Development of tools and paradigms to assess brain cortical activity during cognitive tasks

Monitoring brain cortical activity is essential to decipher and understand neurophysiological behaviour. A wide amount of tools and experimental setups has been developed to stimulate, record and analyze brain activity. The identification of quantitative metrics to assess this activity during specific tasks remains an essential requirement, as it could lead to improve diagnostics, describe objectively self-assessed condition, or track variation during long-term studies. This thesis introduces the development of tools and paradigms to assess brain cortical activity during cognitive tasks. It introduces a complete set of analyses based on EEG signals, under two main scopes, schizophrenia and postural control. The first part of the work evaluates the impact of a potential therapeutic solution for patients with schizophrenia. A longitudinal study case is introduced, where psychometrics data are compared with three types of analysis from EEG data: temporal, spectral and connectivity. The small sample size prevents us to draw definitive conclusion, however, this work reveals the interest to use EEG-based metrics to complete the standard psychometrics assessment. The second part of the work focuses on postural control, using a novel measurement setup, called BioVRSea, combining virtual reality and a moving platform. The brain cortical activity of more than 150 healthy individuals have been investigated during this experiment. A robust neurophysiological reference has been identified using power spectral density. Moreover, combining brain connectivity and microstate segmentation, network dynamics reveal a coherent brain remodeling throughout the acquisition, strengthening our current knowledge regarding complex postural control. The current work highlights the concrete benefit of using EEG signal to decipher brain cortical activity. The tools developed in this thesis are of interest to build a neurophysiological signature of specific cognitive tasks, that will be crucial for a further understanding of neurodegenerative disease.Að fylgjast með starfsemi heilaberki er nauðsynlegt til að útskýra og skilja tauga-lífeðlisfræðilega hegðun. Fjölbreytt útval af tólum og tilraunauppsetningum hefur verið þróað til að örva, vista og greina heilastarfsemi. Það er nauðsynleg krafa að finna magnmælingu til að greina þessa starfsemi í ákveðnu verkefni, því það gæti leitt að beturumbættu greiningarferli, útskýrt hlutlægu sjálfsmats ástandi, eða fylgst með breytingum í lang-tíma rannsóknum. Þessi ritgerð kynnir þróunn tóla og hugmyndafræði til að meta heilaberka starfsemi við vitræn verkefni. Ritgerðinn kynnir heilt safn af greiningum byggt á EEG merkj- um, í tvem megin sviðum, geðklofa og líkamsstöðustjórnun. Fyrsti hluti verkefnisins metur áhrifin af mögulegum meðferðarlegum lausnum fyrir sjúklinga með geðklofa. Kynnt er langtímarannsóknartilvik, þar sem þar sem sálfræðigögn eru borin saman við þrenns konar greiningar úr heilarita gögnum: tímabundnum, litrófs- og tengingum. Lítil úrtaksstærð kemur í veg fyrir að við getum dregið endanlegar ályktanir, en þessi vinna sýnir áhugann á því að nota heilalínuritaða mælikvarða til að ljúka stöðluðu sálfræðimati. Annar hluti verksins fjallar um líkamsstöðustýringu, með því að nota nýja mæling- aruppsetningu, sem kallast BioVRSea, sem sameinar sýndarveruleika og hreyfanlegan vettvang. Heilabarkarvirkni af meira en 150 heilbrigðra einstaklinga hefur verið rann- sökuð í þessari tilraun. Öflug taugalífeðlisfræðileg tilvísun hefur fundist með því að nota kraftrófsþéttleika. Þar að auki, með því að sameina heilatengingu og örstöðu- skiptingu, sýnir netverkun samfellda endurgerð heilans í gegnum tökuna, sem styrkir núverandi þekkingu okkar varðandi flókna líkamsstöðustjórnun. Þessi rannsókn undirstrikar raunverulegan ávinning af því að nota EEG merki til að ráða virkni heilabarka. Tólin sem þróuð eru í þessari ritgerð eru mikilvæg til að byggja upp taugalífeðlisfræðilega undirskrift ákveðinna vitræna verkefna, sem mikilvæg eru fyrir frekari skilning á taugahrörnunarsjúkdómum

