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.

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.

Improving the flexibility of genetically encoded voltage indicators via intermolecular FRET.

A new family of genetically encoded voltage indicators (GEVIs) has been developed based on intermolecular Förster resonance energy transfer (FRET). To test the hypothesis that the GEVI ArcLight functions via interactions between the fluorescent protein (FP) domains of neighboring probes, the FP of ArcLight was replaced with either a FRET donor or acceptor FP. We discovered relatively large FRET signals only when cells were cotransfected with both the FRET donor and acceptor GEVIs. Using a cyan fluorescent protein donor and an RFP acceptor, we were able to observe a voltage-dependent signal with an emission peak separated by over 200 nm from the excitation wavelength. The intermolecular FRET strategy also works for rhodopsin-based probes, potentially improving their flexibility as well. Separating the FRET pair into two distinct proteins has important advantages over intramolecular FRET constructs. The signals are larger because the voltage-induced conformational change moves two FPs independently. The expression of the FRET donor and acceptor can also be restricted independently, enabling greater cell type specificity as well as refined subcellular voltage reporting.

Mechanism of ArcLight derived GEVIs involves electrostatic interactions that can affect proton wires.

The genetically encoded voltage indicators ArcLight and its derivatives mediate voltage-dependent optical signals by intermolecular, electrostatic interactions between neighboring fluorescent proteins (FPs). A random mutagenesis event placed a negative charge on the exterior of the FP, resulting in a greater than 10-fold improvement of the voltage-dependent optical signal. Repositioning this negative charge on the exterior of the FP reversed the polarity of voltage-dependent optical signals, suggesting the presence of "hot spots" capable of interacting with the negative charge on a neighboring FP, thereby changing the fluorescent output. To explore the potential effect on the chromophore state, voltage-clamp fluorometry was performed with alternating excitation at 390 nm followed by excitation at 470 nm, resulting in several mutants exhibiting voltage-dependent, ratiometric optical signals of opposing polarities. However, the kinetics, voltage ranges, and optimal FP fusion sites were different depending on the wavelength of excitation. These results suggest that the FP has external, electrostatic pathways capable of quenching fluorescence that are wavelength specific. One mutation to the FP (E222H) showed a voltage-dependent increase in fluorescence when excited at 390 nm, indicating the ability to affect the proton wire from the protonated chromophore to the H222 position. ArcLight-derived sensors may therefore offer a novel way to map how conditions external to the ß-can structure can affect the fluorescence of the chromophore and transiently affect those pathways via conformational changes mediated by manipulating membrane potential.

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.

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.

A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation.

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Due to the high dimension and small sample size, conventional approaches to decentralized differentially private algorithms suffer in terms of utility. When centralizing the data is not possible, we investigate the benefit of enabling limited collaboration in the form of generating jointly distributed random noise. We show that such (anti) correlated noise improves the privacy-utility trade-off, and can reach the same level of utility as the corresponding non-private algorithm for certain parameter choices. We validate this benefit using synthetic and real neuroimaging datasets. We conclude that it is possible to achieve meaningful utility while preserving privacy, even in complex signal processing systems.

Biophysical Parameters of GEVIs: Considerations for Imaging Voltage.

Genetically encoded voltage indicators (GEVIs) continue to evolve, resulting in many different probes with varying strengths and weaknesses. Developers of new GEVIs tend to highlight their positive features. A recent article from an independent laboratory has compared the signal/noise ratios of a number of GEVIs. Such a comparison can be helpful to investigators eager to try to image the voltage of excitable cells. In this perspective, we will present examples of how the biophysical features of GEVIs affect the imaging of excitable cells in an effort to assist researchers when considering probes for their specific needs.

Decentralized dynamic functional network connectivity: State analysis in collaborative settings.

