A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method for analyzing brain dynamics using HMMs and graph models.
A new mathematical approach detects frequency-based alterations in brain networks.
GNNs improve brain activity forecasting in fMRI studies.
Learning a similarity metric has gained much attention recently, where the goal is to learn a function that maps input patterns to a target space while preserving the semantic distance in the input space. While most related work focused on images, we focus instead on learning a similarity metric for neuroimages, such a…
Network analysis of human brain connectivity is critically important for understanding brain function and disease states. Embedding a brain network as a whole graph instance into a meaningful low-dimensional representation can be used to investigate disease mechanisms and inform therapeutic interventions. Moreover, by …
Brain networks have received considerable attention given the critical significance for understanding human brain organization, for investigating neurological disorders and for clinical diagnostic applications. Structural brain network (e.g. DTI) and functional brain network (e.g. fMRI) are the primary networks of inte…
Derives a biologically plausible neural network for Slow Feature Analysis.
DBGDGM models dynamic brain graphs for better understanding brain function.
A new geometric framework embeds correlation matrices into Euclidean space for scalable brain network analysis.
While statistical analysis of a single network has received a lot of attention in recent years, with a focus on social networks, analysis of a sample of networks presents its own challenges which require a different set of analytic tools. Here we study the problem of classification of networks with labeled nodes, motiv…
Resting-state functional MRI (rs-fMRI) in functional neuroimaging techniques have improved in brain disorders, dysfunction studies via mapping the topology of the brain connections, i.e. connectopic mapping. Since, there are the slight differences between healthy and unhealthy brain regions and functions, investigation…
Study adapts -TCVAE for fMRI to recover nonlinear brain components.
Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks…
Unified framework detects dynamic community structure in brain networks across individuals.
Fine-grained atlases improve fMRI analysis of brain activity.
Unified model learns joint and individual features from brain imaging data.
Paper analyzes shapes of brain arterial networks using statistical methods.
In this paper, we consider the problem of estimating multiple graphical models simultaneously using the fused lasso penalty, which encourages adjacent graphs to share similar structures. A motivating example is the analysis of brain networks of Alzheimer's disease using neuroimaging data. Specifically, we may wish to e…
There has been huge interest in studying human brain connectomes inferred from different imaging modalities and exploring their relationship with human traits, such as cognition. Brain connectomes are usually represented as networks, with nodes corresponding to different regions of interest (ROIs) and edges to connecti…
Autoencoders identify brain networks linked to stress and genotype.
Novel approach combines local and global brain changes for AD prediction.
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BO…
Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. Increasingly the focus of network analysis is shifting towards using network information in some other prediction or inference task rather than just analyzing th…
Analyzing and understanding the structure of complex relational data is important in many applications including analysis of the connectivity in the human brain. Such networks can have prominent patterns on different scales, calling for a hierarchically structured model. We propose two non-parametric Bayesian hierarchi…
Financial markets modeled like brain networks using dMNC.
Novel tRSA combines geometry and topology for brain and model analysis.
Brain decoding involves the determination of a subject's cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemp…
DynDepNet learns dynamic brain graphs from fMRI data for better prediction performance.
Schizophrenia, a mental disorder that is characterized by abnormal social behavior and failure to distinguish one's own thoughts and ideas from reality, has been associated with structural abnormalities in the architecture of functional brain networks. Using various methods from network analysis, we examine the effect …
New methods compare neural network models using geometric and topological summaries.
VNNs transfer well across datasets for brain age prediction using cortical thickness features.
3D CNNs interpret brain MRI differences between men and women.
ACERL embeds networks into a low-dimensional space preserving structural and semantic properties.
Extracts causal brain dynamics across multiple scales.
This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary time-series analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and dis…
A new kernel measures brain network similarities, improving disease classification.
PR-GNN identifies salient brain regions for ASD biomarkers.
New method predicts AD progression using MEG brain networks.
Paper introduces a framework for diagnosing Alzheimer's disease using higher-order topological features from fMRI.
The paper generates future brain imaging sequences for Alzheimer's disease detection.
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on marmoset brains uses two-photon microscopy fluorescence images of axonal projections to collect the neuron connectivity from defined brain r…
The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segment brain tumors and to classify different categories of tumors from d…
Brain imaging analysis on clinically acquired computed tomography (CT) is essential for the diagnosis, risk prediction of progression, and treatment of the structural phenotypes of traumatic brain injury (TBI). However, in real clinical imaging scenarios, entire body CT images (e.g., neck, abdomen, chest, pelvis) are t…
Framework identifies brain connectivity alterations for MDD patients using limited rs-fMRI data.
This research uses Siamese networks to identify partial mouse brain images from the Allen atlas.
Synthesizes computational approaches to understand neural timescales.
The paper characterizes brain states and transitions using functional MRI data.
Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias…