Evidence map›Paper›PMID 42064680›Full record

ReviewNeuroimage. Reports2026

Neuroimaging of the monoaminergic system in Parkinson's disease: A narrative review.

Yavuz Samanci, Sonny Tan, Yasin Temel, Ali Jahanshahi

Abstract readReview
In one paragraph

Review in Neuroimage. Reports, 2026. 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

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

4 authors.

Yavuz SamanciDepartment of Neurosurgery, Maastricht University Medical Centre, Maastricht, the Netherlands.
Sonny TanInstitute for Mental Health and Neurosciences, Maastricht University, Maastricht, the Netherlands.
Yasin TemelDepartment of Neurosurgery, Maastricht University Medical Centre, Maastricht, the Netherlands.
Ali JahanshahiDepartment of Neurosurgery, Maastricht University Medical Centre, Maastricht, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by both motor and non-motor symptoms, primarily attributed to dopaminergic dysfunction in the substantia nigra pars compacta. However, growing evidence indicates that serotonergic and noradrenergic alterations also contribute significantly to PD pathophysiology and progression. This growing understanding has driven the development of advanced neuroimaging techniques aimed at visualizing not only dopaminergic deficits but also serotonergic and noradrenergic alterations, providing deeper insights into PD pathophysiology and progression. Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) have been instrumental in visualizing dopaminergic deficits, particularly dopamine transporter loss, aiding in diagnosis and disease progression tracking. While PET and SPECT remain crucial in assessing dopaminergic dysfunction, novel MRI techniques, including neuromelanin-sensitive MRI, iron-sensitive MRI, diffusion-weighted imaging, and pharmacological MRI, have emerged as promising non-invasive alternatives for evaluating monoaminergic dysfunction in PD. In this narrative review, we have discussed the recent neuroimaging advancements in assessing monoaminergic dysfunction in PD, highlighting how these advances not only improve our understanding of the distinct contributions of dopaminergic, noradrenergic, and serotonergic systems to motor and non-motor symptoms, but also offer promising biomarkers for disease progression and treatment response.

Indexed as

Basal gangliaBrainstemMagnetic resonance imagingMonoaminesParkinson's disease

Identifiers

PMID42064680
PMCPMC13127200

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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.