Biology subjects

Eyler, L.

Publications and source records attributed to Eyler, L..

5 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↗

bioRxiv2020

Atypical genomic patterning of the cerebral cortex in autism with poor early language outcome

Cortical regional identities develop through anterior-posterior (A-P) and dorsal-ventral (D-V) prenatal genomic patterning gradients. Here we find that A-P and D-V genomic patterning of cortical surface area (SA) and thickness (CT) is intact in typically developing and autistic toddlers with good language outcome, but is absent in autistic toddlers with poor early language outcome. Genes driving this effect are prominent in midgestational A-P and D-V gene expression gradients and prenatal cell types driving SA and CT variation (e.g., progenitor cells versus excitatory neurons). These genes are also important for vocal learning, human-specific evolution, and prenatal co-expression networks enriched for high-penetrance autism risk genes. Autism with poor early language outcome may be linked to atypical genomic cortical patterning starting in prenatal periods and which impacts later development of regional functional specialization and circuit formation. One Sentence SummaryGenomic patterning of the cortex is atypical in autistic toddlers with poor early language outcome.

neuroscience↗

bioRxiv2019

Brain Aging in Major Depressive Disorder: Results from the ENIGMA Major Depressive Disorder working group

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset. MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain predicted age difference (brain-PAD). FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohens d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004). InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates. FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

neuroscience↗

bioRxiv2017

Toddlers later diagnosed with autism exhibit multiple structural abnormalities in temporal corpus callosum fibers

Interhemispheric functional connectivity abnormalities are often reported in autism and it is thus not surprising that structural defects of the corpus callosum (CC) are consistently found using both traditional MRI and DTI techniques. Past DTI studies however, have subdivided the CC into 2 or 3 segments without regard for where fibers may project to within the cortex, thus placing limitations on our ability to understand the nature, timing and neurobehavioral impact of early CC abnormalities in autism. Leveraging a unique cohort of 97 toddlers (68 autism; 29 typical) we utilized a novel technique that identified seven CC tracts according to their cortical projections. Results revealed that younger (<2.5 years old), but not older toddlers with autism exhibited abnormally low mean, radial, and axial diffusivity values in the CC tracts connecting the occipital lobes and the temporal lobes. Fractional anisotropy and the cross sectional area of the temporal CC tract were significantly larger in young toddlers with autism. These findings indicate that water diffusion is more restricted and unidirectional in the temporal CC tract of young toddlers who develop autism. Such results may be explained by a potential overabundance of small caliber axons generated by excessive prenatal neural proliferation as proposed by previous genetic, animal model, and postmortem studies of autism. Furthermore, early diffusion measures in the temporal CC tract of the young toddlers were correlated with outcome measures of autism severity at later ages. These findings regarding the potential nature, timing, and location of early CC abnormalities in autism add to accumulating evidence, which suggests that altered inter-hemispheric connectivity, particularly across the temporal lobes, is a hallmark of the disorder.

neuroscience↗