Individualized Mapping of Aberrant Cortical Thickness via Stochastic Cortical Self-Reconstruction
This addresses the need for more accurate and individualized diagnostics in clinical neurology and psychiatry, though it appears incremental as it builds on existing reference models with a novel deep learning approach.
The paper tackled the problem of detecting individual cortical thickness abnormalities in neurology and psychiatry by developing the Stochastic Cortical Self-Reconstruction (SCSR) method, which achieved significantly lower reconstruction errors and better disease discrimination than established methods, including identifying undetected cortical thinning in preterm infants.
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.