Hydrophobic residues in S1 modulate enzymatic function and voltage sensing in voltage-sensing phosphatase.

The voltage-sensing domain (VSD) is a four-helix modular protein domain that converts electrical signals into conformational changes, leading to open pores and active enzymes. In most voltage-sensing proteins, the VSDs do not interact with one another, and the S1-S3 helices are considered mainly scaffolding, except in the voltage-sensing phosphatase (VSP) and the proton channel (Hv). To investigate its contribution to VSP function, we mutated four hydrophobic amino acids in S1 to alanine (F127, I131, I134, and L137), individually or in combination. Most of these mutations shifted the voltage dependence of activity to higher voltages; however, not all substrate reactions were the same. The kinetics of enzymatic activity were also altered, with some mutations significantly slowing down dephosphorylation. The voltage dependence of VSD motions was consistently shifted to lower voltages and indicated a second voltage-dependent motion. Additionally, none of the mutations broke the VSP dimer, indicating that the S1 impact could stem from intra- and/or intersubunit interactions. Lastly, when the same mutations were introduced into a genetically encoded voltage indicator, they dramatically altered the optical readings, making some of the kinetics faster and shifting the voltage dependence. These results indicate that the S1 helix in VSP plays a critical role in tuning the enzyme's conformational response to membrane potential transients and influencing the function of the VSD.

S1 hydrophobic residues modulate voltage sensing phosphatase enzymatic function and voltage sensing.

The voltage sensing domain (VSD) is a four-helix modular protein domain that converts electrical signals into conformational changes, leading to open pores and active enzymes. In most voltage sensing proteins, the VSDs do not interact with one another and the S1-S3 helices are considered mainly as scaffolding. The two exceptions are the voltage sensing phosphatase (VSP) and the proton channel (Hv). VSP is a voltage-regulated enzyme and Hvs are channels that only have VSDs. To investigate the S1 contribution to VSP function, we individually mutated four hydrophobic amino acids in S1 to alanine (F127, I131, I134 and L137). We also combined these mutations to generate quadruple mutation designated S1-Q. Most of these mutations shifted the voltage dependence of activity to higher voltages though interestingly, not all substrate reactions were the same. The kinetics of enzymatic activity were also altered with some mutations significantly slowing down dephosphorylation. The voltage dependence of VSD motions were consistently shifted to lower voltages and indicated a second voltage dependent motion. Co-immunoprecipitation demonstrated that none of the mutations broke the VSP dimer indicating that the S1 impact could stem from intrasubunit and/or intersubunit interactions. Lastly, when the same alanine mutations were introduced into a genetically encoded voltage indicator, they dramatically altered the optical readings, making some of the kinetics faster and shifting the voltage dependence. These results indicate that the S1 helix in VSP plays a critical role in tuning the enzymes conformational response to membrane potential transients and influencing the function of the VSD.

We don't know what you did last summer. On the importance of transparent reporting of reaction time data pre-processing.

In behavioral, cognitive, and social sciences, reaction time measures are an important source of information. However, analyses on reaction time data are affected by researchers' analytical choices and the order in which these choices are applied. The results of a systematic literature review, presented in this paper, revealed that the justification for and order in which analytical choices are conducted are rarely reported, leading to difficulty in reproducing results and interpreting mixed findings. To address this methodological shortcoming, we created a checklist on reporting reaction time pre-processing to make these decisions more explicit, improve transparency, and thus, promote best practices within the field. The importance of the pre-processing checklist was additionally supported by an expert consensus survey and a multiverse analysis. Consequently, we appeal for maximal transparency on all methods applied and offer a checklist to improve replicability and reproducibility of studies that use reaction time measures.

Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data.

Large amounts of neuroimaging and omics data have been generated for studies of mental health. Collaborations among research groups that share data have shown increased power for new discoveries of brain abnormalities, genetic mutations, and associations among genetics, neuroimaging and behavior. However, sharing raw data can be challenging for various reasons. A federated data analysis allowing for collaboration without exposing the raw dataset of each site becomes ideal. Following this strategy, a decentralized parallel independent component analysis (dpICA) is proposed in this study which is an extension of the state-of-art Parallel ICA (pICA). pICA is an effective method to analyze two data modalities simultaneously by jointly extracting independent components of each modality and maximizing connections between modalities. We evaluated the dpICA algorithm using neuroimage and genetic data from patients with schizophrenia and health controls, and compared its performances under various conditions with the centralized pICA. The results showed dpICA is robust to sample distribution across sites as long as numbers of samples in each site are sufficient. It can produce the same imaging and genetic components and the same connections between those components as the centralized pICA. Thus our study supports dpICA is an accurate and effective decentralized algorithm to extract connections from two data modalities.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping.

