bioRxiv Science⌕ Search

Biology subjects

Norden, M.

Publications and source records attributed to Norden, M..

2 recordsLinked to original sources

Experimental change in personality: Overexpression of GDNF in the rat striatum converts the low exploratory phenotype into highly explorative

Major vulnerability factors for psychiatric disorders such as depression, that often prevent complete remission and lead to relapses, are temperamental. In a rat model of clustered persistent high anxiety/low motivation, we have found that overexpression of glial-cell-line-derived neurotrophic factor (GDNF) by intra-striatally administered adeno-associated virus vector strikingly converts the passive coping style of low exploratory rats into an active one, similar to high exploratory rats. This conversion of behavioural strategy developed gradually over repeated testing, and was associated with increased catecholamine metabolism in several brain regions and changes in the regulation of serotonin neurotransmission. An increase in in vivo dopamine transporter availability in the striatum was necessary for the phenotype conversion. Associated changes in striatal gene expression included key players in monoamine storage and epitranscriptomic regulation. The increase in GDNF signalling also caused alterations in levels and regional covariation of oxidative metabolism, indicative of persistent reorganization of neural activity throughout the brain. Thus, neurotrophic factors, GDNF in particular, may play a pivotal role in the development, persistence and alteration of personality traits, and therefore constitute a potential target for treatment of chronic, relapsing psychiatric disorders.

neuroscience↗

transferGWAS: GWAS of images using deep transfer learning

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSMedical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. ResultsWe validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. Contactmatthias.kirchler@hpi.de, christoph.lippert@hpi.de Supplementary informationOur method is implemented in Python and available at https://github.com/mkirchler/transferGWAS/

genetics↗