Functional Connectivity in Parkinson’s: Biomarker or Bystander?

Ideas and Inspiration
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Functional connectivity (FC) changes in Parkinson’s disease (PD) are all over the map — literally and metaphorically. From disruptions in the default mode network to rewiring in sensorimotor circuits, resting-state fMRI (rs-fMRI) studies have consistently highlighted altered FC across disease stages.

But the key questions remain:
Are these changes clinically meaningful? Mechanistically relevant? Or just noisy side effects of neurodegeneration?

The evidence is mixed. Some studies show FC alterations that correlate with motor and cognitive symptoms, while others reveal inconsistent patterns — highly sensitive to datasets, preprocessing pipelines, or motion artifacts.

A more robust approach in PD research has been spatial covariance analysis, which has uncovered reproducible, disease-specific network patterns, particularly using FDG-PET:

  • Parkinson’s Disease-Related Pattern (PDRP): This pattern shows increased glucose metabolism in the putamen, pallidum, thalamus, pons, and cerebellum, and reduced metabolism in the premotor and posterior parietal cortices. Its expression correlates with motor symptom severity (Spetsieris & Eidelberg, 2011; Mure et al., 2011; Tomše et al., 2017; Matthews et al., 2018; Memes et al., 2020).
  • Parkinson’s Disease Cognition-Related Pattern (PDCP): Identified in non-demented PD patients, PDCP is associated with cognitive impairment and characterized by reduced activity in ventral default mode network (DMN) regions, while anterior and posterior DMN components remain relatively preserved (Schindelbeck et al., 2021).

These patterns have also been replicated using rs-fMRI, showing topographical similarity to those derived from FDG-PET (Vo et al., 2017).

In our recent (under-review) study, we applied an explainable AI framework to rs-fMRI data using independent component analysis (ICA), aiming to map functional analogs of PDRP and PDCP. We extended this to the prodromal phase by studying non-manifesting carriers (NMCs) of GBA1 and LRRK2 mutations. Additionally, we also found that NMCs showed significantly higher expression of fPDRP compared to non-carriers — aligning with higher MDS prodromal PD risk scores, and suggesting these patterns may be early indicators of network dysfunction even before symptom onset.

:rocket: Takeaway:
By combining rs-fMRI biomarkers with PET findings, genetics, and explainable AI, we may move closer to identifying specific and early neural signatures of PD — ones that go beyond group-level trends and might inform individual-level risk prediction.

Would be interesting to hear the DCoP members opinion about the utility of brain functional imaging and their potential to serve as early and/or progression markers in PD.

For further details:

  • Spetsieris PG, Eidelberg D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: Methodological issues. Neuroimage 2011; 54:2899–2914.
  • Vo A, Sako W, Fujita K, Peng S, et al. Parkinson’s disease-related network topographies characterized with resting state functional MRI. Hum Brain Mapp. 2017;38(2):617-630.
  • Matthews DC, Lerman H, Lukic A, Andrews RD, et al. FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin. 2018 20:572-579.
  • Mure H, Hirano S, Tang CC, Isaias IU, et al. Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage. 2011;54(2):1244-1253.
  • Tomše P, Jensterle L, Grmek M, Zaletel K, et al. Abnormal metabolic brain network associated with Parkinson’s disease: replication on a new European sample. Neuroradiology. 2017;59(5):507-515.
  • Meles SK, Renken RJ, Pagani M, Teune LK, et al. Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts. Eur J Nucl Med Mol Imaging. 2020;47(2):437-450.
  • Schindlbeck KA, Vo A, Mattis PJ, Villringer K, et al. Cognition-Related Functional Topographies in Parkinson’s Disease: Localized Loss of the Ventral Default Mode Network. Cereb Cortex. 2021;31(11):5139-5150.
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This is so interesting! Imaging is an area I don’t have much experience in, so thanks for your comprehensive summary.

I’m curious how sensitive to individual differences fMRI can be? Do you need to baseline for a given patient, to get best tracking of differences over time? Or is there enough between-patient similarity that you can detect PDRP and PDCP from a single scan?

As to the utility of functional imaging, I can see its potential as a non-invasive diagnostic, although not as easy to administer as something like a blood test! Therefore its utility will really be determined by how much diagnostic information can be gleaned, especially in the early stages of PD. I’m excited to watch for future developments in this area!

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Great question, @vcatterson! fMRI does pick up on individual differences—things like physiology and neural reserve can vary a lot from person to person. Having a baseline scan for each individual really helps track changes more accurately over time. That said, studies on PDRP and PDCP show there’s enough consistency in these brain patterns that they can often be detected from a single scan, especially when using advanced analysis methods.

While functional imaging is promising, it’s unlikely to become the gold standard for early PD diagnosis anytime soon. Still, based on our recent experience and findings in the pre-clinical stages; it could be a valuable second-line tool for risk stratification and monitoring disease progression, since it reflects the overall impact of brain degeneration.

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Thanks for your detailed reply! Very interesting to hear that there is person-to-person consistency in the PDRP and PDCP patterns.

Your suggestion that functional imaging could be a second-line tool makes sense to me. Perhaps in an ideal scenario, a blood-based biomarker could highlight the early signs of PD, leading to functional imaging for that patient to predict the type of symptoms and comorbidities they are at higher risk for. In this way the technique would give patients and clinicians valuable information about the future outlook.

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