Correcting for motion is an important consideration in infant functional near-infrared spectroscopy studies. We tested the performance of conventional motion correction methods and compared probe motion and data quality metrics for data collected at different infant ages (5, 7, and 12 months) and during different methods of stimulus presentation (video versus live). While 5-month-olds had slower maximum head speed than 7- or 12-month-olds, data quality metrics and hemodynamic response recovery errors were similar across ages. Data quality was also similar between video and live stimulus presentation. Motion correction algorithms, such as wavelet filtering and targeted principal component analysis, performed well for infant data using infant-specific parameters, and parameters may be used without fine-tuning for infant age or method of stimulus presentation. We recommend using wavelet filtering with iqr=0.5; however, a range of parameters seemed acceptable. We do not recommend using trial rejection alone, because it did not improve hemodynamic response recovery as compared to no correction at all. Data quality metrics calculated from uncorrected data were associated with hemodynamic response recovery error, indicating that full simulation studies may not be necessary to assess motion correction performance.
Changes in heart rate are a useful physiological measure in infant studies. We present an algorithm for calculating the heart rate (HR) from oxyhemoglobin pulsation in functional near-infrared spectroscopy (fNIRS) signals. The algorithm is applied to data collected from 10 infants, and the HR derived from the fNIRS signals is compared against the HR as calculated by electrocardiography. We show high agreement between the two HR signals for all infants (r>0.90), and also compare stimulus-related HR responses as measured by the two methods and find good agreement despite high levels of movement in the infants. This algorithm can be used to measure changes in HR in infants participating in fNIRS studies without the need for additional HR sensors.
Accurate segmentation of structural magnetic resonance images is critical for creating subject-specific forward models for functional neuroimaging source localization. In this work, we present an innovative segmentation algorithm that generates accurate head tissue layer thicknesses that are needed for diffuse optical tomography (DOT) data analysis. The presented algorithm is compared against other publicly available head segmentation methods. The proposed algorithm has a root mean square scalp thickness error of 1.60 mm, skull thickness error of 1.96 mm, and summed scalp and skull error of 1.49 mm. We also introduce a segmentation evaluation metric that evaluates the accuracy of tissue layer thicknesses in regions of the head where optodes are typically placed. The presented segmentation algorithm and evaluation metric are tools for improving the localization accuracy of neuroimaging with DOT, and also multimodal neuroimaging such as combined electroencephalography and DOT.
Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occuring at the surface of
the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these
veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the
NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS
signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the
cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic
geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during
motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo
measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR)
is equal to 16-22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the
cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73-79% of the cortical contribution
to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and
therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of
ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein
contributions, our finger tapping results do reveal the importance of considering the pial contribution.