Guillaume Huguet

Martineau Jean-Louis

Élise Douard

Charles-Olivier Martin

C.O. Martin

Nadine Younis

Petra Tamer

Anne M. Maillard

Borja Rodriguez-Herreros

Aurélie Pain

Sonia Richetin

Leila Kushan

Dmitry Isaev … (voir 26 de plus)

Kathryn Alpert

Anjani Ragothaman

Jessica A. Turner

Wei Wang

T. Ho

Tiffany C. Ho

Lianne Schmaal

Ana I. Silva

Marianne B.M. van den Bree

V. Marianne

David E.J. Linden

M. J. Owen

Marie Owen

Jeremy Hall

Sarah Lippé

Guillaume Dumas

Bogdan Draganski

Boris A. Gutman

Ida E. Sønderby

Ole A. Andreassen

Laura Schultz

Laura Almasy

David C. Glahn

Carrie E. Bearden

Paul M. Thompson

Sébastien Jacquemont

OBJECTIVE Copy number variants (CNVs) are well-known genetic pleiotropic risk factors for multiple neurodevelopmental and psychiatric disord… (voir plus)ers (NPDs), including autism (ASD) and schizophrenia. Little is known about how different CNVs conferring risk for the same condition may affect subcortical brain structures and how these alterations relate to the level of disease risk conferred by CNVs. To fill this gap, the authors investigated gross volume, vertex-level thickness, and surface maps of subcortical structures in 11 CNVs and six NPDs. METHODS Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (CNVs at 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2; age range, 6-80 years; 340 males) and 782 control subjects (age range, 6-80 years; 387 males) as well as ENIGMA summary statistics for ASD, schizophrenia, attention deficit hyperactivity disorder, obsessive-compulsive disorder, bipolar disorder, and major depression. RESULTS All CNVs showed alterations in at least one subcortical measure. Each structure was affected by at least two CNVs, and the hippocampus and amygdala were affected by five. Shape analyses detected subregional alterations that were averaged out in volume analyses. A common latent dimension was identified, characterized by opposing effects on the hippocampus/amygdala and putamen/pallidum, across CNVs and across NPDs. Effect sizes of CNVs on subcortical volume, thickness, and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and schizophrenia. CONCLUSIONS The findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions, as well distinct effects, with some CNVs clustering with adult-onset conditions and others with ASD. These findings provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD and why a single CNV increases the risk for a diverse set of NPDs.

2023-07-11

American Journal of Psychiatry (publié)

Neural FIM for learning Fisher information metrics from point cloud data

Oluwadamilola Fasina

Yanlei Zhang

Maximilian Nickel

Ian Adelstein

Although data diffusion embeddings are ubiquitous in unsupervised learning and have proven to be a viable technique for uncovering the under… (voir plus)lying intrinsic geometry of data, diffusion embeddings are inherently limited due to their discrete nature. To this end, we propose neural FIM, a method for computing the Fisher information metric (FIM) from point cloud data - allowing for a continuous manifold model for the data. Neural FIM creates an extensible metric space from discrete point cloud data such that information from the metric can inform us of manifold characteristics such as volume and geodesics. We demonstrate Neural FIM's utility in selecting parameters for the PHATE visualization method as well as its ability to obtain information pertaining to local volume illuminating branching points and cluster centers embeddings of a toy dataset and two single-cell datasets of IPSC reprogramming and PBMCs (immune cells).

2023-07-02

Proceedings of the 40th International Conference on Machine Learning (publié)

proceedings.mlr.press

Graph Fourier MMD for Signals on Graphs

Samuel Leone

Aarthi Venkat

While numerous methods have been proposed for computing distances between probability distributions in Euclidean space, relatively little at… (voir plus)tention has been given to computing such distances for distributions on graphs. However, there has been a marked increase in data that either lies on graph (such as protein interaction networks) or can be modeled as a graph (single cell data), particularly in the biomedical sciences. Thus, it becomes important to find ways to compare signals defined on such graphs. Here, we propose Graph Fourier MMD (GFMMD), a novel distance between distributions and signals on graphs. GFMMD is defined via an optimal witness function that is both smooth on the graph and maximizes difference in expectation between the pair of distributions on the graph. We find an analytical solution to this optimization problem as well as an embedding of distributions that results from this method. We also prove several properties of this method including scale invariance and applicability to disconnected graphs. We showcase it on graph benchmark datasets as well on single cell RNA-sequencing data analysis. In the latter, we use the GFMMD-based gene embeddings to find meaningful gene clusters. We also propose a novel type of score for gene selection called "gene localization score" which helps select genes for cellular state space characterization.

2023-05-20

SampTA/2023/Conference (publié)

openreview.net

Single-cell analysis reveals inflammatory interactions driving macular degeneration

Manik Kuchroo

Marcello DiStasio

Eric Song

Eda Calapkulu

Le Zhang

Maryam Ige

Amar H. Sheth

Abdelilah Majdoubi

Madhvi Menon

Abhinav Godavarthi

Yu Xing

Scott Gigante

Holly R. Steach

Jessie Huang

Janhavi Narain

Kisung You

George Mourgkos

Rahul M. Dhodapkar … (voir 5 de plus)

Matthew Hirn

Bastian Rieck

Brian P. Hafler

Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate thera… (voir plus)pies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer’s disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.

