Evidence map›Paper›PMID 34755293›Full record

ArticleBrain imaging and behavior2022

Altered structural brain networks in linguistic variants of frontotemporal dementia.

Salvatore Nigro, Benedetta Tafuri, Daniele Urso, Roberto De Blasi, Alessia Cedola, Giuseppe Gigli, Giancarlo Logroscino, Frontotemporal Lobar Degeneration Neuroimaging Initiative*

Open access · hybridAbstract read
In one paragraph

Article in Brain imaging and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.7field-weighted citation impact, top 17% of its field
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

15 citing papers in PubMed, 20 citations in OpenAlex.

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  10. Frontotemporal lobar degeneration.Nature reviews. Disease primers · 2023
    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

8 authors at 5 institutions in 2 countries.

Salvatore NigroInstitute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy.
Benedetta TafuriCenter for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, Tricase, Lecce, Italy.
Daniele UrsoCenter for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, Tricase, Lecce, Italy.
Roberto De BlasiCenter for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, Tricase, Lecce, Italy.
Alessia CedolaInstitute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy.
Giuseppe GigliInstitute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy.
Giancarlo LogroscinoCenter for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, Pia Fondazione Cardinale G. Panico, Tricase, Lecce, Italy. giancarlo.logroscino@uniba.it.ORCID http://orcid.org/0000-0003-0423-3242
Frontotemporal Lobar Degeneration Neuroimaging Initiative*
Istituto di Nanotecnologia · ITUniversity of Bari Aldo Moro · ITFerioli & Gianotti (Italy) · ITKing's College London · GBUniversity of Salento · IT

Funding

The Frontotemporal Lobar Degeneration Neuroimaging InitiativeR01AG032306 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ROSEN, HOWARD J · 2009 to 2013
$10.0M
foundation for the national institutes of health R01, AG032306NIA NIH HHS R01 AG032306regione puglia D.G.R. n. 2117 of 21.11.2018
6 · The paper itself

Abstract

Semantic (svPPA) and nonfluent (nfvPPA) variants of primary progressive aphasia (PPA) have recently been associated with distinct patterns of white matter and functional network alterations in left frontoinsular and anterior temporal regions, respectively. Little information exists, however, about the topological characteristics of gray matter covariance networks in these two PPA variants. In the present study, we used a graph theory approach to describe the structural covariance network organization in 34 patients with svPPA, 34 patients with nfvPPA and 110 healthy controls. All participants underwent a 3 T structural MRI. Next, we used cortical thickness values and subcortical volumes to define subject-specific connectivity networks. Patients with svPPA and nfvPPA were characterized by higher values of normalized characteristic path length compared with controls. Moreover, svPPA patients had lower values of normalized clustering coefficient relative to healthy controls. At a regional level, patients with svPPA showed a reduced connectivity and impaired information processing in temporal and limbic brain areas relative to controls and nfvPPA patients. By contrast, local network changes in patients with nfvPPA were focused on frontal brain regions such as the pars opercularis and the middle frontal cortex. Of note, a predominance of local metric changes was observed in the left hemisphere in both nfvPPA and svPPA brain networks. Taken together, these findings provide new evidences of a suboptimal topological organization of the structural covariance networks in svPPA and nfvPPA patients. Moreover, we further confirm that distinct patterns of structural network alterations are related to neurodegenerative mechanisms underlying each PPA variant.

Indexed as

Aphasia, Primary ProgressiveFrontotemporal DementiaBrainHumansMagnetic Resonance ImagingSemanticsBrain networksGraph analysisGray matter structural covariance networksNonfluent variant of primary progressive aphasiaSemantic variant of primary progressive aphasia

Identifiers

PMID34755293
PMCPMC9107413
OpenAlexW3212968437

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.