Beyond differences in means robust graphical methods to compare two groups in neuroscience, bioRxiv, 2017-03-28
AbstractIf many changes are necessary to improve the quality of neuroscience research, one relatively simple step could have great pay-offs to promote the adoption of detailed graphical methods, combined with robust inferential statistics. Here we illustrate how such methods can lead to a much more detailed understanding of group differences than bar graphs and t-tests on means. To complement the neuroscientist’s toolbox, we present two powerful tools that can help us understand how groups of observations differ the shift function and the difference asymmetry function. These tools can be combined with detailed visualisations to provide complementary perspectives about the data. We provide implementations in R and Matlab of the graphical tools, and all the examples in the article can be reproduced using R scripts.
biorxiv neuroscience 100-200-users 2017Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems, bioRxiv, 2017-03-23
AbstractAdvances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.
biorxiv neuroscience 0-100-users 2017Modern machine learning outperforms GLMs at predicting spikes, bioRxiv, 2017-02-25
AbstractNeuroscience has long focused on finding encoding models that effectively ask “what predicts neural spiking?” and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a GLM. Here we compared the predictive performance of GLMs to three leading machine learning methods feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. In general, the modern methods (particularly XGBoost and the ensemble) produced more accurate spike predictions and were less sensitive to the preprocessing of features. This discrepancy in performance suggests that standard feature sets may often relate to neural activity in a nonlinear manner not captured by GLMs. Encoding models built with machine learning techniques, which can be largely automated, more accurately predict spikes and can offer meaningful benchmarks for simpler models.
biorxiv neuroscience 100-200-users 2017Developmental diversification of cortical inhibitory interneurons, bioRxiv, 2017-02-03
ABSTRACTDiverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.
biorxiv neuroscience 100-200-users 2017A computational toolbox and step-by-step tutorial for the analysis of neuronal population dynamics in calcium imaging data, bioRxiv, 2017-01-29
The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a tutorial for the use of a comprehensive computational toolbox for the analysis of neuronal population activity imaging. It consists of tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters’ features against surrogate control datasets. They are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets, and provide a case study to explain its implementation.
biorxiv neuroscience 0-100-users 2017Untangling intelligence, psychopathy, antisocial personality disorder, & conduct problems A meta-analytic review, bioRxiv, 2017-01-18
AbstractSubstantial research has investigated the association between intelligence and psychopathic traits. The findings to date have been inconsistent and have not always considered the multi-dimensional nature of psychopathic traits. Moreover, there has been a tendency to confuse psychopathy with other closely related, clinically significant disorders. The current study represents a meta-analysis conducted to evaluate the direction and magnitude of the association of intelligence with global psychopathy, as well as its factors and facets, and related disorders (Antisocial Personality Disorder, Conduct Disorder, and Oppositional Defiant Disorder). Our analyses revealed a small, significant, negative relationship between intelligence and total psychopathy (r = -.07, p = .001). Analysis of factors and facets found differential associations, including both significant positive (e.g., interpersonal facet) and negative (e.g., affective facet) associations, further affirming that psychopathy is a multi-dimensional construct. Additionally, intelligence was negatively associated with Antisocial Personality Disorder (r = -.13, p = .001) and Conduct Disorder (r = -.11, p = .001), but positively with Oppositional Defiant Disorder (r = .06, p = .001). There was significant heterogeneity across studies for most effects, but the results of moderator analyses were inconsistent. Finally, bias analyses did not find significant evidence for publication bias or outsized effects of outliers.
biorxiv neuroscience 0-100-users 2017