Brain network dynamics in the alpha band during a complex postural control task

Creative Commons Attribution license.Objective.To decipher brain network dynamic remodeling from electroencephalography (EEG) during a complex postural control (PC) task combining virtual reality and a moving platform.Approach.EEG (64 electrodes) data from 158 healthy subjects were acquired. The experiment is divided into several phases, and visual and motor stimulation is applied progressively. We combined advanced source-space EEG networks with clustering algorithms to decipher the brain networks states (BNSs) that occurred during the task.Main results.The results show that BNS distribution describes the different phases of the experiment with specific transitions between visual, motor, salience, and default mode networks coherently. We also showed that age is a key factor that affects the dynamic transition of BNSs in a healthy cohort.Significance.This study validates an innovative approach, based on a robust methodology and a consequent cohort, to quantify the brain networks dynamics in the BioVRSea paradigm. This work is an important step toward a quantitative evaluation of brain activities during PC and could lay the foundation for developing brain-based biomarkers of PC-related disorders.Peer reviewe

Motion sickness susceptibility and visually induced motion sickness as diagnostic signs in Parkinson's disease

Funding Information: A financial support was received from the Métropole du Grand Nancy, Grand Est region, northeastern France. This research was also funded by the Association France Parkinson, an association promoting research and assisting patients. Publisher Copyright: © 2022 PAGEPress Publications. All rights reserved.Postural instability and loss of vestibular and somatosensory acuity are among the signs encountered in Parkinson's disease (PD). Visual dependency is described in PD. These modifications of sensory input hierarchy are predictors of motion sickness (MS). The aim of this study was to assess MS susceptibility and the effects of real induced MS in posture. Sixty-three PD patients, whose medication levels (levodopa) reflected the severity of the pathology were evaluated, and 27 healthy controls, filled a MS questionnaire; 11 PD patients and 41 healthy controls were assessed by posturography using virtual reality. The levels of levodopa predicted visual MS (p=0.01), but not real induced MS susceptibility. PD patients did not experience postural instability in virtual reality, contrary to healthy controls. Since PD patients do not seem to feel vestibular stimulated MS, they may not rely on vestibular and somatosensory inputs during the stimulation. However, they feel visually induced MS more with higher levels of levodopa. Levodopa amount can increase visual dependency for postural control. The strongest MS predictors must be studied in PD to better understand the effect of visual stimulation and its absence in vestibular stimulation.Peer reviewe

Predicting postural control adaptation measuring EEG, EMG, and center of pressure changes : BioVRSea paradigm

Funding Information: This work has been funded by Landspitali Research Fund (Grant number: 935836; Landspítali Háskólasjúkrahús). Publisher Copyright: Copyright © 2022 Stehle, Aubonnet, Hassan, Recenti, Jacob, Petersen and Gargiulo.Introduction: Postural control is a sensorimotor mechanism that can reveal neurophysiological disorder. The present work studies the quantitative response to a complex postural control task. Methods: We measure electroencephalography (EEG), electromyography (EMG), and center of pressure (CoP) signals during a virtual reality (VR) experience called BioVRSea with the aim of classifying different postural control responses. The BioVRSea paradigm is based on six different phases where motion and visual stimulation are modulated throughout the experiment, inducing subjects to a different adaptive postural control strategy. The goal of the study is to assess the predictability of those responses. During the experiment, brain activity was recorded from a 64-channel EEG, muscle activity was determined with six wireless EMG sensors placed on lower leg muscles, and individual movement measured by the CoP. One-hundred and seventy-two healthy individuals underwent the BioVRSea paradigm and 318 features were extracted from each phase of the experiment. Machine learning techniques were employed to: (1) classify the phases of the experiment; (2) assess the most notable features; and (3) identify a quantitative pattern for healthy responses. Results: The results show that the EEG features are not sufficient to predict the distinct phases of the experiment, but they can distinguish visual and motion onset stimulation. EMG features and CoP features, when used jointly, can predict five out of six phases with a mean accuracy of 74.4% (±8%) and an AUC of 0.92. The most important feature to identify the different adaptive strategies is the Squared Root Mean Distance of points on Medio-Lateral axis (RDIST_ML). Discussion: This work shows the importance and the feasibility of a quantitative evaluation in a complex postural control task and demonstrates the potential of EEG, CoP, and EMG for assessing pathological conditions. These predictive systems pave the way for developing an objective assessment of pathological behavior PC responses. This will be a first step in identifying individual disorders and treatment options.Peer reviewe

