Adult lifespan effects on functional specialization along the hippocampal long axis DOI Creative Commons
Caitlin R. Bowman, Cara Charles,

Saisha M. Birr

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 5, 2024

Abstract There has been increasing attention to differences in function along the hippocampal long axis, with posterior regions proposed have properties that are well suited representing fine-grained details and coarser representations anterior regions. Whether axis functional specialization persists into older age is not understood, despite documented memory changes age. In this study, we used a large database of fMRI data (n =323 humans both sexes included) from across adult lifespan (ages 18-88) determine degree differentiation posterior-anterior axis. Our first approach was measure similarity among signals within each subregion. We found intra-region most subregion became more similar age, but did relate episodic performance. As second approach, measured connectivity between subregions rest brain. The profiles distinct one another age-related reductions were strongest for intermediate portion hippocampus. contrast, remained relatively stable lifespan, stronger hippocampus cingulate associated better adults, suggesting may help some adults compensate preserve memory. Significance Statement an understanding give rise multifaceted memories. Yet, whether understood. Here, show exaggerates due largely portions Anterior sometimes performance These findings suggest declines be normal part healthy aging, offset decline by upregulating function, which turn helps maintain

Language: Английский

Anterior-temporal network hyperconnectivity is key to Alzheimer's disease: from ageing to dementia DOI Creative Commons
Léa Chauveau,

Brigitte Landeau,

Sophie Dautricourt

et al.

Brain, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Abstract Curing Alzheimer’s disease remains hampered by an incomplete understanding of its pathophysiology and progression. Exploring dysfunction in medial temporal lobe networks, particularly the anterior-temporal (AT) posterior-medial (PM) systems, may provide key insights, as these networks exhibit functional connectivity alterations along entire continuum, potentially influencing propagation. However, specific changes each network their clinical relevance across stages are not yet fully understood. This requires considering commonly used biomarkers, progression, individual variability, age confounds. Here, we leveraged monocentric longitudinal data from 261 participants spanning adult lifespan continuum. The sample included cognitively unimpaired adults aged 19 to 85 years (n = 209; eight out 64 older over 60 were Aβ-positive) Aβ-positive patients fulfilling diagnostic criteria for mild cognitive impairment (MCI, n 26; 18 progressed Alzheimer-dementia within seven years) or type dementia 26). Participants underwent structural resting-state (f) MRI, florbetapir FDG-PET, global assessments, with up three visits a maximum period 47 months. Network was assessed using seed-based analyses perirhinal parahippocampal cortices seeds, data-driven masks reflecting AT PM networks. Generalized additive linear mixed models run assess age-specific effects Alzheimer’s-related alterations. In this context, explored various markers pathological severity, including cerebral amyloid uptake, glucose metabolism, hippocampal volume, cognition, staging, time onset. Our findings revealed distinct patterns linked normal aging disease. Advancing throughout adulthood associated lower more subtle connectivity, while characterised hyperconnectivity without connectivity. Specifically, higher MCI compared controls positively burden, hypometabolism, atrophy, deficits adults, ranging demented. Additionally, correlated faster progression patients. comprehensive approach allowed reveal that excessive is intrinsically These insights guide future research better understand cascading events leading hold promise developing prognostic tools therapeutic interventions targeting

Language: Английский

Citations

2
Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease DOI Creative Commons
Jingjing Gao, Jiaxin Liu, Yuhang Xu

et al.

Frontiers in Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: June 30, 2023

Introduction Alzheimer's disease (AD) is a neurodegenerative that significantly impacts the quality of life patients and their families. Neuroimaging-driven brain age prediction has been proposed as potential biomarker to detect mental disorders, such AD, aiding in studying its effects on functional networks. Previous studies have shown individuals with AD display impaired resting-state connections. However, most used structural magnetic resonance imaging (MRI), limited based MRI (rs-fMRI). Methods In this study, we applied graph neural network (GNN) model controls predict ages using rs-fMRI AD. We compared performance GNN traditional machine learning models. Finally, post hoc was also identify critical regions Results The experimental results demonstrate our can normal data from ADNI database. Moreover differences between chronological were more significant than controls. Our suggest associated accelerated aging connectivity an effective tool for predicting age. Discussion study provides evidence promising modality research, proves be Furthermore, hippocampus, parahippocampal gyrus, amygdala are verified.

Language: Английский

Citations

21
Optimizing functional brain network analysis by incorporating nonlinear factors and frequency band selection with machine learning models DOI Creative Commons
Kai Hu,

Baohua Zhong,

Renjie Tian

et al.

Medicine, Journal Year: 2025, Volume and Issue: 104(9), P. e41667 - e41667

Published: Feb. 28, 2025

The accurate assessment of the brain’s functional network is seen as crucial for understanding complex relationships between different brain regions. Hidden information within frequency bands, which often overlooked by traditional linear correlation-based methods such Pearson correlation (PC) and partial correlation, fails to be revealed, leading neglect more intricate nonlinear factors. These limitations were aimed overcome in this study combination fast continuous wavelet transform normalized mutual (NMI) develop a novel approach. Original time-domain signals from resting-state magnetic resonance imaging decomposed into domains using transform, adjacency matrices constructed enhance feature separation across Both aspects regions comprehensively considered through integration coefficient NMI. construction networks was enabled adaptive selection optimal band combinations. model facilitated extraction tree models with extreme gradient boosting. It demonstrated comparative analysis that method outperformed baseline PC NMI, achieving an area under curve 0.9054. introduction factors found increase precision 14.25% recall 17.14%. Importantly, approach optimized original data without significantly altering topology. Overall, innovation advances function, offering potential future research clinical applications.

