General sexual desire, but not desire for uncommitted sexual relationships, tracks changes in women’s hormonal status, bioRxiv, 2017-06-27
AbstractSeveral recent longitudinal studies have investigated the hormonal correlates of both young adult women’s general sexual desire and, more specifically, their desire for uncommitted sexual relationships. Findings across these studies have been mixed, potentially because each study tested only small samples of women (Ns = 43, 33, and 14). Here we report results from a much larger (N = 375) longitudinal study of hormonal correlates of young adult women’s general sexual desire and their desire for uncommitted sexual relationships. Our analyses suggest that within-woman changes in general sexual desire are negatively related to progesterone, but are not related to testosterone or cortisol. We observed some positive relationships for estradiol, but these were generally only significant for solitary sexual desire. By contrast with our results for general sexual desire, analyses showed no evidence that changes in women’s desire for uncommitted sexual relationships are related to their hormonal status. Together, these results suggest that changes in hormonal status contribute to changes in women’s general sexual desire, but do not influence women’s desire for uncommitted sexual relationships.
biorxiv animal-behavior-and-cognition 100-200-users 2017Evaluating Metagenome Assembly on a Simple Defined Community with Many Strain Variants, bioRxiv, 2017-06-26
AbstractWe evaluate the performance of three metagenome assemblers, IDBA, MetaSPAdes, and MEGAHIT, on short-read sequencing of a defined “mock” community containing 64 genomes (Shakya et al. (2013)). We update the reference metagenome for this mock community and detect several additional genomes in the read data set. We show that strain confusion results in significant loss in assembly of reference genomes that are otherwise completely present in the read data set. In agreement with previous studies, we find that MEGAHIT performs best computationally; we also show that MEGAHIT tends to recover larger portions of the strain variants than the other assemblers.
biorxiv bioinformatics 100-200-users 2017Inferring single-trial neural population dynamics using sequential auto-encoders, bioRxiv, 2017-06-21
Neuroscience is experiencing a data revolution in which simultaneous recording of many hundreds or thousands of neurons is revealing structure in population activity that is not apparent from single-neuron responses. This structure is typically extracted from trial-averaged data. Single-trial analyses are challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. Here we introduce Latent Factor Analysis via Dynamical Systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. LFADS uses a nonlinear dynamical system (a recurrent neural network) to infer the dynamics underlying observed population activity and to extract ‘de-noised’ single-trial firing rates from neural spiking data. We apply LFADS to a variety of monkey and human motor cortical datasets, demonstrating its ability to predict observed behavioral variables with unprecedented accuracy, extract precise estimates of neural dynamics on single trials, infer perturbations to those dynamics that correlate with behavioral choices, and combine data from non-overlapping recording sessions (spanning months) to improve inference of underlying dynamics. In summary, LFADS leverages all observations of a neural population’s activity to accurately model its dynamics on single trials, opening the door to a detailed understanding of the role of dynamics in performing computation and ultimately driving behavior.
biorxiv neuroscience 100-200-users 2017Dark Control Towards a Unified Account of Default Mode Function by Markov Decision Processes, bioRxiv, 2017-06-15
AbstractThe default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its highest energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. Specifically, we answer the question whether the neurobiological properties of the DMN collectively provide the computational building blocks necessary for a Markov Decision Process. We argue that our formal account of DMN function naturally accommodates as special cases previous interpretations based on (1) predictive coding, (2) semantic associations, and (3) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.
biorxiv neuroscience 100-200-users 2017An open resource for transdiagnostic research in pediatric mental health and learning disorders, bioRxiv, 2017-06-14
ABSTRACTTechnological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics, and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n = 664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).
biorxiv neuroscience 100-200-users 2017Epigenetic maintenance of DNA methylation after evolutionary loss of the de novo methyltransferase, bioRxiv, 2017-06-14
ABSTRACTAfter the initial establishment of symmetric cytosine methylation patterns by de novo DNA methyltransferases (DNMTs), maintenance DNMTs mediate epigenetic memory by propagating the initial signal. We find that CG methylation in the yeast Cryptococcus neoformans is dependent on a purely epigenetic mechanism mediated by the single DNMT encoded by the genome, Dnmt5. Purified Dnmt5 is a maintenance methyltransferase that strictly requires a hemimethylated substrate, and methylation lost by removal of Dnmt5 in vivo is not restored by its mitotic or meiotic reintroduction. Phylogenetic analysis reveals that the ancestral species had a second methyltransferase, DnmtX, whose gene was lost between 50 and 150 Mya. Expression of extant DnmtXs in C. neoformans triggers de novo methylation. These data indicate that DNA methylation has been maintained epigenetically by the Dnmt5 system since the ancient loss of the DnmtX de novo enzyme, implying remarkably long-lived epigenetic memory.Single sentence summaryEpigenetic information can be inherited over geological timescales
biorxiv molecular-biology 100-200-users 2017