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

BrainForge: an online data analysis platform for integrative neuroimaging acquisition, analysis, and sharing.

BrainForge is a cloud-enabled, web-based analysis platform for neuroimaging research. This website allows users to archive data from a study and effortlessly process data on a high-performance computing cluster. After analyses are completed, results can be quickly shared with colleagues. BrainForge solves multiple problems for researchers who want to analyze neuroimaging data, including issues related to software, reproducibility, computational resources, and data sharing. BrainForge can currently process structural, functional, diffusion, and arterial spin labeling MRI modalities, including preprocessing and group level analyses. Additional pipelines are currently being added, and the pipelines can accept the BIDS format. Analyses are conducted completely inside of Singularity containers and utilize popular software packages including Nipype, Statistical Parametric Mapping, the Group ICA of fMRI Toolbox, and FreeSurfer. BrainForge also features several interfaces for group analysis, including a fully automated adaptive ICA approach.

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC (The Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation) is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration, and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.

Teaching open and reproducible scholarship: a critical review of the evidence base for current pedagogical methods and their outcomes.

In recent years, the scientific community has called for improvements in the credibility, robustness and reproducibility of research, characterized by increased interest and promotion of open and transparent research practices. While progress has been positive, there is a lack of consideration about how this approach can be embedded into undergraduate and postgraduate research training. Specifically, a critical overview of the literature which investigates how integrating open and reproducible science may influence student outcomes is needed. In this paper, we provide the first critical review of literature surrounding the integration of open and reproducible scholarship into teaching and learning and its associated outcomes in students. Our review highlighted how embedding open and reproducible scholarship appears to be associated with (i) students' scientific literacies (i.e. students' understanding of open research, consumption of science and the development of transferable skills); (ii) student engagement (i.e. motivation and engagement with learning, collaboration and engagement in open research) and (iii) students' attitudes towards science (i.e. trust in science and confidence in research findings). However, our review also identified a need for more robust and rigorous methods within pedagogical research, including more interventional and experimental evaluations of teaching practice. We discuss implications for teaching and learning scholarship.

Multi-study evaluation of neuroimaging-based prediction of medication class in mood disorders.

Clinicians often face a dilemma in diagnosing bipolar disorder patients with complex symptoms who spend more time in a depressive state than a manic state. The current gold standard for such diagnosis, the Diagnostic and Statistical Manual (DSM), is not objectively grounded in pathophysiology. In such complex cases, relying solely on the DSM may result in misdiagnosis as major depressive disorder (MDD). A biologically-based classification algorithm that can accurately predict treatment response may help patients suffering from mood disorders. Here we used an algorithm to do so using neuroimaging data. We used the neuromark framework to learn a kernel function for support vector machine (SVM) on multiple feature subspaces. The neuromark framework achieves up to 95.45% accuracy, 0.90 sensitivity, and 0.92 specificity in predicting antidepressant (AD) vs. mood stabilizer (MS) response in patients. We incorporated two additional datasets to evaluate the generalizability of our approach. The trained algorithm achieved up to 89% accuracy, 0.88 sensitivity, and 0.89 specificity in predicting the DSM-based diagnosis on these datasets. We also translated the model to distinguish responders to treatment from nonresponders with up to 70% accuracy. This approach reveals multiple salient biomarkers of medication-class of response within mood disorders.

Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility.

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTAC's federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.

What's in a Badge? A Computational Reproducibility Investigation of the Open Data Badge Policy in One Issue of Psychological Science.

In April 2019, Psychological Science published its first issue in which all Research Articles received the Open Data badge. We used that issue to investigate the effectiveness of this badge, focusing on the adherence to its aim at Psychological Science: sharing both data and code to ensure reproducibility of results. Twelve researchers of varying experience levels attempted to reproduce the results of the empirical articles in the target issue (at least three researchers per article). We found that all 14 articles provided at least some data and six provided analysis code, but only one article was rated to be exactly reproducible, and three were rated as essentially reproducible with minor deviations. We suggest that researchers should be encouraged to adhere to the higher standard in force at Psychological Science. Moreover, a check of reproducibility during peer review may be preferable to the disclosure method of awarding badges.

A trafficking motif alters GEVI activity implicating persistent protein interactions at the membrane.

Efficient plasma-membrane expression is critical for genetically encoded voltage indicators (GEVIs). To improve the plasma-membrane expression, we introduced multiple combinations of plasma-membrane trafficking motifs at different positions to members of the Bongwoori family of GEVIs. An improvement from 20% to 27% in the ΔF/F/100 mV depolarization of the plasma membrane was observed when a Golgi transport motif was inserted near the N-terminus in conjunction with an endoplasmic reticulum release motif near the C-terminus of the protein. Unfortunately, this variant was also slower. The weighted tau on of the variant (25 ms) was more than double the original construct (11 ms). The weighted tau off was >20 ms compared with 10 ms for the original GEVI. The voltage range of the GEVI was also shifted to more negative potentials. Insertion of spacer amino acids between the fluorescent-protein domain and the endoplasmic reticulum release motif at the C-terminus rescued the speed of both the tau on and tau off while restoring the voltage range and maintaining the improved voltage-dependent optical signal. These results suggest that while trafficking motifs do improve plasma-membrane expression, they may also mediate persistent associations that affect the functioning of the protein.

Conserved Amino Acids Residing Outside the Voltage Field Can Shift the Voltage Sensitivity and Increase the Signal Speed and Size of Ciona Based GEVIs.

To identify potential regions of the voltage-sensing domain that could shift the voltage sensitivity of Ciona intestinalis based Genetically Encoded Voltage Indicators (GEVIs), we aligned the amino acid sequences of voltage-gated sodium channels from different organisms. Conserved polar residues were identified at multiple transmembrane/loop junctions in the voltage sensing domain. Similar conservation of polar amino acids was found in the voltage-sensing domain of the voltage-sensing phosphatase gene family. These conserved residues were mutated to nonpolar or oppositely charged amino acids in a GEVI that utilizes the voltage sensing domain of the voltage sensing phosphatase from Ciona fused to the fluorescent protein, super ecliptic pHluorin (A227D). Different mutations shifted the voltage sensitivity to more positive or more negative membrane potentials. Double mutants were then created by selecting constructs that shifted the optical signal to a more physiologically relevant voltage range. Introduction of these mutations into previously developed GEVIs resulted in Plos6-v2 which improved the dynamic range to 40% ΔF/F/100 mV, a 25% increase over the parent, ArcLight. The onset time constant of Plos6-v2 is also 50% faster than ArcLight. Thus, Plos6-v2 appears to be the GEVI of choice.

Individualized spatial network predictions using Siamese convolutional neural networks: A resting-state fMRI study of over 11,000 unaffected individuals.

Individuals can be characterized in a population according to their brain measurements and activity, given the inter-subject variability in brain anatomy, structure-function relationships, or life experience. Many neuroimaging studies have demonstrated the potential of functional network connectivity patterns estimated from resting functional magnetic resonance imaging (fMRI) to discriminate groups and predict information about individual subjects. However, the predictive signal present in the spatial heterogeneity of brain connectivity networks is yet to be extensively studied. In this study, we investigate, for the first time, the use of pairwise-relationships between resting-state independent spatial maps to characterize individuals. To do this, we develop a deep Siamese framework comprising three-dimensional convolution neural networks for contrastive learning based on individual-level spatial maps estimated via a fully automated fMRI independent component analysis approach. The proposed framework evaluates whether pairs of spatial networks (e.g., visual network and auditory network) are capable of subject identification and assesses the spatial variability in different network pairs' predictive power in an extensive whole-brain analysis. Our analysis on nearly 12,000 unaffected individuals from the UK Biobank study demonstrates that the proposed approach can discriminate subjects with an accuracy of up to 88% for a single network pair on the test set (best model, after several runs), and 82% average accuracy at the subcortical domain level, notably the highest average domain level accuracy attained. Further investigation of our network's learned features revealed a higher spatial variability in predictive accuracy among younger brains and significantly higher discriminative power among males. In sum, the relationship among spatial networks appears to be both informative and discriminative of individuals and should be studied further as putative brain-based biomarkers.

A Multimodal Multi-Shank Fluorescence Neural Probe for Cell-Type-Specific Electrophysiology in Multiple Regions across a Neural Circuit.

Cell-type-specific, activity-dependent electrophysiology can allow in-depth analysis of functional connectivity inside complex neural circuits composed of various cell types. To date, optics-based fluorescence recording devices enable monitoring cell-type-specific activities. However, the monitoring is typically limited to a single brain region, and the temporal resolution is significantly low. Herein, a multimodal multi-shank fluorescence neural probe that allows cell-type-specific electrophysiology from multiple deep-brain regions at a high spatiotemporal resolution is presented. A photodiode and an electrode-array pair are monolithically integrated on each tip of a minimal-form-factor silicon device. Both fluorescence and electrical signals are successfully measured simultaneously in GCaMP6f expressing mice, and the cell type from sorted neural spikes is identified. The probe's capability of combined electro-optical recordings for cell-type-specific electrophysiology at multiple brain regions within a neural circuit is demonstrated. The new experimental paradigm to enable the precise investigation of functional connectivity inside and across complex neural circuits composed of various cell types is expected.

