Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data

Cardiac responses (e.g., heart rate changes) due to an autonomous response to sensory stimuli have been reported in several studies. This study investigated whether heart rate information extracted from functional near-infrared spectroscopy (fNIRS) data can be used to assess the discrimination of speech sounds in sleeping infants. This study also investigated the adaptation of the heart rate response over multiple, sequential stimulus presentations. fNIRS data were recorded from 23 infants with no known hearing loss, aged 2 to 10 months. Speech syllables were presented using a habituation/dishabituation test paradigm: the infant's heart rate response was first habituated by repeating blocks of one speech sound; then, the heart rate response was dishabituated with the contrasting (novel) speech sound. This stimulus presentation sequence was repeated for as long as the infants were asleep. The group-level average heart rate response to the novel stimulus was greater than that to the habituated first sound, indicating that sleeping infants were able to discriminate the speech sound contrast. A significant adaptation of the heart rate responses was seen over the session duration. The dishabituation response could be a valuable marker for speech discrimination, especially when used in conjunction with the fNIRS hemodynamic response.

This study aimed at understanding the neurobiological mechanisms associated with inattention induced by traumatic brain injury (TBI). To eliminate the potential confounding caused by the heterogeneity of TBI, we focused on young adults postsports-related concussion (SRC). Functional near-infrared spectroscopy (fNIRS) data were collected from 27 young adults post-SRC and 27 group-matched normal controls (NCs), while performing a visual sustained attention task. Task responsive cortical activation maps and pairwise functional connectivity among six regions of interest were constructed for each subject. Correlations among the brain imaging measures and clinical measures of attention were calculated in each group. Compared to the NCs, the SRC group showed significantly increased brain activation in left middle frontal gyrus (MFG) and increased functional connectivity between right inferior occipital cortex (IOC) bilateral calcarine gyri (CG). The left MFG activation magnitude was significantly negatively correlated with the hyperactive/impulsive symptom severity measure in the NCs, but not in the patients. The right hemisphere CG-IOC functional connectivity showed a significant positive correlation with the hyperactive/impulsive symptom severity measure in patients, but not in NCs. The current data suggest that abnormal left MFG activation and hyper-communications between right IOC and bilateral CG during visual attention processing may significantly contribute to behavioral manifestations of attention deficits in patients with TBI.

Functional near-infrared spectroscopy (fNIRS) is impacted by signal contamination from superficial hemodynamics. It is important to develop methods that account for such contamination and provide accurate measurements of cerebral hemodynamics. We aim to investigate whether simulated data with two- or three-layer tissue models are able to reproduce in vivo data collected with dual-slope (DS) frequency-domain (FD) near-infrared spectroscopy (NIRS) on human subjects during brain activation. We performed Monte Carlo simulations to generate DS FD-NIRS data from two- and three-layer media with a range of layer thicknesses and optical properties. We collected in vivo data with DS FD-NIRS (source-detector distances: 25 and 37mm, wavelengths: 690 and 830nm, and modulation frequency: 140MHz) over the occipital lobe of human subjects during visual stimulation. Simulated and in vivo data were analyzed with diffusion theory for a homogeneous medium, and results were compared for each DS FD-NIRS data type. We found that the main qualitative features of in vivo data could be reproduced by simulated data from a three-layer medium, with a second layer (representing the cerebrospinal fluid in the subarachnoid space) that is less absorbing and less scattering than the other two layers, and with a top layer thickness that represents the combined scalp and skull thickness. A three-layer model is a viable improvement over a homogeneous model to analyze DS FD-NIRS data (or any other fNIRS data) to generate more accurate measurements of cerebral hemodynamic changes without a need for large datasets for tomographic reconstructions.

