scholarly journals Decomposition of spontaneous fluctuations in tumour oxygenation using BOLD MRI and independent component analysis

2015 ◽  
Vol 113 (8) ◽  
pp. 1168-1177 ◽  
Author(s):  
Miguel R Gonçalves ◽  
S Peter Johnson ◽  
Rajiv Ramasawmy ◽  
R Barbara Pedley ◽  
Mark F Lythgoe ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Blood Oxygenation ◽  
Independent Component ◽  
Physiological Basis ◽  
Bold Mri ◽  
Vessel Maturation ◽  
Novel Method ◽  
Tumour Oxygenation ◽  
Spontaneous Fluctuations

Abstract Background: Solid tumours can undergo cycles of hypoxia, followed by reoxygenation, which can have significant implications for the success of anticancer therapies. A need therefore exists to develop methods to aid its detection and to further characterise its biological basis. We present here a novel method for decomposing systemic and tumour-specific contributions to fluctuations in tumour deoxyhaemoglobin concentration, based on magnetic resonance imaging measurements. Methods: Fluctuations in deoxyhaemoglobin concentration in two tumour xenograft models of colorectal carcinoma were decomposed into distinct contributions using independent component analysis. These components were then correlated with systemic pulse oximetry measurements to assess the influence of systemic variations in blood oxygenation in tumours, compared with those that arise within the tumour itself (tumour-specific). Immunohistochemical staining was used to assess the physiological basis of each source of fluctuation. Results: Systemic fluctuations in blood oxygenation were found to contribute to cycling hypoxia in tumours, but tumour-specific fluctuations were also evident. Moreover, the size of the tumours was found to influence the degree of systemic, but not tumour-specific, oscillations. The degree of vessel maturation was related to the amplitude of tumour-specific, but not systemic, oscillations. Conclusions: Our results provide further insights into the complexity of spontaneous fluctuations in tumour oxygenation and its relationship with tumour pathophysiology. These observations could be used to develop improved drug delivery strategies.

scholarly journals Erratum: Decomposition of spontaneous fluctuations in tumour oxygenation using BOLD MRI and independent component analysis

2016 ◽  
Vol 114 (12) ◽  
pp. e13-e13 ◽  
Author(s):  
Miguel R Gonçalves ◽  
S Peter Johnson ◽  
Rajiv Ramasawmy ◽  
R Barbara Pedley ◽  
Mark F Lythgoe ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Bold Mri ◽  
Tumour Oxygenation ◽  
Spontaneous Fluctuations

Statistical Independence and Independent Component Analysis

2014 ◽  
Vol 553 ◽  
pp. 564-569
Author(s):  
Yaseen Unnisa ◽  
Danh Tran ◽  
Fu Chun Huang
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Statistical Testing ◽  
P Value ◽  
Statistical Independence ◽  
Mixing Matrix ◽  
Recent Method ◽  
Novel Method ◽  
Source Signals

Independent Component Analysis (ICA) is a recent method of blind source separation, it has been employed in medical image processing and structural damge detection. It can extract source signals and the unmixing matrix of the system using mixture signals only. This novel method relies on the assumption that source signals are statistically independent. This paper looks at various measures of statistical independence (SI) employed in ICA, the measures proposed by Bakirov and his associates, and the effects of levels of SI of source signals on the output of ICA. Firstly, two statistical independent signals in the form of uniform random signals and a mixing matrix were used to simulate mixture signals to be anlysed byfastICApackage, secondly noise was added onto the signals to investigate effects of levels of SI on the output of ICA in the form of soure signals, the mixing and unmixing matrix. It was found that for p-value given by Bakirov’s SI statistical testing of the null hypothesis H0is a good indication of the SI between two variables and that for p-value larger than 0.05, fastICA performs satisfactorily.

Segregation of frontoparietal and cerebellar components within saccade and vergence networks using hierarchical independent component analysis of fMRI

2011 ◽  
Vol 28 (3) ◽  
pp. 247-261 ◽  
Author(s):  
YELDA ALKAN ◽  
BHARAT B. BISWAL ◽  
PAUL A. TAYLOR ◽  
TARA L. ALVAREZ
Keyword(s):  
Independent Component Analysis ◽  
Functional Activity ◽  
Component Analysis ◽  
Blood Oxygenation ◽  
Independent Component ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Group Level ◽  
Independent Components ◽  
Comparison Results

AbstractPurpose: Cortical and subcortical functional activity stimulated via saccade and vergence eye movements were investigated to examine the similarities and differences between networks and regions of interest (ROIs). Methods: Blood oxygenation level-dependent (BOLD) signals from stimulus-induced functional Magnetic Resonance Imaging (MRI) experiments were analyzed studying 16 healthy subjects. Six types of oculomotor experiments were conducted using a block design to study both saccade and vergence circuits. The experiments included a simple eye movement task and a more cognitively demanding prediction task. A hierarchical independent component analysis (ICA) process began by analyzing individual subject data sets with spatial ICA to extract spatial independent components (sIC), which resulted in three ROIs. Using the time series from each of the three ROIs per subject, per oculomotor experiment, a temporal ICA was used to compute individual temporal independent components (tICs). For each of the three ROIs, the individual tICs from multiple subjects were entered into a second temporal ICA to compute group-level tICs for comparison. Results: Two independent spatial maps were observed for each subject (one sIC showing activity in the frontoparietal regions and another sIC in the cerebellum) during the six oculomotor tasks. Analysis of group-level tICs revealed an increased latency in the cerebellar region when compared to the frontoparietal region. Conclusion: Shared neuronal behavior has been reported in the frontal and parietal lobes, which may in part explain the segregation of frontoparietal functional activity into one sIC. The cerebellum uses multiple time scales for motor learning. This may result in an increased latency observed in the BOLD signal of the cerebellar group-level tIC when compared to the frontal and parietal group-level tICs. The increased latency offers a possible explanation to why ICA dissects the cerebellar activity into an sIC. The hierarchical ICA process used to calculate group-level tICs can yield insight into functional connectivity within complex neural networks.

BCG Artifact Removal Using Improved Independent Component Analysis Approach

2017 ◽  
Vol 5 (1) ◽  
pp. 130 ◽  
Author(s):  
Manjula B.M. ◽  
Chirag Sharma
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Reaction Force ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Acquisition ◽  
Artifact Removal ◽  
Recent Advancement ◽  
Floor Vibrations ◽  
Novel Method

<p>Recent advancement in bio-medical field has attracted researchers toward BCG signal processing for monitoring the health activities. There have been various techniques for monitoring physical activities such as (SCG) Seismocardiography, Electrocardiography (ECG) etc. BCG signal is a measurement of reaction force applied for cardiac ejection of blood. Various measurement schemes and systems have been developed for BCG detection and measurement such as tables, beds, weighing scale and chairs. Weighing scales have been promising method for measurement of BCG signal because of less cost of implementation, smaller size etc. but these devices still suffer from the artifact which are induced due to subject movement or motion during signal acquisition or it can be caused due to floor vibrations. Artifact removal is necessary for efficient analysis and health monitoring. In this work we address the issue of artifact removal in BCG signal by proposing a novel method of signal processing. According to proposed approach raw signal is pre-processed and parsed to independent component analysis which provides the decomposed components and later k-means is applied to detect the components which are responsible for artifact and removed. Proposed approach is compared with existing method and shows better performance in terms of artifact removal.</p>

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