Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique, largely used in paediatric applications. However, there is not a standardized protocol for the functional NIRS (fNIRS) data analysis. In particular, there is not a standard method for the removal of motion artefacts, that are the most significant sources of noise in fNIRS and can completely affect the quality of the data acquired, especially for child and infant data, which are often noisier than adult data. The present thesis is within this framework firstly aiming at the identification of an adequate pre-processing pipeline to be used for children datasets. Data of 22 typically developing children (mean age 11.4 years) were considered. The data were acquired at the Scientific Institute E. Medea (Bosisio Parini, LC). The participants were given a visuospatial n-back working memory task. The second aim of the thesis was the extraction from NIRS signals of task related results obtained by applying the developed pre-processing pipeline. In this study, we compared the efficiency of five motion artefact correction techniques including two principal component analyses, wavelet, kurtosis wavelet and a combination of wavelet and moving average. To compare the efficacy of these methods, different metrics related to the physiology of the hemodynamic response were derived. Our results suggest that the wavelet with moving average technique yield the best outcomes. Task related results obtained by applying the selected pre-processing pipeline were extracted from NIRS signals, thus allowing the investigation of the brain activity underlying the spatial working memory. General linearized model (GLM) was applied to the data and the effect of the task cognitive load was investigated by evaluating the area between the oxy- and deoxy-haemoglobin curves. Results showed a main involvement of the frontal right cortical area with an increasing cortical activation at the increase of the difficulty of the task. The present work successfully explored and confirm the capability of fNIRS technique to efficiently study cognitive functions in children populations.
La spettroscopia nel vicino infrarosso (NIRS) è una tecnica non invasiva di neuroimaging, largamente utilizzata in applicazioni pediatriche. Tuttavia, non esiste un protocollo standard per l’analisi dei dati della NIRS funzionale (fNIRS). In particolare, non esiste un metodo standard per la rimozione degli artefatti da movimento, che sono la principale fonte di rumore nella fNIRS e possono completamente compromettere la qualità dei dati acquisiti, specialmente per dati di infanti e bambini, che sono spesso più rumorosi di quelli degli adulti. All’interno di questo contesto, questa tesi punta per prima cosa all’identificazione di un adeguato schema di elaborazione per dataset di bambini. Sono stati considerati i dati di 22 bambini (età media 11.4 anni) con sviluppo tipico, acquisiti all’Istituto Scientifico E. Medea (Bosisio Parini, LC). I partecipanti hanno svolto un compito visuo-spaziale di working memory con n-back. Il secondo obiettivo di questa tesi è stato l’estrazione dal segnale nirs di risultati relativi al compito, ottenuti applicando lo schema precedentemente sviluppato. In questo studio, abbiamo confrontato l’efficacia di 5 tecniche per la rimozione degli artefatti: due PCA, wavelet, kurtosis wavelet e una combinazione di wavelet e media mobile (MA). Per confrontarne l’efficacia, sono state utilizzate diverse metriche legate alla fisiologia della risposta emodinamica. I nostri risultati suggeriscono che la tecnica MA produce i migliori esiti. I risultati legati al compito sono stati estratti dal segnale NIRS, così da permettere l’indagine delle attività del cervello sottoposto ad un compito di working memory spaziale. Un modello lineare generalizzato (GLM) è stato applicato ai dati e gli effetti del carico cognitivo del compito è stato indagato, valutando l’area tra le curve di emoglobina ossigenata e deossigenata. I risultati mostrano un principale coinvolgimento dell’area corticale frontale destra con un incremento dell’attività corticale all’aumento della difficoltà del compito. Questo lavoro ha esplorato con successo e ha confermato le capacità delle fNIRS per lo studio efficiente delle funzioni cognitive in popolazioni di bambini.
Exploring working memory in children through fNIRS : methodological setup and experimental results
BACCHETTA, ANDREA
2017/2018
Abstract
Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique, largely used in paediatric applications. However, there is not a standardized protocol for the functional NIRS (fNIRS) data analysis. In particular, there is not a standard method for the removal of motion artefacts, that are the most significant sources of noise in fNIRS and can completely affect the quality of the data acquired, especially for child and infant data, which are often noisier than adult data. The present thesis is within this framework firstly aiming at the identification of an adequate pre-processing pipeline to be used for children datasets. Data of 22 typically developing children (mean age 11.4 years) were considered. The data were acquired at the Scientific Institute E. Medea (Bosisio Parini, LC). The participants were given a visuospatial n-back working memory task. The second aim of the thesis was the extraction from NIRS signals of task related results obtained by applying the developed pre-processing pipeline. In this study, we compared the efficiency of five motion artefact correction techniques including two principal component analyses, wavelet, kurtosis wavelet and a combination of wavelet and moving average. To compare the efficacy of these methods, different metrics related to the physiology of the hemodynamic response were derived. Our results suggest that the wavelet with moving average technique yield the best outcomes. Task related results obtained by applying the selected pre-processing pipeline were extracted from NIRS signals, thus allowing the investigation of the brain activity underlying the spatial working memory. General linearized model (GLM) was applied to the data and the effect of the task cognitive load was investigated by evaluating the area between the oxy- and deoxy-haemoglobin curves. Results showed a main involvement of the frontal right cortical area with an increasing cortical activation at the increase of the difficulty of the task. The present work successfully explored and confirm the capability of fNIRS technique to efficiently study cognitive functions in children populations.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/145214