Biology subjects

Giorgio, J.

Publications and source records attributed to Giorgio, J..

6 recordsLinked to original sources

bioRxiv2026

Noise-referenced multi-shell diffusion-weighted imaging for regional cerebrospinal fluid thermometry

We developed a multi-shell diffusion-weighted imaging approach for separate ventricular and subarachnoid cerebrospinal fluid thermometry. The approach uses measured image noise to assess agreement among temperature estimates. In 12 participants, temperature was estimated from four non-zero diffusion weightings. For each voxel, disagreement between these estimates was compared with that expected from image noise, accounting for their shared reference image. Voxels meeting this consistency criterion were retained for regional analysis. Mean diffusion-derived temperature ({+/-} SD) was 38.4 {+/-} 0.4 {degrees}C in the lateral ventricles and 36.0 {+/-} 1.1 {degrees}C in the subarachnoid space. Ventricular estimates were consistently higher than subarachnoid estimates across all participants, with a mean difference of 2.4 {+/-} 1.2 {degrees}C (95% confidence interval 1.6 -3.1 {degrees}C). This difference persisted under alternative noise models and temperature estimators. Subarachnoid estimates increased with retained voxel count; in a post hoc analysis of the six participants with the largest retained counts, ventricular estimates remained higher by 1.6 {+/-} 0.7 {degrees}C. Absolute temperature accuracy and the physiological origin of the regional difference require independent validation. This approach provides an internal consistency check for CSF thermometry and supports separate assessment of ventricular and subarachnoid temperatures.

neuroscience↗

bioRxiv2026

Dopamine Compensates for Amyloid-Induced Default Mode Network Dysfunction to Support Learning

Throughout the preclinical phase of Alzheimers disease (AD) {beta}-amyloid (A{beta}) accumulates preferentially within the default mode network (DMN), yet the functional and behavioural consequences of this pathological burden remain poorly understood. Using task-based fMRI combined with A{beta}, tau, and dopamine PET in cognitively normal older adults, we show that A{beta} burden impairs learning independent of tau, but this learning performance is recovered with higher dorsolateral striatal dopamine synthesis capacity. Investigating the neural mechanisms that support this learning, we observe that A{beta} positive individuals show attenuated DMN activity to error related feedback, a metric that relates to poorer learning. When estimating the effective connectivity during feedback, computational modelling reveals that A{beta} induces dis-inhibition of the DMN during error processing. Critically, dopamine synthesis capacity in the dorsolateral striatum rebalances effective connectivity between the DMN and frontostriatal network, thereby opposing A{beta} related disruption. These findings establish a systems-level framework in which A{beta} impairs learning by disrupting dynamic DMN modulation during feedback, a disruption for which dopaminergic function can partially compensate. This suggests that learning in the presence of A{beta} may be subserved by dopamine-dependent network rebalancing, a candidate mechanism of cognitive resilience to support learning in preclinical AD.

neuroscience↗

bioRxiv2026

Characterisation of Posterior Predominant Amyloid PET Binding Across Multiple Cohorts

The standard approach to quantify amyloid (A{beta}) PET averages uptake within a single cortical mask that assumes no clinically relevant spatial heterogeneity in uptake patterns. Here, in a sample of 12,379 clinically impaired participants taken from four phenotypically diverse cohorts we use data-driven approaches to discover heterogeneous patterns of A{beta}-PET binding, uncovering a reproducible and clinically relevant pattern of posterior predominant A{beta}-PET binding. In particular, A{beta}-PET positive participants who have posterior predominant binding are less likely to be APOE-{varepsilon}4 carriers, more severely impaired, have thinner cortex in posterior regions, and greater posterior tau PET burden. Furthermore, in a subsample of participants with neuropathological assessment, participants with posterior predominant A{beta} binding have a higher likelihood of having cerebral amyloid angiopathy at autopsy. These findings suggest that A{beta}-PET accumulates along two orthogonal axes with biological and clinical relevance, indicating that the standard approach to assess A{beta}-PET is insufficient to capture meaningful signal from A{beta}-PET imaging. This work has implications for the diagnosis and treatment of Alzheimers disease and extends our understanding of the mechanisms governing variable A{beta}-PET distribution and its downstream effects.

