A ganglionic intestinointestinal reflex activated by acute noxious challenge.

To investigate noxious stimulation-responsive neural circuits that could influence the gut, we recorded from intestinally directed (efferent) nerve filaments dissected from mesenteric nerves close to the small intestine in anesthetized rats. These exhibited baseline multiunit activity that was almost unaffected by vagotomy (VagX) and reduced only slightly by cutting the splanchnic nerves. The activity was halved by hexamethonium (Hex) treatment. When an adjacent gut segment received an intraluminal stimulus 2,4,6-trinitrobenzenesulfonate (TNBS) in 30% ethanol, mesenteric efferent nerve activity increased for more than 1 h. The increased activity was almost unaffected by bilateral vagotomy or splanchnic nerve section, indicating a lack of central nervous involvement, but it was 60% reduced by hexamethonium. Spike sorting discriminated efferent single and predominantly single-unit spike trains that responded to TNBS, were unaffected by splachnectomy but were silenced by hexamethonium. After noxious stimulation of one segment, the adjacent segment showed no evidence of suppression of gut motility or vasoconstriction. We conclude that luminal application of a noxious stimulus to the small intestine activates an entirely peripheral, intestinointestinal reflex pathway. This pathway involves enteric intestinofugal neurons that excite postganglionic sympathetic neurons via a nicotinic synapse. We suggest that the final sympathetic efferent neurons that respond to a tissue damaging stimulus are distinct from vasoconstrictor, secretomotor, and motility inhibiting neurons.NEW & NOTEWORTHY An intraluminal noxious chemical stimulus applied to one segment of small intestine increased mesenteric efferent nerve activity to an adjacent segment. This was identified as a peripheral ganglionic reflex that did not require vagal or spinal connections. Hexamethonium blocked most, but not all, ongoing and reflex mesenteric efferent activity. The prevertebral sympathetic efferent neurons that are activated likely affect inflammatory and immune functions of other gut segments.

Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture.

PURPOSE: The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. METHODS: Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution. RESULTS: SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. CONCLUSION: Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Using neural networks to autonomously assess adequacy in intraoperative cholangiograms.

BACKGROUND: Intraoperative cholangiography (IOC) is a contrast-enhanced X-ray acquired during laparoscopic cholecystectomy. IOC images the biliary tree whereby filling defects, anatomical anomalies and duct injuries can be identified. In Australia, IOC are performed in over 81% of cholecystectomies compared with 20 to 30% internationally (Welfare AIoHa in Australian Atlas of Healthcare Variation, 2017). In this study, we aim to train artificial intelligence (AI) algorithms to interpret anatomy and recognise abnormalities in IOC images. This has potential utility in (a) intraoperative safety mechanisms to limit the risk of missed ductal injury or stone, (b) surgical training and coaching, and (c) auditing of cholangiogram quality. METHODOLOGY: Semantic segmentation masks were applied to a dataset of 1000 cholangiograms with 10 classes. Classes corresponded to anatomy, filling defects and the cholangiogram catheter instrument. Segmentation masks were applied by a surgical trainee and reviewed by a radiologist. Two convolutional neural networks (CNNs), DeeplabV3+ and U-Net, were trained and validated using 900 (90%) labelled frames. Testing was conducted on 100 (10%) hold-out frames. CNN generated segmentation class masks were compared with ground truth segmentation masks to evaluate performance according to a pixel-wise comparison. RESULTS: The trained CNNs recognised all classes.. U-Net and DeeplabV3+ achieved a mean F1 of 0.64 and 0.70 respectively in class segmentation, excluding the background class. The presence of individual classes was correctly recognised in over 80% of cases. Given the limited local dataset, these results provide proof of concept in the development of an accurate and clinically useful tool to aid in the interpretation and quality control of intraoperative cholangiograms. CONCLUSION: Our results demonstrate that a CNN can be trained to identify anatomical structures in IOC images. Future performance can be improved with the use of larger, more diverse training datasets. Implementation of this technology may provide cholangiogram quality control and improve intraoperative detection of ductal injuries or ductal injuries.

Hybrid adiabatic pulse with asymmetry (HAPY): An asymmetric adiabatic pulse with an application in pulsed arterial spin labeling at 7T.

