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O'Leary, A.

Publications and source records attributed to O'Leary, A..

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↗

DeepInsight: A general framework for interpreting wide-band neural activity

AO_SCPLOWBSTRACTC_SCPLOWRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

neuroscience↗