Smita Krishnaswamy

Understanding how neurons communicate and coordinate their activity is essential for unraveling the brain’s complex functionality. To anal… (voir plus)yze the intricate spatiotemporal dynamics of neural signaling, we developed Geometric Scattering Trajectory Homology (neuro-GSTH), a novel framework that captures time-evolving neural signals and encodes them into low-dimensional representations. GSTH integrates geometric scattering transforms, which extract multiscale features from brain signals modeled on anatomical graphs, with t-PHATE, a manifold learning method that maps the temporal evolution of neural activity. Topological descriptors from computational homology are then applied to characterize the global structure of these neural trajectories, enabling the quantification and differentiation of spatiotemporal brain dynamics. We demonstrate the power of neuro-GSTH in neuroscience by applying it to both simulated and biological neural datasets. First, we used neuro-GSTH to analyze neural oscillatory behavior in the Kuramoto model, revealing its capacity to track the synchronization of neural circuits as coupling strength increases. Next, we applied neuro-GSTH to neural recordings from the visual cortex of mice, where it accurately reconstructed visual stimulus patterns such as sinusoidal gratings. Neuro-GSTH-derived neural trajectories enabled precise classification of stimulus properties like spatial frequency and orientation, significantly outperforming traditional methods in capturing the underlying neural dynamics. These findings demonstrate that neuro-GSTH effectively identifies neural motifs—distinct patterns of spatiotemporal activity—providing a powerful tool for decoding brain activity across diverse tasks, sensory inputs, and neurological disorders. Neuro-GSTH thus offers new insights into neural communication and dynamics, advancing our ability to map and understand complex brain functions.

2024-10-13

bioRxiv (prépublication)

Neuro-GSTH: A Geometric Scattering and Persistent Homology Framework for Uncovering Spatiotemporal Signatures in Neural Activity

Dhananjay Bhaskar

Jessica Moore

Yanlei Zhang

Feng Gao

Bastian Rieck

Guy Wolf

Helen Pushkarskaya

Firas Khasawneh

Elizabeth Munch

Valentina Greco

Christopher Pittenger

2024-10-13

bioRxiv (prépublication)

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

Chen Liu

Danqi Liao

Alejandro Parada-Mayorga

Alejandro Ribeiro

Marcello DiStasio

The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (voir plus) biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.

2024-10-04

ArXiv (prépublication)

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

Chen Liu

Danqi Liao

Alejandro Parada-Mayorga

Alejandro Ribeiro

Marcello DiStasio

The proliferation of digital microscopy images, driven by advances in automated whole slide scanning, presents significant opportunities for… (voir plus) biomedical research and clinical diagnostics. However, accurately annotating densely packed information in these images remains a major challenge. To address this, we introduce DiffKillR, a novel framework that reframes cell annotation as the combination of archetype matching and image registration tasks. DiffKillR employs two complementary neural networks: one that learns a diffeomorphism-invariant feature space for robust cell matching and another that computes the precise warping field between cells for annotation mapping. Using a small set of annotated archetypes, DiffKillR efficiently propagates annotations across large microscopy images, reducing the need for extensive manual labeling. More importantly, it is suitable for any type of pixel-level annotation. We will discuss the theoretical properties of DiffKillR and validate it on three microscopy tasks, demonstrating its advantages over existing supervised, semi-supervised, and unsupervised methods.

2024-10-04

ArXiv (prépublication)

ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics

Siddharth Viswanath

Dhananjay Bhaskar

David R. Johnson

João Felipe Rocha

Egbert Castro

Jackson Grady

Alex T. Grigas

Michael Perlmutter

Corey S. O'Hern

Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress… (voir plus) has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.

2024-10-01

arXiv (publié)

Latent Representation Learning for Multimodal Brain Activity Translation

Arman Afrasiyabi

Dhananjay Bhaskar

Erica L. Busch

Laurent Caplette

Rahul Singh

Guillaume Lajoie

Nicholas B. Turk-Browne

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (voir plus)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

2024-09-27

ArXiv (prépublication)

Latent Representation Learning for Multimodal Brain Activity Translation

Arman Afrasiyabi

Dhananjay Bhaskar

Erica Lindsey Busch

Laurent Caplette

Rahul Singh

Guillaume Lajoie

Nicholas B Turk-Browne

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (voir plus)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.

