Paper List
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SpikGPT: A High-Accuracy and Interpretable Spiking Attention Framework for Single-Cell Annotation
This paper addresses the core challenge of robust single-cell annotation across heterogeneous datasets with batch effects and the critical need to ide...
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Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time
This paper addresses the core challenge of efficiently and accurately sampling the conformational landscape of biomolecules from diffusion-based struc...
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Personalized optimization of pediatric HD-tDCS for dose consistency and target engagement
This paper addresses the critical limitation of one-size-fits-all HD-tDCS protocols in pediatric populations by developing a personalized optimization...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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Consistent Synthetic Sequences Unlock Structural Diversity in Fully Atomistic De Novo Protein Design
This paper addresses the core pain point of low sequence-structure alignment in existing synthetic datasets (e.g., AFDB), which severely limits the pe...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
Hybrid eTFCE–GRF: Exact Cluster-Size Retrieval with Analytical pp-Values for Voxel-Based Morphometry
University of Cambridge | National University of Singapore | University of Dundee
30秒速读
IN SHORT: This paper addresses the computational bottleneck in voxel-based neuroimaging analysis by providing a method that delivers exact cluster-size retrieval and analytical inference simultaneously, eliminating the need for slow permutation testing while removing discretization errors.
核心创新
- Methodology Proposes a hybrid algorithm that combines eTFCE's union-find data structure for exact cluster-size retrieval with pTFCE's analytical Gaussian Random Field (GRF) inference, achieving both properties for the first time.
- Methodology Introduces a six-experiment Monte Carlo validation protocol demonstrating nominal family-wise error rate (FWER) control (0/200 rejections, 95% CI [0.0%, 1.9%]), no power loss (Dice ≥0.999), and high cross-variant concordance (r > 0.99).
- Software Develops and releases pytfce, an open-source, pure-Python package that achieves 4.6x to 75x speedup over the reference R implementation and is more than three orders of magnitude faster than permutation-based TFCE.
主要结论
- The hybrid eTFCE-GRF method successfully controls the family-wise error rate at the nominal level, with 0 false positives out of 200 tests (95% CI [0.0%, 1.9%]).
- Statistical power is preserved with Dice coefficients ≥0.999 compared to baseline pTFCE at sufficient signal strength, and cross-variant concordance exceeds r=0.99.
- Runtime improvements are substantial: the baseline implementation is 75x faster than R pTFCE (~5 seconds vs. ~375 seconds), while the hybrid variant is 4.6x faster (~85 seconds) with the added benefit of exact cluster-size retrieval.
摘要: Threshold-free cluster enhancement (TFCE) improves sensitivity in voxel-wise neuroimaging inference by integrating cluster extent across all thresholds, but its reliance on permutation testing makes it prohibitively slow for large datasets. Probabilistic TFCE (pTFCE) replaces permutations with analytical Gaussian random field (GRF) pp-values, which reduces runtime by more than an order of magnitude, yet relies on a fixed threshold grid that introduces discretisation error. Exact TFCE (eTFCE) eliminates this discretisation by computing the integral exactly via a union-find data structure, but still requires permutations for inference. We propose a hybrid method that combines eTFCE’s union-find data structure for exact cluster-size retrieval with pTFCE’s analytical GRF inference. The union-find builds the full cluster hierarchy in a single pass over sorted voxels and enables exact cluster-size queries at any threshold in near-constant time; GRF theory then converts these sizes into analytical pp-values without permutations. We validate the method through a six-experiment Monte Carlo study on synthetic phantoms (64364^{3}, 80 subjects): null family-wise error rate is controlled at the nominal level (0/200 rejections, 95% CI [0.0%,1.9%][0.0\%,1.9\%]); power curves match baseline pTFCE (Dice ≥0.999\geq 0.999 at sufficient signal); smoothness estimation error is below 1%; and cross-variant concordance exceeds r=0.99r=0.99. On real brain data from UK Biobank (N=500N=500, within-vendor) and IXI (N=563N=563, cross-vendor), the method detects biologically plausible scanner, age, and sex effects; on IXI, significance maps form strict subsets of the reference R pTFCE output, which supports conservative family-wise error control. Both methods are implemented in pytfce, a pure-Python package with no R or FSL dependencies, available on PyPI. The baseline reimplementation completes whole-brain voxel-based morphometry in ∼5{\sim}5 s (75×75\times faster than R pTFCE), while the hybrid variant completes in ∼85{\sim}85 s (4.6×4.6\times faster) with the advantage of exact cluster-size retrieval; both are more than three orders of magnitude faster than permutation-based TFCE.