Using high density EEG to assess TMS treatment in patients with schizophrenia.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadWe present preliminary results from the ongoing study entitled "Icelandic AVH-TMS" which aim is to study the effectiveness of repetitive transcranial magnetic stimulation (rTMS) treatment for patients with schizophrenia and with persistent auditory verbal hallucinations (AVH) using symptoms and psychometric scales and high-density EEG system (256 channels). The aim of the present work was to describe cortical topography of the auditory evoked responses like P50 and N100-P300 complex in healthy participants and patients with schizophrenia and to define a robust methodology of signal quantification using dense-array EEG. Preliminary data is shown for three healthy participants and three patients in baseline conditions and for two patients we show the results recorded before and after 10 days rTMS treatment. Our results show differences in sensory gating (P50 suppresion) and a stronger N100-P300 response to rare audio stimulus after the treatment. Moreover we show the value of assessing brain electrical activity from high-density EEG (256 channels) analyzing the results in different regions of interest. However, it is premature and hazardous to assume that rTMS treatment effectiveness in patients with AVH can be assessed using P50 suppression ratio. Keywords: P300; P50; Transcranial magnetic stimulation; high density EEG; schizophrenia

Toward New Assessment of Knee Cartilage Degeneration

Funding Information: The authors would like to thank the project RESTORE for their contribution to this study, Marco Ghiselli and Kristján Örn Jóhannesson from the National University Hospital of Iceland for the medical image acquisition, Vicenzo Cangiano for his help in medical image segmentation. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study is part of the European project RESTORE ( https://restoreproject.eu/ ), funded by the European Union’s Horizon 2020 research and innovation program (grant agreement ID: 814558). This work has also been funded by Landspitalin Science fund (grant number: 960221). Publisher Copyright: © The Author(s) 2022. Publisher Copyright: © The Author(s) 2022.Objective: Assessment of human joint cartilage is a crucial tool to detect and diagnose pathological conditions. This exploratory study developed a workflow for 3D modeling of cartilage and bone based on multimodal imaging. New evaluation metrics were created and, a unique set of data was gathered from healthy controls and patients with clinically evaluated degeneration or trauma. Design: We present a novel methodology to evaluate knee bone and cartilage based on features extracted from magnetic resonance imaging (MRI) and computed tomography (CT) data. We developed patient specific 3D models of the tibial, femoral, and patellar bones and cartilages. Forty-seven subjects with a history of degenerative disease, traumatic events, or no symptoms or trauma (control group) were recruited in this study. Ninety-six different measurements were extracted from each knee, 78 2D and 18 3D measurements. We compare the sensitivity of different metrics to classify the cartilage condition and evaluate degeneration. Results: Selected features extracted show significant difference between the 3 groups. We created a cumulative index of bone properties that demonstrated the importance of bone condition to assess cartilage quality, obtaining the greatest sensitivity on femur within medial and femoropatellar compartments. We were able to classify degeneration with a maximum recall value of 95.9 where feature importance analysis showed a significant contribution of the 3D parameters. Conclusion: The present work demonstrates the potential for improving sensitivity in cartilage assessment. Indeed, current trends in cartilage research point toward improving treatments and therefore our contribution is a first step toward sensitive and personalized evaluation of cartilage condition.Peer reviewe

Toward Predicting Motion Sickness Using Virtual Reality and a Moving Platform Assessing Brain, Muscles, and Heart Signals.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMotion sickness (MS) and postural control (PC) conditions are common complaints among those who passively travel. Many theories explaining a probable cause for MS have been proposed but the most prominent is the sensory conflict theory, stating that a mismatch between vestibular and visual signals causes MS. Few measurements have been made to understand and quantify the interplay between muscle activation, brain activity, and heart behavior during this condition. We introduce here a novel multimetric system called BioVRSea based on virtual reality (VR), a mechanical platform and several biomedical sensors to study the physiology associated with MS and seasickness. This study reports the results from 28 individuals: the subjects stand on the platform wearing VR goggles, a 64-channel EEG dry-electrode cap, two EMG sensors on the gastrocnemius muscles, and a sensor on the chest that captures the heart rate (HR). The virtual environment shows a boat surrounded by waves whose frequency and amplitude are synchronized with the platform movement. Three measurement protocols are performed by each subject, after each of which they answer the Motion Sickness Susceptibility Questionnaire. Nineteen parameters are extracted from the biomedical sensors (5 from EEG, 12 from EMG and, 2 from HR) and 13 from the questionnaire. Eight binary indexes are computed to quantify the symptoms combining all of them in the Motion Sickness Index (I MS ). These parameters create the MS database composed of 83 measurements. All indexes undergo univariate statistical analysis, with EMG parameters being most significant, in contrast to EEG parameters. Machine learning (ML) gives good results in the classification of the binary indexes, finding random forest to be the best algorithm (accuracy of 74.7 for I MS ). The feature importance analysis showed that muscle parameters are the most relevant, and for EEG analysis, beta wave results were the most important. The present work serves as the first step in identifying the key physiological factors that differentiate those who suffer from MS from those who do not using the novel BioVRSea system. Coupled with ML, BioVRSea is of value in the evaluation of PC disruptions, which are among the most disturbing and costly health conditions affecting humans.Landspitali University Hospital, Reykjavi

