Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest

Abstract

Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.

Functional Integration Between Brain Regions at Rest Occurs in Multiple-Frequency Bands

“…Using similar acquisition methods, Lee et al (2012) have demonstrated interhemispheric connectivity in the sensory motor cortex from BOLD fluctuations in higher frequency ranges ( > 0.25 Hz) than traditionally used in resting-state fMRI studies (0.01-0.1 Hz). Similarly, Boubela et al (2013) have shown the presence of the default mode and frontal-parietal networks by applying ICA to BOLD fMRI data acquired at higher sampling rate (TR = 354 msec, sampling frequency = 2.82 Hz).…”

“…Although earlier studies have described the presence of different RSNs in higher frequency bands than LFFs ( > 0.1 Hz), these studies were mainly performed using a single approach to study RSNs (Boubela et al, 2013;Lee et al, 2012). Moreover, these reports focused on specific networks rather than on the entire collection of RSNs (Boubela et al, 2013;Lee et al, 2012;Wu et al, 2008).…”

“…Moreover, these reports focused on specific networks rather than on the entire collection of RSNs (Boubela et al, 2013;Lee et al, 2012;Wu et al, 2008). Finally, the BOLD signal frequencies > 0.1 Hz were generally combined in a single-frequency band to derive RSNs (Boubela et al, 2013;Lee et al, 2012).…”

Fast fMRI can detect oscillatory neural activity in humans

“…In general, the fact that fMRI temporal resolution is limited by slow neurovascular coupling has restricted the majority of fMRI studies to studying dynamics in the <0.1-Hz range, because periodic oscillations above that frequency range are expected to be vanishingly small. Despite this evidence, recent studies performed during the resting state have suggested that there are significant neuronally driven blood oxygenation level-dependent (BOLD) contributions to fMRI signals at frequencies above 0.1 Hz (24)(25)(26)(27). However, a challenge in interpreting fMRI oscillatory dynamics measured during the resting state is that the underlying brain activity is not known.…”

Grand challenges in biomedical physics

“…These novel developments help to cover a wide range of spatio-temporal resolution and sensitivity (Figure 1; adopted from Meikle et al, 2005), complementing each other in both parameters displayed, as well as energy levels (SAR) to be applied. Another important parameter would be the temporal resolution, in particular relative to physiological motion, to help increase functional contrast-to-noise ratio by specifically reducing physiological noise (Boubela et al, 2013). Otherwise, group averaging may be rather limited to extract more subtle functional differences (Biswal et al, 2010;Kalcher et al, 2012), at least in functional MRI.…”