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Gowerlabs specialises in high-density technology, providing hundreds of data channels. As a result, it is possible to move beyond the typical channel-by-channel fNIRS analysis and use the same measurements to produce three-dimensional images of human brain function. The team at Gowerlabs have helped to pioneer this imaging approach, known as Diffuse Optical Tomography (DOT). Here, we describe the science behind image reconstruction and DOT.
What is the difference between DOT and traditional fNIRS?
Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are both non-invasive imaging techniques used to study brain activity. DOT requires more channels than fNIRS, meaning that not all fNIRS systems are capable of DOT. fNIRS measures changes in blood oxygenation levels in the brain using near-infrared light. It relies on the differential absorption of light by oxygenated and deoxygenated hemoglobin. DOT, on the other hand, is based on the measurement of light propagation through tissues. It uses multiple light sources and detectors to reconstruct the spatial distribution of optical properties within the tissue.
How does DOT work?
When near-infrared light travels through tissue, it does not travel in a straight line. Instead, each photon moves in a random direction from one scattering event to the next. In this scenario, the light field is referred to as diffuse. If our goal is to produce images, the diffuse nature of the near-infrared light field presents a significant challenge, because the path taken by any given photon is impossible to predict.
Despite their diffuse nature, fNIRS measurements contain significant spatial information. A given fNIRS measurement will only be sensitive to the volume of tissue beneath the source and detector, and the brain's haemodynamic response to increased neuronal activity is usually well localised to a specific region. Multi-channel fNIRS measurements are therefore highly suited for mapping human brain function.
Multi-channel fNIRS measurements require arrays of optical fibres, and the design of an array will dictate how much spatial information can be extracted from the resulting data. Traditional fNIRS experiments use arrays that contain only a single source-detector distance (usually 30 mm for adults), with the channels arranged in a grid-like pattern. While simple, these arrays provide no information about the depth at which a measured change in haemoglobin concentration has occurred.
However, by arranging the channels of an array so that their sensitivity profiles partially overlap, and by inputting all the measured data into a sophisticated image reconstruction algorithm, it is possible to produce 3D images of haemoglobin changes that contain significantly more spatial information than can be obtained by analysing all the channels individually.
Image reconstruction using DOT
The reconstruction of DOT images is what's known as an inverse problem: we must determine the distribution of haemoglobin changes in the brain that best explains the changes in optical intensity we observe at the scalp.
In practice, the solution to this inverse problem has three phases. First, it is necessary to construct an anatomically accurate computer representation of the head and brain of your subject. Second, we must establish a model of how the near-infrared light we detect has travelled through the tissues of the head from the optical fibre array: the paths taken by the detected light dictate the areas of the brain to which a given channel is sensitive and forms a solution to what is known as the forward problem. Finally, DOT images are reconstructed by inverting our solution to the forward problem and combining it with the measured data.
The reconstruction of DOT images is the best way of exploiting the rich information contained within fNIRS measurements. At Gowerlabs, we are committed to developing and promoting DOT approaches, which are undoubtedly the future of optical neuroimaging.
Advantages of DOT
In addition to the features of fNIRS, including being non-invasive, portable, cost-effective, and providing good temporal resolution and tolerance to motion artefacts, DOT offers significant advantages beyond this.
1. Improved source separation: High-density fNIRS systems can better distinguish between nearby sources and detectors, reducing the potential for signal contamination and crosstalk. This improves the accuracy of the measurements and helps to minimise interference from neighbouring brain regions.
2. Depth sensitivity: DOT has better depth sensitivity compared to fNIRS. It can provide information about brain activity in deeper brain regions, making it suitable for studying cortical and subcortical structures.
3. Enhanced coverage: With more optodes, a high-density system can cover a larger area of the scalp, providing broader coverage of the brain. This is particularly beneficial when studying brain regions that are spread out or have complex anatomical structures.
4. Increased sensitivity: The higher number of optodes in a high-density system allows for more comprehensive coverage of the cortical surface. This increased coverage enhances the sensitivity of the system, enabling researchers to detect smaller changes in oxygenation levels and potentially capture more subtle brain activity.
5. Three-dimensional imaging: DOT can reconstruct three-dimensional images of brain activity, providing spatial information about the distribution of neural activation. This is particularly useful for localising brain regions and understanding their functional connectivity.
6. Higher spatial resolution: DOT can achieve higher spatial resolution compared to fNIRS. By using multiple source-detector pairs, DOT can capture more detailed spatial information, allowing for better localization of brain activity.
7. Enhanced spatial mapping: The increased spatial resolution and coverage of a high-density system facilitate more accurate spatial mapping of brain activity. This can be particularly valuable when investigating functional connectivity or mapping brain networks.
8. Potential for clinical applications: DOT has shown promise in various clinical applications, such as the assessment of brain injuries, monitoring of neurodevelopmental disorders, and detection of brain abnormalities. Its non-invasive nature and ability to provide quantitative measurements make it a valuable tool in clinical research and diagnostics.
Why not DOT?
DOT is more complicated than fNIRS: data analysis can be more complex with DOT compared to fNIRS. This complexity arises due to the higher spatial resolution and depth sensitivity of DOT, which leads to larger datasets and more intricate signal processing requirements. To help with this, there are a number of freely available pipelines and data processing tools. We also provide a bespoke Research Support service to assist you with your application.
fNIRS provides faster temporal resolution: while it is true that fNIRS provides faster temporal resolution compared to DOT, it is important to consider the overall accuracy and reliability of the data. DOT offers superior spatial resolution, which is essential for understanding complex neural processes and mapping brain regions involved in specific tasks or functions. The enhanced spatial resolution of DOT compensates for its slightly slower temporal resolution, making it a more advantageous technique in terms of overall data quality and interpretability. Our full System Specification is available here.
fNIRS systems are more portable: historically, higher density meant more extensive equipment. With advancing technologies such as LUMO, you do not have to choose between channel count and portability. Check out our Applications page to see how LUMO is used across the globe.
fNIRS systems are more cost effective: low density systems are typically cheaper than high density due to the relative complexities of the technologies. LUMO is designed to be modular and scalable to ensure accessibility for all budgets. Contact Us for a quote and to find out how LUMO can fit your research goals.