Biology subjects

Ng, H. X.

Publications and source records attributed to Ng, H. X..

3 recordsLinked to original sources

bioRxiv2025

Cognitive Performance and Brain-Predicted Age Difference in Bipolar Disorder

Neuroimaging-derived brain-predicted age difference (brain-PAD) is a promising marker of advanced brain aging, but its link to cognitive function in bipolar disorder (BD) is not well understood, especially when comparing across publicly available algorithms trained on diverse, large sample datasets and to algorithms trained on local cohorts with rich multimodal imaging data. Our study compares algorithms used to estimate brain-PAD in terms of their clinical relevance to cognition in BD. We included 44 euthymic BD I individuals and 73 HCs who completed the Delis-Kaplan Executive Function System, and we selected nine scores from this battery for further analyses. Raw scores were log-transformed, scaled, and subjected to PCA; PC1 indexed overall executive function. Four brain-PAD algorithms (PHOTON, BrainageR, DenseNet, Multimodal) were applied to T1-weighted MRI data; the multimodal algorithm also included Diffussion Tensor Imaging (DTI), Arterial Spin Labeling (ASL), functional Magnetic resonance imaging (fMRI) and resting state Magnetic resonance imaging (rsMRI) data. For each algorithm, we regressed brain-PAD on age, sex, and their interaction to obtain residuals, then used those residualized brain-PADs (which we refer to subsequently as brain-PADs throughout the text) to predict PC1. We then directly assessed if there were group differences in the relationship of brain-PAD to cognitive function by including an interaction term between group x brain-PAD. We found no significant group x brain-PAD interaction across all four algorithms. Given that, we then combined BD and HC and explored whether brain-PAD was a meaningful predictor of cognitive performance. Multimodal brain-PAD emerged as a strong negative predictor of cognitive performance (Beta Estimate = -0.084, SE = 0.024, t = -3.50, p < 0.001), indicating that those with older-appearing brains, as indexed by the brain-PAD, scored lower on PC1. BrainageR brain-PAD also significantly predicted PC1 (Beta Estimate = -0.031, SE = 0.0116, t = -2.71, p < 0.01), and DenseNet brain-PAD showed a modest effect (Beta Estimate = -0.0355, SE = 0.0177, t = -2.00, p < 0.05). PHOTON brain-PAD demonstrated a negative trend with PC1 (Beta Estimate = -0.024, SE = 0.0127, t = -1.92, p = 0.06). Residualized brain-PAD, after accounting for age and sex, was inversely associated with a composite metric of executive functioning, particularly for an algorithm integrating a range of imaging modalities. Our findings demonstrate how brain aging patterns captured by a neuroimaging-based, ML-derived composite metric could be associated with cognitive performance across algorithms trained on a variety of data granularity and sample sizes.

neuroscience↗

bioRxiv2025

Comparison of Brain Age Algorithms in Bipolar Disorder

Advances in computational methods have accelerated the application of machine learning to analyze large complex biological data. By applying machine learning algorithms to neuroimaging data, researchers have estimated the "biological age of the brain" i.e., brain age, and used it as a composite metric for indexing brain health, as opposed to using individual features of the brain extracted from neuroimaging data. These machine learning algorithms/models, often known as "brain age" algorithms/models, may take supervised or unsupervised approaches and may utilize one or many imaging modalities during training. We applied 3 regression-based algorithm and 1 neural network-based algorithm trained on varying sample sizes of healthy comparison (HC) participants to estimate the brain age of 73 HC and 44 individuals with bipolar disorder (BD) in our neuroimaging study. Out of the four, 3 were pre-trained off-the-shelf algorithms and1 was developed and trained on multimodal neuroimaging data from a local cohort. The multimodal algorithm was trained on 51 age-matched HCs and tested on the remaining 22 HCs and 44 BDs. The brain predicted age difference (brain-PAD) score was calculated by subtracting the chronological age from the predicted age. Across four brain age prediction algorithms evaluated in HC, BrainageR and DenseNet demonstrated the highest predictive accuracy (r = 0.83; 0.89) and lowest mean absolute errors (MAE = 5.94; 7.26). However, PHOTON (r = 0.65, MAE = 7.71) showed greatest sensitivity to BD as demonstrated by our logistic regression model where the PHOTON brain-PAD was a significant predictor (beta = 0.064, p < 0.05) of BD. Analyses using ICC revealed that agreement levels varied, with PHOTON achieving the highest ICC with DenseNet (0.78) and BrainageR (0.73), which suggests they may pick up similar brain features as opposed to the multimodal algorithm (0.17- 0.43) These results suggest that regularized linear models trained on large samples that explicitly exclude individuals with psychiatric diagnoses (i.e., PHOTON in this case) may be most sensitive to case-control differences despite having lower predictive accuracy. Our findings can serve as a starting point and quantitative reference for future efforts for researchers working with datasets that are similarly constrained by sample size but include unique combinations of imaging modalities.

neuroscience↗

bioRxiv2023

Polyglucosan body density in the aged mouse hippocampus is controlled by a novel modifier locus on chromosome 1

In aged humans and mice, aggregates of hypobranched glycogen molecules called polyglucosan bodies (PGBs) accumulate in hippocampal astrocytes. PGBs are known to drive cognitive decline in neurological diseases but remain largely unstudied in the context of typical brain aging. Here, we show that PGBs arise in autophagy-dysregulated astrocytes of the aged C57BL/6J mouse hippocampus. To map the genetic cause of age-related PGB accumulation, we quantified PGB burden in 32 fully sequenced BXD-recombinant inbred mouse strains, which display a 400-fold variation in hippocampal PGB burden at 16-18 months of age. A major modifier locus was mapped to chromosome 1 at 72-75 Mb, which we defined as the Pgb1 locus. To evaluate candidate genes and downstream mechanisms by which Pgb1 controls the aggregation of glycogen, extensive hippocampal transcriptomic and proteomic datasets were produced for aged mice of the BXD family. We utilized these datasets to identify Smarcal1 and Usp37 as potential regulators of PGB accumulation. To assess the effect of PGB burden on age-related cognitive decline, we performed phenome-wide association scans, transcriptomic analyses as well as conditioned fear memory and Y-maze testing. Importantly, we did not find any evidence suggesting a negative impact of PGBs on cognition. Taken together, our study demonstrates that the Pgb1 locus controls glycogen aggregation in astrocytes of the aged hippocampus without affecting age-related cognitive decline.

genetics↗