MRI in Parkinson Disease: Expanding Usability for Better Diagnostics
Many emerging techniques are not yet commonly available and lack normative databases.
The differential diagnosis of parkinsonian disorders remains a significant clinical challenge, with diagnostic error rates as high as 24% for Parkinson disease (PD).1 The ever-increasing elderly population further underscores the need for the correct and early diagnosis of these syndromes to allow for timely intervention. Magnetic resonance imaging (MRI) has proven useful in the diagnostic workup for PD over the past 3 decades, evolving from limited use for excluding symptomatic parkinsonism due to other pathologies to its present utility in differentiating PD from atypical parkinsonian disorders on the basis of abnormalities in the basal ganglia and infratentorial structures.2
“With MRI, we are now looking at the brain structurally, functionally, and metabolically, and we are seeing advances in evaluating the brain as related to various symptoms and correlating those with different structural changes,” Ritesh A. Ramdhani, MD, assistant professor of neurology and neurosurgery at New York University Langone Health's Fresco Institute for Parkinson's & Movement Disorders, told Neurology Advisor. MRI may be used to identify PD biomarkers that can inform diagnosis, track disease progression, and elucidate the neurobiological underpinnings of symptoms.3
Qualitative changes in the substantia nigra (SN) can be detected by iron- and neuromelanin-sensitive (NS) MRI, and quantitative markers can be revealed via diffusion-weighted imaging, MR volumetry, and iron-sensitive imaging. Alterations in functional connectivity can be identified with functional MRI, and researchers are exploring “which connectivity changes are unique to the Parkinson's brain compared to the normal brain, and whether these changes are related to patient symptomatology or if they are a precursor of what's to come,” Dr Ramdhani told Neurology Advisor.
Many emerging techniques, such as free water elimination diffusion tensor imaging, are not yet commonly available and lack normative databases. Meanwhile, changes in dorsolateral nigral hyperactivity (DNH) and neuromelanin signaling, as assessed via iron-sensitive and NS-MRI, have amassed adequate evidence for clinical application. A 2017 meta-analysis of 10 studies reported that the sensitivity and specificity of DNH absence was 97.7% and 94.6% in patients with PD vs controls using iron-sensitive MRI sequences.4
In a study that used 3T NS-MRI to compare patients with PD with controls, there was high accuracy using an automated diagnostic tool, with the highest discriminative power observed for contralateral atrophy in the SN pars compacta (SNc) (area under the curve (AUC), 0.93-0.94; sensitivity, 91%-92%; specificity, 89%).5
Thus far, much of the focus on imaging techniques for PD has centered on earlier diagnosis and links between radiographic changes and disease progression or treatment. “Nevertheless, MRI is beginning to play a crucial role in therapeutic intervention,” Amber Van Laar, MD, a neurologist at the University of Pittsburgh Medical Center Comprehensive Movement Disorders Clinic in Pennsylvania, told Neurology Advisor. “MRI-guided surgery is utilized in deep-brain stimulation lead implantation,” and this approach is more anatomically accurate that traditional procedures, with similar outcomes and risk.10 MRI-guided surgery is also being used to ensure precise delivery of the viral payload in clinical trials of gene therapy for PD.11
On the Horizon
Ongoing developments in MRI technology offer additional possibilities for earlier and more accurate detection of PD. “There is increasing interest in identifying sub-regions of the SN through susceptibility-weighted imaging,” according to Dr Van Laar. The nigrosome-1 in the ventrolateral SN has been found to be particularly affected in PD, with 98% depletion of dopamine-containing neurons observed in an earlier study.6 While this area shows a “swallow-tail” appearance on high-resolution susceptibility-weighted imaging (SWI) in healthy individuals, this feature is not seen in patients with PD, suggesting that assessment of the SN for the typical swallow-tail appearance could become a valuable and simple diagnostic tool for PD.7
The quantification of nigral iron through a variety of techniques — including T2*, SWI, quantitative susceptibility mapping (QSM), and R2* — represents another promising technique to detect PD.8 “This has the implication of potentially identifying patients much earlier in the disease process, which is a critical step in development of therapies to slow or halt disease progression,” said Dr Van Laar. In addition, this modality allows for the in vivo investigation of associations between regional iron and clinical symptoms. In a study comparing QSM and R2*, QSM was found to have higher sensitivity for PD-related changes in the SNc, as well as closer correlation with clinical parameters, compared with R2*.9
“A key improvement that would further advance the use of MRI technology in movement disorders is the ability to find distinguishing features between Parkinson's disease and other atypical parkinsonisms,” noted Dr Van Laar. Along with aiding in detection and diagnosis, such an advance could influence the direction of therapeutic research and new treatment development. “This is especially important for diseases like multiple system atrophy, progressive supranuclear palsy, and corticobasal degeneration that presently have no adequate treatments.”
Although most of these advances have yet to be translated clinically, Dr Ramdhani suspects they will ultimately contribute to improvements in PD treatment. To that end, there is a clear need for uniform methodology and technological approaches in research on this area. “A lot of it is wonderful work, but there is variability in the findings because the technology is so new, and different types of imaging and methods are being used at the various study sites,” he stated. “We need longitudinal studies in which the imaging is standardized across the board. That's the only way to make that bridge from the bench to the bedside.”
- Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain J Neurol. 2002;125(Pt 4):861-870.
- Mahlknecht P, Hotter A, Hussl A, Esterhammer R, Schocke M, Seppi K. Signiﬁcance of MRI in diagnosis and differential diagnosis of Parkinson's disease. Neurodegener Dis. 2010;7:300-318.
- Lehericy S, Vaillancourt DE, Seppi K, et al. The role of high-field magnetic resonance imaging in parkinsonian disorders: pushing the boundaries forward. Mov Disord. 2017;32(4):510-525.
- Mahlknecht P, Krismer F, Poewe W, Seppi K. Meta-analysis of dorsolateral nigral hyperintensity on magnetic resonance imaging as a marker for Parkinson's disease. Mov Disord. 2017;32(4):619-623.
- Castellanos G, Fernández-Seara MA, Lorenzo-Betancor O, et al. Automated neuromelanin imaging as a diagnostic biomarker for Parkinson's disease. Mov Disord. 2015;30(7):945-952.
- Damier P, Hirsch EC, Agid Y, Graybiel AM. The substantia nigra of the human brain. II. Patterns of loss of dopamine-containing neurons in Parkinson's disease. Brain. 1999;122(Pt 8):1437-1448.
- Schwarz ST, Afzal M, Morgan PS, Bajaj N, Gowland PA, Auer DP. The ‘swallow tail' appearance of the healthy nigrosome – a new accurate test of Parkinson's disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS ONE. 2014;9(4):e93814.
- Guan X, Xu X, Zhang M. Region-specific iron measured by MRI as a biomarker for Parkinson's disease [published online May 17, 2017]. Neurosci Bull. doi:10.1007/s12264-017-0138-x
- Du G, Liu T, Lewis MM, et al. Quantitative susceptibility mapping of the midbrain in Parkinson's disease. Mov Disord. 2016;31(3):317–324.
- Lee PS, Richardson RM. Interventional MRI–guided deep brain stimulation lead implantation [published online July 4, 2017]. Neurosurg Clin N Am. doi:10.1016/j.nec.2017.05.007
- Blits B, Petry H. Perspective on the road toward gene therapy for Parkinson's disease. Front Neuroanat. 2016;10:128.