Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.15.267310v1?rss=1 Authors: Ji, J. L., Helmer, M., Fonteneau, C., Burt, J. B., Tamayo, Z., Demsar, J., Adkinson, B., Savic, A., Preller, K., Moujaes, F., Vollenweider, F. X., Martin, W., Repovs, G., Murray, J. D., Anticevic, A. Abstract: Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlights a need to develop a neurobiologically-grounded, quantitatively stable mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 cross-diagnostic PSD patients, we derived and replicated a data-driven dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits, which was predictive at the single patient level. In turn, these data-reduced symptom axes mapped onto distinct and replicable univariate brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) did not show stable results. Instead, we show that a univariate brain-behavioral space (BBS) mapping can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and gene transcriptomic maps. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable quantitative path that can be iteratively optimized for personalized clinical biomarker endpoints. Copy rights belong to original authors. Visit the link for more info