Language: Английский

Citations

0
Investigating the relationship between brain age and Alzheimer’s disease: A deep learning approach with multimodal MRI DOI
Zhengning Wang, Jiaxin Liu, Fang Chen

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 109, P. 107926 - 107926

Published: May 6, 2025

Language: Английский

Citations

0
A 3D pseudo-continuous arterial spin labeling study of altered cerebral blood flow correlation networks in mild cognitive impairment and Alzheimer's disease DOI Creative Commons
Meng Li,

Tianjia Zhu,

Yan Kang

et al.

Frontiers in Aging Neuroscience, Journal Year: 2024, Volume and Issue: 16

Published: April 24, 2024

To investigate the abnormalities of three-dimensional pseudo-continuous arterial spin labeling (3D PCASL) based cerebral blood flow (CBF) correlation networks in mild cognitive impairment (MCI) and Alzheimer's disease (AD).

Language: Английский

Citations

1
Wavelet transform-based frequency self-adaptive model for functional brain network DOI

Yupan Ding,

Xiaowen Xu, Li‐Ling Peng

et al.

Cerebral Cortex, Journal Year: 2023, Volume and Issue: 33(22), P. 11181 - 11194

Published: Sept. 26, 2023

The accurate estimation of functional brain networks is essential for comprehending the intricate relationships between different regions. Conventional methods such as Pearson Correlation and Sparse Representation often fail to uncover concealed information within diverse frequency bands. To address this limitation, we introduce a novel frequency-adaptive model based on wavelet transform, enabling selective capture highly correlated band sequences. Our approach involves decomposing original time-domain signal from resting-state magnetic resonance imaging into distinct domains, thus constructing an adjacency matrix that offers enhanced separation features across Comparative analysis demonstrates superior performance our proposed over conventional techniques, showcasing improved clarity distinctiveness. Notably, achieved highest accuracy rate 89.01% using Wavelet Transform, outperforming Transform with 81.32%. Importantly, method optimizes raw data without significantly altering feature topology, rendering it adaptable various network approaches. Overall, innovation holds potential advance understanding function furnish more samples future research clinical applications.

Language: Английский

Citations

3
Medial temporal lobe hyperconnectivity is key to Alzheimer’s disease: Insight from physiological aging to dementia DOI Open Access
Léa Chauveau,

Brigitte Landeau,

Sophie Dautricourt

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 23, 2023

Abstract Curing Alzheimer’s disease (AD) remains hampered by an incomplete understanding of its pathophysiology and progression. Dysfunction within medial temporal lobe networks may provide key insights, as AD proteins seem to propagate specifically through the anterior-temporal (AT) posterior-medial (PM) systems. Using monocentric longitudinal data from 267 participants spanning physiological aging full continuum, we found that advancing age was associated with decreased PM connectivity increased AT over adult life. When assessing AD-relevant changes, all AD-associated clinicopathological features, including elevated amyloid burden, AD-typical glucose hypometabolism, hippocampal atrophy, greater cognitive impairment faster progression MCI AD-dementia, were consistently linked hyperconnectivity in healthy AD-demented older adults. Our comprehensive approach allowed us reveal excessive network is a pivotal mechanism catalysing pathological process AD. Such findings hold promise for early diagnosis therapeutic strategies targeting these specific alterations.

Language: Английский

Citations

1
Intranasal insulin effect on cognitive and/or memory impairment: a systematic review and meta-analysis DOI

María Dolores Gómez‐Guijarro,

Iván Cavero‐Redondo, Alicia Saz‐Lara

et al.

Cognitive Neurodynamics, Journal Year: 2024, Volume and Issue: 18(5), P. 3059 - 3073

Published: June 13, 2024

Language: Английский

Citations

0
The neurophysiological mechanisms of medial prefrontal-perirhinal cortex circuit mediating temporal order memory decline in early stage of AD rats DOI Creative Commons

Linan Zhuo,

Keliang Pang,

Jiajie Dai

et al.

Neurobiology of Disease, Journal Year: 2024, Volume and Issue: 199, P. 106584 - 106584

Published: June 28, 2024

The temporal component of episodic memory has been recognized as a sensitive behavioral marker in early stage Alzheimer's disease (AD) patients. However, parallel studies AD animals are currently lacking, and the underlying neural circuit mechanisms remain poorly understood. Using novel App

Language: Английский

Citations

0
Adult lifespan effects on functional specialization along the hippocampal long axis DOI Creative Commons
Caitlin R. Bowman, Cara Charles,

Saisha M. Birr

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 5, 2024

Abstract There has been increasing attention to differences in function along the hippocampal long axis, with posterior regions proposed have properties that are well suited representing fine-grained details and coarser representations anterior regions. Whether axis functional specialization persists into older age is not understood, despite documented memory changes age. In this study, we used a large database of fMRI data (n =323 humans both sexes included) from across adult lifespan (ages 18-88) determine degree differentiation posterior-anterior axis. Our first approach was measure similarity among signals within each subregion. We found intra-region most subregion became more similar age, but did relate episodic performance. As second approach, measured connectivity between subregions rest brain. The profiles distinct one another age-related reductions were strongest for intermediate portion hippocampus. contrast, remained relatively stable lifespan, stronger hippocampus cingulate associated better adults, suggesting may help some adults compensate preserve memory. Significance Statement an understanding give rise multifaceted memories. Yet, whether understood. Here, show exaggerates due largely portions Anterior sometimes performance These findings suggest declines be normal part healthy aging, offset decline by upregulating function, which turn helps maintain

Language: Английский

Citations

0