neuroscience↗

bioRxiv2025

Neuropathologically validated MRI to tau PET synthesis via Covariate-modulated attention networks

Tau PET is a powerful tool to assess tau pathology in vivo; however, in comparison to MRI, its development is more recent, is rarely available at scale, and substantially more difficult to acquire. Here, we present Covariate-Modulated Attention UNet (CoMA-UNet) to synthesize subject-specific 3D tau PET from T1 MRI while incorporating in the synthesis procedure readily available covariates. Across six external validation datasets, CoMA-UNet reproduced regional patterns of tau PET uptake showing strong agreement with true PET that was generalizable across tracers. Next, we submitted the synthetic tau PET to a series of downstream clinically relevant tasks. First, MMSE associations between the synthetic tau PET were statistically indistinguishable from true PET. Second, the synthetic tau PET achieved out-of-sample diagnostic classification of dementia with an AUROC=0.99. Third, in two independent autopsy cohorts, voxel wise synthetic tau PET images closely followed neuropathologically defined Braak-stages. Fourth, when fine-tuned on a small subset of atypical presentations of AD, CoMA-UNet preserved phenotype-relevant tau topographies and recovered clinically meaningful structure across atypical clinical phenotypes. These findings demonstrate that the novel CoMA-UNet MRI-based synthesis augmented with covariate information can approximate tau PET with sufficient accuracy for downstream scientific and clinical applications.

neuroscience↗

bioRxiv2024

Imaging synaptic density in ageing and Alzheimer's Disease with -SynVesT-1

Monitoring synaptic injury in neurodegenerative diseases may provide new insights into the evolution of the degenerative process as well as a potential mechanism to target for preservation of function. Synaptic density imaging with PET is a relatively new approach to this issue. However, there are remaining questions about technical approaches to data analysis including reference region selection, and how specific phenotypic presentations and symptoms of Alzheimers Disease (AD) are reflected in alterations in synaptic density. MethodsUsing an SV2A PET ligand radiolabeled with the 18F isotope ([18F]-SynVesT-1) we performed sensitivity analyses to determine the optimal reference tissue modelling approach to derive whole brain ratio images. Using these whole brain images from a sample of young adults, older adults, and patients with varied phenotypic presentations of AD we then contrast regional SV2A density and in vivo AD biomarkers. ResultReference tissue optimisation concluded that a cerebellar grey matter reference region is best for deriving whole brain ratio images. Using these whole brain ratio images, we find a strong inverse association between [18F]-SynVesT-1 PET uptake and amyloid beta and tau PET deposition. Finally, we find that individuals with lower temporal grey matter volume but higher temporal [18F]-SynVesT-1 PET uptake show preserved performance on the MMSE. Conclusions[18F]-SynVesT-1 PET shows a close association with in vivo AD pathology and preserved SV2A density may be a possible marker for resilience to neurodegeneration.

neuroscience↗

bioRxiv2020

Predicting future regional tau accumulation in asymptomatic and early Alzheimer's disease

The earliest stages of Alzheimers disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future tau accumulation, translating predictive information from deep phenotyping cohorts at early stages of AD to cognitively normal individuals. In particular, we use machine learning to quantify interactions between key pathological markers ({beta}-amyloid, medial temporal atrophy, tau and APOE 4) at early and asymptomatic stages of AD. We next derive a predictive index that stratifies individuals based on future pathological tau accumulation, highlighting two critical features for optimal clinical trial design. First, future tau accumulation provides a better outcome measure compared to changes in cognition. Second, stratification based on multimodal data compared to {beta}-amyloid alone reduces the sample size required to detect a clinically meaningful change in tau accumulation. Further, we extend our machine learning approach to derive individualised trajectories of future pathological tau accumulation in early AD patients and accurately predict regional future rate of tau accumulation in an independent sample of cognitively unimpaired individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design at asymptomatic and early stages of AD. One Sentence SummaryOur machine learning approach combines baseline multimodal data to make individualised predictions of future pathological tau accumulation at prodromal and asymptomatic stages of Alzheimers disease with high accuracy and regional specificity.

neuroscience↗