(variable) Frequency-based Layer Identification Procedure (FLIP and vFLIP) for spectrolaminar analysis
Data files
Nov 07, 2023 version files 806.35 MB
-
data.mat
-
FLIP_Algorithm_Introduction.pdf
-
FLIPAnalysis.m
-
main_FLIP_script.m
-
Published_main_FLIP_script.pdf
-
README.md
Jan 17, 2024 version files 806.35 MB
-
data.mat
-
FLIP_Algorithm_Introduction.pdf
-
FLIPAnalysis.m
-
main_FLIP_script.m
-
Published_main_FLIP_script.pdf
-
README.md
Abstract
The mammalian cerebral cortex is anatomically organized into a six-layer motif. In this dataset and associated paper (Mendoza-Halliday et al., 2023) we show that a corresponding laminar motif of neuronal activity patterns exists across the cortex in the power of local field potentials (LFP). Using laminar probes, we recorded LFPs in five macaque monkeys in 14 cortical areas across the cortical hierarchy. The laminar locations of recordings were histologically identified via electrolytic lesions. Across all areas, we found a ubiquitous spectrolaminar pattern characterized by an increasing deep-to-superficial layer gradient of high-frequency power peaking in layers 2/3 and an increasing superficial-to-deep gradient of alpha-beta power peaking in layers 5/6. Our results suggest the existence of a canonical layer- and frequency-based mechanism for cortical computation.
To facilitate the detection of these spectrolaminar patterns, we are releasing data associated with "Study 1" and "Study 2" as reported in Mendoza-Halliday et al., 2023. This comprises relative power of LFPs recorded in 4 macaque monkeys from areas V4, 7A, MT, MST, LIP, and LPFC. The FLIP and vFLIP algorithms were created to facilitate electrophysiological characterization of cortical layers based on power analysis of the local field potential recordings (LFP) that are provided in these datasets. The algorithms can also be used to perform the same analysis on any other multi-channel LFP dataset. We have verified that the spectrolaminar pattern is highly preserved in macaque monkeys, marmosets, and humans, but is more dissimilar in mouse.
README: (variable) Frequency-based Layer Identification Procedure (FLIP and vFLIP) for Spectrolaminar analysis
https://doi.org/10.5061/dryad.9w0vt4bnp
FLIP Algorithm File Descriptions
Getting Started
There are three files available for download: FLIPAnalysis.m, main_FLIP_script, and data.mat
The only file necessary to perform the FLIP algorithm on an existing non-normalized powermatrix is FLIPAnalysis.m. The provided tutorial details how to utilize FLIP. Two supplementary files are provided:
data.mat contains the data used in the paper, and main_FLIP_script.m is a script that interact with the FLIPAnalysis function.
main_FLIP_script is helpful in demonstrating how FLIP can be used in data analysis.
File and function Descriptions
Published_main_FLIP_script.pdf
This PDF contains the author's run of main_FLIP_script.m and associated figures produced by that script, so that users can verify they are getting the expected figure outputs when they run main_FLIP_script.m themselves.
data.mat
This data file contains a struct that encompasses the data recorded in experimentation.
There are five fields contained in the struct: CSD, relpow, meta, example1_vlPFC_lfp, and example2_7A_lfp
- CSD: Current Source Density data contained in a three-dimensional matrix, the first dimension represents each probe, the second dimension represents each channel, and the third dimension represents each time point (from -0.1s pre-stimulus to +0.5s post-stimulus) included in the analysis. The units of CSD are z-score change from baseline (using the standard error across trials to perform the z-score at each point in time). Positive values denote sources and negative values denote sinks.
- relpow: Relative Power data is contained in a three-dimensional matrix, like CSD, the first dimension represents each probe, the second dimension represents each channel, and the third dimension represents each frequency (from 1-150 Hz) included in the analysis. The units of the relpow data are normalized units between 0 and 1 (1=the channel with the highest power at that frequency).
- example1_vIPFC_lfp: a sample LFP dataset, a three-dimensional matrix, the first dimension represents probe channels, the second dimension represents trials, and the third dimension represents time. Units of these local field potentials (LFP) are in microvolts by time (sampling rate = 1,000 Hz)
- example2_7A_lfp: also a sample LFP dataset, a three-dimensional matrix, the first dimension represents probe channels, the second dimension represents trials, and the third dimension represents time. Units of these local field potentials (LFP) are in microvolts by time (sampling rate = 1,000 Hz)
- meta: Metadata about each probe used in the analysis. The rows of meta correspond to the rows of CSD and relpow. Filename: The filename of the associated session, including the date of recording. probenumber: The probenumber on that session. study_number refers to study 1/2 reported in Mendoza-Halliday et al., 2023. monkey_number identifies the research subject. brain_area_num identifies the brain area numerically. interchannel_distance identifies the distance between contacts on a probe in units of microns. brain_area denotes each area's name. crossover_default is the cross identified by the FLIP algorithm with default settings. crossover_VFLIP is cross-identified by the vFLIP algorithm. CSD_sink_channel is the channel corresponding to the "early" current sink identified with a manual inspection method.
FLIPAnalysis.m
This file contains the FLIP and vFLIP algorithms.
main_FLIP_script.m
This file contains a few sample interactions with the FLIPAnalysis function.
To use this file, ensure the data.mat and FLIPAnalysis.m files are downloaded. Additionally, ensure it is in the same folder as FLIPAnalysis.m or add FLIPAnalysis.m to the path.
FLIP Algorithm Introduction.pdf
A step-by-step tutorial that reviews how to use the FLIP and vFLIP algorithms for spectrolaminar analysis.
Methods
Mulit-channel electrode arrays were implanted into different areas of the primate visual cortex. Local field potentials were acquired, and processed for their spectral power in frequencies ranging from 1–150Hz spanning gamma (50–150 Hz) and alpha/beta (10–30 Hz) ranges. A relative power metric was computed and analyzed for the spectrolaminar pattern across layers. This pattern defines the anatomical position of layers 2/3 (peak of gamma relative power), layers 5/6 (peak of alpha/beta relative power), and layer 4 (cross-over between gamma and alpha/beta).