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Abstract Phenotypic variation among species is a product of evolutionary changes to developmental programs^1,2. However, how these changes generate novel morphological traits remains largely unclear. Here we studied the genomic and developmental basis of the mammalian gliding membrane, or patagium—an adaptative trait that has repeatedly evolved in different lineages, including in closely related marsupial species. Through comparative genomic analysis of 15 marsupial genomes, both from gliding and non-gliding species, we find that theEmx2locus experienced lineage-specific patterns of acceleratedcis-regulatory evolution in gliding species. By combining epigenomics, transcriptomics and in-pouch marsupial transgenics, we show thatEmx2is a critical upstream regulator of patagium development. Moreover, we identify differentcis-regulatory elements that may be responsible for driving increasedEmx2expression levels in gliding species. Lastly, using mouse functional experiments, we find evidence thatEmx2expression patterns in gliders may have been modified from a pre-existing program found in all mammals. Together, our results suggest that patagia repeatedly originated through a process of convergent genomic evolution, whereby regulation ofEmx2was altered by distinctcis-regulatory elements in independently evolved species. Thus, different regulatory elements targeting the same key developmental gene may constitute an effective strategy by which natural selection has harnessed regulatory evolution in marsupial genomes to generate phenotypic novelty. 

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere. 

Working memory (WM) is critical to many aspects of cognition, but it frequently fails. Much WM research has focused on capacity limits, but even for single, simple features, the fidelity of individual representations is limited. Why is this? One possibility is that, because of neural noise and interference, neural representations do not remain stable across a WM delay, nor do they simply decay, but instead, they may “drift” over time to a new, less accurate state. We tested this hypothesis in a functional magnetic resonance imaging study of a match/nonmatch WM recognition task for a single item with a single critical feature: orientation. We developed a novel pattern-based index of “representational drift” to characterize ongoing changes in brain activity patterns throughout the WM maintenance period, and we were successfully able to predict performance on the match/nonmatch recognition task using this representational drift index. Specifically, in trials where the target and probe stimuli matched, participants incorrectly reported more nonmatches when their activity patterns drifted away from the target. In trials where the target and probe did not match, participants incorrectly reported more matches when their activity patterns drifted toward the probe. On the basis of these results, we contend that neural noise does not cause WM errors merely by degrading representations and increasing random guessing; instead, one means by which noise introduces errors is by pushing WM representations away from the target and toward other meaningful (yet incorrect) configurations. Thus, we demonstrate that behaviorally meaningful drift within representation space can be indexed by neuroimaging.