2024-09-27

ArXiv (prépublication)

Abstract PR-05: Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk

Cathy C. Garcia

Aarthi Venkat

Daniel C. McQuaid

Sherry Agabiti

Alex Tong

Rebecca Cardone

Richard G. Kibbey

Mandar Deepak Muzumdar

For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (voir plus)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis. Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.

2024-09-15

Cancer Research (publié)

Abstract PR-05: Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk

Cathy C. Garcia

Aarthi Venkat

Daniel C. McQuaid

Sherry Agabiti

Alex Tong

Rebecca Cardone

Richard G. Kibbey

Mandar Deepak Muzumdar

For a long time, the pancreas was thought to have separate cellular compartments that functioned distinctly from one another. The endocrine … (voir plus)pancreas (islets of Langerhans) regulates glucose homeostasis, while the exocrine pancreas (acini and ducts) produces and secretes digestive enzymes. However, it has recently become clear that the endocrine and exocrine compartments communicate with one another, and dysfunction in one leads to dysfunction in the other, resulting in diabetes or pancreatitis. However, whether and how the endocrine pancreas drives the development of pancreatic ductal adenocarcinoma (PDAC), an exocrine tumor, remains unresolved. Strikingly, we found that genetic ablation of insulin-producing islet beta (β) cells (Akita) in a faithful Kras/Trp53-driven PDAC model (KPC: Kras LSL-G12D /+; Trp 53172 /+; Pdx1-Cre) suppressed PDAC progression. Conversely, obesity-induced β cell hormone dysregulation promoted Kras-driven PDAC development. Single-cell RNA sequencing (scRNA-seq) analysis of wild-type and obese mice (high-fat diet-fed and leptin-deficient (Lep ob/ob )) revealed increased expression of the peptide hormone cholecystokinin (CCK) in a subset of β cells concordant with increasing obesity, and transgenic β cell overexpression of CCK was sufficient to promote exocrine tumorigenesis in KC mice. Combined in silico (pseudotime (TrajectoryNET) and archetypal (AANet) analysis) and experimental (CreER) lineage tracing demonstrated that CCK-expressing β cells originated from a pre-existing immature β cell population (virgin β cells). Grainger causality analysis of transcriptional networks uncovered a stress-induced JNK-cJun pathway that promotes CCK expression β cells, which we confirmed using JNK inhibitors in β cell models. Together, our findings identify cellular and molecular mechanisms of β cell adaptation to obesity that contribute to obesity-driven pancreatic cancer. Furthermore, we define a critical role for endocrine-exocrine signaling in PDAC progression and stress-induced β cell pathways which could be leveraged to target the endocrine pancreas to subvert exocrine tumorigenesis. Citation Format: Cathy Garcia, Aarthi Venkat, Daniel McQuaid, Sherry Agabiti, Alex Tong, Rebecca Cardone, Richard Kibbey, Smita Krishnaswamy, Mandar Muzumdar. Endocrine beta-cell stress promotes pancreatic ductal adenocarcinoma through endocrine-exocrine cell crosstalk [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr PR-05.

2024-09-15

Cancer Research (publié)

Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics

Xingzhi Sun

Charles Xu

João Felipe Rocha

Chen Liu

Benjamin Hollander-Bodie

Laney Goldman

Marcello DiStasio

Michael Perlmutter

In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (voir plus)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.

2024-09-14

ArXiv (prépublication)

Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics

Xingzhi Sun

Charles Xu

João F. Rocha

Chen Liu

Benjamin Hollander-Bodie

Laney Goldman

Marcello DiStasio

Michael Perlmutter

In many data-driven applications, higher-order relationships among multiple objects are essential in capturing complex interactions. Hypergr… (voir plus)aphs, which generalize graphs by allowing edges to connect any number of nodes, provide a flexible and powerful framework for modeling such higher-order relationships. In this work, we introduce hypergraph diffusion wavelets and describe their favorable spectral and spatial properties. We demonstrate their utility for biomedical discovery in spatially resolved transcriptomics by applying the method to represent disease-relevant cellular niches for Alzheimer’s disease.

2024-09-14

ArXiv (prépublication)

Geometry-Aware Generative Autoencoders for Metric Learning and Generative Modeling on Data Manifolds

Xingzhi Sun

Danqi Liao

Kincaid MacDonald

Yanlei Zhang

Guillaume Huguet

Guy Wolf

Ian Adelstein

Tim G. J. Rudner