Use of MRI to monitor Parkinson's disease - Future Medicine

2 downloads 4 Views 1MB Size Report
1Department of Kinesiology & Nutrition, University of Illinois at Chicago, Chicago, IL, USA 2Department of Bioengineering,. University of Illinois at Chicago, ...

For reprint orders, please contact: [email protected]


Use of MRI to monitor Parkinson’s disease

Practice Points

Peggy J Planetta1, Janey Prodoehl1, Daniel M Corcos1,2,3,4 & David E Vaillancourt†1,2

„„ Qualitative structural abnormalities are rarely observed in Parkinson’s disease (PD), and the abnormalities

that are observed are not specific to PD. „„ In de novo and moderate PD, iron deposition (R2*) is increased in the substantia nigra pars compacta

compared with healthy individuals on a group level, and R2* values correlate significantly with clinical severity in de novo PD patients.

„„ Region-of-interest volumetry of the subthalamic nucleus and putamen can differentiate between

medication-resistant PD patients and healthy individuals, and the red nucleus and putaminal volumes correlate significantly with clinical severity. „„ Diffusion tensor imaging focused on the ventrolateral, caudal substantia nigra pars compacta can

differentiate between de novo PD patients and healthy individuals with 100% sensitivity and specificity. „„ Magnetic resonance spectroscopy of particular high-energy phosphates in the putamen is able to

discriminate between healthy individuals and individuals with early or advanced PD. „„ In de novo PD, the thalamus, primary motor area, supplementary motor area and each nucleus of the

basal ganglia (BG) are hypoactive, and the blood oxygenation level-dependent signal in the BG and thalamus correlates negatively with clinical severity. In the later disease stages, these areas continue to be hypoactive and the cerebellum and motor cortex may be hyperactive, perhaps reflecting compensation for the dysfunctional BG. „„ There is emerging evidence that combining different structural magnetic resonance measures increases

the sensitivity and specificity of disease detection in PD. „„ MRI techniques show strong potential as trait and state biomarkers of PD. However, longitudinal research

is necessary to evaluate disease progression.

Department of Kinesiology & Nutrition, University of Illinois at Chicago, Chicago, IL, USA 2Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA 3Department of Physical Therapy, University of Illinois at Chicago, Chicago, IL, USA 4 Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA † Author for correspondence: Department of Neurology & Rehabilitation, University of Illinois at Chicago, Chicago, IL, USA; [email protected] 1

10.2217/NMT.10.6 © 2011 PJ Planetta, J Prodoehl, DM Corcos, DE Vaillancourt

Neurodegen. Dis. Manage. (2011) 1(1), 67–77

ISSN 1758-2024


Review  Planetta, Prodoehl, Corcos & Vaillancourt SUMMARY

Objective biological markers of Parkinson’s disease (PD) are pivotal for confirming diagnosis, monitoring disease progression, and evaluating therapeutic interventions and disease-modifying agents. Structural and functional MRI provide an in vivo means to investigate the cortical and subcortical regions known to be affected in PD. In this article, we summarize how several MRI techniques, namely conventional MRI, iron-based MRI, volume-based MRI, diffusion tensor imaging, magnetic resonance spectroscopy and functional MRI have been used to assess the neurobiological changes related to the motor features of PD. We also discuss promising new research in which multiple MRI techniques are combined to achieve greater sensitivity and specificity of disease detection. Longitudinal research is necessary to establish MRI techniques as viable disease-state biomarkers of PD. Parkinson’s disease (PD) is a progressive neuro­ degenerative disorder that adversely affects motor control in approximately 2% of people over the age of 65 years [1] . The classical motor symptoms of PD are bradykinesia, rigidity and rest tremor. In addition, dysfunction often occurs beyond the motor system and may include autonomic, affective, sensory and cognitive deficits [2–4] . The pathophysiology of the motor features of PD involves the selective loss of dopaminergic neurons in the ventrolateral and caudal tiers of the substantia nigra pars compacta (SNc), which projects to the striatum [5] . Several processes have been proposed to underlie this nigral degeneration in PD, including mitochondrial dysfunction [6,7] and iron-related oxidative stress [8] . The diagnosis of PD still relies on the clini­ cal judgment of a neurologist, and despite care­ fully designed criteria, misdiagnoses plague clinical medicine [9,10] . Incorrect diagnoses can negatively impact the outcomes of therapeutic interventions and clinical trials. As such, the development of objective biological markers is critical to confirm diagnosis (trait), track disease progression (state) and assess therapeutic inter­ ventions and potential disease-modifying agents. Neuroimaging techniques provide an objective and potentially more accurate in vivo means to assess the structure and function of the cortical and subcortical structures affected in PD. Although two neuroimaging techniques, namely PET and single photon emission com­ puterized tomography, meet many of the criteria for a viable biomarker of PD [11] , they also have several disadvantages. PET and single photon emission computerized tomography are invasive techniques that rely upon radioactive tracers. Typically only one tracer is used in a given ses­ sion, thus limiting data collection to a particular binding site in the brain. These techniques have been reviewed extensively elsewhere [11–18] . Here we provide an overview of noninvasive magnetic resonance (MR) neuroimaging research as it


Neurodegen. Dis. Manage. (2011) 1(1)

relates to the structural and functional changes associated with the motor symptoms of PD. We discuss conventional MRI, iron-based MRI, volume-based MRI, diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS) and functional MRI (fMRI), with an empha­ sis on recent findings. All of these techniques, except conventional MRI, have the potential to serve as biomarkers of PD (Figure  1) . We also review a promising new method, namely multimodal MRI, in which MR measures are combined to increase the sensitivity and spe­ cificity of distinguishing PD patients from healthy individuals. Conventional MRI MRI is a noninvasive, radiation-free technique using magnetic fields and radio frequency pulses that was commercialized in the early 1980s to visualize tissues in vivo. The contrast seen in MRI is generated from intrinsic differences in hydrogen protons between tissue types and their associated relaxation rates. Generally, the standard T1‑ and T2‑weighted pulse sequences are used to visualize anatomy (gray–white matter contrast) and pathology (edema), respectively. These structural MR images are routinely assessed visually in early-stage PD patients, primarily to rule out alternative pathologies, including multiple sclerosis, tumors, vascular lesions, inflammation and atypical parkinso­ nian disorders. Structural MRI abnormalities are rarely observed, especially in early PD [19,20] . In fact, abnormalities have been reported in less than 20% of PD patients [21] . Furthermore, many of the changes that are observed, such as hippocampal, putaminal and cortical atro­ phy, are not specific to PD [21–25] . In advanced PD there may be observable abnormalities on T2‑weighted images. It has been shown that the width of the SNc was smaller in moderate to severely impaired PD than controls [26–29] , and that the width was correlated negatively with

