Magneticresonanceimaging(MRI)methodsinParkinson sdisease

Chapter

1Magnetic resonance imaging (MRI)methods in Parkinson’s diseaseSilvia Mangia, ShalomMichaeli, and Paul Tuite

IntroductionThe revolution created by magnetic resonanceimaging (MRI) is a result of its ability to utilize theintrinsic differences in the magnetic properties oftissues to provide information about function, struc-ture, and chemistry. This book provides backgroundon a variety of MRI methods and focuses on theirapplication to neurological conditions that manifestwith movement disorders. These disorders includeneurodegenerative conditions such as Parkinson’sdisease (PD), Huntington’s disease, and ataxia whichare characterized by progressive neuronal loss withaccompanying neuropathological changes. Mean-while other movement disorders are not clearlydegenerative and tend to have more subtle structural,functional, and neurochemical changes.

The focus of many chapters herein will be on PD,which constitutes the second most common neuro-degenerative disease after Alzheimer’s disease (AD),and is the principle focus of many clinicians andresearchers in the movement disorders field. It isstated that approximately 1% of those over 65 yearsof age have PD [1]. As a result there is substantialfocus on PD research, and the discoveries made inbasic neuroscience that relate to pathogenic mechan-isms of PD (or AD for that matter) are often relevantfor lesser known neurodegenerative parkinsonianconditions such as multiple system atrophy (MSA)or progressive supranuclear palsy (PSP). Along withthe progress made in basic science research, MRIdevelopments have been applied to PD and havebegun to show promise as a surrogate biomarker inresearch studies. As well these techniques may bemore useful clinically in looking at other parkinso-nian conditions such as MSA and PSP. While MRI ispresently not able directly to image dopaminergic

neuronal loss that underlies degenenerative parkinso-nian conditions, it can provide complementary datato those obtained with nuclear tracer imaging. First,this chapter will provide an overview of MRI methodswhich will be discussed in much greater depth insubsequent chapters, and we will cover nascent tech-niques that we are employing at the University ofMinnesota that may ultimately help provide animaging biomarker of disease.

Iron imaging in PDInitial MRI work in PD began in the mid 1980s asseveral groups focused on the paramagnetic effects ofiron, which is present in increasing quantities in thebasal ganglia in PD [2]. This was followed by thefrequently referenced work by Gorell et al. in 1995who utilized T2 and T2* imaging of the substantianigra (SN) and showed a separation between thosewith PD and control participants; the change in relax-ation time constants was attributed to increased ironin patients with PD [3]. T2* imaging is especiallysensitive to local magnetic field inhomogeneitiesinduced for example by iron. The focus on iron inPD imaging has remained an important topic andresearchers have often utilized T2*, or its reciprocalR2*, in nigral imaging protocols. Visually obviouschanges have been observed at 7 tesla (T) in patientswith PD as compared to controls (Figure 1.1) [4].When the term “iron” is employed in PD imagingmanuscripts, this typically refers to non-heme iron asopposed to heme iron, and also relates to bound ironthat may be present in ferritin or neuromelanin [5].However, a small pool of free labile iron may actuallybe a pathogenic culprit as opposed to iron that issafely bound and tucked away. Nonetheless the over-all increase in bound iron seen in PD – which is more

Magnetic Resonance Imaging in Movement Disorders, ed. Paul Tuite and Alain Dagher. Published by Cambridge UniversityPress. © Cambridge University Press 2013.

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than occurs with “normal aging” – may represent amarker of disease and possibly a source for increasedfree and presumably pathogenic iron [5]. Otherimportant issues to consider about brain iron arethe higher concentrations seen in men, as well as thepotential for effects from dietary and environmentaltoxins. Serum ceruloplasmin levels may help addresspotential confounds in imaging studies [6]. Anotherfactor is the location of iron; whether it is present inneurons or glia such as astrocytes or oligodendrocytes[7]. It is currently not possible to determine whetherthe imaging abnormalities in PD reflect changes inneuromelanin in dopaminergic neurons or ferritin inglia or neurons. Additionally, while iron changes havebeen noted in PD, it remains to be determined if iron-sensitive MRI methods will serve as a surrogate ofdisease – to monitor progression or characterize theseverity of disease. This topic will be covered morecompletely in Chapter 2 by Wayne Martin.

Meanwhile other iron-sensitive methods havebeen recently developed. These include adiabatic T2ρ

[8, 9], magnetization transfer (MT) imaging [10–15],and susceptibility-weighted imaging (SWI) [16–20].

Susceptibility-weighted imaging (SWI)SWI is a technique that is available on clinical MRIplatforms which exploits the differences in magneticsusceptibility between tissues. SWI is based on gradi-ent echo (GRE) pulse sequences with long echo time(TE), and it achieves enhanced image contrast fordetecting susceptibility variations from combiningmagnitude and phase data of the GRE acquisitions.Since iron induces magnetic susceptibility differences,SWI is inherently sensitive to the presence of iron,and is able to demonstrate anatomical structure aswell as potentially providing measures of iron content[21]. Much of the recent excitement in movementdisorders has arisen from studies conducted at 7T –including work in enhanced visualization of brainsurgical targets [16–20], as discussed in Chapter 8.

Magnetization transfer imaging (MTI)MTI utilizes the transfer of magnetization betweenbulk (free) water protons and protons associated withmacromolecules [22] and is generally accepted as acorrelate of tissue integrity. The detection of the

Figure 1.1 Three-dimensional T2*-weighted gradient echo MRI at 7T of themidbrain. These two PD cases (H&Y 1 and3 ¼ Hoehn & Yahr stage 1 and 3,respectively) show substantially serratedsubstantia nigra (SN) in their lateralboundaries compared with normalcontrols that may represent increasediron accumulation. (Cho et al., 2011 [4].)

Chapter 1: MRI methods in Parkinson’s disease

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magnetization transfer (MT) effect in clinical practiceis usually limited to the measurement of MT ratios(MTRs), that is, ratios of signal intensity measuredwith and without the saturation pulse [22]. However,MTRs have appeared to be more useful in atypicalparkinsonian conditions than in PD except for onegroup which showed the potential utility of MTR inPD [10, 12–15]. In contrast to MTR, we havedeveloped an easy-to-implement quantitative MTmethod to estimate MT parameters, which relies onan inversion-prepared MT protocol [11]. By progres-sively incrementing the duration of the off-resonancecontinuous wave (CW) pulse, the T1 of water in thepresence of saturation (T1sat) and the steady-state (SS)magnetization (Mss) can be estimated. In addition, theforward exchange rate from the solid to the free pool,kf, can be calculated as (1 � Mss/M0)/T1sat. The basicsource of instability in the fitting procedure of T1sat

and Mss originates from the impossibility of usingMT pulses that are long enough to achieve the steadystate, due to safety limitations imposed by the specificabsorption rate (SAR) of radiofrequency (RF) powerdeposition. To circumvent this limitation, wedeveloped a protocol where two consecutive sets ofmeasurements are acquired with the magnetizationinitially along the �z or +z-axis, that is with or with-out an on-resonance inversion prior to the off-resonance irradiation. Whereas the usage of inversionrecovery approaches has been suggested to measureMT effects [23, 24], the implementation of an on-resonance inversion pulse prior to the off-resonanceirradiation has not been exploited so far. We haveused the inversion-prepared MT protocol togetherwith adiabatic T1ρ in a study which aimed at assessingthe integrity of the brainstem structures of PD [25].Results from this study will be discussed in the nextsection.

Adiabatic rotating frame relaxationmethodsConventionally, MRI contrast is generated by thetissue variation of free-precession longitudinal (timeconstant, T1) and/or transverse (time constant, T2)relaxation of the 1H2O MR signal. These time con-stants are measured in the laboratory frame, in whichthe direction of the main magnetic field defines thelongitudinal or z-axis. The free precession relaxationrate constant (R1) is particularly sensitive to magneticfluctuations that occur as a result of molecular

motion at frequencies near the Larmor precessionfrequency (ω0), which falls in the MHz range. How-ever, there is reason also to probe lower frequencies intissue (i.e., in the kHz range) in order to evaluate forpathology. Rotating frame relaxation rate constants,R1ρ and R2ρ, characterize relaxation during radio-frequency (RF) irradiation when the magnetizationvector is aligned along or perpendicular to the direc-tion of the effective magnetic field (ωeff), respectively,where the effective magnetic field depends on theapplied RF (ω1) and the frequency offset (Δω). R1ρ

and R2ρ reflect the features of the spin dynamic pro-cesses and depend on the properties of the RF irradi-ation [26, 27]. Importantly, R1ρ and R2ρ are drivenprincipally by dipolar fluctuations at frequencies nearthe effective precession frequency. Since ωeff can be“tuned” by adjustment of the RF amplitude and thefrequency offset, R1ρ and R2ρ can provide experimen-tal access to the relevant lower frequencies in therange of kHz.

