Schoemaker, D. | Search bioRxiv

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↗

bioRxiv2021

Amyloid-β and tau pathologies relate to distinctive brain dysconnectomics in preclinical autosomal-dominant Alzheimer's disease

The human brain is composed of functional networks that have a modular topology, where brain regions are organized into communities that form internally dense (segregated) and externally sparse (integrated) subnetworks that underlie higher-order cognitive functioning. It is hypothesized that amyloid-{beta} and tau pathology in preclinical Alzheimers disease (AD) spread through functional networks, disrupting neural communication that results in cognitive dysfunction. We used high-resolution (voxel-level) graph-based network analyses to test whether in vivo amyloid-{beta} and tau burden was associated with the segregation and integration of brain functional connections, and episodic memory, in cognitively-unimpaired Presenilin-1 E280A carriers who are expected to develop early-onset AD dementia in approximately 13 years on average. Compared to non-carriers, mutation carriers exhibited less functional segregation and integration in posterior default-mode network (DMN) regions, particularly the precuneus, and in the retrospenial cortex, which has been shown to link medial temporal regions and cortical regions of the DMN. Mutation carriers also showed greater functional segregation and integration in regions connected to the salience network, including the striatum and thalamus. Greater tau burden was associated with lower segregated and integrated functional connectivity of DMN regions, particularly the precuneus and medial prefrontal cortex. In turn, greater tau pathology was related to higher segregated and integrated functional connectivity in the retrospenial cortex, and the anterior cingulate cortex, a hub of the salience network. These findings enlighten our understanding of how AD-related pathology distinctly alters the brains functional architecture in the preclinical stage, possibly contributing to pathology propagation and ultimately resulting in dementia. SIGNIFICANCE STATEMENTAmyloid-{beta} and tau pathologies in Alzheimers disease (AD) are hypothesized to propagate through functional networks. Research is needed to elucidate how AD-related pathologies alter the brains functional connections years before symptom-onset to help predict pathology accumulation and dementia risk. Using high-resolution (voxel-level) graph-based network analyses and PET imaging, we showed that greater tau burden was associated with largely dysconnectivity of posterior default-mode network brain regions, and increased connectivity of structures associated with the salience network and others that are critical for integrating information across neural systems in cognitively-unimpaired Presenilin-1 E280A carriers. Findings enlighten how amyloid-{beta} and tau relate to distinct patterns of functional connectivity in regions that underlie memory functioning and are critical for information processing, helping predict disease progression.

neuroscience↗