future science group

MRI to monitor Parkinson’s disease  clinical severity [29] . However, there was consid­ erable overlap in SNc widths between the PD and control groups. In moderate PD patients, Watanabe and colleagues showed that the occur­ rence of mild hyperintensity of the outer puta­ minal margin on T2‑weighted images increased with magnetic field strength (0.35–1.5 T) [30] . Although the overall occurrence was less, a similar pattern was observed in patients with multiple system atrophy, suggesting that this hyperintensity is not specific to PD. Iron-based MRI In healthy individuals, iron has been shown to accumulate as a function of age in most brain structures, including the basal ganglia (BG)  [31,32] . In PD, there is compelling post­ mortem evidence of increased iron deposition in the substantia nigra (SN), primarily in the SNc  [33–41] , with later disease stages showing greater iron deposition [42] . The increased iron levels in the SNc of PD may underlie the death of nigral dopaminergic cells in this region [35] . Although the cause of increased iron deposition in PD is not completely understood, two pos­ sibilities include the mutation of genes involved in the transport and binding of iron and the disruption of the blood–brain barrier in the SN, allowing iron to pass through [43] . While some T 2 MRI studies sensitive to iron content in the SN failed to differentiate between PD and controls [23,24] , research using advanced pulse sequences has been success­ ful  [44–47] . Hutchinson and colleagues differ­ entiated between six PD patients with a range of severity and six controls based on a combi­ nation of inversion-recovery sequences in the SNc at 1.5 T  [45] . They also reported a strong positive correlation between the index of iron deposition and clinical severity, suggesting that this test may be a sensitive biomarker of PD and its progression. Recent research using 3 T MRI has shown that de novo PD patients had increased regional iron content, as measured indirectly by R 2* (i.e., 1/T2*), only in the lateral SNc contra­ lateral to the most affected side of the body [48] . Furthermore, the lateralized motor score from the most affected side correlated with the degree of nigral pathology obtained from the opposite lateral SNc. However, the R 2* values could not differentiate between individuals. Importantly, the R 2* values in the SNc of controls correlated positively with previous reports that measured

future science group


Functional magnetic resonance imaging

Iron-based imaging (R2*)

Diffusion tensor imaging Potential magnetic resonance-based biomarkers of Parkinson’s disease

Volume-based imaging

Magnetic resonance spectroscopy

Figure 1. Potential magnetic resonance-based biomarkers of Parkinson’s disease.

iron content directly. Péran et al. corroborated the increased iron deposition in the whole SN of moderate PD on medication, and also showed increased iron in the thalamus [20] . However, there was again overlap between the PD and control groups on the iron deposition measure, and no significant correlation between R 2* and clinical severity. Volume-based techniques In volume-based MRI, the volume of certain structures is calculated from structural MR images under the assumption that cell loss will result in measurable atrophy. There are two main approaches: region of interest (ROI)-based MR volumetry and voxel-based morpho­metry (VBM). ROI-based MR volumetry involves manually segmenting brain structures on an individual basis and then calculating their respective volumes. VBM is a more recent tech­ nique that measures volume loss using statisti­ cal ana­lysis of differences in tissue types across the entire brain in an operator-independent and automated fashion [49] . Although more objective, VBM is currently inappropriate for routine clini­ cal work-up because it relies on detecting volume


Review  Planetta, Prodoehl, Corcos & Vaillancourt differences on a group-wise basis [49] . However, this technique may prove useful in evaluating therapeutic interventions. Currently, VBM studies are not very pro­ mising for disease detection in PD. Tessa and colleagues showed no differences in whole brain, gray matter or white matter volumes calculated from T1‑weighted images in de novo PD and controls  [50] . Similarly, Geng and col­ leagues showed no difference in whole brain volume between early PD, advanced PD and controls  [51] . However, another study reported reduced white matter volume in the right tem­ poral lobe of de novo PD compared with con­ trols, and suggested this ­difference may be in the subcortex [52] . Although the SN is known to have cell loss in PD, ROI volumetry studies have not con­ sistently reflected this degeneration. Using 3 T MRI, Menke and colleagues showed that the volume of the entire SN was significantly smaller in mild-to-moderate PD compared with con­ trols [53] . However, there was overlap between the groups, and SN volume did not correlate significantly with clinical severity in the patients. Another study also reported reduced volume in the SN as well as in the putamen, globus pallidus (GP) and prefrontal cortex of moderate-to-severe PD [54] . Conversely, Geng and colleagues found no difference in SN volume between early PD, advanced PD and controls, perhaps because the SN was not carefully segmented from the subthalamic nucleus (STN) [51] . However, the volume of the putamen was lower in early and advanced PD compared with controls, with the volume being lower in advanced than early PD. Furthermore, putaminal volume correlated neg­ atively with clinical severity. While there was no overlap in putaminal volume between advanced PD and controls, there was overlap between early PD and controls. GP volume was also reduced in advanced PD compared with controls. This suggests that putaminal volume could be used to detect and monitor PD. Recently, Colpan and Slavin used 3 T MRI to calculate the volumes of the STN, a frequent tar­ get of surgical intervention in PD, and the adja­ cent red nucleus (RN), in medication-resistant PD and controls [55] . STN volume was smaller and RN volume was larger in PD compared with controls. Furthermore, there was a positive corre­ lation between RN volume and clinical severity. There was no overlap in STN volume between groups, but there was a large overlap in RN


