Global motion detection and censoring in high‐density diffuse optical tomography

Abstract

Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography (HD‐DOT) with hundreds to thousands of source‐detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near‐infrared spectroscopy (fNIRS). This limitation restricts the application of HD‐DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation‐based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.

Amplitude of fNIRS Resting-State Global Signal Is Related to EEG Vigilance Measures: A Simultaneous fNIRS and EEG Study

“…Later, most resting-state fMRI studies have adopted GSR as a pre-processing approach: the global signal component is regressed out of preprocessed BOLD signals prior to computation of connectivity measures and therefore regionally focused connectivity patterns are reported (Fox et al, 2009). Similarly, in recent fNIRS studies of resting state brain, a global component has been recognized in the measurements from regularly distanced optodes (White et al, 2009;Mesquita et al, 2010;Tong and Frederick, 2010;Eggebrecht et al, 2014;Tachtsidis and Scholkmann, 2016;Duan et al, 2018;Wyser et al, 2020) and from short-distanced optodes (White et al, 2009;Gregg et al, 2010;Mesquita et al, 2010;Eggebrecht et al, 2014;Tachtsidis and Scholkmann, 2016;Duan et al, 2018;Sherafati et al, 2020;Wyser et al, 2020). To date, there is no wellestablished pre-processing routine in resting state fNIRS studies although multiple efforts are being made (Huppert et al, 2009;Ye et al, 2009;Xu et al, 2014;Santosa et al, 2018).…”

“…The most common RSFC analysis of fNIRS data involves evaluating the temporal relationship between time series of the preprocessed data from recording units, for example, through the Pearson's correlation. A global component has been observed in fNIRS measurements and commonly removed for the purpose of attenuating systematic noises at the resting state (White et al, 2009;Gregg et al, 2010;Mesquita et al, 2010;Eggebrecht et al, 2014;Tachtsidis and Scholkmann, 2016;Duan et al, 2018;Sherafati et al, 2020;Wyser et al, 2020). Whereas removing superficial contributions from short-distanced channels to fNIRS is increasingly employed to attenuate the systematic noises (Saager and Berger, 2005;Gagnon et al, 2011), data from both long-distanced and short-distanced channels commonly suggest a global component exist in fNIRS measurements and distribute across wide regions (Zhang et al, 2005(Zhang et al, , 2007(Zhang et al, , 2009Kohno et al, 2007;Gregg et al, 2010;Tong and Frederick, 2010;Novi et al, 2016;Sato et al, 2016).…”

Prefrontal cortex supports speech perception in listeners with cochlear implants

“…Sourcedetector (SD) pair light level measurements were converted to log-ratio by calculating the temporal mean of a given SD-pair measurement as the baseline for that measurement. Noisy measurements were empirically defined as those that have greater than 7.5% temporal standard deviation in the least noisy (lowest mean motion) 60 seconds of each run (Eggebrecht, Ferradal et al 2014, Sherafati, Snyder et al 2020. Then, channels with greater than 33% noisy first or second nearest neighbor measurements (nn1 and nn2) were excluded (Fig.…”

“…To define the left and right auditory ROIs, we used a previously published fMRI resting state dataset (Sherafati, Snyder et al 2020) that was masked by the field of view of our HD-DOT system. We defined the left and right auditory ROIs by selecting a 5 mm radius seed in the contralateral hemisphere ([70.5, -24, 3], [-67.5, -27, 3]) and finding the Pearson correlation between the time-series of the seed region with all other voxels in the field of view.…”

Mecp2 deletion results in profound alterations of developmental and adult functional connectivity

“…Data cleaning was also performed by discarding all frames with a standard deviation greater than 1 in the global variance of the temporal derivatives in the oxyhemoglobin signal within the 0.4-4.0 Hz band, a method adapted from Sherafati et al 50 . All calcium functional connectivity analysis was performed in the 0.4-4.0 Hz delta band.…”