Biology subjects

Nolin, S. A.

Publications and source records attributed to Nolin, S. A..

4 recordsLinked to original sources

bioRxiv2026

Greater Amyloid Burden in Cognitive Networks in Preclinical Alzheimer's Disease

BackgroundIn preclinical Alzheimers disease (pAD), regional patterns of amyloid-{beta} (A{beta}) deposition are well characterized but it is unclear how this process varies across functional networks. ObjectiveDetermine how A{beta} accumulation in functional networks ("network-amyloid burden" [NAB]) varies by age, network type (cognitive vs. non-cognitive), and A{beta} status (A{beta}+/A{beta}-), and relates to cognition. Methods157 cognitively unimpaired adults (45-84 years; n=28 A{beta}+ per neuroradiological read) underwent brain MRI, amyloid PET (18F-florbetapir), and neuropsychological testing. NAB was calculated as the mean standard uptake value ratio within 7 networks categorized as cognitive (fronto-parietal, default mode, ventral and dorsal attention, limbic) or non-cognitive (somato-motor, visual). Linear mixed models tested how NAB varies across age, networks (by type and each separately), A{beta} status, and their interactions, and relationships between NAB and cognition. ResultsNAB increased with age, most prominently in fronto-parietal and default mode networks. NAB was higher in cognitive than non-cognitive networks, and this difference was more pronounced in A{beta}+ individuals. NAB was not significantly associated with cognition. ConclusionsCognitive brain networks are more vulnerable to amyloid accumulation with aging and in pAD than non-cognitive networks. Cognitive NAB may be useful for early detection and as a target for intervention in pAD.

neuroscience↗

bioRxiv2026

Human Cerebral Cortex Organization Characterized by Functional PET-FDG "Metabolic Connectivity"

PurposeIn this study, we characterize the spatiotemporal organization of resting-state metabolic connectivity (RSMC) in the human brain, as measured by [18F]- fluorodeoxyglucose (FDG) functional PET (fPET-FDG). We examine the relationship between RSMC organization and resting-state functional connectivity (RSFC) derived from functional magnetic resonance imaging and other known cortical organizational principles. MethodsResting-state fPET-FDG data from 24 individuals were obtained from a publicly available repository. We characterized local metabolic organization using connectivity-based boundary mapping, with adaptations to account for the low signal-to-noise ratio of fPET-FDG data. We then estimated global metabolic organization through community detection-based network and principal gradient analyses. Furthermore, we examined how metabolic connectivity is shaped by temporal-frequency-specific components of fPET-FDG signal. Finally, we contextualized metabolic organization by relating metabolic gradients to anatomical, functional, and energetic reference measures. ResultsAt the local scale, boundary mapping results indicated structured transitions shaped by a combination of both fast and slow fPET-FDG signals, partly overlapping with RSFC boundary maps. Globally, RSMC analyses revealed a robust metabolic structure organized along a superior-inferior cortical gradient. This pattern remained consistent across network community detection and principal gradient analyses and was primarily driven by low-frequency, minute-scale fPET-FDG dynamics. The identified large-scale metabolic profile aligns closely with several known anatomical and energetic constraints. ConclusionThis study characterizes the spatiotemporal organizational principles of RSMC, deepening insight into the brains energetic framework and providing a basis for future cognitive and clinical investigations of metabolic connectivity organization.

neuroscience↗

bioRxiv2026

Energetic implications of fMRI-based nodal complex network metrics: a complex picture unfolds across diverse brain states

Functional MRI-based graph theory has provided profound insights into the brains functional organization, yet the neuroenergetic meaning of widely used graph-theoretical metrics remains poorly understood. Although resting-state research suggests a positive coupling between network topology and glucose metabolism, it remains unclear whether this relationship reflects a general principle of brain organization or a state-specific phenomenon. Here, we test the neuroenergetic interpretability of nodal graph-theoretical metrics by linking complex network topology to cerebral glucose consumption across diverse brain states. Leveraging simultaneous functional PET-MRI, we directly compare state-dependent fluctuations in glucose consumption and network topology during sensory, cognitive, and arousal conditions. We further assess metabolic-topological couplings in disease through a meta-analysis of resting-state FDG-PET and fMRI studies involving Alzheimers disease, Parkinsons disease, major depressive disorder, and schizophrenia. Our results show that nodal graph-theoretical metrics exhibit state- and network-dependent metabolic associations, with coupling patterns diverging across experimental and disease contexts. Notably, frontoparietal and attentional networks show more conserved metabolic-topological coupling than other large-scale networks across states. These findings underscore a dynamic, complex interplay between metabolic demand and complex network organization, highlighting the need for a nuanced interpretation of the energetic underpinnings of nodal graph-theoretical metrics in health and disease.

neuroscience↗

bioRxiv2023

"Surviving and Thriving": Evidence for Cortical GABA Stabilization in Cognitively-Intact Oldest-Old Adults

Cortical GABA levels are reduced in older age; age-related differences in GABA may be associated with age-related cognitive change. The nature of age-related GABA differences in the highest-functioning stratum of the oldest-old (85+) population is not yet known. We extend our previously-reported Individual Participant Data Meta-Analysis of GABA levels (Porges et al., 2021) across the lifespan with four novel datasets sampling the cognitively-intact oldest-old. The slope of age-related GABA differences in cognitively-intact oldest-old adults flattens after roughly age 80. We interpret these findings as an effect of survivorship: inclusion in the study required intact cognition, and too great a reduction of GABA levels may not be compatible with neurophysiological function needed for intact cognition. This work contributes to a growing body of evidence suggesting that successful cognitive aging may require intact GABAergic function, as well as further characterizing successful aging amongst oldest-old adults.

neuroscience↗