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1,201 results on '"Murphy, Kieran A."'

1. Which bits went where? Past and future transfer entropy decomposition with the information bottleneck

Author

Murphy, Kieran A., Yin, Zhuowen, and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Computer Science - Information Theory
Abstract

Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the originating process's past and that of the receiving process's future. We employ the information bottleneck (IB) to compress the time series and identify the transferred entropy. We apply our method to decompose the transfer entropy in several synthetic recurrent processes and an experimental mouse dataset of concurrent behavioral and neural activity. Our approach highlights the nuanced dynamics within information flow, laying a foundation for future explorations into the intricate interplay of temporal processes in complex systems., Comment: NeurIPS 2024 workshop "Machine learning and the physical sciences" Camera ready

Published
2024

2. Comparing the information content of probabilistic representation spaces

Author

Murphy, Kieran A., Dillavou, Sam, and Bassett, Dani S.

Subjects
Computer Science - Machine Learning
Abstract

Probabilistic representation spaces convey information about a dataset, and to understand the effects of factors such as training loss and network architecture, we seek to compare the information content of such spaces. However, most existing methods to compare representation spaces assume representations are points, and neglect the distributional nature of probabilistic representations. Here, instead of building upon point-based measures of comparison, we build upon classic methods from literature on hard clustering. We generalize two information-theoretic methods of comparing hard clustering assignments to be applicable to general probabilistic representation spaces. We then propose a practical method of estimation that is based on fingerprinting a representation space with a sample of the dataset and is applicable when the communicated information is only a handful of bits. With unsupervised disentanglement as a motivating problem, we find information fragments that are repeatedly contained in individual latent dimensions in VAE and InfoGAN ensembles. Then, by comparing the full latent spaces of models, we find highly consistent information content across datasets, methods, and hyperparameters, even though there is often a point during training with substantial variety across repeat runs. Finally, we leverage the differentiability of the proposed method and perform model fusion by synthesizing the information content of multiple weak learners, each incapable of representing the global structure of a dataset. Across the case studies, the direct comparison of information content provides a natural basis for understanding the processing of information., Comment: Code: https://github.com/murphyka/representation-space-info-comparison

Published
2024

3. Mechanical prions: Self-assembling microstructures

Author

Ouellet, Mathieu, Bassett, Dani S., Bassett, Lee C., Murphy, Kieran A., and Patankar, Shubhankar P.

Subjects
Condensed Matter - Soft Condensed Matter, Physics - Biological Physics
Abstract

Prions are misfolded proteins that transmit their structural arrangement to neighboring proteins. In biological systems, prion dynamics can produce a variety of complex functional outcomes. Yet, an understanding of prionic causes has been hampered by the fact that few computational models exist that allow for experimental design, hypothesis testing, and control. Here, we identify essential prionic properties and present a biologically inspired model of prions using simple mechanical structures capable of undergoing complex conformational change. We demonstrate the utility of our approach by designing a prototypical mechanical prion and validating its properties experimentally. Our work provides a design framework for harnessing and manipulating prionic properties in natural and artificial systems., Comment: Added supplements, 25 pages, 11 figures

Published
2024

5. Machine-learning optimized measurements of chaotic dynamical systems via the information bottleneck

Author

Murphy, Kieran A. and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Computer Science - Information Theory, Nonlinear Sciences - Chaotic Dynamics
Abstract

Deterministic chaos permits a precise notion of a "perfect measurement" as one that, when obtained repeatedly, captures all of the information created by the system's evolution with minimal redundancy. Finding an optimal measurement is challenging, and has generally required intimate knowledge of the dynamics in the few cases where it has been done. We establish an equivalence between a perfect measurement and a variant of the information bottleneck. As a consequence, we can employ machine learning to optimize measurement processes that efficiently extract information from trajectory data. We obtain approximately optimal measurements for multiple chaotic maps and lay the necessary groundwork for efficient information extraction from general time series., Comment: Project page: https://distributed-information-bottleneck.github.io

Published
2023

6. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome.

Author

Ge, Ruiyang, Ching, Christopher, Bassett, Anne, Kushan, Leila, Antshel, Kevin, van Amelsvoort, Therese, Bakker, Geor, Butcher, Nancy, Campbell, Linda, Chow, Eva, Craig, Michael, Crossley, Nicolas, Cunningham, Adam, Daly, Eileen, Doherty, Joanne, Durdle, Courtney, Emanuel, Beverly, Fiksinski, Ania, Forsyth, Jennifer, Fremont, Wanda, Goodrich-Hunsaker, Naomi, Gudbrandsen, Maria, Gur, Raquel, Jalbrzikowski, Maria, Kates, Wendy, Lin, Amy, Linden, David, McCabe, Kathryn, McDonald-McGinn, Donna, Moss, Hayley, Murphy, Declan, Murphy, Kieran, Owen, Michael, Villalon-Reina, Julio, Repetto, Gabriela, Roalf, David, Ruparel, Kosha, Schmitt, J, Schuite-Koops, Sanne, Angkustsiri, Kathleen, Sun, Daqiang, Vajdi, Ariana, van den Bree, Marianne, Vorstman, Jacob, Thompson, Paul, Vila-Rodriguez, Fidel, and Bearden, Carrie

Subjects
22q11 deletion syndrome, gray matter volume, magnetic resonnance imaging, source-based morphometry, Female, Humans, Adolescent, Male, DiGeorge Syndrome, Magnetic Resonance Imaging, Brain, Psychotic Disorders, Gray Matter
Abstract

22q11.2 deletion syndrome (22q11DS) is the most frequently occurring microdeletion in humans. It is associated with a significant impact on brain structure, including prominent reductions in gray matter volume (GMV), and neuropsychiatric manifestations, including cognitive impairment and psychosis. It is unclear whether GMV alterations in 22q11DS occur according to distinct structural patterns. Then, 783 participants (470 with 22q11DS: 51% females, mean age [SD] 18.2 [9.2]; and 313 typically developing [TD] controls: 46% females, mean age 18.0 [8.6]) from 13 datasets were included in the present study. We segmented structural T1-weighted brain MRI scans and extracted GMV images, which were then utilized in a novel source-based morphometry (SBM) pipeline (SS-Detect) to generate structural brain patterns (SBPs) that capture co-varying GMV. We investigated the impact of the 22q11.2 deletion, deletion size, intelligence quotient, and psychosis on the SBPs. Seventeen GMV-SBPs were derived, which provided spatial patterns of GMV covariance associated with a quantitative metric (i.e., loading score) for analysis. Patterns of topographically widespread differences in GMV covariance, including the cerebellum, discriminated individuals with 22q11DS from healthy controls. The spatial extents of the SBPs that revealed disparities between individuals with 22q11DS and controls were consistent with the findings of the univariate voxel-based morphometry analysis. Larger deletion size was associated with significantly lower GMV in frontal and occipital SBPs; however, history of psychosis did not show a strong relationship with these covariance patterns. 22q11DS is associated with distinct structural abnormalities captured by topographical GMV covariance patterns that include the cerebellum. Findings indicate that structural anomalies in 22q11DS manifest in a nonrandom manner and in distinct covarying anatomical patterns, rather than a diffuse global process. These SBP abnormalities converge with previously reported cortical surface area abnormalities, suggesting disturbances of early neurodevelopment as the most likely underlying mechanism.

Published
2024

7. Intrinsically motivated graph exploration using network theories of human curiosity

Author

Patankar, Shubhankar P., Ouellet, Mathieu, Cervino, Juan, Ribeiro, Alejandro, Murphy, Kieran A., and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Social and Information Networks
Abstract

Intrinsically motivated exploration has proven useful for reinforcement learning, even without additional extrinsic rewards. When the environment is naturally represented as a graph, how to guide exploration best remains an open question. In this work, we propose a novel approach for exploring graph-structured data motivated by two theories of human curiosity: the information gap theory and the compression progress theory. The theories view curiosity as an intrinsic motivation to optimize for topological features of subgraphs induced by nodes visited in the environment. We use these proposed features as rewards for graph neural-network-based reinforcement learning. On multiple classes of synthetically generated graphs, we find that trained agents generalize to longer exploratory walks and larger environments than are seen during training. Our method computes more efficiently than the greedy evaluation of the relevant topological properties. The proposed intrinsic motivations bear particular relevance for recommender systems. We demonstrate that next-node recommendations considering curiosity are more predictive of human choices than PageRank centrality in several real-world graph environments., Comment: 15 pages, 5 figures in main text, and 18 pages, 9 figures in supplement

Published
2023

8. Information decomposition in complex systems via machine learning

Author

Murphy, Kieran A. and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Condensed Matter - Soft Condensed Matter, Computer Science - Information Theory, Physics - Data Analysis, Statistics and Probability
Abstract

One of the fundamental steps toward understanding a complex system is identifying variation at the scale of the system's components that is most relevant to behavior on a macroscopic scale. Mutual information provides a natural means of linking variation across scales of a system due to its independence of functional relationship between observables. However, characterizing the manner in which information is distributed across a set of observables is computationally challenging and generally infeasible beyond a handful of measurements. Here we propose a practical and general methodology that uses machine learning to decompose the information contained in a set of measurements by jointly optimizing a lossy compression of each measurement. Guided by the distributed information bottleneck as a learning objective, the information decomposition identifies the variation in the measurements of the system state most relevant to specified macroscale behavior. We focus our analysis on two paradigmatic complex systems: a Boolean circuit and an amorphous material undergoing plastic deformation. In both examples, the large amount of entropy of the system state is decomposed, bit by bit, in terms of what is most related to macroscale behavior. The identification of meaningful variation in data, with the full generality brought by information theory, is made practical for studying the connection between micro- and macroscale structure in complex systems., Comment: Project page: https://distributed-information-bottleneck.github.io/

Published
2023

9. Chromatin regulators in the TBX1 network confer risk for conotruncal heart defects in 22q11.2DS.

Author

Zhao, Yingjie, Wang, Yujue, Shi, Lijie, McDonald-McGinn, Donna, Crowley, T, McGinn, Daniel, Tran, Oanh, Miller, Daniella, Lin, Jhih-Rong, Zackai, Elaine, Johnston, H, Chow, Eva, Vorstman, Jacob, Vingerhoets, Claudia, van Amelsvoort, Therese, Gothelf, Doron, Swillen, Ann, Breckpot, Jeroen, Vermeesch, Joris, Eliez, Stephan, Schneider, Maude, van den Bree, Marianne, Owen, Michael, Kates, Wendy, Repetto, Gabriela, Shashi, Vandana, Schoch, Kelly, Digilio, M, Unolt, Marta, Putotto, Carolina, Marino, Bruno, Pontillo, Maria, Armando, Marco, Vicari, Stefano, Angkustsiri, Kathleen, Campbell, Linda, Busa, Tiffany, Heine-Suñer, Damian, Murphy, Kieran, Murphy, Declan, García-Miñaúr, Sixto, Fernández, Luis, Zhang, Zhengdong, Goldmuntz, Elizabeth, Gur, Raquel, Emanuel, Beverly, Zheng, Deyou, Marshall, Christian, Bassett, Anne, Wang, Tao, Morrow, Bernice, and Bearden, Carrie

Abstract

Congenital heart disease (CHD) affecting the conotruncal region of the heart, occurs in 40-50% of patients with 22q11.2 deletion syndrome (22q11.2DS). This syndrome is a rare disorder with relative genetic homogeneity that can facilitate identification of genetic modifiers. Haploinsufficiency of TBX1, encoding a T-box transcription factor, is one of the main genes responsible for the etiology of the syndrome. We suggest that genetic modifiers of conotruncal defects in patients with 22q11.2DS may be in the TBX1 gene network. To identify genetic modifiers, we analyzed rare, predicted damaging variants in whole genome sequence of 456 cases with conotruncal defects and 537 controls, with 22q11.2DS. We then performed gene set approaches and identified chromatin regulatory genes as modifiers. Chromatin genes with recurrent damaging variants include EP400, KAT6A, KMT2C, KMT2D, NSD1, CHD7 and PHF21A. In total, we identified 37 chromatin regulatory genes, that may increase risk for conotruncal heart defects in 8.5% of 22q11.2DS cases. Many of these genes were identified as risk factors for sporadic CHD in the general population. These genes are co-expressed in cardiac progenitor cells with TBX1, suggesting that they may be in the same genetic network. The genes KAT6A, KMT2C, CHD7 and EZH2, have been previously shown to genetically interact with TBX1 in mouse models. Our findings indicate that disturbance of chromatin regulatory genes impact the TBX1 gene network serving as genetic modifiers of 22q11.2DS and sporadic CHD, suggesting that there are some shared mechanisms involving the TBX1 gene network in the etiology of CHD.

