Exploring the Impact of Analysis Software on Task fMRI Results, bioRxiv, 2018-03-20

AbstractA wealth of analysis tools are available to fMRI researchers in order to extract patterns of task variation and, ultimately, understand cognitive function. However, this ‘methodological plurality’ comes with a drawback. While conceptually similar, two different analysis pipelines applied on the same dataset may not produce the same scientific results. Differences in methods, implementations across software packages, and even operating systems or software versions all contribute to this variability. Consequently, attention in the field has recently been directed to reproducibility and data sharing. Neuroimaging is currently experiencing a surge in initiatives to improve research practices and ensure that all conclusions inferred from an fMRI study are replicable.In this work, our goal is to understand how choice of software package impacts on analysis results. We use publically shared data from three published task fMRI neuroimaging studies, reanalyzing each study using the three main neuroimaging software packages, AFNI, FSL and SPM, using parametric and nonparametric inference. We obtain all information on how to process, analyze, and model each dataset from the publications. We make quantitative and qualitative comparisons between our replications to gauge the scale of variability in our results and assess the fundamental differences between each software package. While qualitatively we find broad similarities between packages, we also discover marked differences, such as Dice similarity coefficients ranging from 0.000 - 0.743 in comparisons of thresholded statistic maps between software. We discuss the challenges involved in trying to reanalyse the published studies, and highlight our own efforts to make this research reproducible.

biorxiv neuroscience 200-500-users 2018

Third-generation in situ hybridization chain reaction multiplexed, quantitative, sensitive, versatile, robust, bioRxiv, 2018-03-20

ABSTRACTIn situ hybridization based on the mechanism of hybridization chain reaction (HCR) has addressed multi-decade challenges to imaging mRNA expression in diverse organisms, offering a unique combination of multiplexing, quantitation, sensitivity, resolution, and versatility. Here, with third-generation in situ HCR, we augment these capabilities using probes and amplifiers that combine to provide automatic background suppression throughout the protocol, ensuring that even if reagents bind non-specifically within the sample they will not generate amplified background. Automatic background suppression dramatically enhances performance and robustness, combining the benefits of higher signal-to-background with the convenience of using unoptimized probe sets for new targets and organisms. In situ HCR v3.0 enables multiplexed quantitative mRNA imaging with subcellular resolution in the anatomical context of whole-mount vertebrate embryos, multiplexed quantitative mRNA flow cytometry for high-throughput single-cell expression profiling, and multiplexed quantitative single-molecule mRNA imaging in thick autofluorescent samples.SUMMARYIn situ hybridization chain reaction (HCR) v3.0 exploits automatic background suppression to enable multiplexed quantitative mRNA imaging and flow cytometry with dramatically enhanced ease-of-use and performance.

biorxiv developmental-biology 0-100-users 2018

All-optical electrophysiology reveals brain-state dependent changes in hippocampal subthreshold dynamics and excitability, bioRxiv, 2018-03-14

AbstractA technology to record membrane potential from multiple neurons, simultaneously, in behaving animals will have a transformative impact on neuroscience research1. Parallel recordings could reveal the subthreshold potentials and intercellular correlations that underlie network behavior2. Paired stimulation and recording can further reveal the input-output properties of individual cells or networks in the context of different brain states3. Genetically encoded voltage indicators are a promising tool for these purposes, but were so far limited to single-cell recordings with marginal signal to noise ratio (SNR) in vivo4-6. We developed improved near infrared voltage indicators, high speed microscopes and targeted gene expression schemes which enabled recordings of supra- and subthreshold voltage dynamics from multiple neurons simultaneously in mouse hippocampus, in vivo. The reporters revealed sub-cellular details of back-propagating action potentials, correlations in sub-threshold voltage between multiple cells, and changes in dynamics associated with transitions from resting to locomotion. In combination with optogenetic stimulation, the reporters revealed brain state-dependent changes in neuronal excitability, reflecting the interplay of excitatory and inhibitory synaptic inputs. These tools open the possibility for detailed explorations of network dynamics in the context of behavior.

biorxiv neuroscience 100-200-users 2018

 

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