Functional Networks in the Anesthetized Rat Brain Revealed by Independent Component Analysis of Resting-State fMRI

2010 ◽  
Vol 103 (6) ◽  
pp. 3398-3406 ◽  
Author(s):  
R. Matthew Hutchison ◽  
Seyed M. Mirsattari ◽  
Craig K. Jones ◽  
Joseph S. Gati ◽  
L. Stan Leung

The rodent brain is organized into functional networks that can be studied through examination of synchronized low-frequency spontaneous fluctuations (LFFs) of the functional magnetic resonance imaging -blood-oxygen-level-dependent (BOLD) signal. In this study, resting networks of LFFs were estimated from the whole-brain BOLD signals using independent component analysis (ICA). ICA provides a hypothesis-free technique for determining the functional connectivity map that does not require a priori selection of a seed region. Twenty Long-Evans rats were anesthetized with isoflurane (1%, n = 10) or ketamine/xylazine (50/6 mg · kg−1 · h−1 ip, n = 10) and imaged for 5–10 min in a 9.4 T MR scanner without experimental stimulation or task requirement. Independent, synchronous LFFs of BOLD signals were found to exist in clustered, bilaterally symmetric regions of both cortical and subcortical structures, including primary and secondary somatosensory cortices, motor cortices, visual cortices, posterior and anterior cingulate cortices, hippocampi, caudate-putamen, and thalamic and hypothalamic nuclei. The somatosensory and motor cortices typically demonstrated both symmetric and asymmetric components with unique frequency profiles. Similar independent network components were found under isoflurane and ketamine/xylazine anesthesia. The report demonstrates, for the first time, 12 independent resting networks that are bilaterally synchronous in different cortical and subcortical areas of the rat brain.

NeuroImage ◽  
2012 ◽  
Vol 60 (4) ◽  
pp. 2073-2085 ◽  
Author(s):  
Vitaly I. Dobromyslin ◽  
David H. Salat ◽  
Catherine B. Fortier ◽  
Elizabeth C. Leritz ◽  
Christian F. Beckmann ◽  
...  

2011 ◽  
Vol 219-220 ◽  
pp. 1121-1125 ◽  
Author(s):  
Rui Chen ◽  
Yu Lin Lan ◽  
Reza Asharif Mohammad

This paper proposed a digital audio watermarking scheme based on independent component analysis (ICA) in DWT domain. The embedding process make full use of the multi-resolution characteristic of discrete wavelet transform (DWT), performing 3-level DWT. Selecting the low frequency coefficient appropriately as the embed location to make sure of the balance between the transparency and robustness. Then constructing the ICA model to embed the watermarking. The extraction process is similar with ICA’s goal, it’s used in extraction makes the scheme simple for implementation. The experiment results show that the proposed scheme has good robustness against common attacks, as well as transparency.


NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117278
Author(s):  
Yali Huang ◽  
Yang Yang ◽  
Lei Hao ◽  
Xuefang Hu ◽  
Peiguang Wang ◽  
...  

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

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.


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