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brainlife.io: a decentralized and open-source cloud platform to support neuroscience research.
Neuroscience is advancing standardization and tool development to support rigor and transparency. Consequently, data pipeline complexity has increased, hindering FAIR (findable, accessible, interoperable and reusable) access. brainlife.io was developed to democratize neuroimaging research. The platform provides data standardization, management, visualization and processing and automatically tracks the provenance history of thousands of data objects. Here, brainlife.io is described and evaluated for validity, reliability, reproducibility, replicability and scientific utility using four data modalities and 3,200 participants.
Post-stroke upper limb recovery is correlated with dynamic resting-state network connectivity.
Motor recovery is still limited for people with stroke especially those with greater functional impairments. In order to improve outcome, we need to understand more about the mechanisms underpinning recovery. Task-unbiased, blood flow-independent post-stroke neural activity can be acquired from resting brain electrophysiological recordings and offers substantial promise to investigate physiological mechanisms, but behaviourally relevant features of resting-state sensorimotor network dynamics have not yet been identified. Thirty-seven people with subcortical ischaemic stroke and unilateral hand paresis of any degree were longitudinally evaluated at 3 weeks (early subacute) and 12 weeks (late subacute) after stroke. Resting-state magnetoencephalography and clinical scores of motor function were recorded and compared with matched controls. Magnetoencephalography data were decomposed using a data-driven hidden Markov model into 10 time-varying resting-state networks. People with stroke showed statistically significantly improved Action Research Arm Test and Fugl-Meyer upper extremity scores between 3 weeks and 12 weeks after stroke (both P < 0.001). Hidden Markov model analysis revealed a primarily alpha-band ipsilesional resting-state sensorimotor network which had a significantly increased life-time (the average time elapsed between entering and exiting the network) and fractional occupancy (the occupied percentage among all networks) at 3 weeks after stroke when compared with controls. The life-time of the ipsilesional resting-state sensorimotor network positively correlated with concurrent motor scores in people with stroke who had not fully recovered. Specifically, this relationship was observed only in ipsilesional rather in contralesional sensorimotor network, default mode network or visual network. The ipsilesional sensorimotor network metrics were not significantly different from controls at 12 weeks after stroke. The increased recruitment of alpha-band ipsilesional resting-state sensorimotor network at subacute stroke served as functionally correlated biomarkers exclusively in people with stroke with not fully recovered hand paresis, plausibly reflecting functional motor recovery processes.
Correction: Modified minimal-size fragments of heparan sulfate as inhibitors of endosulfatase-2 (Sulf-2).
Correction for 'Modified minimal-size fragments of heparan sulfate as inhibitors of endosulfatase-2 (Sulf-2)' by Alice Kennett et al., Chem. Commun., 2024, 60, 436-439, https://doi.org/10.1039/D3CC02565A.
The bistable mitotic switch in fission yeast.
In favorable conditions, eukaryotic cells proceed irreversibly through the cell division cycle (G1-S-G2-M) in order to produce two daughter cells with the same number and identity of chromosomes of their progenitor. The integrity of this process is maintained by 'checkpoints' that hold a cell at particular transition points of the cycle until all requisite events are completed. The crucial functions of these checkpoints seem to depend on irreversible bistability of the underlying checkpoint control systems. Bistability of cell cycle transitions has been confirmed experimentally in frog egg extracts, budding yeast cells and mammalian cells. For fission yeast cells, a recent paper by Patterson et al. (2021) provides experimental evidence for an abrupt transition from G2 phase into mitosis, and we show that these data are consistent with a stochastic model of a bistable switch governing the G2/M checkpoint. Interestingly, our model suggests that their experimental data could also be explained by a reversible/sigmoidal switch, and stochastic simulations confirm this supposition. We propose a simple modification of their experimental protocol that could provide convincing evidence for (or against) bistability of the G2/M transition in fission yeast.
A cognitive map for value-guided choice in ventromedial prefrontal cortex.
