# Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction

arXiv: https://arxiv.org/abs/2403.06837 · HTML: https://mlsanity.com/paper/2403.06837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 68 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible Evaluation](https://mlsanity.com/paper/1904.07773/) (2019)
- [Detect and Correct Bias in Multi-Site Neuroimaging Datasets](https://mlsanity.com/paper/2002.05049/) (2020)
- [Spherical U-Net on Cortical Surfaces: Methods and Applications](https://mlsanity.com/paper/1904.00906/) (2019)
- [Neural deformation fields for template-based reconstruction of cortical surfaces from MRI](https://mlsanity.com/paper/2401.12938/) (2024)

## Built on by (most cited first, 1 of 1)

- [International Transfer of Stochastic Cortical Self-Reconstruction](https://mlsanity.com/paper/2608.07092/) (2026)

## Abstract (arXiv)

Understanding individual differences in cortical structure is key to advancing diagnostics in neurology and psychiatry. Reference models aid in detecting aberrant cortical thickness, yet site-specific biases limit their direct application to unseen data, and region-wise averages prevent the detection of localized cortical changes. To address these limitations, we developed the Stochastic Cortical Self-Reconstruction (SCSR), a novel method that leverages deep learning to reconstruct cortical thickness maps at the vertex level without needing additional subject information. Trained on over 25,000 healthy individuals, SCSR generates highly individualized cortical reconstructions that can detect subtle thickness deviations. Our evaluations on independent test sets demonstrated that SCSR achieved significantly lower reconstruction errors and identified atrophy patterns that enabled better disease discrimination than established methods. It also hints at cortical thinning in preterm infants that went undetected by existing models, showcasing its versatility. Finally, SCSR excelled in mapping highly resolved cortical deviations of dementia patients from clinical data, highlighting its potential for supporting diagnosis in clinical practice.