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2021 Neuroscience Graduate Program Virtual Retreat
Keynote lecture: "Generating New Neurons and Protecting Old Neurons in Vision Restoration"

Keynote Speaker: Bo Chen, PhD - Icahn School of Medicine at Mount Sinai

 May 28, 2021 @ 9:00 a.m.

Calcium-independent astrocytic lipid release modulates neuronal excitability - Faculty Candidate Seminar

Nathan Anthony Smith, M.S, Ph.D. - Director of Basic Neuroscience Research
Center for Neuroscience Research
Principal Investigator, Center for Neuroscience Research
Children's National Hospital
Assistant Professor of Pediatrics & Pharmacology and Physiology
George Washington University School of Medicine and Health Sciences

Accumulating data point to a key role of Ca2+-dependent gliotransmitter release as a modulator of neuronal networks. Here, we tested the hypothesis that astrocytes in response to agonist exposure also release lipid modulators through activation of Ca2+-independent phospholipase A2 (iPLA2) activity. We found that cultured rat astrocytes treated with selective ATP and glutamatergic agonists released arachidonic acid (AA) and/or its derivatives, including the endogenous cannabinoid 2-arachidonoyl-sn-glycerol (2AG) and prostaglandin E2 (PGE2). Surprisingly, buffering of cytosolic Ca2+ resulted in a sharp increase in agonist-induced astrocytic lipid release. In addition, astrocytic release of PGE2 enhanced miniature excitatory post-synaptic potentials (mEPSPs) by inhibiting the opening of neuronal Kv channels in brain slices. This study provides the first evidence for the existence of a Ca2+-independent pathway regulating the release of PGE2 from astrocytes, and furthermore demonstrates a functional role for astrocytic lipid release in the modulation of synaptic activity.

 Mar 04, 2021 @ 4:00 p.m.

Host: University of Rochester
School of Medicine and Dentistry
Department of Neuroscience and the Del Monte Institute for Neuroscience

Inferring the Cortical Computations of Hearing - Faculty Candidate Seminar

Sam V Norman-Haignere, Ph.D. - Columbia University
Mind Brain Behavior Institute

To understand the meaning of a sentence, recognize a familiar voice in a crowd, or pick out the melody in a song, the brain must rapidly recognize, remember, and synthesize information across timescales spanning milliseconds to seconds. Human auditory cortex is essential to this process, but many basic questions about its organization and computational properties remain unanswered, in part due to the inherent challenge of probing responses to high-dimensional natural stimuli using coarse and noisy neuroimaging methods. In this talk, I will describe three methodological advances that make it possible to: (1) infer brain organization from responses to high-dimensional natural stimuli (2) test whether a computational model can explain a neural response by comparing natural and “model-matched” stimuli and (3) infer how complex sensory responses integrate temporal information in natural stimuli. By applying these methods to neuroimaging data (fMRI, functional ultrasound) and human intracranial recordings, I have uncovered some of the dominant response dimensions that organize human auditory cortex, inferred computational principles that underlie their response, and begun to unravel how auditory cortex flexibly integrates across the multiscale temporal structure that defines natural sounds like speech and music.

 Feb 11, 2021 @ 3:30 p.m.

Host: University of Rochester School of Medicine & Dentistry
Department of Biostatistics & Computational Biology
Del Monte Neuroscience Institute

Integrative Network Learning for Multi-modality Biomarker Data - Faculty Candidate Seminar

Shanghong Xie, Ph.D. - Postdoctoral Research Scientist
Department of Biostatistics
Mailman School of Public Health
Columbia University

Biomarkers are often organized into networks of connected regions. The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same underlying biological model. In addition, substantial heterogeneity in networks between individuals and subgroups of individuals is observed. In this talk, I will present a node-wise biomarker graphical model to leverage the shared mechanism between multi-modality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network and the information from the external modality is incorporated to model the distribution of the underlying biological network. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington’s disease by using sMRI data and DTI data. The identified network connections are used as new features to improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis. I will also discuss other methods and applications in the end.

 Jan 14, 2021 @ 3:30 p.m.

Host: Department of Biostatistics & Computational Biology
Del Monte Neuroscience Institute