Rotating frame relaxation constants can be meas-ured during the application of adiabatic pulses [28].In this case, the adiabatic R1ρ(t) and R2ρ(t) are time-dependent longitudinal and transverse relaxation rateconstants, which characterize decay of magnetizationduring application of RF pulses operating in the adia-batic regime, that is, when the adiabatic condition(i.e., jγ�1dα/dtj << Beff), is well satisfied. Here α isthe angle between Beff and the first rotating framez0-axis. The pulses used for the measurements areadiabatic full passage (AFP) of the hyperbolic secantfamily (HSn) (where n ¼ 1, 2, 4, 8,. . .). Detaileddescriptions of HSn adiabatic pulse modulation func-tions are given in Michaeli et al. [26, 27]. Briefly, HSnpulses are stretched versions of the hyperbolic secant(HS) pulse [29] and n denotes the stretching factor.According to this description, the original hyperbolicsecant pulse is referred to as the HS1 pulse. As nbecomes larger, the HSn pulse amplitude modulation(AM) function becomes flatter. Thus, the time evolu-tion of magnetization during an HSn pulse canchange significantly with a change of n. During adia-batic pulses the R1ρ(t) is time dependent as a result ofmodulating the pulse functions, ω1(t) and ωRF(t).Here, ω1(t) and ωRF(t) each have units of radian/s.We have shown that the instantaneous relaxation rateconstants during AFP pulse resemble pulse modula-tion functions and their magnitude depends on theeffective frequency ωeff(t). If the magnetization M isinitially not perturbed and the train of the AFP pulses


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is applied, the relaxation is governed solely by R1ρ

mechanisms. If magnetization M is initially excitedto 90° and is located in the xy plane of thelaboratory frame and the train of AFP pulses isapplied, relaxation is solely R2ρ. Overall, adiabaticR1ρ (¼ 1/T1ρ) and R2ρ (¼ 1/T2ρ) provide novel tissueMRI contrast [26, 27, 30], and allow for determin-ation of fundamental MR parameters, which are rota-tional correlation times at specific sites, τc, apparentpopulations, and exchange relaxation rate constants[31]. The R1ρ and R2ρ measured during adiabaticpulses were demonstrated to be sensitive to neuralintegrity and iron accumulation, respectively [8, 9,31, 32]. In a validation study of an aphakia mousemodel, T1ρ was indeed able to separate aphakia versuswild-type mice in the substantia nigra pars compacta(SNc), where there is a congenital absence of dopa-minergic neurons (Figure 1.2) [31]. In human studiesof PD patients and healthy controls, we have shownthe ability of adiabatic methods to detect midbrainchanges in PD (Figure 1.3) [8, 9]. Table 1.1 shows theseparation of group data using these techniques [8].Additional recent work has shown that adiabaticmethods were able to demonstrate midbrain andmedullary changes in PD as compared to controls,as demonstrated in Figure 1.4 [25]. In this figure, therepresentative R1ρ and R1sat maps from control (top)and PD (bottom) subjects are shown. All PD patientswere Hoehn and Yahr stage 2, average motor UnifiedParkinson Disease Rating Scale (part III) scores were33.8 (off medication), and average disease duration

was 6 years. Analyses were performed with ten age-matched control subjects. The regions of interest(ROIs) are indicated on the T2-weighted images.The differences between the R1ρ values measuredfrom a rostral region used as internal control persubject (here identified by ROI-1) minus the R1ρ

values measured from medullary nuclei (i.e., ROI-5and ROI-1 versus ROI-6) were altered in patientsrelative to control subjects (p ¼ 0.004 and p ¼0.033, respectively). Differences in R1ρ values weresix and eight times larger in patients than in controlswhen comparing ROI-1 versus ROI-5 and ROI-1versus ROI-6, respectively. Since R1ρ values in ROI-1were not different between patients and controls (p ¼0.25), these findings represent a change in imagingparameters from areas that contain medullary nucleithat are known to be affected in PD. Interestingly, nostatistical differences were observed between patientsand controls when considering R1sat. This could beattributed to differential sensitivity to the exchangeregime between T1ρ and T1sat [11]. Together, thefindings of this study might indicate functionalchanges that occur prior to neuronal death withinthe medullary nuclei.