As neuroimaging data increase in complexity and related analytical problems follow suite, more researchers are drawn to collaborative frameworks that leverage data sets from multiple data-collection sites to balance out the complexity with an increased sample size. Although centralized data-collection approaches have dominated the collaborative scene, a number of decentralized approaches-those that avoid gathering data at a shared central store-have grown in popularity. We expect the prevalence of decentralized approaches to continue as privacy risks and communication overhead become increasingly important for researchers. In this article, we develop, implement and evaluate a decentralized version of one such widely used tool: dynamic functional network connectivity. Our resulting algorithm, decentralized dynamic functional network connectivity (ddFNC), synthesizes a new, decentralized group independent component analysis algorithm (dgICA) with algorithms for decentralized k-means clustering. We compare both individual decentralized components and the full resulting decentralized analysis pipeline against centralized counterparts on the same data, and show that both provide comparable performance. Additionally, we perform several experiments which evaluate the communication overhead and convergence behavior of various decentralization strategies and decentralized clustering algorithms. Our analysis indicates that ddFNC is a fine candidate for facilitating decentralized collaboration between neuroimaging researchers, and stands ready for the inclusion of privacy-enabling modifications, such as differential privacy.

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings.

The field of neuroimaging has recently witnessed a strong shift towards data sharing; however, current collaborative research projects may be unable to leverage institutional architectures that collect and store data in local, centralized data centers. Additionally, though research groups are willing to grant access for collaborations, they often wish to maintain control of their data locally. These concerns may stem from research culture as well as privacy and accountability concerns. In order to leverage the potential of these aggregated larger data sets, we require tools that perform joint analyses without transmitting the data. Ideally, these tools would have similar performance and ease of use as their current centralized counterparts. In this paper, we propose and evaluate a new Algorithm, decentralized joint independent component analysis (djICA), which meets these technical requirements. djICA shares only intermediate statistics about the data, plausibly retaining privacy of the raw information to local sites, thus making it amenable to further privacy protections, for example via differential privacy. We validate our method on real functional magnetic resonance imaging (fMRI) data and show that it enables collaborative large-scale temporal ICA of fMRI, a rich vein of analysis as of yet largely unexplored, and which can benefit from the larger-N studies enabled by a decentralized approach. We show that djICA is robust to different distributions of data over sites, and that the temporal components estimated with djICA show activations similar to the temporal functional modes analyzed in previous work, thus solidifying djICA as a new, decentralized method oriented toward the frontiers of temporal independent component analysis.

Mapping of excitatory and inhibitory postsynaptic potentials of neuronal populations in hippocampal slices using the GEVI, ArcLight.

To understand the circuitry of the brain, it is essential to clarify the functional connectivity among distinct neuronal populations. For this purpose, neuronal activity imaging using genetically-encoded calcium sensors such as GCaMP has been a powerful approach due to its cell-type specificity. However, calcium (Ca2+) is an indirect measure of neuronal activity. A more direct approach would be to use genetically encoded voltage indicators (GEVIs) to observe subthreshold, synaptic activities. The GEVI, ArcLight, which exhibits large fluorescence transients in response to voltage, was expressed in excitatory neurons of the mouse CA1 hippocampus. Fluorescent signals in response to the electrical stimulation of the Schaffer collateral axons were observed in brain slice preparations. ArcLight was able to map both excitatory and inhibitory inputs projected to excitatory neurons. In contrast, the Ca2+ signal detected by GCaMP6f, was only associated with excitatory inputs. ArcLight and similar voltage sensing probes are also becoming powerful paradigms for functional connectivity mapping of brain circuitry.

Voltage and Calcium Imaging of Brain Activity.

Sensors for imaging brain activity have been under development for almost 50 years. The development of some of these tools is relatively mature, whereas qualitative improvements of others are needed and are actively pursued. In particular, genetically encoded voltage indicators are just now starting to be used to answer neurobiological questions and, at the same time, more than 10 laboratories are working to improve them. In this Biophysical Perspective, we attempt to discuss the present state of the art and indicate areas of active development.

Body weight and body composition changes during military training and deployment involving the use of combat rations: a systematic literature review.

Dismounted military personnel operate in physically and psychologically demanding environments, with energy intake from combat rations often falling short of their requirements, leading to reductions in body weight and changes in body composition, which can impact both their health and performance. This review systematically investigated the effects of the continual use of combat rations for periods of 3-40 d on body weight and/or body composition in military personnel engaged in training or deployment. In all, ten databases were searched from their inception until October 2016. Outcome data were described narratively, with studies assessed for quality and risk of bias. A total of thirty studies undertaken over 3-34 d were included. Studies were rated positive, neutral or negative in quality according to the Academy of Nutrition and Dietetics Quality Checklist, with many at risk of bias. Reductions in mean body weight varied, from a negligible decrease of 0·1 % during 8 d of combat training to a substantial decrease of approximately 8·3 % during 12 d of energy restriction during a US Army Ranger course. Decreases in fat mass, fat-free mass and percentage body fat were also reported. There is thus evidence that the continual use of combat rations for periods of 3-34 d results in reductions in body weight and body composition changes which, in some scenarios, may impact on the performance of troops. Body weight and composition should be routinely monitored before and after field activities, and at more regular intervals depending on the length, intensity and type of activity being undertaken.

