Improving the analysis of near-infrared spectroscopy data with multivariate classification of hemodynamic patterns: a theoretical formulation and validation

Objective. The statistical analysis of functional near infrared spectroscopy (fNIRS) data based on the general linear model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as 'active' or 'not active' with a multivariate classifier based on linear discriminant analysis (LDA). Approach. This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis. Main results. The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results. Significance. The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.EC/H2020/641858/EU/Understanding and predicting developmental language abilities and disorders in multilingual Europe/PREDICTABL

Reproducibility of infant fNIRS studies: a meta-analytic approach

Significance Concerns about the reproducibility of experimental findings have recently emerged in many disciplines, from psychology to medicine and neuroscience. As NIRS is a relatively recent brain imaging technique, the question of reproducibility has not yet been systematically addressed. Aim The current study seeks to test the replicability of effects observed in NIRS experiments assessing young infants’ rule-learning ability. Approach We conducted meta-analyses and mixed-effects modeling-based inferential statistics to determine whether effect sizes were replicable and comparable in a sample of 23 NIRS studies investigating infants’ abilities to process repetition- and diversity-based regularities in linguistic and nonlinguistic auditory and visual sequences. Additionally, we tested whether effect sizes were modulated by different factors such as the age of participants or the laboratory. We obtained NIRS data from 12 published and 11 unpublished studies. The 23 studies involved a total of 487 infants, aged between 0 and 9 months, tested in four different countries (Canada, France, Italy, and USA). Results Our most important finding is that study and laboratory were never significant moderators of variation in effect sizes, indicating that results replicated reliably across the different studies and labs included in the sample. We observed small-to-moderate effect sizes, similar to effect sizes found with other neuroimaging and behavioral techniques in the developmental literature. In line with existing findings, effect sizes were modulated by the participants’ age and differed across the different regularities tested, with repetition-based regularities giving rise to the strongest effects; in particular, the overall magnitude of this effect in the left temporal region was 0.27 when analyzing the entire dataset. Conclusions Meta-analysis is a useful tool for assessing replicability and cross-study variability. Here, we have shown that infant NIRS studies in the language domain replicate robustly across various NIRS machines, testing sites, and developmental populations.This study was funded by the ERC Consolidator Grant “BabyRhythm” nr. 773202 to Judit Gervain, the Marie Curie Individual Fellowship EF-ST “BabyMindReader” nr. 101031716 to Jessica Gemignani, and the Basque Foundation for Science Ikerbasque and the Spanish Ministry of Science and Innovation [grant nr. PID2019-105100RJ-I00] to Irene de la Cruz-Pavía

Erweiterung der Analyse von Funktionalitäten der Nah-Infrarot-Spektroskopie (fNIRS) Daten mit multivariaten Techniken : Bewerbung für eine Alphabetisierungsstudie für Kinder

