Massimiliano Passaretti (left) and Yu-Wei Chang (right) at NEME 2025. (Photo courtesy of Clarion Hotel Draken.)Graph theory and deep learning pipelines
Yu-Wei Chang, Massimiliano Passaretti NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 12:45 – 14:00 Place: Clarion Hotel Draken
This workshop begins with a practical introduction to graph theory, then guides participants through BRAPH 2 to build connectomes, compute graph measures, and run group comparisons, followed by a hands-on deep-learning pipeline. It demonstrates a unified GUI/command-line workflow, a unique architecture of BRAPH 2, helping participants move smoothly from the GUI to scripts. This workshop also guides participants to reproduce multiplex and deep-learning results on their computers from the BRAPH 2 bioRxiv preprint.
From images to graphs, this plenary shows how parcellations and tractography become connectomes and how network analysis reveals brain-network signatures. (Image by Y.-W. Chang.)Network analysis of neuroimaging data, and deep learning pipelines
Yu-Wei Chang NEMES 2025, 24-26 September, 2025 Date: 25 September 2025 Time: 09:00 – 09:45 Place: Clarion Hotel Draken
This plenary presents a practical framework for analysing neuroimaging data with network science and deep learning. It moves from modality-specific preprocessing to graph construction (single-layer and multiplex), then covers core graph measures, group inference, and brain-surface visualization, highlighting recent work from Associate Professor Joana B. Pereira’s group (Department of Clinical Neuroscience, Karolinska Institutet). It also introduces deep-learning pipelines for neuroimaging data: reservoir-computing memory capacity analysis, GapNet for handling missing data, and a robust feature-attribution method combined with SNP (single nucleotide polymorphism) information. The plenary concludes with the BRAPH 2 framework, which supports these pipelines and extends to other ongoing projects (e.g., light-sheet microscopy, Raman spectroscopy).
BRAPH 2 Genesis enables swift creation of custom, reproducible software distributions—tackling the growing complexity of neuroscience by streamlining analysis across diverse data types and workflows. (Image by B. Zufiria-Gerbolés and Y.-W. Chang.)BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for replicable network analysis in neurosciences Yu-Wei Chang, Blanca Zufiria Gerbolés, Joana B Pereira, Giovanni Volpe Date: 6 August 2023 Time: 11:00 AM PDT
As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.
In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. (Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
ArXiv: 2507.15772
Detecting stress in plants is crucial for both open-farm and controlled-environment agriculture. Biomolecules within plants serve as key stress indicators, offering vital markers for continuous health monitoring and early disease detection. Raman spectroscopy provides a powerful, non-invasive means to quantify these biomolecules through their molecular vibrational signatures. However, traditional Raman analysis relies on customized data-processing workflows that require fluorescence background removal and prior identification of Raman peaks of interest-introducing potential biases and inconsistencies. Here, we introduce DIVA (Deep-learning-based Investigation of Vibrational Raman spectra for plant-stress Analysis), a fully automated workflow based on a variational autoencoder. Unlike conventional approaches, DIVA processes native Raman spectra-including fluorescence backgrounds-without manual preprocessing, identifying and quantifying significant spectral features in an unbiased manner. We applied DIVA to detect a range of plant stresses, including abiotic (shading, high light intensity, high temperature) and biotic stressors (bacterial infections). By integrating deep learning with vibrational spectroscopy, DIVA paves the way for AI-driven plant health assessment, fostering more resilient and sustainable agricultural practices.
BRAPH 2 Genesis enables swift creation of custom, reproducible software distributions—tackling the growing complexity of neuroscience by streamlining analysis across diverse data types and workflows. (Image by B. Zufiria-Gerbolés and Y.-W. Chang.)BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences
Yu-Wei Chang, Blanca Zufiria-Gerbolés, Pablo Emiliano Gómez-Ruiz, Anna Canal-Garcia, Hang Zhao, Mite Mijalkov, Joana Braga Pereira, Giovanni Volpe
bioRxiv: 10.1101/2025.04.11.648455
As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.
Memory capacity in aging. A Brain reservoir computing architecture with uniform random signals applied to all nodes. (Image from the article.)Computational memory capacity predicts aging and cognitive decline
Mite Mijalkov, Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira
Nature Communications 16, 2748 (2025)
doi: 10.1038/s41467-025-57995-0
Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.
High-resolution display of “The Kiss” on Retina E-Paper vs. iPhone 15: Photographs comparing the display of “The Kiss” on an iPhone 15 and Retina E-paper. The surface area of the Retina E-paper is ~ 1/4000 times smaller than the iPhone 15. (Image by the Authors of the manuscript.)Retina electronic paper with video-rate-tunable 45000 pixels per inch
Ade Satria Saloka Santosa, Yu-Wei Chang, Andreas B. Dahlin, Lars Osterlund, Giovanni Volpe, Kunli Xiong
arXiv: 2502.03580
As demand for immersive experiences grows, displays are moving closer to the eye with smaller sizes and higher resolutions. However, shrinking pixel emitters reduce intensity, making them harder to perceive. Electronic Papers utilize ambient light for visibility, maintaining optical contrast regardless of pixel size, but cannot achieve high resolution. We show electrically tunable meta-pixels down to ~560 nm in size (>45,000 PPI) consisting of WO3 nanodiscs, allowing one-to-one pixel-photodetector mapping on the retina when the display size matches the pupil diameter, which we call Retina Electronic Paper. Our technology also supports video display (25 Hz), high reflectance (~80%), and optical contrast (~50%), which will help create the ultimate virtual reality display.
Brain region connectivity. (Image by the Authors of the manuscript.)Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity
Shuyang Yao, Arvid Harder, Fahimeh Darki, Yu-Wei Chang , Ang Li, Kasra Nikouei, Giovanni Volpe, Johan N Lundström, Jian Zeng , Naomi Wray, Yi Lu, Patrick F Sullivan, Jens Hjerling-Leffler
Nature Communications 16, 395 (2025)
doi: 10.1038/s41467-024-55611-1
medRxiv: 10.1101/2024.01.18.24301478
Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.
Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Yu-Wei Chang.)GapNet: Neural network training with highly incomplete datasets
Presentation in group meeting of Prof. Michael Strano, Department of Chemical Engineering, Massachusetts Institute of Technology, USA and DiSTAP, Singapore-MIT Alliance for Research and Technology, Singapore. Date: 23 February 2024
Neural network training requires complete data. We have introduced GapNet, which can train neural networks with incomplete data, using medical data. This approach can be generalized for integrating spectrum data across different frequency ranges, allowing the neural network to combine important information from diverse spectrum datasets.
