Effects of exposure to colored light on cerebral and systemic physiology in humans

Humans in industrialized societies have become independent of the natural day and night cycle due to the invention and use of artificial light. Colored light is an element of everyday life, which affects various human functions. The main aim of this PhD thesis is to comprehensively investigate the effects of exposure to colored light on cerebral and human physiology. To achieve this goal, 201 healthy right-handed adults were recruited for 20 different colored light conditions. By using systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) neuroimaging, each subject was measured 2-4 times on different days resulting in 676 single measurements. The SPA-fNIRS approach combines the measurement of brain activity and systemic physiological changes. fNIRS is a non-invasive neuroimaging technique employed to measure changes in cerebral hemodynamics and oxygenation. There is an interaction between these and changes in systemic physiology: consequently, the SPA-fNIRS generally enables us to identify and understand these interactions. We simultaneously assessed the effects of colored light exposure (CLE) in the visual cortex (VC), prefrontal cortex (PFC) and systemic physiology. Such a comprehensive study has not been carried out yet, and an integrative view of how the color of light affects the brain and systemic physiology is lacking. In general, CLE has relatively long-lasting effects on cerebral and systemic physiology in humans, and yellow light leads to higher brain activation in the PFC than the other colored lights. Yellow CLE is associated with more active and positive emotions, including happiness, joy, hope, and cheerfulness. We also show that long-term colored light exposures induce wavelength-dependent modulations of brain responses in the VC. Violet and blue lights elicit higher changes in cerebral parameters compared to the other colored lights during the CLE and recovery phase. Our results show that CLE affects individual humans differently. In particular, blue light leads to eight different hemodynamic response patterns, while the typical hemodynamic response pattern (increase in oxygenated ([O2Hb]) and decrease in deoxygenated ([HHb]) hemoglobin) is still observed and valid at the group-level analysis. The SPA-fNIRS approach is able to show that systemic and cerebral physiology interact. Experimental findings in most parts of this research display that inter-subject variability of hemodynamic responses is partially explained by systemic physiological changes. The finding of this research that blue light has an activating effect in the VC should be taken into consideration when assessing the impact of modern light sources such as screens and light-emitting diodes (LEDs) on the human body. Our findings that yellow light leads to higher PFC activation be tested as a potentially beneficial tool in chromotherapy, i.e., a complementary medicine method, to balance “energy” lacking in physical, emotional, and mental levels. Although yellow light, i.e., CLE in general term, influences humans in several positive ways, it should be noted that each individual reacts differently to the CLE, implying that colored light therapy has to be also adjusted to each individual. Therefore, further research should clarify which color in CLE benefits whom. In a civilization that is rapidly exposed to new and increasing lighting, the findings of this research are relevant for the scientific community, medical professionals, and society

Novel Technologies for the Diagnosis and Treatment of Posttraumatic Stress Disorder

The brain and the heart share an active and reciprocal dialogue, continuously modulating each other's function. For individuals who have experienced traumatic events, the reminders of these events affect both the brain and heart due to this intimate relationship, and can later develop into posttraumatic stress disorder (PTSD) due to the repeated activation of trauma-related neuropathways and autonomic imbalance. Electrical stimulation of the vagus nerve —the longest cranial nerve, which regulates the autonomic state—using an implantable device is a potential treatment method to address such imbalance. Noninvasive vagal nerve stimulation (nVNS) devices offer inexpensive and low-risk alternatives to surgical implants, but their effects on the physiology are not well understood. Real-time, noninvasively obtained biomarkers are required to tailor therapy and to close the loop for automated delivery. This dissertation focuses on identifying and developing noninvasive technologies for nVNS in the context of PTSD. Identification of noninvasive measures that can diagnose and treat PTSD is imperative for at-home usage and for developing closed-loop systems. This research first focuses on how noninvasive sensing modalities could be instrumented and used in conjunction with signal processing and machine learning methods to quantify an individual’s autonomic state. Second, a mechanistic, sham-controlled, randomized, double blind study on the use of nVNS for dampening stress response is investigated in multiple dimensions: downstream physiological effects and biochemical biomarkers, with a particular focus on real-time physiological biomarkers and their potential for closing the loop for machine learning guided personalized neuromodulation. The broader impacts of this research cover accessible, low-cost diagnosis and treatment options for patients with stress-related neuropsychiatric disorders, which are important public health problems and projected to increase due to COVID-19 pandemic. The sensing modalities, algorithms, biomarkers, and methodologies detailed in this dissertation lay the groundwork for future efforts to objectively diagnose and treat neuropsychiatric disorders remotely, outside of clinical settings.Ph.D

Towards simultaneous electroencephalography and functional near-infrared spectroscopy for improving diagnostic accuracy in prolonged disorders of consciousness: a healthy cohort study