Neurodegen. Dis. Manage. (2011) 1(1)

volume. Thus, there is potential for STN volume to serve as a trait biomarker and RN volume a state biomarker of PD. Using similar methods, another group reported no difference in volume between early PD patients on medication and controls for six subcortical ROIs, including the SN and RN [20] ; STN was not measured. This discrepancy in RN volume across the two stud­ ies may be due to the inherent differences in the groups tested; that is, the pathogenesis of PD in medication-resistant and -responsive patients may differ. Diffusion tensor imaging Diffusion tensor imaging is a relatively new MRI technique that is sensitive to the magni­ tude and directionality (anisotropy) of water molecule diffusion in three dimensions. Water molecules tend to diffuse in parallel with highly organized tissues. As such, measuring water diffusion can serve as an indirect measure of tissue microstructure. Typically this technique is used to examine the integrity of white matter tracts in the brain, espe­ cially in the case of injury and disease [56–58] . The magnitude of diffusion is quantified by calculat­ ing the mean diffusivity (MD), also known as the apparent diffusion coefficient. This method aver­ ages diffusion across all directions on a voxel-wise basis. Small MD values indicate constrained dif­ fusion and presumably highly organized tissue, whereas large MD values indicate unconstrained water diffusion and presumably tissue degenera­ tion. One common method of quantifying the direction of diffusion is by calculating fractional anisotropy (FA) on each voxel. FA values close to 0 represent isotropic diffusion, while values close to 1 represent anisotropic diffusion [59] . In addition to providing information about white matter, there is compelling evidence using a murine model that DTI can be used as an in­direct measure of cell loss [60] . Given that the SNc has lost approximately 50% of its dopamin­ ergic neurons by the time motor symptoms become apparent [61–63] , researchers have inves­ tigated whether DTI measures of the SN could differentiate between PD patients and healthy individuals. Using 1.5 T MRI, two initial stud­ ies showed that patients with early-to-advanced PD had lower FA values in the SN than con­ trols  [64,65] , and that clinical severity correlated negatively with FA values [65] . Another study showed a borderline increase in MD, with no decrease in FA in the SN [66] .

future science group

MRI to monitor Parkinson’s disease  Given the known pattern of nigral degen­ eration in PD, Vaillancourt and colleagues used 3 T MRI to calculate FA values separately for the caudal, middle and rostral subregions of the SN [67] . These authors were able to differentiate de novo PD patients from controls with 100% sensitivity and specificity based on the FA values in the caudal SN. To date, no other biomarker of PD has reached this level of detection. However, FA values in these three regions did not corre­ late significantly with clinical severity, perhaps due to the limited range of severity tested. It is possible that the low sensitivity of the earlier DTI studies was the result of averaging FA values across the entire SN and/or using a lower field strength scanner. In support of the former view, Menke and colleagues reported no difference in FA values in the whole SN between mildto-moderate PD and controls at 3 T [53] . In a much larger group of subjects, Péran and col­ leagues did observe a significant difference in FA values in the whole SN on a group level [20] . An important next step will be to investigate the relationship between DTI of the caudal SN and clinical severity using PD patients with a wide range of severities. Beyond the SN, whole-brain DTI research has shown that there is an increase of the 25th percentile of the FA histogram in de novo PD compared with controls [50] . However, there was overlap between the groups on the FA histo­ gram para­meters and no significant correla­ tion with disease severity. In mild-to-moderate PD another study showed that MD increased and FA decreased in genu of the corpus cal­ losum and superior longitudinal fasciculus, and MD increased in the cingulum, on a group-wise basis  [66] . Although these studies suggest that there is a subtle loss of gray and white matter in early PD, it is unclear whether these measurements can be used to monitor disease progression. Magnetic resonance spectroscopy Magnetic resonance spectroscopy uses radio­ frequency waves to measure the concentration of particular chemical compounds or meta­ bolites in single or multiple voxels of interest in the brain [68] . The most widely used approach is proton (1H)‑MRS followed by phospho­ rus (31P)‑MRS. Some of the most commonly detected low-energy metabolites in 1H‑MRS are N‑acetyl aspartate (NAA) as a marker of neuronal injury or loss [69] , creatine (Cr) as a

future science group


marker of energy metabolism, choline (Cho) as a marker for cell membrane turnover and the excitatory neurotransmitter glutamate. 31P‑MRS evaluates high-energy phosphates including b‑ATP and phosphocreatines, both of which reflect mitochondrial function. By measuring these compounds, MRS can provide insight into biochemical abnormalities or changes related to pathological conditions. It may also be useful in evaluating the response to therapeutic interven­ tions such as coenzyme Q10, which is deficient in the mitochondria of PD patients [70] . Early studies using 1.5 T 1H-MRS in the BG have shown inconsistent results. Ellis and col­ leagues observed a significant reduction in the NAA:Cho ratio from the putamen contralateral to the most affected side of the body in de novo PD, but not in the more advanced levodopatreated group or the control group [71] . Similarly, in patients on long-term levodopa therapy with motor complications, a significant reduction in the putaminal NAA:Cr ratio was reported [72] . These studies suggest that the NAA:Cho ratio in the putamen may be affected by levodopa therapy. However, several other studies have failed to show significant reductions in meta­ bolites or metabolite ratios in the putamen of early to advanced PD [73–76] . In a combined 3 T 1H‑ and 31P‑MRS study, Hattingen and colleagues showed that there was a significant difference in b‑ATP in the contra­ lateral midbrain of both early and advanced PD groups compared with controls, as well as in the ipsilateral midbrain between the early PD and control groups [77] . However, these measures were not able to differentiate PD and controls on an individual basis. In the putamen, b‑ATP and phosphocreatine measurements were able to discriminate individuals from both PD groups and controls. This is consistent with the results of an earlier 31P-MRS imaging study that also showed a decrease in ATP in the BG and brain­ stem [78] . These results suggest that there is mitochondrial dysfunction in the midbrain and putamen even in early PD, and that high-energy phosphate MRS in the putamen may be useful in detecting PD. Two studies using MRS have investigated whether there are significant metabolic changes in motor cortex associated with de  novo PD. Using 1.5 T MRS one of these studies found that Cho:Cr and NAA:Cr ratios were abnor­ mally low in the primary motor cortex (M1) of de novo PD, but that the Cho:Cr ratio increased


