One of the most important techniques in learning about the functioning of healthy and diseased brains has involved examining neural activity in laboratory animals under varying circumstances. Fundamental knowledge has been obtained by determining experimental conditions, often intended to mimic pathological states, during which neurons in a particular location become increasingly active or inactive. Neural information is represented and communicated through series of action potentials, or "spike trains," and interest often centers on the evolution of neural activity over time in response to some stimulus or behavior. The primary focus of this research is development of methods for analysis of neural spike train data within the statistical framework of nonstationary (time-varying) point processes (processes involving sequences of event times, here spikes). New high-dimensional generalized regression methods will allow characterization of high-dimensional neural stimulus effects that evolve across time, and will allow many diverse effects on neural firing to be considered simultaneously. More powerful use of the data will come from a new variation on statistical clustering in which a covariate is involved-this will combine physiological goals (producing the covariate) with the process of spike identification known as "spike sorting" (which involves clustering). Neuroscientific experiments often produce nonstationary time series, but very frequently these are observed across many repeated trials. By taking advantage of this special circumstance new hierarchical Bayesian models will be developed to reduce dimensionality of complicated tasks, and to reduce the complexity of simultaneously-recorded neural signals. New multiple point process methods based on continuous-time loglinear modeling, conditional-intensity modeling, and functional clustering will provide descriptions of interactions among neural spike trains at multiple time scales. This research will produce a set of tools for identifying change in neural network function following experimental manipulations, such as those producing the cognitive deficits seen in psychiatric disorders.

Public Health Relevance

Neurophysiological experiments increasingly rely on sophisticated statistical analysis of neural activity. By creating new statistical tools for neural data analysis, this research will help advance the understanding of diseases of the nervous system, including those involving cognitive impairment associated with psychiatric disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH064537-10
Application #
8247150
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Glanzman, Dennis L
Project Start
2001-09-26
Project End
2014-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
10
Fiscal Year
2012
Total Cost
$367,550
Indirect Cost
$120,050
Name
Carnegie-Mellon University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
052184116
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213
Vinci, Giuseppe; Ventura, Valérie; Smith, Matthew A et al. (2016) Separating Spike Count Correlation from Firing Rate Correlation. Neural Comput 28:849-81
Wang, Wanjie; Tripathy, Shreejoy J; Padmanabhan, Krishnan et al. (2015) An Empirical Model for Reliable Spiking Activity. Neural Comput 27:1609-23
Ventura, Valérie; Todorova, Sonia (2015) A computationally efficient method for incorporating spike waveform information into decoding algorithms. Neural Comput 27:1033-50
Zhou, Pengcheng; Burton, Shawn D; Snyder, Adam C et al. (2015) Establishing a Statistical Link between Network Oscillations and Neural Synchrony. PLoS Comput Biol 11:e1004549
Perel, Sagi; Schwartz, Andrew B; Ventura, Valérie (2014) Automatic scan test for detection of functional connectivity between cortex and muscles. J Neurophysiol 112:490-9
Todorova, Sonia; Sadtler, Patrick; Batista, Aaron et al. (2014) To sort or not to sort: the impact of spike-sorting on neural decoding performance. J Neural Eng 11:056005
Perel, Sagi; Schwartz, Andrew B; Ventura, Valerie (2014) Single-snippet analysis for detection of postspike effects. Neural Comput 26:40-56
Rager, Danielle M; Alvares, Darren; Birznieks, Ingvars et al. (2013) Generating tactile afferent stimulation patterns for slip and touch feedback in neural prosthetics. Conf Proc IEEE Eng Med Biol Soc 2013:5922-5
Perez, Oswaldo; Kass, Robert E; Merchant, Hugo (2013) Trial time warping to discriminate stimulus-related from movement-related neural activity. J Neurosci Methods 212:203-10
Amarasingham, Asohan; Harrison, Matthew T; Hatsopoulos, Nicholas G et al. (2012) Conditional modeling and the jitter method of spike resampling. J Neurophysiol 107:517-31

Showing the most recent 10 out of 40 publications