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Researchers implementing non-invasive neuroimaging have reported distinct load-dependent brain activity patterns in older adults compared with younger adults. Although findings are mixed, these age-related patterns are often associated with compensatory mechanisms of cognitive decline even in the absence of direct comparisons between brain activity and cognitive performance. This study investigated the effects of cognitive load on brain-behavior relationships in younger and older adults using a data-driven, multivariate partial least squares (PLS) analysis of functional near-infrared spectroscopy (fNIRS) data. We measured bilateral prefrontal brain activity in 31 older and 27 younger adults while they performed single and dual 2-back tasks. Behavioral PLS analysis was used to determine relationships between performance metrics (reaction time and error rate) and brain oxygenation (HbO) and deoxygenation (HbR) patterns across groups and task loads. Results revealed significant age-group differences in brain-behavior relationships. In younger adults, increased brain activity (i.e., increased HbO and decreased HbR) was associated with faster reaction times and better accuracy in the single task, indicating sufficient neural capacity. Conversely, older adults showed a negative correlation between HbR and error rates in the single task; however, in the dual task, they demonstrated a positive relationship between HbO and performance, indicative of compensatory mechanisms under the higher cognitive load. Overall, older adults' showed relationships with either HbR or HbO, but not both, indicating that the robustness of the relationship between brain activity and behavior varies across task load conditions. Our PLS approach revealed distinct load-dependent brain activity between age groups, providing further insight into neurocognitive aging patterns, such as compensatory mechanisms, by emphasizing the variability and complexity of brain-behavior relationships. Our findings also highlight the importance of considering task complexity and cognitive demands in interpreting age-related brain activity patterns.

Mobile devices have been transformed from voice communication tools to advanced tools for consuming multimedia contents. The extensive use of such mobile devices entails watching multimedia contents on small LCD panels. However, the most of video sequences are captured for normal viewing on standard TV or HDTV, but for cost reasons, merely resized and delivered without additional editing. This may give the small-display-viewers uncomfortable experiences in understanding what is happening in a scene. For instance, in a soccer video sequence taken by a long-shot camera technique, the tiny objects (e.g., soccer ball and players) may not be clearly viewed on the small LCD panel. Thus, an intelligent display technique needs to be developed to provide small-display-viewers with better experience. To this end, one of the key technologies is to determine region of interest (ROI) and display the magnified ROI on the screen, where ROI is a part of the scene that viewers pay more attention to than other regions. In this paper, which is an extension from our prior work, we focus on soccer video display for mobile devices, and a fully automatic and computationally efficient method is proposed. Instead of taking generic approaches utilizing visually salient features, we take domain-specific approach to exploit the attributes of the soccer video. The proposed scheme consists of two stages: shot classification, and ROI determination. The experimental results show that the proposed scheme offers useful tools for intelligent video display for multimedia mobile devices.

Functional near infrared spectroscopy (fNIRS) is non-invasive brain imaging techniques that detects the cortical activity by measuring the change in the concentration of oxy-hemoglobin and de-oxy hemoglobin. It uses near infrared light of two wave lengths, 760 nm and 830 nm. NIRS is emerging neuro imaging modality with high temporal resolution. The advantage of NIRS system over other neuro imaging modalities is low cost, portable, safe and somehow results in short period of time. The scalp remains intact throughout the experiment. In this study we present a method for identification of brain activity by using fNIRS data. The general linear model has been used in study with predicted blood oxygen level dependent (BOLD) response signal and its delayed versions. The normalized least mean square (NLMS) algorithm has been used for identification of unknown parameters in the model recursively. A one way t-test has been performed for the significance of results.