Review  Planetta, Prodoehl, Corcos & Vaillancourt following 6 months of pharmacological therapy with a dopamine receptor agonist [79] . Another study used similar methods and found that these metabolite ratios were similar in the pre­ supplementary motor area (pre-SMA) of de novo PD patients and controls [80] . Similarly, Camicioli and colleagues showed that the NAA:Cr ratio was lower in the pre-SMA of early-to-moderate PD patients on medication, but there was over­ lap between the groups and the ratio did not correlate significantly with clinical severity [81] . Other studies have shown reduced metabolite ratios in the posterior cingulate cortex of earlyto-moderate PD [82] , and in each cortical lobe of early PD [74] . Taken together, these studies indicate that there is neuronal loss and metabolic abnormalities beyond the BG in PD, even in the early disease stages. One limitation of MRS is that voxel selec­ tion is performed manually using conventional MR images, and is thus open to operator error. Another important limitation is that the imaged chemical compounds are found in such low concentrations in the brain that data must be acquired over a relatively large area and time period to attain an adequate signal. Given the small volumes of the BG nuclei, these structures are difficult to image. The use of high-field MR systems will allow for greater spatial and tem­ poral resolution, thus increasing the utility of the technique in detecting and monitoring PD. Functional MRI Another technique that has potential to serve as a state biomarker of PD is fMRI. fMRI is a variant of conventional MRI that was developed in the early 1990s to assess brain function in  vivo  [83,84] . The most commonly used technique is blood oxygenation leveldependent (BOLD) contrast fMRI, which relies on the magnetic properties of blood to measure the changes in local hemodynamics associated with neural activity, namely input and intra­ cortical processing [85,86] . A timely review on the role of fMRI in the diagnosis of several movement disorders, including PD, has been published recently [87] . Using 3  T fMRI, Spraker and colleagues showed that the thalamus, M1, SMA and each nucleus of the BG were hypoactive in de novo PD during a visually guided precision grip force task that required frequent switching between contraction and relaxation [88] . In a follow-up study, this group reported a significant negative


Neurodegen. Dis. Manage. (2011) 1(1)

correlation between overall clinical severity and BOLD activation in most of the BG nuclei [89] . Furthermore, BOLD activation in the contra­ lateral GP pars interna was most closely related to tremor, and activation in the thalamus and all BG nuclei except the ipsilateral GP pars interna and bilateral SN was most closely related to bradykinesia. The reduced activation in the cortical areas was not correlated significantly with clinical severity. These studies suggest that fMRI of the BG and thalamus has strong potential as a state biomarker in de novo PD. In a study of the cortex Buhmann and col­ leagues demonstrated that BOLD activation at 1.5 T in contralateral M1 and bilateral SMA was reduced in de  novo PD compared with controls during a simple finger opposition task [90] . Following the intake of dopaminergic medication, the BOLD signal in these regions increased and motor performance improved. Furthermore, there was a strong correlation between motor performance and the BOLD signal in contralateral M1 for all patients, and in SMA for most patients. These findings are in opposition to other studies that have reported increased BOLD signal in motor cortex and the cerebellum [91–94] . Increased cortical and cerebellar BOLD activation is hypothesized to reflect neural reorganization to compen­ sate for dysfunctional BG [91,92] . Yu and col­ leagues reported hypoactivity in the putamen, SMA and pre-SMA, and hyperactivity in the cerebellum and M1, in moderate PD during automatic and cognitively controlled thumb pressing movements [94] . There was a signifi­ cant negative correlation between BOLD signal in the ipsilateral cerebellum and contralateral putamen. The BOLD signal in motor cortex was positively correlated with upper extremity rigidity. Along with the fact that differences in cerebellar BOLD signal are not significant in de  novo PD [88] , these results suggest that hyperactivation in the cerebellum and motor cortex is a compensatory response to dysfunc­ tional BG [93] , and thus may not be apparent in the early disease stages. Another method for investigating brain func­ tion is resting-state fMRI, which examines the level of spontaneous BOLD coactivation of different regions while at rest [95] . In doing so, this technique provides insight into normal and abnormal functional connectivity patterns. The application of group-wise resting-state fMRI methods to individuals is currently being

future science group

MRI to monitor Parkinson’s disease 

future science group

Future perspective Although MR techniques are not currently validated as trait and state biomarkers of PD, they certainly hold promise, especially based on recent technological and methodological advances. In particular, there is strong potential for multimodal structural MRI and fMRI to detect PD and to monitor disease progression. As we move forward, it is important to recog­ nize that evaluating disease progression using cross-sectional designs is inadequate. Only long­itudinal studies can show true change over time. A correlation between clinical severity and MRI measures in cross-sectional studies 1.0 0.9



IV 0.8 0.7 0.6 0.5 0.4




Multimodal MRI There is a developing interest in using multiple MR techniques to improve our ability to differ­ entiate between PD patients and controls. Using 3 T MRI, Menke and colleagues assessed SN volume from advanced T1‑weighted images and FA values and connectivity from DTI [53] . The SN volume was smaller in the early-to-moderate PD group than the control group, but there was overlap between the groups. The FA values did not discriminate between the groups. However, when SN volume and DTI connectivity with the thalamus were combined, classification increased to 100% sensitivity and 80% specificity. Recently, Péran and colleagues indirectly measured brain volume, iron deposition and microstructural damage in moderate PD patients and controls [20] . They calculated the volume, R 2* values, MD and FA values in sev­ eral subcortical structures. By combining the R 2* and FA values in the SN and MD value in the putamen or caudate via logistic regres­ sion, the authors were able to discriminate PD patients and controls with greater than 95% accuracy (Figure 2) . The maximum discriminant power of any given MR value on its own was only 83%. However, no single value correlated significantly with clinical severity. Recent work

by Du and colleagues that combined R 2* and DTI has supported this finding [102] . Taken together, these studies highlight the strong potential of multi­modal imaging to improve disease detection in PD.


explored [96] , and this technique may prove to be a useful diagnostic tool in the future. Current evidence indicates that connectivity of the motor network is abnormal in PD [97–99] . Recently Wu and colleagues reported decreased connectiv­ ity in the SMA, dorsolateral prefrontal cortex and putamen, but increased connectivity in the cerebellum, primary motor cortex and parietal cortex in a group of early PD patients compared with controls [97] . When levodopa was admin­ istered to the patients, the connectivity differ­ ences between the two groups were reduced in most regions. In addition, some studies have reported a significant correlation between con­ nectivity measures and clinical severity [97,99] , while others have not [98] . An important consideration in using fMRI to monitor PD, particularly on an individual basis, is the stability of the BOLD signal. It has been shown that there is considerable intersubject var­ iability in the onset latency, amplitude and shape of the hemodynamic response, as well as the spa­ tial extent of activation [100] . Furthermore, the methods used to process fMRI data can affect the intrasubject variability across sessions [101] .


0.2 0.1

















0.0 0.0





0.5 0.6 Specificity





Figure 2. Sensitivity and specificity of multimodal structural MRI. In the center box (A) the combinations of magnetic resonance parameters that were able to significantly discriminate between patients with Parkinson’s disease and controls are shown. Colors indicate the parameters (red = R2*, green = mean diffusivity and blue = fractional anisotropy) while acronyms identify the anatomical regions of interest indicated. Combinations are numbered from I to IV based on discriminant power (95–98%). (B) Receiver operating charactertistic curves associated with each significant combination. CN: Caudate nucleus; l: Left; Put: Putamen; r: Right; SN: Substantia nigra. Reproduced with permission from [20] © Oxford University Press.


Review  Planetta, Prodoehl, Corcos & Vaillancourt does not mean there is a longitudinal change. Likewise, an insignificant correlation in crosssectional studies does not mean that a longitudi­ nal change would not occur. Future work needs to evaluate MRI measures in the context of dis­ ease progression, as well as provide differential diagnosis from atypical parkinsonian disorders. MRI is particularly well suited to longitudinal studies of PD, in that it is non­i nvasive and already an integral part of healthcare systems in most countries. 11

Bibliography Papers of special note have been highlighted as: of interest of considerable interest n








de Rijk M, Launer L, Berger K et al.: Prevalence of Parkinson’s disease in Europe: a collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group. Neurology 54(11 Suppl. 5), S21–S23 (2000). Ziemssen T, Reichmann H: Non-motor dysfunction in Parkinson’s disease. Parkinsonism Relat. Disord. 13(6), 323–332 (2007). Chaudhuri K, Naidu Y: Early Parkinson’s disease and non-motor issues. J. Neurol. 255(Suppl. 5), 33–38 (2008). Park A, Stacy M: Non-motor symptoms in Parkinson’s disease. J. Neurol. 256(Suppl. 3), 293–298 (2009).

Büeler H: Impaired mitochondrial dynamics and function in the pathogenesis of Parkinson’s disease. Exp. Neurol. 218(2), 235–246 (2009).


Zheng B, Liao Z, Locascio J et al.: PGC‑1a, a potential therapeutic target for early intervention in Parkinson’s disease. Sci. Transl. Med. 2(52), 52ra73 (2010).


Gerlach M, Ben-Shachar D, Riederer P, Youdim M: Altered brain metabolism of iron as a cause of neurodegenerative diseases? J. Neurochem. 63(3), 793–807 (1994).


Litvan I, Bhatia K, Burn D et al.: Movement Disorders Society Scientific Issues Committee report: SIC Task Force appraisal of clinical diagnostic criteria for Parkinsonian disorders. Mov. Disord. 18(5), 467–486 (2003).



Tolosa E, Wenning G, Poewe W: The diagnosis of Parkinson’s disease. Lancet Neurol. 5(1), 75–86 (2006).

Parts of the research described in this article were supported by NIH grants R01-NS-52318, R01-NS-58487, R01-NS-40902 and R01-NS-28127, and the Michael J Fox Foundation for Parkinson’s Research. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Brooks D, Frey K, Marek K et al.: Assessment of neuroimaging techniques as biomarkers of the progression of Parkinson’s disease. Exp. Neurol. 184(Suppl. 1), S68–S79 (2003).

12 Brooks D: Neuroimaging in Parkinson’s

disease. NeuroRx 1(2), 243–254 (2004). 13 Seibyl J, Jennings D, Tabamo R, Marek K:

Neuroimaging trials of Parkinson’s disease progression. J. Neurol. 251(Suppl. 7), vII9–vII13 (2004). 14



Utter A, Basso M: The basal ganglia: an overview of circuits and function. Neurosci. Biobehav. Rev. 32(3), 333–342 (2008).


Financial & competing interests disclosure

Au W, Adams J, Troiano A, Stoessl A: Parkinson’s disease: in vivo assessment of disease progression using positron emission tomography. Brain Res. Mol. Brain Res. 134(1), 24–33 (2005). Ravina B, Eidelberg D, Ahlskog J et al.: The role of radiotracer imaging in Parkinson disease. Neurology 64(2), 208–215 (2005). Eckert T, Tang C, Eidelberg D: Assessment of the progression of Parkinson’s disease: a metabolic network approach. Lancet Neurol. 6(10), 926–932 (2007).

caudate – moderate Parkinson’s disease (PD) patients and controls were discriminated with greater than 95% accuracy. 21

Yekhlef F, Ballan G, Macia F, Delmer O, Sourgen C, Tison F: Routine MRI for the differential diagnosis of Parkinson’s disease, MSA, PSP, and CBD. J. Neural Transm. 110(2), 151–169 (2003).

22 Rutledge J, Hilal S, Silver A, Defendini R,

Fahn S: Study of movement disorders and brain iron by MR. AJR Am. J. Roentgenol. 149(2), 365–379 (1987). 23 Antonini A, Leenders K, Meier D, Oertel W,

Boesiger P, Anliker M: T2 relaxation time in patients with Parkinson’s disease. Neurology 43(4), 697–700 (1993). 24 Vymazal J, Righini A, Brooks R et al.: T1 and

T2 in the brain of healthy subjects, patients with Parkinson disease, and patients with multiple system atrophy: relation to iron content. Radiology 211(2), 489–495 (1999).

