Analysis of Morris Water Maze data with Bayesian statistical methods

Neuroscientists commonly use a Morris Water Maze to assess learning in rodents. In his kind of a maze, the subjects learn to swim toward a platform hidden in opaque water as they orient themselves according to the cues on the walls. This protocol presents a challenge to statistical analysis, because an artificial cut-off must be set for those experimental subjects that do not reach the platform so as they do not drown from exhaustion. This fact leads to the data being right censored. In our experimental data, which compares learning in rodents that have chemically induced symptoms of schizophrenia to a control group of rodents a cut-off of 60 seconds was used, and is the mode of the distribution. Utilizing Bayesian inferential procedures, we account for the censoring in the data and compare the results of learning between the treatment and control group

Temporal Dynamics of Hippocampal and Medial Prefrontal Cortex Interactions During the Delay Period of a Working Memory-Guided Foraging Task

Abstract: Connections between the hippocampus (HC) and medial prefrontal cortex (mPFC) are critical for working memory; however, the precise contribution of this pathway is a matter of debate. One suggestion is that it may stabilize retrospective memories of recently encountered task-relevant information. Alternatively, it may be involved in encoding prospective memories, or the internal representation of future goals. To explore these possibilities, simultaneous extracellular recordings were made from mPFC and HC of rats performing the delayed spatial win-shift on a radial maze. Each trial consisted of a training-phase (when 4 randomly chosen arms were open) and test phase (all 8 arms were open but only previously blocked arms contained food) separated by a 60-s delay. Theta power was highest during the delay, and mPFC units were more likely to become entrained to hippocampal theta as the delay progressed. Training and test phase performance were accurately predicted by a linear classifier, and there was a transition in classification for training-phase to test-phase activity patterns throughout the delay on trials where the rats performed well. These data suggest that the HC and mPFC become more strongly synchronized as mPFC circuits preferentially shift from encoding retrospective to prospective informatio

Rich-Club Organization in Effective Connectivity among Cortical Neurons.

The performance of complex networks, like the brain, depends on how effectively their elements communicate. Despite the importance of communication, it is virtually unknown how information is transferred in local cortical networks, consisting of hundreds of closely spaced neurons. To address this, it is important to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512-electrode array (60 μm spacing) to record spontaneous activity at 20 kHz from up to 500 neurons simultaneously in slice cultures of mouse somatosensory cortex for 1 h at a time. We applied a previously validated version of transfer entropy to quantify information transfer. Similar to in vivo reports, we found an approximately lognormal distribution of firing rates. Pairwise information transfer strengths also were nearly lognormally distributed, similar to reports of synaptic strengths. Some neurons transferred and received much more information than others, which is consistent with previous predictions. Neurons with the highest outgoing and incoming information transfer were more strongly connected to each other than chance, thus forming a “rich club.” We found similar results in networks recorded in vivo from rodent cortex, suggesting the generality of these findings. A rich-club structure has been found previously in large-scale human brain networks and is thought to facilitate communication between cortical regions. The discovery of a small, but information-rich, subset of neurons within cortical regions suggests that this population will play a vital role in communication, learning, and memory.SIGNIFICANCE STATEMENT Many studies have focused on communication networks between cortical brain regions. In contrast, very few studies have examined communication networks within a cortical region. This is the first study to combine such a large number of neurons (several hundred at a time) with such high temporal resolution (so we can know the direction of communication between neurons) for mapping networks within cortex. We found that information was not transferred equally through all neurons. Instead, ∼70% of the information passed through only 20% of the neurons. Network models suggest that this highly concentrated pattern of information transfer would be both efficient and robust to damage. Therefore, this work may help in understanding how the cortex processes information and responds to neurodegenerative diseases

High-Degree Neurons Feed Cortical Computations

Recent work has shown that functional connectivity among cortical neurons is highly varied, with a small percentage of neurons having many more connections than others. Also, recent theoretical developments now make it possible to quantify how neurons modify information from the connections they receive. Therefore, it is now possible to investigate how information modification, or computation, depends on the number of connections a neuron receives (in-degree) or sends out (out-degree). To do this, we recorded the simultaneous spiking activity of hundreds of neurons in cortico-hippocampal slice cultures using a high-density 512-electrode array. This preparation and recording method combination produced large numbers of neurons recorded at temporal and spatial resolutions that are not currently available in any in vivo recording system. We utilized transfer entropy (a well-established method for detecting linear and nonlinear interactions in time series) and the partial information decomposition (a powerful, recently developed tool for dissecting multivariate information processing into distinct parts) to quantify computation between neurons where information flows converged. We found that computations did not occur equally in all neurons throughout the networks. Surprisingly, neurons that computed large amounts of information tended to receive connections from high out-degree neurons. However, the in-degree of a neuron was not related to the amount of information it computed. To gain insight into these findings, we developed a simple feedforward network model. We found that a degree-modified Hebbian wiring rule best reproduced the pattern of computation and degree correlation results seen in the real data. Interestingly, this rule also maximized signal propagation in the presence of network-wide correlations, suggesting a mechanism by which cortex could deal with common random background input. These are the first results to show that the extent to which a neuron modifies incoming information streams depends on its topological location in the surrounding functional network

2009- 2010 UNLV McNair Journal

Journal articles based on research conducted by undergraduate students in the McNair Scholars Program Table of Contents Biography of Dr. Ronald E. McNair Statements: Dr. Neal J. Smatresk, UNLV President Dr. Juanita P. Fain, Vice President of Student Affairs Dr. William W. Sullivan, Associate Vice President for Retention and Outreach Mr. Keith Rogers, Deputy Executive Director of the Center for Academic Enrichment and Outreach McNair Scholars Institute Staf

2011-2012 UNLV McNair Journal

Journal articles based on research conducted by undergraduate students in the McNair Scholars Program Table of Contents Biography of Dr. Ronald E. McNair Statements: Dr. Neal J. Smatresk, UNLV President Dr. Juanita P. Fain, Vice President of Student Affairs Dr. William W. Sullivan, Associate Vice President for Retention and Outreach Mr. Keith Rogers, Deputy Executive Director of the Center for Academic Enrichment and Outreach McNair Scholars Institute Staf