Evidence map›Paper›PMID 27656119›Full record

ArticleFrontiers in neuroscience2016

Exploring Patterns of Alteration in Alzheimer's Disease Brain Networks: A Combined Structural and Functional Connectomics Analysis.

Fulvia Palesi, Gloria Castellazzi, Letizia Casiraghi, Elena Sinforiani, Paolo Vitali, Claudia A M Gandini Wheeler-Kingshott, Egidio D'Angelo

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Review
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  4. Review
  5. Therapeutic relevance of an EU-GMP certifiedFrontiers in pharmacology · 2026
    Article
  6. Article
  7. Article
  8. Article
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  10. Review
  11. Article
  12. Connectomic analysis of Alzheimer's disease using percolation theory.Network neuroscience (Cambridge, Mass.) · 2022
    Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
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  19. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Fulvia PalesiDepartment of Physics, University of PaviaPavia, Italy; Brain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy.
Gloria CastellazziBrain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Electrical, Computer and Biomedical Engineering, University of PaviaPavia, Italy.
Letizia CasiraghiBrain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Brain and Behavioural Sciences, University of PaviaPavia, Italy.
Elena SinforianiNeuroradiology Unit, C. Mondino National Neurological Institute Pavia, Italy.
Paolo VitaliNeuroradiology Unit, C. Mondino National Neurological InstitutePavia, Italy; Brain MRI 3T Mondino Research Center, C. Mondino National Neurological InstitutePavia, Italy.
Claudia A M Gandini Wheeler-KingshottDepartment of Brain and Behavioural Sciences, University of PaviaPavia, Italy; Brain MRI 3T Mondino Research Center, C. Mondino National Neurological InstitutePavia, Italy; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of NeurologyLondon, UK.
Egidio D'AngeloBrain Connectivity Center, C. Mondino National Neurological InstitutePavia, Italy; Department of Brain and Behavioural Sciences, University of PaviaPavia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a severe derangement of cognitive functions, primarily memory, in elderly subjects. As far as the functional impairment is concerned, growing evidence supports the "disconnection syndrome" hypothesis. Recent investigations using fMRI have revealed a generalized alteration of resting state networks (RSNs) in patients affected by AD and mild cognitive impairment (MCI). However, it was unclear whether the changes in functional connectivity were accompanied by corresponding structural network changes. In this work, we have developed a novel structural/functional connectomic approach: resting state fMRI was used to identify the functional cortical network nodes and diffusion MRI to reconstruct the fiber tracts to give a weight to internodal subcortical connections. Then, local and global efficiency were determined for different networks, exploring specific alterations of integration and segregation patterns in AD and MCI patients compared to healthy controls (HC). In the default mode network (DMN), that was the most affected, axonal loss, and reduced axonal integrity appeared to compromise both local and global efficiency along posterior-anterior connections. In the basal ganglia network (BGN), disruption of white matter integrity implied that main alterations occurred in local microstructure. In the anterior insular network (AIN), neuronal loss probably subtended a compromised communication with the insular cortex. Cognitive performance, evaluated by neuropsychological examinations, revealed a dependency on integration and segregation of brain networks. These findings are indicative of the fact that cognitive deficits in AD could be associated not only with cortical alterations (revealed by fMRI) but also with subcortical alterations (revealed by diffusion MRI) that extend beyond the areas primarily damaged by neurodegeneration, toward the support of an emerging concept of AD as a "disconnection syndrome." Since only AD but not MCI patients were characterized by a significant decrease in structural connectivity, integrated structural/functional connectomics could provide a useful tool for assessing disease progression from MCI to AD.

Indexed as

ADconnectomicsdisconnection syndromeprobabilistic tractographyresting state networks

Identifiers

PMID27656119
PMCPMC5013043

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.