Functional Near-infrared Diffuse Optical Spectroscopy (fNIRS) to Explore Mental Health

Mental health disorders have been a long-term concern for society in general and have thus been increasingly studied by the scientific community. Common psychiatric disorders such as depression, anxiety, bipolar disorder, or schizophrenia not only severely affect individual wellbeing, but can also present a risk to the safety and stability within societies. Moreover, aging and accidents have substantially expanded the incidence of psychiatric problems in general populations and as a consequence, psychiatric disorders resulting from stroke, head injuries or neurodegeneration (e.g., Alzheimer’s disease) have become an increasing burden over recent years. In the past, the clinical assessment of mental health has heavily relied on subjective questionnaires and score sheets, due to a lack of objective measuring modalities. However, a few medical imaging technologies, such as CT and MRI, have been utilized to objectively diagnose a variety of cerebral diseases through localizing morphological deficits inside the brain. Nevertheless, many mental health disorders, including depression, anxiety and schizophrenia, especially at an early stage, can be undetectable from changes to cerebral structure. Instead, the malfunctions of the brain usually precede any morphological changes so alternative methods are needed to detect the pathology as early as possible.

Near-infrared diffuse optical spectroscopy (NIRS) has been attracting the attention of scientists and clinicians in the psychiatric field as a low cost and highly sensitive approach to assess cerebral haemodynamics that are closely associated with human functional capacities (e.g., cognitive and emotional functions). Over the past forty years, owing to the faster sampling rate and higher sensitivity in probing brain cortex activations than the morphologic responses probed by CT or MRI, NIRS was utilized to build functional brain networks (namely fNIRS) that have been translated into clinical use for the diagnosis and therapeutic evaluation of various mental diseases including depression and schizophrenia. Additionally, many advanced algorithms for analysing fNIRS data e.g., principle component analysis (PCA), independent component analysis (ICA), general linear model (GLM), functional connectivity density (FCD), graph theory and deep learning, have increased the identifying accuracy that underpins the utility of the technique. Therefore, these advances in analysis have greatly enhanced the clinicians’ capabilities for characterizing specific psychiatric diseases relevant to functional deficits.

To promote the latest progress in the applications of fNIRS for probing mental health disorders, we invite the submission of original research or review articles to this Research Topic. The focus of this Research Topic is on new principles, technologies, or applications of fNIRS modalities for assessing mental health, as well as relevant analysis approaches for differentiating a variety of psychiatric disorders such as depression, anxiety, bipolar disorder, schizophrenia and cognitive impairment.

Potential fNIRS modalities and analysis approaches for detecting mental health disorders include, but are not limited to, the following subject areas:

• Technical improvements in fNIRS (optical design, instrument, probe, etc.)
• New modalities in fNIRS (e.g., diffuse correlation spectroscopy-DCS)
• fNIRS expansion for brain imaging (e.g., diffuse optical tomography-DOT)
• Functional brain network methodologies with fNIRS
• Clinical applications with fNIRS
• Algorithms for fNIRS signal/image analysis
• Advanced fNIRS protocol for cognitive or emotional activations