The prefrontal cortex is crucial for economic decision-making and representing the value of options. However, how such representations facilitate flexible decisions remains unknown. We reframe economic decision-making in prefrontal cortex in line with representations of structure within the medial temporal lobe because such cognitive map representations are known to facilitate flexible behaviour. Specifically, we framed choice between different options as a navigation process in value space. Here we show that choices in a 2D value space defined by reward magnitude and probability were represented with a grid-like code, analogous to that found in spatial navigation. The grid-like code was present in ventromedial prefrontal cortex (vmPFC) local field potential theta frequency and the result replicated in an independent dataset. Neurons in vmPFC similarly contained a grid-like code, in addition to encoding the linear value of the chosen option. Importantly, both signals were modulated by theta frequency - occurring at theta troughs but on separate theta cycles. Furthermore, we found sharp-wave ripples - a key neural signature of planning and flexible behaviour - in vmPFC, which were modulated by accuracy and reward. These results demonstrate that multiple cognitive map-like computations are deployed in vmPFC during economic decision-making, suggesting a new framework for the implementation of choice in prefrontal cortex.
PET-measured human dopamine synthesis capacity and receptor availability predict trading rewards and time-costs during foraging.
Foraging behavior requires weighing costs of time to decide when to leave one reward patch to search for another. Computational and animal studies suggest that striatal dopamine is key to this process; however, the specific role of dopamine in foraging behavior in humans is not well characterized. We use positron emission tomography (PET) imaging to directly measure dopamine synthesis capacity and D1 and D2/3 receptor availability in 57 healthy adults who complete a computerized foraging task. Using voxelwise data and principal component analysis to identify patterns of variation across PET measures, we show that striatal D1 and D2/3 receptor availability and a pattern of mesolimbic and anterior cingulate cortex dopamine function are important for adjusting the threshold for leaving a patch to explore, with specific sensitivity to changes in travel time. These findings suggest a key role for dopamine in trading reward benefits against temporal costs to modulate behavioral adaptions to changes in the reward environment critical for foraging.
The first year of a new era.
What happened when eLife decided to eliminate accept/reject decisions after peer review?
Generative replay underlies compositional inference in the hippocampal-prefrontal circuit.
Human reasoning depends on reusing pieces of information by putting them together in new ways. However, very little is known about how compositional computation is implemented in the brain. Here, we ask participants to solve a series of problems that each require constructing a whole from a set of elements. With fMRI, we find that representations of novel constructed objects in the frontal cortex and hippocampus are relational and compositional. With MEG, we find that replay assembles elements into compounds, with each replay sequence constituting a hypothesis about a possible configuration of elements. The content of sequences evolves as participants solve each puzzle, progressing from predictable to uncertain elements and gradually converging on the correct configuration. Together, these results suggest a computational bridge between apparently distinct functions of hippocampal-prefrontal circuitry and a role for generative replay in compositional inference and hypothesis testing.
Lifehistory Trade-Offs Influence Women’s Reproductive Strategies
Objective: In a UK national census sample, women from the upper and lower socioeconomic (SES) classes achieve parity in completed family size, despite marked differences in both birth rates and offspring survival rates. We test the hypothesis that women adopt reproductive strategies that manipulate age at first reproduction to achieve this. Methods: We use a Monte-Carlo modeling approach parameterized with current UK lifehistory data to simulate the reproductive lifehistories of 64,000 individuals from different SES classes, with parameter values at each successive time step drawn from a statistical distribution defined by the census data. Results: We show that, if they are to achieve parity with women in the higher socioeconomic classes, women in lower socioeconomic classes must begin reproducing 5.65 years earlier on average than women in the higher SES classes in order to offset the higher class-specific mortality and infertility rates that they experience. The model predicts very closely the observed differences in age at first reproduction in the census data. Conclusions: Opting to delay reproduction in order to purse an education-based professional career may be a high risk strategy that many lower SES women are unwilling and unable to pursue. As a result, reproducing as early as possible may be the best strategy available to them.
Sleep effort and its measurement: A scoping review.
Insomnia disorder is characterized by disruption in sleep continuity and an overall dissatisfaction with sleep. A relevant feature of insomnia is sleep effort, which refers to both cognitive and behavioural conscious attempts to initiate sleep. The Glasgow Sleep Effort Scale is a self-report tool developed to assess this construct. The objective of the current scoping review was to map how sleep effort has been discussed in the literature and operationalized through its respective measure. Medline/PubMed, Scopus, Web of Science and PsycInfo databases were used to search for potential studies. The search query used in databases was the specific name of the self-reported tool itself (Glasgow Sleep Effort Scale) and "sleep effort" term. This scoping review followed JBI guidelines. To be included, records pertaining to any type of study that mentioned the Glasgow Sleep Effort Scale were considered. No language constraint was used. At the end, 166 initial records were retrieved. From those, 46 records met eligibility criteria and were analysed. Among the main findings, it was observed that the Glasgow Sleep Effort Scale has been increasingly used in recent years, with a notable observed upward trend, especially in the last 2 years. In addition to the original measure, only three published adapted versions of the instrument were identified. This suggests that there is limited research on adapting the scale for different populations or contexts. Sleep effort has been increasingly studied in the last few years. Nonetheless, more research on the Glasgow Sleep Effort Scale tool is recommended, including cross-cultural adaptations.
