It is increasingly appreciated that we should move towards integrated categorical-dimensional approaches for conceptualizing and investigating mental disorders (Chabernaud et al., 2011; Coghill and Sonuga-Barke, 2012). In this context, categorical effects comprise systematic differences between patients and controls, whereas dimensional effects reflect a continuum of a certain measure/phenotype in which patients might represent the end of that spectrum. As an example in the research field of ADHD, Chabernaud et al. (2011) and Elton et al. (2014) have investigated categorical and dimensional mechanisms related to this disorder and both found neural underpinnings associated with categorical and/or dimensional ADHD-related behavioral measures. However, in these research papers, and in current literature in general, there is little awareness of the methodological implications associated with these types of analyses. Chapter 3 aimed to raise such awareness by explaining that, due to high correlation between categorical and dimensional variables, some typically used models increase false positive rate while others suffer from reduced sensitivity (York, 2012; Mumford et al., 2015). To overcome these limitations, I proposed a modelling framework by utilizing selective orthogonalization of categorical and dimensional variables. As a proof of principle I applied this framework to investigate functional connectivity of the executive control network in ADHD. These results demonstrated how current modelling approaches indeed lead to reduced sensitivity and/or false interpretation of the obtained results.
Our framework on the other hand provided a more refined characterization of the results while maintaining sensitivity to the effects of interest.
Next to categorical-dimensional views on mental disorders; mental disorders are also more and more believed to be multifactorial in its underlying pathology, affecting multiple independent neural systems (Makris et al., 2009; Menon, 2011).
This aspect is known as ‘equifinality’, meaning different pathophysiological mechanism can result in similar behavioral symptoms. Resting-state fMRI allows to model different functional neural networks and therefore investigate this multifactorial aspect of mental disorders. However, to date researchers have typically employed univariate modeling procedures to model a specific network. By not acknowledging the multivariate nature of the data (i.e. simultaneous activation of multiple networks) these approaches are suboptimal and result in less specific networks (Smith et al., 2012). Moreover, in line with this limitation, researchers often define and investigated brain regions or networks using a ‘hard’ parcellation of networks in which every voxel is assigned to a single region/network. However, networks share anatomical infrastructure and brain regions can be involved in multiple networks, which complicates the interpretation of findings base on ‘hard’
parcellation. Fortunately, these drawbacks can be addressed by conducting
multivariate modeling of neural networks using ICA (which allows spatial overlap between networks/components) and dual regression (Beckmann et al., 2009;
Filippini et al., 2009). Accordingly, we combined this method with the proposed methodology in this thesis into a single study to investigate functional connectivity in the context of ADHD. Specifically, we used ICA-AROMA to preprocess rfMRI data, from which we estimated a set of functional networks using dual regression, which were then investigated for categorical and dimensional mechanisms related to ADHD.
This functional connectivity study, see Chapter 4, identified a set of significant findings throughout different networks in which functional connectivity was associated with categorical and/or dimensional measures of ADHD; comprising the default mode, executive control, cerebellum, visual and motor networks. The anatomical locations of these findings are highly consistent with current literature in ADHD but the study provided a more detailed characterization of the findings by specifically relating them to dissociable networks and identifying its categorical-dimensional complexity.
A nice example regards the dimensional finding that increased functional connectivity of the paracingulate cortex within the visual–medial network was associated with inattentive behavior. Importantly, the exact same region was found to be significantly associated with ADHD in a neural systems meta-analysis of fMRI studies by Cortese et al. (2012). See Figure 1 for a comparison of their results with our finding presented in Chapter 4. However, this meta-analysis assigned all findings to specific neural systems using a ‘hard’ parcellation of the brain from a study by Thomas Yeo et al. (2011); in which the paracingulate cortex was assigned to the executive control network (ECN; in their work referred to as the ventral attention network). The authors interpreted this finding in line with a prominent hypothesis proposed by Nigg and Casey (2005), stating that ECN hypoactivation underpins deficits in detecting environmental (ir)regularities, leading to ADHD-related problems in modulating behavior according to these environmental changes.
