Data leakage inflates prediction performance in connectome-based machine learning models

Abstract Predictive modeling is a central technique in neuroimaging to identify brain-behavior relationships and test their generalizability to unseen data. However, data leakage undermines the validity of predictive models by breaching the separation between training and test data. Leakage is always an incorrect practice but still pervasive in machine learning. Understanding its effects on neuroimaging predictive models can inform how leakage affects existing literature. Here, we investigate the effects of five forms of leakage–involving feature selection, covariate correction, and dependence between subjects–on functional and structural connectome-based machine learning models across four datasets and three phenotypes. Leakage via feature selection and repeated subjects drastically inflates prediction performance, whereas other forms of leakage have minor effects. Furthermore, small datasets exacerbate the effects of leakage. Overall, our results illustrate the variable effects of leakage and underscore the importance of avoiding data leakage to improve the validity and reproducibility of predictive modeling

Foxa2 and Pet1 Direct and Indirect Synergy Drive Serotonergic Neuronal Differentiation

18 páginas, 7 figurasNeuronal programming by forced expression of transcription factors (TFs) holds promise for clinical applications of regenerative medicine. However, the mechanisms by which TFs coordinate their activities on the genome and control distinct neuronal fates remain obscure. Using direct neuronal programming of embryonic stem cells, we dissected the contribution of a series of TFs to specific neuronal regulatory programs. We deconstructed the Ascl1-Lmx1b-Foxa2-Pet1 TF combination that has been shown to generate serotonergic neurons and found that stepwise addition of TFs to Ascl1 canalizes the neuronal fate into a diffuse monoaminergic fate. The addition of pioneer factor Foxa2 represses Phox2b to induce serotonergic fate, similar to in vivo regulatory networks. Foxa2 and Pet1 appear to act synergistically to upregulate serotonergic fate. Foxa2 and Pet1 co-bind to a small fraction of genomic regions but mostly bind to different regulatory sites. In contrast to the combinatorial binding activities of other programming TFs, Pet1 does not strictly follow the Foxa2 pioneer. These findings highlight the challenges in formulating generalizable rules for describing the behavior of TF combinations that program distinct neuronal subtypes.The research in the Mahony lab was supported by the NIH grant R35GM144135. The research in the Flames lab was supported by ERC-StG- 2011-281920; ERC-Co- 2020-101002203; and PID2020-115635RB-I00. The research in the Mazzoni lab was supported by the NIH/NIA grant 5R21AG067174.Peer reviewe

Unravelling the Neural Basis of Spatial Delusions After Stroke

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Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress