1H magnetic resonance spectroscopy of autosomal ataxias

Brain Research 1049 (2005) 191 – 202 www.elsevier.com/locate/brainres

Research Report 1

H magnetic resonance spectroscopy of autosomal ataxias Martin Viaua, Luc Marchandb, Ce´line Bardc, Yvan Boulangera,* a

De´partement de radiologie, Hoˆpital Saint-Luc du CHUM, 1058 St-Denis, Montre´al, Que´bec, Canada H2X 3J4 b Service de neurologie, Hoˆtel-Dieu du CHUM, 3840 St-Urbain, Montre´al, Que´bec, Canada H2W 1T8 c De´partement de radiologie, Hoˆtel-Dieu du CHUM, 3840 St-Urbain, Montre´al, Que´bec, Canada H2W 1T8 Accepted 9 May 2005 Available online 15 June 2005

Abstract Multiple forms of autosomal ataxia exist which can be identified by genetic testing. Due to their wide variety, the identification of the appropriate genetic test is difficult but could be aided by magnetic resonance data. In this study, magnetic resonance spectroscopy (MRS) and imaging (MRI) data were recorded for 20 ataxia patients of six different types and compared to 20 normal subjects. Spectra were acquired in the pons, left frontal lobe, left basal ganglia, left cerebellar hemisphere and vermis. Both metabolite spectra and absolute metabolite concentrations were determined. Differences in metabolite levels were observed between ataxia patients and control subjects and between ataxia patients of different types. A number of correlations were found between metabolite ratios, atrophy levels, number of repeats on the small and large allele, age at examination, symptoms duration and age at symptoms onset for ataxia patients. These MR characteristics are expected to be useful for the identification of the ataxia type. D 2005 Elsevier B.V. All rights reserved. Theme: Disorders of the nervous system Topic: Degenerative disease: other Keywords: Ataxia; Magnetic resonance spectroscopy; Magnetic resonance imaging; Cerebral metabolites

1. Introduction A large number of autosomally transmitted ataxias are related to a genetic feature [32] which is the basis for their new classification, replacing symptom-based classifications such as that of Harding which was lacking sensitivity [14]. However, clinical diagnosis remains difficult because of the high variability among the same genotype [7,38] and the common symptoms of different genetic types [1]. These facts, combined with the increasing availability of diagnostic genetic tests for ataxias and the little information gained from a negative genetic test, explain why the positive detection rate remains low [28]. Since the cost of a single genetic test is comparable to that of a magnetic

* Corresponding author. Fax: +1 514 412 7292. E-mail address: [email protected] (Y. Boulanger). URL: http://pages.infinit.net/khiat/ (Y. Boulanger). 0006-8993/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2005.05.015

resonance examination [33], the use of such imaging techniques could prove to be cost-effective for the selection process of the most probable genetic tests for ataxias [37]. Characteristic MRI features have already been identified for most types of ataxia [37]. The main feature, atrophy, is highly variable among individuals of a specific genetic subtype and is often occurring in the same regions for ataxia patients of different types. The most frequently atrophied structures are those of the cerebellar hemispheres, the vermis and the pons. The spinal cord and basal ganglia can also be affected but to a lesser extent. In a number of cases, atrophy is related to the number of trinucleotide repeats or to the disease duration. Nevertheless, specific atrophy patterns were found for a few genetic types which are helpful to differentiate from a number of other possibilities. MRS could possibly show higher specificity to differentiate between particular genetic types of ataxia due to the

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higher number of MRS markers as compared to MRI. MRS showed that Friedreich’s ataxia (FRDA) is characterized by a decreased N-acetylaspartate/creatine ratio (NAA/Cr) in the brainstem and cerebellum hemisphere, while other metabolites were found to be in the normal range [27]. Spinocerebellar ataxia type 2 (SCA2) is characterized by decreased NAA/Cr and choline/creatine ratios (Cho/Cr) in the cerebellar hemisphere and vermis [2]. The presence of lactate in the vermis, but not in the pons, and in the cerebellar hemisphere is helpful to distinguish between SCA2 and spinocerebellar ataxia type 6 (SCA6) [2,27]. The NAA/Cr and Cho/Cr ratios were found to be reduced in the cerebellar hemisphere without reaching the significance threshold [12]. SCA6 showed a decrease of NAA/Cr in the cerebellum structures, however, less pronounced than for SCA2, without other metabolic abnormalities [2]. To our knowledge, no MRS study exists for autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) and spinocerebellar ataxia type 8 (SCA8). In this study, brain MRI and MRS data were acquired for patients with six different types of autosomal ataxias and control subjects. Comparisons of MR parameters were made between patients and control subjects and between patients with different types of ataxias. Correlations between MR parameters and clinical or genetic data were also examined. These analyses were performed in order to assess the possibility of using such parameters to guide the selection of the appropriate genetic test and to gain further insight into the underlying pathologies.

