Biology subjects

Vuoksimaa, E.

Publications and source records attributed to Vuoksimaa, E..

5 recordsLinked to original sources

bioRxiv2026

petVAE: A Data-Driven Model for Identifying Amyloid PET Subgroups Across the Alzheimer's Disease Continuum

Amyloid-{beta} (A{beta}) PET imaging is a core biomarker and is sufficient for the biological diagnosis of Alzheimers disease (AD). Here, we aimed to identify biologically meaningful subgroups across the continuum of A{beta} accumulation using a data-driven deep learning approach, without imposing predefined thresholds for A{beta} negativity or positivity. We analyzed 3,110 A{beta} PET scans from the Alzheimers Disease Neuroimaging Initiative and Anti-Amyloid Treatment in Asymptomatic Alzheimers Disease studies to develop petVAE, a two-dimensional variational autoencoder. The model accurately reconstructed scans without prior labeling, selection by scanner or region of interest. Latent representations of scans extracted from petVAE were used to visualize and cluster the AD continuum. Clustering yielded four groups: two predominantly A{beta} negative (A{beta}-, A{beta}-+) and two predominantly A{beta} positive (A{beta}+, A{beta}++). All clusters differed in standardized uptake value ratio (p < 1.64x10-) and cerebrospinal fluid (CSF) A{beta} (p < 0.02), demonstrating petVAEs ability to assign scans along the A{beta} continuum. Extreme clusters (A{beta}-, A{beta}++) resembled conventional A{beta} negative and positive groups and differed in cognition, APOE {varepsilon}4 prevalence, A{beta} and tau CSF biomarkers (p < 3x10-). Intermediate clusters (A{beta}-+, A{beta}+) showed higher odds of carrying at least one APOE {varepsilon}4 allele versus A{beta}- (p < 0.03). Participants in A{beta}+ or A{beta}++ clusters exhibited faster progression to AD (A{beta}+ hazard ratio = 2.42, A{beta}++ HR = 9.43; p < 1.17x10-). Thus, petVAE was capable of reconstructing PET scans while extracting latent features that capture the AD continuum and define biologically meaningful subgroups, enabling data-driven characterization of preclinical disease stages.

bioinformatics↗

bioRxiv2025

Cerebral cortical structures linked to intelligence

Understanding the neural basis of intelligence in humans remains an ongoing scientific pursuit. Early studies with small samples identified potential regions but lacked consistency across findings. Recent large-scale magnetic resonance imaging (MRI) datasets, using intelligence measures focused on verbal-numerical reasoning, now offer more robust opportunities for discovery. In this study (N=11,289), we showed the dorsolateral prefrontal cortex as exhibiting the strongest effect size and a significant causal relationship with intelligence, where larger surface area predicts higher intelligence as revealed by Mendelian randomization analyses. Additional regions, including the orbitofrontal and temporal cortices, also showed causal links to intelligence. These regions are critical for working memory, executive function, and language. Reverse causality analyses further indicated that higher intelligence contributes to increased total surface area and greater cortical thickness in the perisylvian language region. Our findings replicate prior evidence of a bidirectional relationship between total surface area and intelligence and further offer novel insights into regional cortical associations and causal effect. Collectively, these findings support a polyregional cortical configuration of intelligence, highlighting the dorsolateral prefrontal cortex--a key hub for cognitive ability.

neuroscience↗

bioRxiv2023

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (A{beta}) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimers disease (AD). We aimed to develop a deep learning model to predict A{beta} cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 A{beta} PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical A{beta}. We also compared the brain regions relevance for the models CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted A{beta} CSF values correlated strongly with measured A{beta} CSF values (r=0.81; p<0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction (q<0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted A{beta} CSF concentration from A{beta} PET scans, offering potential clinical utility for A{beta} level determination and early AD detection.

neuroscience↗

bioRxiv2020

DNA methylation signatures of aggression and closely related constructs: A meta-analysis of epigenome-wide studies across the lifespan

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N=15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha=1.2x10-7; Bonferroni correction). In cord blood samples of 2,425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r=0.74, p=0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripherl blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range=3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.

genetics↗

bioRxiv2017

Brain structure mediates the association between height and cognitive ability

Height and general cognitive ability (GCA) are positively associated, but the underlying mechanisms of this relationship are unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to study if the association between height and cognitive ability is mediated by cortical size. We used genetically, ontogenetically and phylogenetically distinct cortical metrics of cortical surface area (SA) and cortical thickness (CT). Our results indicate that the well-replicated height-GCA association is accounted for by individual differences in total cortical SA (highly heritable metric related to global brain size), and not mean CT, and that the genetic association between SA and GCA underlies the phenotypic height-GCA relationship.

neuroscience↗