Behaviour-correlated profiles of cerebellar-cerebral functional connectivity observed in independent neurodevelopmental disorder cohorts.
The cerebellum, through its connectivity with the cerebral cortex, plays an integral role in regulating cognitive and affective processes, and its dysregulation can result in neurodevelopmental disorder (NDD)-related behavioural deficits. Identifying cerebellar-cerebral functional connectivity (FC) profiles in children with NDDs can provide insight into common connectivity profiles and their correlation to NDD-related behaviours. 479 participants from the Province of Ontario Neurodevelopmental Disorders (POND) network (typically developing = 93, Autism Spectrum Disorder = 172, Attention Deficit/Hyperactivity Disorder = 161, Obsessive-Compulsive Disorder = 53, mean age = 12.2) underwent resting-state functional magnetic resonance imaging and behaviour testing (Social Communication Questionnaire, Toronto Obsessive-Compulsive Scale, and Child Behaviour Checklist - Attentional Problems Subscale). FC components maximally correlated to behaviour were identified using canonical correlation analysis. Results were then validated by repeating the investigation in 556 participants from an independent NDD cohort provided from a separate consortium (Healthy Brain Network (HBN)). Replication of canonical components was quantified by correlating the feature vectors between the two cohorts. The two cerebellar-cerebral FC components that replicated to the greatest extent were correlated to, respectively, obsessive-compulsive behaviour (behaviour feature vectors, rPOND-HBN = -0.97; FC feature vectors, rPOND-HBN = -0.68) and social communication deficit contrasted against attention deficit behaviour (behaviour feature vectors, rPOND-HBN = -0.99; FC feature vectors, rPOND-HBN = -0.78). The statistically stable (|z| > 1.96) features of the FC feature vectors, measured via bootstrap re-sampling, predominantly comprised of correlations between cerebellar attentional and control network regions and cerebral attentional, default mode, and control network regions. In both cohorts, spectral clustering on FC loading values resulted in subject clusters mixed across diagnostic categories, but no cluster was significantly enriched for any given diagnosis as measured via chi-squared test (p > 0.05). Overall, two behaviour-correlated components of cerebellar-cerebral functional connectivity were observed in two independent cohorts. This suggests the existence of generalizable cerebellar network differences that span across NDD diagnostic boundaries.
The role of self-referential and social processing in the relationship between pubertal status and difficulties in mental health and emotion regulation in adolescent girls in the UK.
Adolescence is marked by the onset of puberty, which is associated with an increase in mental health difficulties, particularly in girls. Social and self-referential processes also develop during this period: adolescents become more aware of others' perspectives, and judgements about themselves become less favourable. In the current study, data from 119 girls (from London, UK) aged 9-16 years were collected at two-time points (between 2019 and 2021) to investigate the relationship between puberty and difficulties in mental health and emotion regulation, as well as the role of self-referential and social processing in this relationship. Structural equation modelling showed that advanced pubertal status predicted greater mental health and emotion regulation difficulties, including depression and anxiety, rumination and overall difficulties in emotion regulation, and in mental health and behaviour. Advanced pubertal status also predicted greater perspective-taking abilities and negative self-schemas. Exploratory analyses showed that negative self-schemas mediated the relationships between puberty and rumination, overall emotion regulation difficulties, and depression (although these effects were small and would not survive correction for multiple comparisons). The results suggest that advanced pubertal status is associated with higher mental health and emotion regulation problems during adolescence and that negative self-schemas may play a role in this association. RESEARCH HIGHLIGHTS: This study investigates the relationship between puberty, mental health, emotion regulation difficulties, and social and self-referential processing in girls aged 9-16 years. Advanced pubertal status was associated with worse mental health and greater emotion regulation difficulties, better perspective-taking abilities and negative self-schemas. Negative self-schemas may play a role in the relationships between advanced pubertal status and depression, and advanced pubertal status and emotion regulation difficulties, including rumination.
