EDITORIAL
Diffusion MRI in the Diagnosis of Parkinsonism and Tremor phane Lehe ricy, MD, PhD* Ste Institut du Cerveau et de la Moelle epiniere (ICM), Centre de NeuroImagerie de Recherche (CENIR), ^pital Pitie -Salpe ^trie`re, Paris, France CRICM, UPMC/Inserm U975, CNRS 7225, Ho
In the early stages, the differentiation of patients with Parkinson’s disease (PD), atypical parkinsonian disorders (eg, progressive supranuclear palsy [PSP] and the Parkinson’s variant of multiple system atrophy [MSA-P]), and essential tremor is difficult. Clinical presentations can be misleading. Imaging techniques commonly used for these diseases include positron emission tomography and single-photon emission computed tomography to assess dopaminergic neurotransmission. The results obtained by conventional magnetic resonance imaging (MRI) have been mostly disappointing. Recently, the use of new MRI techniques has improved the capacity of MRI to detect changes in PD patients and to differentiate between PD, other parkinsonian syndromes, and essential tremor. These techniques have facilitated the identification of new MRI biomarkers that allow the detection of changes associated with these diseases in brain regions such as the substantia nigra, the brainstem, the cerebellum, the basal ganglia, and the cortex. To determine candidate MRI biomarkers (ie, quantitative parameters), diffusion tensor imaging (DTI) and T2* relaxometry have been the most studied techniques so far.1 DTI provides information concerning water diffusion in the brain. Diffusion imaging can quantify the magnitude of water diffusion using global indexes such as mean diffusivity and the orientation of water diffusion using indexes such as fractional anisotropy (FA). Relaxometry provides T2 and T2* relaxation times and R2 (1/T2) and R2* relaxation rate constants, which characterize the exponential decay of the transverse component of water magnetization after perturbation by a radiofrequency pulse. Relaxation times depend on the molecular structure of tissues. Many experiments have shown that the measurement of R2
-----------------------------------------------------------ricy, Service de neuroradiolo*Correspondence to: Dr. Stephane Lehe gie, Groupe Hospitalier Pitie-Salpetriere, 47 Bd de l’Hopital, 75651 Paris Cedex 13, France; [email protected]
Relevant conflicts of interest/financial disclosures: Nothing to report. Full financial disclosures and author roles may be found in the online version of this article. Received: 20 May 2013; Revised: 27 July 2013; Accepted: 4 August 2013 Published online 21 October 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.25662
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and R2* allows noninvasive estimation of iron content.2 In patients with PD, the changes mainly observed in the substantia nigra include an increase in R2*, indicating an increased iron load and a reduction in FA.1 Patients with PSP presented extensive changes in the midbrain, superior cerebellar peduncle, basal ganglia, white matter, and cortical regions with increased diffusivity and shortened T2.3 In patients with MSA-P, changes were predominate in the basal ganglia, the cerebellum, the pons, and the middle cerebellar peduncle as well as in the cortex.3 As a consequence, various quantitative measures of the pontine and midbrain areas have been suggested to accurately discriminate PSP and MSA-P patients from PD patients and healthy controls. In patients with essential tremor, changes have been observed in the dentate nucleus and the superior cerebellar peduncles.4 Overall, new MRI techniques appear to provide biomarkers that can detect neuropathological features and allow the study of mechanisms underlying neurodegeneration in parkinsonian disorders and essential tremor. These biomarkers may be useful for early diagnosis. In this issue of Movement Disorders, Prodoehl and colleagues used DTI to differentiate subjects with PD, atypical parkinsonism (MSA-P and PSP), and essential tremor from healthy control subjects.5 They measured diffusion imaging metrics in a set of manually drawn regions of interest, which included the basal ganglia, the red nucleus, the dentate nucleus, and the middle and superior cerebellar peduncles. Receiver operating characteristic analyses provided excellent differentiation between the groups. In these analyses, the area under the curve (AUC) was between 0.96 and 0.99 for all the comparisons, the sensitivity ranged from 86% to 94%, and the specificity ranged from 87% to 100%. DTI measurements accurately distinguished all patients from healthy controls (AUC, 0.98) as well as patients with parkinsonism from healthy controls (AUC, 0.99). Patients with PD were distinguished from patients with atypical parkinsonism using measurements in the putamen, the substantia nigra, and the dentate nucleus and from patients with essential tremor using measurements in the caudate nucleus and
D I F F U S I O N