Published
2023

10. Interpretability with full complexity by constraining feature information

Author

Murphy, Kieran A. and Bassett, Dani S.

Subjects
Computer Science - Machine Learning
Abstract

Interpretability is a pressing issue for machine learning. Common approaches to interpretable machine learning constrain interactions between features of the input, rendering the effects of those features on a model's output comprehensible but at the expense of model complexity. We approach interpretability from a new angle: constrain the information about the features without restricting the complexity of the model. Borrowing from information theory, we use the Distributed Information Bottleneck to find optimal compressions of each feature that maximally preserve information about the output. The learned information allocation, by feature and by feature value, provides rich opportunities for interpretation, particularly in problems with many features and complex feature interactions. The central object of analysis is not a single trained model, but rather a spectrum of models serving as approximations that leverage variable amounts of information about the inputs. Information is allocated to features by their relevance to the output, thereby solving the problem of feature selection by constructing a learned continuum of feature inclusion-to-exclusion. The optimal compression of each feature -- at every stage of approximation -- allows fine-grained inspection of the distinctions among feature values that are most impactful for prediction. We develop a framework for extracting insight from the spectrum of approximate models and demonstrate its utility on a range of tabular datasets., Comment: project page: https://distributed-information-bottleneck.github.io

Published
2022

11. Characterizing information loss in a chaotic double pendulum with the Information Bottleneck

Author

Murphy, Kieran A. and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Computer Science - Information Theory, Nonlinear Sciences - Chaotic Dynamics
Abstract

A hallmark of chaotic dynamics is the loss of information with time. Although information loss is often expressed through a connection to Lyapunov exponents -- valid in the limit of high information about the system state -- this picture misses the rich spectrum of information decay across different levels of granularity. Here we show how machine learning presents new opportunities for the study of information loss in chaotic dynamics, with a double pendulum serving as a model system. We use the Information Bottleneck as a training objective for a neural network to extract information from the state of the system that is optimally predictive of the future state after a prescribed time horizon. We then decompose the optimally predictive information by distributing a bottleneck to each state variable, recovering the relative importance of the variables in determining future evolution. The framework we develop is broadly applicable to chaotic systems and pragmatic to apply, leveraging data and machine learning to monitor the limits of predictability and map out the loss of information., Comment: NeurIPS 2022 workshop paper (Machine learning and the physical sciences); project page: distributed-information-bottleneck.github.io

Published
2022

13. The Distributed Information Bottleneck reveals the explanatory structure of complex systems

Author

Murphy, Kieran A. and Bassett, Dani S.

Subjects
Computer Science - Machine Learning, Condensed Matter - Soft Condensed Matter, Computer Science - Information Theory
Abstract

The fruits of science are relationships made comprehensible, often by way of approximation. While deep learning is an extremely powerful way to find relationships in data, its use in science has been hindered by the difficulty of understanding the learned relationships. The Information Bottleneck (IB) is an information theoretic framework for understanding a relationship between an input and an output in terms of a trade-off between the fidelity and complexity of approximations to the relationship. Here we show that a crucial modification -- distributing bottlenecks across multiple components of the input -- opens fundamentally new avenues for interpretable deep learning in science. The Distributed Information Bottleneck throttles the downstream complexity of interactions between the components of the input, deconstructing a relationship into meaningful approximations found through deep learning without requiring custom-made datasets or neural network architectures. Applied to a complex system, the approximations illuminate aspects of the system's nature by restricting -- and monitoring -- the information about different components incorporated into the approximation. We demonstrate the Distributed IB's explanatory utility in systems drawn from applied mathematics and condensed matter physics. In the former, we deconstruct a Boolean circuit into approximations that isolate the most informative subsets of input components without requiring exhaustive search. In the latter, we localize information about future plastic rearrangement in the static structure of a sheared glass, and find the information to be more or less diffuse depending on the system's preparation. By way of a principled scheme of approximations, the Distributed IB brings much-needed interpretability to deep learning and enables unprecedented analysis of information flow through a system.

Published
2022

15. A normative chart for cognitive development in a genetically selected population

Author

Fiksinski, Ania M, Bearden, Carrie E, Bassett, Anne S, Kahn, René S, Zinkstok, Janneke R, Hooper, Stephen R, Tempelaar, Wanda, McDonald-McGinn, Donna, Swillen, Ann, Emanuel, Beverly, Morrow, Bernice, Gur, Raquel, Chow, Eva, van den Bree, Marianne, Vermeesch, Joris, Warren, Stephen, Owen, Michael, van Amelsvoort, Therese, Eliez, Stephan, Gothelf, Doron, Arango, Celso, Kates, Wendy, Simon, Tony, Murphy, Kieran, Repetto, Gabriela, Suner, Damian Heine, Vicari, Stefano, Cubells, Joseph, Armando, Marco, Philip, Nicole, Campbell, Linda, Garcia-Minaur, Sixto, Schneider, Maude, Shashi, Vandana, Vorstman, Jacob, and Breetvelt, Elemi J

Subjects
Brain Disorders, Serious Mental Illness, Pediatric, Mental Health, Schizophrenia, Clinical Research, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adult, Cognition, DiGeorge Syndrome, Humans, Intelligence Tests, 22q11DS International Consortium on Brain and Behavior, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Certain pathogenic genetic variants impact neurodevelopment and cause deviations from typical cognitive trajectories. Understanding variant-specific cognitive trajectories is clinically important for informed monitoring and identifying patients at risk for comorbid conditions. Here, we demonstrate a variant-specific normative chart for cognitive development for individuals with 22q11.2 deletion syndrome (22q11DS). We used IQ data from 1365 individuals with 22q11DS to construct variant-specific normative charts for cognitive development (Full Scale, Verbal, and Performance IQ). This allowed us to calculate Z-scores for each IQ datapoint. Then, we calculated the change between first and last available IQ assessments (delta Z-IQ-scores) for each individual with longitudinal IQ data (n = 708). We subsequently investigated whether using the variant-specific IQ-Z-scores would decrease required sample size to detect an effect with schizophrenia risk, as compared to standard IQ-scores. The mean Z-IQ-scores for FSIQ, VIQ, and PIQ were close to 0, indicating that participants had IQ-scores as predicted by the normative chart. The mean delta-Z-IQ-scores were equally close to 0, demonstrating a good fit of the normative chart and indicating that, as a group, individuals with 22q11DS show a decline in IQ-scores as they grow into adulthood. Using variant-specific IQ-Z-scores resulted in 30% decrease of required sample size, as compared to the standard IQ-based approach, to detect the association between IQ-decline and schizophrenia (p

Published
2022

16. Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold

Author

Murphy, Kieran, Esteves, Carlos, Jampani, Varun, Ramalingam, Srikumar, and Makadia, Ameesh

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses. To this end, we introduce a method to estimate arbitrary, non-parametric distributions on SO(3). Our key idea is to represent the distributions implicitly, with a neural network that estimates the probability given the input image and a candidate pose. Grid sampling or gradient ascent can be used to find the most likely pose, but it is also possible to evaluate the probability at any pose, enabling reasoning about symmetries and uncertainty. This is the most general way of representing distributions on manifolds, and to showcase the rich expressive power, we introduce a dataset of challenging symmetric and nearly-symmetric objects. We require no supervision on pose uncertainty -- the model trains only with a single pose per example. Nonetheless, our implicit model is highly expressive to handle complex distributions over 3D poses, while still obtaining accurate pose estimation on standard non-ambiguous environments, achieving state-of-the-art performance on Pascal3D+ and ModelNet10-SO(3) benchmarks., Comment: Additional implementation details

Published
2021

18. Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision

Author

Murphy, Kieran A., Jampani, Varun, Ramalingam, Srikumar, and Makadia, Ameesh

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Representational learning forms the backbone of most deep learning applications, and the value of a learned representation is intimately tied to its information content regarding different factors of variation. Finding good representations depends on the nature of supervision and the learning algorithm. We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set. Our key insight is that by seeking correspondence between elements of different sets, we learn strong representations that exclude the inactive factors of variation and isolate the active factors that vary within all sets. As a consequence of focusing on the active factors, our method can leverage a mix of set-supervised and wholly unsupervised data, which can even belong to a different domain. We tackle the challenging problem of synthetic-to-real object pose transfer, without pose annotations on anything, by isolating pose information which generalizes to the category level and across the synthetic/real domain gap. The method can also boost performance in supervised settings, by strengthening intermediate representations, as well as operate in practically attainable scenarios with set-supervised natural images, where quantity is limited and nuisance factors of variation are more plentiful., Comment: CVPR 2022. Code: https://github.com/google-research/google-research/tree/master/isolating_factors

Published
2021

19. Genetic contributors to risk of schizophrenia in the presence of a 22q11.2 deletion.

Author

Cleynen, Isabelle, Engchuan, Worrawat, Hestand, Matthew S, Heung, Tracy, Holleman, Aaron M, Johnston, H Richard, Monfeuga, Thomas, McDonald-McGinn, Donna M, Gur, Raquel E, Morrow, Bernice E, Swillen, Ann, Vorstman, Jacob AS, Bearden, Carrie E, Chow, Eva WC, van den Bree, Marianne, Emanuel, Beverly S, Vermeesch, Joris R, Warren, Stephen T, Owen, Michael J, Chopra, Pankaj, Cutler, David J, Duncan, Richard, Kotlar, Alex V, Mulle, Jennifer G, Voss, Anna J, Zwick, Michael E, Diacou, Alexander, Golden, Aaron, Guo, Tingwei, Lin, Jhih-Rong, Wang, Tao, Zhang, Zhengdong, Zhao, Yingjie, Marshall, Christian, Merico, Daniele, Jin, Andrea, Lilley, Brenna, Salmons, Harold I, Tran, Oanh, Holmans, Peter, Pardinas, Antonio, Walters, James TR, Demaerel, Wolfram, Boot, Erik, Butcher, Nancy J, Costain, Gregory A, Lowther, Chelsea, Evers, Rens, van Amelsvoort, Therese AMJ, van Duin, Esther, Vingerhoets, Claudia, Breckpot, Jeroen, Devriendt, Koen, Vergaelen, Elfi, Vogels, Annick, Crowley, T Blaine, McGinn, Daniel E, Moss, Edward M, Sharkus, Robert J, Unolt, Marta, Zackai, Elaine H, Calkins, Monica E, Gallagher, Robert S, Gur, Ruben C, Tang, Sunny X, Fritsch, Rosemarie, Ornstein, Claudia, Repetto, Gabriela M, Breetvelt, Elemi, Duijff, Sasja N, Fiksinski, Ania, Moss, Hayley, Niarchou, Maria, Murphy, Kieran C, Prasad, Sarah E, Daly, Eileen M, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan G, Buzzanca, Antonio, Fabio, Fabio Di, Digilio, Maria C, Pontillo, Maria, Marino, Bruno, Vicari, Stefano, Coleman, Karlene, Cubells, Joseph F, Ousley, Opal Y, Carmel, Miri, Gothelf, Doron, Mekori-Domachevsky, Ehud, Michaelovsky, Elena, Weinberger, Ronnie, Weizman, Abraham, Kushan, Leila, Jalbrzikowski, Maria, Armando, Marco, Eliez, Stéphan, Sandini, Corrado, and Schneider, Maude

Subjects
International 22q11.2DS Brain and Behavior Consortium, Prevention, Serious Mental Illness, Genetics, Human Genome, Schizophrenia, Neurosciences, Mental Health, Clinical Research, Pediatric, Brain Disorders, 2.1 Biological and endogenous factors, Mental health, Psychiatry, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences
Abstract

Schizophrenia occurs in about one in four individuals with 22q11.2 deletion syndrome (22q11.2DS). The aim of this International Brain and Behavior 22q11.2DS Consortium (IBBC) study was to identify genetic factors that contribute to schizophrenia, in addition to the ~20-fold increased risk conveyed by the 22q11.2 deletion. Using whole-genome sequencing data from 519 unrelated individuals with 22q11.2DS, we conducted genome-wide comparisons of common and rare variants between those with schizophrenia and those with no psychotic disorder at age ≥25 years. Available microarray data enabled direct comparison of polygenic risk for schizophrenia between 22q11.2DS and independent population samples with no 22q11.2 deletion, with and without schizophrenia (total n = 35,182). Polygenic risk for schizophrenia within 22q11.2DS was significantly greater for those with schizophrenia (padj = 6.73 × 10-6). Novel reciprocal case-control comparisons between the 22q11.2DS and population-based cohorts showed that polygenic risk score was significantly greater in individuals with psychotic illness, regardless of the presence of the 22q11.2 deletion. Within the 22q11.2DS cohort, results of gene-set analyses showed some support for rare variants affecting synaptic genes. No common or rare variants within the 22q11.2 deletion region were significantly associated with schizophrenia. These findings suggest that in addition to the deletion conferring a greatly increased risk to schizophrenia, the risk is higher when the 22q11.2 deletion and common polygenic risk factors that contribute to schizophrenia in the general population are both present.

