Evidence map›Paper›PMID 41349953›Full record

ArticleProgress in neurobiology2026

Divergence of cortical neurophysiology across different neurodegenerative disorders compared to healthy ageing.

Michael Trubshaw, Oliver Kohl, Chetan Gohil, Mats W J van Es, Andrew J Quinn, Katie Yoganathan, Evan Edmond, Malcolm Proudfoot, Nahid Zokaei, Vanessa Raymont and 11 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Progress in neurobiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

21 authors.

Michael TrubshawNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Oliver KohlOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Chetan GohilOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Mats W J van EsOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Andrew J QuinnOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK; Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK.
Katie YoganathanNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Evan EdmondNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Malcolm ProudfootNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Nahid ZokaeiOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Vanessa RaymontDepartment of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK.
Jemma PittOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Tony ThayanandanOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Alexander G ThompsonNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
Kevin TalbotNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
Michele T HuNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK.
Marlou Nadine PerquinMedical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK.
Ece KocagoncuMedical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK.
James B RoweMedical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, CB2 7EF, UK; Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, CB2 0SP, UK.
Mark W WoolrichOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK.
Anna C NobreOxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK; Wu Tsai Institute and Department of Psychology, Yale University, New Haven, CT, USA. Electronic address: kia.nobre@yale.edu.
Martin R TurnerNuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK; Oxford Centre for Human Brain Activity, University of Oxford, Oxford, OX3 7JX, UK. Electronic address: martin.turner@ndcn.ox.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurodegenerative diseases involve disruption of healthy brain network communication occurring before the emergence of symptoms. Magnetoencephalography (MEG) is sensitive to the magnetic fields generated by cortical neuronal activity, and is the most spatio-temporally accurate method of directly assessing neuronal activity non-invasively. We used MEG to directly compare three neurodegenerative disorders with a large healthy cohort to characterise patterns of activity deviating from healthy ageing. Task-free MEG recordings were acquired from patients with Alzheimer's disease (AD, n = 29), Parkinson's disease (PD, n = 25), amyotrophic lateral sclerosis (ALS, n = 33) and healthy controls (HC, n = 191). Healthy ageing trajectories for metrics including spectral power (local neuronal recruitment), connectivity (long-range communication), 1/f exponent (power spectrum slope, which may reflect inhibition), and oscillatory speed were extracted. These metrics were compared pairwise between HC and patient groups, controlling for age and sex. The modelled trajectories of healthy ageing included increasing beta power and oscillatory speed, with reduced power spectrum slope. PD, AD, and ALS groups all showed reductions in beta power and slowing of oscillatory activity compared to matched HC. In AD, older patients showed lower beta power compared with younger patients. Compared with matched HC, the power spectrum slope was uniquely reduced in ALS, in contrast to the increase seen in PD and AD. Gamma connectivity increased in AD and ALS. MEG has unique potential as a source of biomarkers that might be used to detect deviation from healthy ageing if applied at an earlier presymptomatic stage of neurodegeneration than current tools permit. It might also provide outcome measures for prevention trials.

Indexed as

AgingAlzheimer DiseaseAmyotrophic Lateral SclerosisCerebral CortexHealthy AgingNeurodegenerative DiseasesParkinson DiseaseAgedAged, 80 and overFemaleHumansMagnetoencephalographyMaleMiddle AgedAgeingBiomarkerMagnetoencephalographyNetworksNeurodegeneration

Identifiers

PMID41349953
PMCPMC7618949

What OpenQuestion holds

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Registered trials

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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.