Brain network alterations in Attention-Deficit and Hyperactivity Disorder: towards an integrative perspective based on systems neuroscience

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Attention-Deficit and Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental disorders, affecting mainly the school-age population but also having a moderate prevalence rate into adulthood. It is characterized by symptoms of inattention, impulsivity, and hyperactivity improper for the patient’s age. However, this agedependent characterization of ADHD makes the diagnosis such a problematic issue: the maturation rate is different for each child, making the evaluation of what is age-proper a subjective and difficult question. All of this leads to the ubiquitous question of ADHD, namely, whether there is overdiagnosis of the disease or if it even exists. That is why studying the brain is crucial in ADHD, because finding effective biomarkers able to characterize the disease will allow us to diagnose it more accurately. Magnetic Resonance Imaging (MRI) is one of the most powerful and versatile tools for studying the brain, providing information about both its structure and activity. Traditional MRI studies have focused on analyzing properties of specific brain regions in terms of their shape (e.g., the volume of a structure) or their relation with a cognitive function (e.g., if a structure activates during object recognition), finding multiple alterations in ADHD [8]. However, these widespread regions that present abnormalities are connected between them and with other areas forming the brain network, and their alterations may indeed represent different parts of a more global phenomenon [8, 9]. There are four main neurobiological models that explain ADHD: the maturational lag hypothesis, the dual-pathway model, the Default Mode Network (DMN) interference hypothesis, and multinetwork models. The maturational lag hypothesis is based on ADHD diagnostic criteria and posits that the brain of people with this condition will resemble a younger one [10]. The dual-pathway model proposes two different processing streams for the main symptoms of ADHD: inattention is related to alterations in the corticostriatal executive circuits, while impulsivity/hyperactivity is associated with abnormalities in emotional processing [11, 12]. The DMN interference hypothesis posits that this functional network is not properly suppressed during goal-directed tasks, which is translated into intrusion of inner mental activity [13]. Finally, multinetwork models approach the neurobiology of people with ADHD as an alteration of multiple functional networks [14, 15]. All of these models have received substantial support from neuroimaging studies, which suggests that all of them are correct but incomplete descriptions of the brain profile of people with ADHD. The present dissertation aims to determine whether there is an alteration of the global brain organization in people with ADHD that may underlie the features that characterize the different neurobiological models of the disorder. For that, we will apply two different graph-theory methods based on systems science to the restingstate functional Magnetic Resonance Imaging data of adults and children with ADHD. The two proposed metrics are Stepwise Functional Connectivity (SFC) and Local and Distant Functional Connectivity (LFC and DFC). The first one measures the integration of information from sensory cortices to areas related to high-order cognitive functions, and in Study 1 [16], it will be applied to a sample of medication-naïve adults with ADHD. LFC and DFC study topological properties with physical distance restrictions, that is, the level of connectivity of each voxel with those around it or those far away. This method will be applied to a sample of children with ADHD in Study 2 [17] and the same sample of adults used in Study 1 in Study 3 [18]. Our results consist of alterations in widespread regions that overlap with most functional networks [19]. Specifically, in adults with ADHD, we observed a decrease in integration in the DMN that locally affects the Posterior Cingulate Cortex and its functional connectivity with the medial Prefrontal Cortex. Additionally, the integration of sensory information in these areas was also found to be reduced in the same sample. The integration of the DMN and its development into cortical hubs is a crucial process in the maturation of the brain [20], which relates this finding with a maturational lag. In both children and adults with ADHD, we also observed a lack of segregation between the DMN, the Ventral Attentional Network, and the Frontoparietal Network in a frontal area of the brain. The developmental trajectory of this area consists of the differentiation of three regions, each of them pertaining to one of these networks [21], and thus, it is a sign of brain immaturity. Also, overconnectivity (lack of segregation) between these networks underlies the DMN interference hypothesis and is indeed a multinetwork alteration [14, 22]. We also found abnormalities in the Visual Network in the form of increased integration of information in these areas while decreased local functional integration of the region, which reflects a behavior more typical of associative than sensory cortices [23, 24]. Finally, local connectivity of sensorimotor cortices presents different maturation trends between ADHD and controls while predicting ADHD symptomatology in all of them. In conclusion, our results suggest that for understanding ADHD, we cannot focus just on a few areas related to high-order cognitive functions, but the whole brain functional network is compromised. This goes in line with a recent meta-analysis [8] that was unable to find convergence in specific regions abnormalities and proposed an analysis based on network interactions. Altogether, this dissertation reflects the need to approach ADHD from a systems neuroscience perspective that encompasses all the currently available models instead of proposing alternative reductionist ones.
Mención Internacional en el título de doctor
Attention deficit hyperactivity disorder, Brain, Functional connectivity, Network analysis, Systems neuroscience
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