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Habelt, B.

Publications and source records attributed to Habelt, B..

2 recordsLinked to original sources

Multi-Lab Testing of Early Preclinical Discoveries Identifies Promising Treatments

A fundamental challenge in drug development is the frequent failure of early laboratory research to translate into clinical benefit. One promising solution is to confirm findings from exploratory single-laboratory studies across multiple laboratories before clinical testing. We investigated this approach following the conduct of preclinical multi-laboratory studies across different fields of medicine. For this, we evaluated effect sizes, experimental rigor, and a set of criteria to identify determinants of confirmation success. When tested under increased rigor, only a fraction of multi-laboratory studies confirmed the initial results. The underlying effect size reduction was associated with outcome-relevant experimental differences between exploratory and confirmatory stages. In summary, multi-laboratory studies proved highly informative and served as an effective filter for promising treatments.

scientific communication and education↗

A multimodal neuroprosthetic interface to record, modulate and classify electrophysiological correlates of cognitive function

Most mental disorders are characterised by impaired cognitive function and behaviour control. Their often chronic reoccurring nature and the lack of efficient therapies necessitate the development of new treatment strategies. Brain-computer interfaces, equipped with multiple sensing and stimulation abilities, offer a new toolbox, whose suitability for diagnosis and therapy of mental disorders has not yet been explored. Here, we developed a soft and multimodal neuroprosthesis to measure and modulate prefrontal neurophysiological features of neuropsychiatric symptoms. We implanted the device epidurally above the medial prefrontal cortex of rats and obtained auditory event-related brain potentials reflecting intact neural stimulus processing and alcohol-induced neural impairments. Moreover, implant-driven electrical and pharmacological stimulation enabled successful modulation of neural activity. Finally, we developed machine learning algorithms which can deal with sparsity in the data and distinguish effects with high accuracy. Our work underlines the potential of multimodal bioelectronic systems to enable a personalised and optimised therapy.

neuroscience↗