The ability to remember to perform intentions at a specific time (time-based) or after the appearance of a cue (event-based) is defined as Prospective Memory (PM). It allows to flexibly manage everyday tasks by executing them at the most appropriate moment, making PM a crucial part of our daily life. The PM field has grown considerably in recent years, but despite the scientific community recognizing its importance, different aspects of PM research need further investigation. Many studies have been focusing on the processes underlying event-based tasks, while only a few investigated the underpinnings of time-based PM. The experimental design of studies has sometimes lacked ecological validity, creating situations that are far off real-life PM. Finally, many studies have employed Electroencephalography (EEG) to study PM because of the centrality of temporal dimension in PM and the excellent temporal resolution of EEG, but none of them used this technique to investigate functional connectivity during PM processes. To fill these gaps, the present study aims to explore the functional connectivity of time-based and event-based PM tasks by implementing a pseudo-naturalistic design while recording EEG. Capturing the functional connectivity patterns intrinsic to the EEG signal requires a method sensible to the dynamic states encompassed by neural activity. A promising novel method called Hidden Markov Modelling (HMM) was adopted, because of its ability to identify stable patterns of whole-brain activity without any prior knowledge over the data. HMM was employed to obtain six recurrent brain states, with significant differences in the time spent in those states between conditions. Results confirmed the key role of Dorsal Attention Network in time-based PM as proposed by the AtoDI model of Cona and colleagues (2015), as well as the allocation of attentional resources towards internal processes in PM conditions. Additionally, a configuration resembling posterior Default Mode Network supported the retrieval of intention in PM tasks.
The ability to remember to perform intentions at a specific time (time-based) or after the appearance of a cue (event-based) is defined as Prospective Memory (PM). It allows to flexibly manage everyday tasks by executing them at the most appropriate moment, making PM a crucial part of our daily life. The PM field has grown considerably in recent years, but despite the scientific community recognizing its importance, different aspects of PM research need further investigation. Many studies have been focusing on the processes underlying event-based tasks, while only a few investigated the underpinnings of time-based PM. The experimental design of studies has sometimes lacked ecological validity, creating situations that are far off real-life PM. Finally, many studies have employed Electroencephalography (EEG) to study PM because of the centrality of temporal dimension in PM and the excellent temporal resolution of EEG, but none of them used this technique to investigate functional connectivity during PM processes. To fill these gaps, the present study aims to explore the functional connectivity of time-based and event-based PM tasks by implementing a pseudo-naturalistic design while recording EEG. Capturing the functional connectivity patterns intrinsic to the EEG signal requires a method sensible to the dynamic states encompassed by neural activity. A promising novel method called Hidden Markov Modelling (HMM) was adopted, because of its ability to identify stable patterns of whole-brain activity without any prior knowledge over the data. HMM was employed to obtain six recurrent brain states, with significant differences in the time spent in those states between conditions. Results confirmed the key role of Dorsal Attention Network in time-based PM as proposed by the AtoDI model of Cona and colleagues (2015), as well as the allocation of attentional resources towards internal processes in PM conditions. Additionally, a configuration resembling posterior Default Mode Network supported the retrieval of intention in PM tasks.
Functional connectivity patterns associated with time-based and event-based Prospective Memory
BUZZI RESCHINI, DAVIDE
2023/2024
Abstract
The ability to remember to perform intentions at a specific time (time-based) or after the appearance of a cue (event-based) is defined as Prospective Memory (PM). It allows to flexibly manage everyday tasks by executing them at the most appropriate moment, making PM a crucial part of our daily life. The PM field has grown considerably in recent years, but despite the scientific community recognizing its importance, different aspects of PM research need further investigation. Many studies have been focusing on the processes underlying event-based tasks, while only a few investigated the underpinnings of time-based PM. The experimental design of studies has sometimes lacked ecological validity, creating situations that are far off real-life PM. Finally, many studies have employed Electroencephalography (EEG) to study PM because of the centrality of temporal dimension in PM and the excellent temporal resolution of EEG, but none of them used this technique to investigate functional connectivity during PM processes. To fill these gaps, the present study aims to explore the functional connectivity of time-based and event-based PM tasks by implementing a pseudo-naturalistic design while recording EEG. Capturing the functional connectivity patterns intrinsic to the EEG signal requires a method sensible to the dynamic states encompassed by neural activity. A promising novel method called Hidden Markov Modelling (HMM) was adopted, because of its ability to identify stable patterns of whole-brain activity without any prior knowledge over the data. HMM was employed to obtain six recurrent brain states, with significant differences in the time spent in those states between conditions. Results confirmed the key role of Dorsal Attention Network in time-based PM as proposed by the AtoDI model of Cona and colleagues (2015), as well as the allocation of attentional resources towards internal processes in PM conditions. Additionally, a configuration resembling posterior Default Mode Network supported the retrieval of intention in PM tasks.File | Dimensione | Formato | |
---|---|---|---|
BuzziReschini_Tesi.pdf
accesso aperto
Dimensione
2.12 MB
Formato
Adobe PDF
|
2.12 MB | Adobe PDF | Visualizza/Apri |
The text of this website © Università degli studi di Padova. Full Text are published under a non-exclusive license. Metadata are under a CC0 License
https://hdl.handle.net/20.500.12608/72144