Estimation of the transmission delays in the basal ganglia of the macaque monkey and subsequent predictions about oscillatory activity under dopamine depletion.

The timescales of the dynamics of a system depend on the combination of the timescales of its components and of its transmission delays between components. Here, we combine experimental stimulation data from 10 studies in macaque monkeys that reveal the timing of excitatory and inhibitory events in the basal ganglia circuit, to estimate its set of transmission delays. In doing so, we reveal possible inconsistencies in the existing data, calling for replications, and we propose two possible sets of transmission delays. We then integrate these delays in a model of the primate basal ganglia that does not rely on direct and indirect pathways' segregation and show that extrastriatal dopaminergic depletion in the external part of the globus pallidus and in the subthalamic nucleus is sufficient to generate ß-band oscillations (in the high part, 20-35 Hz, of the band). More specifically, we show that D2 and D5 dopamine receptors in these nuclei play opposing roles in the emergence of these oscillations, thereby explaining how completely deactivating D5 receptors in the subthalamic nucleus can, paradoxically, cancel oscillations.

Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences.

Despite increasing representation in graduate training programs, a disproportionate number of women leave academic research without obtaining an independent position that enables them to train the next generation of academic researchers. To understand factors underlying this trend, we analyzed formal PhD and postdoctoral mentoring relationships in the life sciences during the years 2000 to 2020. Student and mentor gender are both associated with differences in rates of student's continuation to positions that allow formal academic mentorship. Although trainees of women mentors are less likely to take on positions as academic mentors than trainees of men mentors, this effect is reduced substantially after controlling for several measurements of mentor status. Thus, the effect of mentor gender can be explained at least partially by gender disparities in social and financial resources available to mentors. Because trainees and mentors tend to be of the same gender, this association between mentor gender and academic continuation disproportionately impacts women trainees. On average, gender homophily in graduate training is unrelated to mentor status. A notable exception to this trend is the special case of scientists having been granted an outstanding distinction, evidenced by membership in the National Academy of Sciences, being a grantee of the Howard Hughes Medical Institute, or having been awarded the Nobel Prize. This group of mentors trains men graduate students at higher rates than their most successful colleagues. These results suggest that, in addition to other factors that limit career choices for women trainees, gender inequities in mentors' access to resources and prestige contribute to women's attrition from independent research positions.

A biologically constrained spiking neural network model of the primate basal ganglia with overlapping pathways exhibits action selection.

Action selection has been hypothesized to be a key function of the basal ganglia, yet the nuclei involved, their interactions and the importance of the direct/indirect pathway segregation in such process remain debated. Here, we design a spiking computational model of the monkey basal ganglia derived from a previously published population model, initially parameterized to reproduce electrophysiological activity at rest and to embody as much quantitative anatomical data as possible. As a particular feature, both models exhibit the strong overlap between the direct and indirect pathways that has been documented in non-human primates. Here, we first show how the translation from a population to an individual neuron model was achieved, with the addition of a minimal number of parameters. We then show that our model performs action selection, even though it was built without any assumption on the activity carried out during behaviour. We investigate the mechanisms of this selection through circuit disruptions and found an instrumental role of the off-centre/on-surround structure of the MSN-STN-GPi circuit, as well as of the MSN-MSN and FSI-MSN projections. This validates their potency in enabling selection. We finally study the pervasive centromedian and parafascicular thalamic inputs that reach all basal ganglia nuclei and whose influence is therefore difficult to anticipate. Our model predicts that these inputs modulate the responsiveness of action selection, making them a candidate for the regulation of the speed-accuracy trade-off during decision-making.

Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference.

Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can reveal the brain's global network architecture and also abnormalities involved in neurological and mental disorders. However, the reliability of connection inferences from dMRI-based fiber tracking is still debated, due to low sensitivity, dominance of false positives, and inaccurate and incomplete reconstruction of long-range connections. Furthermore, parameters of tracking algorithms are typically tuned in a heuristic way, which leaves room for manipulation of an intended result. Here we propose a general data-driven framework to optimize and validate parameters of dMRI-based fiber tracking algorithms using neural tracer data as a reference. Japan's Brain/MINDS Project provides invaluable datasets containing both dMRI and neural tracer data from the same primates. A fundamental difference when comparing dMRI-based tractography and neural tracer data is that the former cannot specify the direction of connectivity; therefore, evaluating the fitting of dMRI-based tractography becomes challenging. The framework implements multi-objective optimization based on the non-dominated sorting genetic algorithm II. Its performance is examined in two experiments using data from ten subjects for optimization and six for testing generalization. The first uses a seed-based tracking algorithm, iFOD2, and objectives for sensitivity and specificity of region-level connectivity. The second uses a global tracking algorithm and a more refined set of objectives: distance-weighted coverage, true/false positive ratio, projection coincidence, and commissural passage. In both experiments, with optimized parameters compared to default parameters, fiber tracking performance was significantly improved in coverage and fiber length. Improvements were more prominent using global tracking with refined objectives, achieving an average fiber length from 10 to 17 mm, voxel-wise coverage of axonal tracts from 0.9 to 15%, and the correlation of target areas from 40 to 68%, while minimizing false positives and impossible cross-hemisphere connections. Optimized parameters showed good generalization capability for test brain samples in both experiments, demonstrating the flexible applicability of our framework to different tracking algorithms and objectives. These results indicate the importance of data-driven adjustment of fiber tracking algorithms and support the validity of dMRI-based tractography, if appropriate adjustments are employed.

Quantifying vocal effort from the shape of the one-third octave long-term-average spectrum of speech.

Vocal effort is a major source of variability in speech processing. The present study examines its spectral effects from calibrated data recorded in 1977. The 97 talkers were instructed to vary their vocal effort in five degrees. Each sequence was represented by its sound level and its 1/3 octave long-term-average spectrum. After normalization to a common arbitrary level, comparing each spectrum to the others demonstrated that the original sound level could be recovered within a 5 dB error margin. A principal component analysis brought out several spectral features involved in the quantitative relationship between spectral shape and sound level.

Intellectual synthesis in mentorship determines success in academic careers.

As academic careers become more competitive, junior scientists need to understand the value that mentorship brings to their success in academia. Previous research has found that, unsurprisingly, successful mentors tend to train successful students. But what characteristics of this relationship predict success, and how? We analyzed an open-access database of 18,856 researchers who have undergone both graduate and postdoctoral training, compiled across several fields of biomedical science with an emphasis on neuroscience. Our results show that postdoctoral mentors were more instrumental to trainees' success compared to graduate mentors. Trainees' success in academia was also predicted by the degree of intellectual synthesis between their graduate and postdoctoral mentors. Researchers were more likely to succeed if they trained under mentors with disparate expertise and integrated that expertise into their own work. This pattern has held up over at least 40 years, despite fluctuations in the number of students and availability of independent research positions.

Correction to: 'Intercomparison of photogrammetry software for three-dimensional vegetation modelling'.

[This corrects the article DOI: 10.1098/rsos.172192.].

Cross-scale integration of knowledge for predicting species ranges: a metamodeling framework.

AIM: Current interest in forecasting changes to species ranges have resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity, and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. LOCATION: Eastern North America (as an example). METHODS: Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence-absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. RESULTS: For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. MAIN CONCLUSIONS: We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

US forest response to projected climate-related stress: a tolerance perspective.

Although it is widely recognized that climate change will require a major spatial reorganization of forests, our ability to predict exactly how and where forest characteristics and distributions will change has been rather limited. Current efforts to predict future distribution of forested ecosystems as a function of climate include species distribution models (for fine-scale predictions) and potential vegetation climate envelope models (for coarse-grained, large-scale predictions). Here, we develop and apply an intermediate approach wherein we use stand-level tolerances of environmental stressors to understand forest distributions and vulnerabilities to anticipated climate change. In contrast to other existing models, this approach can be applied at a continental scale while maintaining a direct link to ecologically relevant, climate-related stressors. We first demonstrate that shade, drought, and waterlogging tolerances of forest stands are strongly correlated with climate and edaphic conditions in the conterminous United States. This discovery allows the development of a tolerance distribution model (TDM), a novel quantitative tool to assess landscape level impacts of climate change. We then focus on evaluating the implications of the drought TDM. Using an ensemble of 17 climate change models to drive this TDM, we estimate that 18% of US ecosystems are vulnerable to drought-related stress over the coming century. Vulnerable areas include mostly the Midwest United States and Northeast United States, as well as high-elevation areas of the Rocky Mountains. We also infer stress incurred by shifting climate should create an opening for the establishment of forest types not currently seen in the conterminous United States.

An individual-based forest model links canopy dynamics and shade tolerances along a soil moisture gradient.

