Large-scale recordings of neural activity are providing new opportunities to study network-level dynamics. However, the sheer volume of data and its dynamical complexity are critical barriers to uncovering and interpreting these dynamics. Deep learning methods are a promising approach due to their ability to uncover meaningful relationships from large, complex, and noisy datasets. When applied to high-D spiking data from motor cortex (M1) during stereotyped behaviors, they offer improvements in the ability to uncover dynamics and their relation to subjects’ behaviors on a millisecond timescale. However, applying such methods to less-structured behaviors, or in brain areas that are not well-modeled by autonomous dynamics, is far more challenging, because deep learning methods often require careful hand-tuning of complex model hyperparameters (HPs). Here we demonstrate AutoLFADS, a large-scale, automated model-tuning framework that can characterize dynamics in diverse brain areas without regard to behavior. AutoLFADS uses distributed computing to train dozens of models simultaneously while using evolutionary algorithms to tune HPs in a completely unsupervised way. This enables accurate inference of dynamics out-of-the-box on a variety of datasets, including data from M1 during stereotyped and free-paced reaching, somatosensory cortex during reaching with perturbations, and frontal cortex during cognitive timing tasks. We present a cloud software package and comprehensive tutorials that enable new users to apply the method without needing dedicated computing resources.

No abstract

No abstract

Since organisms develop and thrive in the face of constant perturbations due to environmental and genetic variation, species may evolve resilient genetic architectures. We sought evidence for this process, known as canalization, through comparison of the prevalence of phenotypes as a function of polygenic score (PGS) across environments in the UK Biobank cohort study. Contrasting seven diseases and three categorical phenotypes with respect to 151 exposures in 408,925 people, the deviation between the prevalence-risk curves was observed to increase monotonically with PGS percentile in one fifth of the comparisons, suggesting extensive PGS×Environment (PGS×E) interaction. After adjustment for the dependency of allelic effect sizes on increased prevalence in the perturbing environment, cases where polygenic influences are greater or less than expected are seen to be particularly pervasive for educational attainment, obesity, and the metabolic condition type-2 diabetes. Inflammatory bowel disease analysis shows fewer interactions but confirms that smoking and some aspects of diet influence risk. Notably, body mass index has more evidence for decanalization (increased genetic influence at the extremes of polygenic risk) whereas waist-to-hip ratio shows canalization, reflecting different evolutionary pressures on the architectures of these weight-related traits. An additional ten percent of comparisons showed evidence for an additive shift of prevalence independent of PGS between exposures. These results provide the first widespread evidence for canalization protecting against disease in humans, and have implications for personalized medicine as well as understanding the evolution of complex traits. The findings can be explored through an R shiny app at https://canalization-gibsonlab.shinyapps.io/rshiny/.

In many brain areas, neural populations act as a coordinated network whose state is tied to behavior on a moment-by-moment basis and millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe network-scale computation, as it can measure the activity of many individual neurons, monitor multiple layers simultaneously, and sample from identified cell types. However, estimating network states and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities, and limitations on temporal resolution. Here we describe RADICaL, a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically-recorded spikes. It incorporates a novel network training strategy that exploits the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers network states more accurately than previous methods, particularly for high-frequency components. In real 2p recordings from sensorimotor areas in mice performing a “water grab” task, RADICaL infers network states with close correspondence to single-trial variations in behavior, and maintains high-quality inference even when neuronal populations are substantially reduced.

Background: The complex and not yet fully understood etiology of Alzheimer’s disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms. Objective: The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer’s disease (AsymAD), and AD. Methods: Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning. Results: A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.85) or Control (AUC = 0.89) from AD (AUC = 0.96). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, REBEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, PRKAR1B, ENPP6, HAPLN2, PSMD4, and CMAS. Conclusion: Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

No abstract

No abstract