Event Abstract Back to Event Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS Jochem W. Rieger1*, Jakob Scheunemann1, Klas Ihme2, Frank Köster2, Meike Jipp2 and Anirudh Unni1 1 University of Oldenburg, Department of Psychology, Germany 2 Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institute of Transportation Systems, Germany Introduction and Goal The concept of mental workload is of particular importance in human factors and neuroergonomics because mental workload levels that saturate processing capacities will likely decrease performance and cognitive flexibility. Driving, like many other dynamic control tasks, concurrently imposes demands on multiple cognitive resources. In order to assess driver’s cognitive capacity utilization, we need to distinguish loads imposed on different cognitive resources and to characterize their interactions. It is currently not clearly understood how different cognitive task demands interact at the brain level and whether the neuronal markers that have been attributed to certain cognitive resources remain discriminative and predictive of cognitive states when humans operate in complex natural task environments such as driving. Hence, our goal is to investigate how changes in cognitive task demands related to increased lateral control in driving, a visuospatial control task, interacts with working memory load (WML) at the brain level and to predict the driving demand level from multichannel functional near infrared spectroscopy (fNIRS) deoxyhemoglobin (HbR) brain activation measurements. Methods We collected data from participants driving on a highway with moderate concurrent traffic and low or high levels of lateral control demands (driving outside and within construction sites with a narrower lane width) in the realistic Virtual-Reality lab at DLR, Braunschweig (Fischer et al., 2014). In order to manipulate WML levels, participants (n=15) performed an integrated speed regulation task in which they had to remember sequences of speed of varying length. The task resembles a number span task and required the adjustment of the speed when a new speed sign occurred but according to the sign shown n-signs back and to remember the new sign. We introduced five different WML levels (i.e. 0-back to 4-back) and new speed signs appeared every 20 seconds (for details see Unni et al. 2017). We recorded almost whole head fNIRS measurements at a rate close to 2Hz using two 16-channel NIRScout systems (NIRx Medizintechnik GmbH, Germany) during the 60 minutes of the experiment. We then used all fNIRS channels in a multivariate logistic ridge regression classifier with a 5-fold cross-validation for temporally resolved prediction of driving demand level (i.e. whether they drove within or outside the construction site). To gain insights into specific brain areas associated with driving demand level, we performed a univariate group-level analysis where we calculated the prediction model weights from a 5-fold cross-validated univariate logistic ridge regression of channel-wise fNIRS HbR separately for each WML level. Results On average over participants, we correctly predicted the driving demand (lateral control) level in 74.88% of the timepoints in an out-of-sample cross-validation of classifiers trained on the fNIRS data separately for each WML level. Mean accuracy scores significantly varied across WML levels (range 62% to 85% correct; F(4,70) = 7.32, p < .001) with better predictions for intermediate WML levels (1-back to 3-back). Training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46%). These results indicate that cognitive resources recruited by lateral lane control, a visuospatial control task, interact with resources recruited by working memory at the brain level. In order to better understand the nature of these interactions, we analyzed how predictivity of brain regions for driving demands develops over WML levels. Figure 1B depicts group-level brain maps of classification separability from the univariate logistic ridge regression model. Activation changes in the parietal and lateral dorsal frontal areas allow for prediction of increased lateral control demand. The frontal areas (putative BA9/46/10) are known to be involved in tactical planning and execution of behavior and the parietal in visuomotor control. The maps show increasing predictivity of these areas with increasing WML levels up to WML-level 2. At WML level 3 and in particular level 4, the discriminative patterns diminish, indicating an interaction between WML and driving demands. We compared these brain maps to the results from Unni et al. 2017, who showed that it was possible to predict WML independent of lateral controls demands from the fNIRS data (average correlation between predicted and induced WML r = 0.61). Figure 1A shows brain areas that predict WML over different lateral control demand levels. In particular, the inferior frontal gyrus (IFG) and the temporo-occipital areas are predictive to the current WML level. The comparison of the anatomical locations of predictive maxima in Figures 1A (marked by white shapes) and 1B suggests only partial overlap between the brain resources predictive of the different task demands. Discussion We demonstrated fNIRS brain level indicators for interaction between WML and driving complexity. The prediction of driving demands from fNIRS data was possible for each WML level separately but dropped to guessing level when we tried to predict driving demands across WML levels. Conversely, we demonstrated the continuous