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The mitigating effects of fish passes to aid fish dispersal are well recognised; however, non-sports fish species, juveniles and weak swimmers have mostly been neglected when developing solutions to fish passage issues. We studied the juvenile (fork length, FL <60 mm) swimming ability of a weakly swimming fish species, īnanga (Galaxias maculatus), up an artificial ramp to evaluate the effects of fish size, ramp slope (5°, 15°, 25°), water velocity and the potential need for resting opportunities (pools) for a successful ascent. Water velocity significantly increased and fish passage success significantly decreased with ramp angle, with only one fish passing the 25° ramp. On average, fish had to be 10 mm larger to ascend a 15° ramp angle than at 5°. Pools did not enhance passage success in this study. To facilitate fish passage at barriers for weakly swimming fish species like juvenile īnanga, ramps should provide a rough surface substrate and have an optimal angle of 5° for a 3-m ramp. [ABSTRACT FROM AUTHOR]

We applied structural equation modeling to positron emission tomography data in humans to examine functional interactions between the right medial temporal lobe (MTL) and selected right neocortical regions in relation to visual recognition memory. Using a priori knowledge about anatomical connections between these regions as a guiding constraint, we modeled the pattern of interactions [i.e. covariances in regional cerebral blood flow (rCBF)] associated with episodic memory retrieval of spatial location and compared it with the pattern for retrieval of object identity. We also compared these patterns with those associated with perceptual matching of spatial location and object identity. Although displaying no difference in average rCBF across tasks, the right MTL showed domain-specific qualitative differences in interactions with posterior dorsal (parieto-occipital sulcus, supramarginal gyrus) and ventral regions (fusiform gyrus, superior temporal sulcus) but not with a prefrontal region. MTL interactions involving dorsal regions were positive in the spatial retrieval task but negative for object retrieval. Interactions involving ventral regions showed the reverse pattern. No comparable changes were observed during perceptual matching. Using control models, we demonstrated the neuroanatomical specificity of these results. Our results provide support for the notion that the nature of interactions between the MTL and posterior neocortex depends on the domain of information to-be-recovered. [ABSTRACT FROM AUTHOR]

A sensory-sensory learning paradigm was used to measure neural changes in humans during acquisition of an association between an auditory and visual stimulus. Three multivariate partial least-squares (PLS) analyses of positron emission tomography data identified distributed neural systems related to (i) processing the significance of the auditory stimulus, (ii) mediating the acquisition of the behavioral response, and (iii) the spatial overlap between those two systems. The system that processed the significance of the tone engaged primarily right hemisphere regions and included dorso-lateral prefrontal cortex, putamen, and inferior parietal and temporal cortices. Activity changes in left occipital cortex were also identified, most likely reflecting the learned expectancy of the upcoming visual event. The system related to behavior was similar to that which coded the significance of the tone, including dorsal occipital cortex. The PLS analysis of the concordance between these two systems showed substantial regional overlap, and included occipital, dorsolateral prefrontal, and limbic cortices. However, activity in dorsomedial prefrontal cortex was strictly related to processing the auditory stimulus and not to behavior. Taken together, the PLS analyses identified a system that contained a sensory-motor component (comprised of occipital, temporal association and sensorimotor cortices) and a medial prefrontal-limbic component, that as a group simultaneously embodied the learning-related response to the stimuli and the subsequent change in behavior. [ABSTRACT FROM AUTHOR]

