毕业设计&课设-用于导入和分析TDT系统上收集的光度数据的Matlab脚本.zip

# photometry-neural-data-analysis Matlab scripts for batch import and analysis of photometry data from TDT-based systems. Data processing is divided into four phases; behavior-specific files differ in behavioral data analysis specifics but share common strategy for processing photometry data and generating PSTHs. Some statistical and behavioral analyses reported in Steinberg et al. Neuron 2020 are not included in this code as they were conducted in other programs. *********************************************** 1) Data extraction and preparation for analysis, QC check What you can expect this script to do: Control signal is fitted to calcium dependent signal and subtracted to correct for motion artifacts and bleaching Fitted signal is z-score normalized to facilitate comparisons across subjects Graphs are generated for each subject of raw, fitted and z-scored data for QC check Key output data stored in "Photometry_zScore" structure array containing the following fields: (1) Subject IDs, (2) Raw datafile names, (3) Sampling rate, (4) Fitted/normalized photometry data, (5) Photometry timestamps, (6+) TTL timestamps for external events What you need to customize: Input/output data filenames Specify TDT data sources(photometry data + TTL inputs to sync behavior/stimuli) Assumptions: 2-color input: 470 nm (calcium dependent signal) and 405 nm (calcium independent control) Requires additional standalone functions (provided, credit: Tom Davidson): tdt2mat, controlFit, deltaFF *********************************************** 2) Batch analysis of individual data What you can expect this script to do: Identify timestamps for key behavioral events that are separated in time to avoid re-sampling the same data twice Generate PSTH graphs for these events for each subject - batch processing for exploratory visualization of individual variability Key output data stored in structure arrays: Photometry_PSTH Behavior_ts Behavior PSTH (some programs) What you need to customize: Input/output data filenames Specify relationship between TTL inputs and behavioral events Specify time window for analysis Assumptions: Raw data have been previously extracted and normalized as in step (1) Requires two additional functions (provided): processPhotDataRow_normDat (to align photometry data across trials to generate PSTH, credit: Tom Davidson) opacity (to generate fills for shaded error bars for graphs) *********************************************** 3) Generate group average PSTH graphs What you can expect this script to do: Generate group average graphs of behavioral data and photometry responses as PSTH What you need to customize: Input/output data filenames Specify time windows for analysis Assumptions: Raw data have been previously analyzed through steps 1 and 2 Requires one additional function (provided): opacity (to generate fills for shaded error bars for graphs) *********************************************** 4) Statistical analysis of group graphs What you can expect this script to do: Compare pre/post event behavior or neural activity using paired t-tests Print summary results in excel spreadsheet Key output data is stored in a structure array "Statistics" with 9 fields: Name - Description of comparison Comparitor1 - name of data1 Comparitor2 - name of data2 Hypothesis - 1 if null hypothesis can be rejected at the 5% level, 0 if not pValue - obvious Mean1 - group mean for the first comparitor Mean2 - group mean for the second comparitor Values1 - comparitor 1 individual data used to generate group average Values2 - comparitor 2 individual data used to generate group average What you need to customize: Input/output data filenames Specify analysis window Assumptions: Raw data have been previously analyzed through steps 1 and 2 Feel free to contact me with questions - I'm happy to clarify anything in the code. [elizabeth.steinberg at gmail]