time-resolved predictions of WML over different levels of driving demands in an earlier analysis performed on the same data (Unni et al. 2017). These results indicate an asymmetric pattern of interaction between tasks at the brain level. Moreover, despite the functional interaction, the predictive brain activation patterns show little overlap between the resources predictive for the different tasks. Interpreted in the framework of a multi-resource theory (Wickens, 2002), our brain recordings indicate that working memory and visuomotor coordination in vehicle control recruit different resources and more so with increasing task demand. Moreover, the two tasks appear to interact at a common, task unspecific cognitive resource at the brain level. The changing pattern of driving demand related brain areas across WML levels could indicate potential changes in the multitasking strategy with high levels of WML demand. Figure Caption Figure 1. (A) Weighted averaged group-level univariate correlation HbR brain maps showing brain areas sensitive to changes in WML independent of driving complexity. White shapes mark WML prediction maxima in all maps. (B) Weighted mean of channel-wise predictivity (Tjur R-squared coefficients) for driving demands at the different WML levels. Data for the two analyses were recorded in the same session with concurrent manipulation of WML and lateral control demands. Figure 1 Acknowledgements This work was funded by a grant of the Volkswagen Foundations to the Center for Critical Systems Engineering, a DFG-grant RI1511/2-1 to JWR, and KO 1990/5-1 to FK. References Fischer, M., Richter, A., Schindler, J., Plättner, J., Temme, G., Kelsch, J., & et al. (2014). Modular and Scalable Driving Simulator Hardware and Software for the Development of Future Driver Assistence and Automation Systems. In New Developments in Driving Simulation Design and Experiments: Driving Simulation Conference 2014. Paris, France. Retrieved from http://elib.dlr.de/90638/ Unni, A., Ihme, K., Jipp, M., & Rieger, J. W. (2017). Assessing the driver’s current level of working memory load with high density functional near-infrared spectroscopy: A realistic driving simulator study. Frontiers in Human Neuroscience, 11. https://doi.org/10.3389/fnhum.2017.00167 Wickens, C. D. (2002). Multiple resources and performance prediction, 3(2), 159–177. https://doi.org/10.1080/14639220210123806 Keywords: fNIRS, driving simulator, driving demands, lateral control demands, working memory, visuospatial attention, visuomotor control, Working memory load, Task interaction, task interaction at brain level, parietal cortex, inferior frontal cortex, occitpito temporal cortex Conference: 2nd International Neuroergonomics Conference, Philadelphia, PA, United States, 27 Jun - 29 Jun, 2018. Presentation Type: Oral Presentation Topic: Neuroergonomics Citation: Rieger JW, Scheunemann J, Ihme K, Köster F, Jipp M and Unni A (2019). Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS. Conference Abstract: 2nd International Neuroergonomics Conference. doi: 10.3389/conf.fnhum.2018.227.00141 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 27 Mar 2018; Published Online: 27 Sep 2019. * Correspondence: Prof. Jochem W Rieger, University of Oldenburg, Department of Psychology, Oldenburg, Lower Saxony, 26111, Germany, jochem.rieger@uni-oldenburg.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Jochem W Rieger Jakob Scheunemann Klas Ihme Frank Köster Meike Jipp Anirudh Unni Google Jochem W Rieger Jakob Scheunemann Klas Ihme Frank Köster Meike Jipp Anirudh Unni Google Scholar Jochem W Rieger Jakob Scheunemann Klas Ihme Frank Köster Meike Jipp Anirudh Unni PubMed Jochem W Rieger Jakob Scheunemann Klas Ihme Frank Köster Meike Jipp Anirudh Unni Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Functional near-infrared spectroscopy (fNIRS) is a non-intrusive way to measure cortical hemodynamic activity. Predicting cognitive workload from fNIRS data has taken on a diffuse set of methods. To be applicable in real-world settings, models are needed, which can perform well across different sessions as well as different subjects. However, most existing works assume that training and testing data come from the same subjects and/or cannot generalize well across never-before-seen subjects. Additional challenges imposed by fNIRS data include not only the high variations in inter-subject fNIRS data but also the variations in intra-subject data collected across different blocks of sessions. To address these challenges, we propose an effective method, referred to as the block-wise domain adaptation (BWise-DA), which explicitly minimizes intra-session variance as well by viewing different blocks from the same subject and same session as different domains. We minimize the intra-class domain discrepancy and maximize the inter-class domain discrepancy accordingly. In addition, we propose an MLPMixer-based model for workload prediction. Experimental results demonstrate that the proposed model provides better performance compared to three different baseline models on three publicly-available workload datasets. Two of the datasets are collected from n-back tasks and one of them is from finger-tapping. Moreover, the experimental results show that our proposed contrastive learning method can also be leveraged to improve the performance of the baseline models. We also present a visualization study showing that the models are paying attention to the right regions in the brain, which are known to be involved in the respective tasks.

