The purpose of this proposal is to investigate the effect of the spontaneous slow cortical oscillation in rat primary somatosensory cortex during anesthesia on the vibrissa evoked response of all cortical lamina. The slow oscillation is a non-stationary (temporally changing) brain state and such activity has been shown to induce trial-to-trial variability of the cortical response to stimulus in both animals and humans.
We aim to combine laminar electrophysiological recordings with insights from statistical data modeling and compartmental neuron simulations to test the hypothesis that the laminar extra-cellular local field potential (LFP) immediately preceding the stimulus can be used to form highly reliable predictions of the stimulus evoked LFP and population spiking (MUA) response. This will be accomplished using a three pronged approach. First we will collect simultaneous recordings of the LFP in all cortical layers using the laminar electrodes currently in use in our group. Second, we will use statistical analysis to examine the laminar dynamics of the spontaneous slow oscillation and its effect upon the magnitude and dynamics of the vibrissa evoked laminar response. Third, we will create a compartmental neuronal network model to simulate the laminar LFP oscillations and theoretically investigate the dependence of the response on transmembrane currents activated during different phases of the oscillation. Understanding the interactions between external stimuli and the internal state of the cortex is vital for achieving one NINDS's strategic planning goals: to """"""""unravel the complexities of information transfer within the brain"""""""". The proposed five year training program is designed to complement and build upon the candidate's theoretical background. It will provide him with expertise in three areas vital to his success as a neuroscientist, experimental electrophysiology, statistical modeling and compartmental neuron simulations. The broad long term goal is to understand how the brain can reliably perceive single presentations of an external stimulus. Relevance: Several neurological disorders, such as epilepsy are characterized by altered cortical states. Normal sleep oscillations, such as the slow oscillation central to this proposal, can develop into seizures resembling the 3 Hz spike wave complexes of absence epilepsy. Elucidating the interactions between synchronized non-stationary brain states and external stimuli is an important step towards understanding seizure genesis.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25NS052422-03
Application #
7616784
Study Section
NST-2 Subcommittee (NST)
Program Officer
Chen, Daofen
Project Start
2007-05-01
Project End
2012-04-30
Budget Start
2009-05-01
Budget End
2010-04-30
Support Year
3
Fiscal Year
2009
Total Cost
$176,952
Indirect Cost
Name
Massachusetts General Hospital
Department
Type
DUNS #
073130411
City
Boston
State
MA
Country
United States
Zip Code
02199
Haslinger, Robert; Pipa, Gordon; Lewis, Laura D et al. (2013) Encoding through patterns: regression tree-based neuronal population models. Neural Comput 25:1953-93
Haslinger, Robert; Pipa, Gordon; Lima, Bruss et al. (2012) Context matters: the illusive simplicity of macaque V1 receptive fields. PLoS One 7:e39699
Schumacher, Johannes; Haslinger, Robert; Pipa, Gordon (2012) Statistical modeling approach for detecting generalized synchronization. Phys Rev E Stat Nonlin Soft Matter Phys 85:056215
Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon (2011) Applying the multivariate time-rescaling theorem to neural population models. Neural Comput 23:1452-83
Coleman, Jason E; Nahmani, Marc; Gavornik, Jeffrey P et al. (2010) Rapid structural remodeling of thalamocortical synapses parallels experience-dependent functional plasticity in mouse primary visual cortex. J Neurosci 30:9670-82
Haslinger, Robert; Pipa, Gordon; Brown, Emery (2010) Discrete time rescaling theorem: determining goodness of fit for discrete time statistical models of neural spiking. Neural Comput 22:2477-506
Haslinger, Robert; Klinkner, Kristina Lisa; Shalizi, Cosma Rohilla (2010) The computational structure of spike trains. Neural Comput 22:121-57