Baseline connectome modular abnormalities in the childhood phase of a longitudinal study on individuals with chromosome 22q11.2 deletion syndrome

Abstract

Occurring in at least 1 in 3,000 live births, chromosome 22q11.2 deletion syndrome (22q11DS) produces a complex phenotype that includes a constellation of medical complications such as congenital cardiac defects, immune deficiency, velopharyngeal dysfunction, and characteristic facial dysmorphic features. There is also an increased incidence of psychiatric diagnosis, especially intellectual disability and ADHD in childhood, lifelong anxiety, and a strikingly high rate of schizophrenia spectrum disorders, which occur in around 30% of adults with 22q11DS. Using innovative computational connectomics, we studied how 22q11DS affects high-level network signatures of hierarchical modularity and its intrinsic geometry in 55 children with confirmed 22q11DS and 27 Typically Developing (TD) children. Results identified 3 subgroups within our 22q11DS sample using a K-means clustering approach based on several midline structural measures-of-interests. Each subgroup exhibited distinct patterns of connectome abnormalities. Subtype 1, containing individuals with generally healthy-looking brains, exhibited no significant differences in either modularity or intrinsic geometry when compared with TD. By contrast, the more anomalous 22q11DS Subtypes 2 and 3 brains revealed significant modular differences in the right hemisphere, while Subtype 3 (the most anomalous anatomy) further exhibited significantly abnormal connectome intrinsic geometry in the form of left-right temporal disintegration. Taken together, our findings supported an overall picture of (a) anteriorposteriorly differential interlobar frontotemporal/frontoparietal dysconnectivity in Subtypes 2 and 3 and (b) differential intralobar dysconnectivity in Subtype 3. Our ongoing studies are focusing on whether these subtypes and their connnectome signatures might be valid biomarkers for predicting the degree of psychosis-proneness risk found in 22q11DS. Hum Brain Mapp 39:232-248, 2018.V C 2017 Wiley Periodicals, Inc.

Opposing white matter microstructure abnormalities in 22q11.2 deletion and duplication carriers

“…As displayed in Table 2 , the most frequent DSM-V diagnoses in 22q-del and 22q-dup were anxiety disorders, autism spectrum disorder, ADHD, and mood disorders. As described previously [ 6 , 55 , 56 ], 22q-del reported more prodromal psychotic symptoms than 22q-dup and HC. However, only four 22q-del were diagnosed with overt psychotic disorder.…”

“…In addition, the Structured Interview for Psychosis-Risk Syndromes (SIPS) [ 53 ] was administered to assess for psychotic and prodromal (i.e., psychosis-risk) symptoms. Previous research has suggested that many individuals with the 22q-del present with prodromal symptoms [ 54 ] and up to 25% develop psychosis [ 55 , 56 , 6 ]. 22q-dup, on the other hand, does not have an elevated risk for psychosis [ 14 ], and in fact, there is some evidence suggesting a lower risk for psychosis than in the general population [ 15 ].…”

Schizophrenia-related microdeletion causes defective ciliary motility and brain ventricle enlargement via microRNA-dependent mechanisms in mice

“…Structural neuroimaging studies of children with 22q11DS have reported multiple volumetric brain abnormalities 43,44,81 . A recent study indicated the existence of three subgroups within 22q11DS based on severity of changes in midline structures 82 . The changes we observed in 22q11DS mouse models and Dgcr8 +/− mice were relatively mild and correspond to subgroup 1 or 2 of this classification.…”

Structural control energy of resting‐state functional brain states reveals less cost‐effective brain dynamics in psychosis vulnerability

“…Affected white‐matter bundles mostly include long‐range frontal‐frontal, frontal‐occipital, and fronto‐parietal connections (Kikinis et al, 2016; Olszewski et al, 2017; Roalf et al, 2017; Scariati et al, 2016; Tylee et al, 2017). Only a few studies have thus far examined the characteristics of structural whole‐brain networks (Kikinis et al, 2013; Ottet, Schaer, Cammoun, et al, 2013; Ottet, Schaer, Debbané, et al, 2013; Padula et al, 2017; Váša et al, 2016; Zhan et al, 2018), also mostly reporting fronto‐temporal, fronto‐parietal (Ottet, Schaer, Cammoun, et al, 2013; Ottet, Schaer, Debbané, et al, 2013; Zhan et al, 2018), and limbic dysconnectivity (Ottet, Schaer, Cammoun, et al, 2013; Padula et al, 2015). From a topological perspective, Ottet et al reported longer path lengths and disconnectivity of the brain's hub regions, specifically, in the frontal lobes (Ottet, Schaer, Debbané, et al, 2013), and Váša et al (2016) uncovered a “de‐centralization” in 22q11DS with a rerouting of shortest network paths to circumvent an affected core that included frontal, parietal, and subcortical regions.…”