The use of functional Near-Infrared Spectroscopy is experiencing a rapid growth in use in every area of neuroscientific research, but one of the most prolific areas of application is the field of language development in children. If over the past decades there have been incredible advancements on the technology side, the same can not be said about data analysis techniques: for fNIRS, an ad-hoc standardized data analysis procedure is still lacking; there is no general consensus around the best set of pre-processing steps to be performed; there is no standard choice of which hemoglobin component should be used to infer functional brain activity (oxygenated or deoxygenated hemoglobin); the statistical analysis has been carried out for a long time with methods borrowed from the fMRI practice. Only recently, effort has been made in defining fNIRS-specific techniques, both at single-channel and multichannel level. The first contribution of this work was to introduce a single-channel classifier that combines features from both oxy- and deoxyhemoglobin and that employs Linear Discriminant Analysis (LDA) to classify channels as ‘active’ or ‘not-active’. Its performances were compared to those achieved by the General Linear Model (GLM) and it was found that the LDA-based classifier not only yields higher classification accuracies but those accuracies are more stable across different subjects, i.e. the multivariate method was more robust with respect to intersubjective variability of the hemodynamic response. The second contribution of this work was to investigate the impact of literacy on functional brain organization, with both single-channel (univariate) and multi-channel(multivariate) analysis approaches. Using a large fNIRS dataset including children of various ages and literacy levels, we were able to show that the univariate approach has a unique strength in localizing the effects under investigation. On the other hand, the multi-channel analysis approach did not produce a statistically significant effect, most likely because the experimental design was not optimally suited for the trial-by-trial classification; nevertheless, it highlighted a trend that had eluded the univariate analysis. We conclude that both types of analysis should be routinely employed, because their complementary strengths can answer to different questions, but also that the single channel analysis should be rendered more robust by using both hemoglobin components and that a data-driven approach such as the one proposed may mitigate the shortcomings of the model-based single-channel analysis. Finally, in order to perform a multivariate pattern analysis, the experimental paradigm should be designed as to include enough trials for classification.Der Einsatz der funktionellen Nahinfrarotspektroskopie nimmt in allen Bereichen der neurowissenschaftlichen Forschung rasant zu, und eines der produktivsten Anwendungsgebiete ist das Feld der Sprachentwicklung bei Kindern. Wenn es in den letzten Jahrzehnten technologisch gesehen unglaubliche Fortschritte gegeben hat, kann man das Gleiche nicht über Datenanalyseverfahren sagen: Für fNIRS fehlt noch ein ad-hoc standardisiertes Datenanalyseverfahren; es gibt keinen allgemeinen Konsens über die beste Reihe von durchzuführenden Vorverarbeitungsschritten; es gibt keine Standardauswahl, welche Hämoglobinkomponente verwendet werden sollte, um funktionelle Hirnaktivität abzuleiten (sauerstoffreiches oder sauerstoffarmes Hämoglobin); die statistische Analyse wird seit langem mit Methoden durchgeführt, die der fMRI-Praxis entnommen wurden. Erst kürzlich wurden Anstrengungen unternommen, um fNIRS-spezifische Techniken zu definieren, sowohl auf Einkanal- als auch auf Mehrkanalebene. Der erste Beitrag dieser Arbeit war die Einführung eines Einkanalklassifizierers, der Merkmale von Oxy- und Desoxyhämoglobin kombiniert und der die Linear Discriminant Analysis einsetzt, um Kanäle als "aktiv" oder "nicht aktiv" zu klassifizieren. Seine Leistungen wurden mit denen des General Linear Model verglichen und es wurde festgestellt, dass der LDA-basierte Klassifikator nicht nur höhere Klassifikationsgenauigkeiten liefert, sondern dass diese Genauigkeiten bei verschiedenen Probanden stabiler sind, d.h. die multivariate Methode war robuster in Bezug auf intersubjektive Variabilität der hämodynamischen Reaktion. Der zweite Beitrag dieser Arbeit war die Untersuchung der Auswirkungen der Alphabetisierung auf die funktionelle Gehirnorganisation, mit einkanaligen (univariaten) und mehrkanaligen (multivariaten) Analysemethoden. Anhand eines großen fNIRSDatensatzes mit Kindern unterschiedlichen Alters und Alphabetisierungsgrades konnten wir zeigen, dass der univariate Ansatz eine einzigartige Stärke bei der Lokalisierung der untersuchten Effekte hat. Andererseits zeigte der Multi-Channel-Analyse-Ansatz keinen statistisch signifikanten Effekt, wahrscheinlich weil das experimentelle Design nicht optimal für die Klassifizierung im Versuch geeignet war; dennoch zeigte er einen Trend auf, der sich der univariaten Analyse entzogen hatte. Wir kommen zu dem Schluss, dass beide Arten der Analyse routinemäßig eingesetzt werden sollten, weil ihre komplementären Stärken auf unterschiedliche Fragen antworten können, aber auch, dass die Einkanalanalyse durch den Einsatz beider Hämoglobinkomponenten robuster gemacht werden sollte und dass ein datengetriebener Ansatz wie der vorgeschlagene die Mängel der modellbasierten Einkanalanalyse mildern kann. Schließlich, um eine multivariate Musteranalyse durchzuführen, sollte das experimentelle Paradigma so gestaltet werden, dass es genügend Versuche für die Klassifizierung enthält

SVILUPPO DI UN METODO DI PATTERN RECOGNITION PER L'IDENTIFICAZIONE NEL SEGNALE ELETTROENCEFALOGRAFICO DEI FUSI DEL SONNO: IMPLICAZIONI PER I MECCANISMI DI CONSOLIDAMENTO DELLE MEMORIE

Il presente lavoro di tesi consiste nell'implementazione e validazione di un algoritmo per la rilevazione dei fusi del sonno nel segnale elettroencefalografico. Oltre alla rilevazione temporale dei fusi, l'algoritmo estrae da ciascuno di essi delle caratteristiche, e queste caratteristiche vengono correlate con i risultati di un task atto a coinvolgere i processi di consolidamento della memoria. In tal modo, si vuole valutare il coinvolgimento dei fusi del sonno nel processi di consolidamento della memoria

