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Objective: In parametric fMRI studies the relationship between the amplitude of the hemodynamic response and electrophysiological or behavioral parameters is commonly analyzed using the general linear model (GLM). We examined ways of using single-trial response time (RT) in the analysis of a decision-making task to better isolate task-specific activation.
Methods: fMRI and RT data were recorded in twenty-one subjects performing a visual-oddball-task. Four explanatory variables (EVs) were generated for the GLM-analysis: A conventional (constant impulse) EV, a constant epoch EV informed using subjects' average RT, a variable impulse EV and a variable epoch EV both informed using single-trial RT. EVs were tested individually and as orthogonalized pairs.
Results: The individual EVs all detected similar extensive patterns of activation, while orthogonalized EVs were mainly correlated with BOLD signal variance in sensorimotor and parietal areas. Orthogonalizing the variable epoch EV to the constant epoch EV isolated cortical regions resembling the "dorsal frontoparietal attention network" from activation detected by the conventional (i.e., constant impulse) analysis.
Conclusion: For short event durations, the activation detected by individual EVs is very similar, but orthogonalized, parametrically informed EVs can improve isolation of task-specific BOLD signal change.
Significance: Different approaches for integrating parametric timing measures in fMRI analyses can significantly influence outcomes, refining or confounding findings.
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http://dx.doi.org/10.1016/j.clinph.2012.11.001 | DOI Listing |
MAGMA
September 2025
Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3585CX, Utrecht, The Netherlands.
Objective: Within gradient-spoiled transient-state MR sequences like Magnetic Resonance Fingerprinting or Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT), it is examined whether an optimized RF phase modulation can help to improve the precision of the resulting relaxometry maps.
Methods: Using a Cramer-Rao based method called BLAKJac, optimized sequences of RF pulses have been generated for two scenarios (amplitude-only modulation and amplitude + phase modulation) and for several conditions. These sequences have been tested on a phantom, a healthy human brain and a healthy human leg, to reconstruct parametric maps ( and ) as well as their standard deviations.
MAGMA
September 2025
Department of Medical Imaging, (766), Radboud University Medical Center, Geert Grooteplein 10Radboudumc, P.O. Box 9101, 6500 HB, Nijmegen, the Netherlands.
Objective: To improve B field homogeneity in prostate MR imaging and spectroscopy using a custom-designed 16-channel external local shim coil array.
Methods: In vivo prostate imaging was performed in seven healthy volunteers (mean age: 40.7 years) without bowel preparation.
Ann 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.
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August 2025
The Clinical Hospital of Chengdu Brain Sciences Institute, University of Electronic Sciences and Technology of China (UESTC), 2006 Xiyuan Avenue, West Hi Tech Zone, 611731, Chengdu, China.
This commentary reflects three decades of interaction between the Cuban neuroinformatics tradition and the statistical parametric mapping (SPM) framework. From the early development of neurometrics in Cuba to global initiatives like the Global Brain Consortium, our trajectory has paralleled and intersected with that of SPM. We highlight shared commitments to generative modeling, Bayesian inference, and population-level brain mapping, as shaped through collaborations, workshops, and joint theoretical work with Karl Friston and his group.
View Article and Find Full Text PDFCereb Cortex
August 2025
Research Imaging Institute, University of Texas Health Science Center at San Antonio, 8403 Floyd Curl Drive, San Antonio, TX 78229, United States.
Statistical Parametric Mapping (SPM) adheres to rigorous methodological standards, including: spatial normalization, inter-subject averaging, voxel-wise contrasts, and coordinate reporting. This rigor ensures that a thematically diverse literature is amenable to meta-analysis. BrainMap is a community database (www.
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