Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: i) Difference in the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average {\Delta}SNR (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%, respectively for all the EEG recordings. On the other hand, the two-stage motion artifacts removal technique i.e. WPD-CCA has produced the best average {\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta} (41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta} using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed.Comment: 25 pages, 10 figures and 2 table

Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS

IntroductionUpper limb rehabilitation assessment plays a pivotal role in the recovery process of stroke patients. The current clinical assessment tools often rely on subjective judgments of healthcare professionals. Some existing research studies have utilized physiological signals for quantitative assessments. However, most studies used single index to assess the motor functions of upper limb. The fusion of surface electromyography (sEMG) and functional near-infrared spectroscopy (fNIRS) presents an innovative approach, offering simultaneous insights into the central and peripheral nervous systems.MethodsWe concurrently collected sEMG signals and brain hemodynamic signals during bilateral elbow flexion in 15 stroke patients with subacute and chronic stages and 15 healthy control subjects. The sEMG signals were analyzed to obtain muscle synergy based indexes including synergy stability index (SSI), closeness of individual vector (CV) and closeness of time profile (CT). The fNIRS signals were calculated to extract laterality index (LI).ResultsThe primary findings were that CV, SSI and LI in posterior motor cortex (PMC) and primary motor cortex (M1) on the affected hemisphere of stroke patients were significantly lower than those in the control group (p < 0.05). Moreover, CV, SSI and LI in PMC were also significantly different between affected and unaffected upper limb movements (p < 0.05). Furthermore, a linear regression model was used to predict the value of the Fugl-Meyer score of upper limb (FMul) (R2 = 0.860, p < 0.001).DiscussionThis study established a linear regression model using force, CV, and LI features to predict FMul scale values, which suggests that the combination of force, sEMG and fNIRS hold promise as a novel method for assessing stroke rehabilitation

Functional Connectivity During Handgrip Motor Fatigue in Older Adults Is Obesity and Sex-Specific

The prevalence of obesity in older adults, particularly in females, is increasing rapidly and is associated with declines in both the brain and physical health. Both the obese and the female populations have shown greater motor fatigue than their counterparts, however, the central neural mechanisms for fatigue are unclear. The present study measured fatigue-related functional connectivity across frontal and sensorimotor areas using functional near-infrared spectroscopy (fNIRS). Fifty-nine older adults (30 non-obese and 29 obese) performed submaximal handgrip motor fatigue until voluntary exhaustion. Functional connectivity and cerebral hemodynamics were compared across eight cortical areas during motor fatigue and across obesity and sex groups along with neuromuscular fatigue outcomes (i.e., endurance time, strength loss, and force steadiness). Both obesity- and sex-specific functional architecture and mean activation differences during motor fatigue in older adults were observed, which were accompanied by fatigue-related changes in variability of force steadiness that differed between groups. While primary indicators of fatigue, i.e., endurance and strength loss, did not differ between groups, the motor steadiness changes indicated different neural adaptation strategies between the groups. These findings indicate that obesity and sex differences exist in brain function in older adults, which may affect performance during motor fatigue

Neural and Psychological Coordination in Social Communication and Interaction

Dynamic, naturalistic study of social interactions in humans is a small but growing literature. Emerging from this work is the theory that social interaction creates a “merged mind” between interlocutors – they come into psychological, behavioral, and neural alignment in order to better predict each other and coordinate as one social unit. However social interaction is diverse, so more work is needed to understand the specific nature of alignment between people in a variety of interactive contexts. In particular, it’s unclear how heterogeneities among members of an interaction impact their ability to align. This work aims to help address this gap by first evaluating and improving ways to collect neuroimaging data in naturalistic, social settings (Chapter 2). Then, empirical research is presented that examines how personal similarity factors impact the extent of alignment during personal disclosure interactions, where one person speaks and the other listens (Chapter 3). Finally, further empirical research investigates different types of alignment that may be present in a dyadic back-and-forth discussion in a joint decision-making paradigm. How this work contributes to a broader understanding of the ways people communicate and work together, and how this research can continue with improved methods, is discussed

