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235 Research products

  • Neuroinformatics
  • 2023-2023
  • Open Access
  • UK Research and Innovation

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pablo Lemos; Niall Jeffrey; Miles Cranmer; Shirley Ho; +1 Authors

    We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery. 12 pages, 6 figures, under review

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    Machine Learning: Science and Technology
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    https://doi.org/10.48550/arxiv...
    Article . 2022
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Machine Learning: Sc...arrow_drop_down
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      Machine Learning: Science and Technology
      Article . 2023 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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      https://doi.org/10.48550/arxiv...
      Article . 2022
      License: arXiv Non-Exclusive Distribution
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    Authors: Ramanan, Siddharth; Halai, Ajay D; Garcia-Penton, Lorna; Perry, Alistair G; +10 Authors

    Abstract Background Clinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants. Methods Forty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes). Results Four behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant’s characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations. Conclusions Cognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

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    Apollo
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    Alzheimer’s Research & Therapy
    Article . 2023 . Peer-reviewed
    License: CC BY
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      Apollo
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      Alzheimer’s Research & Therapy
      Article . 2023 . Peer-reviewed
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    Authors: Antonio Stanziola; José A. Pineda-Pardo; Bradley Treeby;

    Transcranial ultrasound simulations are increasingly used to predict in situ exposure parameters for ultrasound therapies in the brain. However, there can be considerable uncertainty in estimating the acoustic medium properties of the skull and brain from computed tomography (CT) images. Here, we show how the resulting uncertainty in the simulated acoustic field can be predicted in a computationally efficient way using linear uncertainty propagation. Results for a representative transcranial simulation using a focused bowl transducer at 500 kHz show good agreement with unbiased uncertainty estimates obtained using Monte Carlo. Comment: 6 pages, 5 figures

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    JASA Express Letters
    Article . 2023 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2022
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      JASA Express Letters
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      https://doi.org/10.48550/arxiv...
      Article . 2022
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    Authors: Gul Hameed Khan; Nadeem Ahmad Khan; Muhammad Awais Bin Altaf; Qammer Abbasi;

    This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children’s Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

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    Sensors
    Other literature type . Article . 2023 . Peer-reviewed
    License: CC BY
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    Sensors
    Article . 2023
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      Sensors
      Other literature type . Article . 2023 . Peer-reviewed
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      Sensors
      Article . 2023
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    Authors: M. Beccaria; G. P. Korchemsky; A. A. Tseytlin;

    We apply the localization technique to compute the free energy on four-sphere and the circular BPS Wilson loop in the four-dimensional $\cal N$=2 superconformal $Sp(2N)$ gauge theory containing vector multiplet coupled to four hypermultiplets in fundamental representation and one hypermultiplet in rank-2 antisymmetric representation. This theory is unique among similar $\cal N$=2 superconformal models that are planar-equivalent to $\cal N$=4 SYM in that the corresponding localization matrix model has the interaction potential containing single-trace terms only. We exploit this property to show that, to any order in large $N$ expansion and an arbitrary 't Hooft coupling $\lambda$, the free energy and the Wilson loop satisfy Toda lattice equations. Solving these equations at strong coupling, we find remarkably simple expressions for these observables which include all corrections in $1/N$ and $1/\sqrt{\lambda}$. We also compute the leading exponentially suppressed ${\cal O}(e^{-\sqrt{\lambda}})$ corrections and consider a generalization to the case when the fundamental hypermultiplets have a non-zero mass. The string theory dual of this $\cal N$=2 gauge theory should be a particular orientifold of AdS$_5 \times S^5$ type IIB string and we discuss the string theory interpretation of the obtained strong-coupling results. Comment: 41 pages. v2: minor correction; v3: eqs.(2.41) and (2.43) corrected

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    Journal of High Energy Physics
    Article . 2023 . Peer-reviewed
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    Article . 2022
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      Journal of High Energy Physics
      Article . 2023 . Peer-reviewed
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    Authors: Luca Manneschi; Andrew C. Lin; Eleni Vasilaki;

    ``Sparse'' neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, ``sparsity'' is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created when high spiking thresholds prevent neuronal activity. Here, we introduce sparsity into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to contribute to decision-making but suppressing information from neurons with high thresholds. This approach, which we term ``SpaRCe,'' optimizes the sparsity level of the reservoir without affecting the reservoir dynamics. The read-out weights and the thresholds are learned by an online gradient rule that minimizes an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing interneuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe on classification problems and find that threshold learning improves performance compared to standard reservoir computing. SpaRCe alleviates the problem of catastrophic forgetting, a problem most evident in standard echo state networks (ESNs) and recurrent neural networks in general, due to increasing the number of task-specialized neurons that are included in the network decisions.