Stroke causes pronounced and widespread slowing of neural activity. Despite decades of work exploring these abnormal neural dynamics and their associated functional impairments, their causes remain largely unclear. To close this gap in understanding, we applied a neurophysiological corticothalamic circuit model to simulate magnetoencephalography (MEG) power spectra recorded from chronic stroke patients. Comparing model-estimated physiological parameters to those of controls, patients demonstrated significantly lower intrathalamic inhibition in the lesioned hemisphere, despite the absence of direct damage to the thalamus itself. We hypothesized that this disinhibition could instead be related to secondary degeneration of the thalamus, for which growing evidence exists in the literature. Further analyses confirmed that spectral slowing correlated significantly with overall secondary degeneration of the ipsilesional thalamus, encompassing decreased thalamic volume, altered tissue microstructure, and decreased blood flow. Crucially, this relationship was mediated by model-estimated thalamic disinhibition, suggesting a causal link between secondary thalamic degeneration and abnormal brain dynamics via thalamic disinhibition. Finally, thalamic degeneration was correlated significantly with poorer cognitive and language outcomes, but not lesion volume, reinforcing that thalamus damage may account for additional individual variability in poststroke disability. Overall, our findings indicate that the frequently observed poststroke slowing reflects a disruption of corticothalamic circuit dynamics due to secondary thalamic dysfunction, and highlights the thalamus as an important target for understanding and potentially treating poststroke brain dysfunction., Competing Interests: Competing interests statement:The authors declare no competing interest.

Compensatory density-dependent (DD) processes play an integral role in fisheries management by underpinning fundamental population demographics. However, DD processes are often assessed only for specific life stages, likely resulting in misleading evaluations of population limitations. Here, we assessed the relative roles of intra- and inter-life stage DD interactions in shaping the population dynamics of perennial freshwater fish with demographically open populations. Specifically, we monitored populations of amphidromous banded kōkopu (Galaxias fasciatus), giant kōkopu (Galaxias argenteus), and shortjaw kōkopu (Galaxias postvectis) in five streams where migratory post-larvae are fished and in three no-take ("closed") streams located on New Zealand's South Island for two years. Using mark-recapture data, we investigated whether fishing altered densities of "small" (non-territorial recruits ≤1-year-old) and "large" (territorial fish >1-year-old) kōkopu size classes, and how subsequent density shifts affected the apparent survival and growth of each class while controlling for other confounding factors (e.g., habitat characteristics). We found that closed areas had substantially greater biomass of small kōkopu, particularly following the two-month fishing season. Despite this greater influx of recruits, there was no difference in the biomass of large kōkopu at the species level, or as a combined assemblage between stream types. This indicated that although fishing of post-larvae reduced recruit influxes into adult habitats, there was no subsequent evidence of recruitment-limitation within adult populations. Instead, kōkopu demographics were underpinned by intra- and inter-life stage DD competition and predation. Greater large fish densities played a key role in regulating the survival, growth, and/or presence of various kōkopu classes. In contrast, greater small fish densities had positive effects on the growth of opportunistic and insectivorous congeners, likely due to cannibalism and altered foraging behaviors, respectively. Our study details the prominent role of intra- and inter-life stage DD interactions in regulating the population dynamics of perennial migratory freshwater fishes, even in populations with inhibited recruit and juvenile availability. We emphasize the importance for fisheries management to implement recruitment dependencies and complex interactions between distinct life stages to avoid deleterious DD responses and ensure population persistence., (© 2024 The Author(s). Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America.)

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2024 Massachusetts Institute of Technology.)

Canonical correlation analysis (CCA) and partial least squares correlation (PLS) detect linear associations between two data matrices by computing latent variables (LVs) having maximal correlation (CCA) or covariance (PLS). This study compared the similarity and generalizability of CCA- and PLS-derived brain-behavior relationships. Data were accessed from the baseline Adolescent Brain Cognitive Development (ABCD) dataset ( N > 9,000, 9-11 years). The brain matrix consisted of cortical thickness estimates from the Desikan-Killiany atlas. Two phenotypic scales were examined separately as the behavioral matrix; the Child Behavioral Checklist (CBCL) subscale scores and NIH Toolbox performance scores. Resampling methods were used to assess significance and generalizability of LVs. LV 1 for the CBCL brain relationships was found to be significant, yet not consistently stable or reproducible, across CCA and PLS models (singular value: CCA = .13, PLS = .39, p < .001). LV 1 for the NIH brain relationships showed similar relationships between CCA and PLS and was found to be stable and reproducible (singular value: CCA = .21, PLS = .43, p < .001). The current study suggests that stability and reproducibility of brain-behavior relationships identified by CCA and PLS are influenced by the statistical characteristics of the phenotypic measure used when applied to a large population-based pediatric sample., Competing Interests: Competing Interests: The authors have declared that no competing interests exist., (© 2024 Massachusetts Institute of Technology.)

