matlab scripts for characterising multiple single-unit ...

MATLAB SCRIPTS FOR CHARACTERISING MULTIPLE SINGLE-UNIT SPIKE TRAINS: A STUDY OF RAT MEDIAL PREFRONTAL CORTEX & HIPPOCAMPUS Georgina Fenton1,2, Margarita Zachariou3 & Rob Mason1 1School

Neuronal Networks Laboratory

of Life Sciences, University of Nottingham Medical School UK, 2Department of Biology, University of Leicester UK & 3Department of Computer Science, University of Cyprus, Cyprus

INTRODUCTION • During in vivo multi-electrode electrophysiological studies in our laboratory it became evident that a more time efficient process for screening of correlated neural spike train activity Interpretation of multi-electrode electrophysiological data sets require time efficient processing and data visualisation during screening of spike train activity, correlated neural activity between units and characterising Up-Down states. We illustrate some of the scripts the lab developed & applied to our studies on the dynamics of neural ensemble interaction within the medial prefrontal cortical (mPFC) sub-regions and between the mPFC and hippocampus in the rat. Projects using the scripts: Central control of micturition in anaesthetised & awake-behaving rat - Fear conditioning - Epilepsy - Auditory sensory gating - VTA-mPFC ; Hpc-mPFC; mPFC-thalamus neural interactions • The data illustrated is an 16-channel MEA in mPFC (channels 1-8 “superficial mPFC”; channels 9-16 “deep mPFC”) before (BASAL) and following local intracortical administration of GABAA receptor antagonist GABAzine

METHODS Lister-hooded rats (male, 300-400g; n = 6) were anaesthetised with isoflurane (50%N2O:50%O2) and 8-channel microelectrode arrays (MEAs) implanted in the VTA; hippocampus and/or mPFC (cingulate gyrus & prelimbic areas) and hippocampus. Multiple single-unit and local field potentials (LFPs) were recorded simultaneously. All experiments were performed in accordance with the Animals (Scientific Procedures) Act 1986 UK and subject to local ethical review. ELECTROPHYSIOLOGY: • Single-unit & LFP activity were monitored using either a (i) Plexon Recorder 16-channel system with a synchronised Plexon CinePlex video recording system to image micturition behaviour or (ii) Plexon MAP 32-channel system for follow-up/non-recovery anaesthetised studies. DATA ANALYSIS: • Only data from animals with confirmed electrode placements in mPFC, VTA or hippocampus were analysed. Single-units were sorted using both automatic and manual sorting techniques in Offline Sorter (Plexon Inc.) – threshold + TM sorting algorithms. • Analysis of single-unit and LFP data were conducted using NeuroExplorer (www.neuroexplorer.com) and customised MATLAB scripts - PDC analysis [6] to evaluate directionality between the mPFC and thalamus. Raw data (Plexon MAP / Recorder system) *.plx file →

Data Analysis Work Flow:

OfflineSorter *.plx / *.nex file → NeuroEXplorer NEX 1-D review & initial analysis export to Matlab as *.mat file - custom scripts:

•

UNIT ACTIVITY ANALYSIS (custom-written scripts)

- spike

•

LFP ACTIVITY ANALYSIS (custom-written scripts)

- LFPanalysis suite; SeizureDetection analysis; Sensory Gating (TC 50) analysis; generalised Partial Directed Coherence (gPDC); Up/Down-state analysis

RESULTS

firing profile analysis; x-Correlation Grid analysis; x-Correlation (temporal epoch visualisation); Synchrony Index ; Grainger Causality analysis; NeuroSpec Coherence analysis; gPDC

Post-GABAzine (750-850s)

BASAL (0-100s)

Units –

Units –

mPFC superficial layers

mPFC superficial layers

mPFC deep layers

mPFC deep layers

LFPs - mPFC

LFPs - mPFC

SpikeTrainClusterAnalyser script: • • •

Firing rate statistics were calculated with firing rate histograms with 1-min bins normalized to a user-defined mean baseline firing of individual units. Z-score normalization was used to allow comparison across the unit populations (single or group experiments) with various firing rates. K-means cluster and hierarchical cluster analysis were used to detect any predominant patterns of responses to drug-treatment or stimulus-evoked events. To aid user identification, computed clusters were visualised with silhouette verification & 3-D principal component analysis and the sorted multiple unit recordings displayed as colour-coded spike rastergrams (z-axis colour proportional to firing rate). Plots allow users to visualise & compare spike train neural activity changes induced by behavioural- or drug-induced events.

x-Correlation Grid analysis script: • • • •

The first script centres on graphical visualisation and evaluation of cross-correlation histograms (CCHs) of multiple unit-pairs from a recorded population, the resultant grid displaying reference (y-axis) vs. target (x-axis) units, with the z-axis colour proportional to the degree of correlation. Correlated activity was computed in 1ms bins for user-defined lag times (e.g. ±100, 500 or 1000ms). The mean ± SD of uncorrelated activity was computed from the (user-defined, e.g. ± 30, 100 or 200ms) “outer uncorrelated shoulders” of the correlogram. Computed correlogram parameters include - Correlation Strength (K)-index, i.e. peak (or trough) count/arithmetic mean (shoulder) count, and the half-peak width (PW50)-index. A mouse-click on an individual grid pixel produces a conventional cross-correlogram (CCH) figure with descriptional statistics for that given unit-pair. Peak Z-score

mPFC (sig 0108)

Reference Unit

0-300s

300-1800s

Correlation Strength (K)-index

half-peak width

+

half-peak width (PW50)-index

Confidence limits (95%)

Hpc (sig 0916)

mouse cursor “click” generates CCH

Target Units

Synchrony Index • Script computes / plots synchrony within user-assigned population(s) of recorded single-units – computed over successive 1s epochs (left panel) & 2s epochs (right panel):

Average Synchrony Indices - green = mPFC superficial mean SI = 0.237 - blue = mPFC deep mean SI = 0.34

mPFC superficial unit raster plots

mPFC deep layers

Lab References: Lab web site:

Coomber et al (2009) Synapse 62: 746-755; Dissanayake et al (2009) Brain Research 1298: 153-160; Taxidis et al (2010) Biol. Cybernetics 102 (4): 327-340; Fenton et al (2013) Neuroscience 233: 146-156

www.nottingham.ac.uk/neuronal-networks