the substantia nigra. Measurements in the striatum and the middle cerebellar peduncles distinguished patients with MSA-P from those with PSP. Although previous studies addressing the categorization of patients with movement disorders have presented accurate results for comparisons of particular groups of patients, such as atypical parkinsonism versus PD and healthy controls3 or essential tremor versus PD and healthy controls,4 this report provides the first study with comparisons of PD, atypical parkinsonism, and essential tremor altogether. Further, the distinctions between patients with PSP and MSA-P were stronger than in previous studies because a larger number of regions were sampled. Quantitative MRI techniques may thus provide useful diagnostic markers of these diseases. There are some limitations to this study. The number of subjects included in each patient group was small (12 to 15 per group); thus, future studies should include larger independent samples to confirm these results. Previous studies have shown that classification results may be lower than initially reported when a different population of patients is studied or when the study is performed by a different team.6 Classification results are also influenced by other factors including choice of algorithms, differences in learning and testing patient sets, differences in the populations studied (sample size, stage of the disease), imaging parameters and the quality of imaging data, and image preprocessing steps. The reliability of biomarkers should be confirmed by independent studies and with larger numbers of subjects as done in the framework of the Parkinson’s Progression Markers Initiative (http://www.ppmi-info.org/) or the Alzheimer’s Disease Neuroimaging Initiative.7 The interand intrasite reproducibility of measurements in different patient populations as well as the efficiency of detecting longitudinal changes should be evaluated. From clinical practice, normative values across sites that take into account the variability in MRI scanners are also lacking. The results provided here clearly show that diffusion imaging markers have the potential to quantify pathology and follow disease progression in PD, atypical parkinsonism, and essential tremor patients. Future work should investigate whether diffusion markers correlate with disease progression, which would allow for the monitoring of disease status and responses to therapeutic interventions. In addition, new biomarkers should be validated using experimental models. Ideally, these markers will allow the detection of preclinical changes.
M R I
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D I A G N O S I S
O F
P D
A N D
T R E M O R
Methods may also be enhanced by using improved diffusion acquisition parameters with smaller isotropic voxels, more sophisticated approaches for the processing of diffusion images,8 high-dimensional classification methods for automated discrimination,6,7 and a combination of approaches such as combined diffusion and relaxometry.9 And finally, magnetic resonance microscopy using ultra-high-field MRI at 7 T may detect new structural changes in the substantia nigra.10 Quantitative MRI methods thus offer new ways of studying brain microanatomy and changes in tissue properties by providing surrogate parameters that characterize neurodegeneration. The combined advances in MRI acquisition and image analysis techniques therefore provide increasingly effective methods for the investigation of brain neurodegenerative processes involved in movement disorders as well as improved diagnostic tools, as suggested in the article by Prodroehl et al.5
References 1.
Lehericy S, Sharman MA, Dos Santos CL, Paquin R, Gallea C. Magnetic resonance imaging of the substantia nigra in Parkinson’s disease. Mov Disord. 2012;27:822–830.
2.
Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005;23:1–25.
3.
Seppi K, Poewe W. Brain magnetic resonance imaging techniques in the diagnosis of parkinsonian syndromes. Neuroimaging Clin N Am. 2010;20:29–55.
4.
Nicoletti G, Manners D, Novellino F, et al. Diffusion tensor MRI changes in cerebellar structures of patients with familial essential tremor. Neurology. 2010;74:988–994.
5.
Prodoehl J, Li H, Planetta PJ, et al. Diffusion tensor imaging of Parkinson’s disease, atypical parkinsonism and essential tremor. Mov Disord. 2013;28:1816–1822.
6.
Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011;56:766–781.
7.
Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2012;8:S1–S68.
8.
Seunarine KK, Alexander DC. Multiple fibers: beyond the diffusion tensor. In: Johansen-Berg H, Behrens TE, eds. Diffusion MRI: From Quantitative Measurement to In-Vivo Neuroanatomy. London, UK: Academic Press, Elsevier; 2009:55–71.
9.
Peran P, Cherubini A, Assogna F, et al. Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain. 2010;133:3423–3433.
10.
Kwon DH, Kim JM, Oh SH, et al. Seven-Tesla magnetic resonance images of the substantia nigra in Parkinson disease. Ann Neurol. 2012;71:267–277.