Knowledge of ecosystem-size influences on river populations and communities is integral to the balancing of human and environmental needs for water. The multiple dimensions of dendritic river networks complicate understanding of ecosystem-size influences, but could be resolved by the development of scaling relationships. We highlight the importance of physical constraints limiting predator body sizes, movements, and population sizes in small rivers, and where river contraction limits space or creates stressful conditions affecting community stability and food webs. Investigations of the scaling and contingency of these processes will be insightful because of the underlying generality and scale independence of such relationships. Doing so will also pinpoint damaging water-management practices and identify which aspects of river size can be most usefully manipulated in river restoration., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Enjoying music consistently engages key structures of the neural auditory and reward systems such as the right superior temporal gyrus (R STG) and ventral striatum (VS). Expectations seem to play a central role in this effect, as preferences reliably vary according to listeners' uncertainty about the musical future and surprise about the musical past. Accordingly, VS activity reflects the pleasure of musical surprise, and exhibits stronger correlations with R STG activity as pleasure grows. Yet the reward value of musical surprise - and thus the reason for these surprises engaging the reward system - remains an open question. Recent models of predictive neural processing and learning suggest that forming, testing, and updating hypotheses about one's environment may be intrinsically rewarding, and that the constantly evolving structure of musical patterns could provide ample opportunity for this procedure. Consistent with these accounts, our group previously found that listeners tend to prefer melodic excerpts taken from real music when it either validates their uncertain melodic predictions (i.e., is high in uncertainty and low in surprise) or when it challenges their highly confident ones (i.e., is low in uncertainty and high in surprise). An independent research group (Cheung et al., 2019) replicated these results with musical chord sequences, and identified their fMRI correlates in the STG, amygdala, and hippocampus but not the VS, raising new questions about the neural mechanisms of musical pleasure that the present study seeks to address. Here, we assessed concurrent liking ratings and hemodynamic fMRI signals as 24 participants listened to 50 naturalistic, real-world musical excerpts that varied across wide spectra of computationally modeled uncertainty and surprise. As in previous studies, liking ratings exhibited an interaction between uncertainty and surprise, with the strongest preferences for high uncertainty/low surprise and low uncertainty/high surprise. FMRI results also replicated previous findings, with music liking effects in the R STG and VS. Furthermore, we identify interactions between uncertainty and surprise on the one hand, and liking and surprise on the other, in VS activity. Altogether, these results provide important support for the hypothesized role of the VS in deriving pleasure from learning about musical structure., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor MT declared a past co-authorship with the author BG., (Copyright © 2023 Gold, Pearce, McIntosh, Chang, Dagher and Zatorre.)

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt., (© 2023 Massachusetts Institute of Technology.)

