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Record W4414160321 · doi:10.1093/braincomms/fcaf308

Mapping the distribution of neurotransmitters to resting-state functional connectivity in Parkinson’s disease

2025· article· en· W4414160321 on OpenAlexfundno aff
Weihua Li, Nicholas P Lao–Kaim, Runtian Li, Antonio Martín‐Bastida, Andreas–Antonios Roussakis, Graham E. Searle, Natalie Valle‐Guzman, Viswas Dayal, Dilan Athauda, Zinovia Kefalopoulou, Philipp Mahlknecht, Alistair Church, Kathryn J. Peall, Håkan Widner, Gesine Paul, Roger A. Barker, Paola Piccini

Bibliographic record

VenueBrain Communications · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
FundersNIHR Cambridge Biomedical Research CentreNIHR Imperial Biomedical Research CentreSeventh Framework ProgrammeMedical Research CouncilMedical Research Council CanadaEuropean CommissionNational Institute for Health and Care ResearchImperial College Healthcare NHS TrustImperial College London
KeywordsCovarianceFunctional connectivityRegressionPositron emission tomographyDefault mode networkBrain mappingPattern recognition (psychology)Resting state fMRICovariance function

Abstract

fetched live from OpenAlex

Abstract Dopamine and serotonin are two major monoamine neurotransmitters associated with Parkinson's disease (PD), but their spatial distribution and relationship to underlying functional brain architecture are not fully understood. We assessed 30 patients with PD at baseline using structural MRI, resting-state functional MRI (rs-fMRI), 11C-PE2I and 11C-DASB PET, along with comprehensive clinical evaluations of motor and non-motor symptoms. Of these, 15 patients with PD who completed the same assessments after 19 months were included in the longitudinal analysis. rs-fMRI was used to assess functional connectivity, while 11C-PE2I and 11C-DASB PET were used to evaluate interregional homogeneity of dopamine and serotonin levels, referred to as PET covariance. Functional connectivity and PET covariance were estimated using a region-of-interest (ROI)-based approach with 138 ROIs from the Automated Anatomical Labelling 3 atlas, excluding cerebellar regions. These ROIs were further grouped into eight networks: visual, sensorimotor, attention, limbic, frontoparietal, default mode, subcortical and brainstem. At baseline, linear regression revealed that functional connectivity was positively associated with both 11C-PE2I PET covariance (β-values ranging from 0.575 to 0.790, P < 0.001) and 11C-DASB PET covariance (β-values ranging from 0.356 to 0.773, P < 0.001) across all networks. Longitudinally, we found positive correlations between baseline functional connectivity and both 11C-PE2I PET change covariance and 11C-DASB PET change covariance (β-values ranging from 0.166 to 0.576 and 0.312 to 0.671, respectively, P < 0.001) across all networks. These correlations remained significant after controlling for the Euclidean distance between ROIs, indicating that the association is independent of spatial proximity. For both tracers, absolute PET uptake across seed ROIs was positively associated with correspondent regression-derived functional connectivity-PET β-weights, which represent the relationship between PET uptake in target ROIs and their functional connectivity to the seed. This association between target functional connectivity and PET uptake was correlated with PD motor and non-motor severity across different brain regions in a manner that was dependent on the neurotransmitter system evaluated. Our findings suggest that in patients with PD, dopamine and serotonin levels covary among brain regions that are highly functionally connected. This implies that the spatial distribution of these neurotransmitters follows the organizational principles of the brain's functional connectomes, which are associated with features of the disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.083
GPT teacher head0.299
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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