Evidence map›Paper›PMID 39497149›Full record

SynthesisAlzheimer's research & therapy2024

Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review.

Matteo Costanzo, Carolina Cutrona, Giorgio Leodori, Leonardo Malimpensa, Fabrizia D'antonio, Antonella Conte, Daniele Belvisi

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Alzheimer's research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–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

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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.

Matteo CostanzoDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy.
Carolina CutronaDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy.
Giorgio LeodoriDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy.
Leonardo MalimpensaIRCCS Neuromed, Via Atinense 18, Pozzilli, 86077, IS, Italy.
Fabrizia D'antonioDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy.
Antonella ConteDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy.
Daniele BelvisiDepartment of Human Neurosciences, Sapienza University of Rome, Viale dell'Università 30, Rome, 00185, RM, Italy. daniele.belvisi@uniroma1.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alzheimer disease (AD) remains a significant global health concern. The progression from preclinical stages to overt dementia has become a crucial point of interest for researchers. This paper reviews the potential of neurophysiological biomarkers in predicting AD progression, based on a systematic literature search following PRISMA guidelines, including 55 studies. EEG-based techniques have been predominantly employed, whereas TMS studies are less common. Among the investigated neurophysiological measures, spectral power measurements and event-related potentials-based measures, including P300 and N200 latencies, have emerged as the most consistent and reliable biomarkers for predicting the likelihood of conversion to AD. In addition, TMS-based indices of cortical excitability and synaptic plasticity have also shown potential in assessing the risk of conversion to AD. However, concerns persist regarding the methodological discrepancies among studies, the accuracy of these neurophysiological measures in comparison to established AD biomarkers, and their immediate clinical applicability. Further research is needed to validate the predictive capabilities of EEG and TMS measures. Advancements in this area could lead to cost-effective, reliable biomarkers, enhancing diagnostic processes and deepening our understanding of AD pathophysiology.

Indexed as

Alzheimer DiseaseBiomarkersDisease ProgressionElectroencephalographyEvoked PotentialsHumansTranscranial Magnetic StimulationBiomarkersAlzheimer diseaseBiomarkersElectroencephalographyEvent-related potentialsNeurophysiologyTranscranial magnetic stimulation

Identifiers

PMID39497149
PMCPMC11533378

What OpenQuestion holds

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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.