An integrated neurovascular investigation of cognitive aging

Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting state functional connectivity (rsFC) and gray and white matter status. In addition, research has demonstrated that individual variance in cognitive aging is associated with cardiovascular health. In this dissertation, I integrate these factors into a cascade model and show how they might jointly and hierarchically account for individual differences in cognitive aging. The aim here is to have a framework that provides a starting point from which mechanistic pathways can be revealed and tested, ultimately advancing our knowledge for preventing or reducing age- related cognitive decline. In Chapter 1, I first review the factors that promote healthy cognitive aging and discuss the motivation for the focus on rsFC in later chapters. In Chapter 2, I introduce a cascade framework for cognitive aging that integrates the factors important for healthy brain health in aging. The results demonstrate for the first time that optically-measured cerebral arterial elasticity is strongly associated with segregation measures, and replicate previous findings of strong relationships between brain structure, brain function and cognition. In addition, the pattern of associations between these different factors is consistent with a hierarchical cascade framework linking them, suggesting that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging. Extending these findings, Chapter 3 demonstrates that the results in Chapter 2 are not limited to parcellations derived from young-adult populations and can be extended to age-cohort- based parcellations. In Chapter 4, the main rsFC measure used in this dissertation– segregation – is investigated. Specifically, while network segregation is without doubt important as an index of brain health and cognitive function, the age-related changes in its topography has not been fully explored in previous studies due to various methodological constraints. In this chapter, I employ a distribution-based analysis to examine how decreased segregation is topographically changed with aging, manifesting in age-related cognitive declines. The results show that connectivity between networks is in fact systematically increased during aging, and that age-related decreases in segregation as a result of age-related adjustments in connectivity between networks are not simply the result of increased neural noise. Finally, Chapter 5 summarizes the results of previous chapters and discusses implications and future directions for the work in this dissertation. Overall, the research here demonstrates that individual variations in cognitive aging are connected to neurovascular factors in a cascade fashion, and implicates how we might optimize future interventions aimed at mitigating cognitive aging. Further, it extends our current understanding of how age changes the modular organization of functional networks, allowing us greater insight into how cognitive aging might be affected. Taken together with research showing the intervention effects of exercise, the current research supports the importance and potential of a healthy and active lifestyle for promoting healthy cognitive aging

Processing pipeline per segnali NIRS acquisiti in continua per 7 giorni in neonati pretermine

Sviluppo di una nuova processing pipeline per segnali NIRS acquisiti in continua per 7 giorni in resting state su un neonato pretermine. Lo scopo principale è di correggere gli artefatti da movimento che corrompono i segnali NIRS, permettendo di ricavare stime fisiologiche di ossigenazione cerebrale. Scopo secondario, la valutazione delle correlazioni tra ossigenazione cerebrale ed il segnale glicemico acquisito in contemporanea ai dati NIRS durante i primi 7 giorni di vita del neonato.ope

Understanding the role of cerebrovascular health in cognitive aging: a multi-modal noninvasive human neuroimaging approach

