The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli
Understanding how brains process sensory signals in natural environments is one of the key goals of 21st century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2016-11-01
|
| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2016.00604/full |
| _version_ | 1818492341201141760 |
|---|---|
| author | Michael J Crosse Michael J Crosse Giovanni M Di Liberto Adam Bednar Adam Bednar Edmund C Lalor Edmund C Lalor |
| author_facet | Michael J Crosse Michael J Crosse Giovanni M Di Liberto Adam Bednar Adam Bednar Edmund C Lalor Edmund C Lalor |
| author_sort | Michael J Crosse |
| collection | DOAJ |
| description | Understanding how brains process sensory signals in natural environments is one of the key goals of 21st century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter – often referred to as a temporal response function – that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. |
| first_indexed | 2024-12-10T17:41:45Z |
| format | Article |
| id | doaj.art-c052378e30654422af3b1ddca8456829 |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-10T17:41:45Z |
| publishDate | 2016-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-c052378e30654422af3b1ddca84568292022-12-22T01:39:21ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612016-11-011010.3389/fnhum.2016.00604219245The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuliMichael J Crosse0Michael J Crosse1Giovanni M Di Liberto2Adam Bednar3Adam Bednar4Edmund C Lalor5Edmund C Lalor6Trinity College DublinAlbert Einstein College of MedicineTrinity College DublinTrinity College DublinUniversity of RochesterTrinity College DublinUniversity of RochesterUnderstanding how brains process sensory signals in natural environments is one of the key goals of 21st century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter – often referred to as a temporal response function – that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.http://journal.frontiersin.org/Journal/10.3389/fnhum.2016.00604/fullreverse correlationsensory processingEEG/MEGsystem identificationstimulus reconstruction |
| spellingShingle | Michael J Crosse Michael J Crosse Giovanni M Di Liberto Adam Bednar Adam Bednar Edmund C Lalor Edmund C Lalor The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli Frontiers in Human Neuroscience reverse correlation sensory processing EEG/MEG system identification stimulus reconstruction |
| title | The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli |
| title_full | The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli |
| title_fullStr | The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli |
| title_full_unstemmed | The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli |
| title_short | The multivariate temporal response function (mTRF) toolbox: a MATLAB toolbox for relating neural signals to continuous stimuli |
| title_sort | multivariate temporal response function mtrf toolbox a matlab toolbox for relating neural signals to continuous stimuli |
| topic | reverse correlation sensory processing EEG/MEG system identification stimulus reconstruction |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2016.00604/full |
| work_keys_str_mv | AT michaeljcrosse themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT michaeljcrosse themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT giovannimdiliberto themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT adambednar themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT adambednar themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT edmundclalor themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT edmundclalor themultivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT michaeljcrosse multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT michaeljcrosse multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT giovannimdiliberto multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT adambednar multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT adambednar multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT edmundclalor multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli AT edmundclalor multivariatetemporalresponsefunctionmtrftoolboxamatlabtoolboxforrelatingneuralsignalstocontinuousstimuli |