Faculty Opinions recommendation of Investigations into resting-state connectivity using independent component analysis.

Author(s):  
Aapo Hyvärinen
2005 ◽  
Vol 360 (1457) ◽  
pp. 1001-1013 ◽  
Author(s):  
Christian F Beckmann ◽  
Marilena DeLuca ◽  
Joseph T Devlin ◽  
Stephen M Smith

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.


2021 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre-Yves Hervé ◽  
...  

2020 ◽  
Vol 80 (3-4) ◽  
pp. 111-125
Author(s):  
Olli Rajasilta ◽  
Jetro J. Tuulari ◽  
Malin Björnsdotter ◽  
Noora M. Scheinin ◽  
Satu J. Lehtola ◽  
...  

2014 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Caroline Malherbe ◽  
Arnaud Messé ◽  
Eric Bardinet ◽  
Mélanie Pélégrini-Issac ◽  
Vincent Perlbarg ◽  
...  

2020 ◽  
Author(s):  
Jiun-Yiing Hu ◽  
Evgeniya Kirilina ◽  
Till Nierhaus ◽  
Smadar Ovadia-Caro ◽  
Michelle Livne ◽  
...  

AbstractObjectiveTo identify, characterize, and automatically classify hypoperfusion-related changes in the blood oxygenation level dependent (BOLD) signal in acute stroke using spatial independent component analysis of resting-state functional MRI data.MethodsWe applied spatial independent component analysis to resting-state functional MRI data of 37 stroke patients scanned within 24 hours of symptom onset, 17 of whom received follow-up scans the next day. All patients also received dynamic susceptibility contrast MRI. After denoising and manually classifying the components, we extracted a set of temporal and spatial features from each independent component and used a generalized linear model to automatically identify components related to tissue hypoperfusion.ResultsOur analysis revealed “Hypoperfusion spatially-Independent Components” (HICs) whose BOLD signal spatial patterns resembled regions of delayed perfusion depicted by dynamic susceptibility contrast MRI. These HICs were detected even in the presence of excessive patient motion, and disappeared following successful tissue reperfusion. The unique spatial and temporal features of HICs allowed them to be distinguished with high accuracy from other components in a user-independent manner (AUC = 0.95, accuracy = 0.96, sensitivity = 1.00, specificity = 0.96).InterpretationOur study presents a new, non-invasive method for assessing blood flow in acute stroke that minimizes interpretative subjectivity and is robust to severe patient motion.


2018 ◽  
Author(s):  
Dόra Szabό ◽  
Kálmán Czeibert ◽  
Ádám Kettinger ◽  
Márta Gácsi ◽  
Attila Andics ◽  
...  

ABSTRACTResting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained animals of either sex and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. Using resting-state functional magnetic resonance imaging (rs-fMRI), in this exploratory study we found multiple resting-state networks in dogs, which resemble the pattern described in humans. We report the following dog resting-state networks: default mode network (DMN), visual network (VIS), sensorimotor network (SMN), combined auditory (AUD)-saliency (SAL) network and cerebellar network (CER). The DMN, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give us insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.


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