The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

How natural speech is represented in the auditory cortex constitutes a major challenge for cognitive neuroscience. Although many single-unit and neuroimaging studies have yielded valuable insights about the processing of speech and matched complex sounds, the mechanisms underlying the analysis of speech dynamics in human auditory cortex remain largely unknown. Here, we show that the phase pattern of theta band (4-8 Hz) responses recorded from human auditory cortex with magnetoencephalography (MEG) reliably tracks and discriminates spoken sentences and that this discrimination ability is correlated with speech intelligibility. The findings suggest that an approximately 200 ms temporal window (period of theta oscillation) segments the incoming speech signal, resetting and sliding to track speech dynamics. This hypothesized mechanism for cortical speech analysis is based on the stimulus-induced modulation of inherent cortical rhythms and provides further evidence implicating the syllable as a computational primitive for the representation of spoken language.

Accurate cochlear frequency-position functions based on physiological data would facilitate the interpretation of physiological and psychoacoustic data within and across species. Such functions might aid in developing cochlear models, and cochlear coordinates could provide potentially useful spectral transforms of speech and other acoustic signals. In 1961, an almost-exponential function was developed (Greenwood, 1961b, 1974) by integrating an exponential function fitted to a subset of frequency resolution-integration estimates (critical bandwidths). The resulting frequency-position function was found to fit cochlear observations on human cadaver ears quite well and, with changes of constants, those on elephant, cow, guinea pig, rat, mouse, and chicken (Békésy, 1960), as well as in vivo (behavioral-anatomical) data on cats (Schucknecht, 1953). Since 1961, new mechanical and other physiological data have appeared on the human, cat, guinea pig, chinchilla, monkey, and gerbil. It is shown here that the newer extended data on human cadaver ears and from living animal preparations are quite well fit by the same basic function. The function essentially requires only empirical adjustment of a single parameter to set an upper frequency limit, while a "slope" parameter can be left constant if cochlear partition length is normalized to 1 or scaled if distance is specified in physical units. Constancy of slope and form in dead and living ears and across species increases the probability that the function fitting human cadaver data may apply as well to the living human ear. This prospect increases the function's value in plotting auditory data and in modeling concerned with speech and other bioacoustic signals, since it fits the available physiological data well and, consequently (if those data are correct), remains independent of, and an appropriate means to examine, psychoacoustic data and assumptions.