Biology subjects

Cadenhead, K.

Publications and source records attributed to Cadenhead, K..

3 recordsLinked to original sources

bioRxiv2022

Linking Choroid Plexus Enlargement with Plasma Analyte and Structural Phenotypes in Clinical High Risk for Psychosis: A Multisite Neuroimaging Study

BackgroundChoroid plexus (ChP) enlargement exists in first-episode and chronic psychosis, but whether enlargement occurs before psychosis onset is unknown. This study investigated whether ChP volume is enlarged in individuals with clinical high-risk (CHR) for psychosis and whether these changes are related to clinical, neuroanatomical, and plasma analytes. MethodsClinical and neuroimaging data from the North American Prodrome Longitudinal Study 2 (NAPLS2) was used for analysis. 509 participants (169 controls, 340 CHR) were recruited. Conversion status was determined after 2-years of follow-up, with 36 psychosis converters. The lateral ventricle ChP was manually segmented from baseline scans. A subsample of 31 controls and 53 CHR had plasma analyte and neuroimaging data. ResultsCompared to controls, CHR (d=0.23, p=0.017) and non-converters (d=0.22, p=0.03) demonstrated higher ChP volumes, but not in converters. In CHR, greater ChP volume correlated with lower cortical (r=-0.22, p<0.001), subcortical gray matter (r=-0.21, p<0.001), and total white matter volume (r=-0.28,p<0.001), as well as larger lateral ventricle volume (r=0.63,p<0.001). Greater ChP volume correlated with makers functionally associated with the lateral ventricle ChP in CHR [CCL1 (r=-0.30, p=0.035), ICAM1 (r=0.33, p=0.02)], converters [IL1{beta} (r=0.66, p=0.004)], and non-converters [BMP6 (r=-0.96, p<0.001), CALB1 (r=-0.98, p<0.001), ICAM1 (r=0.80, p=0.003), SELE (r=0.59, p=0.026), SHBG (r=0.99, p<0.001), TNFRSF10C (r=0.78, p=0.001)]. ConclusionsCHR and non-converters demonstrated significantly larger ChP volumes compared to controls. Enlarged ChP was associated with neuroanatomical alterations and analyte markers functionally associated with the ChP. These findings suggest that the ChP may be a key explanatory biomarker in CHR for psychosis.

neuroscience↗

bioRxiv2017

Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared underlying neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, are implicated in the progression to full psychosis.

neuroscience↗

bioRxiv2017

Toward leveraging big data in human functional connectomics: Generalization of brain graphs across scanners, sessions, and paradigms

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating brain graphic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the reproducibility of functional connectomic measures across multiple sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Higher reliabilities were detected for cognitive tasks (vs rest) and for weighted networks (vs binary networks). While network diagnostics for several primary functional systems were consistently reliable independently of paradigm, those for cognitive-emotional systems were reliable predominantly when challenged by task. Different data aggregation approaches yielded significantly different reliability. In addition, we showed that after accounting for observed reliability, satisfactory statistical power can be achieved in the multisite context with a total sample size of approximately 250 when the effect size is at least moderate. Our findings provide direct evidence for the generalizability of brain graphs for both resting and task paradigms in large consortia and encourage the use of multisite, multisession scans to enhance power for human functional connectomic studies.

neuroscience↗