scholarly journals Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

2018 ◽  
Author(s):  
Dόra Szabό ◽  
Kálmán Czeibert ◽  
Ádám Kettinger ◽  
Márta Gácsi ◽  
Attila Andics ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Component Analysis ◽  
Independent Component ◽  
Group Level ◽  
Resting State Networks ◽  
Wide Range ◽  
Spatially Distributed

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.

scholarly journals Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dóra Szabó ◽  
Kálmán Czeibert ◽  
Ádám Kettinger ◽  
Márta Gácsi ◽  
Attila Andics ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Component Analysis ◽  
Independent Component ◽  
Anterior Cingulate ◽  
Group Level ◽  
Resting State Networks ◽  
Posterior Cingulate ◽  
Wide Range ◽  
Spatially Distributed

Abstract Resting-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 individuals of both sexes and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. In this exploratory study, using resting-state functional magnetic resonance imaging (rs-fMRI), we found several such networks: a network involving prefrontal, anterior cingulate, posterior cingulate and hippocampal regions; sensorimotor (SMN), auditory (AUD), frontal (FRO), cerebellar (CER) and striatal networks. The network containing posterior cingulate regions, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

scholarly journals Resting‐state networks of the neonate brain identified using independent component analysis

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

scholarly journals Group independent component analysis reveals consistent resting-state networks across multiple sessions

2008 ◽  
Vol 1239 ◽  
pp. 141-151 ◽  
Author(s):  
Sharon Chen ◽  
Thomas J. Ross ◽  
Wang Zhan ◽  
Carol S. Myers ◽  
Keh-Shih Chuang ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Component Analysis ◽  
Independent Component ◽  
Resting State Networks ◽  
Group Independent Component Analysis

Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis

2012 ◽  
Vol 18 (9) ◽  
pp. 1251-1258 ◽  
Author(s):  
Anthony Faivre ◽  
Audrey Rico ◽  
Wafaa Zaaraoui ◽  
Lydie Crespy ◽  
Françoise Reuter ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Resting State ◽  
Surrogate Marker ◽  
Component Analysis ◽  
Independent Component ◽  
Resting State Networks ◽  
Frontoparietal Network ◽  
Multiple Sclerosis Patients

Objective: The present study aims to determine the clinical counterpart of brain resting-state networks reorganization recently evidenced in early multiple sclerosis. Methods: Thirteen patients with early relapsing–remitting multiple sclerosis and 14 matched healthy controls were included in a resting state functional MRI study performed at 3 T. Data were analyzed using group spatial Independent Component Analysis using concatenation approach (FSL 4.1.3) and double regression analyses (SPM5) to extract local and global levels of connectivity inside various resting state networks (RSNs). Differences in global levels of connectivity of each network between patients and controls were assessed using Mann–Whitney U-test. In patients, relationship between clinical data (Expanded Disability Status Scale and Multiple Sclerosis Functional Composite Score – MSFC) and global RSN connectivity were assessed using Spearman rank correlation. Results: Independent component analysis provided eight consistent neuronal networks involved in motor, sensory and cognitive processes. For seven RSNs, the global level of connectivity was significantly increased in patients compared with controls. No significant decrease in RSN connectivity was found in early multiple sclerosis patients. MSFC values were negatively correlated with increased RSN connectivity within the dorsal frontoparietal network ( r = −0.811, p = 0.001), the right ventral frontoparietal network ( r = − 0.587, p = 0.045) and the prefronto-insular network ( r = −0.615, p = 0.033). Conclusions: This study demonstrates that resting state networks reorganization is strongly associated with disability in early multiple sclerosis. These findings suggest that resting state functional MRI may represent a promising surrogate marker of disease burden.

Template free identification of resting state networks based on independent component analysis

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S123
Author(s):  
V. Schöpf ◽  
C.H. Kasess ◽  
A. Weissenbacher ◽  
R. Lanzenberger ◽  
C. Windischberger ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Component Analysis ◽  
Independent Component ◽  
Resting State Networks ◽  
Template Free
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