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<p>Low-dimensional manifolds underlie mental and cognitive representations, and characterizing individual differences is critical to neuropsychiatric research. Existing manifold learning methods either distort subject-specific features through population-level alignment or limit cross-subject comparisons with individualized models. We introduce a scalable, subject-specific manifold learning framework with two modules: one capturing individual spatial variation via individualized spatial weights, and an efficient variant reducing computational cost with minimal performance loss. Simulations show that population-level alignment distorts manifold geometry, whereas our approach preserves subject-specific structure. Applied to movie-watching fMRI, our modules improve behavioral label recovery and reconstruction accuracy. In clinical resting-state fMRI, the efficient module identifies individualized spatial activation patterns highlighting sensorimotor, learning, and emotion-related regions. Further validation on an independent resting-state dataset demonstrates 95% test-retest identification accuracy. Together, these results show our framework is robust, reproducible, and broadly applicable across computational neuroscience and clinical research.</p>

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A scalable subject-specific manifold learning framework for neuroimaging data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月29日

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  • Eloy Geenjaar,
  • Tülay Adalí,
  • Shashwath Meda,
  • Godfrey Pearlson,
  • Sergey Plis,
  • Vince Calhoun

摘要

Low-dimensional manifolds underlie mental and cognitive representations, and characterizing individual differences is critical to neuropsychiatric research. Existing manifold learning methods either distort subject-specific features through population-level alignment or limit cross-subject comparisons with individualized models. We introduce a scalable, subject-specific manifold learning framework with two modules: one capturing individual spatial variation via individualized spatial weights, and an efficient variant reducing computational cost with minimal performance loss. Simulations show that population-level alignment distorts manifold geometry, whereas our approach preserves subject-specific structure. Applied to movie-watching fMRI, our modules improve behavioral label recovery and reconstruction accuracy. In clinical resting-state fMRI, the efficient module identifies individualized spatial activation patterns highlighting sensorimotor, learning, and emotion-related regions. Further validation on an independent resting-state dataset demonstrates 95% test-retest identification accuracy. Together, these results show our framework is robust, reproducible, and broadly applicable across computational neuroscience and clinical research.