Large-scale functional network reorganization in 22q11.2 deletion syndrome revealed by modularity analysis

The 22q11.2 deletion syndrome (22q11DS) is associated with cognitive impairments and a 41% risk of developing schizophrenia. While several studies performed on patients with 22q11DS showed the presence of abnormal functional connectivity in this syndrome, how these alterations affect large-scale network organization is still unknown. Here we performed a network modularity analysis on whole-brain functional connectomes derived from the resting-state fMRI of 40 patients with 22q11DS and 41 healthy control participants, aged between 9 and 30 years old. We then split the sample at 18 years old to obtain two age subgroups and repeated the modularity analyses. We found alterations of modular communities affecting the visuo-spatial network and the anterior cingulate cortex (ACC) in both age groups. These results corroborate previous structural and functional studies in 22q11DS that showed early impairment of visuo-spatial processing regions. Furthermore, as ACC has been linked to the development of psychotic symptoms in 22q11DS, the early impairment of its functional connectivity provide further support that ACC alterations may provide potential biomarkers for an increased risk of schizophrenia. Finally, we found an abnormal modularity partition of the dorsolateral prefrontal cortex (DLPFC) only in adults with 22q11DS, suggesting the presence of an abnormal development of functional network communities during adolescence in 22q11DS. (C) 2016 Elsevier Ltd. All rights reserved.

Identifying 22q11.2 Deletion Syndrome and Psychosis Using Resting-State Connectivity Patterns

Jonas Richiardi, Marie Schaer, Maude Schneider, Dimitri Nestor Alice Van De Ville

The clinical picture associated with 22q11.2 deletion syndrome (22q11DS) includes mild mental retardation and an increased risk of schizophrenia. While the clinical phenotype has been related to structural brain network alterations, there is only scarce information about functional connectivity in 22q11DS. However, such studies could lead to a better comprehension of the disease and reveal potential biomarkers for psychosis. A connectivity decoding approach was used to discriminate between 42 patients with 22q11DS and 41 controls using resting-state connectivity. The same method was then applied within the 22q11DS group to identify brain connectivity patterns specifically related to the presence of psychotic symptoms. An accuracy of 84 % was achieved in differentiating patients with 22q11DS from controls. The discriminative connections were widespread, but predominantly located in the bilateral frontal and right temporal lobes, and were significantly correlated to IQ. An 88 % accuracy was obtained for identification of existing psychotic symptoms within the patients group. The regions containing most discriminative connections included the anterior cingulate cortex (ACC), the left superior temporal and the right inferior frontal gyri. Functional connectivity alterations in 22q11DS affect mostly frontal and right temporal lobes and are related to the syndrome's mild mental retardation. These results also provide evidence that resting-state connectivity can potentially become a biomarker for psychosis and that ACC plays an important role in the development of psychotic symptoms.

Springer Verlag2014

Altered structural network architecture is predictive of the presence of psychotic symptoms in patients with 22q11.2 deletion syndrome

Maria Carmela Padula, Marie Schaer, Maude Schneider, Dimitri Nestor Alice Van De Ville

22q11.2 deletion syndrome (22q11DS) represents a homogeneous model of schizophrenia particularly suitable for the search of neural biomarkers of psychosis. Impairments in structural connectivity related to the presence of psychotic symptoms have been reported in patients with 22q11DS. However, the relationships between connectivity changes in patients with different symptomatic profiles are still largely unknown and warrant further investigations. In this study, we used structural connectivity to discriminate patients with 22q11DS with (N = 31) and without (N = 31) attenuated positive psychotic symptoms. Different structural connectivity measures were used, including the number of streamlines connecting pairs of brain regions, graph theoretical measures, and diffusion measures. We used univariate group comparisons as well as predictive multivariate approaches. The univariate comparison of connectivity measures between patients with or without attenuated positive psychotic symptoms did not give significant results. However, the multivariate prediction revealed that altered structural network architecture discriminates patient subtypes (accuracy = 67.7%). Among the regions contributing to the classification we found the anterior cingulate cortex, which is known to be associated to the presence of psychotic symptoms in patients with 22q11DS. Furthermore, a significant discrimination (accuracy = 64%) was obtained with fractional anisotropy and radial diffusivity in the left inferior longitudinal fasciculus and the right cingulate gyrus. Our results point to alterations in structural network architecture and white matter microstructure in patients with 22q11DS with attenuated positive symptoms, mainly involving connections of the limbic system. These alterations may therefore represent a potential biomarker for an increased risk of psychosis that should be further tested in longitudinal studies.

Elsevier Sci Ltd2017

Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome Daniela

Fikret Isik Karahanoglu, Maria Carmela Padula, Marie Schaer, Dimitri Nestor Alice Van De Ville

BACKGROUND: Prodromal positive psychotic symptoms and anxiety are two strong risk factors for schizophrenia in 22q11.2 deletion syndrome (22q11DS). The analysis of large-scale brain network dynamics during rest is promising to investigate aberrant brain function and identify potentially more reliable biomarkers. METHODS: We retrieved and examined dynamic properties of large-scale functional brain networks using innovation-driven coactivation patterns. The study included resting-state functional magnetic resonance scans from 78 patients with 22q11DS and 85 healthy control subjects. After group comparison of temporal brain network activation properties, functional signatures of prodromal psychotic symptoms and anxiety were extracted using multivariate partial least squares correlation. RESULTS: Patients with 22q11DS had shorter activation in cognitive brain networks, longer activation in emotion processing networks, and generally increased segregation between brain networks. The functional signature of prodromal psychotic symptoms confirmed an implication of cingulo-prefrontal salience network activation duration and coupling. Further, the functional signature of anxiety uncovered an implication of amygdala activation and coupling, indicating differential roles of dorsal and ventral subdivisions of the anterior cingulate and medial prefrontal cortices. Coupling of amygdala with the dorsal anterior cingulate and medial prefrontal cortices was promoting anxiety, whereas coupling with the ventral anterior cingulate and medial prefrontal cortices had a protective function. CONCLUSIONS: Using innovation-driven coactivation patterns for dynamic large-scale brain network analysis, we uncovered patterns of brain network activation duration and coupling that are relevant in clinical risk factors for psychosis in 22q11DS. Our results confirm that the dynamic nature of brain network activation contains essential function to develop clinically relevant imaging markers of psychosis vulnerability.