A fully-automated independent component classifier for reducing artifact in analysis of functional MRI

© 2015 Dr. Kaushik BhaganagarapuBrain imaging techniques, specifically, functional Magnetic Resonance Imaging (fMRI) has played a significant role in aiding our understanding of brain function. Taking advantage of the coupling between blood oxygen concentration and neural activity, fMRI has the ability to noninvasively map brain functions, providing researchers and clinicians new insights into the understanding of the human brain. However, interpretation of data obtained from fMRI experiments is challenging due to limited knowledge about the origins of the signal, low signal to noise ratio and the massive amount of data generated. Data driven techniques, specifically Independent Component Analysis (ICA) are increasingly being used to generate potentially valuable information on the nature of signal and noise in fMRI data. Unlike traditional methods for analysing fMRI data, where a design paradigm and assumptions about the haemodynamic processes are required, ICA offers a hypothesis free method to gain further insights in identifying the spatial location of brain activity. However, an enduring issue with data-driven analysis is the interpretation of results. To address this, we developed a novel algorithm, dubbed, Spatially Organised Component Klassifikator (SOCK), that identifies and removes noise from an ICA of fMRI data. The SOCK method does not require temporal information about an fMRI paradigm; does not require the user to train the algorithm; does not require querying external databases; requires only the fMRI images (additional acquisition of anatomical imaging not required); is able to identify a high proportion of noise related components without removing components that are likely to be of neuronal origin; can be applied to any fMRI studies; is automated, requiring minimal or no human intervention and is freely available in an open-source software package. In this thesis, we describe the SOCK algorithm and demonstrate its effectiveness in separating noise from signal by applying it to resting state, task-based and event related fMRI data, thus leading to additional insights in identifying spatial and temporal patterns of brain activity

Impact of automated ICA-based denoising of fMRI data in acute stroke patients

Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients. The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom. The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility (p < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity (p < 0.001). Patient-trained FIX resulted in higher RSN identifiability (p < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA. The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX with appropriate patient data proved valuable, particularly in improving the signal-to-noise ratio