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Kahn, D.

Publications and source records attributed to Kahn, D..

2 recordsLinked to original sources

The Healthy Brain Network Biobank: An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

neuroscience

Correlated Evolution of Metabolic Functions over the Tree of Life

We are interested in the structure and evolution of metabolism in order to better understand its complexity. We study metabolic functions in 1459 species within which several hundreds of thousands of families of homologous genes have been identified [17]. Given a protein sequence, PRIAM search [5] delivers probabilities of the presence of several thousand enzymes (ECs). This allows us to infer reaction sets and to construct a metabolic network for an organism, given its set of sequences.\n\nWe then propagate these ECs to the ancestral nodes of the species tree using maximimum likelihood methods. These evolutionary scenarios are systematically compared using pairwise mutual information. We identify co-evolving enzyme sets from the graph of these relationships using community detection algorithms [1,3]. This sheds light on the structure of the metabolic networks in terms of co-evolving metabolic modules. These modules are also interpreted from a functional perspective using stoichiometric models of metabolic networks.

evolutionary biology