Traditionally, the DMN has been investigated during sustained periods of rest or task, hence, as an end result of a state-to-state transition (Buckner, Andrews-Hanna, &
Schacter, 2008). However, the understanding of the neural processes during these transitions has been lacking. In Chapter 2 we developed and validated a novel experimental task, suitable for reliable examination of neural modulation in terms of DMN, task-relevant regions and SN, during state-to-state transitions. Importantly, this paradigm extends the classical task-switching paradigm by inclusion of rest trials and, hence, enables the investigation of state anticipatory neural processes (i.e. task- and rest-related). In Chapter 3 we applied this task to individuals with ADHD and found a potential deficit in future state-dependent brain network engagement, which added to the existing evidence of state regulation deficits in ADHD (Metin et al., 2012; Sonuga-Barke et al., 2010). Moreover, the finding of difficulty in “switching-on” future state-dependent brain networks suggest an extension of existing anticipatory training schemes, based on slow cortical potentials (e.g., anticipation-related contingent negative variation), to include not only tasks requiring cognitive engagement, but also rest anticipation (Heinrich et al., 2014; Mayer, Wyckoff, & Strehl, 2013). In addition, the state-switching paradigm, targeting the anticipatory neural modulation during state-to-state switching, can help reveal the neural underpinnings of other psychopathological conditions as well. The
151 findings of Chapter 4 and Chapter 5 have several important implications. First, they bring forward the importance of connectomics approaches in the study of psychopathological conditions in general and specifically ADHD. This approach focuses on the notion that even the smallest and simplest of tasks involves the coordination of multiple brain regions from different brain networks (Griffa, Baumann, Thiran, & Hagmann, 2013; Shenton et al., 2014; Smith et al., 2013). Second, they point to the relevance of studying the relationship between structural and functional brain connectivity, which may not be straightforward, but will help to evaluate the causality between structural white matter alterations and functional disturbances, as well as their effects on developmental trajectory (Damoiseaux & Greicius, 2009; Hagmann et al., 2010; Huang et al., 2013;
Konrad & Eickhoff, 2010). Furthermore, the potential imbalance between SN and attention networks observed in Chapter 4, may introduce a novel target for the existing psychological intervention strategies.
Limitations
There are several limitations of the current doctoral dissertation that need to be recognised. First, in Chapter 2 and Chapter 3 we used a state-switching task to study anticipatory neural modulation of state-dependent brain networks. While this task provided important inferences and extended the classical task-switching paradigm, the inclusion of rest trials to the cued task-switching experimental design was challenging.
The major constraint regarding rest trials was the temporal differentiation between the anticipatory period-related neural response, and the actual initiation of rest phase and the related brain activation. In task trials the anticipatory phase and actual task initiation was separated with the appearance of a target, however, rest trials were lacking that. Thus, we cannot be sure that rest anticipatory period did not involve any of the neural activity
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related to rest initiation, and future research should attempt to more reliably single out the anticipatory phase for a state with no direct state initiation indication.
Second, it is known that fMRI suffers for an inherently low temporal resolution as opposed to its high spatial resolution. Generally the switch anticipatory phase is very time-constrained. As a result, the related neural processes have to occur very rapidly, and fMRI might not capture all the temporal aspects of anticipatory neural modulation.
Therefore, combining fMRI and electroencephalography (EEG), which has an excellent temporal resolution, should be considered in order to increase our understanding of these time scale sensitive neural events.
Third, the connectivity results of both Chapter 4 and Chapter 5 are highly dependent on the brain parcellation scheme used to define the brain network nodes.
Despite the fact that the atlas employed for node definition in both chapters is widely used across other brain connectivity studies (Bassett & Bullmore, 2009; Griffa et al., 2013;
Smith et al., 2013), this brings forward the importance of investigating and developing reliable and representative brain parcellation templates (Schultz et al., 2014).
Fourth, the deterministic tractography algorithm, applied in Chapter 5, suffers from a limited capacity to account for the fibre directionality, which may result in the loss of the existing fibres constituting a network (Gong et al., 2009; Tournier, Mori, & Leemans, 2011). Therefore, more advanced acquisition methods such as, diffusion spectrum magnetic resonance imaging (DSI) (Wedeen et al., 2008) or high angular resolution diffusion imaging (HARDI) (Hess, Mukherjee, Han, Xu, & Vigneron, 2006), that enable the reconstruction of multiple fibre orientations should be considered. However, the drawback of these methods is that they increase the acquisition time, which is a crucial limiting factor in clinical populations like ADHD (Hong et al., 2014).
153 Fifth, the history and duration of stimulant and other psychoactive medication use, across all studies, was not taken into account. However, there exists evidence that psychoactive medication use may exert differential effects on both functional and structural brain organisation, which may have influenced our results (Shaw et al., 2009;
Spencer et al., 2013).
Sixth, our ADHD group represents a community-based, well-functioning sample, which is known to be related to less severe symptom expression and impairment as compared to clinic-based ADHD groups (Brassett-Harknett & Butler, 2007), and which could have mediated our results. Moreover, the considerable overlap among the participant samples across all studies may limit the generalizability of the findings.