The brain’s vasculature undergoes age-related physiological and anatomical changes similarly to the rest of the cardiovascular system. However, the health of the cerebrovasculature may be related to cognitive ability. Thus, it is critical to determine the effect of cerebrovascular health on cognition and the mediators of cerebrovascular health across the lifespan. Since aerobic fitness is known to alleviate both cognitive and volumetric losses in the brain, it is important to investigate some of the possible mechanisms underlying these beneficial changes. In one experiment, we investigated the role that cardiorespiratory fitness (CRF) plays in determining the relationship between aging and cerebral blood flow (CBF) in a group of older adults (ages 55-85). Using arterial spin labeling (ASL) to quantify CBF, we found that blood flow in the gray matter was positively correlated with CRF and negatively correlated with age (Zimmerman et al., 2014). Subsequent analyses revealed that CRF fully mediated the effects of age on CBF in the gray matter, but not in the white matter. Whether this same effect holds true for younger adults is unknown. In the next study, using a large sample of resting cerebral blood flow measured with arterial spin labelling in younger adults, we demonstrate that the relationship between cardiorespiratory fitness and cerebral blood flow is negative. Although the relationship is weak, the observation demonstrates that the interpretation of resting cerebral blood flow as a measure of cerebrovascular health should be made with caution. In order to gain an improved understanding of how cerebrovascular health impacts cognitive aging and relates to CRF, ASL was used in a third study to investigate both the resting and activation CBF in healthy older adults ranging in age from 56-88. To this end, we analyzed measures of both baseline CBF and changes in CBF during activation from a visual task. We found that the change in CBF in the visual cortex to a reversing checkerboard stimulus, but not the baseline CBF, was associated with neuropsychological measures of executive function. While baseline CBF was correlated with age and CRF, the change in CBF was correlated with the participants’ pulse pressure. These results indicate that the measures of baseline CBF and activation-related CBF are separable measures of vascular health in older age that relate differentially to measures of physiology and cognition. Because reactivity measures are dynamic and contain a temporal component, we were interested in whether improved temporal resolution could reveal differences in the time course of cerebrovascular reactivity across age or arterial compliance. In this final study, multi-distance, frequency-domain near infrared spectroscopy (NIRS) was used to measure changes in oxy- and deoxy-hemoglobin concentrations induced by breath holding in the right prefrontal cortex concurrently with ASL in a magnetic resonance imaging (MRI) scanner. Studying participants ranging in age from 55-87, we found that the superior temporal resolution of NIRS allowed us to observe differences in the speed of the oxy-hemoglobin response to a period of breath holding between our older and younger participants and between participants split by arterial compliance, where older individuals and participants with stiffer arteries tended to have a delayed hemodynamic response. This finding highlights the usefulness of utilizing a multi-modal neuroimaging approach for the investigation of time-sensitive aspects of cerebrovascular health. Overall, this set of experiments highlights the complexity of measures of cerebrovascular health both across the lifespan and in their relationships to cognition. We demonstrated that in older adults, resting CBF was related to CRF, and CRF mediated age-related declines in CBF. Interestingly, the relationship between CBF and CRF reversed in a sample of younger adults. Further analysis revealed that resting CBF did not predict cognitive decline in a sample of older adults. In contrast, the level of task-related change in CBF did positively relate to executive functioning. Pursuing measures of reactivity further, we found that adding NIRS measures provided temporal resolution that allowed us to see differences in the timing of cerebrovascular reactivity across age and arterial compliance in the brain. These timing differences may be complementary to the amplitude differences found through ASL, and future research will continue to resolve these separable components of cerebrovascular health

Separating Signal from Noise in High-Density Diffuse Optical Tomography

High-density diffuse optical tomography (HD-DOT) is a relatively new neuroimaging technique that detects the changes in hemoglobin concentrations following neuronal activity through the measurement of near-infrared light intensities. Thus, it has the potential to be a surrogate for functional MRI (fMRI) as a more naturalistic, portable, and cost-effective neuroimaging system. As in other neuroimaging modalities, head motion is the most common source of noise in HD-DOT data that results in spurious effects in the functional brain images. Unlike other neuroimaging modalities, data quality assessment methods are still underdeveloped for HD-DOT. Therefore, developing robust motion detection and motion removal methods in its data processing pipeline is a crucial step for making HD-DOT a reliable neuroimaging modality. In particular, our lab is interested in using HD-DOT to study the brain function in clinical populations with metal implants that cannot be studied using fMRI due to their contraindications. Two of these populations are patients having movement disorders (Parkinson Disease or essential tremor) with deep brain stimulation (DBS) implants and individuals with cochlear implants (CI). These two groups both receive tremendous benefit from their implants at the statistical level; however, there is significant single-subject variability. Our overarching goal is to use HD-DOT to find the relationships between the neuronal function and the behavioral measures in these populations to optimize the contact location of these implant surgeries. However, one of the challenges in analyzing the data in these subjects, especially in patients with DBS, is their high levels of motion due to tremors when their DBS implant is turned off. This further motivates the importance of the methods presented herein for separating signal from noise in HD-DOT data. To this end, I will first assess the efficacy of state-of-the-art motion correction methods introduced in the fNIRS literature for HD-DOT. Then, I will present a novel global metric inspired by motion detection methods in fMRI called GVTD (global variance of the temporal derivatives). Our results show that GVTD-based motion detection not only outperforms other comparable motion detection methods in fNIRS, but also outperforms motion detection with accelerometers. I will then present my work on collecting and processing HD-DOT data for two clinical populations with metal implants in their brain and the preliminary results for these studies. Our results in PD patients show that HD-DOT can reliably map neuronal activity in this group and replicate previously published results using PET and fMRI. Our results in the CI users provide evidence for the recruitment of the prefrontal cortex in processing speech to compensate for the decreased activity in the temporal cortex. These findings support the theory of cognitive demand increase in effortful listening situations. In summary, the presented methods for separating signal from noise enable direct comparisons of HD-DOT images with those of fMRI in clinical populations with metal implants and equip this modality to be used as a surrogate for fMRI