PURPOSE: A new class of asymmetric adiabatic radiofrequency (RF) pulses, Hybrid Adiabatic Pulse with asYmmetry (HAPY), is designed to be used as the labeling pulse for Pulsed Arterial Spin labeling (PASL) at 7T to reduce overall specific absorption rate (SAR) while maintaining high labeling efficiency with B 0 $$ {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities. METHODS: Realistic Δ B 0 $$ \Delta {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ distributions were extracted from multiple in vivo scans. The proposed class of asymmetric pulses was parameterized and optimized considering these conditions. Simulation and phantoms experiments were performed to compare the optimized pulses with HS-3, GOIA, and trFOCI pulses. In vivo experiments were conducted to demonstrate the application of HAPY in PICORE PASL at 7T, compared with the GOIA and trFOCI pulses. RESULTS: HAPYs with different amounts of pulse energy reduction are obtained by the proposed optimization framework. Both simulation and phantom experiments demonstrate that HAPY achieves high labeling efficiency and high selectivity along the critical side despite B 0 $$ {\mathrm{B}}_0 $$ off-resonance and low B 1 + $$ {\mathrm{B}}_1^{+} $$ amplitude. In vivo experiments reveal that HAPY is able to generate robust perfusion signal with less overall SAR or shorter pulse repetition time, compared to the GOIA and trFOCI pulses. CONCLUSION: The HAPY class of pulses, obtained via systematic optimization tailored to the application of PASL at 7T, reduces power deposition without affecting labeling efficiency, which provides a prospect of further exploiting the benefits of ultra-high field in ASL.

Image Registration in Longitudinal Bone Assessment Using Computed Tomography.

PURPOSE OF REVIEW: Rigid image registration is an important image processing tool for the assessment of musculoskeletal chronic disease. In this paper, we critically review applications of rigid image registration in terms of similarity measurement methods over the past three years (2019-2022) in the context of monitoring longitudinal changes to bone microstructure and mechanical properties using computed tomography. This review identifies critical assumptions and trade-offs underlying different similarity measurement methods used in image registration and demonstrates the effect of using different similarity measures on registration outcomes. RECENT FINDINGS: Image registration has been used in recent studies for: correcting positional shifts between longitudinal scans to quantify changes to bone microstructural and mechanical properties over time, developing registration-based workflows for longitudinal assessment of bone properties in pre-clinical and clinical studies, and developing and validating registration techniques for longitudinal studies. In evaluating the recent literature, it was found that the assumptions at the root of different similarity measures used in rigid image registration are not always confirmed and reported. Each similarity measurement has its advantages and disadvantages, as well as underlying assumptions. Breaking these assumptions can lead to poor and inaccurate registration results. Thus, care must be taken with regards to the choice of similarity measurement and interpretation of results. We propose that understanding and verifying the assumptions of similarity measurements will enable more accurate and efficient quantitative assessments of structural changes over time.

Empirical Modelling Workflow for Resolution Invariant Assessment of Osteophytes.

OBJECTIVE: Traditional quantitative analysis of bone microstructure in micro-computed tomography (microCT) is dependent on animal scale and requires parametric tuning in new implementations. This study aims to develop an automated and resolution-invariant 3D image processing workflow for quantitative assessment of osteophytes. METHODS: In this workflow, cortical bone was segmented from microCT scans, and a 3D sphere-fitting transform was performed to obtain a thickness map, for which each voxel is assigned a thickness value corresponding to the size of the largest sphere containing the voxel that fits entirely within the cortical bone. From the thickness map, a 1-voxel thick outer surface was extracted to model surface roughness. The thickness values of the outer surface were empirically estimated by a series of known statistical distributions. Resulting parameters describing best-fit distributions, along with other cortical bone metrics, were analysed to determine sensitivity to osteoarthritis and the presence of osteophytes. RESULTS: The workflow was validated using microCT scans and histological gradings of rabbit and rat tibiofemoral joints. Visual inspection shows that samples with osteoarthritis and the presence of osteophytes have more surface voxels assigned small thickness values. The distribution of surface thickness values for each animal is best described by Gamma distributions, whose shape parameter is consistently sensitive to osteoarthritis and the presence of osteophytes. CONCLUSION: Combining traditional image processing with empirical distribution fitting provides an automated, objective, and resolution-invariant workflow for osteophyte assessment. SIGNIFICANCE: The proposed method is simple, yet elegant in its implementation, and can be readily used in new implementations.

Inter-scanner Aß-PET harmonization using barrel phantom spatial resolution matching.

