Resting-State Networks Revealed with Whole-head Near-Infrared Spectroscopy
|J Mehnert1,2, D S Margulies2, C Schmitz1,3, J Steinbrink1, H Obrig1,2,4, A Villringer2
1Berlin NeuroImaging Center, Charité, Berlin, Germany/2Max-Planck Institute for Human Cognitive and Brain Science, Leipzig, Germany/3NIRx Medizintechnik GmbH, Berlin, Germany/4Clinic for Cognitive Neurology, University Hospital, Leipzig, Germany
Introduction: The use of correlation analysis on low-frequency fluctuations in fMRI data has been well established for determining functional connectivity in the absence of task, termed ‘resting-state networks’. Only recently, similar methods have been applied to optical imaging data (White and Culver, 2008). Here we report the correlation analysis of low-frequency oscillations (Obrig et al., 2000) with the aim of detecting functional connectivity networks. The current study establishes the feasibility of optical measurements for studying resting-state and offers the potential to extend this field of research to subject groups not suited for fMRI studies.
Methods: Cortical oxygenation changes of 4 healthy adults (1 female) were measured at rest with eyes-open (2 blocks of 15 min duration) using a near-infrared spectroscopy (NIRS) imaging system (DYNOT 232 by NIRx Medizintechnik GmbH, Berlin, Germany). The sensor array consisted of 52 probes (32 detector fibers, 20 emitter fibers) with an inter-optode distance of approx. 2.5 cm, providing a total of 80 channels. The array covered nearly the whole head, only excluding the occipital areas. For each subject we selected a subset of 5 minutes of data for further analysis, which was not contaminated by movement artifacts. Data were low-pass filtered at 0.01 Hz, and the global mean of all channels was regressed out to reduce the influence of extra-cerebral signal (e.g., oxygenation changes in the skin). Subsequently, correlation analysis was performed for the oxygenated and deoxygenated hemoglobin signal for each channel separately. Three individual regions-of-interest were used as the basis for functional connectivity correlation analysis.
Results: Our findings indicate the detection of three well-known functional networks: (1) bilateral motor areas, (2) bilateral temporal lobes and (3) the ‘default-mode’ network (medial-frontal and bilateral parietal cortical areas). Furthermore, our results show a high intra-subject consistency for both oxygenated and deoxygenated hemoglobin, as well as high inter-subject reliability. To further validate the findings a correlation between resting-state and functional activation data is currently being performed. Also inter-subject comparison will be aided by projection on a standardized brain surface by means of probability mapping (Jurcak et al., 2007).
Conclusions: Using optical imaging methods to detect and interpret functional connectivity can be used to cross-validate findings in resting-state fMRI and may help to extend the approach to further investigate the developmental effects in children and infants, to facilitate bed-side measurements, or to study patients which cannot be examined by fMRI (e.g. deep-brain stimulation).
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