DWT–CEM: an algorithm for scale-temporal clustering in fMRI

Abstract

The number of studies using functional magnetic resonance imaging (fMRI) has grown very rapidly since the first description of the technique in the early 1990s. Most published studies have utilized data analysis methods based on voxel-wise application of general linear models (GLM). On the other hand, temporal clustering analysis (TCA) focuses on the identification of relationships between cortical areas by measuring temporal common properties. In its most general form, TCA is sensitive to the low signal-to-noise ratio of BOLD and is dependent on subjective choices of filtering parameters. In this paper, we introduce a method for wavelet-based clustering of time-series data and show that it may be useful in data sets with low signal-to-noise ratios, allowing the automatic selection of the optimum number of clusters. We also provide examples of the technique applied to simulated and real fMRI datasets.

Disentangling Multispectral Functional Connectivity With Wavelets

“…Since their development, wavelets have become an important tool in fMRI analysis (Bullmore et al, 2004 ). Several methodological studies have shown the usefulness of combining wavelet filtering with various connectivity metrics to better characterize FC networks (Achard and Bullmore, 2007 ; Sato et al, 2007 ; Chang and Glover, 2010 ; Eryilmaz et al, 2011 ; Guo et al, 2012 ; Schröter et al, 2012 ). These and other methods have been extended into investigations of fMRI based biomarkers for neurological diseases such as addiction (Salomon et al, 2012 ; Lam et al, 2013 ), depression (Salomon et al, 2011 ; Meng et al, 2013 ), Parkinson's (Skidmore et al, 2011 ), Alzheimer's (Supekar et al, 2008 ; Wang et al, 2013 ), and schizophrenia (Alexander-Bloch et al, 2010 ; Bassett et al, 2012 ).…”

Wavelet correlation between subjects: A time-scale data driven analysis for brain mapping using fMRI

“…Wavelets have also been used to help solve problems related to resampling (wavestrap), modeling of time series and in multiple hypothesis testing (Bullmore et al, 2003;Suckling et al, 2006). Wavelets have also been used for temporal multi-resolution analysis in fMRI (Alexandera et al, 2000;Escolá et al, 2007;Sato et al, 2007). This paper introduces a new method for exploratory analysis, based on a combination of inter-subject correlation analysis proposed by Hasson et al (2004) and Hejnar et al (2006) and multiresolution analysis using wavelets.…”

“…Thus, approaches providing a more general flexibility can be attractive as alternative and complementary tools. Examples of alternative data-driven methods are: independent component analysis (ICA), (Calhoun et al, 2004;McKeown et al, 2003) and cluster analysis (Dimitriadou et al, 2004;Windischberger et al, 2003;Sato et al, 2007). These techniques provide exploratory data analyses and do not require the explicit specification of the HRF.…”