INTRODUCTION: The standardized uptake value ratio (SUVR) is used to measure amyloid beta-positron emission tomography (Aß-PET) uptake in the brainDifferences in PET scanner technologies and image reconstruction techniques can lead to variability in PET images across scanners. This poses a challenge for Aß-PET studies conducted in multiple centers. The aim of harmonization is to achieve consistent Aß-PET measurements across different scanners. In this study, we propose an Aß-PET harmonization method of matching spatial resolution, as measured via a barrel phantom, across PET scanners. Our approach was validated using paired subject data, for which patients were imaged on multiple scanners. METHODS: In this study, three different PET scanners were evaluated: the Siemens Biograph Vision 600, Siemens Biograph molecular computed tomography (mCT), and Philips Gemini TF64. A total of five, eight, and five subjects were each scanned twice with [18F]-NAV4694 across Vision-mCT, mCT-Philips, and Vision-Philips scanner pairs. The Vision and mCT scans were reconstructed using various iterations, subsets, and post-reconstruction Gaussian smoothing, whereas only one reconstruction configuration was used for the Philips scans. The full-width at half-maximum (FWHM) of each reconstruction configuration was calculated using [18F]-filled barrel phantom scans with the Society of Nuclear Medicine and Molecular Imaging (SNMMI) phantom analysis toolkit. Regional SUVRs were calculated from 72 brain regions using the automated anatomical labelling atlas 3 (AAL3) atlas for each subject and reconstruction configuration. Statistical similarity between SUVRs was assessed using paired (within subject) t-tests for each pair of reconstructions across scanners; the higher the p-value, the greater the similarity between the SUVRs. RESULTS: Vision-mCT harmonization: Vision reconstruction with FWHM = 4.10 mm and mCT reconstruction with FWHM = 4.30 mm gave the maximal statistical similarity (maximum p-value) between regional SUVRs. Philips-mCT harmonization: The FWHM of the Philips reconstruction was 8.2 mm and the mCT reconstruction with the FWHM of 9.35 mm, which gave the maximal statistical similarity between regional SUVRs. Philips-Vision harmonization: The Vision reconstruction with an FWHM of 9.1 mm gave the maximal statistical similarity between regional SUVRs when compared with the Philips reconstruction of 8.2 mm and were selected as the harmonized for each scanner pair. CONCLUSION: Based on data obtained from three sets of participants, each scanned on a pair of PET scanners, it has been verified that using reconstruction configurations that produce matched-barrel, phantom spatial resolutions results in maximally harmonized Aß-PET quantitation between scanner pairs. This finding is encouraging for the use of PET scanners in multi-center trials or updates during longitudinal studies. Highlights: Question: Does the process of matching the barrel phantom-derived spatial resolution between scanners harmonize amyloid beta-standardized uptake value ratio (Aß-SUVR) quantitation? Pertinent findings: It has been validated that reconstruction pairs with matched barrel phantom-derived spatial resolution maximize the similarity between subjects paired Aß-PET (positron emission tomography) SUVR values recorded on two scanners. Implications for patient care: Harmonization between scanners in multi-center trials and PET camera updates in longitudinal studies can be achieved using a simple and efficient phantom measurement procedure, beneficial for the validity of Aß-PET quantitation measurements.

Impact of PET Reconstruction on Amyloid-ß Quantitation in Cross-Sectional and Longitudinal Analyses.

