Biology subjects

Chong, J. S. X.

Publications and source records attributed to Chong, J. S. X..

3 recordsLinked to original sources

bioRxiv2026

Widespread use of invalid statistical tests in biomedical machine learning

Cross-validation is routinely used to compare performance in biomedical artificial intelligence. Standard tests ignore correlation across cross-validation folds, inflating false-positive rates. In a PRISMA-guided meta-analysis of 184 studies (impact factor [≥] 15) across 30 biomedical fields, 97% use invalid tests. Among studies with abstract-level claims supported by invalid tests, 59% rely on a spurious comparison: one that loses significance after correcting for this correlation. On average, regaining significance requires a 43% larger effect. Extending these findings to the broader literature, we estimate that spurious comparisons occur in nearly two in three studies and support abstract-level claims in nearly one in three studies. Simulations confirm false-positive rates paradoxically approach 100% when cross-validation is repeated to improve stability. We introduce SHARP, a redesign of cross-validation, which best balances false-positive control and power among 13 benchmarked tests. These results reveal widespread fragility in biomedical artificial intelligence and offer a practical route to valid comparisons.

bioinformatics↗

bioRxiv2024

Evaluation of Brain Age as a Specific Marker of Brain Health

Brain age is widely regarded as a powerful marker of general brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting specific health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well-accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimers Disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance as direct models. Overall, our results do not discount brain age as a useful marker of general brain health, but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.

neuroscience↗

bioRxiv2024

Additive effects of cerebrovascular disease functional connectome phenotype and plasma p-tau181 on longitudinal neurodegeneration and cognitive outcomes

INTRODUCTIONWe investigated the effects of multiple cerebrovascular disease (CeVD) neuroimaging markers on brain functional connectivity (FC), and how such CeVD-related FC changes interact with plasma p-tau181 (Alzheimers disease (AD) marker) to influence downstream neurodegeneration and cognitive changes. METHODSMultivariate associations between four CeVD markers and whole-brain FC in 529 participants across the dementia spectrum were examined using partial least squares correlation. Interactive effects of CeVD-related FC patterns and p-tau181 on longitudinal grey matter volume and cognitive changes were investigated using linear mixed-effects models. RESULTSWe identified a brain FC phenotype associated with high CeVD burden across all markers. Further, expression of this general CeVD-related FC phenotype and p-tau181 contributed additively, but not synergistically, to baseline and longitudinal grey matter volumes and cognitive changes. DISCUSSIONOur findings suggest that CeVD exerts global effects on the brain connectome and highlight the additive nature of AD and CeVD on neurodegeneration and cognition.

neuroscience↗