No abstract

The ability to detect dynamic changes in brain activity during affective processing within individual subjects in real-time can advance our understanding of the neural mechanisms of emotion, psychiatric illness, and therapeutic intervention. We investigated whether activity in limbic and paralimbic regions elicited by blocks of aversive (AV) and neutral (NEU) pictures can be detected by real-time fMRI. Real-time analysis of signal change during each block revealed that activations in insula and medial frontal cortex were more frequent during AV than NEU epochs. Single subject and group analysis off-line with conventional statistical parametric mapping methods matched the results obtained in real-time. Detecting cortico-limbic brain activation during perception and experience of emotionally salient visual stimuli with real-time fMRI technology is feasible.

Background/Introduction: There is considerable interest in using real-time functional magnetic resonance imaging (fMRI) for monitoring functional connectivity dynamics. To date, the majority of real-time resting-state fMRI studies have examined a limited number of brain regions. This is, in part, due to the computational demands of traditional seed-and independent component analysis-based methods, in particular when using increasingly available high-speed fMRI methods. Methods: This study describes a computationally efficient, real-time, seed-based, resting-state fMRI analysis pipeline using moving averaged sliding-windows (ASW) with partial correlations and regression of motion parameters and signals from white matter and cerebrospinal fluid. Results: Analytical and numerical analyses of ASW correlation and sliding-window regression as a function of window width show selectable bandpass filter characteristics and effective suppression of artifactual correlations resulting from signal drifts and transients. The analysis pipeline is compatible with multislab echo-volumar imaging and simultaneous multislice echo-planar imaging with repetition times as short as 136 msec. High-speed, resting-state fMRI data in healthy controls demonstrate the effectiveness of this approach for minimizing artifactual correlations in white and gray matter, which was comparable to conventional regression across the entire scan. Integrating sliding-window averaging (width: W 1 ) within a second-level sliding-window (width: W 2 ) enabled monitoring of intra-and internetwork correlation dynamics of up to 12 resting-state networks with bandpass filter characteristics determined by the first-level sliding-window and temporal resolution W 1 + W 2 . Conclusions: The computational performance and confound tolerance make this seed-based, resting-state fMRI approach suitable for real-time monitoring of data quality and resting-state connectivity dynamics in neuroscience and clinical research studies.