Area-based fuzzy membership forest cover comparison between MODIS NPP and Forest Inventory and Analysis (FIA) across eastern U.S. forest.

This research assessed the accuracy of the moderate resolution imaging spectroradiometer's (MODIS) land cover classification of softwood and hardwood using a fuzzy-based approach for 31 easternmost states in the U.S. Our main objective was to quantitatively evaluate spatially explicit land cover classifications of MODIS net primary product (NPP) scheme using the USDA Forest Service's (FS) field-based, tree-specific Forest Inventory Analysis (FIA). We used a grid of 648 km2 hexagons as base mapping units and interpreted our results at the USDA FS level IV ecological regions. Forest area was calculated for both MODIS and FIA and were found to be strongly correlated (Pearson's r = 0.875, p < 0.01), which suggests the two classifications are comparable. Area-based fuzzy memberships of softwood and hardwood forest were determined for both MODIS and FIA for each hexagon. We used cross-entropy (H c) to evaluate the accuracy of the MODIS classification. Our results determined that the accuracy of MODIS forest cover classification was not uniform for all ecological regions. Tree species importance values (IV) and Shannon's diversity index (H s) were calculated to examine species abundance and heterogeneity, which may partially explain discrepancies between MODIS and FIA classifications. The greatest misclassifications were due to (1) MODIS underestimating softwood forest cover and (2) MODIS confusing forest cover with other land covers such as grassland, cropland, or woody savanna. Our results provide a guideline for users to understand the degree of uncertainty of MODIS forest cover classifications in the eastern USA.

Combinatorial mutagenesis of the voltage-sensing domain enables the optical resolution of action potentials firing at 60 Hz by a genetically encoded fluorescent sensor of membrane potential.

ArcLight is a genetically encoded fluorescent voltage sensor using the voltage-sensing domain of the voltage-sensing phosphatase from Ciona intestinalis that gives a large but slow-responding optical signal in response to changes in membrane potential (Jin et al., 2012). Fluorescent voltage sensors using the voltage-sensing domain from other species give faster yet weaker optical signals (Baker et al., 2012; Han et al., 2013). Sequence alignment of voltage-sensing phosphatases from different species revealed conserved polar and charged residues at 7 aa intervals in the S1-S3 transmembrane segments of the voltage-sensing domain, suggesting potential coil-coil interactions. The contribution of these residues to the voltage-induced optical signal was tested using a cassette mutagenesis screen by flanking each transmembrane segment with unique restriction sites to allow for the testing of individual mutations in each transmembrane segment, as well as combinations in all four transmembrane segments. Addition of a counter charge in S2 improved the kinetics of the optical response. A double mutation in the S4 domain dramatically reduced the slow component of the optical signal seen in ArcLight. Combining that double S4 mutant with the mutation in the S2 domain yielded a probe with kinetics <10 ms. Optimization of the linker sequence between S4 and the fluorescent protein resulted in a new ArcLight-derived probe, Bongwoori, capable of resolving action potentials in a hippocampal neuron firing at 60 Hz. Additional manipulation of the voltage-sensing domain could potentially lead to fluorescent sensors capable of optically resolving neuronal inhibition and subthreshold synaptic activity.

Imaging voltage in zebrafish as a route to characterizing a vertebrate functional connectome: promises and pitfalls of genetically encoded indicators.

