Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural k -Core Network Analysis

Daianu, Madelaine; Jahanshad, Neda; Nir, Talia M.; Toga, Arthur W.; Jack, Clifford R.; Weiner, Michael W.; Thompson, for the Alzheimer's Disea, Paul M.

doi:10.1089/brain.2012.0137

Cited by 173Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsComplex Network Analysis Techniques

Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural k -Core Network Analysis

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Daianu, Madelaine, Jahanshad, Neda, Nir, Talia M., Toga, Arthur W., Jack, Clifford R., Weiner, Michael W., Thompson, for the Alzheimer's Disea, Paul M.·2013·Brain Connectivity

DOI 10.1089/brain.2012.0137PMC PMC3749712

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Abstract

Brain connectivity analyses show considerable promise for understanding how our neural pathways gradually break down in aging and Alzheimer's disease (AD). Even so, we know very little about how the brain's networks change in AD, and which metrics are best to evaluate these changes. To better understand how AD affects brain connectivity, we analyzed anatomical connectivity based on 3-T diffusion-weighted images from 111 subjects (15 with AD, 68 with mild cognitive impairment, and 28 healthy elderly; mean age, 73.7±7.6 SD years). We performed whole brain tractography based on the orientation distribution functions, and compiled connectivity matrices showing the proportions of detected fibers interconnecting 68 cortical regions. We computed a variety of measures sensitive to anatomical network topology, including the structural backbone--the so-called "k-core"--of the anatomical network, and the nodal degree. We found widespread network disruptions, as connections were lost in AD. Among other connectivity measures showing disease effects, network nodal degree, normalized characteristic path length, and efficiency decreased with disease, while normalized small-worldness increased, in the whole brain and left and right hemispheres individually. The normalized clustering coefficient also increased in the whole brain; we discuss factors that may cause this effect. The proportions of fibers intersecting left and right cortical regions were asymmetrical in all diagnostic groups. This asymmetry may intensify as disease progressed. Connectivity metrics based on the k-core may help understand brain network breakdown as cognitive impairment increases, revealing how degenerative diseases affect the human connectome.

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Some studies have observed conflicting results, including lower characteristic path length in DTI, gray matter, fMRI, EEG, and MEG connectomes in patients with AD.
“However, some studies have observed conflicting results, including lower characteristic path length in DTI (Daianu et al., 2013), gray matter (Tijms, Moller et al., 2013), fMRI (Sanz-Arigita et al., 2010), EEG (de Haan et al., 2009), and magneto-encephalogram (MEG; Stam et al., 2009) connectomes in patients with AD.”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Daianu et al., 2013
Clinical studies have noted decreased network efficiency in patients with AD.
“Clinical studies have also noted decreased network efficiency in patients with AD (Daianu et al., 2013; Fischer et al., 2015; Lo et al., 2010; Reijmer et al., 2013)”
From Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer’s disease (2018) · cites this paper as Daianu et al., 2013