Early deficits in an in vitro striatal microcircuit model carrying the Parkinson's GBA-N370S mutation.
Understanding medium spiny neuron (MSN) physiology is essential to understand motor impairments in Parkinson's disease (PD) given the architecture of the basal ganglia. Here, we developed a custom three-chambered microfluidic platform and established a cortico-striato-nigral microcircuit partially recapitulating the striatal presynaptic landscape in vitro using induced pluripotent stem cell (iPSC)-derived neurons. We found that, cortical glutamatergic projections facilitated MSN synaptic activity, and dopaminergic transmission enhanced maturation of MSNs in vitro. Replacement of wild-type iPSC-derived dopamine neurons (iPSC-DaNs) in the striatal microcircuit with those carrying the PD-related GBA-N370S mutation led to a depolarisation of resting membrane potential and an increase in rheobase in iPSC-MSNs, as well as a reduction in both voltage-gated sodium and potassium currents. Such deficits were resolved in late microcircuit cultures, and could be reversed in younger cultures with antagonism of protein kinase A activity in iPSC-MSNs. Taken together, our results highlight the unique utility of modelling striatal neurons in a modular physiological circuit to reveal mechanistic insights into GBA1 mutations in PD.
Motor Complications in Parkinson's Disease: Results from 3343 Patients Followed for up to 12 Years.
BACKGROUND: Motor complications are well recognized in Parkinson's disease (PD), but their reported prevalence varies and functional impact has not been well studied. OBJECTIVES: To quantify the presence, severity, impact and associated factors for motor complications in PD. METHODS: Analysis of three large prospective cohort studies of recent-onset PD patients followed for up to 12 years. The MDS-UPDRS part 4 assessed motor complications and multivariable logistic regression tested for associations. Genetic risk score (GRS) for Parkinson's was calculated from 79 single nucleotide polymorphisms. RESULTS: 3343 cases were included (64.7% male). Off periods affected 35.0% (95% CI 33.0, 37.0) at 4-6 years and 59.0% (55.6, 62.3) at 8-10 years. Dyskinesia affected 18.5% (95% CI 16.9, 20.2) at 4-6 years and 42.1% (38.7, 45.5) at 8-10 years. Dystonia affected 13.4% (12.1, 14.9) at 4-6 years and 22.8% (20.1, 25.9) at 8-10 years. Off periods consistently caused greater functional impact than dyskinesia. Motor complications were more common among those with higher drug doses, younger age at diagnosis, female gender, and greater dopaminergic responsiveness (in challenge tests), with associations emerging 2-4 years post-diagnosis. Higher Parkinson's GRS was associated with early dyskinesia (0.026 ≤ P ≤ 0.050 from 2 to 6 years). CONCLUSIONS: Off periods are more common and cause greater functional impairment than dyskinesia. We confirm previously reported associations between motor complications with several demographic and medication factors. Greater dopaminergic responsiveness and a higher genetic risk score are two novel and significant independent risk factors for the development of motor complications.
Comparing the performance of global, geographically weighted and ecologically weighted species distribution models for Scottish wildcats using GLM and Random Forest predictive modeling
Species distribution modeling has emerged as a foundational method to predict occurrence and suitability of species in relation to environmental variables to advance ecological understanding and guide conservation planning. Recent research, however, has shown that species-environmental relationships and habitat model predictions are often nonstationary in space, time and ecological context. This calls into question modeling approaches that assume a global, stationary ecological realized niche and use predictive modeling to describe it. This paper explores this issue by comparing the performance of predictive models for wildcat hybrid occurrence based on (1) global pooled data across individuals, (2) geographically weighted aggregation of individual models, (3) ecologically weighted aggregation of individual models, and (4) combinations of global, geographical and ecological weighting. Our study system included GPS telemetry data from 14 individual wildcat hybrids across Scotland. We developed predictive models both using Generalized Linear Models (GLM) and Random Forest machine learning to compare the performance of these differing algorithms and how they compare in stationary and nonstationary analyses. We validated the predicted models in four different ways. First, we used independent hold-out data from the 14 collared wildcat hybrids. Second, we used data from 8 additional GPS collared wildcat hybrids from a previous study that were not included in the training sample. Third, we used sightings data sent in by the public and researchers and validated by expert opinion. Fourth, we used data collected by camera trap surveys between 2012 – 2021 from various sources to produce a combined camera trap dataset showing where wildcats and wildcat hybrids had been detected. Our results show that validation using hold-out data from the individuals used to train the model provides highly biased assessment of true model performance in other locations, with Random Forest in particular appearing to perform exceptionally (and inaccurately) well when validated by data from the same individuals used to train the models. Very different results were obtained when the models were validated using independent data from the three other sources. Each of these three independent validation data sets gave a different result in terms of the best overall model. The average of independent validation across these three validation datasets suggested that the best overall model produced for potential wildcat occurrence and habitat suitability was obtained by an ensemble average of the global Generalized Linear Model (GLM) and Random Forest models with the ecologically weighted GLM and Random Forest models. This suggests that the debate over whether which of GLM vs machine learning approaches is superior or whether global vs aggregated nonstationary modeling is superior may be a false choice. The results presented here show that the best prediction applies a combination of all of these approaches in an ensemble modeling framework.