However, the ‘soft’ group-ICA parcellation used in this chapter showed that this region is not only involved in the ECN but also in the visual-medial network.
Notably, it was specifically the functional connectivity of the paracingulate cortex within this visual network that showed the signification association with inattentive
behavior. Combined with the notions that visual areas can be involved in suppressing spatial attention to stimuli and might play a compensatory role for impaired functioning of brain regions within the ECN, this finding allowed refining the hypothesis on spatial and visual attention processing in ADHD. Specifically, Chapter 4 hypothesizes on the basis of these findings that ADHD-related deficits in detecting environmental (ir)regularities relate to visual attention processing regulated by the paracingulate cortex, possibly as a neural compensatory mechanism for dysfunction of the ECN. Importantly, these results stress out the importance of appropriate multivariate modeling of neural networks.
Figure 1 - Comparison of the significant results located at the posterior part of the paracingulate cortex in respectively the neural systems’ meta-analysis of Cortese et al. (2012) and neural systems’ research conducted in Chapter 5 (i.e. Pruim et al.). The two left figures where adapted from the paper by Cortese et.al. (adapted from Cortese et al. (2012)). The upper figures show the significant clusters obtained in both studies. The lower left figure shows the (non-overlapping) network templates identified by Thomas Yeo et al. (2011) on the basis of which Cortese et.al. assigned their findings to the executive control network (ECN; in their work referred to as ventral attention network). The lower right figure illustrates the visual network identified in Chapter 5 using group-ICA (allowing spatial overlap between estimated networks), which showed involvement of the posterior paracingulate, next to it also being part of the estimated ECN. Importantly, the significant cluster (top right) regards the association of functional connectivity of paracingulate cortex within the visual network with ADHD (specifically: a dimensional association with inattentive behavior).
Importantly, all the developed and proposed methodology in this thesis converges in these results, illustrating their (complementary) benefit for modeling functional brain architecture in mental disorders. First, effective cleaning of the rfMRI data using ICA-AROMA (Chapter 1 and 2) resulted in increased sensitivity such that we could derive very specific network templates (e.g. identify the paracingulate cortex as part of the visual network; see supplementary Figure 3 in Chapter 1 and Figure 1 in Chapter 4), and replicate the main finding of a meta-analysis of 55 fMRI studies. Second, due to the categorical-dimensional modeling framework (Chapter 3) we could characterize this finding as a dimensional mechanism related to inattentive behavior. And third, using multivariate network modeling (Chapter 4) we were able to associate this finding specifically to the visual network rather than the (expected) ECN which allowed more accurate interpretation and refinement of a hypothesis on visual-attention in ADHD.
For further validation of the findings and to explore genetic-neural-cognitive-behavioral pathways, the functional connectivity results in Chapter 4 were related to four ADHD-related cognitive measures (working memory, response inhibition, reaction time variability, reward sensitivity) and two dopamine neurotransmission-related genetic variants (in the genes DAT1 and DRD4) (Faraone et al., 2014). The results for example suggest two categorical-dimensional pathways within the ECN.
The first, comprising a pathway in which the DRD4 gene modulates functional connectivity of fronto-striatal pathways within the ECN, affecting timing and inhibitory control. The second, comprising a pathway in which the DAT1 gene is related to connectivity within putamen/insula, unspecific to cognitive domains.
Moreover, we found a categorical mechanism related to ADHD-diagnosis of functional connectivity of the PCC/precuneus within the default mode network; this is considered to be a key locus in ADHD pathology (Castellanos et al., 2008).
Interestingly, this finding was unrelated to cognitive ADHD-related measures and the locus is found in many psychiatric disorders, suggesting that pathology/dysfunctioning of the PCC/precuneus is relevant yet unspecific to ADHD (Broyd et al., 2009; Leech and Sharp, 2014).
Accordingly, by using a neural systems approach, disentangling categorical-dimensional and relating the findings to genetic and cognitive measures of ADHD, Chapter 4 was able provide new insights into the neurobiological mechanisms (i.e.
genetic-neural-cognitive-behavioral pathways) of ADHD.