Preparing fMRI Data for Statistical Analysis

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1. Related Not relevant

   Multiscale statistical testing for connectome-wide association studies in fMRI

   P. Bellec, Y. Benhajali, F. Carbonell et al. · 2014 · Quantitative Methods · AI summary available

   Abstract

   Alterations in brain connectivity have been associated with a variety of clinical disorders using functional magnetic resonance imaging (fMRI). We investigated empirically how the number of brain parcels (or scale) impacted the results of a mass univariate general linear model (GLM) on connectomes. The brain parcels used as nodes in the connectome analysis were functionnally defined by a group cluster analysis. We first validated that a classic Benjamini-Hochberg procedure with parametric GLM tests did control appropriately the false-discovery rate (FDR) at a given scale. We then observed on realistic simulations that there was no substantial inflation of the FDR across scales, as long as th…

2. Related Not relevant

   Statistical power and prediction accuracy in multisite resting-state fMRI connectivity

   Christian Dansereau, Yassine Benhajali, Celine Risterucci et al. · 2016 · NeuroImage.Vol 149, p. 220-232 (2017) · AI summary available

   Abstract

   Connectivity studies using resting-state functional magnetic resonance imaging are increasingly pooling data acquired at multiple sites. While this may allow investigators to speed up recruitment or increase sample size, multisite studies also potentially introduce systematic biases in connectivity measures across sites. In this work, we measure the inter-site effect in connectivity and its impact on our ability to detect individual and group differences. Our study was based on real, as opposed to simulated, multisite fMRI datasets collected in N=345 young, healthy subjects across 8 scanning sites with 3T scanners and heterogeneous scanning protocols, drawn from the 1000 functional connectom…

3. Related Not relevant

   Data-driven decomposition of brain dynamics with principal component analysis in different types of head impacts

   Xianghao Zhan, Yuzhe Liu, Nicholas J. Cecchi et al. · 2021 · Quantitative Methods · AI summary available

   Abstract

   Strain and strain rate are effective traumatic brain injury predictors. Kinematics-based models estimating these metrics suffer from significant different distributions of both kinematics and the injury metrics across head impact types. To address this, previous studies focus on the kinematics but not the injury metrics. We have previously shown the kinematic features vary largely across head impact types, resulting in different patterns of brain deformation. This study analyzes the spatial distribution of brain deformation and applies principal component analysis (PCA) to extract the representative patterns of injury metrics (maximum principal strain (MPS), MPS rate (MPSR) and MPSXMPSR) in …

4. Related Not relevant

   Preterm Birth Analysis Using Nonlinear Methods (a preliminary study)

   Tijana T. Ivancevic, Lakhmi C. Jain, John Pattison et al. · 2008 · Quantitative Methods · AI summary available

   Abstract

   In this report we review modern nonlinearity methods that can be used in the preterm birth analysis. The nonlinear analysis of uterine contraction signals can provide information regarding physiological changes during the menstrual cycle and pregnancy. This information can be used both for the preterm birth prediction and the preterm labor control. Keywords: preterm birth, complex data analysis, nonlinear methods

5. Related Not relevant

   Classification of anatomic structures in head and neck by CT-based radiomics

   Yoichi Watanabe, A. Biswas, K. Rangarajan et al. · 2022 · Quantitative Methods · AI summary available

   Abstract

   Background and Purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we analyzed the radiomics features of 22 anatomical structures in the head and neck area in CT images. Furthermore, we studied whether CT radiomics can classify anatomical structures of the head and neck using unsupervised machine-learning techniques. Materials and methods: We obtained IMRT/VMAT treatment planning data from 36 patients treated for head and neck cancers in a single institution. There were 1357 contours of more than 22 anatomical structures drawn on…

6. Related Not relevant

   The Latent Space Hypothesis: Toward Universal Medical Representation Learning

   Salil Patel · 2025 · Quantitative Methods · AI summary available

   Abstract

   Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although these modalities appear disparate, many encode convergent information about a single underlying physiological state. The Latent Space Hypothesis frames each observation as a projection of a unified, hierarchically organized manifold -- much like shadows cast by the same three-dimensional object. Within this learned geometric representation, an individual's health status occupies a point, disease progression traces a trajectory, and therapeutic intervention corresponds to a directed vector. Interpreting heterogeneous evidence in a shared space provi…

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   Sparsity is All You Need: Rethinking Biological Pathway-Informed Approaches in Deep Learning

   Isabella Caranzano, Corrado Pancotti, Cesare Rollo et al. · 2025 · Quantitative Methods · AI summary available

   Abstract

   Biologically-informed neural networks typically leverage pathway annotations to enhance performance in biomedical applications. We hypothesized that the benefits of pathway integration does not arise from its biological relevance, but rather from the sparsity it introduces. We conducted a comprehensive analysis of all relevant pathway-based neural network models for predictive tasks, critically evaluating each study's contributions. From this review, we curated a subset of methods for which the source code was publicly available. The comparison of the biologically informed state-of-the-art deep learning models and their randomized counterparts showed that models based on randomized informati…

8. Related Not relevant

   Evaluating Integrative Strategies for Incorporating Phenotypic Features in Spatial Transcriptomics

   Levin M Moser, Ahmad Kamal Hamid, Esteban Miglietta et al. · 2025 · Quantitative Methods · AI summary available

   Abstract

   Spatial transcriptomics (ST) technologies not only offer an unprecedented opportunity to interrogate intact biological samples in a spatially informed manner, but also set the stage for integration with other imaging-based modalities. However, how to best exploit spatial context and integrate ST with imaging-based modalities remains an open question. To address this, particularly under real-world experimental constraints such as limited dataset size, class imbalance, and bounding-box-based segmentation, we used a publicly available murine ileum MERFISH dataset to evaluate whether a minimally tuned variational autoencoder (VAE) could extract informative low-dimensional representations from ce…