Introduction to fNIRS (English)

Categories: Introductory fNIRS
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About Course

Functional Near-Infrared Spectroscopy (fNIRS) is a rapidly growing neuroimaging modality, valued for its portability, tolerance to movement, and suitability across diverse populations – from infants to older adults, in labs and in the real world. Yet producing high-quality fNIRS research demands a solid grasp of the physical principles, physiological processes, and practical challenges that shape every dataset.

This course offers a comprehensive foundation for anyone entering the field. From the physical principles to the frontiers of hyperscanning and ecological neuroscience, you will build the conceptual toolkit needed to understand what fNIRS actually measures, why signal quality matters, and where the field is heading, thus ensuring your future work is both rigorous and reproducible.

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What Will You Learn?

  • Describe the fundamental physical principles governing near-infrared light propagation in biological tissue
  • Explain why the Modified Beer-Lambert Law is a powerful but simplified model of light transport, and what assumptions it rests on
  • Distinguish between different fNIRS device types — including CW, FD, TD, and speckle-based systems — and understand what each can and cannot measure
  • Connect neural activity to the hemodynamic signals fNIRS detects, from neurovascular coupling to the hemodynamic response function
  • Identify the major sources of noise in fNIRS data and apply appropriate signal processing strategies
  • Appreciate the breadth of fNIRS applications beyond the standard lab paradigm, including infant research, ecological settings, and hyperscanning

Course Content

What is fNIRS?
Where fNIRS comes from, what it measures in principle, and where it sits among other neuroimaging tools.

How does fNIRS work?
The physical logic from light entering tissue to a number quantifying the detected light in an fNIRS device

Brain Hemodynamics and Neurovascular Coupling
NIRS does not measure neural activity directly; it measures the vascular response that follows it. This unit builds the physiological bridge between the two. We start at the level of neurons and synapses, trace the cascade through metabolism and blood flow regulation, and arrive at the haemodynamic signals we actually record: changes in oxyhaemoglobin (ΔHbO) and deoxyhaemoglobin (ΔHbR). We examine what these signals represent biophysically, how to interpret them, and what the canonical Hemodynamic Response Function looks like. A recurring theme is the distinction between different physiological quantities that are often conflated: CMRO₂, cerebral blood flow, blood volume, oxygen saturation, and hemoglobin concentration are related but not equivalent, and confusing them leads to misinterpretation.

Collecting fNIRS Data
Not all fNIRS systems are the same. This unit provides a broad overview of the hardware landscape, from the ubiquitous Continuous Wave (CW) systems to Frequency Domain (FD) and Time Domain (TD) instruments, and the emerging class of speckle-based devices that measure blood flow rather than hemoglobin concentration. A central message is that the choice of hardware determines what you can and cannot measure: for example, TD and FD systems can independently quantify absorption and scattering, and therefore provide absolute hemoglobin concentrations, while CW systems are almost always limited to relative changes and must assume a fixed DPF. We also cover the practical side: how sources and detectors are arranged on the head (the montage), and what "good coupling" between optode and scalp actually means.

Processing fNIRS Data
Collecting fNIRS data is relatively accessible, but clean, interpretable data is not. This unit confronts the reality that the fNIRS signal is dominated by noise: physiological fluctuations from the heart, respiration, and slow vasomotion can be an order of magnitude larger than the neural signal of interest. Motion artifacts are arguably one of the defining challenges of the modality. We work through the major noise sources and introduce strategies to address each before considering statistical analysis. On the way, you will understand why simple averaging over blocks is insufficient, and why the General Linear Model (GLM) has been the standard framework. The unit closes with an overview of alternative quantification approaches for data analysis, including inter-subject correlation and functional connectivity, and the broader challenge of pipeline standardization.

fNIRS Challenges and Future Frontiers
The final unit steps back from technical details to consider the bigger picture: what fNIRS uniquely enables, and what still stands in its way. We explore three of the most exciting directions in the field: hyperscanning and social/developmental neuroscience, ecological validity through wearable deployment, and the study of populations that other neuroimaging modalities struggle to accommodate. We also return to the challenge of pipeline standardization and reproducibility as an open problem that newcomers entering the field have an opportunity (and a responsibility!) to address.

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