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Diffusion MRI in the Diagnosis of Parkinsonism and Tremor phane Lehe ricy, MD, PhD* Ste Institut du Cerveau et de la Moelle epiniere (ICM), Centre de NeuroImagerie de Recherche (CENIR), ^pital Pitie -Salpe ^trie`re, Paris, France CRICM, UPMC/Inserm U975, CNRS 7225, Ho
In the early stages, the differentiation of patients with Parkinson’s disease (PD), atypical parkinsonian disorders (eg, progressive supranuclear palsy [PSP] and the Parkinson’s variant of multiple system atrophy [MSA-P]), and essential tremor is difficult. Clinical presentations can be misleading. Imaging techniques commonly used for these diseases include positron emission tomography and single-photon emission computed tomography to assess dopaminergic neurotransmission. The results obtained by conventional magnetic resonance imaging (MRI) have been mostly disappointing. Recently, the use of new MRI techniques has improved the capacity of MRI to detect changes in PD patients and to differentiate between PD, other parkinsonian syndromes, and essential tremor. These techniques have facilitated the identification of new MRI biomarkers that allow the detection of changes associated with these diseases in brain regions such as the substantia nigra, the brainstem, the cerebellum, the basal ganglia, and the cortex. To determine candidate MRI biomarkers (ie, quantitative parameters), diffusion tensor imaging (DTI) and T2* relaxometry have been the most studied techniques so far.1 DTI provides information concerning water diffusion in the brain. Diffusion imaging can quantify the magnitude of water diffusion using global indexes such as mean diffusivity and the orientation of water diffusion using indexes such as fractional anisotropy (FA). Relaxometry provides T2 and T2* relaxation times and R2 (1/T2) and R2* relaxation rate constants, which characterize the exponential decay of the transverse component of water magnetization after perturbation by a radiofrequency pulse. Relaxation times depend on the molecular structure of tissues. Many experiments have shown that the measurement of R2
-----------------------------------------------------------ricy, Service de neuroradiolo*Correspondence to: Dr. Stephane Lehe gie, Groupe Hospitalier Pitie-Salpetriere, 47 Bd de l’Hopital, 75651 Paris Cedex 13, France; [email protected]
Relevant conflicts of interest/financial disclosures: Nothing to report. Full financial disclosures and author roles may be found in the online version of this article. Received: 20 May 2013; Revised: 27 July 2013; Accepted: 4 August 2013 Published online 21 October 2013 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/mds.25662
1762
Movement Disorders, Vol. 28, No. 13, 2013
and R2* allows noninvasive estimation of iron content.2 In patients with PD, the changes mainly observed in the substantia nigra include an increase in R2*, indicating an increased iron load and a reduction in FA.1 Patients with PSP presented extensive changes in the midbrain, superior cerebellar peduncle, basal ganglia, white matter, and cortical regions with increased diffusivity and shortened T2.3 In patients with MSA-P, changes were predominate in the basal ganglia, the cerebellum, the pons, and the middle cerebellar peduncle as well as in the cortex.3 As a consequence, various quantitative measures of the pontine and midbrain areas have been suggested to accurately discriminate PSP and MSA-P patients from PD patients and healthy controls. In patients with essential tremor, changes have been observed in the dentate nucleus and the superior cerebellar peduncles.4 Overall, new MRI techniques appear to provide biomarkers that can detect neuropathological features and allow the study of mechanisms underlying neurodegeneration in parkinsonian disorders and essential tremor. These biomarkers may be useful for early diagnosis. In this issue of Movement Disorders, Prodoehl and colleagues used DTI to differentiate subjects with PD, atypical parkinsonism (MSA-P and PSP), and essential tremor from healthy control subjects.5 They measured diffusion imaging metrics in a set of manually drawn regions of interest, which included the basal ganglia, the red nucleus, the dentate nucleus, and the middle and superior cerebellar peduncles. Receiver operating characteristic analyses provided excellent differentiation between the groups. In these analyses, the area under the curve (AUC) was between 0.96 and 0.99 for all the comparisons, the sensitivity ranged from 86% to 94%, and the specificity ranged from 87% to 100%. DTI measurements accurately distinguished all patients from healthy controls (AUC, 0.98) as well as patients with parkinsonism from healthy controls (AUC, 0.99). Patients with PD were distinguished from patients with atypical parkinsonism using measurements in the putamen, the substantia nigra, and the dentate nucleus and from patients with essential tremor using measurements in the caudate nucleus and
D I F F U S I O N