Relaxations along a fictitiousfield (RAFF)A potential limitation to the widespread exploitationof rotating frame relaxation in PD is the requiredRF power delivered to the sample (i.e., specific

Figure 1.2 T1ρ time constants measuredin different areas of aphakia (ak) andwild-type (w-t) mice using HS1 AFPpulses. SNc ¼ substantia nigra parscompacta; SNr ¼ substantia nigrareticulate; VTA ¼ ventral tegmentalarea. (Michaeli et al., 2009 [31].) Thesefindings show that in ak mice withabsence of SNc neurons there is anincrease in T1ρ as compared to the w-tmice in this region.


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absorption rate – SAR), which can result in tissueheating. However, RF power can sometimes bereduced by using off-resonance irradiation to createthe locking field, Beff [33, 34]. Recently we havedeveloped a novel rotating frame relaxation

experiment called Relaxation Along a Fictitious Field(RAFF). RAFF does not require an initial rotation ofthe magnetization to a specific locking angle. As com-pared to CW T1ρ and adiabatic T1ρ and T2ρ methods,RAFF experiments can be performed with reduced RFpower because the spin-locking field (i.e., the so-called fictitious field E) is produced by taking advan-tage of both amplitude and frequency modulationfunctions (AM sine and FM cosine, respectively) oper-ating in a sub-adiabatic condition [34]. RAFF com-prises T1ρ and T2ρ mechanisms by exploitingrelaxation in a second rotating frame. RAFF was ableto provide a greater contrast in tissues of the SN ascompared to T1ρ and T2ρ, and specifically it was betterthan all other methods in separating the SN into itsvarious subregions, that is, the pars compacta frompars reticulata [35]. Additional studies are warrantedto sort out its utility.

Table 1.1. Averaged calculated T2ρ, T1ρ, and T2 time constantsin PD patients and controls

Timeconstants (ms)

T2ρa T2

b T1ρc

Controls 72.3 ± 3.4 59.4 ± 4.3 156 ± 6.7

PD 64.6 ± 3.4 57.1 ± 4.2 178 ± 11.6a Significant difference between PD and controls (p < 0.01, two-tailed).

b No difference between PD and controls (p ¼ 0.32, two-tailed).c Significant difference between PD and controls (p ¼ 0.036, two-tailed).

(b) T2

PD

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04030 50 60 70 80 90 100

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080 130 180 230


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Controls

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15

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5

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Figure 1.3 (a) Comparison between T2ρ relaxograms in the SN area from PD patients and controls. (b) Comparison between T2 relaxogramsin the SN area from PD patients and controls. (c) Comparison between T1ρ relaxograms in the SN between PD patients and controls. ROIobtained from 8 healthy controls and 8 patients with PD. (Michaeli et al., 2007 [8].) ROI, region of interest.


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Volumetric measurements anddiffusion tensor imaging (DTI)Meanwhile researchers have pursued other imagingmethods to confirm the known loss of neurons andshrinkage of the SN in PD, with measurements ofarea or volumes beginning in the early 1990s [36].These attempts utilized a preselected ROI and sub-sequently compared affected to unaffected individ-uals. However, there has been greater refinementwith the utilization of methods that do not employa-priori ROIs. One such method is voxel-basedmorphometry (VBM), in which there is standard-ization of data and then voxel-by-voxel comparisonbetween group data to determine if there are differ-ences in signal intensity. VBM discussions will becovered in an assortment of chapters as they relate

to different movement disorders. These methodsutilize 1.5 or 3T T1 anatomical data, which maynot be sufficiently sensitive to detect structuralchanges in PD until there is substantial diseaseprogression and the presence of accompanyingdementia [37, 38]. Conditions such as Huntington’sdisease, MSA, PSP, along with AD and atypicaldementias may lend themselves to greater utiliza-tion of these methods because atrophy is moreprominent and therefore detected earlier. Oneexample of the potential utility of VBM is from arapid eye movement sleep behavior disorder (RBD)study that showed changes in a population of indi-viduals with RBD as opposed to controls – RBDmay represent a precursor to PD in some situations(Figure 1.5) [39]. Meanwhile, researchers are utiliz-ing higher field strengths along with potentially

[S-1] [S-1]

Figure 1.4 Rotating frame R1ρ maps(middle column), MT rate maps (R1sat)(right column), with relative T2-weighted(T2w) images (left column) fromrepresentative control subject (top row)and PD patient (bottom row). Regions ofinterest (ROIs) – as depicted on T2wimages – were obtained in maps from sixareas: (1) medial raphe nucleus; (2) dorsalraphe nucleus; (3) nucleus raphe pontis;(4) nucleus raphe magnus; (5) nucleusraphe pallidus; (6) nucleus raphe obscuris.(Tuite et al., 2012 [25].) See text for details.

