Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.Published versio

Wearable, high-density fNIRS and diffuse optical tomography technologies: a perspective

Recent progress in optoelectronics has made wearable and high-density functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) technologies possible for the first time. These technologies have the potential to open new fields of real-world neuroscience by enabling functional neuroimaging of the human cortex at a resolution comparable to fMRI in almost any environment and population. In this perspective article, we provide a brief overview of the history and the current status of wearable high-density fNIRS and DOT approaches, discuss the greatest ongoing challenges, and provide our thoughts on the future of this remarkable technology

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI

Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for “target” vs. “non-target” (dataset A) and symbol “O” vs. symbol “X” (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques

Towards a wireless open source instrument: functional Near-Infrared Spectroscopy in mobile neuroergonomics and BCI applications

Brain-Computer Interfaces (BCIs) and neuroergonomics research have high requirements regarding robustness and mobility. Additionally, fast applicability and customization are desired. Functional Near-Infrared Spectroscopy (fNIRS) is an increasingly established technology with a potential to satisfy these conditions. EEG acquisition technology, currently one of the main modalities used for mobile brain activity assessment, is widely spread and open for access and thus easily customizable. fNIRS technology on the other hand has either to be bought as a predefined commercial solution or developed from scratch using published literature. To help reducing time and effort of future custom designs for research purposes, we present our approach toward an open source multichannel stand-alone fNIRS instrument for mobile NIRS-based neuroimaging, neuroergonomics and BCI/BMI applications. The instrument is low-cost, miniaturized, wireless and modular and openly documented on www.opennirs.org. It provides features such as scalable channel number, configurable regulated light intensities, programmable gain and lock-in amplification. In this paper, the system concept, hardware, software and mechanical implementation of the lightweight stand-alone instrument are presented and the evaluation and verification results of the instrument\u27s hardware and physiological fNIRS functionality are described. Its capability to measure brain activity is demonstrated by qualitative signal assessments and a quantitative mental arithmetic based BCI study with 12 subjects

Pathogen- and Host-Directed Antileishmanial Effects Mediated by Polyhexanide (PHMB)

BACKGROUND:Cutaneous leishmaniasis (CL) is a neglected tropical disease caused by protozoan parasites of the genus Leishmania. CL causes enormous suffering in many countries worldwide. There is no licensed vaccine against CL, and the chemotherapy options show limited efficacy and high toxicity. Localization of the parasites inside host cells is a barrier to most standard chemo- and immune-based interventions. Hence, novel drugs, which are safe, effective and readily accessible to third-world countries and/or drug delivery technologies for effective CL treatments are desperately needed. METHODOLOGY/PRINCIPAL FINDINGS:Here we evaluated the antileishmanial properties and delivery potential of polyhexamethylene biguanide (PHMB; polyhexanide), a widely used antimicrobial and wound antiseptic, in the Leishmania model. PHMB showed an inherent antileishmanial activity at submicromolar concentrations. Our data revealed that PHMB kills Leishmania major (L. major) via a dual mechanism involving disruption of membrane integrity and selective chromosome condensation and damage. PHMB's DNA binding and host cell entry properties were further exploited to improve the delivery and immunomodulatory activities of unmethylated cytosine-phosphate-guanine oligodeoxynucleotides (CpG ODN). PHMB spontaneously bound CpG ODN, forming stable nanopolyplexes that enhanced uptake of CpG ODN, potentiated antimicrobial killing and reduced host cell toxicity of PHMB. CONCLUSIONS:Given its low cost and long history of safe topical use, PHMB holds promise as a drug for CL therapy and delivery vehicle for nucleic acid immunomodulators

Towards neuroscience of the everyday world (NEW) using functional near infrared spectroscopy

Published in final edited form as: Curr Opin Biomed Eng. 2021 June ; 18: doi:10.1016/j.cobme.2021.100272.Functional near-infrared spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability, and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional magnetic resonance imaging, expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and electroencephalography for routine, increasingly unconstrained, and mobile brain imaging is leading toward a new domain that we term “Neuroscience of the Everyday World” (NEW). In this light, we review recent advances in hardware, study design, and signal processing, and discuss challenges and future directions.U01EB029856 - National Institutes of HealthAccepted manuscrip

A bio-psycho-social exercise program (RÜCKGEWINN) for chronic low back pain in rehabilitation aftercare - Study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>There is strong, internationally confirmed evidence for the short-term effectiveness of multimodal interdisciplinary specific treatment programs for chronic back pain. However, the verification of long-term sustainability of achieved effects is missing so far. For long-term improvement of pain and functional ability high intervention intensity or high volume seems to be necessary (> 100 therapy hours). Especially in chronic back pain rehabilitation, purposefully refined aftercare treatments offer the possibility to intensify positive effects or to increase their sustainability. However, quality assured goal-conscious specific aftercare programs for the rehabilitation of chronic back pain are absent.</p> <p>Methods/Design</p> <p>This study aims to examine the efficacy of a specially developed bio-psycho-social chronic back pain specific aftercare intervention (RÜCKGEWINN) in comparison to the current usual aftercare (IRENA) and a control group that is given an educational booklet addressing pain-conditioned functional ability and back pain episodes. Overall rehabilitation effects as well as predictors for compliance to the aftercare programs are analysed. Therefore, a multicenter prospective 3-armed randomised controlled trial is conducted. 456 participants will be consecutively enrolled in inpatient and outpatient rehabilitation and assigned to either one of the three study arms. Outcomes are measured before and after rehabilitation. Aftercare programs are assessed at ten month follow up after dismissal form rehabilitation.</p> <p>Discussion</p> <p>Special methodological and logistic challenges are to be mastered in this trial, which accrue from the interconnection of aftercare interventions to their residential district and the fact that the proportion of patients who take part in aftercare programs is low. The usability of the aftercare program is based on the transference into the routine care and is also reinforced by developed manuals with structured contents, media and material for organisation assistance as well as training manuals for therapists in the aftercare.</p> <p>Trial Registration</p> <p>Trial Registration number: NCT01070849</p