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    IEEE Transactions on Neural Networks and Learning Systems
    Other literature type . Article . 2021 . 2023 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2019
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      IEEE Transactions on Neural Networks and Learning Systems
      Other literature type . Article . 2021 . 2023 . Peer-reviewed
      License: CC BY
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      https://doi.org/10.48550/arxiv...
      Article . 2019
      License: arXiv Non-Exclusive Distribution
      Data sources: Datacite
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    Authors: Andrea L. Conroy; Dibyadyuti Datta; Angelika Hoffmann; Samuel C. Wassmer;

    Severe falciparum malaria is a medical emergency and a leading cause of death and neurodisability in endemic areas. Common complications include acute kidney injury (AKI) and cerebral malaria, and recent studies have suggested links between kidney and brain dysfunction in Plasmodium falciparum infection. Here, we review these new findings and present the hypothesis of a pivotal pathogenic crosstalk between the kidneys and the brain in severe falciparum malaria. We highlight the evidence of a role for distant organ involvement in the development of cerebral malaria and subsequent neurocognitive impairment post-recovery, describe the challenges associated with current diagnostic shortcomings for both AKI and brain involvement in severe falciparum malaria, and explore novel potential therapeutic strategies.

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    https://doi.org/10.48350/17836...
    Article . 2023
    License: CC BY
    Data sources: Datacite
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    Trends in Parasitology
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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      https://doi.org/10.48350/17836...
      Article . 2023
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      Trends in Parasitology
      Article . 2023 . Peer-reviewed
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    Authors: Sevda T. Boyanova; Ethlyn Lloyd-Morris; Christopher Corpe; K. Miraz Rahman; +10 Authors

    AbstractBlood-brain barrier (BBB) dysfunction may be involved in the increased sensitivity of Alzheimer’s disease (AD) patients to antipsychotics, including amisulpride. Studies indicate that antipsychotics interact with facilitated glucose transporters (GLUT), including GLUT1, and that GLUT1 BBB expression decreases in AD. We tested the hypotheses that amisulpride (charge: +1) interacts with GLUT1, and that BBB transport of amisulpride is compromised in AD.GLUT1 substrates and inhibitors, and GLUT-interacting antipsychotics were identified by literature review and their physicochemical characteristics summarised. Interactions between amisulpride, and GLUT1 were studied usingin silicoapproaches and the human cerebral endothelial cell line, hCMEC/D3. Brain distribution of [3H]amisulpride was determined usingin situperfusion in wild type (WT) and 5xFamilial AD (5xFAD) mice. With transmission electron microscopy (TEM) we investigated brain capillary degeneration in WT and 5xFAD mice, and human samples. Western blots determined BBB transporter expression in mouse and human.Literature review revealed that, although D-glucose has no charge, charged molecules can interact with GLUT1. GLUT1 substrates are smaller (184.95±6.45g/mol) than inhibitors (325.50±14.40g/mol), and GLUT-interacting antipsychotics (369.38±16.04). Molecular docking showed beta-D-glucose (free energy binding: –15.39kcal/mol) and amisulpride (–29.04kcal/mol) interact with GLUT1. Amisulpride did not affect [14C]D-glucose accumulation in hCMEC/D3. 5xFAD mice showed increased brain [3H]amisulpride uptake, and no cerebrovascular space changes compared to WT. TEM revealed brain capillary degeneration in human AD. There was no significant effect of AD on mouse GLUT1 and P-gp BBB expression, and in human GLUT1 expression. In contrast, caudate P-glycoprotein expression was decreased in human AD capillaries versus controls.This study provides new details about the BBB transport of amisulpride, evidence that amisulpride interacts with GLUT1, and that BBB transporter expression is altered in AD. This suggests that antipsychotics exacerbate the cerebral hypometabolism in AD. Further research into the mechanism of amisulpride transport by GLUT1 is important for improving antipsychotics safety.

