Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25656

A Bayesian Distance-Weighted Ising Model for Task-Related Regional Analysis of Neuroimaging Data

Heesang Lee·Jongrak Park·Seoyoung Choi·Bokgyeong Kang·Nial Friel·Sanghoon Han·Jaewoo Park
View original source ↗Read source document ↗Export metadata ↓

Abstract

Neuroimaging analysis commonly involves classifying cognitive conditions, identifying task-relevant brain regions, and examining functional connectivity. These objectives are typically addressed separately, without a model-based framework. In contrast, we propose a Bayesian distance-weighted Ising model that integrates classification, regional importance, and connectivity analysis. The model captures dependence among task labels based on connectivities between functional signals, while region-of-interest (ROI)-specific interaction parameters account for spatial heterogeneity and quantify regional contributions to task discrimination. However, inference is challenging because the signals may be observed on unequal time grids and the likelihood function contains an intractable normalising constant. To address this, we use a basis expansion and an auxiliary variable Markov chain Monte Carlo algorithm. We apply the method to functional near-infrared spectroscopy data from younger and older adults, comparing resting-state and task-related activity. The model achieves high classification accuracy and reveals age-dependent patterns of regional importance and connectivity. Among younger adults, the left orbitofrontal cortex contributes more strongly to task discrimination than the dorsolateral prefrontal and frontopolar cortices, whereas regional differences are less evident among older adults. Older adults also exhibit greater bilateral connectivity between corresponding ROIs, consistent with reduced hemispheric lateralisation.

Explore related subjects

stat.AP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Heesang Lee, Jongrak Park, Seoyoung Choi, Bokgyeong Kang, Nial Friel, Sanghoon Han, Jaewoo Park. 2026-10-19. A Bayesian Distance-Weighted Ising Model for Task-Related Regional Analysis of Neuroimaging Data. https://arxiv.org/abs/2610.25656

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Optimal product launch in uncertain competitive markets: A multi-agent adversarial risk analysis framework

Product launching is a nuanced problem that entails choosing numerous decision variables, including timing, pricing, quality, and marketing strategy. Furthermore, decision-makers must account for multiple uncertainty sources. These include endogenous factors, such as product quality and the strategic decisions of competitors and consumers; as well as exogenous elements, like market evolution and regulations. This paper proposes a novel general framework to support product launch decisions in the presence of multiple competitors and buyers using adversarial risk analysis, mitigating the strong common-knowledge assumptions typical of standard game-theoretic approaches in this domain. We also introduce variants that account for multiple product purchases and integrate discrete choice models. To overcome the inherent computational intractability of modeling multi-agent strategic uncertainty, we deploy a simulation-optimization approach leveraging Bayesian optimization. Finally, we illustrate our proposed framework through a software launch case study focused on optimal timing, pricing, and quality decisions.

stat.AP↗
arXiv2026

A multivariate index for estimating seasonal shifts from temperature curves

Seasonal timing is often studied using fixed calendar seasons, temperature thresholds, or a fixed smoothing parameter applied across all years. These may be sensitive to changes in variability over time. To address this, we propose the Seasonal Adaptive LocalisabLe Index (SALLI), a multivariate index, for characterising seasonality from daily temperature curves observed over complete seasons. SALLI uses Significant Zero Crossings of Derivatives (SiZer) to identify significant increases and decreases across a range of smoothing levels. It uses this information to select a smoothing level for each year. The method assumes one main warm peak and one cool trough per annual cycle. For each year, SALLI estimates the annual temperature curve and the midpoints of the four seasons and temperatures. These estimates describe changes in both seasonal timing and temperature. We illustrate SALLI using Australian maximum temperature data from 1965 to 2024. Comparisons of SALLI estimates over the first and last decades show not only a warming, but a shift to earlier seasons, with the midpoints of spring, summer, and winter occurring roughly a week earlier. All four seasonal midpoints become warmer, with the largest increases occurring in summer (1.37 $^\circ$C) and spring (1.22 $^\circ$C). Warming is evident across time, whereas the clearest shift in seasonal timing emerges after the 2000s. By estimating both timing and temperature at each seasonal midpoint, SALLI locates where changes occur on the annual temperature curve, providing information that is more robust and data-driven than fixed thresholds, fixed calendar seasons, or a single smoothing choice.

stat.AP↗
arXiv2026

Financial Literacy and the Functional Shape of Subjective Time Deformation in Intertemporal Choice

Intertemporal decisions are commonly summarized by a single discount parameter, although individuals with similar overall discounting intensity may value short-, medium-, and long-term delays differently. This study investigates whether financial literacy is associated with these horizon-specific differences by treating reconstructed subjective time as a functional rather than scalar outcome. Data were collected through a multilingual online questionnaire administered to a convenience and snowball sample of 140 adults, who provided hypothetical equivalence judgments for a future reward of EUR 100 over 13 horizons from 7 days to 5 years, a three-item financial-literacy measure, and measures of temporal perception. Individual discount trajectories were reconstructed and analyzed with function-on-scalar regression, allowing the association with financial literacy to vary across the temporal domain. Because the normalized subjective-time trajectory is not identifiable for flat or reconstruction-degenerate discount profiles, the primary functional analysis included 59 participants, while complementary analyses used the full analytical sample. The primary model provided no evidence of a robust linear association between financial literacy and subjective-time deformation. An exploratory categorical specification indicated heterogeneity across financial-literacy levels and departure from a linear-score representation, although individual contrasts were imprecise and sensitive to specification. The relationship between financial literacy and intertemporal valuation may thus be more complex than a monotonic score effect, and functional data analytics can reveal horizon-specific structure that scalar summaries obscure. Given the convenience sample and the reduced identified sample, the results are exploratory and require validation in larger independent samples.

stat.AP↗