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Record W2943930278 · doi:10.1016/j.nicl.2019.101856

Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations

2019· article· en· W2943930278 on OpenAlexafffund
Jessie Fanglu Fu, Ivan S. Klyuzhin, Jessamyn McKenzie, Nicole Neilson, Elham Shahinfard, Katie Dinelle, Martin J. McKeown, A. Jon Stoessl, Vesna Sossi

Bibliographic record

VenueNeuroImage Clinical · 2019
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaTRIUMFCanadian Institutes of Health ResearchCanada Research ChairsHealth ResearchMichael J. Fox Foundation for Parkinson's Research
KeywordsPutamenStriatumDopaminergicParkinson's diseaseNeuroscienceDopaminePsychologyDiseaseMedicineInternal medicine

Abstract

fetched live from OpenAlex

Most neurodegenerative diseases are known to affect several aspects of brain function, including neurotransmitter systems, metabolic and functional connectivity. Diseases are generally characterized by common clinical characteristics across subjects, but there are also significant inter-subject variations. It is thus reasonable to expect that in terms of brain function, such clinical behaviors will be related to a general overall multi-system pattern of disease-induced alterations and additional brain system-specific abnormalities; these additional abnormalities would be indicative of a possible unique system response to disease or subject-specific propensity to a specific clinical progression. Based on the above considerations we introduce and validate the use of a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction, to analyze multi-tracer PET data to identify common (reflecting functional similarities) and unique (reflecting functional differences) information provided by each tracer/target. We apply the method to [11C]-DTBZ (VMAT2 marker) and [11C]-MP (DAT marker) data from 15 early Parkinson's disease (PD) subjects; the behavior of these two tracers/targets is well characterized providing robust reference information for the method's outcome. Highly significant common subject profiles were identified that decomposed the characteristic dopaminergic changes into three distinct orthogonal spatial patterns: 1) disease-induced asymmetry between the less and more affected dorsal striatum; 2) disease-induced gradient with caudate and ventral striatum being relatively spared compared to putamen; 3) progressive loss in the less affected striatum, which correlated significantly with disease duration (p < 0.01 for DTBZ, p < 0.05 for MP). These common spatial patterns reproduce all known aspects of these two targets/tracers. In addition, orthogonality of the patterns may indicate different mechanisms underlying disease initiation or progression. Information unique to each tracer revealed a residual striatal asymmetry when targeting VMAT2, consistent with the notion that VMAT2 density is highly related to terminal degeneration; and a residual DAT disease-induced gradient in the striatum with relative DAT preservation in the substantia nigra. This finding may be indicative either of a possible DAT specific early disease compensation and/or related to disease origin. These results demonstrate the applicability and relevance of the joint pattern analysis approach to datasets obtained with two PET tracers; this data driven method, while recapitulating known aspects of the PD-induced tracer/target behaviour, was found to be statistically more robust and provided additional information on (i) correlated behaviors of the two systems, identified as orthogonal patterns, possibly reflecting different disease-induced alterations and (ii) system specific effects of disease. It is thus expected that this approach will be very well suited to the analysis of multi-tracer and/or multi-modality data and to relating the outcomes to different aspects of 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.045
GPT teacher head0.338
Teacher spread0.292 · 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

Citations29
Published2019
Admission routes2
Has abstractyes

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