Classification of temporal ICA components for separating global noise from fMRI data: reply to power
We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence an...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Elsevier
2019
|
| _version_ | 1797077741935263744 |
|---|---|
| author | Glasser, MF Coalson, TS Bijsterbosch, JD Harrison, SJ Harms, MP Anticevic, A Van Essen, DC Smith, SM |
| author_facet | Glasser, MF Coalson, TS Bijsterbosch, JD Harrison, SJ Harms, MP Anticevic, A Van Essen, DC Smith, SM |
| author_sort | Glasser, MF |
| collection | OXFORD |
| description | We respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach. |
| first_indexed | 2024-03-07T00:22:20Z |
| format | Journal article |
| id | oxford-uuid:7cfc0aac-f477-4b41-a059-7fcf4bc15157 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T00:22:20Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:7cfc0aac-f477-4b41-a059-7fcf4bc151572022-03-26T21:00:25ZClassification of temporal ICA components for separating global noise from fMRI data: reply to powerJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7cfc0aac-f477-4b41-a059-7fcf4bc15157EnglishSymplectic Elements at OxfordElsevier2019Glasser, MFCoalson, TSBijsterbosch, JDHarrison, SJHarms, MPAnticevic, AVan Essen, DCSmith, SMWe respond to a critique of our temporal Independent Components Analysis (ICA) method for separating global noise from global signal in fMRI data that focuses on the signal versus noise classification of several components. While we agree with several of Power's comments, we provide evidence and analysis to rebut his major criticisms and to reassure readers that temporal ICA remains a powerful and promising denoising approach. |
| spellingShingle | Glasser, MF Coalson, TS Bijsterbosch, JD Harrison, SJ Harms, MP Anticevic, A Van Essen, DC Smith, SM Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title | Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title_full | Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title_fullStr | Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title_full_unstemmed | Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title_short | Classification of temporal ICA components for separating global noise from fMRI data: reply to power |
| title_sort | classification of temporal ica components for separating global noise from fmri data reply to power |
| work_keys_str_mv | AT glassermf classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT coalsonts classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT bijsterboschjd classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT harrisonsj classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT harmsmp classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT anticevica classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT vanessendc classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower AT smithsm classificationoftemporalicacomponentsforseparatingglobalnoisefromfmridatareplytopower |