Evidence map›Paper›PMID 39281738›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Connectome-based predictive modeling of brain pathology and cognition in Autosomal Dominant Alzheimer's Disease.

Vaibhav Tripathi, Joshua Fox-Fuller, Vincent Malotaux, Ana Baena, Nikole Bonillas Felix, Sergio Alvarez, David Aguillon, Francisco Lopera, David C Somers, Yakeel T Quiroz

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Vaibhav TripathiDepartment of Psychological and Brain Sciences, Boston University.
Joshua Fox-FullerDepartment of Psychological and Brain Sciences, Boston University.
Vincent MalotauxMassachusetts General Hospital, Harvard Medical School.
Ana BaenaGrupo de Neurociencias, Universidad de Antioquia, Medellin, Colombia.
Nikole Bonillas FelixMassachusetts General Hospital, Harvard Medical School.
Sergio AlvarezHospital Pablo Tobon Uribe, Medellin, Colombia.
David AguillonGrupo de Neurociencias, Universidad de Antioquia, Medellin, Colombia.
Francisco LoperaGrupo de Neurociencias, Universidad de Antioquia, Medellin, Colombia.
David C SomersDepartment of Psychological and Brain Sciences, Boston University.
Yakeel T QuirozMassachusetts General Hospital, Harvard Medical School.

Funding

Relationship between tau pathology and cognitive impairment in autosomal dominant Alzheimer's diseaseR01AG054671 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI QUIROZ, YAKEEL T. · 2017 to 2021
$3.8M
Nerve growth factor (NGF) metabolic dysfunction as a marker of cognitive decline in autosomal dominant Alzheimer's diseaseRF1AG077627 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI AGUILLON, DAVID FERNANDO, CUELLO, A CLAUDIO · 2022 to 2022
$2.0M
NIA NIH HHS R01 AG054671NIA NIH HHS RF1 AG077627
6 · The paper itself

Abstract

introductionAutosomal Dominant Alzheimer's Disease (ADAD) through genetic mutations can result in near complete expression of the disease. Tracking AD pathology development in an ADAD cohort of Presenilin-1 (

methodsResting-state functional Magnetic Resonance Imaging (fMRI) and Positron-Emission Tomography (PET) scans were acquired in a group of

resultsCPM models strongly predicted tau concentrations and cognitive scores within the carrier group. The connectivity patterns between the temporal cortex, default mode network, and other memory networks were the most informative of tau burden. DISCUSSION: These results indicate that resting-state fMRI methods can complement PET methods in early detection and monitoring of disease progression in ADAD.

Indexed as

ADADCPMpredictive modelingrsfMRI

Identifiers

PMID39281738
PMCPMC11398447

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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.