Published
2021

20. Prioritizing Genetic Contributors to Cortical Alterations in 22q11.2 Deletion Syndrome Using Imaging Transcriptomics

Author

Forsyth, Jennifer K, Mennigen, Eva, Lin, Amy, Sun, Daqiang, Vajdi, Ariana, Kushan-Wells, Leila, Ching, Christopher RK, Villalon-Reina, Julio E, Thompson, Paul M, Jonas, Rachel K, Pacheco-Hansen, Laura, Bakker, Geor, van Amelsvoort, Therese, Antshel, Kevin M, Fremont, Wanda, Kates, Wendy R, Campbell, Linda E, McCabe, Kathryn L, Craig, Michael C, Daly, Eileen, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan G, Murphy, Kieran C, Fiksinski, Ania, Koops, Sanne, Vorstman, Jacob, Crowley, T Blaine, Emanuel, Beverly S, Gur, Raquel E, McDonald-McGinn, Donna M, Roalf, David R, Ruparel, Kosha, Schmitt, J Eric, Zackai, Elaine H, Durdle, Courtney A, Goodrich-Hunsaker, Naomi J, Simon, Tony J, Bassett, Anne S, Butcher, Nancy J, Chow, Eva WC, Vila-Rodriguez, Fidel, Cunningham, Adam, Doherty, Joanne L, Linden, David E, Moss, Hayley, Owen, Michael J, van den Bree, Marianne, Crossley, Nicolas A, Repetto, Gabriela M, and Bearden, Carrie E

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Research, Genetics, Pediatric, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, 22q11 Deletion Syndrome, Brain Cortical Thickness, Case-Control Studies, Cerebral Cortex, DNA Copy Number Variations, Gene Expression Profiling, Gene Expression Regulation, Developmental, Haploinsufficiency, Humans, Magnetic Resonance Imaging, MicroRNAs, Mitochondrial Proteins, RNA-Binding Proteins, Receptors, Purinergic P2, copy number variant, cortical thickness, DGCR8, gene expression, surface area, 22q11.2 ENIGMA Consortium, Psychology, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

22q11.2 deletion syndrome (22q11DS) results from a hemizygous deletion that typically spans 46 protein-coding genes and is associated with widespread alterations in brain morphology. The specific genetic mechanisms underlying these alterations remain unclear. In the 22q11.2 ENIGMA Working Group, we characterized cortical alterations in individuals with 22q11DS (n = 232) versus healthy individuals (n = 290) and conducted spatial convergence analyses using gene expression data from the Allen Human Brain Atlas to prioritize individual genes that may contribute to altered surface area (SA) and cortical thickness (CT) in 22q11DS. Total SA was reduced in 22q11DS (Z-score deviance = -1.04), with prominent reductions in midline posterior and lateral association regions. Mean CT was thicker in 22q11DS (Z-score deviance = +0.64), with focal thinning in a subset of regions. Regional expression of DGCR8 was robustly associated with regional severity of SA deviance in 22q11DS; AIFM3 was also associated with SA deviance. Conversely, P2RX6 was associated with CT deviance. Exploratory analysis of gene targets of microRNAs previously identified as down-regulated due to DGCR8 deficiency suggested that DGCR8 haploinsufficiency may contribute to altered corticogenesis in 22q11DS by disrupting cell cycle modulation. These findings demonstrate the utility of combining neuroanatomic and transcriptomic datasets to derive molecular insights into complex, multigene copy number variants.

Published
2021

22. Using common genetic variation to examine phenotypic expression and risk prediction in 22q11.2 deletion syndrome

Author

Davies, Robert W, Fiksinski, Ania M, Breetvelt, Elemi J, Williams, Nigel M, Hooper, Stephen R, Monfeuga, Thomas, Bassett, Anne S, Owen, Michael J, Gur, Raquel E, Morrow, Bernice E, McDonald-McGinn, Donna M, Swillen, Ann, Chow, Eva WC, van den Bree, Marianne, Emanuel, Beverly S, Vermeesch, Joris R, van Amelsvoort, Therese, Arango, Celso, Armando, Marco, Campbell, Linda E, Cubells, Joseph F, Eliez, Stephan, Garcia-Minaur, Sixto, Gothelf, Doron, Kates, Wendy R, Murphy, Kieran C, Murphy, Clodagh M, Murphy, Declan G, Philip, Nicole, Repetto, Gabriela M, Shashi, Vandana, Simon, Tony J, Suñer, Damiàn Heine, Vicari, Stefano, Scherer, Stephen W, Bearden, Carrie E, and Vorstman, Jacob AS

Subjects
Biomedical and Clinical Sciences, Health Sciences, Genetics, Schizophrenia, Serious Mental Illness, Behavioral and Social Science, Prevention, Brain Disorders, Clinical Research, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adolescent, Adult, Aged, Child, Child, Preschool, Cognitive Dysfunction, Cohort Studies, DiGeorge Syndrome, Female, Genetic Variation, Humans, Intellectual Disability, Male, Middle Aged, Multifactorial Inheritance, Phenotype, Risk Factors, Young Adult, International 22q11.2 Brain and Behavior Consortium, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

The 22q11.2 deletion syndrome (22q11DS) is associated with a 20-25% risk of schizophrenia. In a cohort of 962 individuals with 22q11DS, we examined the shared genetic basis between schizophrenia and schizophrenia-related early trajectory phenotypes: sub-threshold symptoms of psychosis, low baseline intellectual functioning and cognitive decline. We studied the association of these phenotypes with two polygenic scores, derived for schizophrenia and intelligence, and evaluated their use for individual risk prediction in 22q11DS. Polygenic scores were not only associated with schizophrenia and baseline intelligence quotient (IQ), respectively, but schizophrenia polygenic score was also significantly associated with cognitive (verbal IQ) decline and nominally associated with sub-threshold psychosis. Furthermore, in comparing the tail-end deciles of the schizophrenia and IQ polygenic score distributions, 33% versus 9% of individuals with 22q11DS had schizophrenia, and 63% versus 24% of individuals had intellectual disability. Collectively, these data show a shared genetic basis for schizophrenia and schizophrenia-related phenotypes and also highlight the future potential of polygenic scores for risk stratification among individuals with highly, but incompletely, penetrant genetic variants.

Published
2020

23. Memory in nonmonotonic stress relaxation of a granular system

Author

Murphy, Kieran A., Kruppe, Jonathon W., and Jaeger, Heinrich M.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

We demonstrate experimentally that a granular packing of glass spheres is capable of storing memory of multiple strain states in the dynamic process of stress relaxation. Modeling the system as a non-interacting population of relaxing elements, we find that the functional form of the predicted relaxation requires a quantitative correction which grows in severity with each additional memory and is suggestive of interactions between elements. Our findings have implications for the broad class of soft matter systems that display memory and anomalous relaxation., Comment: 5 pages, 5 figures

Published
2019
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24. Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions

Author

Marshall, Christian R., Merico, Daniele, Thiruvahindrapuram, Bhooma, Wang, Zhouzhi, Scherer, Stephen W., Howrigan, Daniel P, Ripke, Stephan, Bulik-Sullivan, Brendan, Farh, Kai-How, Fromer, Menachem, Goldstein, Jacqueline I., Huang, Hailiang, Lee, Phil, Daly, Mark J., Neale, Benjamin M., Belliveau, Richard A., Jr., Bergen, Sarah E., Bevilacqua, Elizabeth, Chambert, Kimberley D., O'Dushlaine, Colm, Scolnick, Edward M., Smoller, Jordan W., Moran, Jennifer L., Palotie, Aarno, Petryshen, Tracey L., Wu, Wenting, Greer, Douglas S., Antaki, Danny, Shetty, Aniket, Gujral, Madhusudan, Brandler, William M., Malhotra, Dheeraj, Fuentes Fajarado, Karin V., Maile, Michelle S., Holmans, Peter A., Carrera, Noa, Craddock, Nick, Escott-Price, Valentina, Georgieva, Lyudmila, Hamshere, Marian L., Kavanagh, David, Legge, Sophie E., Pocklington, Andrew J., Richards, Alexander L., Ruderfer, Douglas M., Williams, Nigel M., Kirov, George, Owen, Michael J., Pinto, Dalila, Cai, Guiqing, Davis, Kenneth L., Drapeau, Elodie, Friedman, Joseph I, Haroutunian, Vahram, Parkhomenko, Elena, Reichenberg, Abraham, Silverman, Jeremy M., Buxbaum, Joseph D., Domenici, Enrico, Agartz, Ingrid, Djurovic, Srdjan, Mattingsdal, Morten, Melle, Ingrid, Andreassen, Ole A., Jönsson, Erik G., Söderman, Erik, Albus, Margot, Alexander, Madeline, Laurent, Claudine, Levinson, Douglas F., Amin, Farooq, Atkins, Joshua, Cairns, Murray J., Scott, Rodney J., Tooney, Paul A., Wu, Jing Qin, Bacanu, Silviu A., Bigdeli, Tim B., Reimers, Mark A., Webb, Bradley T., Wolen, Aaron R., Wormley, Brandon K., Kendler, Kenneth S., Riley, Brien P., Kähler, Anna K., Magnusson, Patrik K.E., Hultman, Christina M., Bertalan, Marcelo, Hansen, Thomas, Olsen, Line, Rasmussen, Henrik B., Werge, Thomas, Mattheisen, Manuel, Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Roffman, Joshua L., Byerley, William, Cahn, Wiepke, Kahn, René S, Strengman, Eric, Ophoff, Roel A., Carr, Vaughan J., Catts, Stanley V., Henskens, Frans A., Loughland, Carmel M., Michie, Patricia T., Pantelis, Christos, Schall, Ulrich, Jablensky, Assen V., Kelly, Brian J., Campion, Dominique, Cantor, Rita M., Cheng, Wei, Cloninger, C. Robert, Svrakic, Dragan M, Cohen, David, Cormican, Paul, Donohoe, Gary, Morris, Derek W., Corvin, Aiden, Gill, Michael, Crespo-Facorro, Benedicto, Crowley, James J., Farrell, Martilias S., Giusti-Rodríguez, Paola, Kim, Yunjung, Szatkiewicz, Jin P., Williams, Stephanie, Curtis, David, Pimm, Jonathan, Gurling, Hugh, McQuillin, Andrew, Davidson, Michael, Weiser, Mark, Degenhardt, Franziska, Forstner, Andreas J., Herms, Stefan, Hoffmann, Per, Hofman, Andrea, Cichon, Sven, Nöthen, Markus M., Del Favero, Jurgen, DeLisi, Lynn E., McCarley, Robert W., Levy, Deborah L., Mesholam-Gately, Raquelle I., Seidman, Larry J., Dikeos, Dimitris, Papadimitriou, George N., Dinan, Timothy, Duan, Jubao, Sanders, Alan R., Gejman, Pablo V., Gershon, Elliot S., Dudbridge, Frank, Eichhammer, Peter, Eriksson, Johan, Salomaa, Veikko, Essioux, Laurent, Fanous, Ayman H., Knowles, James A., Pato, Michele T., Pato, Carlos N., Frank, Josef, Meier, Sandra, Schulze, Thomas G., Strohmaier, Jana, Witt, Stephanie H., Rietschel, Marcella, Franke, Lude, Karjalainen, Juha, Freedman, Robert, Olincy, Ann, Freimer, Nelson B., Purcell, Shaun M., Roussos, Panos, Stahl, Eli A., Sklar, Pamela, Giegling, Ina, Hartmann, Annette M., Konte, Bettina, Rujescu, Dan, Godard, Stephanie, Hirschhorn, Joel N., Pers, Tune H., Price, Alkes, Esko, Tõnu, Gratten, Jacob, Lee, S. Hong, Visscher, Peter M., Wray, Naomi R., Mowry, Bryan J., de Haan, Lieuwe, Meijer, Carin J., Hansen, Mark, Ikeda, Masashi, Iwata, Nakao, Joa, Inge, Kalaydjieva, Luba, Keller, Matthew C., Kennedy, James L., Zai, Clement C., Knight, Jo, Lerer, Bernard, Liang, Kung-Yee, Lieberman, Jeffrey, Stroup, T. Scott, Lönnqvist, Jouko, Suvisaari, Jaana, Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, McDonald, Colm, McIntosh, Andrew M., Blackwood, Douglas H.R., Metspalu, Andres, Milani, Lili, Milanova, Vihra, Mokrab, Younes, Collier, David A., Müller-Myhsok, Bertram, Murphy, Kieran C., Murray, Robin M., Powell, John, Myin-Germeys, Inez, Van Os, Jim, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Pulver, Ann E., Nicodemus, Kristin K., Nisenbaum, Laura, Nordin, Annelie, Adolfsson, Rolf, O'Callaghan, Eadbhard, Oh, Sang-Yun, O'Neill, F. Anthony, Paunio, Tiina, Pietiläinen, Olli, Perkins, Diana O., Quested, Digby, Savitz, Adam, Li, Qingqin S., Schwab, Sibylle G., Shi, Jianxin, Spencer, Chris C.A., Thirumalai, Srinivas, Veijola, Juha, Waddington, John, Walsh, Dermot, Wildenauer, Dieter B., Bramon, Elvira, Darvasi, Ariel, Posthuma, Danielle, St. Clair, David, Shanta, Omar, Klein, Marieke, Park, Peter J., Weinberger, Daniel, Moran, John V., Gage, Fred H., Vaccarino, Flora M., Gleeson, Joseph, Mathern, Gary, Courchesne, Eric, Roy, Subhojit, Bizzotto, Sara, Coulter, Michael, Dias, Caroline, D'Gama, Alissa, Ganz, Javier, Hill, Robert, Huang, August Yue, Khoshkhoo, Sattar, Kim, Sonia, Lodato, Michael, Miller, Michael, Borges-Monroy, Rebeca, Rodin, Rachel, Zhou, Zinan, Bohrson, Craig, Chu, Chong, Cortes-Ciriano, Isidro, Dou, Yanmei, Galor, Alon, Gulhan, Doga, Kwon, Minseok, Luquette, Joe, Viswanadham, Vinay, Jones, Attila, Rosenbluh, Chaggai, Cho, Sean, Langmead, Ben, Thorpe, Jeremy, Erwin, Jennifer, Jaffe, Andrew, McConnell, Michael, Narurkar, Rujuta, Paquola, Apua, Shin, Jooheon, Straub, Richard, Abyzov, Alexej, Bae, Taejeong, Jang, Yeongjun, Wang, Yifan, Gage, Fred, Linker, Sara, Reed, Patrick, Wang, Meiyan, Urban, Alexander, Zhou, Bo, Zhu, Xiaowei, Pattni, Reenal, Amero, Aitor Serres, Juan, David, Lobon, Irene, Marques-Bonet, Tomas, Moruno, Manuel Solis, Perez, Raquel Garcia, Povolotskaya, Inna, Soriano, Eduardo, Averbuj, Dan, Ball, Laurel, Breuss, Martin, Yang, Xiaoxu, Chung, Changuk, Emery, Sarah B., Flasch, Diane A., Kidd, Jeffrey M., Kopera, Huira C., Kwan, Kenneth Y., Mills, Ryan E., Moldovan, John B., Sun, Chen, Zhao, Xuefang, Zhou, Weichen, Frisbie, Trenton J., Cherskov, Adriana, Fasching, Liana, Jourdon, Alexandre, Pochareddy, Sirisha, Scuderi, Soraya, Sestan, Nenad, Maury, Eduardo A., Sherman, Maxwell A., Genovese, Giulio, Gilgenast, Thomas G., Kamath, Tushar, Burris, S.J., Rajarajan, Prashanth, Flaherty, Erin, Akbarian, Schahram, Chess, Andrew, McCarroll, Steven A., Loh, Po-Ru, Phillips-Cremins, Jennifer E., Brennand, Kristen J., Macosko, Evan Z., Walters, James T.R., O’Donovan, Michael, Sullivan, Patrick, Sebat, Jonathan, Lee, Eunjung A., and Walsh, Christopher A.

