A Newcomer's Guide to Functional Near Infrared Spectroscopy Experiments.

This review presents a practical primer for functional near-infrared spectroscopy (fNIRS) with respect to technology, experimentation, and analysis software. Its purpose is to jump-start interested practitioners considering utilizing a non-invasive, versatile, nevertheless challenging window into the brain using optical methods. We briefly recapitulate relevant anatomical and optical foundations and give a short historical overview. We describe competing types of illumination (trans-illumination, reflectance, and differential reflectance) and data collection methods (continuous wave, time domain and frequency domain). Basic components (light sources, detection, and recording components) of fNIRS systems are presented. Advantages and limitations of fNIRS techniques are offered, followed by a list of very practical recommendations for its use. A variety of experimental and clinical studies with fNIRS are sampled, shedding light on many brain-related ailments. Finally, we describe and discuss a number of freely available analysis and presentation packages suited for data analysis. In conclusion, we recommend fNIRS due to its ever-growing body of clinical applications, state-of-the-art neuroimaging technique and manageable hardware requirements. It can be safely concluded that fNIRS adds a new arrow to the quiver of neuro-medical examinations due to both its great versatility and limited costs.

Dual Layered Models of Light Scattering in the Near Infrared A: Optical Measurements and Simulation.

Intralipid emulsion is often used as optical model substance to mimick living tissue's strong scattering properties. As such it is of considerable importance to utilize realistic parameters for any type of simulation or calculation in context of Near Infrared Spectroscopy. We determined optical properties of diluted Intralipid solutions at often used, realistic volume concentrations ρil and at two NIRS wavelengths (780nm and 850nm) in a double integrating Ulbricht-sphere setup. The results were used in Monte Carlo (MC) simulations of an experiment, described in our companion paper. Both, phantom experiments and MC simulation showed qualitatively similar results and demonstrated the effects of changing the three major NIRS factors, namely the penetrated layer depth (d), the Intralipid concentration ρil and the source-detector separation (SDS). The results demonstrated that light reaching the detectors was inversely proportional to ρil and d. It also showed that very low Intralipid concentrations do not follow the optical properties documented for Intralipid 20%.

Dual Layered Models of Light Scattering in the Near Infrared B: Experimental Results with a Phantom.

Intralipid emulsion is often used as optical model substance to mimic living tissue's strong scattering properties. As such it is of considerable importance to utilize realistic parameters for any type of simulation or calculation in context of Near Infrared Spectroscopy. We determined optical characteristics of diluted Intralipid solutions at often used, realistic volume concentrations ρil and at two wavelengths (780nm and 850nm) in a simple phantom setup featuring multiple sensors with different source-detector-separation (SDS) and penetration depths d. Both, phantom experiments and MC simulation showed qualitatively similar results and demonstrated the influence of the three major NIRS factors, namely the penetrated layer depth (d), the Intralipid concentration ρil and the source-detector separation (SDS). The results demonstrated that light reaching the detectors is inversely proportional to ρil and d. It corroborates the need for differential measurements with at least two SDS to account for superficial large angle scattering.

A Wireless EEG Recording Method for Rat Use inside the Water Maze.

With the continued miniaturisation of portable embedded systems, wireless EEG recording techniques are becoming increasingly prevalent in animal behavioural research. However, in spite of their versatility and portability, they have seldom been used inside water-maze tasks designed for rats. As such, a novel 3D printed implant and waterproof connector is presented, which can facilitate wireless water-maze EEG recordings in freely-moving rats, using a commercial wireless recording system (W32; Multichannel Systems). As well as waterproofing the wireless system, battery, and electrode connector, the implant serves to reduce movement-related artefacts by redistributing movement-related forces away from the electrode connector. This implant/connector was able to successfully record high-quality LFP in the hippocampo-striatal brain regions of rats as they undertook a procedural-learning variant of the double-H water-maze task. Notably, there were no significant performance deficits through its use when compared with a control group across a number of metrics including number of errors and speed of task completion. Taken together, this method can expand the range of measurements that are currently possible in this diverse area of behavioural neuroscience, whilst paving the way for integration with more complex behaviours.

Multimodal 2D Brain Computer Interface.

In this work we used multimodal, non-invasive brain signal recording systems, namely Near Infrared Spectroscopy (NIRS), disc electrode electroencephalography (EEG) and tripolar concentric ring electrodes (TCRE) electroencephalography (tEEG). 7 healthy subjects participated in our experiments to control a 2-D Brain Computer Interface (BCI). Four motor imagery task were performed, imagery motion of the left hand, the right hand, both hands and both feet. The signal slope (SS) of the change in oxygenated hemoglobin concentration measured by NIRS was used for feature extraction while the power spectrum density (PSD) of both EEG and tEEG in the frequency band 8-30Hz was used for feature extraction. Linear Discriminant Analysis (LDA) was used to classify different combinations of the aforementioned features. The highest classification accuracy (85.2%) was achieved by using features from all the three brain signals recording modules. The improvement in classification accuracy was highly significant (p = 0.0033) when using the multimodal signals features as compared to pure EEG features.