Theory & Data Analysis Tools

CRCNS: Collaboration toward an experimentally validated multiscale model of rTMS

Mental health diseases such as depression are a major burden on society and new treatment options are strongly needed. One strategic goal of NIMH is to develop novel therapies based on discoveries in neuroscience. TMS is a non-invasive neuromodulation technique that can directly interfere with brain activity. TMS is FDA-approved for depression treatment, however, shows mixed efficacy.

CRCNS: Optimization of closed-loop control of gamma oscillations

Throughout the brain, specialized systems carry out different but complementary functions, sometimes independently but often in cooperation. However, we do not understand how their activity is dynamically coordinated, and dysregulation of this is associated with many mental health conditions. Neuronal oscillations, which are detectable in local field potentials (LFPs) at various frequencies, are a promising target for this coordination.

CRCNS: Neurocomputational Study of Reward-Related Decision-Making & Uncertainty

Humans and animals often make decisions under uncertainty, whereby each decision affects not only the immediate reward gain but also longer-term information gain. While important advances have been made in understanding human learning and decision-making, there is still a lack of understanding of the different motivational factors that come into play when the behavioral context confers systematically varying amounts of reward and information gain.

CRCNS: Functional Brain Networks with Tensioned Stability for Optimal Processing

Understanding the brain processes underlying alcohol use and misuse are essential for the development of effective treatments for alcohol use disorder or AUD. Human brain imaging has greatly contributed to our current understanding of AUD, but much more remains to be understood. Most recently, human neuroscience has been transformed by the integration of network science and neuroimaging (now coined network neuroscience). Functional brain imaging is used to generate networks to examine interconnected groups of synchronized brain regions.

CRCNS: Diverse effects of GABAergic inputs on a basal ganglia output center

The basal ganglia are a collection of subcortical nuclei studied for their contributions to movement, action selection, habit formation, and reward learning as well as their dysfunction in movement disorders. While basal ganglia processing of cortical inputs and the emergence of the direct and indirect pathway communication channels within the striatum have been the subject of extensive investigation, the integration of these channels at the level of basal ganglia output nuclei including the substantia nigra pars reticulata (SNr) has been relatively understudied.

CRCNS: Multiple Time Scale Memory Consolidation in Neural Networks

Detailed description of the proposed use of the animals, including species, strains, ages, sex, and number to be used; Dissociated, primary cultures will be prepared from the cortex of new born mice of either sex (mus musculus, Postnatal day 0-1). These experiments will be performed using pups obtained from Vglut1-IRES2-Cre strains mated with Floxopatch (Lou et al., 2016) strains so that pyramidal cells will express channel rhodopsin CheRiff and voltage indicator QuasR2. Up to 5 cultures can be grown, which can be used for 5 experiments.

CRCNS: The Role of Statistical Structure for Natural Sound Recognition in Noise

The ability to listen and identify sounds in the presence of competing background noise is a critical function of the healthy auditory system. Humans with normal hearing can easily carry a conversation even with relatively high levels of noise and in complex auditory environments, such as a busy restaurant. Yet, for individuals with hearing loss even moderate levels of background noise can adversely impact sound recognition. Understanding the neural mechanisms that underlie recognition in noise is thus of high clinically relevance.

CRCNS: Multimodal Dynamic Causal Learning for Neuroimaging

CRCNS Research Proposal: Collaborative Research: Multimodal Dynamic Causal Learning for Neuroimaging A Project Description A.1 Introduction Many analyses of fMRI and other neuroimaging data aim to discover the underlying causal or commu- nication structures that generated that activity.1,2 An accurate characterization of these brain structures is important for understanding neural circuits, systems-level neuroscience, and the neural bases of var- ious cognitive psychological phenomena or mental diseases.

An Ecosystem of Technology and Protocols for Adaptive Neuromodulation Research in Humans

Project Summary/Abstract Neurological and psychiatric disorders affect millions of people in the United States and worldwide, and produce a third of all health care costs. Recent research has produced encouraging evidence that adaptive neuromodulation can induce nervous system plasticity that produces long-lasting improvements in certain neurological disorders such as stroke.

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