Explaining inter-individual differences in habitat relationships among wildcat hybrids in Scotland
Little is known about the factors that drive nonstationarity and inter-individual differences in realized habitat niches and species-environment relationships. We explored this topic by developing individual habitat selection models for 14 wildcat hybrids distributed across Scotland, and assessed how differences in their predicted probabilities of occurrence were related to factors including (1) geographic distance, (2) multivariate ecological distance, (3) difference in degree of hybridization and (4) difference in sex (male vs female). We found that the individual models were exceptionally effective in predicting the habitat use and occurrence of the particular individuals on whose data they were trained, but were generally highly divergent and not transferable among individuals. We conducted a reciprocal validation approach where we calculated the AUC for each individual model, predicting the occurrence patterns of the 13 other individuals. We then fit regression and nonparametric splines to evaluate the impacts of geographical distance, ecological distance, hybridization distance and difference in the sex of individuals in the ability of individual wildcat hybrid habitat models to predict the occurrences of other individuals. We found that, of the four factors assessed, ecological distance was supported as being inversely related to ability of a model from one individual to predict occurrence of another individual. The other three factors were not strongly related to differences in reciprocal model predictive ability. This suggests that ecological differences where individual wildcat hybrids reside drive differences in their habitat selection, but that geographical distance, degree of genetic hybridization and difference in the sex of individuals are not consistently associated with differences in model prediction or reciprocal validation performance. These results highlight the effect of ecological limiting factors, and the importance of nonstationary limiting factors in determining the habitat they select, their expressed species-environment relationship and the description of their realized habitat niches.
Variable importance and scale of influence across individual scottish wildcat hybrid habitat models
Understanding the scale dependence of species-habitat relationships is an important area of research in species distribution modeling. There has been little research focused on how habitat selection may depend on individual variation among organisms, geographical location and ecological context of that location. Furthermore, little is known about the extent and drivers of heterogeneity of scale dependence among individuals of a species inhabiting different ecological contexts, and few studies have compared scale dependence and variable importance in a spatially replicated framework. Two of the most important factors for interpreting habitat relationships models include: (1) the relative importance of variables in the model and (2) the spatial scale at which each variable has the largest influence. Based on the existing evidence we hypothesize a priori that landcover variables will generally be the most important predictors, followed by topography, then soil type (which influence both vegetation and prey), Normalized Difference Vegetation Index (NDVI) as an indicator of total vegetation density and perhaps a proxy for prey density, vegetation cover and rabbit abundance. We also expected a priori that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. We expected topographical features to be selected at broad scales, as they influence broad-scale climatic and ecological conditions. We also expected that land cover classes and vegetation cover density to be selected at relatively broad scales given past research showing land cover generally influences habitat selection at relatively broad scales. We expected NDVI and soil type to be selected at finer scales, as their variation influences the distribution of resources and limiting conditions within landscapes. Finally, we expected that rabbit abundance and linear features would affect wildcat hybrid occurrence at the finest scales, given these are resources and conditions that vary over short distances and strongly influence wildcat and wildcat hybrid behavior and habitat use. Our results were consistent with the hypothesis that there may be consistency regarding which variables or variable groups are most important as predictors of wildcat hybrid occurrence in Scotland. Based on previous research we expected that there would be consistent patterns of scale dependence across individual wildcat hybrid models related to different variable groups. Finally, our results identify a clear and consistent trend of increasing frequency of inclusion of variables at increasingly broad scales. This is a linear trend in frequency of variables retained increasing as the scale increased. This suggests a consistent and monotonic pattern of more frequent retention of variables at increasingly broad scales.