Mild Cognitive Impairment (MCI) is a highly heterogeneous condition, which covers a wide spectrum of cognitive and functional impairment, with multiple neuropsychological profiles. Positivity to Alzheimer’s disease (AD) hallmarks, such as amyloid β and tau, complicates MCI picture, and even if these biomarkers are currently used in clinics to monitor cognitive worsening, their predictive power is still flawed. Given its high heterogeneity, MCI characterization remains a critical clinical need. The main aim of this study is to integrate high-density electroencephalography (HDEEG) derived information with a novel digital twin technology to perform a multiparametric characterization of MCI, test its sensitivity to biomarkers positivity in patients (MCI+ and MCI-) and capture more efficiently their cognitive performance. Unlike traditional EEG analyses focused on electrode-level signals, our approach was based on the electrophysiological sources extracted from brain regions then grouped into functional networks. Area under the curve (AUC) and peak amplitude (PA) were computed to describe frequency power in each brain region. The information derived from HD-EEG data has been further enriched by the reconstruction of digital twins of patients, to better understand personalized brain dynamics. Digital twins can be reconstructed thanks to a recent technology, the virtual brain (TVB) modelling, which combines mesoscopic models of neural dynamics with structural MRI data to create a brain “avatar”, made of nodes (brain areas) and edges (axonal bundles derived with tractography), essential to simulate large-scale brain dynamics. Between these mesoscopic models, we chose the Wong-Wang, which allows to estimate subject-specific network excitation and inhibition (E/I, i.e., glutamate and GABA) by modelling NMDA and GABA receptor-mediated cellular activity. Extracting from TVB the personalized E/I parameters, we explore the association between neurotransmitters (i.e., glutamate and GABA) and brain rhythms, which cannot be easily assessed and non-invasively in the human brain before. Backward regressions between EEG and TVB parameters and verbal episodic memory neuropsychological tests were used to quantify the ability of this multiparametric characterization to capture MCI memory performance. 2 AUC revealed a higher sensitivity in capturing the hierarchy of frequency bands within different networks, while PA flatten variations at higher frequency ranges (beta and gamma). Both AUC and PA confirmed delta as the predominant band in our MCI population, with AUC highlighting an increase of delta activity in the limbic regions. In line with the resting-state condition, alpha was the second band observed, predominant in the occipital regions and consequently in the visual network. Interestingly, both AUC and PA confirmed alpha band effectiveness in distinguish MCI+ from MCI-. Positive correlations between frequency bands and TVB-derived parameters were found between alpha and NMDA in the default mode network, gamma and GABA in the somatomotor network and beta and recurrent excitation in the frontoparietal network. Further, the combination of brain rhythms and TVB parameters significantly explained up to 60% of memory performance variance. Overall, our findings reveal new insight into patient brain rhythmic activity, being sensitive to biomarkers positivity in MCI and enhancing the impact of Aβ burden on alpha band power in brain networks. Taking advantage of digital twins reconstruction, we explore non-invasively the correlation between neurotransmitters and oscillatory activity, supporting the role of glutamate and GABA in modulating brain rhythms. The multiparametric characterization obtained from this study effectively captures the MCI neuropsychological heterogeneity, paving the way for more effective personalized treatments.
Il Mild Cognitive Impairment (MCI) è una patologia eterogenea, caratterizzata da molteplici profili neuropsicologici. La positività ai biomarcatori della malattia di Alzheimer (AD), come l'amiloide β e la tau, complica il quadro clinico degli MCI e il potere predittivo di questi biomarcatori risulta tuttora inefficiente. Data la sua elevata eterogeneità, la caratterizzazione degli MCI rimane un'esigenza clinica critica. L'obiettivo principale di questo studio è integrare le informazioni derivate dall’elettroencefalografia ad alta risoluzione (HD-EEG) con una nuova tecnologia di creazione di gemelli digitali dei pazienti, per eseguire una caratterizzazione multiparametrica degli MCI, testare la sua sensibilità alla positività dei biomarcatori (MCI+ e MCI-) e catturare in modo più efficiente le loro prestazioni cognitive. A differenza delle tradizionali analisi EEG incentrate sui segnali a livello degli elettrodi, il nostro approccio si è basato sulle sorgenti elettrofisiologiche estratte dalle regioni cerebrali raggruppate in reti funzionali. L'area sotto la curva (AUC) e l'ampiezza del picco (PA) sono state calcolate per descrivere le frequenze in ciascuna regione cerebrale. Le informazioni derivate dai dati HD-EEG sono state arricchite dalla ricostruzione dei gemelli digitali dei pazienti. Questi gemelli digitali possono essere ricostruiti grazie a una recente tecnologia, il “The Virtual Brain” (TVB), che combina modelli mesoscopici di dinamica neurale con dati strutturali di risonanza magnetica per creare un “avatar” cerebrale e simulare dinamiche cerebrali su larga scala. Tra questi modelli mesoscopici abbiamo scelto il Wong-Wang, che permette di derivare l'eccitazione e l'inibizione in modo soggetto specifico (E/I, cioè glutammato e GABA), modellizzando l'attività cellulare mediata dai recettori NMDA e GABA. Estraendo dal TVB i parametri E/I personalizzati, è stato possibile indagare l'associazione tra i neurotrasmettitori e i ritmi cerebrali, precedentemente non valutabile in modo non