25 Jokinen P, Brück A, Aalto S, Forsback S,

Parkkola R, Rinne J: Impaired cognitive performance in Parkinson’s disease is related to caudate dopaminergic hypofunction and hippocampal atrophy. Parkinsonism Relat. Disord. 15(2), 88–93 (2009).


Antonini A, Isaias I: Single photon-emission computed tomography imaging in early Parkinson’s disease. Expert Rev. Neurother. 8(12), 1853–1864 (2008).


Nandhagopal R, McKeown M, Stoessl A: Functional imaging in Parkinson disease. Neurology 70(16 Pt 2), 1478–1488 (2008).

26 Duguid J, De La Paz R, DeGroot J: Magnetic

Nilsson C, Markenroth Bloch K, Brockstedt S, Lätt J, Widner H, Larsson E: Tracking the neurodegeneration of parkinsonian disorders – a pilot study. Neuroradiology 49(2), 111–119 (2007).

27 Braffman B, Grossman R, Goldberg H et al.:


20 Péran P, Cherubini A, Assogna F et al.:

Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 133(11), 3423–3433 (2010). n


By combining values from different MRI-based techniques – R 2* and fractional anisotropy in the substantia nigra (SN), and mean diffusivity in the putamen or

Neurodegen. Dis. Manage. (2011) 1(1)

resonance imaging of the midbrain in Parkinson’s disease. Ann. Neurol. 20(6), 744–747 (1986). MR imaging of Parkinson disease with spin-echo and gradient-echo sequences. AJR Am. J. Roentgenol. 152(1), 159–165 (1989). 28 Huber S, Chakeres D, Paulson G, Khanna R:

Magnetic resonance imaging in Parkinson’s disease. Arch. Neurol. 47(7), 735–737 (1990). 29 Pujol J, Junqué C, Vendrell P, Grau J,

Capdevila A: Reduction of the substantia nigra width and motor decline in aging and Parkinson’s disease. Arch. Neurol. 49(11), 1119–1122 (1992).

future science group

MRI to monitor Parkinson’s disease  30 Watanabe H, Ito M, Fukatsu H et al.:

Putaminal magnetic resonance imaging features at various magnetic field strengths in multiple system atrophy. Mov. Disord. 25(12), 1916–1923 (2010). 31

42 Youdim M, Ben-Shachar D, Yehuda S,

Connor JR, Crichton RR: Iron, brain ageing and neurodegenerative disorders. Nat. Rev. Neurosci. 5(11), 863–873 (2004).

32 Zecca L, Stroppolo A, Gatti A et al.:

33 Earle K: Studies on Parkinson’s disease

Buderer N, Helpern J: Increased iron-related MRI contrast in the substantia nigra in Parkinson’s disease. Neurology 45(6), 1138–1143 (1995). 45

34 Dexter D, Wells F, Agid F et al.: Increased


Dexter D, Wells F, Lees A et al.: Increased nigral iron content and alterations in other metal ions occurring in brain in Parkinson’s disease. J. Neurochem. 52(6), 1830–1836 (1989).

Assessment of brain iron and neuronal integrity in patients with Parkinson’s disease using novel MRI contrasts. Mov. Disord. 22(3), 334–340 (2007). regional brain iron with magnetic resonance imaging. Parkinsonism Relat. Disord. 15(Suppl. 3), S215–S218 (2009). n

iron content in early Parkinson disease: a potential biomarker of disease status. Neurology 70(16 Pt 2), 1411–1417 (2008). Esterhammer R, Schocke M, Seppi K: Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodegener. Dis. 7(5), 300–318 (2010). 50 Tessa C, Giannelli M, Della Nave R et al.:

40 Griffiths P, Crossman A: Distribution of iron

in the basal ganglia and neocortex in postmortem tissue in Parkinson’s disease and Alzheimer’s disease. Dementia 4(2), 61–65 (1993). 41 Griffiths P, Dobson B, Jones G, Clarke D:

Iron in the basal ganglia in Parkinson’s disease. An in vitro study using extended x‑ray absorption fine structure and cryo-electron microscopy. Brain 122(Pt 4), 667–673 (1999).

future science group


Geng D, Li Y, Zee C: Magnetic resonance imaging-based volumetric ana­lysis of basal ganglia nuclei and substantia nigra in patients with Parkinson’s disease. Neurosurgery 58(2), 256–262; discussion 256–262 (2006). Martin W, Wieler M, Gee M, Camicioli R: Temporal lobe changes in early, untreated Parkinson’s disease. Mov. Disord. 24(13), 1949–1954 (2009).

53 Menke R, Scholz J, Miller K et al.: MRI

characteristics of the substantia nigra in Parkinson’s disease: a combined quantitative T1 and DTI study. Neuroimage 47(2), 435–441 (2009).

Demonstrates that the volume of the subthalamic nucleus could differentiate medication-resistant PD patients from controls on an individual basis, and that red nucleus volume was correlated with disease severity. tensor imaging and its applications to basic neuroscience research. Neuron 51(5), 527–539 (2006).

57 Naggara O, Oppenheim C, Rieu D et al.:

Diffusion tensor imaging in early Alzheimer’s disease. Psychiatry Res. 146(3), 243–249 (2006). 58 Kraus M, Susmaras T, Caughlin B, Walker C,

Sweeney J, Little D: White matter integrity and cognition in chronic traumatic brain injury: a diffusion tensor imaging study. Brain 130(Pt 10), 2508–2519 (2007). 59 Le Bihan D, Mangin J, Poupon C et al.:

Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13(4), 534–546 (2001). 60 Boska M, Hasan K, Kibuule D et al.:

Quantitative diffusion tensor imaging detects dopaminergic neuronal degeneration in a murine model of Parkinson’s disease. Neurobiol. Dis. 26(3), 590–596 (2007).

A whole-brain ana­lysis in de novo Parkinson disease. AJNR Am. J. Neuroradiol. 29(4), 674–680 (2008). 51

Colpan M, Slavin K: Subthalamic and red nucleus volumes in patients with Parkinson’s disease: do they change with disease progression? Parkinsonism Relat. Disord. 16(6), 398–403 (2010).