With the global decline of freshwater fishes, quantifying the body size-specific habitat use of vulnerable species is crucial for accurately evaluating population health, identifying the effects of anthropogenic stressors, and directing effective habitat restoration. Populations of New Zealand's endemic kōkopu species (Galaxias fasciatus, G. argenteus, and G. postvectis) have declined substantially over the last century in response to anthropogenic stressors, including habitat loss, migratory barriers, and invasive species. Despite well-understood habitat associations, key within-habitat features underpinning the reach-scale biomass of small and large kōkopu remain unclear. Here, we investigated whether the total biomass of large (> 90 mm) size classes of each kōkopu species and the composite biomass of all small (≤ 90 mm) kōkopu were associated with components of the physical environment that provided refuge and prey resources across fifty-seven 50-m stream reaches. Because kōkopu are nocturnal, populations were sampled by removal at night using headlamps and hand-nets until reaches were visually depleted. Based on Akaike's information criterion, greater large banded kōkopu biomass was most parsimoniously explained by greater pool volume and forest cover, greater large giant kōkopu biomass by greater bank cover and pool volume, and greater large shortjaw kōkopu biomass by greater substrate size and pool volume. In contrast, greater composite small kōkopu biomass was best explained by smaller substrate size, reduced bank cover, and greater pool volume. Local habitat associations therefore varied among kōkopu species and size classes. Our study demonstrates the importance of considering the ontogenetic shift in species' habitat use and provides an effective modelling approach for quantifying size-specific local habitat use of stream-dwelling fish., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Crichton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Episodic memory decline is an early symptom of Alzheimer's disease (AD) - a neurodegenerative disease that has a higher prevalence rate in older females compared to older males. However, little is known about why these sex differences in prevalence rate exist. In the current longitudinal task fMRI study, we explored whether there were sex differences in the patterns of memory decline and brain activity during object-location (spatial context) encoding and retrieval in a large sample of cognitively unimpaired older adults from the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD) program who are at heightened risk of developing AD due to having a family history (+FH) of the disease. The goal of the study was to gain insight into whether there are sex differences in the neural correlates of episodic memory decline, which may advance knowledge about sex-specific patterns in the natural progression to AD. Our results indicate that +FH females performed better than +FH males at both baseline and follow-up on neuropsychological and task fMRI measures of episodic memory. Moreover, multivariate data-driven task fMRI analysis identified generalized patterns of longitudinal decline in medial temporal lobe activity that was paralleled by longitudinal increases in lateral prefrontal cortex, caudate and midline cortical activity during successful episodic retrieval and novelty detection in +FH males, but not females. Post-hoc analyses indicated that higher education had a stronger effect on +FH females neuropsychological scores compared to +FH males. We conclude that higher educational attainment may have a greater neuroprotective effect in older +FH females compared to +FH males., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Crown Copyright © 2023. Published by Elsevier Inc. All rights reserved.)