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    PLoS ONE
    Article . 2023 . Peer-reviewed
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    Data sources: Crossref
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    PLoS ONE
    Article . 2023
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      PLoS ONE
      Article . 2023 . Peer-reviewed
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      PLoS ONE
      Article . 2023
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    Authors: Nicole S. McKay; Brian A. Gordon; Russ C. Hornbeck; Aylin Dincer; +62 Authors

    AbstractThe Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case–control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual’s point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of ‘sporadic’ AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers.

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    Nature Neuroscience
    Article . 2023 . Peer-reviewed
    License: CC BY
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    DZNE Pub
    Article . 2023
    Data sources: DZNE Pub
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      Nature Neuroscience
      Article . 2023 . Peer-reviewed
      License: CC BY
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      DZNE Pub
      Article . 2023
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    Authors: Hastings, Nataly; Yu, Yi-Lin; Huang, Botian; Middya, Sagnik; +9 Authors

    AbstractAstrocytes are diverse brain cells that form large networks communicating via gap junctions and chemical transmitters. Despite recent advances, the functions of astrocytic networks in information processing in the brain are not fully understood. In culture, brain slices, and in vivo, astrocytes, and neurons grow in tight association, making it challenging to establish whether signals that spread within astrocytic networks communicate with neuronal groups at distant sites, or whether astrocytes solely respond to their local environments. A multi‐electrode array (MEA)‐based device called AstroMEA is designed to separate neuronal and astrocytic networks, thus allowing to study the transfer of chemical and/or electrical signals transmitted via astrocytic networks capable of changing neuronal electrical behavior. AstroMEA demonstrates that cortical astrocytic networks can induce a significant upregulation in the firing frequency of neurons in response to a theta‐burst charge‐balanced biphasic current stimulation (5 pulses of 100 Hz × 10 with 200 ms intervals, 2 s total duration) of a separate neuronal‐astrocytic group in the absence of direct neuronal contact. This result corroborates the view of astrocytic networks as a parallel mechanism of signal transmission in the brain that is separate from the neuronal connectome. Translationally, it highlights the importance of astrocytic network protection as a treatment target.

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    Apollo
    Article . 2023
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    Advanced Science
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    Advanced Science
    Article . 2023 . Peer-reviewed
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      Advanced Science
      Article . 2023
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      Advanced Science
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    Authors: Pablo Lemos; Niall Jeffrey; Miles Cranmer; Shirley Ho; +1 Authors

    We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery. 12 pages, 6 figures, under review

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    Machine Learning: Science and Technology
    Article . 2023 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2022
    License: arXiv Non-Exclusive Distribution
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      Machine Learning: Science and Technology
      Article . 2023 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
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      https://doi.org/10.48550/arxiv...
      Article . 2022
      License: arXiv Non-Exclusive Distribution
      Data sources: Datacite
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    Authors: Ramanan, Siddharth; Halai, Ajay D; Garcia-Penton, Lorna; Perry, Alistair G; +10 Authors

    Abstract Background Clinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants. Methods Forty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes). Results Four behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant’s characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations. Conclusions Cognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

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    Other literature type . 2023
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    Alzheimer’s Research & Therapy
    Article . 2023 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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      Alzheimer’s Research & Therapy
      Article . 2023 . Peer-reviewed
      License: CC BY
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    Authors: Antonio Stanziola; José A. Pineda-Pardo; Bradley Treeby;

    Transcranial ultrasound simulations are increasingly used to predict in situ exposure parameters for ultrasound therapies in the brain. However, there can be considerable uncertainty in estimating the acoustic medium properties of the skull and brain from computed tomography (CT) images. Here, we show how the resulting uncertainty in the simulated acoustic field can be predicted in a computationally efficient way using linear uncertainty propagation. Results for a representative transcranial simulation using a focused bowl transducer at 500 kHz show good agreement with unbiased uncertainty estimates obtained using Monte Carlo. Comment: 6 pages, 5 figures

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