FMRI single trial discovery of spatio‐temporal brain activity patterns. Issue 3 (23rd November 2016)
- Record Type:
- Journal Article
- Title:
- FMRI single trial discovery of spatio‐temporal brain activity patterns. Issue 3 (23rd November 2016)
- Main Title:
- FMRI single trial discovery of spatio‐temporal brain activity patterns
- Authors:
- Allegra, Michele
Seyed‐Allaei, Shima
Pizzagalli, Fabrizio
Baftizadeh, Fahimeh
Maieron, Marta
Reverberi, Carlo
Laio, Alessandro
Amati, Daniele - Abstract:
- Abstract: There is growing interest in the description of short‐lived patterns in the spatiotemporal cortical activity monitored via neuroimaging. Most traditional analysis methods, designed to estimate relatively long‐term brain dynamics, are not always appropriate to capture these patterns. Here we introduce a novel data‐driven approach for detecting short‐lived fMRI brain activity patterns. Exploiting Density Peak Clustering (Rodriguez and Laio [2014]), our approach reveals well localized clusters by identifying and grouping together voxels whose time‐series are similar, irrespective of their brain location, even when very short time windows (∼10 volumes) are used. The method, which we call Coherence Density Peak Clustering (CDPC), is first tested on simulated data and compared with a standard unsupervised approach for fMRI analysis, independent component analysis (ICA). CDPC identifies activated voxels with essentially no false‐positives and proves more reliable than ICA, which is troubled by a number of false positives comparable to that of true positives. The reliability of the method is demonstrated on real fMRI data from a simple motor task, containing brief iterations of the same movement. The clusters identified are found in regions expected to be involved in the task, and repeat synchronously with the paradigm. The methodology proposed is especially suitable for the study of short‐time brain dynamics and single trial experiments, where the event or task ofAbstract: There is growing interest in the description of short‐lived patterns in the spatiotemporal cortical activity monitored via neuroimaging. Most traditional analysis methods, designed to estimate relatively long‐term brain dynamics, are not always appropriate to capture these patterns. Here we introduce a novel data‐driven approach for detecting short‐lived fMRI brain activity patterns. Exploiting Density Peak Clustering (Rodriguez and Laio [2014]), our approach reveals well localized clusters by identifying and grouping together voxels whose time‐series are similar, irrespective of their brain location, even when very short time windows (∼10 volumes) are used. The method, which we call Coherence Density Peak Clustering (CDPC), is first tested on simulated data and compared with a standard unsupervised approach for fMRI analysis, independent component analysis (ICA). CDPC identifies activated voxels with essentially no false‐positives and proves more reliable than ICA, which is troubled by a number of false positives comparable to that of true positives. The reliability of the method is demonstrated on real fMRI data from a simple motor task, containing brief iterations of the same movement. The clusters identified are found in regions expected to be involved in the task, and repeat synchronously with the paradigm. The methodology proposed is especially suitable for the study of short‐time brain dynamics and single trial experiments, where the event or task of interest cannot be repeated for the same subject, as happens, for instance, in problem‐solving, learning and decision‐making. A GUI implementation of our method is available for download athttps://github.com/micheleallegra/CDPC . Hum Brain Mapp 38:1421–1437, 2017 . © 2016 Wiley Periodicals, Inc. … (more)
- Is Part Of:
- Human brain mapping. Volume 38:Issue 3(2017)
- Journal:
- Human brain mapping
- Issue:
- Volume 38:Issue 3(2017)
- Issue Display:
- Volume 38, Issue 3 (2017)
- Year:
- 2017
- Volume:
- 38
- Issue:
- 3
- Issue Sort Value:
- 2017-0038-0003-0000
- Page Start:
- 1421
- Page End:
- 1437
- Publication Date:
- 2016-11-23
- Subjects:
- unsupervised fMRI analysis -- time‐dependent connectivity -- short‐lived brain activity patterns
Brain mapping -- Periodicals
611.81 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1097-0193 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/hbm.23463 ↗
- Languages:
- English
- ISSNs:
- 1065-9471
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4336.031000
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