Decades of electrophysiological work have demonstrated the presence of "spectral slowing" in stroke patients - a prominent shift in the power spectrum towards lower frequencies, most evident in the vicinity of the lesion itself. Despite the reliability of this slowing as a marker of dysfunctional tissue across patient groups as well as animal models, it has yet to be explained in terms of the pathophysiological processes of stroke. To do so requires clear understanding of the neural dynamics that these differences represent, acknowledging the often overlooked fact that spectral power reflects more than just the amplitude of neural oscillations. To accomplish this, we used a combination of frequency domain and time domain measures to disambiguate and quantify periodic (oscillatory) and aperiodic (non-oscillatory) neural dynamics in resting state magnetoencephalography (MEG) recordings from chronic stroke patients. We found that abnormally elevated low frequency power in these patients was best explained by a steepening of the aperiodic component of the power spectrum, rather than an enhancement of low frequency oscillations, as is often assumed. However, genuine oscillatory activity at higher frequencies was also found to be abnormal, with patients showing alpha slowing and diminished oscillatory activity in the beta band. These aperiodic and periodic abnormalities were found to covary, and could be detected even in the un-lesioned hemisphere, however they were most prominent in perilesional tissue, where their magnitude was predictive of cognitive impairment. This work redefines spectral slowing as a pattern of changes involving both aperiodic and periodic neural dynamics and narrows the gap in understanding between non-invasive markers of dysfunctional tissue and disease processes responsible for altered neural dynamics., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Habitat modification and introduced mammalian predators are linked to global species extinctions and declines, but their relative influences can be uncertain, often making conservation management difficult. Using landscape-scale models, we quantified the relative impacts of habitat modification and mammalian predation on the range contraction of a threatened New Zealand riverine duck. We combined 38 years of whio (Hymenolaimus malacorhynchos) observations with national-scale environmental data to predict relative likelihood of occurrence (RLO) under two scenarios using bootstrapped boosted regression trees (BRT). Our models used training data from contemporary environments to predict the potential contemporary whio distribution across New Zealand riverscapes in the absence of introduced mammalian predators. Then, using estimates of environments prior to human arrival, we used the same models to hindcast potential pre-human whio distribution prior to widespread land clearance. Comparing RLO differences between potential pre-human, potential contemporary and observed contemporary distributions allowed us to assess the relative impacts of the two main drivers of decline; habitat modification and mammalian predation. Whio have undergone widespread catastrophic declines most likely linked to mammalian predation, with smaller declines due to habitat modification (range contractions of 95% and 37%, respectively). We also identified areas of potential contemporary habitat outside their current range that would be suitable for whio conservation if mammalian predator control could be implemented. Our approach presents a practical technique for estimating the relative importance of global change drivers in species declines and extinctions, as well as providing valuable information to improve conservation planning., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Whitehead et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Mild cognitive impairment (MCI) is a prevalent and complex condition among older adults that often progresses into Alzheimer's disease (AD). Although MCI affects individuals differently, there are specific indicators of risk commonly associated with the development of MCI. The present study explored the prevalence of seven established MCI risk categories within a large sample of older adults with and without MCI. We explored trends across the different diagnostic groups and extracted the most salient risk factors related to MCI using partial least squares. Neuropsychological risk categories showed the largest differences across groups, with the cognitively unimpaired groups outperforming the MCI groups on all measures. Apolipoprotein E4 (ApoE4) carriers were significantly more common among the more severe MCI group, whereas ApoE4 non-carriers were more common in the healthy controls. Participants with subjective and objective cognitive impairment were trending towards AD-like cerebral spinal fluid (CSF) biomarker levels. Increased age, being male and having fewer years of education were identified as important risk factors of MCI. Higher CSF tau levels were correlated with ApoE4 carrier status, age and a decrease in the ability to carry out daily activities across all diagnostic groups. Amyloid beta 1-42 CSF concentration was positively correlated with cognitive and memory performance and non-ApoE4 carrier status regardless of diagnostic status. Unlike previous research, poor cardiovascular health or being female had no relation to MCI. Altogether, the results highlighted risk factors that were specific to persons with MCI, findings that will inform future research in healthy aging, MCI and AD., (© 2022 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.)

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2022 Jirsa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In organisms with complex life cycles, the various stages occupy different habitats creating demographically open populations. The dynamics of these populations will depend on the occurrence and timing of stochastic influences relative to demographic density dependence, but understanding of these fundamentals, especially in the face of climate warming, has been hampered by the difficulty of empirical studies. Using a logically feasible organism, we conducted a replicated density-perturbation experiment to manipulate late-instar larvae of nine populations of a stream caddisfly, Zelandopsyche ingens, and measured the resulting abundance over 2 years covering the complete life cycle of one cohort to evaluate influences on dynamics. Negative density feedback occurred in the larval stage, and was sufficiently strong to counteract variation in abundance due to manipulation of larval density, adult caddis dispersal in the terrestrial environment as well as downstream drift of newly hatched and older larvae in the current. This supports theory indicating regulation of open populations must involve density dependence in local populations sufficient to offset variability associated with dispersal, especially during recruitment, and pinpoints the occurrence to late in the larval life cycle and driven by food resource abundance. There were large variations in adult, egg mass and early instar abundance that were not related to abundance in the previous stage, or the manipulation, pointing to large stochastic influences. Thus, the results also highlight the complementary nature of stochastic and deterministic influences on open populations. Such density dependence will enhance population persistence in situations where variable dispersal and transitioning between life stages frequently creates mismatches between abundance and the local availability of resources, such as might become more common with climate warming., (© 2022 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

