Superficial Fluctuations in Functional Near-Infrared Spectroscopy during Concurrent Transcranial Magnetic Stimulation.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging modality which, similar to fMRI, measures cerebral hemodynamics associated with neural activity. It has several advantages over fMRI, including low cost, portability, compatibility with metal or electrical medical implants, and ease of integration with electroencephalography (EEG) and transcranial magnetic stimulation (TMS). However, fNIRS signal contains a number of confounding components. Physiological noises due to superficial absorption by the scalp and skull are present in all fNIRS data. Additionally, low-frequency oscillations of respiration, cardiac pulse and movements all obscure the underlying cerebral hemodynamic signals. Our previous work has developed an automatic processing pipeline that effectively removes these physiological noise components from data during voluntary tasks (e.g., a motor task) and an endogenous state (e.g., awake resting) [1], [2]. However, to date it has not been known if the noises behave similarly in recordings involving an externally injected stimulus such as TMS. Therefore, in a unique setup of concurrent fNIRS, EEG and TMS (fNET), this study examined the spatial and temporal profiles of fNIRS data and noises during motor, single pulse and repetitive TMS. Specifically, we compared the multichannel short separation recordings with the regularly distanced long separation data in a whole-head montage. The results showed that superficial fluctuations indeed were present in the TMS-concurrent fNIRS recordings and that the noise components behaved similarly across motor task, single pulse and repetitive TMS at individuals' alpha frequency, which warrants removal of such physiological noises.Clinical Relevance- Compared to fMRI, fNIRS offers a much less expensive alternative for measuring cortical hemodynamics. Importantly, fNIRS can provide a clinic accessible options for concurrent measurement with TMS when TMS is given as treatment to patients with depression or other neurological disorders. Our findings indicate that fNIRS data acquired during concurrent TMS are contaminated by superficial fluctuations and that careful removal of these physiological noises from fNIRS data is critical in obtaining accurate images of cerebral activity with fNIRS.