Biology subjects

Costantino, M.

Publications and source records attributed to Costantino, M..

10 recordsLinked to original sources

bioRxiv2026

Auditory brainstem response amplitude predicts age-related forward masking temporal-processing deficits in normal-hearing listeners

Aging has profound effects on sensory systems, such that older adults can report problems in hearing despite having normal audiometric thresholds. While it is known that older listeners have poorer temporal processing than younger listeners, there is limited understanding of how these deficits interact with effects of sound level, in particular when they are above conversational level (e.g., > 65 dB SPL). Here we wanted to investigate the link between temporal processing and sound level as a function of age in normal-hearing listeners. Using a forward-masking paradigm, auditory brainstem responses (ABRs) and behavioral detection thresholds were measured at different masker levels. It was found that with increasing age and masker sound level, the Wave V amplitudes of older listeners decreased and directly correlated with behavioral detection thresholds of the probe stimulus in the forward-masking paradigm. These results elucidate the fundamental effects of aging on temporal processing and its interactions with sound level in the central auditory system. The results of this study have clinical implications for the concept of normal hearing in the aging population and how older listeners may struggle in different listening environments that are not captured by simple audiometry. Further delineating the important effects of aging will pave the way for earlier clinical diagnosis of hearing disorders and personalized treatments for different forms of age-related hearing loss.

neuroscience↗

bioRxiv2026

Locus-specific gene-context interactions improve polygenic prediction

Polygenic scores (PGS) are a primary output of large-scale genetic studies and are being deployed in clinical and non-clinical settings. However, current PGS assume simple additive models that ignore context-specific genetic effects, which likely reduce their accuracy and robustness. To address this, we developed PGSC, a PGS framework to incorporate locus-specific gene-context interaction effects (GxC). Simulations show PGSC is robust under the additive model and outperforms PGS in realistic settings. Using sex, age, and statin treatment status as contexts in UK Biobank, we find that PGSC outperforms PGS on average across 48 traits, with substantial improvement in some cases, such as GxSex for testosterone, GxAge for bilirubin, and GxStatins for LDL cholesterol. PGSC consistently outperforms a simple genome-wide GxC model, ampPGS, which only outperforms PGS when a context uniformly amplifies all genome-wide additive effects. Critically, PGSC improvements replicate across ancestries in the UK Biobank and in an external cohort, the Mount Sinai Million Health Discovery Program. Finally, we test robustness to log-scale phenotypes and find that ampPGS gains vanish, while the locus-specific GxC components in PGSC persist. Overall, PGSC is a simple, robust framework that demonstrates GxC effects can improve out-of-sample PGS prediction and is a step toward precision treatment.

genetics↗

bioRxiv2026

Optimizing phenotype scale improves genetic analyses in large-scale biobanks

Large-scale biobanks have enabled increasingly complicated genetic analyses across thousands of phenotypes. However, studies rarely consider the appropriate phenotype measurement scale, a problem that can drastically affect inferences on genetic architecture. Here, we introduce SIQReg, a practical solution to this classical problem, which learns a data-driven phenotype scale by minimizing heterogeneity across phenotype quantiles. Applied to complex traits in UK Biobank, SIQReg rejects the default scale for 24/25 traits. Generally, SIQReg scales lie between default and logarithmic, indicating that default-scale traits are neither purely additive nor purely multiplicative. We show that SIQReg improves both non-additive and additive genetic analyses. SIQReg eliminates most non-additive genetic signals (such as 97% of vQTL and 76% of quantile-dependent TWAS genes), indicating they may be statistical artifacts, while preserving biologically plausible non-additive signals. Simultaneously, SIQReg improves power to detect additive signals, increasing GWAS loci, TWAS genes, and PGS prediction accuracy by 11%, 13%, and 10%, respectively, and identifies 50% more high-risk individuals. These gains replicate across ancestry groups. Our results establish SIQReg as a principled approach to phenotype scale transformation that improves genetic analyses of complex traits.