Amyloid-ß (Aß) accumulation in Alzheimer disease (AD) is typically measured using SUV ratio and the centiloid (CL) scale. The low spatial resolution of PET images is known to degrade quantitative metrics because of the partial-volume effect. This article examines the impact of spatial resolution, as determined by the reconstruction configuration, on the Aß PET quantitation in both cross-sectional and longitudinal data. Methods: The cross-sectional study involved 89 subjects with 20-min [18F]florbetapir scans generated on an mCT (44 Aß-negative [Aß-], 45 Aß-positive [Aß+]) using 69 reconstruction configurations, which varied in number of iteration updates, point-spread function, time-of-flight, and postreconstruction smoothing. The subjects were classified as Aß- or Aß+ visually. For each reconstruction, Aß CL was calculated using CapAIBL, and the spatial resolution was calculated as full width at half maximum (FWHM) using the barrel phantom method. The change in CLs and the effect size of the difference in CLs between Aß- and Aß+ groups with FWHM were examined. The longitudinal study involved 79 subjects (46 Aß-, 33 Aß+) with three 20-min [18F]flutemetamol scans generated on an mCT. The subjects were classified as Aß- or Aß+ using a cutoff CL of 20. All scans were reconstructed using low-, medium-, and high-resolution configurations, and Aß CLs were calculated using CapAIBL. Since linear Aß accumulation was assumed over a 10-y interval, for each reconstruction configuration, Aß accumulation rate differences (ARDs) between the second and first periods were calculated for all subjects. Zero ARD was used as a consistency metric. The number of Aß accumulators was also used to compare the sensitivity of CL across reconstruction configurations. Results: In the cross-sectional study, CLs in both the Aß- and the Aß+ groups were impacted by the FWHM of the reconstruction method. Without postreconstruction smoothing, Aß- CLs increased for a FWHM of 4.5 mm or more, whereas Aß+ CLs decreased across the FWHM range. High-resolution reconstructions provided the best statistical separation between groups. In the longitudinal study, the median ARD of low-resolution reconstructed data for the Aß- group was greater than zero whereas the ARDs of higher-resolution reconstructions were not significantly different from zero, indicating more consistent rate estimates in the higher-resolution reconstructions. Higher-resolution reconstructions identified 10 additional Aß accumulators in the Aß- group, resulting in a 22% increased group size compared with the low-resolution reconstructions. Higher-resolution reconstructions reduced the average CLs of the negative group by 12 points. Conclusion: High-resolution PET reconstructions, inherently less impacted by partial-volume effect, may improve Aß PET quantitation in both cross-sectional and longitudinal data. In the cross-sectional analysis, separation of CLs between Aß- and Aß+ cohorts increased with spatial resolution. Higher-resolution reconstructions also exhibited both improved consistency and improved sensitivity in measures of Aß accumulation. These features suggest that higher-resolution reconstructions may be advantageous in early-stage AD therapies.

Filtering induces correlation in fMRI resting state data.

Correlation-based functional MRI connectivity methods typically impose a temporal sample independence assumption on the data. However, the conventional use of temporal filtering to address the high noise content of fMRI data may introduce sample dependence. Violation of the independence assumption has ramifications for the distribution of sample correlation which, if unaccounted for, may invalidate connectivity results. To enable the use of temporal filtering for noise suppression while maintaining the integrity of connectivity results, we derive the distribution of sample correlation between filtered timeseries as a function of the filter frequency response. Corrected distributions are also derived for statistical inference tests of sample correlation between filtered timeseries, including Fisher's z-transformation and the Student's t-test. Crucially, the proposed corrections are valid for any unknown true correlation and arbitrary filter specifications. Empirical simulations demonstrate the potential for temporal filtering to artificially induce connectivity by introducing sample dependence, and verify the utility of the proposed corrections in mitigating this effect. The importance of our corrections is exemplified in a resting state fMRI connectivity analysis: seed-voxel correlation maps generated from filtered data using uncorrected test variates yield an unfeasible number of connections to the left primary motor cortex, suggesting artificially induced connectivity, while maps acquired from filtered data using corrected test variates exhibit bilateral connectivity in the primary motor cortex, in conformance with expected results as seen in the literature.

The equivalence of linear Gaussian connectivity techniques.

The theoretical basis of linear Gaussian connectivity methods for the analysis of fMRI data is examined in this article, resulting in a clarification of methodological dependencies between techniques. In particular, Granger causality connectivity procedures, which describe instantaneous and directed influence between sets of voxel timeseries, are shown to be remappings of correlation-based metrics. Furthermore, the statistical inference tests applied to pairwise Granger causality measures are theoretically shown to be equivalent to inference tests applied to correlation-based metrics. These results are demonstrated empirically using receiver operating characteristic curves derived from vector autoregressive models of various lags, sample size, and noise covariance values. The equivalence of linear Granger causality and correlation-based methods, in both metric and test statistic, renders linear Granger causality a restatement of traditional data-driven methodologies in the context of brain connectivity studies. Furthermore, the equivalence highlights the centrality of partial correlation and partial variance in linear connectivity analyses and bridges the gap between functional and effective connectivity techniques. Consequently, rather than a distinction rooted in methodological difference, the dichotomy between functional and effective connectivity methods is ultimately a function of model configuration realized in choices such as the selection of nodes, the choice to model instantaneous and/or directed influence, and the choice to employ many bivariate models or a single multivariate model. While these theoretical results may be unsurprising to the reader with advanced statistical knowledge, they highlight the importance of a clear understanding of the theoretical basis of connectivity analysis methods for human brain mapping researchers.

