mvdmlab wiki - analysis
https://mvdmlab.host.dartmouth.edu/wiki2/
2026-07-05T04:56:08+00:00mvdmlab wiki
https://mvdmlab.host.dartmouth.edu/wiki2/
https://mvdmlab.host.dartmouth.edu/wiki2/lib/exe/fetch.php?media=wiki:logo.pngtext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)adrlab
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:adrlab&rev=1523964036&do=diff
Overview of data files
A promoted (i.e. pre-processed, annotated, and approved) data folder consists of most of the following files:
* *.Ncs files: continuously sampled data, “local field potentials”
* (*.ntt files: raw tetrode files, these are generally not included)text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)amplipex
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:amplipex&rev=1523964036&do=diff
Loading Amplipex data into fieldtrip
Note: as of Jan/2014, make sure you use the APX_devel branch on GitHub to get the latest version. At some point this will be merged back into master.
Eric's Note (Jan 12/2014): Use session R041-2013-08-06 as a test since it contains an events file within the directory. If you are using another session be sure to run the AMPX_getEvetns script in the util/amplipex/loading folder.text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)basicskills
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:basicskills&rev=1523964036&do=diff
Basic computing skills
Using your operating system's command line interface (aka "shell", "terminal", "CLI")
You should be able to open a command line window (Windows: cmd.exe, Mac: Terminal) and perform basic operations such as navigating, creating and removing folders, and passing optional arguments to common commands liketext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)cosmo2014
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:cosmo2014&rev=1523964036&do=diff
~~DISCUSSION~~
Welcome to the CoSMo 2014 hands-on session on decoding neural ensemble data!
The Tutorial modules below constitute a step-by-step walkthrough that introduces you to a data set of 100+ neurons, recorded simultaneously from hippocampal subfield CA1 as a rat runs a T-maze task, followed by some example decoding analyses.text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)course-w16
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:course-w16&rev=1523964036&do=diff
~~DISCUSSION~~
Welcome! This is the home page for the Winter 2016 edition of the “Neural Data Analysis” course.
Contents
Reference
* Principles of (neural) data analysis
Fundamentals
* Module 1: Setting up (MATLAB, paths, GitHub, accessing data; Week 1)
* Module 2: Introduction to neural data formats and preprocessing (Week 2)
* Module 3: Visualizing raw neural data in MATLAB (Week 3)
Time series data data basics
* Module 4: Anatomy of time series data, sampling theory (Week 4)…text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)course
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:course&rev=1523964036&do=diff
~~DISCUSSION~~
List of Topics
* Good habits for data analysis (paths, backups, versioning, annotation)
* Visualizing neural data in MATLAB
* Anatomy of neural data: time series, sampling, aliasing
* Fourier series, transforms, power spectra
* Filtering: filter design, use, caveats
* Time-frequency analysis: spectrograms
* Time-frequency analysis II: cross-frequency coupling
* Interactions between multiple signals: coherence and other connectivity measures
* Spike train analys…text/html2024-07-03T00:40:52+00:00Anonymous (anonymous@undisclosed.example.com)dataanalysis
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:dataanalysis&rev=1719967252&do=diff
Current version of our lab's data analysis tutorials:
* Neural Systems & Behavior (MBL, Woods Hole)
Archived course materials
* Graduate course: Analysis of Neural Data (Dartmouth, Winter 2016)
* Neural Systems & Behavior (MBL, Woods Hole, Summer 2016)
* PSYC50: The Rhythmic Brain (Dartmouth, Spring 2016)
* Neural Systems & Behavior (MBL, Woods Hole, Summer 2015)
* Neural Systems & Behavior (MBL, Woods Hole, Summer 2014)text/html2024-07-03T00:50:13+00:00Anonymous (anonymous@undisclosed.example.com)fiber-photo
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:fiber-photo&rev=1719967813&do=diff
Intro to fiber photometry preprocessing
Goals:
* Load raw fiber photometry data and quality check it
* Understand the preprocessing steps
* Step by step preprocessing
Resources:
* This module makes heavy use of filtering
Loading some example raw datatext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)matlab-examples
