Alzheimer's Disease (AD) is a neurodegenerative condition characterized by
diverse progression rates among individuals, with changes in cortical thickness
(CTh) closely linked to its progression. Accurately forecasting CTh
trajectories can significantly enhance early diagnosis and intervention
strategies, providing timely care. However, the longitudinal data essential for
these studies often suffer from temporal sparsity and incompleteness,
presenting substantial challenges in modeling the disease's progression
accurately. Existing methods are limited, focusing primarily on datasets
without missing entries or requiring predefined assumptions about CTh
progression. To overcome these obstacles, we propose a conditional score-based
diffusion model specifically designed to generate CTh trajectories with the
given baseline information, such as age, sex, and initial diagnosis. Our
conditional diffusion model utilizes all available data during the training
phase to make predictions based solely on baseline information during inference
without needing prior history about CTh progression. The prediction accuracy of
the proposed CTh prediction pipeline using a conditional score-based model was
compared for sub-groups consisting of cognitively normal, mild cognitive
impairment, and AD subjects. The Bland-Altman analysis shows our
diffusion-based prediction model has a near-zero bias with narrow 95%
confidential interval compared to the ground-truth CTh in 6-36 months. In
addition, our conditional diffusion model has a stochastic generative nature,
therefore, we demonstrated an uncertainty analysis of patient-specific CTh
prediction through multiple realizations.

阿尔茨海默病是一种神经退行性情况，其进展速度因个体而异，与皮层厚度的变化密切相关，准确预测皮层厚度的轨迹可以显著提升早期诊断和干预策略，然而，长期数据经常遭受时间上的稀疏和不完整，我们提出了一种用于生成给定基线信息下皮层厚度轨迹的条件分数扩散模型，该模型在训练阶段利用所有可用数据，仅基于基线信息进行预测，无需先验的皮层厚度进展历史，通过对亚组进行比较，我们发现该模型预测的皮层厚度与真实值在 6-36 个月内几乎无偏差，有较窄的 95％置信区间，此外，我们的条件分数扩散模型具有随机生成的特性，因此我们对患者特定皮层厚度的预测进行了不确定性分析。

基于条件评分的扩散模型用于皮层厚度轨迹预测

Conditional Score-Based Diffusion Model for Cortical Thickness  Trajectory Prediction

The morphological fingerprint in the brain is capable of identifying the
uniqueness of an individual. However, whether such individual patterns are
present in perinatal brains, and which morphological attributes or cortical
regions better characterize the individual differences of ne-onates remain
unclear. In this study, we proposed a deep learning framework that projected
three-dimensional spherical meshes of three morphological features (i.e.,
cortical thickness, mean curvature, and sulcal depth) onto two-dimensional
planes through quasi-conformal mapping, and employed the ResNet18 and
contrastive learning for individual identification. We used the cross-sectional
structural MRI data of 682 infants, incorporating with data augmentation, to
train the model and fine-tuned the parameters based on 60 infants who had
longitudinal scans. The model was validated on 30 longitudinal scanned infant
data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were
achieved, respectively. The sensorimotor and visual cortices were recognized as
the most contributive regions in individual identification. Moreover, the
folding morphology demonstrated greater discriminative capability than the
cortical thickness, which could serve as the morphological fingerprint in
perinatal brains. These findings provided evidence for the emergence of
morphological fingerprints in the brain at the beginning of the third
trimester, which may hold promising implications for understanding the
formation of in-dividual uniqueness in the brain during early development.

本研究提出了一个深度学习框架，通过准保角映射将三维球面网格投影到二维平面上，利用残差神经网络和对比学习进行个体识别，发现在胎儿脑中存在形态指纹，其中感觉运动皮层和视觉皮层是最具贡献的区域，纹理形态学在识别个体方面的能力优于皮层厚度，这对于理解大脑早期发育中个体独特性的形成具有重要意义。

应用准保角映射和对比学习在围产期脑部识别形态指纹

Identification of morphological fingerprint in perinatal brains using  quasi-conformal mapping and contrastive learning

The thickness of the cortical band is linked to various neurological and
psychiatric conditions, and is often estimated through surface-based methods
such as Freesurfer in MRI studies. The DiReCT method, which calculates cortical
thickness using a diffeomorphic deformation of the gray-white matter interface
towards the pial surface, offers an alternative to surface-based methods.
Recent studies using a synthetic cortical thickness phantom have demonstrated
that the combination of DiReCT and deep-learning-based segmentation is more
sensitive to subvoxel cortical thinning than Freesurfer.
While anatomical segmentation of a T1-weighted image now takes seconds,
existing implementations of DiReCT rely on iterative image registration methods
which can take up to an hour per volume. On the other hand, learning-based
deformable image registration methods like VoxelMorph have been shown to be
faster than classical methods while improving registration accuracy. This paper
proposes CortexMorph, a new method that employs unsupervised deep learning to
directly regress the deformation field needed for DiReCT. By combining
CortexMorph with a deep-learning-based segmentation model, it is possible to
estimate region-wise thickness in seconds from a T1-weighted image, while
maintaining the ability to detect cortical atrophy. We validate this claim on
the OASIS-3 dataset and the synthetic cortical thickness phantom of Rusak et
al.

该研究提出了 CortexMorph 方法，该方法利用无监督深度学习直接回归 DiReCT 所需的变形场，结合深度学习的分割模型，可以在几秒钟内从 T1 加权图像中估计区域厚度，同时保持检测皮层萎缩的能力。