Akademik Veri Yönetim Sistemi
Ankişhan H. (Yürütücü)
TÜBİTAK Projesi, 2022 - 2023
Functional near infrared imaging (fNIRS) provides the opportunity to examine cortical cerebral hemodynamic fluctuations by passing two wavelengths of light at the values of 830 nm and 690 nm through the scalp, cerebral spinal fluid and cortical volumes. This provides information about the blood dynamics of tissues by using changes in the absorption of that specific wavelength of light. Among some of the advantages of the fNIRS system over functional magnetic resonance imaging (fMRI)fMRI modality are portability, ease of use, quick set up time, ability to provide separately quantify both oxygenated (HbO) and deoxygenated hemoglobin (HbR) time series from the measured area, having a high temporal resolution (~ 80 ms(> 100Hz), while usinge of non-ionizing radiation is are also another important propertiesy which is are valid for all biosensing systems[1]. However; some of the disadvantages of fNIRS are that its spatial resolution is limited, and it cannot be measuredmake measurements of from the deep regions of the cortex. It appears to provide better motion tolerance compared to functional magnetic resonance imaging (fMRI). Therefore, fNIRS is suitable for patients with restless symptoms and actively participating in the movement. In addition, cochlear implant patients can safely pass fNIRS measurement without exposure to magnetic field.
fMRI and fNIRS data are affected by systemic physiology-based noise sourcess with different characteristics in time and frequency domains, and these effects make it difficult to accurately and precisely measure hemodynamic signals induced by brain activation. Although the sources and contributions of these systemic noises are still not fully characterized and understood, oscillations caused by heartbeat (around 1.25 Hz) [2.3], Mayer Waves (around 0.1 Hz), which are thought to be reflected as variations in blood pressure, are caused by breathing oscillations (around 0.25 Hz) [5,6] and low frequency oscillations (LFO [7]) due to tonal change occurring in order to carry blood in the vessel walls. These oscillations are confused with brain activation-induced hemodynamic markers (in the range of about 0.008-0.2 Hz), which are targeted to be measured by fNIRS, and may cause false positives and false negatives in the brain activation maps obtained. When these signals are mixed with the signal obtained from the activation of nerve cells, it is seen that the most difficult systemic effects to separate are LFOs.
For the purpose of characterizing the source of LFO based noise and correct removal of these effects, this project will include the following work packages;
Development of hardware with modules for ECG, breathing, pulse, oxygen saturation, and body movements
Using the physiological data obtained with the developed equipment to determine the source of the noise in fNIRS and fMRI data, modeling the obtained physiological noise with linear and nonlinear approaches, and separating them from the main system data by application of various filtering approaches
With the system planned to be developed within the scope of the study, a clear understanding of the source of LFO-based noise will be provided. ECG, breathing, heartbeat, oxygen saturation and body movements’ data to be taken simultaneously with fNIRS and fMRI data will make serious contributions in determining the noise source. Although the frequency ranges affected by LFOs have been determined in the existing studies, it is not known exactly how the source relates to components of physiological data. Therefore, in this case, it is of great importance to investigate the contribution of physiological signals of non-neuronal origin derived from the proposed hardware to determine the source of noise.
As a result ofHaving obtained a clear understanding of the source of noise, the separation and cleaning of the noise by fitting it to a linear or non-linear model with certain filtering methods are the other crucial parts of thise study. Another aim of the study is to develop a mathematical approach in order to distinguish embedded physiology-based noises from neural-based hemodynamic signals associated with brain activation in fNIRS and fMRI systems.
The ultimate aim of the study is also to obtain well-cleanred brain signals, and to contribute to the creation of a good database combined with more successful classification algorithms. If neural activity induced signals can be extracted from fNIRS data with high quality and reproducibility, conversion of this technology into wearable sensors for real time decoding of brain activity becomes possible. The potential of a sensor of this nature to be developed will be high in brain-computer interface studies, especially in patients with brain functions locked, and in studies for the defense industry.
The study is of great importance in terms of the scientificscientific and, economic results obtained, and in forming new collaborations. Publishing in international high index journals is among the project aims and obtaining patents for a system capable of analysis and noise reduction, along with the methods proposed within the scope of the project, developing project partnerships by creating international networks will be other important contributions and potential outputs of the project.
It is important for the outcomes of the project that the project manager makes serious contributions to the academic studies, especially on the development of equipment that records, processes, and analyzes physiological signals, and biomedical signal processing. In terms of career development potential, the project manager's future academic studies will gear towards development of new experimental and analytical methods in the field of functional brain imaging, using fNIRS data in conjunction with fMRI and the physiological signal processing tool to be developed. It is anticipated that being able to apply these methods in interdisciplinary studies in the fields of clinical, cognitive and behavioral neuroscience will make important contributions to these fields. In line with this goal, to benefit from the experience of the group that the project manager planned to work with in using and developing two imaging methods (fNIRS and fMRI) and to contribute to their pioneering work in this field; the project supervisor will provide an important technical infrastructure, experience and on-site observation opportunities for future studies.