TheVirtualBrain, an open-source platform for large-scale network modeling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project. Our pipeline generates connectome-based modeling inputs compatible for use with TheVirtualBrain. We leverage the existing multimodal MRI processing pipeline from the UK Biobank made for use with a variety of brain imaging modalities. We add various features and changes to the original UK Biobank implementation specifically for informing large-scale network models, including user-defined parcellations for the construction of matching whole-brain functional and structural connectomes. Changes also include detailed reports for quality control of all modalities, a streamlined installation process, modular software packaging, updated software versions, and support for various publicly available datasets. The pipeline has been tested on various datasets from both healthy and clinical populations and is robust to the morphological changes observed in aging and dementia. In this paper, we describe these and other pipeline additions and modifications in detail, as well as how this pipeline fits into the TheVirtualBrain ecosystem., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Frazier-Logue, Wang, Wang, Sodums, Khosla, Samson, McIntosh and Shen.)

Introduction: Computational brain network modeling using The Virtual Brain (TVB) simulation platform acts synergistically with machine learning (ML) and multi-modal neuroimaging to reveal mechanisms and improve diagnostics in Alzheimer's disease (AD)., Methods: We enhance large-scale whole-brain simulation in TVB with a cause-and-effect model linking local amyloid beta (Aβ) positron emission tomography (PET) with altered excitability. We use PET and magnetic resonance imaging (MRI) data from 33 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI3) combined with frequency compositions of TVB-simulated local field potentials (LFP) for ML classification., Results: The combination of empirical neuroimaging features and simulated LFPs significantly outperformed the classification accuracy of empirical data alone by about 10% (weighted F1-score empirical 64.34% vs. combined 74.28%). Informative features showed high biological plausibility regarding the AD-typical spatial distribution., Discussion: The cause-and-effect implementation of local hyperexcitation caused by Aβ can improve the ML-driven classification of AD and demonstrates TVB's ability to decode information in empirical data using connectivity-based brain simulation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The disclosures are based on the disclosure form of the International Committee of Medical Journal Editors (ICMJE). PR, ARM, and VJ report the following patent application: McIntosh AR, Mersmann J, Jirsa VK, Ritter P. Method and Computing System for Modeling a Primate Brain. Patent Application 137PCT1754. VJ report stock or stock options in Virtual Brain Technologies (VB‐Tech). VB‐Tech performs activities in the domain of brain simulation. There is no relation to field of dementia, nor to the content of the manuscript. All other authors, namely PT, LS, KD, MD, PB, KB, RP, ASp, and ASo, have nothing to declare., (© 2022 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Neurons in the brain are seldom perfectly quiet. They continually receive input and generate output, resulting in highly variable patterns of ongoing activity. Yet the functional significance of this variability is not well understood. If brain signal variability is functionally relevant and serves as an important indicator of cognitive function, then it should be highly sensitive to the precise manner in which a cognitive system is engaged and/or relate strongly to differences in behavioral performance. To test this, we examined EEG activity in younger adults as they performed a cognitive skill learning task and during rest. Several measures of EEG variability and signal strength were calculated in overlapping time windows that spanned the trial interval. We performed a systematic examination of the factors that most strongly influenced the variability and strength of EEG activity. First, we examined the relative sensitivity of each measure to across-subject variation (within blocks) and across-block variation (within subjects). We found that the across-subject variation in EEG variability and signal strength was much stronger than the across-block variation. Second, we examined the sensitivity of each measure to different sources of across-block variation during skill acquisition. We found that key task-driven changes in EEG activity were best reflected in changes in the strength, rather than the variability, of EEG activity. Lastly, we examined across-subject variation in each measure and its relationship with behavior. We found that individual differences in response time measures were best reflected in individual differences in the variability, rather than the strength, of EEG activity. Importantly, we found that individual differences in EEG variability related strongly to stable indicators of subject identity rather than dynamic indicators of subject performance. We therefore suggest that EEG variability may provide a more sensitive subject-driven measure of individual differences than task-driven signal of interest., Competing Interests: Declaration of Competing Interest None., (Copyright © 2022. Published by Elsevier Inc.)

