A Preliminary Assessment of the Impact of Scan Length on ICA Dimensionality & Network Features

An important practical question, given various sources of noise in resting-state blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) images, is how much data to acquire to get identifiable and stable independent component analysis (ICA) networks in MRI scans. In this study, we investigate how ICA dimensionality, stability of the estimated independent components, and the spatiotemporal features of the extracted networks are affected by scan length. Our results show that the length of scan clearly influences the network dimensionality, peak voxel location of the spatial maps, spectral power, and functional network connectivity (FNC). Using minimum description length criteria and entropy-rate based order selection, we observed that the dimensionality decreased as the scan length decreased from 12 to 2 minutes. In addition, due to decreased dimensionality in shorter scans, some networks such as the ventral attention and subcortical networks could not be extracted. But the networks that could be identified in shorter scans including the default-mode, salience, and self-referential networks were stable (cluster quality index Iq > 0.8). Kurtosis of time-courses and spatial maps, as well as the spectral parameters (the dynamic range and fractional of amplitude of low frequency fluctuation; fALFF), and FNC also changed with the scan length with shorter scans having lower values. Consequently, scan length choice can have a significant impact on the of ICA-based restingstate fMRI studies. Future studies using larger sample sizes are warranted to further assess the implications of the scan length and data size on ICA outcome measures and reliability.