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We propose a new, modular, open-source, Python-based 3D+time realistic functional magnetic resonance imaging (fMRI) data simulation software. SNAKE or imulator from eurovascular coupling to cquisition of -space data for xploration of fMRI acquisition techniques. It is the first simulator to simulate the entire chain of fMRI data acquisition, from the spatio-temporal design of evoked brain responses to various 3D sampling strategies of k-space data with multiple coils. We now have the possibility to extend the forward acquisition model to different noise and artifact sources while remaining memory-efficient. Using this in-silico setup, we can provide a realistic and reproducible ground truth for fMRI reconstruction methods in 3D accelerated acquisition settings and explore the influence of critical parameters. This includes the acceleration factor and signal-to-noise ratio (SNR), on downstream tasks of image reconstruction and statistical analysis of evoked brain activity. In this paper, we present three scenarios of increasing complexity to showcase the flexibility, versatility, and fidelity of SNAKE: From a temporally fixed full 3D Cartesian to various 3D non-Cartesian sampling patterns, we can compare-with reproducibility guarantees-how experimental paradigms, acquisition strategies, and reconstruction methods contribute and interact together, affecting the downstream statistical analysis.
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http://dx.doi.org/10.1162/IMAG.a.121 | DOI Listing |
IEEE J Biomed Health Inform
September 2025
Vision Transformer (ViT) applied to structural magnetic resonance images has demonstrated success in the diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, three key challenges have yet to be well addressed: 1) ViT requires a large labeled dataset to mitigate overfitting while most of the current AD-related sMRI data fall short in the sample sizes. 2) ViT neglects the within-patch feature learning, e.
View Article and Find Full Text PDFAnn Acad Med Singap
August 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore.
Introduction: Interpretation and analysis of magnetic resonance imaging (MRI) scans in clinical settings comprise time-consuming visual ratings and complex neuroimage processing that require trained professionals. To combat these challenges, artificial intelligence (AI) techniques can aid clinicians in interpreting brain MRI for accurate diagnosis of neurodegenerative diseases but they require extensive validation. Thus, the aim of this study was to validate the use of AI-based AQUA (Neurophet Inc.
View Article and Find Full Text PDFJAMA Psychiatry
September 2025
School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
Importance: Cannabis is the most commonly used illicit drug, with 10% to 30% of regular users developing cannabis use disorder (CUD), a condition linked to altered hippocampal integrity. Evidence suggests high-intensity interval training (HIIT) enhances hippocampal structure and function, with this form of physical exercise potentially mitigating CUD-related cognitive and mental health impairments.
Objective: To determine the impact of a 12-week HIIT intervention on hippocampal integrity (ie, structure, connectivity, biochemistry) compared with 12 weeks of strength and resistance (SR) training in CUD.
Cereb Cortex
August 2025
Section of Brain Function Information, National Institute for Physiological Sciences, 38 Nishigonaka, Myodaiji, Okazaki, Aichi 444-8585, Japan.
This study aimed to identify brain activity modulations associated with different types of visual tracking using advanced functional magnetic resonance imaging techniques developed by the Human Connectome Project (HCP) consortium. Magnetic resonance imaging data were collected from 27 healthy volunteers using a 3-T scanner. During a single run, participants either fixated on a stationary visual target (fixation block) or tracked a smoothly moving or jumping target (smooth or saccadic tracking blocks), alternating across blocks.
View Article and Find Full Text PDFCereb Cortex
August 2025
Department of Psychology, University of Milano-Bicocca, Milan, Italy.
Semantic composition allows us to construct complex meanings (e.g., "dog house", "house dog") from simpler constituents ("dog", "house").
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