P300 Analysis Using High-Density EEG to Decipher Neural Response to rTMS in Patients With Schizophrenia and Auditory Verbal Hallucinations.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadSchizophrenia is a complex disorder about which much is still unknown. Potential treatments, such as transcranial magnetic stimulation (TMS), have not been exploited, in part because of the variability in behavioral response. This can be overcome with the use of response biomarkers. It has been however shown that repetitive transcranial magnetic stimulation (rTMS) can the relieve positive and negative symptoms of schizophrenia, particularly auditory verbal hallucinations (AVH). This exploratory work aims to establish a quantitative methodological tool, based on high-density electroencephalogram (HD-EEG) data analysis, to assess the effect of rTMS on patients with schizophrenia and AVH. Ten schizophrenia patients with drug-resistant AVH were divided into two groups: the treatment group (TG) received 1 Hz rTMS treatment during 10 daily sessions (900 pulses/session) over the left T3-P3 International 10-20 location. The control group (CG) received rTMS treatment over the Cz (vertex) EEG location. We used the P300 oddball auditory paradigm, known for its reduced amplitude in schizophrenia with AVH, and recorded high-density electroencephalography (HD-EEG, 256 channels), twice for each patient: pre-rTMS and 1 week post-rTMS treatment. The use of HD-EEG enabled the analysis of the data in the time domain, but also in the frequency and source-space connectivity domains. The HD-EEG data were linked with the clinical outcome derived from the auditory hallucinations subscale (AHS) of the Psychotic Symptom Rating Scale (PSYRATS), the Quality of Life Scale (QoLS), and the Depression, Anxiety and Stress Scale (DASS). The general results show a variability between subjects, independent of the group they belong to. The time domain showed a higher N1-P3 amplitude post-rTMS, the frequency domain a higher power spectral density (PSD) in the alpha and beta bands, and the connectivity analysis revealed a higher brain network integration (quantified using the participation coefficient) in the beta band. Despite the small number of subjects and the high variability of the results, this work shows a robust data analysis and an interplay between morphology, spectral, and connectivity data. The identification of a trend post-rTMS for each domain in our results is a first step toward the definition of quantitative neurophysiological parameters to assess rTMS treatment. Keywords: P300; TMS (repetitive transcranial magnetic stimulation); brain connectivity; high-density EEG; schizophrenia; spectral analysis; temporal analysis.United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Neurological Disorders & Stroke (NINDS) Landspitali Scientific fund

Towards defining biomarkers to evaluate concussions using virtual reality and a moving platform (BioVRSea)

Publisher Copyright: © 2022, The Author(s).Current diagnosis of concussion relies on self-reported symptoms and medical records rather than objective biomarkers. This work uses a novel measurement setup called BioVRSea to quantify concussion status. The paradigm is based on brain and muscle signals (EEG, EMG), heart rate and center of pressure (CoP) measurements during a postural control task triggered by a moving platform and a virtual reality environment. Measurements were performed on 54 professional athletes who self-reported their history of concussion or non-concussion. Both groups completed a concussion symptom scale (SCAT5) before the measurement. We analyzed biosignals and CoP parameters before and after the platform movements, to compare the net response of individual postural control. The results showed that BioVRSea discriminated between the concussion and non-concussion groups. Particularly, EEG power spectral density in delta and theta bands showed significant changes in the concussion group and right soleus median frequency from the EMG signal differentiated concussed individuals with balance problems from the other groups. Anterior–posterior CoP frequency-based parameters discriminated concussed individuals with balance problems. Finally, we used machine learning to classify concussion and non-concussion, demonstrating that combining SCAT5 and BioVRSea parameters gives an accuracy up to 95.5%. This study is a step towards quantitative assessment of concussion.Peer reviewe

Human tibial cortical thickness can predict its strength

22d congress of the European Society of Biomechanics, LYON, FRANCE, 10-/07/2016 - 13/07/2016Cortical bone plays a major role in fracture of long bones. Cortical bone thickness and its porosity are supposed to be ones of the determinants of the bone strength. Such parameters can be assessed ex vivo by X-ray microcomputed tomography. The aim of this study is to investigate the link between human tibial strength and its cortical thickness and porosity