Published
2023
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25. The intertwined roles of particle shape and surface roughness in controlling the shear strength of a granular material

Author

Murphy, Kieran A., MacKeith, Arthur K., Roth, Leah K., and Jaeger, Heinrich M.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

Both the shape of individual particles and their surface properties contribute to the strength of a granular material under shear. Here we show the degree to which these two aspects can be intertwined. In experiments on assemblies of 3D printed, convex lens-shaped particles, we measure the stress-strain response under repeated compressive loading and find that the aggregate's shear strength falls rapidly when compared to other particle shapes. We probe the granular material at mm-scales with X-ray computed tomography and $\mu$m-scales with high-resolution surface metrology to look for the cause of the degradation. We find that wear due to accumulated deformation smooths out the lens surfaces in a controlled and systematic manner that correlates with a significant loss of shear strength observed for the assembly as a whole. The sensitivity of lenses to changes in surface properties contrasts with results for assemblies of 3D printed tetrahedra and spheres, which under the same load cycling are found to exhibit only minor degradation in strength. This case study provides insight into the relationship between particle shape, surface wear, and the overall material response, and suggests new strategies when designing a granular material with desired evolution of properties under repeated deformation., Comment: 6 pages, 5 figures

Published
2019

26. Edges control clustering in levitated granular matter

Author

Lim, Melody Xuan, Murphy, Kieran A., and Jaeger, Heinrich M.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

The properties of small clusters depend dramatically on the interactions between their constituent particles. However, it remains challenging to design and tune the interactions between macroscopic particles, such as in a granular material. Here, we use acoustic levitation to trap macroscopic grains and induce forces between them. Our main results show that particles levitated in an acoustic field prefer to make contact along sharp edges. The radius of curvature of the edges directly controls the magnitude of these forces. These highly directional interactions, combined with local contact forces, give rise to a diverse array of cluster shapes. Our results open up new possibilities for the design of specific forces between macroscopic particles, directing their assembly, and actuating their motion., Comment: Submitted to the special edition of Granular Matter, In Memoriam of Robert P. Behringer. For supplementary movies, see https://youtu.be/u_IJsMC69-8 and https://youtu.be/rxNYclVQFUY

Published
2019

27. Altered white matter microstructure in 22q11.2 deletion syndrome: a multisite diffusion tensor imaging study

Author

Villalón-Reina, Julio E, Martínez, Kenia, Qu, Xiaoping, Ching, Christopher RK, Nir, Talia M, Kothapalli, Deydeep, Corbin, Conor, Sun, Daqiang, Lin, Amy, Forsyth, Jennifer K, Kushan, Leila, Vajdi, Ariana, Jalbrzikowski, Maria, Hansen, Laura, Jonas, Rachel K, van Amelsvoort, Therese, Bakker, Geor, Kates, Wendy R, Antshel, Kevin M, Fremont, Wanda, Campbell, Linda E, McCabe, Kathryn L, Daly, Eileen, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan, Craig, Michael, Emanuel, Beverly, McDonald-McGinn, Donna M, Vorstman, Jacob AS, Fiksinski, Ania M, Koops, Sanne, Ruparel, Kosha, Roalf, David, Gur, Raquel E, Eric Schmitt, J, Simon, Tony J, Goodrich-Hunsaker, Naomi J, Durdle, Courtney A, Doherty, Joanne L, Cunningham, Adam C, van den Bree, Marianne, Linden, David EJ, Owen, Michael, Moss, Hayley, Kelly, Sinead, Donohoe, Gary, Murphy, Kieran C, Arango, Celso, Jahanshad, Neda, Thompson, Paul M, and Bearden, Carrie E

Subjects
Paediatrics, Biomedical and Clinical Sciences, Neurosciences, Pediatric, Biomedical Imaging, Mental Health, Clinical Research, Brain Disorders, Mental health, Adolescent, Adult, Anisotropy, Child, DiGeorge Syndrome, Diffusion Magnetic Resonance Imaging, Female, Humans, Male, Middle Aged, White Matter, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

22q11.2 deletion syndrome (22q11DS)-a neurodevelopmental condition caused by a hemizygous deletion on chromosome 22-is associated with an elevated risk of psychosis and other developmental brain disorders. Prior single-site diffusion magnetic resonance imaging (dMRI) studies have reported altered white matter (WM) microstructure in 22q11DS, but small samples and variable methods have led to contradictory results. Here we present the largest study ever conducted of dMRI-derived measures of WM microstructure in 22q11DS (334 22q11.2 deletion carriers and 260 healthy age- and sex-matched controls; age range 6-52 years). Using harmonization protocols developed by the ENIGMA-DTI working group, we identified widespread reductions in mean, axial and radial diffusivities in 22q11DS, most pronounced in regions with major cortico-cortical and cortico-thalamic fibers: the corona radiata, corpus callosum, superior longitudinal fasciculus, posterior thalamic radiations, and sagittal stratum (Cohen's d's ranging from -0.9 to -1.3). Only the posterior limb of the internal capsule (IC), comprised primarily of corticofugal fibers, showed higher axial diffusivity in 22q11DS. 22q11DS patients showed higher mean fractional anisotropy (FA) in callosal and projection fibers (IC and corona radiata) relative to controls, but lower FA than controls in regions with predominantly association fibers. Psychotic illness in 22q11DS was associated with more substantial diffusivity reductions in multiple regions. Overall, these findings indicate large effects of the 22q11.2 deletion on WM microstructure, especially in major cortico-cortical connections. Taken together with findings from animal models, this pattern of abnormalities may reflect disrupted neurogenesis of projection neurons in outer cortical layers.

Published
2020

28. Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: Convergence with idiopathic psychosis and effects of deletion size

Author

Sun, Daqiang, Ching, Christopher RK, Lin, Amy, Forsyth, Jennifer K, Kushan, Leila, Vajdi, Ariana, Jalbrzikowski, Maria, Hansen, Laura, Villalon-Reina, Julio E, Qu, Xiaoping, Jonas, Rachel K, van Amelsvoort, Therese, Bakker, Geor, Kates, Wendy R, Antshel, Kevin M, Fremont, Wanda, Campbell, Linda E, McCabe, Kathryn L, Daly, Eileen, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan, Craig, Michael, Vorstman, Jacob, Fiksinski, Ania, Koops, Sanne, Ruparel, Kosha, Roalf, David R, Gur, Raquel E, Schmitt, J Eric, Simon, Tony J, Goodrich-Hunsaker, Naomi J, Durdle, Courtney A, Bassett, Anne S, Chow, Eva WC, Butcher, Nancy J, Vila-Rodriguez, Fidel, Doherty, Joanne, Cunningham, Adam, van den Bree, Marianne BM, Linden, David EJ, Moss, Hayley, Owen, Michael J, Murphy, Kieran C, McDonald-McGinn, Donna M, Emanuel, Beverly, van Erp, Theo GM, Turner, Jessica A, Thompson, Paul M, and Bearden, Carrie E

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Pediatric, Serious Mental Illness, Clinical Research, Mental Health, Schizophrenia, Brain Disorders, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adolescent, Adult, Cerebral Cortex, Chromosome Deletion, DiGeorge Syndrome, Female, Gray Matter, Humans, Magnetic Resonance Imaging, Male, Psychotic Disorders, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

The 22q11.2 deletion (22q11DS) is a common chromosomal microdeletion and a potent risk factor for psychotic illness. Prior studies reported widespread cortical changes in 22q11DS, but were generally underpowered to characterize neuroanatomic abnormalities associated with psychosis in 22q11DS, and/or neuroanatomic effects of variability in deletion size. To address these issues, we developed the ENIGMA (Enhancing Neuro Imaging Genetics Through Meta-Analysis) 22q11.2 Working Group, representing the largest analysis of brain structural alterations in 22q11DS to date. The imaging data were collected from 10 centers worldwide, including 474 subjects with 22q11DS (age = 18.2 ± 8.6; 46.9% female) and 315 typically developing, matched controls (age = 18.0 ± 9.2; 45.9% female). Compared to controls, 22q11DS individuals showed thicker cortical gray matter overall (left/right hemispheres: Cohen's d = 0.61/0.65), but focal thickness reduction in temporal and cingulate cortex. Cortical surface area (SA), however, showed pervasive reductions in 22q11DS (left/right hemispheres: d = -1.01/-1.02). 22q11DS cases vs. controls were classified with 93.8% accuracy based on these neuroanatomic patterns. Comparison of 22q11DS-psychosis to idiopathic schizophrenia (ENIGMA-Schizophrenia Working Group) revealed significant convergence of affected brain regions, particularly in fronto-temporal cortex. Finally, cortical SA was significantly greater in 22q11DS cases with smaller 1.5 Mb deletions, relative to those with typical 3 Mb deletions. We found a robust neuroanatomic signature of 22q11DS, and the first evidence that deletion size impacts brain structure. Psychotic illness in this highly penetrant deletion was associated with similar neuroanatomic abnormalities to idiopathic schizophrenia. These consistent cross-site findings highlight the homogeneity of this single genetic etiology, and support the suitability of 22q11DS as a biological model of schizophrenia.

