Biology subjects

Fernandez-Lozano, S.

Publications and source records attributed to Fernandez-Lozano, S..

4 recordsLinked to original sources

bioRxiv2026

Hippocampal-to-Ventricle Ratio as a Head-Size-IndependentBiomarker: Sex Differences and Cognitive Associations in 27,680UK Biobank Participants

Hippocampal volume is a key biomarker for Alzheimers disease and brain aging, yet reported sex differences vary substantially depending on head-size adjustment methods. We examined whether the hippocampal-to-ventricle ratio (HVR), a self-normalizing measure, provides more consistent sex difference estimates than conventional adjustment approaches. Using a subset of UK Biobank structural MRI data (N = 27,680; 56% female; ages 45-82), we compared sex differences across four adjustment methods (unadjusted, proportions, stereotaxic, residualized) and in samples matched on age and on intracranial volume (ICV). Hippocampal sex differences reversed direction across methods, ranging from d = -0.89 (males larger, unadjusted) to d = 0.58 (females larger, proportions), with a range of 1.5 standard deviations across analytical choices. In contrast, HVR showed a consistent female advantage (d = 0.52) that persisted in the ICV-matched subsample (d = 0.19), confirming this effect is not a head-size artifact. Males exhibited steeper cross-sectional age-related HVR differences (1.7x female rate, p < 10-50), consistent with males showing ventricular expansion at younger ages. Structural equation modeling revealed that HVR predicted general cognition comparably to hippocampal volume ({beta} = 0.04, overlapping CIs), and unlike residualized hippocampal volume, HVR maintained significant brain-cognition associations in both sexes. We provide sex-specific normative centile curves for clinical application. These findings indicate that apparent hippocampal sex differences largely reflect methodological choices rather than biology, while HVR captures consistent morphological variation related to brain aging that may have clinical utility.

neuroscience↗

bioRxiv2024

Automatization and validation of the hippocampal-to-ventricle ratio in a clinical sample

BackgroundThe hippocampal-to-ventricle ratio (HVR) is a biomarker of medial temporal atrophy, particularly useful in the assessment of neurodegeneration in diseases such as Alzheimers disease (AD). To minimize subjectivity and inter-rater variability, an automated, accurate, precise, and reliable segmentation technique for the hippocampus (HC) and surrounding cerebro-spinal fluid (CSF) filled spaces -- such as the temporal horns of the lateral ventricles -- is essential. MethodsWe trained and evaluated three automated methods for the segmentation of both HC and CSF (Multi-Atlas Label Fusion (MALF), Nonlinear Patch-Based Segmentation (NLPB), and a Convolutional Neural Network (CNN)). We then evaluated these methods, including the widely used FreeSurfer technique, using baseline T1w MRIs of 1,641 participants from the AD Neuroimaging Initiative study with various degree of atrophy associated with their cognitive status on the spectrum from cognitively healthy to clinically probable AD. Our gold standard consisted in manual segmentation of HC and CSF from 80 cognitively healthy individuals. We calculated HC volumes and HVR and compared all methods in terms of segmentation reliability, similarity across methods, sensitivity in detecting between-group differences and associations with age, scores of the learning subtest of the Rey Auditory Verbal Learning Test (RAVLT) and the Alzheimers Disease Assessment Scale 13 (ADAS13) scores. ResultsCross validation demonstrated that the CNN method yielded more accurate HC and CSF segmentations when compared to MALF and NLPB, demonstrating higher volumetric overlap (Dice Kappa = 0.94) and correlation (rho = 0.99) with the manual labels. It was also the most reliable method in clinical data application, showing minimal failures. Our comparisons yielded high correlations between FreeSurfer, CNN and NLPB volumetric values. HVR yielded higher control:AD effect sizes than HC volumes among all segmentation methods, reinforcing the significance of HVR in clinical distinction. AssociationsThe positive association with age was significantly stronger for HVR compared to HC volumes on all methods except FreeSurfer. Memory associations with HC volumes or HVR were only significant for individuals with mild cognitive impairment. Finally, the HC volumes and HVR showed comparable negative associations with ADAS13, particularly in the mild cognitive impairment cohort. ConclusionThis study provides an evaluation of automated segmentation methods centered to estimate HVR, emphasizing the superior performance of a CNN-based algorithm. The findings underscore the pivotal role of accurate segmentation in HVR calculations for precise clinical applications, contributing valuable insights into medial temporal lobe atrophy in neurodegenerative disorders, especially AD. AuthorshipO_LISofia Fernandez-Lozano: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Visualization. C_LIO_LIVladimir Fonov: Software, Data Curation. C_LIO_LIDorothee Schoemaker: Resources, Writing - Review & Editing. C_LIO_LIJens Pruessner: Resources, Writing - Review & Editing. C_LIO_LIOlivier Potvin: Resources, Writing - Review & Editing. C_LIO_LISimon Duchesne: Resources, Writing - Review & Editing. C_LIO_LID. Louis Collins: Conceptualization, Writing - Review & Editing, Supervision. C_LI