Qualitative clinical assessments of the recovery of awareness after severe brain injury require an assessor to differentiate purposeful behaviour from spontaneous behaviour. As many such behaviours are minimal and inconsistent, behavioural assessments are susceptible to diagnostic errors. Advanced neuroimaging tools such as functional magnetic resonance imaging and electroencephalography (EEG) can bypass behavioural responsiveness and reveal evidence of covert awareness and cognition within the brains of some patients, thus providing a means for more accurate diagnoses, more accurate prognoses, and, in some instances, facilitated communication. As each individual neuroimaging method has its own advantages and disadvantages (e.g., signal resolution, accessibility, etc.), this thesis studies on healthy individuals a burgeoning technique of non-invasive electrical and optical neuroimaging—simultaneous EEG and functional near-infrared spectroscopy (fNIRS)—that can be applied at the bedside. Measuring reliable covert behaviours is correlated with participant engagement, instrumental sensitivity and the accurate localisation of responses, aspects which are further addressed over three studies. Experiment 1 quantifies the typical EEG changes in response to covert commands in the absence and presence of an object. This is investigated to determine whether a goal-directed task can yield greater EEG control accuracy over simple monotonous imagined single-joint actions. Experiment 2 characterises frequency domain NIRS changes in response to overt and covert hand movements. A method for reconstructing haemodynamics using the less frequently investigated phase parameter is outlined and the impact of noise contaminated NIRS measurements are discussed. Furthermore, classification performances between frequency-domain and continuous-wave-like signals are compared. Experiment 3 lastly applies these techniques to determine the potential of simultaneous EEG-fNIRS classification. Here a sparse channel montage that would ultimately favour clinical utility is used to demonstrate whether such a hybrid method containing rich spatial and temporal information can improve the classification of covert responses in comparison to unimodal classification of signals. The findings and discussions presented within this thesis identify a direction for future research in order to more accurately translate the brain state of patients with a prolonged disorder of consciousness

Proceedings of the 3rd International Mobile Brain/Body Imaging Conference : Berlin, July 12th to July 14th 2018

The 3rd International Mobile Brain/Body Imaging (MoBI) conference in Berlin 2018 brought together researchers from various disciplines interested in understanding the human brain in its natural environment and during active behavior. MoBI is a new imaging modality, employing mobile brain imaging methods like the electroencephalogram (EEG) or near infrared spectroscopy (NIRS) synchronized to motion capture and other data streams to investigate brain activity while participants actively move in and interact with their environment. Mobile Brain / Body Imaging allows to investigate brain dynamics accompanying more natural cognitive and affective processes as it allows the human to interact with the environment without restriction regarding physical movement. Overcoming the movement restrictions of established imaging modalities like functional magnetic resonance tomography (MRI), MoBI can provide new insights into the human brain function in mobile participants. This imaging approach will lead to new insights into the brain functions underlying active behavior and the impact of behavior on brain dynamics and vice versa, it can be used for the development of more robust human-machine interfaces as well as state assessment in mobile humans.DFG, GR2627/10-1, 3rd International MoBI Conference 201

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Real-world listening effort in adult cochlear implant users

Cochlear implants (CI) are a treatment to provide a sense of hearing to individuals with severe-to-profound sensorineural hearing loss. Even when optimal levels of intelligibility are achieved after cochlear implantation, many CI users complain about the effort required to understand speech in everyday life contexts. This sustained mental exertion, commonly known as “listening effort”, could negatively affect their lives, especially regarding communication, participation, and long-term cognitive health. This thesis aimed to evaluate the listening effort experienced by CI recipients in real-world sound scenarios. The research focused on social listening situations that are particularly common in everyday life such as having conversations in a busy café or communicating through video call. Additionally, some situations that prevailed during the COVID-19 pandemic were also examined (e.g., listening to someone who is wearing a facemask). Multimodal measures of listening effort were employed throughout the research project to obtain a comprehensive assessment. Nonetheless, the primary focus was on measures that quantify objectively the cognitive demands of listening through a CI. To that end, we used a combination of physiological measures, functional near infrared spectroscopy (fNIRS) brain imaging and simultaneous pupillometry, both of which are compatible with CIs and capable of providing insights into the neural underpinnings of effortful listening. We also proposed a novel approach to quantify “listening efficiency”, an integrated behavioural measure that reflects both intelligibility and listening effort. We successfully applied these assessments to 168 CI users and 75 age-matched normally hearing (NH) controls who were recruited throughout the project. We found that CI users experienced high levels of listening effort, even when their intelligibility was optimal under highly favourable listening conditions. Objective measures revealed that CI listeners exhibited significantly inferior listening efficiency than NH controls when listening to speech under moderate levels of cafeteria background noise and when attending online video calls. Physiologically, they showed elevated levels of arousal as revealed by larger and prolonged pupil dilations to baseline compared with NH controls, suggesting high cognitive load and increased need for recovery. The importance of visual cues was evident; the presence of video and captions benefited CI recipients by improving considerably their listening efficiency during online communication. These results were consistent with their subjective ratings of effort, both in the experiments and in daily life. These findings provide objective evidence of the cognitive burden endured by CI listeners in everyday life. In addition, the objective assessments proposed were proved feasible to quantify the performance and cognitive demands of listening through a CI. In particular, listening efficiency showed sensitivity to differences in task demands and between groups, even when intelligibility remained near perfect. We argue that listening efficiency holds potential to become a CI outcome measure