Figure 1.5 Statistical parametricmapping (t-statistic) axial intensityprojection maps rendered onto astereotactically normalized magneticresonance imaging (MRI) scan, showingareas of significant increases (colour code,yellow to orange) of gray matter densityvalues in a cohort of patients withidiopathic rapid eye movement sleepbehaviour disorder versus healthy controlsubjects. The number at the bottom rightcorner of each MRI scan corresponds tothe z coordinate in Talairach space.(Scherfler et al., 2011 [39].)


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more sensitive methods such as T1ρ or others togenerate raw structural data that may providerenewed interest in VBM for PD.

Diffusion tensor imaging (DTI)DTI provides structural data based on the direction-ally restrained diffusion of water within fiber tracts.The movement of water, and hence protons, in whitematter tends to be constrained to the long axis of fiber

tracts, leading to anisotropy, that is, greater randommotion in certain directions. Pathological processesdisturb this natural state of anisotropy and this can beexploited with imaging. Specifically, the loss ofrestriction of water movement within damaged fiberbundles results in reduced anisotropy, which can becharacterized as a reduction in fractional anisotropyor FA. Figure 1.6 (P. Tuite, K. Lim, and B. Mueller,unpublished data) demonstrates FA and SWI imagingof the SN and striatum in PD. Figure 1.7 shows

Figure 1.6 Representative fractional anisotropy (FA) maps from different slices with the corresponding susceptibility weighted imaging (SWI)images in a PD subject demonstrating feasibility of methods on clinical 3T platforms. (Images provided courtesy of P Tuite, K. Lim, andB. Mueller, University of Minnesota, unpublished data.)

Figure 1.7 Statistical parametric mapping (t-statistic) axial intensity projection maps rendered onto a stereotactically normalized magneticresonance imaging (MRI) scan, showing areas of significant increases of mean diffusivity values (colour code, yellow to orange) in a cohort ofpatients with idiopathic rapid eye movement (REM) sleep behaviour disorder versus healthy control subjects. The number at the bottom rightcorner of each MRI scan corresponds to the z coordinate in Talairach space. Schematic drawings below correspond to the MRI and visualizeproposed nuclei involved in REM sleep control. The REM-off region is represented by the periaqueductal gray matter (PAG) in red, and theREM-on region is represented by the precoeruleus (PC) and sublaterodorsal nucleus (SLD) in green. Nuclei in yellow and blue are known toinfluence REM and non-REM sleep circuits. LC ¼ locus coeruleus; PPN ¼ pedunculopontine nucleus. (Scherfler et al., 2011 [39].)


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changes in mean diffusivity in a cohort of individualswith RBD, a possible precursor to PD [39]. DTI hasits limitations in determining directional and spatialanisotropy; hence some individuals have resorted tomethods such as tractography, which include prob-abilistic and streamline tractography. Several groupshave funded but unpublished studies that are evalu-ating these methods as a means for tracking diseaseand differentiating parkinsonian conditions fromone another as well as appreciating the structuralsubstrates that underlie clinical features. A large-scale effort called the human connectome project(www.humanconnectomeproject.org) is under wayto advance DTI methods by generating normativeimaging along with neurobehavioral and genomicdata. This project applies established and novelmethods to evaluate structure and function in thebrain. The hope is that these data will allow areference for disease states such as movementdisorders.

Resting-state MRIThe focus of resting-state MRI is on brain activitythat occurs in the absence of externally triggeredactivity. Even in a “resting state” there are physio-logical variations in brain activity and accompanyingblood flow alterations that manifest as fluctuations inthe MRI blood oxygen level-dependent (BOLD)signal. Spontaneous correlations in BOLD signalcan be utilized to determine the “functional connect-ivity” between different regions. There have been afair number of studies in PD [40–45]. Measurementof this fluctuation can be done using a variety ofmethods, including the amplitude of low frequencyfluctuations or ALFF to assess for an index ofresting-state brain activity based on the blood flowvariability [44]. Resting-state methods allow thedetermination of spontaneously occurring brain net-works, which may distinguish PD from controls;however, in one study 1/3 of those with PD and1/5 of controls had unusable data due to motionartifact, which may be partially due to the need toassess subjects when they had been off medicationsfor at least 12 hours [44]. Hence while resting-statefunctional MRI (fMRI) methods are able to providea rapid and whole brain view of PD their practicalityremains to be determined due to possible need formedication withdrawal for imaging.