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Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer’s disease
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The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases
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A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD)
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“…MRI (dMRI) as an additional approach for tracking AD progression (Jack et al., 2015). dMRI has since been used in numerous studies to understand the effects of AD on white matter (WM) microstructure and brain connectivity (Daianu et al., 2013a,b; Nir et al., 2013; Prasad et al., 2013).”
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Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment
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Metastability in Senescence
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Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis
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The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer’s Disease and Mild Cognitive Impairment
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Disrupted rich club network in behavioral variant frontotemporal dementia and early‐onset A lzheimer's disease
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Common Brain Structural Alterations Associated with Cardiovascular Disease Risk Factors and Alzheimer’s Dementia: Future Directions and Implications
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The Structural and Functional Connectome and Prediction of Risk for Cognitive Impairment in Older Adults
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Connectome-wide network analysis of white matter connectivity in Alzheimer's disease
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Identification of Superficial White Matter Abnormalities in Alzheimer’s Disease and Mild Cognitive Impairment Using Diffusion Tensor Imaging
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The apolipoprotein E epsilon 4 allele is associated with ventricular expansion rate and surface morphology in dementia and normal aging
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“However, some studies have observed conflicting results, including lower characteristic path length in DTI (Daianu et al., 2013), gray matter (Tijms, Moller et al., 2013), fMRI (SanzArigita et al., 2010), EEG (de Haan et al., 2009), and magneto-encephalogram (MEG; Stam et al., 2009) connectomes in…”
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Connectivity network measures predict volumetric atrophy in mild cognitive impairment
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Expression and significance of autonomic nerves and α9 nicotinic acetylcholine receptor in colorectal cancer
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CoreCube: Core Decomposition in Multilayer Graphs
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Detecting Phase-Synchrony Connectivity Anomalies in EEG Signals. Application to Dyslexia Diagnosis
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Hole detection in metabolic connectivity of Alzheimer's disease using kappa-Laplacian.
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Algebraic Connectivity of Brain Networks Shows Patterns of Segregation Leading to Reduced Network Robustness in Alzheimer’s Disease
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Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis
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Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns
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Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment
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Preserved Structural Network Organization Mediates Pathology Spread in Alzheimer’s Disease Spectrum Despite Loss of White Matter Tract Integrity
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Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: A connectome based approach using machine learning
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Biomarker Investigation Using Multiple Brain Measures from MRI Through Explainable Artificial Intelligence in Alzheimer’s Disease Classification
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Multi-Shell Hybrid Diffusion Imaging (HYDI) at 7 Tesla in TgF344-AD Transgenic Alzheimer Rats
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Potential biomarkers for distinguishing people with Alzheimer’s disease from cognitively intact elderly based on the rich-club hierarchical structure of white matter networks
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Generalized model for k -core percolation and interdependent networks
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Predicting Brain Amyloid-β PET Grades with Graph Convolutional Networks Based on Functional MRI and Multi-Level Functional Connectivity
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Authors: Daianu, M., Jahanshad, N., Nir, T. M., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, F. T. A. D.
Affiliations: University of California, Los Angeles (US)
Mayo Clinic (US)
San Francisco VA Medical Center (US)
Department of Neurology, Imaging Genetics Center, Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, California.
ORCID: Jahanshad, Neda
Nir, Talia M.
Toga, Arthur W.
Jack, Clifford R.
Weiner, Michael W.
Venue: Brain Connectivity
Publisher: SAGE Publications
Published: 2013-08
Volume: 3
Issue: 4
Pages: 407-422
ISSN: 2158-0014 (ISSN-L)
2158-0022
DOI: 10.1089/brain.2012.0137
PMC: PMC3749712
OpenAlex: W2079903691
Cited by: 173
References: 62
Citations by year: 2026: 4 · 2025: 9 · 2024: 7 · 2023: 12 · 2022: 10 · 2021: 13 · 2020: 12 · 2019: 14 · 2018: 21 · 2017: 17 · 2016: 16 · 2015: 19 · 2014: 14 · 2013: 5
FWCI: 9.87
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Brain Connectivity (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: Foundation for the National Institutes of Health (P30AG010129, K01 AG030514, U01AG024904, Grant U01 AG024904, AG024904)
Alzheimer's Association (AG010129, U01 AG024904)
Bristol-Myers Squibb (U01 AG024904, AG024904)
Eli Lilly and Company (U01 AG024904, AG024904)
Pfizer (AG024904, U01 AG024904)
AstraZeneca
Biogen (U01 AG024904, AG024904)
F. Hoffmann-La Roche (AG024904, U01 AG024904)
Medpace (U01 AG024904)
Meso Scale Diagnostics (U01 AG024904, AG024904)
Synarc (U01 AG024904, P30 AG010129, K01 AG030514)
Alzheimer's Disease Neuroimaging Initiative (U01AG024904, AG024904, P30 AG010129, K01 AG030514)
Amorfix Life Sciences
BioClinica (U01 AG024904, AG024904)
Novartis Pharmaceuticals Corporation (U01 AG024904, AG024904)
Northern California Institute for Research and Education (U01 AG024904, K01 AG030514, P30 AG010129)
Bayer HealthCare
Eisai (U01 AG024904, AG024904)
Servier (AG024904, U01 AG024904)
IXICO (AG024904, U01 AG024904)
National Institutes of Health (3u01ag024904-10s2, U01-AG024904, Grant U01 AG024904, P30 AG010129 and K01 AG030514, AG010129, AG024904)
Genentech (AG024904, U01 AG024904)
University of California, Los Angeles
University of California, San Diego
Canadian Institutes of Health Research (U01 AG024904, K01 AG030514, P30 AG010129)
National Institute on Aging (U01-AG-024904, P30AG010129, AG 024904, K01AG030514, AG010129, EB008281)
National Institute of Biomedical Imaging and Bioengineering (R01EB008432, R01EB008281, U01 AG024904, AG024904)
Johnson and Johnson Pharmaceutical Research and Development (U01 AG024904)
Janssen Alzheimer Immunotherapy Research And Development (AG024904, U01 AG024904)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords: Alzheimer's disease, normal aging, tractography, diffusion-weighted imaging, mild cognitive impairment, hemispheric lateralization, small-worldness, node degree
MeSH terms: Aged, Aged, 80 and over, Aging, Alzheimer Disease, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Case-Control Studies, Diffusion Tensor Imaging, Cognitive Dysfunction

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Breakdown of Brain Connectivity Between Normal Aging and Alzheimer's Disease: A Structural k -Core Network Analysis

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