genetics↗

bioRxiv2026

Choice of phenotype scale is critical in biobank-based GxE tests

The importance of gene-environment interactions (GxE) for complex human traits is heavily debated. Recently, biobank-based GWAS have revealed many statistically significant GxE signals, though most lack clear evidence of biological significance. Here, we partly explain this discrepancy by showing that many GxE signals simplify to additive effects on a different phenotype scale, a classical concern that is currently underappreciated. Our results clearly distinguish GxSex effects on height, which vanish on the log scale, from GxSex effects on testosterone, where the log scale uncovers biologically meaningful female-specific effects. Across 32 phenotypes in UK Biobank, we find that scaling by a power transformation can explain 46% of PGSxSex interactions, and that simple log transformation can explain 23%, with similar results for other environments. We also show that phenotype scale can substantially impact GWAS discovery and the construction and evaluation of polygenic scores. Finally, we provide a set of guidelines to consider and choose phenotype scale in modern genetic studies.

genetics↗

bioRxiv2025

ATHENA: Automatically Tracking Hands Expertly with No Annotations

Studying naturalistic hand behaviours is challenging due to the limitations of conventional marker-based motion capture, which can be costly, time-consuming, and encumber participants. While markerless pose estimation exists - an accurate, off-the-shelf solution validated for hand-object manipulation is needed. We present ATHENA (Automatically Tracking Hands Expertly with No Annotations), an open-source, Python-based toolbox for 3D markerless hand tracking. To validate ATHENA, we concurrently recorded hand kinematics using ATHENA and an industry-standard optoelectronic marker-based system (OptiTrack). Participants performed unimanual, bimanual, and naturalistic object manipulation and we compared common kinematic variables like grip aperture, wrist velocity, index metacarpophalangeal flexion, and bimanual span. Our results demonstrated high spatiotemporal agreement between ATHENA and OptiTrack. This was evidenced by extremely high matches (R2 > 0.90 across the majority of tasks) and low root mean square differences (< 1 cm for grip aperture, < 4 cm/s for wrist velocity, and < 5-10{degrees} for index metacarpophalangeal flexion). ATHENA reliably preserved trial-to-trial variability in kinematics, offering identical scientific conclusions to marker-based approaches, but with significantly reduced financial and time costs and no participant encumbrance. In conclusion, ATHENA is an accurate, automated, and easy-to-use platform for 3D markerless hand tracking that enables more ecologically valid motor control and learning studies of naturalistic hand behaviours, enhancing our understanding of human dexterity.

neuroscience↗

bioRxiv2023

Assessment of white matter hyperintensity severity using multimodal MRI in Alzheimer's Disease

White matter hyperintensities (WMHs) are clinically significant MRI abnormalities often detected in the elderly and early stages of Alzheimers Disease. They are indicative of vascular pathology but represent a mixture of microstructural tissue alterations that is highly variable between individuals. To better understand these alterations, we leveraged the signal of different MRI contrasts sampled within WMHs, which have differential sensitivity to microstructural properties. Subsequently, we sought to examine the asso of these WMH signal measures to clinically-relevant measures such as cortical and global brain atrophy, cognitive function, diagnostic and demographic differences, and Alzheimers Disease-relevant cardiovascular risk factors. Our sample of 118 subjects was composed of healthy controls (n=30), high-risk of Alzheimers Disease due to familial history (n=47), mild cognitive impairment (n=32), and clinical Alzheimers Disease (n=9) as a means of ascertaining a spectrum of impairment. We sampled the median signal within WMHs on weighted MRI images that are commonly acquired (T1-weighted [T1w], T2-weighted [T2w], T1w/T2w ratio, Fluid-Attenuated Inversion Recovery [FLAIR]), and the relaxation times from quantitative T1 (qT1) and T2* (qT2*) images. Main analyses were performed with a periventricular/deep/superficial white matter parcellation and were repeated with a lobar white matter parcellation. We demonstrated that the correlations between WMH signal measures were variable, suggesting that they are likely influenced by different microstructural properties. We observed that the WMH qT2* and FLAIR measures displayed different age- and disease-related trends compared to normal-appearing white matter, highlighting sensitivity to WMH-specific tissue deterioration. Further, WMH qT2* particularly in periventricular and occipital white matter regions was consistently associated with several of our clinical variables of interest using both parcellation schemes in univariate analyses, and further showed high contributions to a pattern of brain variables that was associated with age and cognitive variables in multivariate Partial Least Squares Correlation analyses. qT1 and FLAIR measures showed consistent clinical relationships in multivariate analyses only, while T1w, T2w, and T1w/T2w ratio measures were not consistently associated with clinical variables. We observed that the qT2* signal was sensitive to clinically-relevant microstructural tissue alterations specific to WMHs. Combining volumetric and signal measures of WMH, particularly qT2* and to a lesser extent qT1 and FLAIR, should be considered to more precisely characterize the severity of WMHs in vivo. These findings may have implications in determining the reversibility of WMHs and potential efficacy of cardio- and cerebrovascular treatments.

