an ambiguous scene, source-space MEG responses and perceptual decision behaviors. In each observer, behavioral analysis revealed the scene features that subtend their decisions. Independent source space analyses revealed the flow of these and other features in cortical activity. We show where (at the junction between occipital cortex and ventral regions), when (up until 170 ms post stimulus) and how (by separating task-relevant and irrelevant features) brain regions reduce the high-dimensional scene to construct task-relevant feature representations in the right fusiform gyrus that support decisions. Our results inform the occipito-temporal pathway mechanisms that reduce and select information to produce behavior.

neuroscience↗

bioRxiv2018

Discovering patterns of pleiotropy in genome-wide association studies

MotivationGenome-wide association studies have had great success in identifying human genetic variants associated with disease, disease risk factors, and other biomedical phenotypes. Many variants are associated with multiple traits, even after correction for trait-trait correlation. Discovering subsets of variants associated with a shared subset of phenotypes could help reveal disease mechanisms, suggest new therapeutic options, and increase the power to detect additional variants with similar pattern of associations. Here we introduce two methods based on a Bayesian framework, SNP And Pleiotropic PHenotype Organization (SAPPHO), one modeling independent phenotypes (SAPPHO-I) and the other incorporating a full phenotype covariance structure (SAPPHO-C). These two methods learn patterns of pleiotropy from genotype and phenotype data, using identified associations to discover additional associations with shared patterns.\n\nResultsThe SAPPHO methods, along with other recent approaches for pleiotropic association tests, were assessed using data from the Atherosclerotic Risk in Communities (ARIC) study of 8,000 individuals, whose gold-standard associations were provided by meta-analysis of 40,000 to 100,000 individuals from the CHARGE consortium. Using power to detect gold-standard associations at genome-wide significance (0.05 family-wise error rate) as a metric, SAPPHO performed best. The SAPPHO methods were also uniquely able to select the most significant variants in a parsimonious model, excluding other less likely variants within a linkage disequilibrium block. For meta-analysis, the SAPPHO methods implement summary modes that use sufficient statistics rather than full phenotype and genotype data. Meta-analysis applied to CHARGE detected 16 additional associations to the gold-standard loci, as well as 124 novel loci, at 0.05 false discovery rate. Reasons for the superior performance were explored by performing simulations over a range of scenarios describing different genetic architectures. With SAPPHO we were able to learn genetic structures that were hidden using the traditional univariate tests.\n\nAvailabilityhttps://bitbucket.org/baderlab/fast/wiki/Home. SAPPHO software is available under the GNU General Public License, v2.

genetics↗

bioRxiv2017

Efficient Information Contents Flow Down from Memory to Predict the Identity of Faces

In the sciences of cognition, an influential idea is that the brain makes predictions about incoming sensory information to reduce inherent ambiguity. In the visual hierarchy, this implies that information content originating in memory-the identity of a face-propagates down to disambiguate incoming stimulus information. However, understanding this powerful prediction-for-recognition mechanism will remain elusive until we uncover the content of the information propagating down from memory. Here, we address this foundational limitation with a task ubiquitous to humans-familiar face identification. We developed a unique computer graphics platform that combines a generative model of random face identity information with the subjectivity of perception. In 14 individual participants, we reverse engineered the predicted information contents propagating down from memory to identify 4 familiar faces. In a follow-up validation, we used the predicted face information to synthesize the identity of new faces and confirmed the causal role of the predictions in face identification. We show these predictions comprise both local 3D surface patches, such as a particularly thin and pointy nose combined with a square chin and a prominent brow, or more global surface characteristics, such as a longer or broader face. Further analyses reveal that the predicted contents are efficient because they represent objective features that maximally distinguish each identity from a model norm. Our results reveal the contents that propagate down the visual hierarchy from memory, showing this coding scheme is efficient and compatible with norm-based coding, with implications for mechanistic accounts of brain and machine intelligence.

neuroscience↗