invasivo. Le regressioni tra i parametri EEG e TVB e i test neuropsicologici di memoria episodica verbale sono state utilizzate per quantificare la capacità di questa caratterizzazione multiparametrica di catturare la memoria degli MCI. L'AUC ha rivelato una maggiore sensibilità rispetto al PA nel catturare la gerarchia delle bande di frequenza all'interno delle diverse reti, soprattutto alle frequenze più elevate 4 (beta e gamma). Sia AUC che PA hanno confermato delta come banda predominante, in particolare nelle regioni limbiche. In linea con la condizione di riposo delle nostre acquisizioni, alfa è stata la seconda banda osservata, predominante nelle regioni occipitali e di conseguenza nella rete visiva. È interessante notare come sia AUC che PA hanno confermato l'efficacia della banda alfa nel distinguere MCI+ da MCI-. Sono state riscontrate correlazioni tra le bande di frequenza e i parametri derivati dal TVB, tra alfa e NMDA nella default mode network, gamma e GABA nella rete somatomotoria e beta ed eccitazione ricorrente nella rete frontoparietale. Inoltre, la combinazione di ritmi cerebrali e parametri TVB ha spiegato in modo significativo fino al 60% della varianza della memoria. Nel complesso, i nostri risultati forniscono una nuova visione dell'attività ritmica cerebrale dei pazienti, essendo sensibili alla positività dei biomarcatori e rivelando l'impatto della β amiloide sull’intensità della banda alfa. Ricostruendo i gemelli digitali dei pazienti MCI, siamo stati in grado di esplorare in modo non invasivo il ruolo del glutammato e del GABA nella modulazione dei ritmi cerebrali. La caratterizzazione multiparametrica ottenuta da questo studio cattura efficacemente l'eterogeneità neuropsicologica degli MCI, aprendo nuove prospettive a trattamenti personalizzati più efficaci.
Brain rhythms and digital twins capture biomarker positivity and memory performance in mild cognitive impairment
AUGELLO, ALBERTO
2023/2024
Abstract
Mild Cognitive Impairment (MCI) is a highly heterogeneous condition, which covers a wide spectrum of cognitive and functional impairment, with multiple neuropsychological profiles. Positivity to Alzheimer’s disease (AD) hallmarks, such as amyloid β and tau, complicates MCI picture, and even if these biomarkers are currently used in clinics to monitor cognitive worsening, their predictive power is still flawed. Given its high heterogeneity, MCI characterization remains a critical clinical need. The main aim of this study is to integrate high-density electroencephalography (HDEEG) derived information with a novel digital twin technology to perform a multiparametric characterization of MCI, test its sensitivity to biomarkers positivity in patients (MCI+ and MCI-) and capture more efficiently their cognitive performance. Unlike traditional EEG analyses focused on electrode-level signals, our approach was based on the electrophysiological sources extracted from brain regions then grouped into functional networks. Area under the curve (AUC) and peak amplitude (PA) were computed to describe frequency power in each brain region. The information derived from HD-EEG data has been further enriched by the reconstruction of digital twins of patients, to better understand personalized brain dynamics. Digital twins can be reconstructed thanks to a recent technology, the virtual brain (TVB) modelling, which combines mesoscopic models of neural dynamics with structural MRI data to create a brain “avatar”, made of nodes (brain areas) and edges (axonal bundles derived with tractography), essential to simulate large-scale brain dynamics. Between these mesoscopic models, we chose the Wong-Wang, which allows to estimate subject-specific network excitation and inhibition (E/I, i.e., glutamate and GABA) by modelling NMDA and GABA receptor-mediated cellular activity. Extracting from TVB the personalized E/I parameters, we explore the association between neurotransmitters (i.e., glutamate and GABA) and brain rhythms, which cannot be easily assessed and non-invasively in the human brain before. Backward regressions between EEG and TVB parameters and verbal episodic memory neuropsychological tests were used to quantify the ability of this multiparametric characterization to capture MCI memory performance. 2 AUC revealed a higher sensitivity in capturing the hierarchy of frequency bands within different networks, while PA flatten variations at higher frequency ranges (beta and gamma). Both AUC and PA confirmed delta as the predominant band in our MCI population, with AUC highlighting an increase of delta activity in the limbic regions. In line with the resting-state condition, alpha was the second band observed, predominant in the occipital regions and consequently in the visual network. Interestingly, both AUC and PA confirmed alpha band effectiveness in distinguish MCI+ from MCI-. Positive correlations between frequency bands and TVB-derived parameters were found between alpha and NMDA in the default mode network, gamma and GABA in the somatomotor network and beta and recurrent excitation in the frontoparietal network. Further, the combination of brain rhythms and TVB parameters significantly explained up to 60% of memory performance variance. Overall, our findings reveal new insight into patient brain rhythmic activity, being sensitive to biomarkers positivity in MCI and enhancing the impact of Aβ burden on alpha band power in brain networks. Taking advantage of digital twins reconstruction, we explore non-invasively the correlation between neurotransmitters and oscillatory activity, supporting the role of glutamate and GABA in modulating brain rhythms. The multiparametric characterization obtained from this study effectively captures the MCI neuropsychological heterogeneity, paving the way for more effective personalized treatments.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14239/28765