COVID-19, diffusione, infodemia

Il presente elaborato tratta due fenomeni caratteristici dell'anno 2020: la diffusione della malattia infettiva COVID-19 e la propagazione di “un'infodemia” ad essa associata ovvero la propagazione di un sovraccarico informativo (spesso contradditorio o addirittura misinformativo e disinformativo) da COVID-19. L’infodemia da COVID-19 ha influenzato ubiquitariamente qualunque attività umana in queste prime fasi della pandemia. Ha infatti influenzato in particolar modo i rischi percepiti del virus e delle sue conseguenze riducendolo in alcuni casi (e quindi stimolando comportamenti sbagliati in termini di contenimento della diffusione) o aumentandolo eccessivamente in altri casi generando allarmismo sociale, paura, ansietà e quindi reazioni anomale e conseguentemente danni psicologici, diventando quindi un fattore di rischio > per molte attività umane. Dunque, lo sviluppo di > di comunicazione pubblica dell’emergenza sta diventando un’area fondamentale della comunicazione di questa fase, in quanto gioca un ruolo chiave sulla percezione del rischio e quest’ultimo è potenzialmente un forte modificatore dell’evoluzione dell’epidemia. La tesi è articolata in tre capitoli: il primo capitolo si occupa in primo luogo di fornire in maniera sintetica informazioni sul nuovo coronavirus SARS-CoV-2 responsabile dell'attuale pandemia e in secondo luogo una panoramica di quello è successo in Italia, dai primi casi segnalati alla seconda ondata epidemica, con particolare attenzione alle misure adottate dal Governo Italiano per contrastare l'epidemia COVID-19, facendo riferimento a materiali provenienti principalmente da fonti ufficiali, quali il sito dell'Organizzazione Mondiale della Sanità, dell'Istituto Superiore di Sanità, del Ministero della Salute, del Governo Italiano. Il secondo capitolo si focalizza sulla descrizione di alcuni modelli epidemiologici che vengono utilizzati per studiare la diffusione di SARS-CoV-2, partendo dal modello di base SIR e arrivando a modelli più complessi che possono descriverne in modo più realistico la sua diffusione. Tuttavia, come già anticipato, la diffusione del nuovo coronavirus è stata accompagnata dalla diffusione di una quantità incontrollata di informazioni provenienti da fonti diverse, spesso in contrasto tra di loro e molto spesso non accurate. Questo "contagio informativo" ha reso più complessa la gestione dell’emergenza sanitaria stessa, andando a pregiudicare la possibilità di trasmettere informazioni chiare e univoche e di ottenere dunque comportamenti omogenei da parte della popolazione. Per questo motivo, il terzo capitolo si concentra sulla descrizione dell’infodemia. In tale capitolo, per inquadrare il fenomeno infodemico, vengono esplicitate: (i) alcune definizioni, tra le quali quelle di “fake news” e di “altri disordini informativi” con connessi alcuni esempi relativi al caso COVID-19; (ii) la sua diffusione, con particolare attenzione allo scenario informativo/ disinformativo in Italia e i suoi effetti sulla percezione del rischio, con focalizzazione in particolar modo sulla prima ondata epidemica; (iii) alcune misure di contrasto alle notizie false a livello nazionale e da parte dell’Organizzazione Mondiale della Sanità

Beyond astronaut's capabilities : the current state of the art

Space agencies have developed extensive expertise with sustaining human presence in low earth orbits and microgravity. Prolonged human presence in space beyond Earth. A Z's orbit presents additional, some still unsolved issues. These are linked to the distance to Earth (impossibility of effective tele-operation, psychological effects linked to remoteness from Earth, required autonomy, the handling of emergencies, long mission durations), and to the environments beyond the Earth magnetosphere (radiation levels, local environments including atmospheres, dust, gravity, day-night cycles). These issues have impacts on the spacecraft design, the mission operations, astronaut selection and preparation and required supporting/enabling technologies. This paper builds upon previous work by Rossini et al., in critically reviewing and updating the current state of scientific research on enhancing astronaut's capabilities to face some of these challenges [1]. In particular, it discusses the pertinence and feasibility of two approaches aiming at enhancing the chances of success of human missions: induced hibernation state and brain-machine interfaces

Vocal brain development in infants of mothers with serious mental illness (CAPRI-Voc):study protocol

Improving the lives of children and adolescents with parental mental illness (CAPRI) remains an urgent political and public health concern for the UK and European Union. Recurrent parental mental illness is believed to lead to fractures in the family, academic and social lives of these children, yet interventions are poorly targeted and non-specific. Part of an interdisciplinary programme of work (the CAPRI Programme; grant number: 682741), CAPRI-Voc aims to achieve two goals: first, to test the feasibility of our longitudinal imaging paradigm in mother-infant pairs where the mother has a diagnosis of severe mental illness. Second, to compare development of vocal processing in these infants with infants in the general population