A microCT imaging protocol for reproducible and efficient quantitative morphometric analysis (QMA) of joint structures of the in situ mouse tibio-femoral joint.

Micro-computed tomography (microCT) offers a three-dimensional (3D), high-resolution technique for the visualisation and analysis of bone microstructure. Using contrast-enhanced microCT, this capability has been expanded in recent studies to include cartilage morphometry and whole joint measures, known together as quantitative morphometric analysis (QMA). However, one of the main challenges in quantitative analysis of joint images is sensitivity to joint pose and alignment, which may influence measures related to both joint space and joint biomechanics. Thus, this study proposes a novel microCT imaging protocol for reproducible and efficient QMA of in situ mouse tibio-femoral joint. This work consists of two parts: an in situ diffusion kinetics study for a known cationic iodinated contrast agent (CA4+) for QMA of the cartilage, and a joint positioning and image processing workflow for whole joint QMA. In the diffusion kinetics study, 8 mice were injected at both of their tibio-femoral joints with distinct CA4+ concentrations and diffusion times. The mice were scanned at different time points after injection, and evaluated using attenuation and cartilage QMA measures. Results show that cartilage segmentation and QMA could be performed for CA4+ solution at a concentration of 48 mg/ml, and that reliable measurement and quantification of cartilage were achieved after 5 min of diffusion following contrast agent injection. We established the joint positioning and image processing workflow by developing a novel positioning device to control joint pose during scanning, and a spherical harmonics-based image processing workflow to ensure consistent alignment during image processing. Both legs of seven mice were scanned 10 times, 5 prior to receiving CA4+ and 5 after, and evaluated using whole joint QMA parameters. Joint QMA evaluation of the workflow showed excellent reproducibility; intraclass correlation coefficients ranged from 0.794 to 0.930, confirming that the imaging protocol enables reproducible and efficient QMA of joint structures in preclinical models, and that contrast agent injection did not cause significant alteration to the measured parameters.

SPHARM-PDM based image preprocessing pipeline for quantitative morphometric analysis (QMA) for in situ joint assessment in rabbit and rat models.

The accessibility of quantitative measurements of joint morphometry depends on appropriate tibial alignment and volume of interest (VOI) selection of joint compartments; often a challenging and time-consuming manual task. In this work, we developed a novel automatic, efficient, and model-invariant image preprocessing pipeline that allows for highly reproducible 3D quantitative morphometric analysis (QMA) of the joint. The pipeline addresses the problem by deploying two modules: an alignment module and a subdivision module. Alignment is achieved by representing the tibia in its basic form using lower degree spherical harmonic basis functions and aligning using principal component analysis. The second module subdivides the joint into lateral and medial VOIs via a watershedding approach based on persistence homology. Multiple repeated micro-computed tomography scans of small (rat) and medium (rabbit) animal knees were processed using the pipeline to demonstrate model invariance. Existing QMA was performed to evaluate the pipeline's ability to generate reproducible measurements. Intraclass correlation coefficient and mean-normalised root-mean-squared error of more than 0.75 and lower than 9.5%, respectively, were achieved for joint centre of mass, joint contact area under virtual loading, joint space width, and joint space volume. Processing time and technical requirements were reduced compared to manual processing in previous studies.

Emergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network.

We establish a simple mechanism by which radially oriented simple cells can emerge in the primary visual cortex. In 1986, R. Linsker. proposed a means by which radially symmetric, spatial opponent cells can evolve, driven entirely by noise, from structure in the initial synaptic connectivity distribution. We provide an analytical derivation of Linsker's results, and further show that radial eigenfunctions can be expressed as a weighted sum of degenerate Cartesian eigenfunctions, and vice-versa. These results are extended to allow for radially dependent cell density, from which we show that, despite a circularly symmetric synaptic connectivity distribution, radially biased orientation selectivity emerges in the third layer when cell density in the first layer, or equivalently, synaptic radius, changes with eccentricity; i.e., distance to the center of the lamina. This provides a potential mechanism for the emergence of radial orientation in the primary visual cortex before eye opening and the onset of structured visual input after birth.

Mapping the association between tau-PET and Aß-amyloid-PET using deep learning.