2023-05-04

Nature Communications (publié)

Using rare genetic mutations to revisit structural brain asymmetry

Jakub Kopal

Kuldeep Kumar

Kimia Shafighi

Karin Saltoun

Claudia Modenato

Clara A. Moreau

Martineau Jean-Louis

Charles-Olivier Martin

Zohra Saci

Nadine Younis

Élise Douard

Khadije Jizi

Alexis Beauchamp-Chatel

Leila Kushan

Ana I. Silva

Marianne B. M. van den Bree

David E. J. Linden

Michael J. Owen

Jeremy Hall … (voir 10 de plus)

Sarah Lippé

Bogdan Draganski

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Paul M. Thompson

Carrie E. Bearden

Robert Zatorre

Sébastien Jacquemont

Danilo Bzdok

Asymmetry between the left and right brain is a key feature of brain organization. Hemispheric functional specialization underlies some of t… (voir plus)he most advanced human-defining cognitive operations, such as articulated language, perspective taking, or rapid detection of facial cues. Yet, genetic investigations into brain asymmetry have mostly relied on common variant studies, which typically exert small effects on brain phenotypes. Here, we leverage rare genomic deletions and duplications to study how genetic alterations reverberate in human brain and behavior. We quantitatively dissected the impact of eight high-effect-size copy number variations (CNVs) on brain asymmetry in a multi-site cohort of 552 CNV carriers and 290 non-carriers. Isolated multivariate brain asymmetry patterns spotlighted regions typically thought to subserve lateralized functions, including language, hearing, as well as visual, face and word recognition. Planum temporale asymmetry emerged as especially susceptible to deletions and duplications of specific gene sets. Targeted analysis of common variants through genome-wide association study (GWAS) consolidated partly diverging genetic influences on the right versus left planum temporale structure. In conclusion, our gene-brain-behavior mapping highlights the consequences of genetically controlled brain lateralization on human-defining cognitive traits.

2023-04-17

bioRxiv (prépublication)

Rare CNVs and phenome-wide profiling highlight brain structural divergence and phenotypical convergence

Jakub Kopal

Kuldeep Kumar

Karin Saltoun

Claudia Modenato

Clara Moreau

Sandra Martin‐Brevet

Martineau Jean‐Louis

Charles-Olivier Martin

Zohra Saci

Nadine Younis

Petra Tamer

Élise Douard

Anne Maillard

Borja Rodríguez‐Herreros

Aurélie Pain

Sonia Richetin

Leila Kushan

Ana Isabel Silva

Marianne van den Bree … (voir 12 de plus)

David E.J. Linden

Michael J. Owen

Jeremy Hall

Sarah Lippé

Bogdan Draganski

Ida E. Sønderby

Ole A. Andreassen

David C. Glahn

Paul M. Thompson

Carrie E. Bearden

Sébastien Jacquemont

Danilo Bzdok

Copy number variations (CNVs) are rare genomic deletions and duplications that can affect brain and behaviour. Previous reports of CNV pleio… (voir plus)tropy imply that they converge on shared mechanisms at some level of pathway cascades, from genes to large-scale neural circuits to the phenome. However, existing studies have primarily examined single CNV loci in small clinical cohorts. It remains unknown, for example, how distinct CNVs escalate vulnerability for the same developmental and psychiatric disorders. Here we quantitatively dissect the associations between brain organization and behavioural differentiation across 8 key CNVs. In 534 CNV carriers, we explored CNV-specific brain morphology patterns. CNVs were characteristic of disparate morphological changes involving multiple large-scale networks. We extensively annotated these CNV-associated patterns with ~1,000 lifestyle indicators through the UK Biobank resource. The resulting phenotypic profiles largely overlap and have body-wide implications, including the cardiovascular, endocrine, skeletal and nervous systems. Our population-level investigation established brain structural divergences and phenotypical convergences of CNVs, with direct relevance to major brain disorders.

2023-03-01

Nature Human Behaviour (inconnu)

Conditional Flow Matching: Simulation-Free Dynamic Optimal Transport

Nikolay Malkin

Yanlei Zhang

2022-12-31

arXiv.org (prépublication)

Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds

María Ramos Zapatero

Christopher J. Tape

Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods a… (voir plus)re currently the state-of-the-art for such computations, but require

2022-12-31

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (publié)

arxiv.org

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction

Edward De Brouwer

Yanlei Zhang

Ian Adelstein

Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensio… (voir plus)nal, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold structure of data by learning a proxy for geodesic distances, no specific theoretical links have been established. Here, we establish such a link via results in Riemannian geometry explicitly connecting heat diffusion to manifold distances. In this process, we also formulate a more general heat kernel based manifold embedding method that we call heat geodesic embeddings. This novel perspective makes clearer the choices available in manifold learning and denoising. Results show that our method outperforms existing state of the art in preserving ground truth manifold distances, and preserving cluster structure in toy datasets. We also showcase our method on single cell RNA-sequencing datasets with both continuum and cluster structure, where our method enables interpolation of withheld timepoints of data. Finally, we show that parameters of our more general method can be configured to give results similar to PHATE (a state-of-the-art diffusion based manifold learning method) as well as SNE (an attraction/repulsion neighborhood based method that forms the basis of t-SNE).

2022-12-31

Advances in Neural Information Processing Systems (publié)

openreview.net

P397. Genomic Deletions and Duplications Show Mirror Effects on Cognitive Ability According to Spatial Patterns of Gene Expression in the Human Brain

Kuldeep Kumar

Sayeh Kazem

Élise Douard

Zohra Saci

Laura Almasy

David Glahn

Guillaume Dumas

Sébastien Jacquemont

2022-04-30

Biological Psychiatry (publié)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover’s Distance

Dennis Shung

Manik Kuchroo

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover’s distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an L(1) space, whose metric we call unbalanced diffusion earth mover’s distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods.

2022-04-26

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (publié)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance

Dennis Shung

Manik Kuchroo

In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observation… (voir plus)s in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an

2021-12-31

IEEE International Conference on Acoustics, Speech and Signal Processing (inconnu)