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:matlab-examples&rev=1523964036&do=diff
* Plotting an ordered raster of place cells
* LFP event detectiontext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)matlab-overview
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:matlab-overview&rev=1523964036&do=diff
Overview of common functions
Utility functions
Utility functions common to multiple data types
* restrict(): restricts times in data object (iv, ts, tsd) to specific intervals
* getd(): helper function to return data corresponding to label target_field
Utility functions associated with specific data typestext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)matlabtricks
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:matlabtricks&rev=1523964036&do=diff
Plotting
* To plot an imagesc with NaNs transparent, do this:
h = imagesc(X,Y,C);
set(h,'alphadata',~isnan(C));
(this is helpful when plotting 2D tuning curves, for instance; pcolor() is not recommended)
* To eliminate the large borders when using subplots, usetext/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)neuralynx
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:neuralynx&rev=1523964036&do=diff
Loading LFP data into fieldtrip
To load *.Ncs (Neuralynx Continuously Sampled, sometimes also referred to as CSC for Continuously Sampled Channel) files in MATLAB, use the following:
fc = FindFiles('*.ncs');
data = ft_read_neuralynx_interp(fc);text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)nsb2014
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:nsb2014&rev=1523964036&do=diff
~~DISCUSSION~~
Contents
* Introduction: Principles of careful data analysis
* Module 1: Good data management habits and tools (paths, backups, versioning, annotation)
* Module 2: Introduction to Neuralynx data formats and preprocessing
* Module 3: Anatomy of neural data: time series, sampling, aliasing
* Module 4: Spike sorting
* Module 5: Visualizing neural data in MATLAB
* Module 6: Fourier series, transforms, power spectra
* Module 7: Filtering: filter design, use, caveats
…text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)nsb2015
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:nsb2015&rev=1523964036&do=diff
~~DISCUSSION~~
Contents
Fundamentals
* Module 1: Setting up (MATLAB, paths, GitHub, lab database)
* Module 2: Introduction to Neuralynx data formats and preprocessing
* Module 3: Visualizing raw neural data in MATLAB (long version to learn the guts, short version to just get stuff done)
Spike data basics
* Module 4: Spike sorting
* Module 11: Spike train analysis: firing rate, interspike interval distributions, auto- and crosscorrelations
* Module 12: Spike train analysis II: tu…text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)nsb2016
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:nsb2016&rev=1523964036&do=diff
~~DISCUSSION~~
Welcome! This is the home page for the data management and analysis tutorials for the NS&B 2016 hippocampus cycle.
Contents
Reference: read this first, and then again later
* Principles of (neural) data analysis
Fundamentals: do these in Week 1
* Module 1: Setting up (MATLAB, paths, GitHub, accessing data)
* Module 2: Introduction to neural data formats and preprocessing
* Module 3: Visualizing raw neural data in MATLAB (Short version)text/html2024-07-02T21:53:51+00:00Anonymous (anonymous@undisclosed.example.com)nsb2017
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:nsb2017&rev=1719957231&do=diff
Welcome! This is the home page for the data management and analysis tutorials for the NS&B mouse cycle.
Contents
Reference: skim these so you know what's here
* Principles of (neural) data analysis
* Overview of common functions
* Example data analysis workflows
Fundamentals: do these in Week 1
* Module 1: Setting up (MATLAB, paths, GitHub, accessing data)
* Module 2: Introduction to neural data formats and preprocessing
* Module 3: Visualizing raw neural data in MATLAB (Short v…text/html2018-04-17T11:20:36+00:00Anonymous (anonymous@undisclosed.example.com)rhythms
https://mvdmlab.host.dartmouth.edu/wiki2/doku.php?id=analysis:rhythms&rev=1523964036&do=diff
~~DISCUSSION~~
Welcome! This is the home page for the optional hands-on (heads-on) 2017 edition of the “Rhythmic Brain” course.
Contents
* Module 1: Setting up, loading and plotting Emotiv data
* Module 2: Signal processing, basics of sampling (aliasing, Nyquist)
* Module 3: Signal processing, Fourier analysis and power spectra
(more advanced modules follow below)
Getting started with MATLAB
Depending on your background and programming experience you might find the following resource…