In Alzheimer's disease, the molecular pathogenesis of the extracellular Aß-amyloid (Aß) instigation of intracellular tau accumulation is poorly understood. We employed a high-resolution PET scanner, with low detection thresholds, to examine the Aß-tau association using a convolutional neural network (CNN), and compared results to a standard voxel-wise linear analysis. The full range of Aß Centiloid values was highly predicted by the tau topography using the CNN (training R2 = 0.86, validation R2 = 0.75, testing R2 = 0.72). Linear models based on tau-SUVR identified widespread positive correlations between tau accumulation and Aß burden throughout the brain. In contrast, CNN analysis identified focal clusters in the bilateral medial temporal lobes, frontal lobes, precuneus, postcentral gyrus and middle cingulate. At low Aß levels, information from the middle cingulate, frontal lobe and precuneus regions was more predictive of Aß burden, while at high Aß levels, the medial temporal regions were more predictive of Aß burden. The data-driven CNN approach revealed new associations between tau topography and Aß burden.

Correcting for Non-stationarity in BOLD-fMRI Connectivity Analyses.

In this work fMRI BOLD datasets are shown to contain slice-dependent non-stationarities. A model containing slice-dependent, non-stationary signal power is proposed to address time-varying signal power during BOLD data acquisition. The impact of non-stationary power on functional MRI connectivity is analytically derived, establishing that pairwise connectivity estimates are scaled by a function of the time-varying signal power, with magnitude upper bound by 1, and that the variance of sample correlation is increased, thereby inducing spurious connectivity. Consequently, we make the observation that time-varying power during acquisition of BOLD timeseries has the propensity to diminish connectivity estimates. To ameliorate the impact of non-stationary signal power, a simple correction for slice-dependent non-stationarity is proposed. Our correction is analytically shown to restore both signal stationarity and, subsequently, the integrity of connectivity estimates. Theoretical results are corroborated with empirical evidence demonstrating the utility of our correction. In addition, slice-dependent non-stationary variance is experimentally determined to be optimally characterized by an inverse Gamma distribution. The resulting distribution of a voxel's signal intensity is analytically derived to be a generalized Student's-t distribution, providing support for the Gaussianity assumption typically imposed by fMRI connectivity methods.

Impact of axonal delay on structure development in a multi-layered network.

The mechanisms underlying how activity in the visual pathway gives rise through neural plasticity to many features observed experimentally in early stages of visual processing was provided by Linsker in a seminal, three-paper series. Owing to the complexity of multi-layer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous, playing little functional role in neural behavior. In this paper, we relax this assumption to examine the impact of distance-dependent axonal propagation delay on neural learning. We show that propagation delay induces low-pass filtering by dispersing arrival times of spikes from presynaptic neurons, providing a natural correlation cancellation mechanism for distal connections. The cut-off frequency decreases as radial propagation delay within a layer increases relative to propagation delay between layers, introducing an upper limit on temporal resolution. Given that the postsynaptic potential acts as a low-pass filter, we show that the effective time constant of each should enable processing of similar scales of temporal information. This has implications for the visual system, in which receptive field size and, thus, propagation delay, increases with eccentricity. Furthermore, network response is frequency dependent since higher frequencies require increased input amplitude to compensate for attenuation. This concords with frequency-dependent contrast sensitivity, which changes with eccentricity and receptive field size. We further show that the proportion of inhibition relative to excitation is larger where radial propagation delay is long relative to inter-laminar delay, and that delay reduces the range in on-center size, providing stability to variations in homeostatic parameters.

Place Cells in Head-Fixed Mice Navigating a Floating Real-World Environment.

The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.

Continuous monitoring of plant sodium transport dynamics using clinical PET.