the substantia nigra. Measurements in the striatum and the middle cerebellar peduncles distinguished patients with MSA-P from those with PSP. Although previous studies addressing the categorization of patients with movement disorders have presented accurate results for comparisons of particular groups of patients, such as atypical parkinsonism versus PD and healthy controls3 or essential tremor versus PD and healthy controls,4 this report provides the first study with comparisons of PD, atypical parkinsonism, and essential tremor altogether. Further, the distinctions between patients with PSP and MSA-P were stronger than in previous studies because a larger number of regions were sampled. Quantitative MRI techniques may thus provide useful diagnostic markers of these diseases. There are some limitations to this study. The number of subjects included in each patient group was small (12 to 15 per group); thus, future studies should include larger independent samples to confirm these results. Previous studies have shown that classification results may be lower than initially reported when a different population of patients is studied or when the study is performed by a different team.6 Classification results are also influenced by other factors including choice of algorithms, differences in learning and testing patient sets, differences in the populations studied (sample size, stage of the disease), imaging parameters and the quality of imaging data, and image preprocessing steps. The reliability of biomarkers should be confirmed by independent studies and with larger numbers of subjects as done in the framework of the Parkinson’s Progression Markers Initiative (http://www.ppmi-info.org/) or the Alzheimer’s Disease Neuroimaging Initiative.7 The interand intrasite reproducibility of measurements in different patient populations as well as the efficiency of detecting longitudinal changes should be evaluated. From clinical practice, normative values across sites that take into account the variability in MRI scanners are also lacking. The results provided here clearly show that diffusion imaging markers have the potential to quantify pathology and follow disease progression in PD, atypical parkinsonism, and essential tremor patients. Future work should investigate whether diffusion markers correlate with disease progression, which would allow for the monitoring of disease status and responses to therapeutic interventions. In addition, new biomarkers should be validated using experimental models. Ideally, these markers will allow the detection of preclinical changes.
M R I
I N
D I A G N O S I S
O F
P D
A N D
T R E M O R
Methods may also be enhanced by using improved diffusion acquisition parameters with smaller isotropic voxels, more sophisticated approaches for the processing of diffusion images,8 high-dimensional classification methods for automated discrimination,6,7 and a combination of approaches such as combined diffusion and relaxometry.9 And finally, magnetic resonance microscopy using ultra-high-field MRI at 7 T may detect new structural changes in the substantia nigra.10 Quantitative MRI methods thus offer new ways of studying brain microanatomy and changes in tissue properties by providing surrogate parameters that characterize neurodegeneration. The combined advances in MRI acquisition and image analysis techniques therefore provide increasingly effective methods for the investigation of brain neurodegenerative processes involved in movement disorders as well as improved diagnostic tools, as suggested in the article by Prodroehl et al.5
References 1.
Lehericy S, Sharman MA, Dos Santos CL, Paquin R, Gallea C. Magnetic resonance imaging of the substantia nigra in Parkinson’s disease. Mov Disord. 2012;27:822–830.
2.
Haacke EM, Cheng NY, House MJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005;23:1–25.
3.
Seppi K, Poewe W. Brain magnetic resonance imaging techniques in the diagnosis of parkinsonian syndromes. Neuroimaging Clin N Am. 2010;20:29–55.
4.
Nicoletti G, Manners D, Novellino F, et al. Diffusion tensor MRI changes in cerebellar structures of patients with familial essential tremor. Neurology. 2010;74:988–994.
5.
Prodoehl J, Li H, Planetta PJ, et al. Diffusion tensor imaging of Parkinson’s disease, atypical parkinsonism and essential tremor. Mov Disord. 2013;28:1816–1822.
6.
Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage. 2011;56:766–781.
7.
Weiner MW, Veitch DP, Aisen PS, et al. The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 2012;8:S1–S68.
8.
Seunarine KK, Alexander DC. Multiple fibers: beyond the diffusion tensor. In: Johansen-Berg H, Behrens TE, eds. Diffusion MRI: From Quantitative Measurement to In-Vivo Neuroanatomy. London, UK: Academic Press, Elsevier; 2009:55–71.
9.
Peran P, Cherubini A, Assogna F, et al. Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain. 2010;133:3423–3433.
10.
Kwon DH, Kim JM, Oh SH, et al. Seven-Tesla magnetic resonance images of the substantia nigra in Parkinson disease. Ann Neurol. 2012;71:267–277.
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