Optical Imaging and Spectroscopy Techniques

This paper conducts a meta-analysis of infant fNIRS research to identify attrition rate trends and predictors, finding an average attrition rate of 34.23% and recommending strategies to reduce it.

This study identifies a novel diagnostic biomarker for acute ischemic stroke using green autofluorescence patterns in skin and fingernails, showing high accuracy in differentiating AIS patients from healthy and non-AIS groups.

This paper compares SA correction methods with and without short-distance channels in fNIRS, finding that methods with SDCs generally perform better but methods without SDCs can still improve signal quality.

This study tests if scale invariance measured by the Hurst exponent can quantify cognitive load using fNIRS during a working memory task, showing higher H during easier tasks.

This study uses near infrared spectroscopy (NIRS) to monitor cerebral ischemia in rats after middle cerebral artery occlusion, finding a significant negative correlation between NIRS-derived parameters and infarct volume.

This study shows that nonlinear analysis of high-frequency fNIRS data reveals distinct neurovascular representations compared to linear analysis, highlighting the importance of considering nonlinear effects in hemodynamic responses.

This paper demonstrates a 3D visualization method for amyloid-beta deposits in Alzheimer's mouse models using MRI and FMT, showing improved depth resolution compared to 2D methods.

This paper introduces PARAFAC for fNIRS artifact correction, showing it improves data quality more than traditional methods.

This paper develops a non-invasive optical imaging method to map functional areas of the primate cortex across multiple modalities in awake marmosets.

This study shows that locomotion during awake imaging alters sensory-evoked haemodynamic responses in mice, emphasizing the need to monitor movement to avoid confounding results.

LIONirs is a MATLAB toolbox for fNIRS data analysis that provides flexibility and simplicity in processing and visualizing brain data with multimodal integration.

This study uses near infrared spectroscopy to measure prefrontal functional connectivity as a biomarker of cognitive reserve, finding a positive correlation with IQ but not motor skills.

This study characterizes Mayer wave oscillations in fNIRS signals using a physiologically informed model and evaluates the impact of short-channel correction on reducing these oscillations.

This study uses multi-exposure speckle imaging to show that isoflurane anesthesia causes rapid and sustained increases in cerebral vessel diameter and blood flow in mice.

This paper develops a method using hyperspectral imaging and deep learning to classify tissues with high accuracy, showing that spectral variability is primarily due to tissue type.

This study compares the predictive power of oxygenated and de-oxygenated hemoglobin signals in fNIRS data for experimental condition classification, finding oxygenated hemoglobin provides better differentiation.

This paper explores using diffuse optical reflectance at different wavelengths to detect cardiac structure, showing sensitivity to tissue properties and potential for real-time mapping.

The paper investigates how anatomical features of different brain regions affect the spatial localization of fiber photometry signals in mice.

This study investigates how auditory cortex activation changes with stimulation duration using fNIRS, finding non-linear responses and identifying 15s as the optimal duration for signal quality.

This study investigates the origin of 1/f-like oxygenation fluctuations in the murine cortex and finds that they are driven by erythrocyte flow dynamics rather than neural activity.

The paper compares optode-layout design approaches for fNIRS using individual fMRI data, finding that incorporating more individual MRI data improves signal quality and sensitivity.

This paper presents a pipeline for fNIRS image reconstruction using MRI templates or individual anatomy, showing high temporal correlations and reliability with a longitudinal child dataset.

The paper examines age-related changes in scalp-to-cortex distances and mappings in infants, children, and adults using realistic head models and photon propagation simulations.

This study compares averaging and GLM-based analyses of fNIRS data from a block-design auditory paradigm, finding similar results but improved detection with systemic hemodynamic correction.

This paper introduces a fiberoptic probe for hemodynamic spectroscopy in freely-moving mice, showing cerebrovascular dysregulation in Alzheimer's and glioma models.

This paper introduces a method to measure absolute cerebral metabolic rate of oxygen in rat brains using optical imaging techniques and validates it against MR and PET measurements.

The paper introduces an open-source MATLAB toolbox for processing and analyzing wide-field optical imaging data in mice, demonstrating its utility in detecting stroke deficits and analyzing resting state data.

This paper introduces a new fNIRS reconstruction method called Maximum Entropy on the Mean (MEM), which provides more accurate and robust reconstructions than the Minimum Norm Estimate (MNE) in both simulations and real data.

This study uses fNIRS and FDA to classify odor detection with high accuracy, showing that fNIRS can distinguish odorant and non-odorant stimuli without motor interference.

This paper introduces a high-sensitivity DCS system with 1024 parallel detection channels, enabling fast and sensitive measurement of blood flow and physiological variations.