neuroscience↗

bioRxiv2022

QRATER: a collaborative and centralized imaging quality control web-based application.

Quality control (QC) is an important part of all scientific analysis, including neuroscience. With manual curation considered the gold standard, there remains a lack of available tools that make manual neuroimaging QC accessible, fast, and easy. In this article we present Qrater, a containerized web-based python application that enables viewing and rating of previously generated QC images. A group of raters with varying amounts of experience in QC evaluated Qrater in three different tasks: QC of MRI raw acquisition (10,196 images), QC of non-linear registration to a standard template (10,196 images) and QC of skull segmentation (6,968 images). We measured the proportion of failed images, timing and intra- and inter-rater agreement. Raters spent vastly different amounts of time on each image depending on their experience and the task at hand. QC of MRI raw acquisition was the slowest. While an expert rater needed approximately one minute, trained raters spent 2-6 minutes evaluating an image. The fastest was the curation of a skull segmentation image, where expert raters spent on average 3 seconds per image before assigning a rating. Rating agreement also varied depending on the experience of the raters and the task at hand: trained raters inter-rater agreement with the experts gold standard ranged from fair to substantial in raw acquisition (Cohens chance corrected kappa agreement scores up to 0.72) and from fair to excellent in linear registration (kappa scores up to 0.82), while the experts inter-rater agreement of the skull segmentation task was excellent (kappa = 0.83). These results demonstrate that Qrater is a useful asset for QC tasks that rely on manual curation of images.

bioinformatics↗

bioRxiv2020

Gray and white matter morphology in substance use disorders: A neuroimaging systematic review and meta-analysis.

Substance use disorders (SUDs) are characterized by a compulsion to seek and consume one or more substances of abuse, with a perceived loss of control and negative emotional state. Repeated use of a substance results in synaptic and morphological changes, secondary to toxicity and SUD pathology in the dopamine striato-thalamo-cortical and limbic pathways. These neuroadaptations seem to vary between studies, which could be related to divergent effects of substances, consumption severity or other unknown factors. We therefore identified studies investigating the effects of SUDs using volumetric whole-brain voxel-based morphometry (VBM) in gray (GM) and white matter (WM). We performed a systematic review and meta-analysis of VBM studies using the anatomic likelihood estimation (ALE) method implemented in GingerALE (PROSPERO pre-registration CRD42017071222). Fifty studies met inclusion criteria and were included in the final quantitative meta-analysis, with a total of 538 foci, 88 experiments and 4370 participants. We found convergence and divergence in brain regions and volume effects (higher vs lower volume) in GM and WM depending on the severity of consumption pattern and type of substance. Convergent pathology was evident across substances in GM of the insula, anterior cingulate cortex, putamen, and thalamus, and in WM of the thalamic radiation and internal capsule bundle. Divergent pathology between occasional use (cortical pathology) and addiction (cortical-subcortical pathology) provides evidence of a possible top-down neuroadaptation. Our findings indicate distinctive brain morphometry alterations in SUDs, which may inform our understanding of disease progression and ultimately therapeutic approaches.

neuroscience↗