Conventional rate control schemes focus on making output bit rate approach a target value and are deficient in ensuring a higher quality of ROI (Region-of-Interest) than others in a frame. In this paper, we propose a new scheme for H.264/AVC, aiming to allocate more bit resource for the encoding of ROI and still maintain the accuracy of the output bit rate. ROI-based rate control algorithms can find their specific advantages in video telephony and video surveillance applications. Our proposed scheme is based on the one implemented in H.264/AVC JM software, but enhanced with several features: ROI determination with saliency map, tunable quality factor for ROI, two-channel (ROI & non-ROI) rate control, and QP adjustment with constraints from temporal and spatial domains, as well as from ROI/non-ROI adjacency boundaries. Experiment results show both advantages in objective PSNR and subjective evaluations for ROI, while making the output bit rate accurate as before.

Evidence suggests that transcranial direct current stimulation (tDCS) combined with repetitive transcranial magnetic stimulation (rTMS) is more effective than either modality alone. However, the underlying neural mechanisms remain unclear. To explore the effects of combined tDCS-rTMS on cortical functional networks and neural regulation processes in chronic insomnia. In this randomised, double-blind, sham-controlled trial, 111 adults with chronic insomnia were allocated to real tDCS+real rTMS or sham tDCS+real rTMS. Participants received 20 weekday sessions over 4 weeks. Of these, 106 completed treatment and 103 provided functional near-infrared spectroscopy (fNIRS) data meeting predefined quality criteria. fNIRS measured oxygenated haemoglobin (Oxy-Hb) responses in prefrontal and temporal regions during a verbal fluency task (VFT) before and after treatment. The primary outcome was haemodynamic activation; secondary outcomes were functional connectivity and sleep quality. Sleep quality improved in both groups, with significantly greater reduction in Pittsburgh Sleep Quality Index (PSQI) scores in the real tDCS+real rTMS group (p<0.05). No between-group differences were observed in VFT performance. However, the combined stimulation group showed significantly increased Oxy-Hb responses across multiple fronto-temporal channels, particularly in bilateral temporal regions, and greater enhancement in mean functional connectivity (p<0.05). Both interventions were well tolerated, with no serious adverse events. This study provides neuroimaging evidence that combined tDCS and rTMS may improve chronic insomnia by enhancing activation of fronto-temporal cortices and strengthening inter-regional functional connectivity. These findings offer objective support for the clinical efficacy of the combined stimulation protocol, link neurophysiological changes to treatment optimisation and suggest potential advantages of this approach for neuromodulation-based therapeutic strategies. These findings suggest that tDCS combined with rTMS may represent a feasible and well tolerated non-pharmacological strategy for chronic insomnia, with potential advantages over rTMS alone at both clinical and neurophysiological levels. This study was registered as a clinical trial with the China Clinical Trial Registration Centre (ChiCTR2100052681).

Classification of fNIRS signals from adolescents with MDD in suicide high- and low-risk groups using machine learning

Non-adjacent dependencies (NADs) are important building blocks for language and extracting them from the input is a fundamental part of language acquisition. Prior event-related potential (ERP) studies revealed changes in the neural signature of NAD learning between infancy and adulthood, suggesting a developmental shift in the learning route for NADs. The present study aimed to specify which brain regions are involved in this developmental shift and whether this shift extends to NAD learning in the non-linguistic domain. In two experiments, 2- and 3-year-old German-learning children were familiarized with either Italian sentences or tone sequences containing NADs and subsequently tested with NAD violations, while functional near-infrared spectroscopy (fNIRS) data were recorded. Results showed increased hemodynamic responses related to the detection of linguistic NAD violations in the left temporal, inferior frontal, and parietal regions in 2-year-old children, but not in 3-year-old children. A different developmental trajectory was found for non-linguistic NADs, where 3-year-old, but not 2-year-old children showed evidence for the detection of non-linguistic NAD violations. These results confirm a developmental shift in the NAD learning route and point to distinct mechanisms underlying NAD learning in the linguistic and the non-linguistic domain.

The spatial complexity of neural signals, which was traditionally quantified by omega complexity, varies inversely with the global functional connectivity level across distinct region-of-interests, thus provides a novel approach in functional connectivity analysis. However, the measures in omega complexity are sensitive to the number of neural time-series. Here, normalized spatial complexity was suggested to overcome the above limitation, and was verified by the functional near-infrared spectroscopy (fNIRS) data from a previous published autism spectrum disorder (ASD) research. By this new method, several conclusions consistent with traditional approaches on the pathological mechanisms of ASD were found, i.e., the prefrontal cortex made a major contribution to the hypo-connectivity of young children with ASD. Moreover, some novel findings were also detected (e.g., significantly higher normalized regional spatial complexities of bilateral prefrontal cortices and the variability of normalized local complexity differential of right temporal lobe, and the regional differences of measures in normalized regional spatial complexity), which could not be successfully detected via traditional approaches. These results confirmed the value of this novel approach, and extended the methodology system of functional connectivity. This novel technique could be applied to the neural signal of other neuroimaging techniques and other neurological and cognitive conditions.

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