21 citations · 25 across the 13 of their papers we have counts for
13 papers
Imaging the Topology of Dynamic Brain Connectivity
Peilin He, Tananun Songdechakraiwut
Functional brain connectivity changes dynamically over time, making its representation challenging for learning on non-Euclidean data. We present a framework that encodes dynamic f…
Bayesian Topological Analysis of Functional Brain Networks
Xukun Zhu, Michael W Lutz, Tananun Songdechakraiwut
Subtle alterations in brain network topology often evade detection by traditional statistical methods. To address this limitation, we introduce a Bayesian inference framework for t…
Leveraging LLMs for Early Alzheimer's Prediction
Tananun Songdechakraiwut
We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation…
Language Models for Longitudinal Clinical Prediction
Tananun Songdechakraiwut, Michael Lutz
We explore a lightweight framework that adapts frozen large language models to analyze longitudinal clinical data. The approach integrates patient history and context within the la…
Connectome-Guided Automatic Learning Rates for Deep Networks
Peilin He, Tananun Songdechakraiwut
The human brain is highly adaptive: its functional connectivity reconfigures on multiple timescales during cognition and learning, enabling flexible information processing. By cont…
Augmenting Bias Detection in LLMs Using Topological Data Analysis
Keshav Varadarajan, Tananun Songdechakraiwut
Recently, many bias detection methods have been proposed to determine the level of bias a large language model captures. However, tests to identify which parts of a large language…
Data-Efficient Neural Training with Dynamic Connectomes
Yutong Wu, Peilin He, Tananun Songdechakraiwut
The study of dynamic functional connectomes has provided valuable insights into how patterns of brain activity change over time. Neural networks process information through artific…
Functional Connectivity Graph Neural Networks
Yang Li, Luopeiwen Yi, Tananun Songdechakraiwut
Real-world networks often benefit from capturing both local and global interactions. Inspired by multi-modal analysis in brain imaging, where structural and functional connectivity…
Functional connectomes of neural networks
Tananun Songdechakraiwut, Yutong Wu
The human brain is a complex system, and understanding its mechanisms has been a long-standing challenge in neuroscience. The study of the functional connectome, which maps the fun…
Topological Continual Learning with Wasserstein Distance and Barycenter
Tananun Songdechakraiwut, Xiaoshuang Yin, Barry D. Van Veen
Continual learning in neural networks suffers from a phenomenon called catastrophic forgetting, in which a network quickly forgets what was learned in a previous task. The human br…
Topological Classification in a Wasserstein Distance Based Vector Space
Tananun Songdechakraiwut, Bryan M. Krause, Matthew I. Banks +2
Classification of large and dense networks based on topology is very difficult due to the computational challenges of extracting meaningful topological features from real-world net…
Fast Topological Clustering with Wasserstein Distance
Tananun Songdechakraiwut, Bryan M. Krause, Matthew I. Banks +2
The topological patterns exhibited by many real-world networks motivate the development of topology-based methods for assessing the similarity of networks. However, extracting topo…
Topological Learning for Brain Networks
Tananun Songdechakraiwut, Moo K. Chung
This paper proposes a novel topological learning framework that integrates networks of different sizes and topology through persistent homology. Such challenging task is made possi…