Published
2020

29. Mapping Subcortical Brain Alterations in 22q11.2 Deletion Syndrome: Effects of Deletion Size and Convergence With Idiopathic Neuropsychiatric Illness

Author

Ching, Christopher RK, Gutman, Boris A, Sun, Daqiang, Villalon Reina, Julio, Ragothaman, Anjanibhargavi, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Lin, Amy, Jonas, Rachel K, Kushan, Leila, Pacheco-Hansen, Laura, Vajdi, Ariana, Forsyth, Jennifer K, Jalbrzikowski, Maria, Bakker, Geor, van Amelsvoort, Therese, Antshel, Kevin M, Fremont, Wanda, Kates, Wendy R, Campbell, Linda E, McCabe, Kathryn L, Craig, Michael C, Daly, Eileen, Gudbrandsen, Maria, Murphy, Clodagh M, Murphy, Declan G, Murphy, Kieran C, Fiksinski, Ania, Koops, Sanne, Vorstman, Jacob, Crowley, T Blaine, Emanuel, Beverly S, Gur, Raquel E, McDonald-McGinn, Donna M, Roalf, David R, Ruparel, Kosha, Schmitt, J Eric, Zackai, Elaine H, Durdle, Courtney A, Goodrich-Hunsaker, Naomi J, Simon, Tony J, Bassett, Anne S, Butcher, Nancy J, Chow, Eva WC, Vila-Rodriguez, Fidel, Cunningham, Adam, Doherty, Joanne, Linden, David E, Moss, Hayley, Owen, Michael J, van den Bree, Marianne, Crossley, Nicolas A, Repetto, Gabriela M, Thompson, Paul M, and Bearden, Carrie E

Subjects
Clinical Research, Serious Mental Illness, Brain Disorders, Mental Health, Schizophrenia, Clinical Trials and Supportive Activities, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, Mental health, Adolescent, Adult, Atrophy, Brain, Brain Mapping, Case-Control Studies, Child, DiGeorge Syndrome, Female, Humans, Hypertrophy, Magnetic Resonance Imaging, Male, Mental Disorders, Middle Aged, Psychotic Disorders, Young Adult, 22q11.2 Deletion Syndrome, Copy Number Variant, Neuroanatomy, Neurodevelopment, Psychosis, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Objective22q11.2 deletion syndrome (22q11DS) is among the strongest known genetic risk factors for schizophrenia. Previous studies have reported variable alterations in subcortical brain structures in 22q11DS. To better characterize subcortical alterations in 22q11DS, including modulating effects of clinical and genetic heterogeneity, the authors studied a large multicenter neuroimaging cohort from the ENIGMA 22q11.2 Deletion Syndrome Working Group.MethodsSubcortical structures were measured using harmonized protocols for gross volume and subcortical shape morphometry in 533 individuals with 22q11DS and 330 matched healthy control subjects (age range, 6-56 years; 49% female).ResultsCompared with the control group, the 22q11DS group showed lower intracranial volume (ICV) and thalamus, putamen, hippocampus, and amygdala volumes and greater lateral ventricle, caudate, and accumbens volumes (Cohen's d values, -0.90 to 0.93). Shape analysis revealed complex differences in the 22q11DS group across all structures. The larger A-D deletion was associated with more extensive shape alterations compared with the smaller A-B deletion. Participants with 22q11DS with psychosis showed lower ICV and hippocampus, amygdala, and thalamus volumes (Cohen's d values, -0.91 to 0.53) compared with participants with 22q11DS without psychosis. Shape analysis revealed lower thickness and surface area across subregions of these structures. Compared with subcortical findings from other neuropsychiatric disorders studied by the ENIGMA consortium, significant convergence was observed between participants with 22q11DS with psychosis and participants with schizophrenia, bipolar disorder, major depressive disorder, and obsessive-compulsive disorder.ConclusionsIn the largest neuroimaging study of 22q11DS to date, the authors found widespread alterations to subcortical brain structures, which were affected by deletion size and psychotic illness. Findings indicate significant overlap between 22q11DS-associated psychosis, idiopathic schizophrenia, and other severe neuropsychiatric illnesses.

Published
2020

30. Complete Sequence of the 22q11.2 Allele in 1,053 Subjects with 22q11.2 Deletion Syndrome Reveals Modifiers of Conotruncal Heart Defects

Author

Zhao, Yingjie, Diacou, Alexander, Johnston, H Richard, Musfee, Fadi I, McDonald-McGinn, Donna M, McGinn, Daniel, Crowley, T Blaine, Repetto, Gabriela M, Swillen, Ann, Breckpot, Jeroen, Vermeesch, Joris R, Kates, Wendy R, Digilio, M Cristina, Unolt, Marta, Marino, Bruno, Pontillo, Maria, Armando, Marco, Di Fabio, Fabio, Vicari, Stefano, van den Bree, Marianne, Moss, Hayley, Owen, Michael J, Murphy, Kieran C, Murphy, Clodagh M, Murphy, Declan, Schoch, Kelly, Shashi, Vandana, Tassone, Flora, Simon, Tony J, Shprintzen, Robert J, Campbell, Linda, Philip, Nicole, Heine-Suñer, Damian, García-Miñaúr, Sixto, Fernández, Luis, Consortium, International 22q11 2 Brain and Behavior, Antonarakis, Stylianos E, Biondi, Massimo, Boot, Erik, Breetvelt, Elemi, Busa, Tiffany, Butcher, Nancy, Buzzanca, Antonino, Carmel, Miri, Cleynen, Isabelle, Cutler, David, Dallapiccola, Bruno, de la Fuente Sanches, María Angeles, Epstein, Michael P, Evers, Rens, Fernandez, Luis, Fritsch, Rosemarie, Algas, Fernando García, Guo, Tingwei, Gur, Raquel, Hestand, Matthew S, Heung, Tracy, Hooper, Stephen, Jin, Andrea, Kushan-Wells, Leila, Laorden-Nieto, Alejandra Teresa, Lattanzi, Guido, Marshall, Christian, McCabe, Kathryn, Michaelovsky, Elena, Ornstein, Claudia, Silversides, Candice, Tran, Oanh, van Duin, Esther DA, Vergaelen, Elfi, Warren, Steve T, Weinberger, Ronnie, Weizman, Abraham, Zhang, Zhengdong, Zwick, Michael, Bearden, Carrie E, Vingerhoets, Claudia, van Amelsvoort, Therese, Eliez, Stephan, Schneider, Maude, Vorstman, Jacob AS, Gothelf, Doron, Zackai, Elaine, Agopian, AJ, Gur, Raquel E, Bassett, Anne S, Emanuel, Beverly S, Goldmuntz, Elizabeth, Mitchell, Laura E, Wang, Tao, and Morrow, Bernice E

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Epidemiology, Health Sciences, Clinical Research, Human Genome, Heart Disease, Cardiovascular, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Case-Control Studies, Chromosome Deletion, Chromosomes, Human, Pair 22, Cohort Studies, Female, Genome-Wide Association Study, Heart Defects, Congenital, Humans, Linkage Disequilibrium, Male, Phenotype, Polymorphism, Single Nucleotide, Proto-Oncogene Mas, Segmental Duplications, Genomic, International 22q11.2 Brain and Behavior Consortium, CRKL, DiGeorge syndrome, TBX1, chromosome 22q11.2 deletion syndrome, complex trait, congenital heart disease, conotruncal heart defects, copy number variation, genetic association, genetic modifier, haploinsufficiency, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

The 22q11.2 deletion syndrome (22q11.2DS) results from non-allelic homologous recombination between low-copy repeats termed LCR22. About 60%-70% of individuals with the typical 3 megabase (Mb) deletion from LCR22A-D have congenital heart disease, mostly of the conotruncal type (CTD), whereas others have normal cardiac anatomy. In this study, we tested whether variants in the hemizygous LCR22A-D region are associated with risk for CTDs on the basis of the sequence of the 22q11.2 region from 1,053 22q11.2DS individuals. We found a significant association (FDR p < 0.05) of the CTD subset with 62 common variants in a single linkage disequilibrium (LD) block in a 350 kb interval harboring CRKL. A total of 45 of the 62 variants were associated with increased risk for CTDs (odds ratio [OR) ranges: 1.64-4.75). Associations of four variants were replicated in a meta-analysis of three genome-wide association studies of CTDs in affected individuals without 22q11.2DS. One of the replicated variants, rs178252, is located in an open chromatin region and resides in the double-elite enhancer, GH22J020947, that is predicted to regulate CRKL (CRK-like proto-oncogene, cytoplasmic adaptor) expression. Approximately 23% of patients with nested LCR22C-D deletions have CTDs, and inactivation of Crkl in mice causes CTDs, thus implicating this gene as a modifier. Rs178252 and rs6004160 are expression quantitative trait loci (eQTLs) of CRKL. Furthermore, set-based tests identified an enhancer that is predicted to target CRKL and is significantly associated with CTD risk (GH22J020946, sequence kernal association test (SKAT) p = 7.21 × 10-5) in the 22q11.2DS cohort. These findings suggest that variance in CTD penetrance in the 22q11.2DS population can be explained in part by variants affecting CRKL expression.

Published
2020

31. Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia

Author

Ripke, Stephan, Neale, Benjamin M., Corvin, Aiden, Walters, James T.R., Farh, Kai-How, Holmans, Peter A., Lee, Phil, Bulik-Sullivan, Brendan, Collier, David A., Huang, Hailiang, Pers, Tune H., Agartz, Ingrid, Agerbo, Esben, Albus, Margot, Alexander, Madeline, Amin, Farooq, Bacanu, Silviu A., Begemann, Martin, Belliveau, Richard A., Jr., Bene, Judit, Bergen, Sarah E., Bevilacqua, Elizabeth, Bigdeli, Tim B., Black, Donald W., Bruggeman, Richard, Buccola, Nancy G., Buckner, Randy L., Byerley, William, Cahn, Wiepke, Cai, Guiqing, Campion, Dominique, Cantor, Rita M., Carr, Vaughan J., Carrera, Noa, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C.K., Chan, Ronald Y.L., Chen, Eric Y.H., Cheng, Wei, Cheung, Eric FC., Chong, Siow Ann, Cloninger, C. Robert, Cohen, David, Cohen, Nadine, Cormican, Paul, Craddock, Nick, Crowley, James J., Curtis, David, Davidson, Michael, Davis, Kenneth L., Degenhardt, Franziska, Del Favero, Jurgen, Demontis, Ditte, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Donohoe, Gary, Drapeau, Elodie, Duan, Jubao, Dudbridge, Frank, Durmishi, Naser, Eichhammer, Peter, Eriksson, Johan, Escott-Price, Valentina, Essioux, Laurent, Fanous, Ayman H., Farrell, Martilias S., Frank, Josef, Franke, Lude, Freedman, Robert, Freimer, Nelson B., Friedl, Marion, Friedman, Joseph I., Fromer, Menachem, Genovese, Giulio, Georgieva, Lyudmila, Giegling, Ina, Giusti-Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., Golimbet, Vera, Gopal, Srihari, Gratten, Jacob, de Haan, Lieuwe, Hammer, Christian, Hamshere, Marian L., Hansen, Mark, Hansen, Thomas, Haroutunian, Vahram, Hartmann, Annette M., Henskens, Frans A., Herms, Stefan, Hirschhorn, Joel N., Hoffmann, Per, Hofman, Andrea, Hollegaard, Mads V., Hougaard, David M., Ikeda, Masashi, Joa, Inge, Julià, Antonio, Kahn, René S., Kalaydjieva, Luba, Karachanak-Yankova, Sena, Karjalainen, Juha, Kavanagh, David, Keller, Matthew C., Kennedy, James L., Khrunin, Andrey, Kim, Yunjung, Klovins, Janis, Knowles, James A., Konte, Bettina, Kucinskas, Vaidutis, Kucinskiene, Zita Ausrele, Kuzelova-Ptackova, Hana, Kähler, Anna K., Laurent, Claudine, Lee, Jimmy, Lee, S. Hong, Legge, Sophie E., Lerer, Bernard, Li, Miaoxin, Li, Tao, Liang, Kung-Yee, Lieberman, Jeffrey, Limborska, Svetlana, Loughland, Carmel M., Lubinski, Jan, Lönnqvist, Jouko, Macek, Milan, Magnusson, Patrik K.E., Maher, Brion S., Maier, Wolfgang, Mallet, Jacques, Marsal, Sara, Mattheisen, Manuel, Mattingsdal, Morten, McCarley, Robert W., McDonald, Colm, McIntosh, Andrew M., Meier, Sandra, Meijer, Carin J., Melegh, Bela, Melle, Ingrid, Mesholam-Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mokrab, Younes, Morris, Derek W., Mors, Ole, Murphy, Kieran C., Murray, Robin M., Myin-Germeys, Inez, Müller-Myhsok, Bertram, Nelis, Mari, Nenadic, Igor, Nertney, Deborah A., Nestadt, Gerald, Nicodemus, Kristin K., Nikitina-Zake, Liene, Nisenbaum, Laura, Nordin, Annelie, O'Callaghan, Eadbhard, O'Dushlaine, Colm, O'Neill, F. Anthony, Oh, Sang-Yun, Olincy, Ann, Olsen, Line, Van Os, Jim, Pantelis, Christos, Papadimitriou, George N., Papiol, Sergi, Parkhomenko, Elena, Pato, Michele T., Paunio, Tiina, Pejovic-Milovancevic, Milica, Perkins, Diana O., Pietiläinen, Olli, Pimm, Jonathan, Pocklington, Andrew J., Powell, John, Price, Alkes, Pulver, Ann E., Purcell, Shaun M., Quested, Digby, Rasmussen, Henrik B., Reichenberg, Abraham, Reimers, Mark A., Richards, Alexander L., Roffman, Joshua L., Roussos, Panos, Ruderfer, Douglas M., Salomaa, Veikko, Sanders, Alan R., Schall, Ulrich, Schubert, Christian R., Schulze, Thomas G., Schwab, Sibylle G., Scolnick, Edward M., Scott, Rodney J., Seidman, Larry J., Shi, Jianxin, Sigurdsson, Engilbert, Silagadze, Teimuraz, Silverman, Jeremy M., Sim, Kang, Slominsky, Petr, Smoller, Jordan W., So, Hon-Cheong, Spencer, Chris C.A., Stahl, Eli A., Stefansson, Hreinn, Steinberg, Stacy, Stogmann, Elisabeth, Straub, Richard E., Strengman, Eric, Strohmaier, Jana, Stroup, T Scott, Subramaniam, Mythily, Suvisaari, Jaana, Svrakic, Dragan M., Szatkiewicz, Jin P., Söderman, Erik, Thirumalai, Srinivas, Toncheva, Draga, Tosato, Sarah, Veijola, Juha, Waddington, John, Walsh, Dermot, Wang, Dai, Wang, Qiang, Webb, Bradley T., Weiser, Mark, Wildenauer, Dieter B., Williams, Nigel M., Williams, Stephanie, Witt, Stephanie H., Wolen, Aaron R., Wong, Emily H.M., Wormley, Brandon K., Xi, Hualin Simon, Zai, Clement C., Zheng, Xuebin, Zimprich, Fritz, Wray, Naomi R., Stefansson, Kari, Visscher, Peter M., Adolfsson, Rolf, Andreassen, Ole A., Blackwood, Douglas H.R., Bramon, Elvira, Buxbaum, Joseph D., Børglum, Anders D., Cichon, Sven, Darvasi, Ariel, Domenici, Enrico, Ehrenreich, Hannelore, Esko, Tõnu, Gejman, Pablo V., Gill, Michael, Gurling, Hugh, Hultman, Christina M., Iwata, Nakao, Jablensky, Assen V., Jönsson, Erik G., Kendler, Kenneth S., Kirov, George, Knight, Jo, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., McCarroll, Steven A., McQuillin, Andrew, Moran, Jennifer L., Mortensen, Preben B., Mowry, Bryan J., Nöthen, Markus M., Ophoff, Roel A., Owen, Michael J., Palotie, Aarno, Pato, Carlos N., Petryshen, Tracey L., Posthuma, Danielle, Rietschel, Marcella, Riley, Brien P., Rujescu, Dan, Sham, Pak C., Sklar, Pamela, St Clair, David, Weinberger, Daniel R., Wendland, Jens R., Werge, Thomas, Daly, Mark J., Sullivan, Patrick F., O'Donovan, Michael C., Qin, Shengying, Sawa, Akira, Kahn, Rene, Hong, Kyung Sue, Shi, Wenzhao, Tsuang, Ming, Itokawa, Masanari, Feng, Gang, Glatt, Stephen J., Ma, Xiancang, Tang, Jinsong, Ruan, Yunfeng, Liu, Ruize, Zhu, Feng, Horiuchi, Yasue, Lee, Byung Dae, Joo, Eun-Jeong, Myung, Woojae, Ha, Kyooseob, Won, Hong-Hee, Baek, Ji Hyung, Chung, Young Chul, Kim, Sung-Wan, Kusumawardhani, Agung, Chen, Wei J., Hwu, Hai-Gwo, Hishimoto, Akitoyo, Otsuka, Ikuo, Sora, Ichiro, Toyota, Tomoko, Yoshikawa, Takeo, Kunugi, Hiroshi, Hattori, Kotaro, Ishiwata, Sayuri, Numata, Shusuke, Ohmori, Tetsuro, Arai, Makoto, Ozeki, Yuji, Fujii, Kumiko, Kim, Se Joo, Lee, Heon-Jeong, Ahn, Yong Min, Kim, Se Hyun, Akiyama, Kazufumi, Shimoda, Kazutaka, Kinoshita, Makoto, Hsu, Yu-Han H., Pintacuda, Greta, Nacu, Eugeniu, Kim, April, Tsafou, Kalliopi, Petrossian, Natalie, Crotty, William, Suh, Jung Min, Riseman, Jackson, Martin, Jacqueline M., Biagini, Julia C., Mena, Daya, Ching, Joshua K.T., Malolepsza, Edyta, Li, Taibo, Singh, Tarjinder, Ge, Tian, Egri, Shawn B., Tanenbaum, Benjamin, Stanclift, Caroline R., Apffel, Annie M., Carr, Steven A., Schenone, Monica, Jaffe, Jake, Fornelos, Nadine, Eggan, Kevin C., and Lage, Kasper

Published
2023
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34. Transforming mesoscale granular plasticity through particle shape

Author

Murphy, Kieran A., Dahmen, Karin A., and Jaeger, Heinrich M.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

When an amorphous material is strained beyond the point of yielding it enters a state of continual reconfiguration via dissipative, avalanche-like slip events that relieve built-up local stress. However, how the statistics of such events depend on local interactions among the constituent units remains debated. To address this we perform experiments on granular material in which we use particle shape to vary the interactions systematically. Granular material, confined under constant pressure boundary conditions, is uniaxially compressed while stress is measured and internal rearrangements are imaged with x-rays. We introduce volatility, a quantity from economic theory, as a powerful new tool to quantify the magnitude of stress fluctuations, finding systematic, shape-dependent trends. For all 22 investigated shapes the magnitude $s$ of relaxation events is well-fit by a truncated power law distribution $P(s)\sim {s}^{-\tau} exp(-s/s^*)$, as has been proposed within the context of plasticity models. The power law exponent $\tau$ for all shapes tested clusters around $\tau=$ 1.5, within experimental uncertainty covering the range 1.3 - 1.7. The shape independence of $\tau$ and its compatibility with mean field models indicate that the granularity of the system, but not particle shape, modifies the stress redistribution after a slip event away from that of continuum elasticity. Meanwhile, the characteristic maximum event size $s^*$ changes by two orders of magnitude and tracks the shape dependence of volatility. Particle shape in granular materials is therefore a powerful new factor influencing the distance at which an amorphous system operates from scale-free criticality. These experimental results are not captured by current models and suggest a need to reexamine the mechanisms driving mesoscale plastic deformation in amorphous systems., Comment: 11 pages, 8 figures. v3 adds a new appendix and figure about event rates and changes several parts the text

Published
2018
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37. Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Author

