Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity

March 2023 in “ bioRxiv (Cold Spring Harbor Laboratory) ”

Dhananjay Bhaskar, Yanlei Zhang, Jessica L. Moore, Feng Gao, Bastian Rieck, Guy Wolf, Firas A. Khasawneh, Elizabeth Munch, J. Adam Noah, Helen Pushkarskaya, Christopher Pittenger, Valentina Greco, Smita Krishnaswamy

Studysummary This study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.

Our plain-language summary. Not medical advice or a treatment recommendation. Consult a qualified healthcare professional before changing treatment. Full disclaimer ·

View this study on biorxiv.org →

Discuss this study in the Community →

Similar Research

5 / 1000+ results

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

research Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

November 2022 in “bioRxiv (Cold Spring Harbor Laboratory)”

In this study, deep learning models accurately predicted gene expression in whole slide images of colorectal cancer, with convolutional neural networks outperforming transformer and graph-based approaches in spatial RNA pattern prediction.

research Scalp disease analysis using deep learning models

March 2023 in “Applied and Computational Engineering”

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

research Amplitude-guided deep reinforcement learning for semi-supervised layer segmentation

January 2026 in “Pattern Recognition”

This study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study

2 citations , September 2025 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft”

This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

research MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network

April 2026 in “Scientific Reports”

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.