ArticleScientific reports2023
TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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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.
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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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Changes in cortical plasticity induced by paired associative stimulation in people with mild cognitive impairment and Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neural circuits · 2026Pooled it
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Exploring easily accessible neurophysiological biomarkers for predicting Alzheimer's disease progression: a systematic review.Alzheimer's research & therapy · 2024Pooled it
- Fornix Mediates Information Propagation in Brain Networks Following DLPFC-Targeted rTMS in Alzheimer's Disease: A Randomized Controlled Trial.CNS neuroscience & therapeutics · 2025Trial
- Clinical utility and prospective of TMS-EEG: Updated review from an international expert group.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026Review
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- Multimodal non-invasive approaches for early Alzheimer's disease detection: a review of neuroelectrophysiological and neuroimaging techniques.Frontiers in psychiatry · 2026Review
- Cortical dysfunction and impulsivity in non-suicidal self-injury: a multimodal study using TMS-EEG and fNIRS.Annals of medicine · 2025Article
- Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss.Biomedicines · 2025Review
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Utilization of Single-Pulse Transcranial-Evoked Potentials in Neurological and Psychiatric Clinical Practice: A Narrative Review.Neurology international · 2024Review
- Research hotspots and trends in transcranial magnetic stimulation for cognitive impairment: A bibliometric analysis from 2014 to 2023.World journal of psychiatry · 2024Article
- The Pathophysiological Underpinnings of Gamma-Band Alterations in Psychiatric Disorders.Life (Basel, Switzerland) · 2024Review
- Article
- Trend-analysis reveals real and placebo rtms effects on addiction craving: a case-control observational study.Frontiers in psychiatry · 2024Article
- Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia.Frontiers in neurology · 2023Article
- Broad repetitive transcranial magnetic stimulation (rTMS) of the precuneus in Alzheimer's disease: A rationale and study design.Alzheimer's & dementia (New York, N. Y.)Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 3 countries.
Funding
Abstract
The combination of TMS and EEG has the potential to capture relevant features of Alzheimer's disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45-80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients' classification as well as longitudinal disease tracking.
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Registered trials
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.