Trubetskoy, Vassily, Pardiñas, Antonio F., Qi, Ting, Panagiotaropoulou, Georgia, Awasthi, Swapnil, Bigdeli, Tim B., Bryois, Julien, Chen, Chia-Yen, Dennison, Charlotte A., Hall, Lynsey S., Lam, Max, Watanabe, Kyoko, Frei, Oleksandr, Ge, Tian, Harwood, Janet C., Koopmans, Frank, Magnusson, Sigurdur, Richards, Alexander L., Sidorenko, Julia, Wu, Yang, Zeng, Jian, Grove, Jakob, Kim, Minsoo, Li, Zhiqiang, Voloudakis, Georgios, Zhang, Wen, Adams, Mark, Agartz, Ingrid, Atkinson, Elizabeth G., Agerbo, Esben, Al Eissa, Mariam, Albus, Margot, Alexander, Madeline, Alizadeh, Behrooz Z., Alptekin, Köksal, Als, Thomas D., Amin, Farooq, Arolt, Volker, Arrojo, Manuel, Athanasiu, Lavinia, Azevedo, Maria Helena, Bacanu, Silviu A., Bass, Nicholas J., Begemann, Martin, Belliveau, Richard A., Bene, Judit, Benyamin, Beben, Bergen, Sarah E., Blasi, Giuseppe, Bobes, Julio, Bonassi, Stefano, Braun, Alice, Bressan, Rodrigo Affonseca, Bromet, Evelyn J., Bruggeman, Richard, Buckley, Peter F., Buckner, Randy L., Bybjerg-Grauholm, Jonas, Cahn, Wiepke, Cairns, Murray J., Calkins, Monica E., Carr, Vaughan J., Castle, David, Catts, Stanley V., Chambert, Kimberley D., Chan, Raymond C. K., Chaumette, Boris, Cheng, Wei, Cheung, Eric F. C., Chong, Siow Ann, Cohen, David, Consoli, Angèle, Cordeiro, Quirino, Costas, Javier, Curtis, Charles, Davidson, Michael, Davis, Kenneth L., de Haan, Lieuwe, Degenhardt, Franziska, DeLisi, Lynn E., Demontis, Ditte, Dickerson, Faith, Dikeos, Dimitris, Dinan, Timothy, Djurovic, Srdjan, Duan, Jubao, Ducci, Giuseppe, Dudbridge, Frank, Eriksson, Johan G., Fañanás, Lourdes, Faraone, Stephen V., Fiorentino, Alessia, Forstner, Andreas, Frank, Josef, Freimer, Nelson B., Fromer, Menachem, Frustaci, Alessandra, Gadelha, Ary, Genovese, Giulio, Gershon, Elliot S., Giannitelli, Marianna, Giegling, Ina, Giusti-Rodríguez, Paola, Godard, Stephanie, Goldstein, Jacqueline I., González Peñas, Javier, González-Pinto, Ana, Gopal, Srihari, Gratten, Jacob, Green, Michael F., Greenwood, Tiffany A., Guillin, Olivier, Gülöksüz, Sinan, Gur, Raquel E., Gur, Ruben C., Gutiérrez, Blanca, Hahn, Eric, Hakonarson, Hakon, Haroutunian, Vahram, Hartmann, Annette M., Harvey, Carol, Hayward, Caroline, Henskens, Frans A., Herms, Stefan, Hoffmann, Per, Howrigan, Daniel P., Ikeda, Masashi, Iyegbe, Conrad, Joa, Inge, Julià, Antonio, Kähler, Anna K., Kam-Thong, Tony, Kamatani, Yoichiro, Karachanak-Yankova, Sena, Kebir, Oussama, Keller, Matthew C., Kelly, Brian J., Khrunin, Andrey, Kim, Sung-Wan, Klovins, Janis, Kondratiev, Nikolay, Konte, Bettina, Kraft, Julia, Kubo, Michiaki, Kučinskas, Vaidutis, Kučinskiene, Zita Ausrele, Kusumawardhani, Agung, Kuzelova-Ptackova, Hana, Landi, Stefano, Lazzeroni, Laura C., Lee, Phil H., Legge, Sophie E., Lehrer, Douglas S., Lencer, Rebecca, Lerer, Bernard, Li, Miaoxin, Lieberman, Jeffrey, Light, Gregory A., Limborska, Svetlana, Liu, Chih-Min, Lönnqvist, Jouko, Loughland, Carmel M., Lubinski, Jan, Luykx, Jurjen J., Lynham, Amy, Macek, Jr, Milan, Mackinnon, Andrew, Magnusson, Patrik K. E., Maher, Brion S., Maier, Wolfgang, Malaspina, Dolores, Mallet, Jacques, Marder, Stephen R., Marsal, Sara, Martin, Alicia R., Martorell, Lourdes, Mattheisen, Manuel, McCarley, Robert W., McDonald, Colm, McGrath, John J., Medeiros, Helena, Meier, Sandra, Melegh, Bela, Melle, Ingrid, Mesholam-Gately, Raquelle I., Metspalu, Andres, Michie, Patricia T., Milani, Lili, Milanova, Vihra, Mitjans, Marina, Molden, Espen, Molina, Esther, Molto, María Dolores, Mondelli, Valeria, Moreno, Carmen, Morley, Christopher P., Muntané, Gerard, Murphy, Kieran C., Myin-Germeys, Inez, Nenadić, Igor, Nestadt, Gerald, Nikitina-Zake, Liene, Noto, Cristiano, Nuechterlein, Keith H., O’Brien, Niamh Louise, O’Neill, F. Anthony, Oh, Sang-Yun, Olincy, Ann, Ota, Vanessa Kiyomi, Pantelis, Christos, Papadimitriou, George N., Parellada, Mara, Paunio, Tiina, Pellegrino, Renata, Periyasamy, Sathish, Perkins, Diana O., Pfuhlmann, Bruno, Pietiläinen, Olli, Pimm, Jonathan, Porteous, David, Powell, John, Quattrone, Diego, Quested, Digby, Radant, Allen D., Rampino, Antonio, Rapaport, Mark H., Rautanen, Anna, Reichenberg, Abraham, Roe, Cheryl, Roffman, Joshua L., Roth, Julian, Rothermundt, Matthias, Rutten, Bart P. F., Saker-Delye, Safaa, Salomaa, Veikko, Sanjuan, Julio, Santoro, Marcos Leite, Savitz, Adam, Schall, Ulrich, Scott, Rodney J., Seidman, Larry J., Sharp, Sally Isabel, Shi, Jianxin, Siever, Larry J., Sigurdsson, Engilbert, Sim, Kang, Skarabis, Nora, Slominsky, Petr, So, Hon-Cheong, Sobell, Janet L., Söderman, Erik, Stain, Helen J., Steen, Nils Eiel, Steixner-Kumar, Agnes A., Stögmann, Elisabeth, Stone, William S., Straub, Richard E., Streit, Fabian, Strengman, Eric, Stroup, T. Scott, Subramaniam, Mythily, Sugar, Catherine A., Suvisaari, Jaana, Svrakic, Dragan M., Swerdlow, Neal R., Szatkiewicz, Jin P., Ta, Thi Minh Tam, Takahashi, Atsushi, Terao, Chikashi, Thibaut, Florence, Toncheva, Draga, Tooney, Paul A., Torretta, Silvia, Tosato, Sarah, Tura, Gian Battista, Turetsky, Bruce I., Üçok, Alp, Vaaler, Arne, van Amelsvoort, Therese, van Winkel, Ruud, Veijola, Juha, Waddington, John, Walter, Henrik, Waterreus, Anna, Webb, Bradley T., Weiser, Mark, Williams, Nigel M., Witt, Stephanie H., Wormley, Brandon K., Wu, Jing Qin, Xu, Zhida, Yolken, Robert, Zai, Clement C., Zhou, Wei, Zhu, Feng, Zimprich, Fritz, Atbaşoğlu, Eşref Cem, Ayub, Muhammad, Benner, Christian, Bertolino, Alessandro, Black, Donald W., Bray, Nicholas J., Breen, Gerome, Buccola, Nancy G., Byerley, William F., Chen, Wei J., Cloninger, C. Robert, Crespo-Facorro, Benedicto, Donohoe, Gary, Freedman, Robert, Galletly, Cherrie, Gandal, Michael J., Gennarelli, Massimo, Hougaard, David M., Hwu, Hai-Gwo, Jablensky, Assen V., McCarroll, Steven A., Moran, Jennifer L., Mors, Ole, Mortensen, Preben B., Müller-Myhsok, Bertram, Neil, Amanda L., Nordentoft, Merete, Pato, Michele T., Petryshen, Tracey L., Pirinen, Matti, Pulver, Ann E., Schulze, Thomas G., Silverman, Jeremy M., Smoller, Jordan W., Stahl, Eli A., Tsuang, Debby W., Vilella, Elisabet, Wang, Shi-Heng, Xu, Shuhua, Adolfsson, Rolf, Arango, Celso, Baune, Bernhard T., Belangero, Sintia Iole, Børglum, Anders D., Braff, David, Bramon, Elvira, Buxbaum, Joseph D., Campion, Dominique, Cervilla, Jorge A., Cichon, Sven, Collier, David A., Corvin, Aiden, Curtis, David, Forti, Marta Di, Domenici, Enrico, Ehrenreich, Hannelore, Escott-Price, Valentina, Esko, Tõnu, Fanous, Ayman H., Gareeva, Anna, Gawlik, Micha, Gejman, Pablo V., Gill, Michael, Glatt, Stephen J., Golimbet, Vera, Hong, Kyung Sue, Hultman, Christina M., Hyman, Steven E., Iwata, Nakao, Jönsson, Erik G., Kahn, René S., Kennedy, James L., Khusnutdinova, Elza, Kirov, George, Knowles, James A., Krebs, Marie-Odile, Laurent-Levinson, Claudine, Lee, Jimmy, Lencz, Todd, Levinson, Douglas F., Li, Qingqin S., Liu, Jianjun, Malhotra, Anil K., Malhotra, Dheeraj, McIntosh, Andrew, McQuillin, Andrew, Menezes, Paulo R., Morgan, Vera A., Morris, Derek W., Mowry, Bryan J., Murray, Robin M., Nimgaonkar, Vishwajit, Nöthen, Markus M., Ophoff, Roel A., Paciga, Sara A., Palotie, Aarno, Pato, Carlos N., Qin, Shengying, Rietschel, Marcella, Riley, Brien P., Rivera, Margarita, Rujescu, Dan, Saka, Meram C., Sanders, Alan R., Schwab, Sibylle G., Serretti, Alessandro, Sham, Pak C., Shi, Yongyong, St Clair, David, Stefánsson, Hreinn, Stefansson, Kari, Tsuang, Ming T., van Os, Jim, Vawter, Marquis P., Weinberger, Daniel R., Werge, Thomas, Wildenauer, Dieter B., Yu, Xin, Yue, Weihua, Holmans, Peter A., Pocklington, Andrew J., Roussos, Panos, Vassos, Evangelos, Verhage, Matthijs, Visscher, Peter M., Yang, Jian, Posthuma, Danielle, Andreassen, Ole A., Kendler, Kenneth S., Owen, Michael J., Wray, Naomi R., Daly, Mark J., Huang, Hailiang, Neale, Benjamin M., Sullivan, Patrick F., Ripke, Stephan, Walters, James T. R., and O’Donovan, Michael C.

Published
2022
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38. Variance of IQ is partially dependent on deletion type among 1,427 22q11.2 deletion syndrome subjects

Author

Zhao, Yingjie, Guo, Tingwei, Fiksinski, Ania, Breetvelt, Elemi, McDonald‐McGinn, Donna M, Crowley, Terrence B, Diacou, Alexander, Schneider, Maude, Eliez, Stephan, Swillen, Ann, Breckpot, Jeroen, Vermeesch, Joris, Chow, Eva WC, Gothelf, Doron, Duijff, Sasja, Evers, Rens, Amelsvoort, Thérèse A, Bree, Marianne, Owen, Michael, Niarchou, Maria, Bearden, Carrie E, Ornstein, Claudia, Pontillo, Maria, Buzzanca, Antonino, Vicari, Stefano, Armando, Marco, Murphy, Kieran C, Murphy, Clodagh, Garcia‐Minaur, Sixto, Philip, Nicole, Campbell, Linda, Morey‐Cañellas, Jaume, Raventos, Jasna, Rosell, Jordi, Heine‐Suner, Damian, Shprintzen, Robert J, Gur, Raquel E, Zackai, Elaine, Emanuel, Beverly S, Wang, Tao, Kates, Wendy R, Bassett, Anne S, Vorstman, Jacob AS, Morrow, Bernice E, and Consortium, on behalf of the International 22q11 2 Brain and Behavior

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Genetics, Acquired Cognitive Impairment, Clinical Research, Brain Disorders, Pediatric, Adolescent, Adult, Child, Chromosome Deletion, Chromosomes, Human, Pair 22, DiGeorge Syndrome, Female, Humans, Intellectual Disability, Intelligence Tests, Male, 22q11.2 deletion syndrome, deletion size, intellectual disability, IQ, low copy repeat, segmental duplication, International 22q11.2 Brain and Behavior Consortium, Clinical sciences
Abstract

The 22q11.2 deletion syndrome is caused by non-allelic homologous recombination events during meiosis between low copy repeats (LCR22) termed A, B, C, and D. Most patients have a typical LCR22A-D (AD) deletion of 3 million base pairs (Mb). In this report, we evaluated IQ scores in 1,478 subjects with 22q11.2DS. The mean of full scale IQ, verbal IQ, and performance IQ scores in our cohort were 72.41 (standard deviation-SD of 13.72), 75.91(SD of 14.46), and 73.01(SD of 13.71), respectively. To investigate whether IQ scores are associated with deletion size, we examined individuals with the 3 Mb, AD (n = 1,353) and nested 1.5 Mb, AB (n = 74) deletions, since they comprised the largest subgroups. We found that full scale IQ was decreased by 6.25 points (p = .002), verbal IQ was decreased by 8.17 points (p = .0002) and performance IQ was decreased by 4.03 points (p = .028) in subjects with the AD versus AB deletion. Thus, individuals with the smaller, 1.5 Mb AB deletion have modestly higher IQ scores than those with the larger, 3 Mb AD deletion. Overall, the deletion of genes in the AB region largely explains the observed low IQ in the 22q11.2DS population. However, our results also indicate that haploinsufficiency of genes in the LCR22B-D region (BD) exert an additional negative impact on IQ. Furthermore, we did not find evidence of a confounding effect of severe congenital heart disease on IQ scores in our cohort.