BACKGROUND: The absorption, translocation, accumulation and excretion of substances are fundamental processes in all organisms including plants, and have been successfully studied using radiotracers labelled with 11C, 13N, 14C and 22Na since 1939. Sodium is one of the most damaging ions to the growth and productivity of crops. Due to the significance of understanding sodium transport in plants, a significant number of studies have been carried out to examine sodium influx, compartmentation, and efflux using 22Na- or 24Na-labeled salts. Notably, however, most of these studies employed destructive methods, which has limited our understanding of sodium flux and distribution characteristics in real time, in live plants. Positron emission tomography (PET) has been used successfully in medical research and diagnosis for decades. Due to its ability to visualise and assess physiological and metabolic function, PET imaging has also begun to be employed in plant research. Here, we report the use of a clinical PET scanner with a 22Na tracer to examine 22Na-influx dynamics in barley plants (Hordeum vulgare L. spp. Vulgare-cultivar Bass) under variable nutrient levels, alterations in the day/night light cycle, and the presence of sodium channel inhibitors. RESULTS: 3D dynamic PET images of whole plants show readily visible 22Na translocation from roots to shoots in each examined plant, with rates influenced by both nutrient status and channel inhibition. PET images show that plants cultivated in low-nutrient media transport more 22Na than plants cultivated in high-nutrient media, and that 22Na uptake is suppressed in the presence of a cation-channel inhibitor. A distinct diurnal pattern of 22Na influx was discernible in curves displaying rates of change of relative radioactivity. Plants were found to absorb more 22Na during the light period, and anticipate the change in the light/dark cycle by adjusting the sodium influx rate downward in the dark period, an effect not previously described experimentally. CONCLUSIONS: We demonstrate the utility of clinical PET/CT scanners for real-time monitoring of the temporal dynamics of sodium transport in plants. The effects of nutrient deprivation and of ion channel inhibition on sodium influx into barley plants are shown in two proof-of-concept experiments, along with the first-ever 3D-imaging of the light and dark sodium uptake cycles in plants. This method carries significant potential for plant biology research and, in particular, in the context of genetic and treatment effects on sodium acquisition and toxicity in plants.

A new algorithm for drift compensation in multi-unit recordings of action potentials in peripheral autonomic nerves over time.

BACKGROUND: Peripheral autonomic nerves control visceral organs and convey information regarding their functional states and are, therefore, potential targets for new therapeutic and diagnostic approaches. Conventionally recorded multi-unit nerve activity in vivo undergoes slow differential drift of signal and noise amplitudes, making accurate monitoring of nerve activity for more than tens of minutes problematic. NEW METHOD: We describe an on-line drift compensation algorithm that utilizes recursive least-squares to estimate the relative change in spike amplitude due to changes in the nerve-electrode interface over time. RESULTS: We tested and refined our approach using simulated data and in vivo recordings from nerves supplying the small intestine under control conditions and in response to gut inflammation over several hours. The algorithm is robust to changes in recording conditions and signal-to-noise ratio and applicable to both single and multi-unit recordings. In uncompensated records, drift prevented "spike families" and single units from being discriminated accurately over hours. After rescaling, these were successfully tracked throughout recordings (up to 3 h). COMPARISON WITH EXISTING METHODS: Existing methods are subjective or compensate for drift using spatial information and spike shape data which is not practical in multi-unit peripheral nerve recordings. In contrast, this method is objective and applicable to data from a single differential multi-unit recording. In comparisons using simulated data the algorithm performed as well as or better than existing methods. CONCLUSIONS: Results suggest our drift compensation algorithm is widely applicable and robust, though conservative, when differentiating prolonged responses from drift in signal. Extracellular nerve recordings; drift compensation; chronic nerve recordings; closed-loop; multi-unit activity; spike discrimination; recursive least squares; real-time.

Visual cues for manual control of headway.

The ability to maintain appropriate gaps to objects in one's environment is important when navigating through a three-dimensional world. Previous research has shown that the visual angle subtended by a lead/approaching object and its rate of change are important variables for timing interceptions, collision avoidance, continuous regulation of braking, and manual control of headway. However, investigations of headway maintenance have required participants to maintain a fixed distance headway and have not investigated how information about own-speed is taken into account. In the following experiment, we asked participants to use a joystick to follow computer-simulated lead objects. The results showed that ground texture, following speed, and the size of the lead object had significant effects on both mean following distances and following distance variance. Furthermore, models of the participants' joystick responses provided better fits when it was assumed that the desired visual extent of the lead object would vary over time. Taken together, the results indicate that while information about own-speed is used by controllers to set the desired headway to a lead object, the continuous regulation of headway is influenced primarily by the visual angle of the lead object and its rate of change. The reliance on visual angle, its rate of change, and/or own-speed information also varied depending on the control dynamics of the system. Such findings are consistent with an optimal control criterion that reflects a differential weighting on different sources of information depending on the plant dynamics. As in other judgements of motion in depth, the information used for controlling headway to other objects in the environment varies depending on the constraints of the task and different strategies of control.