The Virtual Brain (TVB) is now available as open-source services on the cloud research platform EBRAINS (ebrains.eu). It offers software for constructing, simulating and analysing brain network models including the TVB simulator; magnetic resonance imaging (MRI) processing pipelines to extract structural and functional brain networks; combined simulation of large-scale brain networks with small-scale spiking networks; automatic conversion of user-specified model equations into fast simulation code; simulation-ready brain models of patients and healthy volunteers; Bayesian parameter optimization in epilepsy patient models; data and software for mouse brain simulation; and extensive educational material. TVB cloud services facilitate reproducible online collaboration and discovery of data assets, models, and software embedded in scalable and secure workflows, a precondition for research on large cohort data sets, better generalizability, and clinical translation., Competing Interests: Declaration of competing interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

The brain's ability to extract information from multiple sensory channels is crucial to perception and effective engagement with the environment, but the individual differences observed in multisensory processing lack mechanistic explanation. We hypothesized that, from the perspective of information theory, individuals with more effective multisensory processing will exhibit a higher degree of shared information among distributed neural populations while engaged in a multisensory task, representing more effective coordination of information among regions. To investigate this, healthy young adults completed an audiovisual simultaneity judgment task to measure their temporal binding window (TBW), which quantifies the ability to distinguish fine discrepancies in timing between auditory and visual stimuli. EEG was then recorded during a second run of the simultaneity judgment task, and partial least squares was used to relate individual differences in the TBW width to source-localized EEG measures of local entropy and mutual information, indexing local and distributed processing of information, respectively. The narrowness of the TBW, reflecting more effective multisensory processing, was related to a broad pattern of higher mutual information and lower local entropy at multiple timescales. Furthermore, a small group of temporal and frontal cortical regions, including those previously implicated in multisensory integration and response selection, respectively, played a prominent role in this pattern. Overall, these findings suggest that individual differences in multisensory processing are related to widespread individual differences in the balance of distributed versus local information processing among a large subset of brain regions, with more distributed information being associated with more effective multisensory processing. The balance of distributed versus local information processing may therefore be a useful measure for exploring individual differences in multisensory processing, its relationship to higher cognitive traits, and its disruption in neurodevelopmental disorders and clinical conditions., (© 2022 Massachusetts Institute of Technology.)

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes., (Copyright © 2022 Good et al.)

Mechanisms linked to demographic, biogeographic, and food-web processes thought to underpin community stability could be affected by habitat size, but the effects of habitat size on community stability remain relatively unknown. We investigated whether those habitat-size-dependent properties influenced community instability and vulnerability to perturbations caused by disturbance. This is particularly important given that human exploitation is contracting ecosystems, and abiotic perturbations are becoming more severe and frequent. We used a perturbation experiment in which 10 streams, spanning three orders of magnitude in habitat size, were subjected to simulated bed movement akin to a major flood disturbance event. We measured the resistance, resilience, and variability of basal resources, and population and community-level responses across the stream habitat-size gradient immediately before, and at 0.5, 5, 10, 20, and 40 d post-disturbance. Resistance to disturbance consistently increased with stream size in all response variables. In contrast, resilience was significantly higher in smaller streams for some response variables. However, this higher resilience of small ecosystems was insufficient to compensate for their lower resistance, and communities of smaller streams were thus more variable over time than those of larger streams. Compensatory dynamics of populations, especially for predators, stabilized some aspects of communities, but these mechanisms were unrelated to habitat size. Together, our results provide compelling evidence for the links between habitat size and community stability, and should motivate ecologists and managers to consider how changes in the size of habitats will alter the vulnerability of ecosystems to perturbations caused by environmental disturbance., (© 2021 by the Ecological Society of America.)

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development., (© 2021. The Author(s).)

