The brain is organized into networks at multiple resolutions, or scales, yet studies of functional network development typically focus on a single scale. Here, we derive personalized functional ...networks across 29 scales in a large sample of youths (n = 693, ages 8-23 years) to identify multi-scale patterns of network re-organization related to neurocognitive development. We found that developmental shifts in inter-network coupling reflect and strengthen a functional hierarchy of cortical organization. Furthermore, we observed that scale-dependent effects were present in lower-order, unimodal networks, but not higher-order, transmodal networks. Finally, we found that network maturation had clear behavioral relevance: the development of coupling in unimodal and transmodal networks are dissociably related to the emergence of executive function. These results suggest that the development of functional brain networks align with and refine a hierarchy linked to cognition.
A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence ...between two or more brain maps. This “correspondence problem” affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.
•A new method is developed to test the anatomical correspondence between brain maps.•Random rotational permutations generate rigorous null models of correspondence.•The correspondence of structural, functional and resting-state maps is quantified.•These methods are publicly available for future applications.
Acute geriatric units (AGUs) require efficient discharge planning tools. Risk factors for discharge from an AGU to post-acute care (PAC) have not previously been investigated in detail.
The objective ...is to identify risk factors for PAC transfer. The DAMAGE (prospective multicenter cohort) consecutively included more than 3500 subjects aged 75 or older and admitted to an AGU. The patients underwent a comprehensive geriatric assessment (CGA) during their stay in the AGU. Only community-dwelling patients admitted to the AGU from the emergency department were included in the analysis. We recorded the characteristics of the care pathway and identified risk factors for discharge to home or to a PAC facility.
1928 patients were included. Loss of functional independence (a decrease in the Katz activities of daily living (ADL) score between 1 month prior to admission and AGU admission), living alone, social isolation, a high Katz ADL score at home, a low Katz ADL on admission, and delirium on admission were risk factors for transfer to PAC. Obesity, an elevated serum albumin level, and community-acquired infection were associated with discharge to home. Neither sex nor age was a risk factor for home discharge or transfer to PAC.
The present results might help clinicians and discharge planning teams to identify patients at risk of transfer to PAC more reliably and promptly in AGUs.
•We propose a new method CLEAN-R to test and localize intermodal correspondence.•Modeling modality-specific spatial autocorrelations dramatically improves power.•We further improve reproducibility by ...using clusterwise inference.•Type I error is controlled using null maps generated by permuting subjects.•A R package supports the fast implementation of CLEAN-R.
With the increasing availability of neuroimaging data from multiple modalities—each providing a different lens through which to study brain structure or function—new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.
The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS ...Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.
•CuBIDS is a workflow and software package for curating MRI BIDS data.•CuBIDS summarizes the heterogeneity in a MRI BIDS dataset.•CuBIDS prepares BIDS data for successful preprocessing pipeline runs.•CuBIDS helps users perform metadata-based quality control on MRI BIDS data.
Individual differences in cognition during childhood are associated with important social, physical, and mental health outcomes in adolescence and adulthood. Given that cortical surface arealization ...during development reflects the brain's functional prioritization, quantifying variation in the topography of functional brain networks across the developing cortex may provide insight regarding individual differences in cognition. We test this idea by defining personalized functional networks (PFNs) that account for interindividual heterogeneity in functional brain network topography in 9-10 year olds from the Adolescent Brain Cognitive Development℠ Study. Across matched discovery (n = 3525) and replication (n = 3447) samples, the total cortical representation of fronto-parietal PFNs positively correlates with general cognition. Cross-validated ridge regressions trained on PFN topography predict cognition in unseen data across domains, with prediction accuracy increasing along the cortex's sensorimotor-association organizational axis. These results establish that functional network topography heterogeneity is associated with individual differences in cognition before the critical transition into adolescence.
This article proposed and validated an original and automatic method based on synchrotron X-ray microtomography to characterise non-destructively, in 3D, the mineral fillers that may be present in ...fibrous composite materials. The approach consists of (i) obtaining the 3D internal structure of the sample in a non invasive way, (ii) identifying the fillers in the 3D microstructure using appropriate image processing tools, (iii) calculating the filler content on the numerical data, and (iv) validating the representativity of the data sets by evaluating the representative elementary volume. This method was successfully applied in the case of paper samples. The numerical filler content were in good agreement with standards. This method opens new perspectives in terms of characterisation of filler spatial repartition.
Human cortical maturation has been posited to be organized along the sensorimotor-association axis, a hierarchical axis of brain organization that spans from unimodal sensorimotor cortices to ...transmodal association cortices. Here, we investigate the hypothesis that the development of functional connectivity during childhood through adolescence conforms to the cortical hierarchy defined by the sensorimotor-association axis. We tested this pre-registered hypothesis in four large-scale, independent datasets (total n = 3355; ages 5-23 years): the Philadelphia Neurodevelopmental Cohort (n = 1207), Nathan Kline Institute-Rockland Sample (n = 397), Human Connectome Project: Development (n = 625), and Healthy Brain Network (n = 1126). Across datasets, the development of functional connectivity systematically varied along the sensorimotor-association axis. Connectivity in sensorimotor regions increased, whereas connectivity in association cortices declined, refining and reinforcing the cortical hierarchy. These consistent and generalizable results establish that the sensorimotor-association axis of cortical organization encodes the dominant pattern of functional connectivity development.
Different methods for measuring the friction forces are investigated in this paper. We consider the paper-on-paper contact as an example of application. We first underline several drawbacks for the ...two main standard methods, namely the inclined and horizontal plane methods. In particular, the horizontal plane test method often involves stick-slip oscillations that make the measurement of the friction force impossible. We then propose a method for characterizing these oscillations and removing their influence on the friction force measurement. The comparison of the proposed method to standards suggests that our proposed method delivers measurements that are much more accurate and repeatable. We finally discuss the validity of averaging the friction force measured during the sliding movement.
•We examine changes in friction coefficients with the experimental protocol.•We built an experimental device dedicated to the measurement of friction.•The proposed method gives lower dispersions and better accuracies than standard methods.
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to ...understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.
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•We define spatially covarying fiber covariance networks using machine learning•Networks align with major tracts, while also capturing distinct spatial patterns•Most networks show age-related increases in fiber network properties•Fiber covariance networks are linked to developmental changes in executive function
Bagautdinova et al. use machine learning to reveal fiber covariance networks in white matter in a large sample of youth. Most fiber covariance networks show age-related increases in fiber network properties, which are also related to developmental changes in executive function.
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