In vivo magnetic resonancespectroscopy (MRS)MRS has failed to demonstrate great utility in move-ment disorders until recently. This has been due tothe low sensitivity of methods and the low concen-trations of metabolites of interest. High-field MRSwith its greater sensitivity has overcome some limita-tions and will be discussed in several chapters, includ-ing one by Emir and Öz (Chapter 15) who havedemonstrated the ability to measure absolute concen-trations of various neurochemicals within the SN andother brainstem regions.

Meanwhile, MRS imaging (MRSI) has begun tomeasure cerebral metabolic rates of oxygen (CMRO2)and ATP (CMRATP) and to correlate neuroenergeticswith specific brain functions. CMRO2 measurementsare achieved using inhaled 17O2 gas which is ultim-ately incorporated into labeled water (H2

17O) inbrain tissue, which is detectable by in vivo 17O MRS[46, 47]. This method allows the determination of therole of oxygen metabolism in normal brain functionand disease to complement functional MRI studiesthat utilize the BOLD contrast and are sensitive tocerebral blood flow.

Another important development includes in vivo31P MRSI which generates measurements of intracel-lular pH and metabolites of ATP, ADP, and phospho-creatine (PCr), among others [47]. The combinationof MRSI and magnetization transfer imaging allowsfor the measurement of CMRATP, and hence oxidativephosphorylation, a measure of cerebral mitochondrialfunction. This may prove useful in PD and Hunting-ton’s disease, in which mitochondrial dysfunction isthought to play a key role.

Clinical applicationsMRI methods are seemingly making their way intothe clinic; most rapidly by aiding the neurosurgeonin planning targeting for deep brain stimulation(DBS) surgery (Figure 1.8) [48], also discussed inmore detail in Chapter 8. Combining informationfrom different modalities may increase sensitivity todisease states, as shown for example in the combin-ation of structural and iron-sensitive imaging(Figures 1.9, 1.10, and 1.11) [49, 50]. It is hopedthat cross-sectional and longitudinal studies that arefunded through the M. J. Fox Foundation and the


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National Institutes of Health will provide additionalinsights for determining the ability of such methodsto provide a correlate to disease severity and pro-gression. With a recent dramatic increase in sales of7T clinical MRI platforms, there has been greathaste to use higher-field magnets over the 3T butwhether this proves useful as well as feasible for

clinical research studies as well as care of patientswill remain to be seen. At our institution 7T pre-operative images already have altered surgical plan-ning; however, whether 7T can improve outcomeshas not been proven yet, and researchers will needto be creative to find funding to address thisquestion.

Figure 1.8 (A) Postoperative sagittal T1-weighted MRI scan of a patient who underwent insertion of subthalamic stimulators for Parkinson’sdisease. The hypointense signal artifact shows the electrode tip (Medtronic 3387) with four contacts indicated by circles that traverse thesubthalamic nucleus. The electrode contacts are within, and dorsal to, the subthalamic nucleus. Abbreviations: cc, corpus callosum; Cd,caudate; Th, thalamus. (B) The automatic non-linear image matching and automatic labeling (ANIMAL) algorithm was used to integrate eachpatient’s MRI scan with the canonical high-resolution MRI (Colin27), resulting in a common space for evaluation of electrode positions fromdifferent patients. A probabilistic average map of active contacts of subthalamic stimulators associated with the best outcome for motorsymptoms of the contralateral side is shown. (C) The voxel-labeled 3D atlas was integrated with the probabilistic volume map of the mosteffective active electrode contacts in patients with Parkinson’s disease with subthalamic stimulator implants. The subthalamic nucleus isrepresented as a net. A 90% probability map of most effective electrode positions shows they are localized in the dorsolateral subthalamicnucleus, and areas dorsal and posterior to the subthalamic nucleus, including the zona incerta, Forel’s fields, and ventral thalamus. (Sadikotet al., 2011 [48].)


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0.0012

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R2* - mineralization

Volume - atrophy MD - microstructural damage

Thalamus

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Red nucleusSubstantia nigra

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FA - anisotropy47

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SN RN Thal Puta Caud Pall SN RN

Figure 1.9 3D reconstruction of subcortical regions considered in this work (top). Volume (mm3), R2* (s�1), mean diffusivity (mm2 s�1), and

fractional anisotropy mean values from whole subcortical structures (bottom). Light bars ¼ controls, darker bars ¼ Parkinson’s disease.*Significant difference. (Peran et al., 2010 [49].)


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Magneticresonanceimaging(MRI)methodsinParkinson sdisease