Search bioRxivSearch

Biology subjects

Pinar, A.

Publications and source records attributed to Pinar, A..

2 recordsLinked to original sources

Functional Connectivity of Corticostriatal Circuitry and Psychosis-Like Experiences in the General Community

BackgroundPsychotic symptoms are proposed lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in non-clinical populations to frank disorder. Here, we investigate neurobiological correlates of this symptomatologic continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in patients and high-risk individuals, is associated with the severity of subclinical PLEs.\n\nMethodsA community sample of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis (PCA) estimated major dimensions of PLEs from the questionnaires. PCA dimension scores were then correlated with whole-brain voxelwise functional connectivity (FC) maps of the striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol.\n\nResultsPCA identified two dimensions of PLEs accounting for 62.57% of variance in the measures, corresponding to positive and negative PLEs. Reduced FC between the dorsal striatum and prefrontal cortex correlated with higher positive PLEs. Negative PLEs correlated with increased FC between the dorsal striatum and visual and sensorimotor areas. In the ventral corticostriatal system, positive and negative PLEs were both associated with FC between the ventro-rostral putamen and sensorimotor cortices.\n\nConclusionsConsistent with past findings in patients and high-risk individuals, subthreshold positive symptomatology is associated with reduced FC of the dorsal circuit. These findings suggest that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.

neuroscience

RetSynth: Solving all optimal retrosynthesis solutions using dynamically constrained integer linear programming

MotivationNaive determination of all the optimal pathways to production of a target chemical on an arbitrarily defined chassis organism is computationally intractable. Methods like linear integer programming can provide a singular solution to this problem, but fail to provide all optimal pathways.\n\nResultsHere we present RetSynth, an algorithm for determining all optimal biological retrosynthesis solutions, given a starting biological chassis and target chemical. By dynamically scaling constraints, additional pathway search scales relative to the number of fully independent branches in the optimal pathways, and not relative to the number of reactions in the database or size of the metabolic network. This feature allows all optimal pathways to be determined for a very large number of chemicals and for a large corpus of potential chassis organisms.\n\nAvailabilityThis algorithm is distributed as part of the RetSynth software package, under a BSD 2-clause license at https://www.github.com/sandialabs/RetSynth/

synthetic biology