neuroscience↗

bioRxiv2022

The impact of study design choices on significance and generalizability of canonical correlation analysis in neuroimaging studies

This technical note describes the effects of different data reduction methods and sample sizes for neuroimaging studies in the context of canonical correlation analysis (CCA). CCA is a multivariate statistical technique which has gained increasing popularity in neuroimaging research in recent years. Here, we investigate the parcellation methods impact on elucidating neuroanatomical relationships (based on cortical thickness) with known risk factors related to Alzheimers disease risk using data from the UK Biobank. The cortical thickness values were parcellated using four common methods in neuroimaging (atlas-based parcellation, spectral clustering, principal component analysis, and independent component analysis) and results from CCA were compared. The results show that the choice of parcellation technique impacts the strength and significance of the correlation between the brain and behaviours. Principal component analysis and independent component analysis result in the strongest correlations. Additionally, we show that regardless of parcellation technique, smaller sample sizes of participants result in inflated correlation strength and significance.

bioinformatics↗

bioRxiv2022

Menopause, Brain Anatomy, Cognition and Alzheimer's Disease

The menopause transition has been repeatedly associated with decreased cognitive performance and increased incidence of Alzheimers Disease (AD), particularly when it is induced surgically 1,2 or takes place at a younger age 3,4. However, there are very few studies that use neuroimaging techniques to examine the effects of these variables in aggregate and in a large sample. Here, we use data from thousands of participants from the UK Biobank to assess the relationship between menopausal status, menopause type (surgical or natural), and age at menopause with cognition, AD, and neuroanatomical measures derived from magnetic resonance imaging. We find that for brain and cognitive measures, menopausal status, menopause type and age at surgical menopause do not impact the brain; but that there is a positive correlation between anatomy, cognition and age at non-surgical menopause. These results do not align with previous reports in the literature with smaller samples. However, we confirm that both early and surgical menopause are associated with a higher risk of developing AD, indicating that early and abrupt ovarian hormone deprivation might contribute to the development of the disorder.

neuroscience↗

bioRxiv2022

Variation in subcortical anatomy: relating interspecies differences, heritability, and brain-behavior relationships

There has been an immense research focus on the topic of cortical reorganization in human evolution, but much less is known regarding the reorganization of subcortical circuits which are intimate working partners of the cortex. Here, by combining advanced image analysis techniques with comparative neuroimaging data, we systematically map organizational differences in striatal, pallidal and thalamic anatomy between humans and chimpanzees. We relate interspecies differences, a proxy for evolutionary changes, to genetics and behavioral correlates. We show that highly heritable morphological measures are significantly expanded across species, in contrast to previous findings in the cortex. The identified morphological-cognitive latent variables were associated with striatal expansion, and affective latent variables were associated with more evolutionarily-conserved areas in the thalamus and globus pallidus. These findings provide new insight into the architecture of these subcortical hubs and can provide greater information on the role of these structures in health and illness.

neuroscience↗

bioRxiv2021

Sex Differences in Cortical Morphometry during Ageing: Examining the Interplay between Lifestyle and Reproductive Factors

Sex differences in neurodegenerative disorder prevalence have been attributed to life expectancy, modifiable risk factors related to lifestyle and the impact of changes in sex hormones and the reproductive system. Although these factors are known to interact with one another, they are often studied in isolation. Here, we used a multivariate approach to investigate how lifestyle, along with menopause and the number of children, interacts with cortical thickness (CT) in healthy adults. Using CT measures from T1-weighted scans (MPRAGE, 1 mm3 voxels; 124 participants; 67 females; 40-70 years old) from the Cam-CAN dataset. Using a partial least squares decomposition, we identified patterns of covariance between CT and lifestyle factors, menopause and the number of children. In women, we identified significant patterns that linked education, socioeconomic status, social contact and length of reproductive period to CT in the left prefrontal cortex, as well as alcohol consumption, physical activity and menopausal status to CT in the frontal poles. Contrastingly, the results in men were driven by education and anxiety, and involved increased CT in the temporal poles. Our findings suggests that sex differences in cortical anatomy during brain ageing might be driven by interactions between contrasting lifestyles and the female-specific endocrine environment.

neuroscience↗