Decoding continuous behavioral variables from neuroimaging data
The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is view...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2011-06-01
|
| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00075/full |
| _version_ | 1818484178881085440 |
|---|---|
| author | Jessica R Cohen Robert F Asarnow Fred W Sabb Fred W Sabb Robert M Bilder Robert M Bilder Robert M Bilder Susan Y Bookheimer Susan Y Bookheimer Susan Y Bookheimer Barbara J Knowlton Barbara J Knowlton Russell A Poldrack |
| author_facet | Jessica R Cohen Robert F Asarnow Fred W Sabb Fred W Sabb Robert M Bilder Robert M Bilder Robert M Bilder Susan Y Bookheimer Susan Y Bookheimer Susan Y Bookheimer Barbara J Knowlton Barbara J Knowlton Russell A Poldrack |
| author_sort | Jessica R Cohen |
| collection | DOAJ |
| description | The application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods. |
| first_indexed | 2024-12-10T15:51:41Z |
| format | Article |
| id | doaj.art-a115b88ad57249b0929e0eddf0d7dc1c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T15:51:41Z |
| publishDate | 2011-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-a115b88ad57249b0929e0eddf0d7dc1c2022-12-22T01:42:47ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2011-06-01510.3389/fnins.2011.000759881Decoding continuous behavioral variables from neuroimaging dataJessica R Cohen0Robert F Asarnow1Fred W Sabb2Fred W Sabb3Robert M Bilder4Robert M Bilder5Robert M Bilder6Susan Y Bookheimer7Susan Y Bookheimer8Susan Y Bookheimer9Barbara J Knowlton10Barbara J Knowlton11Russell A Poldrack12University of California, BerkeleyUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of California, Los AngelesUniversity of Texas at AustinThe application of statistical machine learning techniques to neuroimaging data has allowed researchers to decode the cognitive and disease states of participants. The majority of studies using these techniques have focused on pattern classification to decode the type of object a participant is viewing, the type of cognitive task a participant is completing, or the disease state of a participant's brain. However, an emerging body of literature is extending these classification studies to the decoding of values of continuous variables (such as age, cognitive characteristics, or neuropsychological state) using high-dimensional regression methods. This review details the methods used in such analyses and describes recent results. We provide specific examples of studies which have used this approach to answer novel questions about age and cognitive and disease states. We conclude that while there is still much to learn about these methods, they provide useful information about the relationship between neural activity and age, cognitive state, and disease state, which could not have been obtained using traditional univariate analytical methods.http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00075/fullfMRImachine learningbiomarkerhigh-dimensional regressionmultivariate decodingpredictive analysis |
| spellingShingle | Jessica R Cohen Robert F Asarnow Fred W Sabb Fred W Sabb Robert M Bilder Robert M Bilder Robert M Bilder Susan Y Bookheimer Susan Y Bookheimer Susan Y Bookheimer Barbara J Knowlton Barbara J Knowlton Russell A Poldrack Decoding continuous behavioral variables from neuroimaging data Frontiers in Neuroscience fMRI machine learning biomarker high-dimensional regression multivariate decoding predictive analysis |
| title | Decoding continuous behavioral variables from neuroimaging data |
| title_full | Decoding continuous behavioral variables from neuroimaging data |
| title_fullStr | Decoding continuous behavioral variables from neuroimaging data |
| title_full_unstemmed | Decoding continuous behavioral variables from neuroimaging data |
| title_short | Decoding continuous behavioral variables from neuroimaging data |
| title_sort | decoding continuous behavioral variables from neuroimaging data |
| topic | fMRI machine learning biomarker high-dimensional regression multivariate decoding predictive analysis |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2011.00075/full |
| work_keys_str_mv | AT jessicarcohen decodingcontinuousbehavioralvariablesfromneuroimagingdata AT robertfasarnow decodingcontinuousbehavioralvariablesfromneuroimagingdata AT fredwsabb decodingcontinuousbehavioralvariablesfromneuroimagingdata AT fredwsabb decodingcontinuousbehavioralvariablesfromneuroimagingdata AT robertmbilder decodingcontinuousbehavioralvariablesfromneuroimagingdata AT robertmbilder decodingcontinuousbehavioralvariablesfromneuroimagingdata AT robertmbilder decodingcontinuousbehavioralvariablesfromneuroimagingdata AT susanybookheimer decodingcontinuousbehavioralvariablesfromneuroimagingdata AT susanybookheimer decodingcontinuousbehavioralvariablesfromneuroimagingdata AT susanybookheimer decodingcontinuousbehavioralvariablesfromneuroimagingdata AT barbarajknowlton decodingcontinuousbehavioralvariablesfromneuroimagingdata AT barbarajknowlton decodingcontinuousbehavioralvariablesfromneuroimagingdata AT russellapoldrack decodingcontinuousbehavioralvariablesfromneuroimagingdata |