56 Mori S, Zhang J: Principles of diffusion

49 Mahlknecht P, Hotter A, Hussl A,

39 Sofic E, Paulus W, Jellinger K, Riederer P,

Youdim M: Selective increase of iron in substantia nigra zona compacta of parkinsonian brains. J. Neurochem. 56(3), 978–982 (1991).


Provides a review of iron-based MRI.

48 Martin W, Wieler M, Gee M: Midbrain

38 Riederer P, Wuketich S: Time course of

nigrostriatal degeneration in parkinson’s disease. A detailed study of influential factors in human brain amine ana­lysis. J. Neural Transm. 38(3–4), 277–301 (1976).


47 Martin W: Quantitative estimation of

37 Jellinger K, Paulus W, Grundke-Iqbal I,

Riederer P, Youdim M: Brain iron and ferritin in Parkinson’s and Alzheimer’s diseases. J. Neural Transm. Park. Dis. Dement. Sect. 2(4), 327–340 (1990).

Aminoff M, Weiner M: Quantitative 1H magnetic resonance spectroscopy and MRI of Parkinson’s disease. Mov. Disord. 17(5), 917–927 (2002).

46 Michaeli S, Oz G, Sorce D et al.:

36 Sofic E, Riederer P, Heinsen H et al.:

Increased iron (III) and total iron content in post mortem substantia nigra of parkinsonian brain. J. Neural Transm. 74(3), 199–205 (1988).

Hutchinson M, Raff U: Structural changes of the substantia nigra in Parkinson’s disease as revealed by MR imaging. AJNR Am. J. Neuroradiol. 21(4), 697–701 (2000).

This study was able to correctly classify 100% of PD patients and 80% of controls using a combination of volumetry (T1‑weighted images) and diffusion tensor imaging connectivity measures in the SN.

54 O’Neill J, Schuff N, Marks WJ, Feiwell R,

44 Gorell J, Ordidge R, Brown G, Deniau J,

including x‑ray fluorescent spectroscopy of formalin fixed brain tissue. J. Neuropathol. Exp. Neurol. 27(1), 1–14 (1968). nigral iron content in postmortem parkinsonian brain. Lancet 2(8569), 1219–1220 (1987).


43 Zecca L, Youdim MB, Riederer P,

Hallgren B, Sourander P: The effect of age on the non-haemin iron in the human brain. J. Neurochem. 3(1), 41–51 (1958). The role of iron and copper molecules in the neuronal vulnerability of locus coeruleus and substantia nigra during aging. Proc. Natl Acad. Sci. USA 101(26), 9843–9848 (2004).


Riederer P: The role of iron in the basal ganglion. Adv. Neurol. 53, 155–162 (1990).



Fearnley J, Lees A: Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain 114(Pt 5), 2283–2301 (1991).

62 Braak H, Del Tredici K, Rüb U, de Vos R,

Jansen Steur E, Braak E: Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 24(2), 197–211 (2003). 63 Hodaie M, Neimat J, Lozano A: The

dopaminergic nigrostriatal system and Parkinson’s disease: molecular events in development, disease, and cell death, and new therapeutic strategies. Neurosurgery 60(1), 17–28; discussion 28–30 (2007).


Review  Planetta, Prodoehl, Corcos & Vaillancourt 64 Yoshikawa K, Nakata Y, Yamada K,


Nakagawa M: Early pathological changes in the parkinsonian brain demonstrated by diffusion tensor MRI. J. Neurol. Neurosurg. Psychiatry 75(3), 481–484 (2004). 65

Chan L, Rumpel H, Yap K et al.: Case control study of diffusion tensor imaging in Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 78(12), 1383–1386 (2007).

Localized proton NMR spectroscopy in the striatum of patients with idiopathic Parkinson’s disease: a multicenter pilot study. Magn. Reson. Med. 33(5), 589–594 (1995).



Bhattacharya S et al.: A proton magnetic resonance spectroscopy study of the striatum and cerebral cortex in Parkinson’s disease. Metab. Brain Dis. 14(1), 45–55 (1999). Pilatus U et al.: Phosphorus and proton magnetic resonance spectroscopy demonstrates mitochondrial dysfunction in early and advanced Parkinson’s disease. Brain 132(Pt 12), 3285–3297 (2009). n

biological nuclear magnetic resonance spectroscopy. Biol. Rev. Camb. Philos. Soc. DOI: 10.1111/j.1469-185X.2010.00157.x (2010) (Epub ahead of print). 69 Boska M, Lewis T, Destache C et al.:

Coenzyme Q10 levels correlate with the activities of complexes I and II/III in mitochondria from parkinsonian and nonparkinsonian subjects. Ann. Neurol. 42(2), 261–264 (1997).

72 Chaudhuri K, Lemmens G, Williams S et al.:

Proton magnetic resonance spectroscopy of the striatum in Parkinson’s disease patients with motor response fluctuations. Parkinsonism Relat. Disord. 2(2), 63–67 (1996). 73 Kickler N, Krack P, Fraix V et al.:

Glutamate measurement in Parkinson’s disease using MRS at 3 T field strength. NMR Biomed. 20(8), 757–762 (2007).


Results of this magnetic resonance spectroscopy study suggest that there is mitochondrial dysfunction in the midbrain and putamen of early and advanced PD patients. Hajnal J, Brooks D: A comparison of 18 F‑dopa PET and inversion recovery MRI in the diagnosis of Parkinson’s disease. Neurology 56(9), 1195–1200 (2001).

85 Logothetis N, Pauls J, Augath M, Trinath T,

Oeltermann A: Neurophysiological investigation of the basis of the fMRI signal. Nature 412(6843), 150–157 (2001). 86 Ekstrom A: How and when the fMRI BOLD

signal relates to underlying neural activity: the danger in dissociation. Brain Res. Rev. 62(2), 233–244 (2010). 87 Nahab FB, Hallett M: Current role of

functional MRI in the diagnosis of movement disorders. Neuroimaging Clin. N. Am. 20(1), 103–110 (2010). 88 Spraker M, Prodoehl J, Corcos D,

Comella C, Vaillancourt D: Basal ganglia hypoactivity during grip force in drug naive Parkinson’s disease. Hum. Brain Mapp. 31(12), 1928–1941 (2010). 89 Prodoehl J, Spraker M, Corcos D,

Comella C, Vaillancourt D: Blood oxygenation level-dependent activation in basal ganglia nuclei relates to specific symptoms in de novo Parkinson’s disease. Mov. Disord. 25(13), 2035–2043 (2010). n


79 Lucetti C, Del Dotto P, Gambaccini G

et al.: Influences of dopaminergic treatment on motor cortex in Parkinson disease: a MRI/MRS study. Mov. Disord. 22(15), 2170–2175 (2007). 80 Martin W, Wieler M, Gee M, Hanstock C,

Camicioli R: Intact presupplementary motor area function in early, untreated Parkinson’s disease. Mov. Disord. 23(12), 1756–1759 (2008).

