Schizotypy in Parkinson's disease predicts dopamine-associated psychosis

Spatial and functional profiles distinguish target sets of Parkinson's disease and antipsychotic drugs with different clinical effects

Several studies have examined the genetic factors shared between Parkinson's disease (PD) and schizophrenia (SZ), but the biological themes underlying their clinical relationships remain less explored. We employed systematic transcriptomic and network analyses to examine the genes targeted by two sets of antipsychotic drugs (APDs) - first-generation APDs inducing Parkinsonism and second-generation APDs typically effective against psychotic symptoms in PD - and two sets of PD drugs, one at risk of psychosis and the other with a lower risk of psychosis. Although global brain expression patterns did not effectively differentiate between the targets of the two sets of APDs, they did differentiate the targets of the two PD drug sets. However, both APD and PD target sets showed differences in mean expression levels in specific brain regions. Moreover, they showed significant enrichment for genes highly expressed in distinct adult and prenatal brain structures relative to the overall distribution of such genes among all brain-expressed genes. Specific neurotransmitter systems, either individually or in combinations, appeared to underlie the clinically informed drug categories, indicating their differential roles in inducing or not inducing PD and psychosis. Additionally, the target sets formed distinct network modules representing different biological mechanisms and exhibited differential proximity to putative PD and SZ risk genes in the human interactome. In summary, our study identified specific spatial and functional features that distinguish the target sets of PD and antipsychotic drugs with different clinical effects.

NoLiTiA: An Open-Source Toolbox for Non-linear Time Series Analysis

In many scientific fields including neuroscience, climatology or physics, complex relationships can be described most parsimoniously by non-linear mechanics. Despite their relevance, many neuroscientists still apply linear estimates in order to evaluate complex interactions. This is partially due to the lack of a comprehensive compilation of non-linear methods. Available packages mostly specialize in only one aspect of non-linear time-series analysis and most often require some coding proficiency to use. Here, we introduce NoLiTiA, a free open-source MATLAB toolbox for non-linear time series analysis. In comparison to other currently available non-linear packages, NoLiTiA offers (1) an implementation of a broad range of classic and recently developed methods, (2) an implementation of newly proposed spatially and time-resolved recurrence amplitude analysis and (3) an intuitive environment accessible even to users with little coding experience due to a graphical user interface and batch-editor. The core methodology derives from three distinct fields of complex systems theory, including dynamical systems theory, recurrence quantification analysis and information theory. Besides established methodology including estimation of dynamic invariants like Lyapunov exponents and entropy-based measures, such as active information storage, we include recent developments of quantifying time-resolved aperiodic oscillations. In general, the toolbox will make non-linear methods accessible to the broad neuroscientific community engaged in time series processing.