Understanding how forested ecosystems respond to climatic changes is a challenging problem as forest self-organization occurs simultaneously across multiple scales. Here, we explore the hypothesis that soil water availability shapes above-ground competition and gap dynamics, and ultimately alters the dominance of shade tolerant and intolerant species along the moisture gradient. We adapt a spatially explicit individual-based model with simultaneous crown and root competitions. Simulations show that the transition from xeric to mesic soils is accompanied by an increase in shade-tolerant species similar to the patterns documented in the North American forests. This transition is accompanied by a change from water to sunlight competitions, and happens at three successive stages: (i) mostly water-limited parkland, (ii) simultaneously water- and sunlight-limited closed canopy forests featuring a very sparse understory, and (iii) mostly sunlight-limited forests with a populated understory. This pattern is caused by contrasting successional dynamics that favour either shade-tolerant or shade-intolerant species, depending on soil moisture and understory density. This work demonstrates that forest patterns along environmental gradients can emerge from spatial competition without physiological trade-offs between shade and growth tolerance. Mechanistic understanding of population processes involved in the forest-parkland-desert transition will improve our ability to explain species distributions and predict forest responses to climatic changes.

The Essential Complexity of Auditory Receptive Fields.

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

3D Tree Dimensionality Assessment Using Photogrammetry and Small Unmanned Aerial Vehicles.

Detailed, precise, three-dimensional (3D) representations of individual trees are a prerequisite for an accurate assessment of tree competition, growth, and morphological plasticity. Until recently, our ability to measure the dimensionality, spatial arrangement, shape of trees, and shape of tree components with precision has been constrained by technological and logistical limitations and cost. Traditional methods of forest biometrics provide only partial measurements and are labor intensive. Active remote technologies such as LiDAR operated from airborne platforms provide only partial crown reconstructions. The use of terrestrial LiDAR is laborious, has portability limitations and high cost. In this work we capitalized on recent improvements in the capabilities and availability of small unmanned aerial vehicles (UAVs), light and inexpensive cameras, and developed an affordable method for obtaining precise and comprehensive 3D models of trees and small groups of trees. The method employs slow-moving UAVs that acquire images along predefined trajectories near and around targeted trees, and computer vision-based approaches that process the images to obtain detailed tree reconstructions. After we confirmed the potential of the methodology via simulation we evaluated several UAV platforms, strategies for image acquisition, and image processing algorithms. We present an original, step-by-step workflow which utilizes open source programs and original software. We anticipate that future development and applications of our method will improve our understanding of forest self-organization emerging from the competition among trees, and will lead to a refined generation of individual-tree-based forest models.

An appraisal of the classic forest succession paradigm with the shade tolerance index.

In this paper we revisit the classic theory of forest succession that relates shade tolerance and species replacement and assess its validity to understand patch-mosaic patterns of forested ecosystems of the USA. We introduce a macroscopic parameter called the "shade tolerance index" and compare it to the classic continuum index in southern Wisconsin forests. We exemplify shade tolerance driven succession in White Pine-Eastern Hemlock forests using computer simulations and analyzing approximated chronosequence data from the USDA FIA forest inventory. We describe this parameter across the last 50 years in the ecoregions of mainland USA, and demonstrate that it does not correlate with the usual macroscopic characteristics of stand age, biomass, basal area, and biodiversity measures. We characterize the dynamics of shade tolerance index using transition matrices and delimit geographical areas based on the relevance of shade tolerance to explain forest succession. We conclude that shade tolerance driven succession is linked to climatic variables and can be considered as a primary driving factor of forest dynamics mostly in central-north and northeastern areas in the USA. Overall, the shade tolerance index constitutes a new quantitative approach that can be used to understand and predict succession of forested ecosystems and biogeographic patterns.

A biologically constrained model of the whole basal ganglia addressing the paradoxes of connections and selection.

The basal ganglia nuclei form a complex network of nuclei often assumed to perform selection, yet their individual roles and how they influence each other is still largely unclear. In particular, the ties between the external and internal parts of the globus pallidus are paradoxical, as anatomical data suggest a potent inhibitory projection between them while electrophysiological recordings indicate that they have similar activities. Here we introduce a theoretical study that reconciles both views on the intra-pallidal projection, by providing a plausible characterization of the relationship between the external and internal globus pallidus. Specifically, we developed a mean-field model of the whole basal ganglia, whose parameterization is optimized to respect best a collection of numerous anatomical and electrophysiological data. We first obtained models respecting all our constraints, hence anatomical and electrophysiological data on the intrapallidal projection are globally consistent. This model furthermore predicts that both aforementioned views about the intra-pallidal projection may be reconciled when this projection is weakly inhibitory, thus making it possible to support similar neural activity in both nuclei and for the entire basal ganglia to select between actions. Second, we predicts that afferent projections are substantially unbalanced towards the external segment, as it receives the strongest excitation from STN and the weakest inhibition from the striatum. Finally, our study strongly suggests that the intrapallidal connection pattern is not focused but diffuse, as this latter pattern is more efficient for the overall selection performed in the basal ganglia.

