**A non-invasive, quantitative study of broadband spectral responses in human visual cortex**
*by Eline R. Kupers, Helena X. Wang, Koaru Amano, Kendrick N. Kay, David J. Heeger, & Jonathan Winawer*
(2018) PLOS ONE 13(3): e0193107.
**[PDF][1]** | **[Code Repository][2]**
**Abstract:**
Currently, non-invasive methods for studying the human brain do not routinely and reliably measure spike-rate-dependent signals, independent of responses such as hemodynamic coupling (fMRI) and subthreshold neuronal synchrony (oscillations and event-related potentials). In contrast, invasive methods—microelectrode recordings and electrocorticography (ECoG)—have recently measured broadband power elevation in field potentials (~50–200 Hz) as a proxy for locally averaged spike rates. Here, we sought to detect and quantify stimulus-related broadband responses using magnetoencephalography (MEG). Extracranial measurements like MEG and EEG have multiple global noise sources and relatively low signal-to-noise ratios; moreover high frequency artifacts from eye movements can be confounded with stimulus design and mistaken for signals originating from brain activity. For these reasons, we developed an automated denoising technique that helps reveal the broadband signal of interest. Subjects viewed 12-Hz contrast-reversing patterns in the left, right, or bilateral visual field. Sensor time series were separated into evoked (12-Hz amplitude) and broadband components (60–150 Hz). In all subjects, denoised broadband responses were reliably measured in sensors over occipital cortex, even in trials without microsaccades. The broadband pattern was stimulus-dependent, with greater power contralateral to the stimulus. Because we obtain reliable broadband estimates with short experiments (~20 minutes), and with sufficient signal-to-noise to distinguish responses to different stimuli, we conclude that MEG broadband signals, denoised with our method, offer a practical, non-invasive means for characterizing spike-rate-dependent neural activity for addressing scientific questions about human brain function.
**Functions to reproduce main results from paper:**
% Add relevant paths
dfdAddPaths;
savePath = [];
%% CASE 1; Download raw data and denoise yourself
% Download NYU sample data
dfdDownloadsampledata(savePath, 1:8, {'raw','controls'});
% Download CiNet sample data
dfdDownloadsampledata(savePath, 9:12, {'raw'});
% Download TSSS, CALM, TSPCA sample data
dfdDownloadsampledata(savePath, [14:2:20,21:28,29:36], {'raw'});
% Denoise data
dfdDenoiseWrapper(1:12,1) % Project out 10 PCs
dfdDenoiseWrapper(1:8,2) % Project out All PCs
dfdDenoiseWrapper(1:8,3) % Control analyses
dfdDenoiseWrapper([14:2:20,21:36],1) % With preprocessed data by TSSS, CALM and TSPCA algorithms
% Reproduce figure 2-14 (except 3)
dfdMakeAllFigures;
%% CASE 2; Download denoised data
% Download denoised data
dfdDowloadsampledata([1:12,14:2:20,21:36],{'denoised 10 pcs'});
dfdDowloadsampledata([1:8],{'denoised all pcs', 'controls');
% Reproduce figure 2-14 (except 3)
dfdMakeAllFigures;
[1]: https://doi.org/10.1371/journal.pone.0193107
[2]: https://github.com/elinekupers/noisepoolPCADenoise