Large neuroimaging datasets, including information about structural connectivity (SC) and functional connectivity (FC), play an increasingly important role in clinical research, where they guide the design of algorithms for automated stratification, diagnosis or prediction. A major obstacle is, however, the problem of missing features [e.g., lack of concurrent DTI SC and resting-state functional magnetic resonance imaging (rsfMRI) FC measurements for many of the subjects]. We propose here to address the missing connectivity features problem by introducing strategies based on computational whole-brain network modeling. Using two datasets, the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and a healthy aging dataset, for proof-of-concept, we demonstrate the feasibility of virtual data completion (i.e., inferring "virtual FC" from empirical SC or "virtual SC" from empirical FC), by using self-consistent simulations of linear and nonlinear brain network models. Furthermore, by performing machine learning classification (to separate age classes or control from patient subjects), we show that algorithms trained on virtual connectomes achieve discrimination performance comparable to when trained on actual empirical data; similarly, algorithms trained on virtual connectomes can be used to successfully classify novel empirical connectomes. Completion algorithms can be combined and reiterated to generate realistic surrogate connectivity matrices in arbitrarily large number, opening the way to the generation of virtual connectomic datasets with network connectivity information comparable to the one of the original data., (Copyright © 2021 Arbabyazd et al.)

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Stefanovski, Meier, Pai, Triebkorn, Lett, Martin, Bülau, Hofmann-Apitius, Solodkin, McIntosh and Ritter.)

Traditionally, resistance and resilience are associated with good ecological health, often underpinning restoration goals. However, degraded ecosystems can also be highly resistant and resilient, making restoration difficult: degraded communities often become dominated by hyper-tolerant species, preventing recolonization and resulting in low biodiversity and poor ecosystem function. Using streams as a model, we undertook a mesocosm experiment to test if degraded community presence hindered biological recovery. We established 12 mesocosms, simulating physically healthy streams. Degraded invertebrate communities were established in half, mimicking the post-restoration scenario of physical recovery without biological recovery. We then introduced a healthy colonist community to all mesocosms, testing if degraded community presence influenced healthy community establishment. Colonists established less readily in degraded community mesocosms, with larger decreases in abundance of sensitive taxa, likely driven by biotic interactions rather than abiotic constraints. Resource depletion by the degraded community likely increased competition, driving priority effects. Colonists left by drifting, but also by accelerating development, reducing time to emergence but sacrificing larger body size. Since degraded community presence prevented colonist establishment, our experiment suggests successful restoration must address both abiotic and biotic factors, especially those that reinforce the 'negative' resistance and resilience which perpetuate degraded communities and are typically overlooked.

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Griffiths, McIntosh and Lefebvre.)

Functional Connectivity (FC) during resting-state or task conditions is not static but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely "order-driven" dynamics, in which the mean FC is preserved, and from a purely "randomness-driven" scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Reciprocal subsidies link ecosystems into meta-ecosystems, but energy transfer to organisms that do not cross boundaries may create sinks, reducing reciprocal subsidy transfer. We investigated how the type of subsidy and top predator presence influenced reciprocal flows of energy, by manipulating the addition of terrestrial leaf and terrestrial insect subsidies to experimental freshwater pond mesocosms with and without predatory fish. Over 18 months, fortnightly addition of subsidies (terrestrial beetle larvae) to top-predators was crossed with monthly addition of subsidies (willow leaves) to primary consumers in mesocosms with and without top predators (upland bullies) in a 2 × 2 × 2 factorial design in four replicate blocks. Terrestrial insect subsidies increased reciprocal flows, measured as the emergence of aquatic insects out of mesocosms, but leaf subsidies dampened those effects. However, the presence of fish and snails, consumers with no terrestrial life stage, usurped and retained the energy within in the aquatic ecosystem, creating a cross-ecosystem bottleneck to energy flow. Thus, changes in species composition of donor or recipient food webs within a meta-ecosystems can alter reciprocal subsidies through cross-ecosystem bottlenecks.