Published
2018

39. Developing a Southern Ocean Marine Ecosystem Model Ensemble To Assess Climate Risks and Uncertainties

Author

Murphy, Kieran, primary, Arcos, L. Denisse Fierro, additional, Rohr, Tyler Weaver, additional, Green, David Bruce, additional, Novaglio, Camilla, additional, Baker, Katherine, additional, Ortega-Cisneros, Kelly, additional, Eddy, Tyler, additional, Harrison, Cheryl Shannon, additional, Hill, Simeon, additional, Keith, Patrick, additional, Cataldo-Mendez, Camila, additional, Petrik, Colleen M, additional, pinkerton, matt, additional, Spence, Paul, additional, Stollberg, Ilaria, additional, Subramaniam, Roshni, additional, Trebilco, Rowan, additional, Tulloch, Vivitskaia, additional, Palacios-Abrantes, Juliano, additional, Bestley, Sophie, additional, Bianchi, Daniele, additional, Boyd, Philip W, additional, Buchanan, Pearse James, additional, Bryndum-Buchholz, Andrea, additional, Coll, Marta, additional, Corney, Stuart Paul, additional, Datta, Samik, additional, Everett, Jason D, additional, Forestier, Romain, additional, Fulton, Beth, additional, Galton-Fenzi, Benjamin Keith, additional, Luzinais, Vianney Guibourd de, additional, Heneghan, Ryan, additional, Mason, Julia G, additional, Maury, Olivier, additional, McMahon, Clive R., additional, Murphy, Eugene J., additional, Richardson, Anthony, additional, Tittensor, Derek, additional, Spillias, Scott, additional, Steenbeek, Jeroen Gerhard, additional, Veytia, Devi, additional, and Blanchard, Julia L., additional

Published
2024
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40. Prospective neuroimaging and neuropsychological evaluation in adults with newly diagnosed focal epilepsy

Author

de BEZENAC, Christophe Emmanuel, primary, Leek, Nicola, additional, Adan, Guleed, additional, Ali, Ahmad, additional, Mohanraj, Rajiv, additional, Biswas, Shubhabrata, additional, Mcginty, Ronan, additional, Murphy, Kieran, additional, Malone, Helen, additional, Baker, Gus, additional, Moore, Perry, additional, Marson, Anthony G, additional, and Keller, Simon S, additional

Published
2024
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42. Autoantibodies and Psychosis

Author

Kelleher, Eric, Barry, Helen, Cotter, David R., Corvin, Aiden, Murphy, Kieran C., Geyer, Mark A., Series Editor, Ellenbroek, Bart A., Series Editor, Marsden, Charles A., Series Editor, Barnes, Thomas R.E., Series Editor, Andersen, Susan L., Series Editor, Khandaker, Golam M., editor, Meyer, Urs, editor, and Jones, Peter B., editor

Published
2020
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44. Rare Genome-Wide Copy Number Variation and Expression of Schizophrenia in 22q11.2 Deletion Syndrome

Author

Bassett, Anne S, Lowther, Chelsea, Merico, Daniele, Costain, Gregory, Chow, Eva WC, van Amelsvoort, Therese, McDonald-McGinn, Donna, Gur, Raquel E, Swillen, Ann, Van den Bree, Marianne, Murphy, Kieran, Gothelf, Doron, Bearden, Carrie E, Eliez, Stephan, Kates, Wendy, Philip, Nicole, Sashi, Vandana, Campbell, Linda, Vorstman, Jacob, Cubells, Joseph, Repetto, Gabriela M, Simon, Tony, Boot, Erik, Heung, Tracy, Evers, Rens, Vingerhoets, Claudia, van Duin, Esther, Zackai, Elaine, Vergaelen, Elfi, Devriendt, Koen, Vermeesch, Joris R, Owen, Michael, Murphy, Clodagh, Michaelovosky, Elena, Kushan, Leila, Schneider, Maude, Fremont, Wanda, Busa, Tiffany, Hooper, Stephen, McCabe, Kathryn, Duijff, Sasja, Isaev, Karin, Pellecchia, Giovanna, Wei, John, Gazzellone, Matthew J, Scherer, Stephen W, Emanuel, Beverly S, Guo, Tingwei, Morrow, Bernice E, and Marshall, Christian R

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Pediatric, Mental Health, Schizophrenia, Clinical Research, Brain Disorders, Human Genome, Biotechnology, Genetics, Prevention, Serious Mental Illness, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adult, Autism Spectrum Disorder, Autistic Disorder, Chromosome Deletion, Chromosome Disorders, Chromosomes, Human, Pair 16, DNA Copy Number Variations, DiGeorge Syndrome, Female, Humans, Intellectual Disability, Male, Middle Aged, International 22q11.2DS Brain and Behavior Consortium, 22q11 Deletion Syndrome, Microdeletion, Psychosis, Structural Variants, Velocardiofacial Syndrome, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Clinical and health psychology
Abstract

ObjectiveChromosome 22q11.2 deletion syndrome (22q11.2DS) is associated with a more than 20-fold increased risk for developing schizophrenia. The aim of this study was to identify additional genetic factors (i.e., "second hits") that may contribute to schizophrenia expression.MethodThrough an international consortium, the authors obtained DNA samples from 329 psychiatrically phenotyped subjects with 22q11.2DS. Using a high-resolution microarray platform and established methods to assess copy number variation (CNV), the authors compared the genome-wide burden of rare autosomal CNV, outside of the 22q11.2 deletion region, between two groups: a schizophrenia group and those with no psychotic disorder at age ≥25 years. The authors assessed whether genes overlapped by rare CNVs were overrepresented in functional pathways relevant to schizophrenia.ResultsRare CNVs overlapping one or more protein-coding genes revealed significant between-group differences. For rare exonic duplications, six of 19 gene sets tested were enriched in the schizophrenia group; genes associated with abnormal nervous system phenotypes remained significant in a stepwise logistic regression model and showed significant interactions with 22q11.2 deletion region genes in a connectivity analysis. For rare exonic deletions, the schizophrenia group had, on average, more genes overlapped. The additional rare CNVs implicated known (e.g., GRM7, 15q13.3, 16p12.2) and novel schizophrenia risk genes and loci.ConclusionsThe results suggest that additional rare CNVs overlapping genes outside of the 22q11.2 deletion region contribute to schizophrenia risk in 22q11.2DS, supporting a multigenic hypothesis for schizophrenia. The findings have implications for understanding expression of psychotic illness and herald the importance of whole-genome sequencing to appreciate the overall genomic architecture of schizophrenia.

Published
2017

46. Source-based morphometry reveals structural brain pattern abnormalities in 22q11.2 deletion syndrome

Author

Psychosociale zorg patientenzorg, Ge, Ruiyang, Ching, Christopher R.K., Bassett, Anne S., Kushan, Leila, Antshel, Kevin M., van Amelsvoort, Therese, Bakker, Geor, Butcher, Nancy J., Campbell, Linda E., Chow, Eva W.C., Craig, Michael, Crossley, Nicolas A., Cunningham, Adam, Daly, Eileen, Doherty, Joanne L., Durdle, Courtney A., Emanuel, Beverly S., Fiksinski, Ania, Forsyth, Jennifer K., Fremont, Wanda, Goodrich-Hunsaker, Naomi J., Gudbrandsen, Maria, Gur, Raquel E., Jalbrzikowski, Maria, Kates, Wendy R., Lin, Amy, Linden, David E.J., McCabe, Kathryn L., McDonald-McGinn, Donna, Moss, Hayley, Murphy, Declan G., Murphy, Kieran C., Owen, Michael J., Villalon-Reina, Julio E., Repetto, Gabriela M., Roalf, David R., Ruparel, Kosha, Schmitt, J. Eric, Schuite-Koops, Sanne, Angkustsiri, Kathleen, Sun, Daqiang, Vajdi, Ariana, van den Bree, Marianne, Vorstman, Jacob, Thompson, Paul M., Vila-Rodriguez, Fidel, Bearden, Carrie E., Psychosociale zorg patientenzorg, Ge, Ruiyang, Ching, Christopher R.K., Bassett, Anne S., Kushan, Leila, Antshel, Kevin M., van Amelsvoort, Therese, Bakker, Geor, Butcher, Nancy J., Campbell, Linda E., Chow, Eva W.C., Craig, Michael, Crossley, Nicolas A., Cunningham, Adam, Daly, Eileen, Doherty, Joanne L., Durdle, Courtney A., Emanuel, Beverly S., Fiksinski, Ania, Forsyth, Jennifer K., Fremont, Wanda, Goodrich-Hunsaker, Naomi J., Gudbrandsen, Maria, Gur, Raquel E., Jalbrzikowski, Maria, Kates, Wendy R., Lin, Amy, Linden, David E.J., McCabe, Kathryn L., McDonald-McGinn, Donna, Moss, Hayley, Murphy, Declan G., Murphy, Kieran C., Owen, Michael J., Villalon-Reina, Julio E., Repetto, Gabriela M., Roalf, David R., Ruparel, Kosha, Schmitt, J. Eric, Schuite-Koops, Sanne, Angkustsiri, Kathleen, Sun, Daqiang, Vajdi, Ariana, van den Bree, Marianne, Vorstman, Jacob, Thompson, Paul M., Vila-Rodriguez, Fidel, and Bearden, Carrie E.

Published
2024

47. Comparing information content of representation spaces for disentanglement with VAE ensembles

Author

Murphy, Kieran A., Dillavou, Sam, Bassett, Dani S., Murphy, Kieran A., Dillavou, Sam, and Bassett, Dani S.

Abstract

Disentanglement is the endeavour to use machine learning to divide information about a dataset into meaningful fragments. In practice these fragments are representation (sub)spaces, often the set of channels in the latent space of a variational autoencoder (VAE). Assessments of disentanglement predominantly employ metrics that are coarse-grained at the model level, but this approach can obscure much about the process of information fragmentation. Here we propose to study the learned channels in aggregate, as the fragments of information learned by an ensemble of repeat training runs. Additionally, we depart from prior work where measures of similarity between individual subspaces neglected the nature of data embeddings as probability distributions. Instead, we view representation subspaces as communication channels that perform a soft clustering of the data; consequently, we generalize two classic information-theoretic measures of similarity between clustering assignments to compare representation spaces. We develop a lightweight method of estimation based on fingerprinting representation subspaces by their ability to distinguish dataset samples, allowing us to identify, analyze, and leverage meaningful structure in ensembles of VAEs trained on synthetic and natural datasets. Using this fully unsupervised pipeline we identify "hotspots" in the space of information fragments: groups of nearly identical representation subspaces that appear repeatedly in an ensemble of VAEs, particularly as regularization is increased. Finally, we leverage the proposed methodology to achieve ensemble learning with VAEs, boosting the information content of a set of weak learners -- a capability not possible with previous methods of assessing channel similarity., Comment: Code: https://github.com/murphyka/representation-space-info-comparison

Published
2024

48. Freestanding loadbearing structures with Z-shaped particles

Author

Murphy, Kieran A., Reiser, Nikolaj, Choksy, Darius, Singer, Clare E., and Jaeger, Heinrich M.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

Architectural structures such as masonry walls or columns exhibit a slender verticality, in contrast to the squat, sloped forms obtained with typical unconfined granular materials. Here we demonstrate the ability to create freestanding, weight-bearing, similarly slender and vertical structures by the simple pouring of suitably shaped dry particles into a mold that is subsequently removed. Combining experiments and simulations we explore a family of particle types that can entangle through their non-convex, hooked shape. We show that Z-shaped particles produce granular aggregates which can either be fluid and pourable, or solid and rigid enough to maintain vertical interfaces and build freestanding columns of large aspect ratio (>10) that support compressive loads without external confinement. We investigate the stability of such columns with uniaxial compression, bending, and vibration tests and compare with other particle types including U-shaped particles and rods. We find a pronounced anisotropy in the internal stress propagation together with strong strain-stiffening, which stabilizes rather than destabilizes the structures under load., Comment: 9 pages, 11 figures

Published
2015

50. Detecting, attributing, and projecting global marine ecosystem and fisheries change: FishMIP 2.0

Author

Blanchard, Julia L., primary, Novaglio, Camilla, additional, Maury, Olivier, additional, Harrison, Cheryl Shannon, additional, Petrik, Colleen M, additional, Arcos, L. Denisse Fierro, additional, Ortega-Cisneros, Kelly, additional, Bryndum-Buchholz, Andrea, additional, Eddy, Tyler, additional, Heneghan, Ryan, additional, Roberts, Kelsey E, additional, Schewe, Jacob, additional, Bianchi, Daniele, additional, Guiet, Jerome, additional, Denderen, Daniel van, additional, Palacios-Abrantes, Juliano, additional, Liu, Xiao, additional, Stock, Charles A. A, additional, Rousseau, Yannick, additional, Büchner, Matthias, additional, Adekoya, Ezekiel, additional, Cheung, William, additional, Christensen, Villy, additional, Coll, Marta, additional, Capitani, Leonardo, additional, Datta, Samik, additional, Fulton, Beth, additional, Fuster, Alba, additional, Garza, Victoria, additional, Lengaigne, Matthieu, additional, lindmark, Max, additional, Murphy, Kieran, additional, Ouled-Cheikh, Jazel, additional, Prasad, Sowdamini P., additional, Oliveros-Ramos, Ricardo, additional, Reum, Jonathan Charles, additional, Rynne, Nina, additional, Scherrer, Kim, additional, Shin, Yunne-Jai, additional, Steenbeek, Jeroen Gerhard, additional, Woodworth-Jefcoats, Phoebe, additional, Wu, Yan-Lun, additional, and Tittensor, Derek, additional

Published
2024
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