Visualizing Oscillations in Brain Slices With Genetically Encoded Voltage Indicators.

Genetically encoded voltage indicators (GEVIs) expressed pan-neuronally were able to optically resolve bicuculline induced spontaneous oscillations in brain slices of the mouse motor cortex. Three GEVIs were used that differ in their timing of response to voltage transients as well as in their voltage ranges. The duration, number of cycles, and frequency of the recorded oscillations reflected the characteristics of each GEVI used. Multiple oscillations imaged in the same slice never originated at the same location, indicating the lack of a "hot spot" for induction of the voltage changes. Comparison of pan-neuronal, Ca2+/calmodulin-dependent protein kinase II α restricted, and parvalbumin restricted GEVI expression revealed distinct profiles for the excitatory and inhibitory cells in the spontaneous oscillations of the motor cortex. Resolving voltage fluctuations across space, time, and cell types with GEVIs represent a powerful approach to dissecting neuronal circuit activity.

Surface-Initiated, Catechol-Containing Polymer Films for Effective Chelation of Aluminum Ions.

We present a new route for obtaining surface-tethered polymer films containing pendant catechol functional groups via surface-initiated activators regenerated by electron-transfer atom-transfer radical polymerization (SI-ARGET ATRP) of glycidyl methacrylate (GMA) and post-polymerization modification of the resulting poly(glycidyl methacrylate) (pGMA) films with dopamine. This method enables a high degree of functionalization of pGMA films with catechol groups at a controlled level, depending on the duration of the post-polymerization modification reaction. The dopamine-pGMA films readily absorbs Al3+ and Zn2+ ions, as verified by quartz crystal microbalance with dissipation (QCM-D) under continuous flow conditions, and demonstrates a four-fold molar selectivity to Al3+ over Zn2+. The ions desorb from the films upon rinsing with pure deionized (DI) water, which regenerates the catechol sites in the dopamine-pGMA film. Subsequent exposure to metal ions after rinsing steps yields reproducible levels of loading.

Motivators of Indiscriminate and Unsafe Supplement Use among Young Australians.

BACKGROUND: There is growing concern about the self-administration of supplements, which can often be indiscriminate, counterproductive to health, and serve as a gateway to more harmful drugs and substances. Research suggests that high uptake of performance- and image-enhancing drugs (PIEDs) is correlated with body image to accentuate masculinity. This study provides insights into limiting unhealthy supplement usage. This research identifies reasons for casual unhealthy supplement use among young adult Australians through the Theory of Planned Behavior (TPB) lens, providing practitioners with insights into developing interventions to deter their use. METHOD: Semi-structured in-depth interviews were conducted with ten participants aged between 18 and 40, using a convenience sample. Leximancer analysis was used to assess word co-occurrence and map to TPB constructs. RESULTS: Leximancer identified positive attitudes, social norms, and perceived behavioral control towards supplement usage. Key themes that influenced supplement use were weight loss, body image, nutrition, training, education, challenges, need, and time. Furthermore, using TPB constructs, affective and instrumental attitudes and prevailing norms were observed when investigating what would cause an individual to use supplements in an unhealthy manner. CONCLUSION: Through understanding the motivations of indiscriminate supplement use across the Australian population, the study has uncovered several social factors that may reduce or limit the practice of unsafe supplement usage.

GEVI cell-type specific labelling and a manifold learning approach provide evidence for lateral inhibition at the population level in the mouse hippocampal CA1 area.

The CA1 area in the mammalian hippocampus is essential for spatial learning. Pyramidal cells are the hippocampus output neurons and their activities are regulated by inhibition exerted by a diversified population of interneurons. Lateral inhibition has been suggested as the mechanism enabling the reconfiguration of pyramidal cell assembly activity observed during spatial learning tasks in rodents. However, lateral inhibition in the CA1 lacks the overwhelming evidence reported in other hippocampal areas such as the CA3 and the dentate gyrus. The use of genetically encoded voltage indicators and fast optical recordings permits the construction of cell-type specific response maps of neuronal activity. Here, we labelled mouse CA1 pyramidal neurons with the genetically encoded voltage indicator ArcLight and optically recorded their response to Schaffer Collaterals stimulation in vitro. By undertaking a manifold learning approach, we report a hyperpolarization-dominated area focused in the perisomatic region of pyramidal cells receiving late excitatory synaptic input. Functional network organization metrics revealed that information transfer was higher in this area. The localized hyperpolarization disappeared when GABAA receptors were pharmacologically blocked. This is the first report where the spatiotemporal pattern of lateral inhibition is visualized in the CA1 by expressing a genetically encoded voltage indicator selectively in principal neurons. Our analysis suggests a fundamental role of lateral inhibition in CA1 information processing.