Multimodale Instrumente und Methoden für Neurotechnologie außerhalb des Labors

In neuroscience and related fields, progress in instrumentation, computational power, and signal processing methods continuously provide novel and increasingly powerful tools toward the investigation of brain activity in real-time and everyday environments. Research into real-life and application-oriented, non-invasive neurotechnology bears a number of multidisciplinary challenges which need to be addressed. Neurophysiological signals have to be measured subtly and safely while reliability and robustness have to be ensured. To this end, new approaches are explored in this thesis that deal with the simultaneous acquisition and utilization of multiple brain and body signals in mobile scenarios. They aim to reduce acquisition restraints for mobile neuroimaging, and at the same time increase the amount of information that is provided by hybrid acquisition equipment. This enables the exploitation of complementary and shared information in the measured modalities toward the development of methods that enhance robustness in the analysis and classification of brain signals. The first contribution of this work comprises the development of novel architectures and devices for the mobile measurement of brain and body signals. Here, the focus lies on functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) instruments. The primary result is M3BA, an architecture for Mobile, Modular, Multimodal Biosignal Acquisition. While miniaturized beyond previous approaches, M3BA offers hybrid and high-precision measurement of fNIRS, EEG, acceleration and other signals while allowing scalability and easy customization. The second contribution targets the generation of evoked multimodal neuroimaging data under realistic environmental but yet well-controlled movement conditions. Making use of M3BA modules in a lightweight wireless headset, a novel, bespoke n-back-based cognitive workload paradigm was designed and administered in a study with 17 freely moving subjects. Using this unique dataset, the third contribution consists of the development of a multimodal Blind-Source-Separation framework for the analysis of fNIRS signals and its application in BLISSA2RD, for the accelerometer-based rejection of movement induced artifacts. Employing it along with other state-of-the-art methods, we ultimately provide a proof of feasibility toward workload classification under challenging, realistic conditions. In this unique approach, and with strict rejection of artifacts, accuracies greater than 80% based on neurophysiological EEG-fNIRS markers is achieved.In den Neurowissenschaften und ihren angrenzenden Feldern ermöglichen Fortschritte in der Messtechnik, Miniaturisierung, Rechenleistung und Signalverarbeitung leistungsstarke Ansätze zur Untersuchung der Gehirnaktivität in Echtzeit und unter zunehmend alltagsähnlichen Bedingungen. Die Erforschung nicht-invasiver Neurotechnologie für anwendungsorientierte Szenarien außerhalb des Labors birgt jedoch eine Vielzahl multidisziplinärer Herausforderungen. Neuartige Ansätze müssen eine unaufdringliche und schadlose Erfassung neurophysiologischer Signale ermöglichen und gleichzeitig Zuverlässigkeit und Robustheit sicher stellen. Zu diesem Zweck werden in dieser Dissertation neue Ansätze untersucht, die sich mit der simultanen Erfassung und Nutzung von multiplen Gehirn- und Körpersignalen in mobilen Szenarien beschäftigen. Durch die Verbindung von Biomedizintechnik, Neurowissenschaften und Maschinellem Lernen sollen die Möglichkeiten bei der Signalerfassung erweitert und die Menge der erfassten Informationen erhöht werden. Diese ermöglicht die Entwicklung multimodaler Methoden zur Verbesserung von Signalqualität und Robustheit. Der erste Teil dieser Arbeit besteht aus der Entwicklung von Grundlagen und Architekturen für den Entwurf neuer Instrumente zur mobilen, miniaturisierten und hybriden Messung von Gehirn- und peripheren Körpersignalen. Dabei liegen die Schwerpunkte auf der funktionellen Nahinfrarot-Spektroskopie (fNIRS) und Elektroenzephalographie (EEG). Das primäre Resultat ist M3BA, eine Mobile, Modulare, Multimodale Biosignalerfassungs-Architektur. Während M3BA gegenüber früherer Ansätze weiter miniaturisiert ist, bietet es hochpräzise hybride fNIRS-EEG- und Accelerometer-Messungen, Skalierbarkeit und einfache Anpassung. Diese Architektur ermöglicht im zweiten Teil die Entwicklung und experimentelle Umsetzung eines neuartigen räumlichen n-back-Paradigmas für die Erfassung der mentalen Arbeitslast in sich frei bewegenden Teilnehmern. Der resultierende Datensatz, mit einem speziell darauf ausgelegten M3BA-Headgear erfasst, bietet eine Vielfalt physiologischer Signale von 17 Probanden unter kontrollierten Bewegungsbedingungen. Unter Verwendung dieses neuen Datensatzes besteht der dritte Teil aus der Entwicklung einer Methode zur Analyse von fNIRS-Signalen und der Accelerometer-basierten Entfernung von fNIRS Bewegungsartefakten mit dem Namen BLISSA2RD. In Kombination dieser Methode mit anderen state-of-the art Ansätzen und unter strikter Artefaktbereinigung wird abschließend die Klassifizierung mentaler Arbeitslast unter herausfordernden, realitätsnahen Bedingungen untersucht. Ein erster Machbarkeitsnachweis wird mit erreichten Klassifikationsgenauigkeiten von > 80% unter Ausnutzung der Multimodalität der Daten erbracht