Melody Informatics: Computational Approaches to Understanding the Relationships Between Human Affective Reasoning and Music

Music is a powerful and complex medium that allows people to express their emotions, while enhancing focus and creativity. It is a universal medium that can elicit strong emotion in people, regardless of their gender, age or cultural background. Music is all around us, whether it is in the sound of raindrops, birds chirping, or a popular song played as we walk along an aisle in a supermarket. Music can also significantly help us regain focus while doing a number of different tasks. The relationship between music stimuli and humans has been of particular interest due to music's multifaceted effects on human brain and body. While music can have an anticonvulsant effect on people's bodily signals and act as a therapeutic stimulus, it can also have proconvulsant effects such as triggering epileptic seizures. It is also unclear what types of music can help to improve focus while doing other activities. Although studies have recognised the effects of music in human physiology, research has yet to systematically investigate the effects of different genres of music on human emotion, and how they correlate with their subjective and physiological responses. The research set out in this thesis takes a human-centric computational approach to understanding how human affective (emotional) reasoning is influenced by sensory input, particularly music. Several user studies are designed in order to collect human physiological data while they interact with different stimuli. Physiological signals considered are: electrodermal activity (EDA), blood volume pulse (BVP), skin temperature (ST), pupil dilation (PD), electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Several computational approaches, including traditional machine learning approaches with a combination of feature selection methods are proposed which can effectively identify patterns from small to medium scale physiological feature sets. A novel data visualisation approach called "Gingerbread Animation" is proposed, which allows physiological signals to be converted into images that are compatible with transfer learning methods. A novel stacked ensemble based deep learning model is also proposed to analyse large-scale physiological datasets. In the beginning of this research, two user studies were designed to collect physiological signals from people interacting with visual stimuli. The computational models showed high efficacy in detecting people's emotional reactions. The results provided motivation to design a third user study, where these visual stimuli were combined with music stimuli. The results from the study showed decline in recognition accuracy comparing to the previous study. These three studies also gave a key insight that people's physiological response provide a stronger indicator of their emotional state, compared with their verbal statements. Based on the outcomes of the first three user studies, three more user studies were carried out to look into people's physiological responses to music stimuli alone. Three different music genres were investigated: classical, instrumental and pop music. Results from the studies showed that human emotion has a strong correlation with different types of music, and these can be computationally identified using their physiological response. Findings from this research could provide motivation to create advanced wearable technologies such as smartwatches or smart headphones that could provide personalised music recommendation based on an individual's physiological state. The computational approaches can be used to distinguish music based on their positive or negative effect on human mental health. The work can enhance existing music therapy techniques and lead to improvements in various medical and affective computing research

Real-world listening effort in adult cochlear implant users

Cochlear implants (CI) are a treatment to provide a sense of hearing to individuals with severe-to-profound sensorineural hearing loss. Even when optimal levels of intelligibility are achieved after cochlear implantation, many CI users complain about the effort required to understand speech in everyday life contexts. This sustained mental exertion, commonly known as “listening effort”, could negatively affect their lives, especially regarding communication, participation, and long-term cognitive health. This thesis aimed to evaluate the listening effort experienced by CI recipients in real-world sound scenarios. The research focused on social listening situations that are particularly common in everyday life such as having conversations in a busy café or communicating through video call. Additionally, some situations that prevailed during the COVID-19 pandemic were also examined (e.g., listening to someone who is wearing a facemask). Multimodal measures of listening effort were employed throughout the research project to obtain a comprehensive assessment. Nonetheless, the primary focus was on measures that quantify objectively the cognitive demands of listening through a CI. To that end, we used a combination of physiological measures, functional near infrared spectroscopy (fNIRS) brain imaging and simultaneous pupillometry, both of which are compatible with CIs and capable of providing insights into the neural underpinnings of effortful listening. We also proposed a novel approach to quantify “listening efficiency”, an integrated behavioural measure that reflects both intelligibility and listening effort. We successfully applied these assessments to 168 CI users and 75 age-matched normally hearing (NH) controls who were recruited throughout the project. We found that CI users experienced high levels of listening effort, even when their intelligibility was optimal under highly favourable listening conditions. Objective measures revealed that CI listeners exhibited significantly inferior listening efficiency than NH controls when listening to speech under moderate levels of cafeteria background noise and when attending online video calls. Physiologically, they showed elevated levels of arousal as revealed by larger and prolonged pupil dilations to baseline compared with NH controls, suggesting high cognitive load and increased need for recovery. The importance of visual cues was evident; the presence of video and captions benefited CI recipients by improving considerably their listening efficiency during online communication. These results were consistent with their subjective ratings of effort, both in the experiments and in daily life. These findings provide objective evidence of the cognitive burden endured by CI listeners in everyday life. In addition, the objective assessments proposed were proved feasible to quantify the performance and cognitive demands of listening through a CI. In particular, listening efficiency showed sensitivity to differences in task demands and between groups, even when intelligibility remained near perfect. We argue that listening efficiency holds potential to become a CI outcome measure