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YaleNeuroConnect is a human functional MRI (fMRI) dataset collected at Yale University that includes functional MRI data (and the respective functional connectomes) obtained under resting-state and six task conditions. There are 302 diagnostically and demographically diverse subjects, each with extensive neuropsychological testing and symptom inventories obtained outside of the MRI. Prior studies have shown that stronger predictive models relating the brain to external measures can be built with connectivity data obtained during continuous performance tasks instead of the more common resting-state. The tasks here were selected to exercise the brain across various cognitive domains. For each subject, 48 minutes of fMRI data and high-resolution 3D brain volumes were obtained. The fMRI data, along with the deep phenotyping data in a diverse subject pool, allow studies of brain parcellation under different conditions, the relationship between cognitive and clinical measures, identification of circuits supporting external measures, and data for the development of brain-based tests. The transdiagnostic nature of the sample allows a sufficient range of symptom scores to test the principles of the Research Domain Criteria framework.
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http://dx.doi.org/10.1101/2025.07.15.25331595 | DOI Listing |
Comput Med Imaging Graph
August 2025
Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China. Electronic address:
Bipolar disorder (BD) is a debilitating mental illness characterized by significant mood swings, posing a substantial challenge for accurate diagnosis due to its clinical complexity. This paper presents CS2former, a novel approach leveraging a dual channel-spatial feature extraction module within a Transformer model to diagnose BD from resting-state functional MRI (Rs-fMRI) and T1-weighted MRI (T1w-MRI) data. CS2former employs a Channel-2D Spatial Feature Aggregation Module to decouple channel and spatial information from Rs-fMRI, while a Channel-3D Spatial Attention Module with Synchronized Attention Module (SAM) concurrently computes attention for T1w-MRI feature maps.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
JAMA Netw Open
September 2025
School of Medicine and Public Health, University of Wisconsin-Madison, Madison.
Importance: It is unclear whether the duration of amyloid-β (Aβ) pathology is associated with neurodegeneration and whether this depends on the presence of tau.
Objective: To examine the association of longitudinal atrophy with Aβ positron emission tomography (PET)-positivity (Aβ+) and the estimated duration of Aβ+ (Aβ+ duration), controlling for tau-positivity.
Design, Setting, And Participants: Data for this longitudinal cohort study were drawn from the Wisconsin Registry for Alzheimer Prevention and the Wisconsin Alzheimer Disease Research Center Clinical Core Study.
Biomed Eng Lett
September 2025
Department of Radiology, Guizhou International Science and Technology Cooperation Base of Precision Imaging for Diagnosis and Treatment, Guizhou Provincial People's Hospital, Guiyang, Guizhou China.
The generated lung nodule data plays an indispensable role in the development of intelligent assisted diagnosis of lung cancer. Existing generative models, primarily based on Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM), have demonstrated effectiveness but also come with certain limitations: GANs often produce artifacts and unnatural boundaries, and due to dataset limitations, they struggle with irregular nodules. While DDPMs are capable of generating a diverse range of nodules, their inherent randomness and lack of control limit their applicability in tasks such as segmentation.
View Article and Find Full Text PDFArtif Intell Med
August 2025
University of Science and Technology of China, 230000, Hefei, China; Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China. Electronic address:
The diagnosis of brain tumors is pivotal for effective treatment, with MRI serving as a commonly used non-invasive diagnostic modality in clinical practices. Fundamentally, brain tumor diagnosis is a type of pattern recognition task that requires the integration of information from multi-modal MRI images. However, existing fusion strategies are hindered by the scarcity of multi-modal imaging samples.
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