The core problem the researchers identify is heterogeneity. Two people diagnosed with Parkinson’s disease on the same day can follow radically different trajectories: one may retain sharp cognition for decades while motor symptoms slowly worsen, whereas another may face rapid cognitive decline within a few years. The perspective attributes this variability to differences in underlying molecular pathways, cellular vulnerabilities, network-level dysfunction, and compensatory mechanisms. Existing staging systems, including the Neuronal Alpha-Synuclein Disease Integrated Staging System and the SynNeurGe research diagnostic criteria, were designed to diagnose and stage the disease but do not fully capture how individual patients differ from one another.

The proposed framework organizes this diversity along six complementary biological axes. The first is the dominant pathological process, meaning the primary molecular and cellular mechanisms driving neurodegeneration in a given patient. The second is the spatial distribution of pathology, describing whether damage concentrates in the brainstem and deep gray nuclei, spreads preferentially through limbic structures, or reaches the neocortex early. The third is propagation architecture, the spatiotemporal trajectory through which pathology appears to travel, including the distinction between brain-first and body-first disease. The fourth is the temporal dynamics of progression, which separates slow from fast progressors and linear from punctuated decline. The fifth is co-pathology burden, the coexistence of Alzheimer-type amyloid and tau pathology, TDP-43 inclusions, and cerebrovascular disease alongside alpha-synuclein. The sixth is system response and resilience, the brain’s capacity to buffer the clinical impact of accumulating damage.

Each dimension can be interrogated with specific MRI techniques, and the authors map these tools explicitly. Quantitative susceptibility mapping quantifies iron accumulation in the substantia nigra, putamen, and dentate nucleus, and a technique called susceptibility source separation can isolate the paramagnetic iron signal to document progressive nigral iron spread. Neuromelanin-sensitive MRI measures pigment-containing neuron integrity in the substantia nigra pars compacta and the locus coeruleus, with regional neuromelanin loss correlating with motor and cognitive severity. Diffusion tensor imaging yields fractional anisotropy and diffusivity measures that index white matter integrity, while T1-weighted volumetry and deformation-based morphometry localize patterns of regional atrophy.

Spatial subtypes emerge clearly from data-driven clustering of these imaging features. The perspective describes brainstem-predominant patterns with early substantia nigra and locus coeruleus involvement, limbic-predominant patterns beginning in the amygdala and temporal cortex, neocortical or cortex-first patterns with early frontal and parietal thinning, and a diffuse-malignant pattern marked by accelerated multi-regional atrophy and worse longitudinal decline in motor function, cognition, and daily living activities. A restricted deep-gray-matter-first pattern shows early nigrostriatal involvement with relative cortical sparing and milder early cortical symptoms. Hemispheric asymmetry in neuromelanin loss and iron deposition contralateral to the side of motor onset has also been documented, reflecting asymmetric nigral involvement.

Propagation architecture is perhaps the most conceptually ambitious dimension. The authors review connectivity-informed epicenter models that combine structural connectivity with regional atrophy to infer where degeneration begins and how it spreads, with cerebellar and midbrain epicenters linked to faster motor progression and cortical or striatal epicenters to milder courses. They also discuss disease reservoir models, in which cortical thinning propagates from a reservoir through regions with strong functional connectivity, and epidemic-style models based on Susceptible-Infected-Removed mathematics that predict spatial and temporal atrophy patterns seeded from the substantia nigra. The authors caution that MRI provides only indirect insights into propagation and cannot independently verify where the pathology originated, so imaging must be interpreted alongside autonomic testing, longitudinal clinical observation, and alpha-synuclein amplification assays.

Temporal dynamics and co-pathology burden round out the picture. Longitudinal neuromelanin MRI and serial quantitative susceptibility mapping can track progressive neuromelanin loss and iron accumulation over time, while repeated volumetry quantifies rates of regional atrophy. Trajectory modeling approaches such as mixed-effects models and latent class growth analyses can distinguish linear from non-linear progression and identify inflection points where decline accelerates. On the co-pathology side, white matter hyperintensities and microbleeds reflect cerebrovascular burden, medial temporal atrophy may suggest concomitant Alzheimer’s pathology, and inferior temporal thinning has been associated with probable REM sleep behavior disorder and higher risk of cognitive impairment. The authors stress that these MRI markers are approximations that require validation with tau and amyloid PET, cerebrospinal fluid biomarkers, and genetic profiling.

The resilience dimension is the most difficult to measure directly, because compensatory mechanisms such as cognitive reserve, synaptic plasticity, and glial support are not visible to any scanner. Instead, the framework relies on imaging-clinical dissociation: patients who maintain function despite substantial atrophy occupy the high-resilience end of the spectrum, while those with severe symptoms despite minimal pathological burden sit at the low end. Resting-state functional MRI can reveal compensatory network reorganization, and cortical thickness in frontal and parietal association cortices is linked to cognitive reserve. The authors argue that resilience-based stratification may be particularly valuable in prodromal patients, where enhancing compensatory capacity before substantial pathology accumulates could delay or prevent clinical onset.

Critically, the authors emphasize that the six dimensions are continuous biological axes rather than discrete categories, and that familiar labels such as cortex-first, body-first, or brain-first should be treated as estimates of recurring patterns rather than distinct disease entities. Each patient occupies a unique position along every axis, generating a multidimensional profile that could be visualized as a radar plot. This framing explains why patients sharing the same diagnosis can experience such different trajectories, and it offers a mechanistic foundation for precision medicine approaches that current diagnostic and staging systems cannot provide.

Turning this vision into clinical practice will require a demanding translational roadmap. The authors call for standardized operational definitions of each dimension, harmonized multimodal datasets spanning imaging, clinical, genetic, and molecular biomarkers, and models that prioritize interpretability over raw predictive performance. Independent multicenter validation must demonstrate stability across imaging platforms and acquisition protocols, and prospective studies must show that stratification genuinely improves prognostic accuracy or patient selection for disease-modifying therapies. Regulatory engagement, ethical safeguards around equitable access and algorithmic transparency, and integration of MRI with fluid, molecular, and PET measures all feature in their roadmap. MRI, the authors conclude, should be viewed as one layer within a broader multimodal framework, but its scalability and accessibility make it the natural foundation for transforming Parkinson’s disease care from a one-size-fits-all approach into truly personalized medicine.

Subject of Research: A multidimensional MRI-based framework for characterizing biological heterogeneity and patient stratification in Parkinson's disease

Article Title: Biological heterogeneity in Parkinson’s disease: implications for MRI-based patient stratification

Article References: Pokotylo, M. M., Brüggemann, N., & Prasuhn, J. (2026). Biological heterogeneity in Parkinson’s disease: implications for MRI-based patient stratification. npj Parkinson's Disease, 12(1), Article 207. https://doi.org/10.1038/s41531-026-01544-z

Image Credits: AI Generated

DOI: 10.1038/s41531-026-01544-z

Keywords: Parkinson's disease, MRI, biological heterogeneity, patient stratification, neuromelanin-sensitive MRI, quantitative susceptibility mapping, diffusion tensor imaging, alpha-synuclein, co-pathology, brain resilience, precision medicine, npj Parkinson's Disease