Neural circuits are non-linear dynamical systems that transform information based on the pattern of input, current state and functional connectivity. To understand how a given stimulus is processed, one would ideally record neural activity across the entire brain of a behaving animal, at cellular or even subcellular resolution, in addition to characterizing anatomical connectivity. Given their transparency and relatively small size, larval zebrafish provide a powerful system for brain-wide monitoring of neural activity. Genetically encoded calcium indicators have been used for this purpose, but cannot directly report hyperpolarization or sub-threshold activity. Voltage indicators, in contrast, have this capability. Here, we test whether two different genetically encoded voltage reporters, ASAP1 and Bongwoori, can be expressed and report activity in the zebrafish brain, using widefield, two-photon and light sheet microscopy. We were unable to express ASAP1 in neurons. Bongwoori, in contrast expressed well, and because of its membrane localization, allowed visualization of axon trajectories in 3D. Bongwoori displayed stimulus-evoked changes in fluorescence, which could be detected in single trials. However, under high laser illumination, puncta on neural membranes underwent spontaneous fluctuations in intensity, suggesting that the probe is susceptible to blinking artefacts. These data indicate that larval zebrafish can be used to image electrical activity in the brain of an intact vertebrate at high resolution, although care is needed in imaging and analysis. Recording activity across the whole brain will benefit from further developments in imaging hardware and indicators.

Genetically Encoded Protein Sensors of Membrane Potential.

Organic voltage-sensitive dyes offer very high spatial and temporal resolution for imaging neuronal function. However these dyes suffer from the drawbacks of non-specificity of cell staining and low accessibility of the dye to some cell types. Further progress in imaging activity is expected from the development of genetically encoded fluorescent sensors of membrane potential. Cell type specificity of expression of these fluorescent protein (FP) voltage sensors can be obtained via several different mechanisms. One is cell type specificity of infection by individual virus subtypes. A second mechanism is specificity of promoter expression in individual cell types. A third, depends on the offspring of transgenic animals with cell type specific expression of cre recombinase mated with an animal that has the DNA for the FP voltage sensor in all of its cells but its expression is dependent on the recombinase activity. Challenges remain. First, the response time constants of many of the new FP voltage sensors are slower (2-10 ms) than those of organic dyes. This results in a relatively small fractional fluorescence change, ΔF/F, for action potentials. Second, the largest signal presently available is only ~40% for a 100 mV depolarization and many of the new probes have signals that are substantially smaller. Large signals are especially important when attempting to detect fast events because the shorter measurement interval results in a relatively small number of detected photons and therefore a relatively large shot noise (see Chap. 1). Another kind of challenge has occurred when attempts were made to transition from one species to another or from one cell type to another or from cell culture to in vivo measurements.Several laboratories have recently described a number of novel FP voltage sensors. Here we attempt to critically review the current status of these developments in terms of signal size, time course, and in vivo function.

The Physiological Consequences of and Recovery following the Australian Special Forces Selection Course.

This study aimed to determine the energy requirements, physiological consequences, and recovery rate from the Australian Special Forces Selection Course. Ninety-three male soldiers (mean ± SD, 28.1 ± 3.6 y, 1.81 ± 0.1 m, 85.1 ± 8.1 kg) volunteered for this study. Body composition via dual-energy x-ray absorptiometry, hormones and resting metabolic rate were assessed before, immediately after, and at one, three, five, and eight weeks post-course. Energy expenditure, assessed via doubly-labelled water during the first 10 days of the course significantly exceeded energy intake (expenditure: 7680 ± 1095 kcal.d-1, intake: 3859 ± 704 kcal.d-1). Body mass (△ -6.8 ± 1.9 kg, p<0.01), fat mass (△ -4.2 ± 1.0 kg, p<0.0001) and lean mass (△ -3.0 ± 1.7 kg, p<0.0001) were significantly reduced in response to the course and returned to baseline 1-3 weeks post-course. Total testosterone, free testosterone, free triiodothyronine, free thyroxine and insulin like growth factor-1 significantly (p<0.001) declined following the course, while cortisol and sex hormone binding globulin increased (p<0.001). All hormones, except insulin like growth factor-1, returned to baseline concentrations within 1-3 weeks post-course. Resting metabolic rate decreased (p<0.01) in response to the course, and subsequently rebounded above baseline levels at one week post-course. The Special Forces Selection Course involved high energy output and a substantial caloric deficit, resulting in body mass loss and significant hormonal disruption that took weeks to recover. These results highlight the energy requirements, physiological consequences, and recovery processes from the Australian Special Forces Selection Course.

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.