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Reproducibility of neuroimaging analyses and aggregation of heterogenous datasets are significant challenges in human subjects imaging research. This stems in part from a lack of an easy to use and universal data format that encompasses all steps of neuroimaging. The BIDS format has become widely adopted, however it is increasingly complex to implement as features are added, with the documentation now exceeding 500 pages. As such, there is a need for standards that can handle the complexity of the data while minimizing the complexity of the format. Here we present a simple but generalizable data sharing specification, called the squirrel format (not related to the squirrel programming language), to share imaging data in a simple, but flexible, specification. It is so named because squirrels are effective at storing significant quantities of food and knowing exactly where and when to find it. The design objectives of the format specification are to 1) store subject information, experimental parameters, raw data, analyzed data, and analysis methods 2) organize data in a human-readable hierarchy 3) enable easy sharing and dissemination of data packages. We developed a relational hierarchy with a structured representation of all steps of neuroimaging data collection and analysis, and a generalizable specification to store any modality of neuroimaging data, which satisfies the design objectives. Additionally, redundancy is minimized by using relational database principles. The specification allows all research data to be classified into one of ten object types, thus simplifying the sharing of neuroimaging data. Like how squirrels employ 'chunking', the squirrel format chunks data into a manageable number of object types. The squirrel format was developed to share neuroimaging data but can be generalized to share any imaging research.
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http://dx.doi.org/10.1007/s12021-025-09732-7 | DOI Listing |
J Neurophysiol
September 2025
Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Repetition suppression, the reduced neural response upon repeated presentation of a stimulus, can be explained by models focussing on bottom-up (i.e. adaptation) or top-down (i.
View Article and Find Full Text PDFIEEE 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 PDFCereb 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
Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France.
Over three decades, statistical parametric mapping has transformed neuroimaging from descriptive mapping to causal inference, placing generative models at the core of causal explanations for brain function. It inspired to a large degree The Virtual Brain, which builds subject-specific digital twins from multimodal data, enabling brain simulations and exploration. Both frameworks converge at parameter estimation, where model and data meet, providing the mathematical manifestation of cause-effect in pathophysiology.
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