71 Ellis C, Lemmens G, Williams S et al.:

Changes in putamen N‑acetylaspartate and choline ratios in untreated and levodopatreated Parkinson’s disease: a proton magnetic resonance spectroscopy study. Neurology 49(2), 438–444 (1997).

Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl Acad. Sci. USA 89(13), 5951–5955 (1992).

78 Hu M, White S, Herlihy A, Chaudhuri K,

Quantitative 1H magnetic resonance spectroscopic imaging determines therapeutic immunization efficacy in an animal model of Parkinson’s disease. J. Neurosci. 25(7), 1691–1700 (2005). 70 Shults C, Haas R, Passov D, Beal M:

84 Ogawa S, Tank D, Menon R et al.:

77 Hattingen E, Magerkurth J,

Demonstrates that diffusion tensor imaging of the caudal SN could differentiate between early stage, de novo PD patients and controls with 100% sensitivity and specificity.

68 Bothwell J, Griffin J: An introduction to

Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl Acad. Sci. USA 89(12), 5675–5679 (1992).

76 Taylor-Robinson S, Turjanski N,

67 Vaillancourt D, Spraker M, Prodoehl J et al.:

High-resolution diffusion tensor imaging in the substantia nigra of de novo Parkinson disease. Neurology 72(16), 1378–1384 (2009).

83 Kwong K, Belliveau J, Chesler D et al.:

75 Holshouser B, Komu M, Möller H et al.:

66 Gattellaro G, Minati L, Grisoli M et al.:

White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. AJNR Am. J. Neuroradiol. 30(6), 1222–1226 (2009).

Tedeschi G, Litvan I, Bonavita S et al.: Proton magnetic resonance spectroscopic imaging in progressive supranuclear palsy, Parkinson’s disease and corticobasal degeneration. Brain 120(Pt 9), 1541–1552 (1997).


Camicioli R, Hanstock C, Bouchard T, Gee M, Fisher N, Martin W: Magnetic resonance spectroscopic evidence for presupplementary motor area neuronal dysfunction in Parkinson’s disease. Mov. Disord. 22(3), 382–386 (2007).

82 Griffith H, den Hollander J, Okonkwo O,

O’Brien T, Watts R, Marson D: Brain metabolism differs in Alzheimer’s disease and Parkinson’s disease dementia. Alzheimers Dement. 4(6), 421–427 (2008).

Neurodegen. Dis. Manage. (2011) 1(1)

Highlights the potential of functional MRI as a state biomarker of PD by showing a significant negative correlation between clinical severity and functional MRI signal in most of the basal ganglia nuclei and thalamus.

90 Buhmann C, Glauche V, Stürenburg H,

Oechsner M, Weiller C, Büchel C: Pharmacologically modulated fMRI – cortical responsiveness to levodopa in drug-naive hemiparkinsonian patients. Brain 126(Pt 2), 451–461 (2003). 91

Sabatini U, Boulanouar K, Fabre N et al.: Cortical motor reorganization in akinetic patients with Parkinson’s disease: a functional MRI study. Brain 123(Pt 2), 394–403 (2000).

92 Haslinger B, Erhard P, Kämpfe N et al.:

Event-related functional magnetic resonance imaging in Parkinson’s disease before and after levodopa. Brain 124(Pt 3), 558–570 (2001).

future science group

MRI to monitor Parkinson’s disease  93 Eckert T, Peschel T, Heinze H, Rotte M:

Increased pre-SMA activation in early PD patients during simple self-initiated hand movements. J. Neurol. 253(2), 199–207 (2006). 94 Yu H, Sternad D, Corcos D, Vaillancourt D:

Role of hyperactive cerebellum and motor cortex in Parkinson’s disease. Neuroimage 35(1), 222–233 (2007). 95 van den Heuvel M, Hulshoff Pol H: Exploring

the brain network: a review on resting-state fMRI functional connectivity. Eur. Neuropsychopharmacol. 20(8), 519–534 (2010). 96 James G, Kelley M, Craddock R et al.:

Exploratory structural equation modeling of resting-state fMRI: applicability of group models to individual subjects. Neuroimage 45(3), 778–787 (2009).

future science group

97 Wu T, Wang L, Chen Y, Zhao C, Li K,

Chan P: Changes of functional connectivity of the motor network in the resting state in Parkinson’s disease. Neurosci. Lett. 460(1), 6–10 (2009). 98 Helmich R, Derikx L, Bakker M,

Scheeringa R, Bloem B, Toni I: Spatial remapping of cortico–striatal connectivity in Parkinson’s disease. Cereb. Cortex 20(5), 1175–1186 (2010). 99 Wu T, Long X, Wang L et al.: Functional

connectivity of cortical motor areas in the resting state in Parkinson’s disease. Hum. Brain Mapp. (2010) (Epub ahead of print).


100 Aguirre GK, Zarahn E, D’esposito M: The

variability of human, BOLD hemodynamic responses. Neuroimage 8(4), 360–369 (1998). 101 Smith SM, Beckmann CF, Ramnani N

et al.: Variability in fMRI: a re-examination of inter-session differences. Hum. Brain Mapp. 24(3), 248–257 (2005). 102 Du G, Lewis MM, Styner M, Yang Q,

Shaffer M, Huang X: Combined R 2* and DTI measures in substantia nigra in Parkinson’s disease. Presented at: 135th Annual Meeting of the American Neurological Association. San Francisco, CA, USA, 12–15 September 2010.


Suggest Documents