Quantitative automated assessment of myocardial perfusion at cardiac catheterization.

Perfusion assessed in the cardiac catheterization laboratory predicts outcomes after myocardial infarction. The aim of this study was to investigate a novel method of assessing perfusion using digital subtraction angiography to generate a time-density curve (TDC) of myocardial blush, incorporating epicardial and myocardial perfusion. Seven pigs underwent temporary occlusion of the left anterior descending coronary artery for 60 minutes. Angiography was performed in the same projections before, during, and after occlusion. Perfusion parameters were obtained from the TDC and compared with Thrombolysis In Myocardial Infarction (TIMI) frame count and myocardial perfusion grade. In addition, safety and feasibility were tested in 8 patients after primary percutaneous coronary intervention. The contrast density differential between the proximal artery and the myocardium derived from the TDC correlated well with TIMI myocardial perfusion grade (R = 0.54, p <0.001). The arterial transit time derived from the TDC correlated with TIMI frame count (R = 0.435, p = 0.011). Using a cutoff of 2.4, the density/time ratio, a ratio of density differential to transit time, had sensitivity and specificity of 100% for coronary arterial occlusion. The positive and negative predictive values were 100%. The generation of a TDC was safe and feasible in 7 patients after acute myocardial infarctions, but the correlation between TDC-derived parameters and TIMI parameters did not reach statistical significance. In conclusion, this novel method of digital subtraction angiography with rapid, automated, quantitative assessment of myocardial perfusion in the cardiac catheterization laboratory correlates well with established angiographic measures of perfusion. Further studies to assess the prognostic value of this technique are warranted.

Women use voice parameters to assess men's characteristics.

The purpose of this study was: (i) to provide additional evidence regarding the existence of human voice parameters, which could be reliable indicators of a speaker's physical characteristics and (ii) to examine the ability of listeners to judge voice pleasantness and a speaker's characteristics from speech samples. We recorded 26 men enunciating five vowels. Voices were played to 102 female judges who were asked to assess vocal attractiveness and speakers' age, height and weight. Statistical analyses were used to determine: (i) which physical component predicted which vocal component and (ii) which vocal component predicted which judgment. We found that men with low-frequency formants and small formant dispersion tended to be older, taller and tended to have a high level of testosterone. Female listeners were consistent in their pleasantness judgment and in their height, weight and age estimates. Pleasantness judgments were based mainly on intonation. Female listeners were able to correctly estimate age by using formant components. They were able to estimate weight but we could not explain which acoustic parameters they used. However, female listeners were not able to estimate height, possibly because they used intonation incorrectly. Our study confirms that in all mammal species examined thus far, including humans, formant components can provide a relatively accurate indication of a vocalizing individual's characteristics. Human listeners have the necessary information at their disposal; however, they do not necessarily use it.

Multiresolution parametric estimation of transparent motions and denoising of fluoroscopic images.

We describe a novel multiresolution parametric framework to estimate transparent motions typically present in X-Ray exams. Assuming the presence if two transparent layers, it computes two affine velocity fields by minimizing an appropriate objective function with an incremental Gauss-Newton technique. We have designed a realistic simulation scheme of fluoroscopic image sequences to validate our method on data with ground truth and different levels of noise. An experiment on real clinical images is also reported. We then exploit this transparent-motion estimation method to denoise two layers image sequences using a motion-compensated estimation method. In accordance with theory, we show that we reach a denoising factor of 2/3 in a few iterations without bringing any local artifacts in the image sequence.

The 6sigma quality approach for quantitative arteriography performance improvement.

Quantitative angiography is a medical application where the user requires tools whose operational results in term of accuracy, precision, robustness and reliability must be extensively assessed and validated for clinical use. In this study, the 6sigma methodology has been applied to analyze the performances of the General Electric QA software method. In particular, the catheter calibration procedure was identified as the weakest function in term of sensitivity to procedure parameters, like point-spread-function, field-of-view, catheter dimensions. It was therefore improved by following a design for 6sigma scheme, and the main parameters that govern the QA accuracy and precision were put under quantifiable control.