2. Materials and methods 2.1. Patients and normal subjects Twenty ataxia patients (6 females, 14 males; 21– 61 years) whose ataxia type was established by genetic testing (Table 1) were recruited by Dr. Luc Marchand, Hoˆtel-Dieu of the Centre Hospitalier de l’Universite´ de Montre´al (CHUM). They comprised 4 patients with ARSACS, 4 patients with FRDA, 8 patients with SCA2, 1 patient with SCA3, 2 patients with SCA6 and 1 patient with SCA8. Patients with a history of alcohol abuse, vitamin E or B12 deficiency, neoplasia or gluten sensitivity were excluded. Twenty normal subjects (5 females, 15 males; 20 –47 years) not suffering of any neurological or psychiatric disease were used as controls. All patients signed an informed consent and were evaluated in accordance with a protocol approved by the Scientific and Ethics Committees of the CHUM. 2.2. Genetic testing and age of onset Genetic data (ataxia type, number of repetitions of nucleotidic triplets and inheritance pattern) were obtained from genetic tests using standard protocols. The age of apparition of ataxia symptoms was obtained from patient records (Table 1). 2.3. MR data acquisition All 1H magnetic resonance experiments were performed on a GE Signa 1.5 T whole-body scanner with spectroscopic

Table 1 Clinical and genetic data for ataxia patients Patient number

Diagnosis

Age (y)/sex (F/M)

Onset of symptoms (decade)

Parental transmission (F/M)/onset of symptoms (decade)

No. of allelic repeats (small/large)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

ARSACS ARSACS ARSACS ARSACS FRDA FRDA FRDA FRDA SCA2 SCA2 SCA2 SCA2 SCA2 SCA2 SCA2 SCA2 SCA3 SCA6 SCA6 SCA8

37/F 41/M 43/M 48/F 21/M 45/F 46/M 61/M 37/M 38/M 38/M 48/M 50/M 52/F 55/F 60/M 29/M 37/M 48/F 31/M

Early 2nd Early 2nd Late 1st Late 1st Early 2nd Early 2nd Mid 2nd Mid 2nd Mid 2nd Late 2nd Mid 3rd Late 4th Late 5th Mid 4th Junction 3rd – 4th Late 3rd Late 2nd Late 2nd Mid 2nd Junction 1st – 2nd

– – – – – – – – F M/junction 4th – 5th M/late 3rd N/A F/late 5th N/A N/A M M F F M

– – – – 540/920 490/690 390/890 620/1120 21/39 21/35 21/38 21/37 21/34 21/39 21/37 N/A 14/79 12/25 12/26 22/190

N/A, not available.

M. Viau et al. / Brain Research 1049 (2005) 191 – 202

capabilities operating at 63.85 MHz (GE Medical Systems, Milwaukee, WI) using the GE 1H headcoil. In order to localize the regions of interest (ROI), a rapid magnetic resonance imaging (MRI) examination of the brain was first performed where axial, coronal and sagittal slices were obtained using the T2 fast spin echo method. In the axial and coronal dimensions, 17 slices were recorded with a repetition time (TR) of 4000 ms, an echo time (TE) of 90 ms and two acquisitions for a total time of 70 s. In the sagittal dimension, 13 slices were obtained with a TR of 5400 ms, a TE of 90 ms and a single acquisition for a total time of 72 s. In all experiments, the matrix size was 256
192, and the slice thickness was 5 mm with a gap of 2.5 mm between slices. Proton magnetic resonance spectra (MRS) were recorded on left – right (LR)
anterior –posterior (AP)
inferior – superior (IS) regions of interest (ROIs) localized in the pons (10
20
20 mm3), left basal ganglia (16
20
27 mm3), left frontal lobe (20
20
20 mm3), left cerebellar hemisphere (20
20
10 mm3) and vermis (15
24.5
17.5 mm3), as defined on the MR images. The locations of the selected ROIs are illustrated in Fig. 1. The excitation was performed using the GE PROBE (proton brain exam) protocol with the PRESS (point-resolved spectroscopy) pulse sequence [3]. Acquisition parameters were: TR, 1500 ms; TE, 30 ms, 135 ms; number of acquisitions, 128; spectral width, 2000 Hz; number of points, 1024; total acquisition time per ROI, 4.36 min. For absolute quantification, in vitro T1 (T1vitro) and T2 (T2vitro) values were determined on the GE proton spectroscopic phantom [31] on a single 20
20
20 mm3 ROI at 20 -C. T1vitro was determined from five progressive saturation experiments [16] using magnetic resonance spectroscopic imaging (MRSI) with the stimulated echo acquisition mode pulse sequence (STEAM) [15]. Acquisition parameters were: TE, 30 ms; mixing time TM, 13.7 ms; spectral width, 8000 Hz; number of points, 2048; TR, 593, 650, 700, 800, 900, 1150, 1500, 1900, 2400, 3000, 3800, 5000, 6000 ms; numbers of acquisitions were 1024 for TR < 1000 ms, 512 for TR between 1000 and 2000 ms, 256 for TR = 2400 and 3000 ms and 128 for TR > 3000 ms. The total acquisition time per ROI varied from 9.1 min to 17.2 min. T2vitro was determined from five Hahn-type SE experiments using MRSI with the PRESS pulse sequence. Acquisition parameters were: TE, 30, 60, 100, 150, 200, 400, 600, 800 ms; spectral width, 2500 Hz; number of points, 2048; TR, 2000 ms; numbers of acquisitions were 128 for TE < 400 ms, 256 for TE = 400 ms and 512 for TE > 400 ms. The total acquisition time per ROI varied from 2.0 min to 18.0 min. 2.4. MR data analysis 2.4.1. Metabolite quantification After completion of the experiment, free induction decays (FIDs) were transferred to a Silicon Graphics Octane

193

workstation (SGI, Mountain View, CA) and processed with the LCModel software, version 6.1-0 (S. Provencher, Oakville, ON, Canada) [29]. The appropriate basis-sets were used with correction for Eddy current effects. Standard parameters were used for in vivo data with a line broadening of 1 Hz, while the in vitro data were processed with edited control parameters, and only the basis-sets corresponding to the phantom composition were used in LCModel. The following metabolite signals were quantified with a TE = 30 ms: mI = myo-inositol, Cho = choline-containing compounds, Cr = creatine + phosphocreatine, Glx = glutamine + glutamate and NAA = N-acetylaspartate with a minor contribution from N-acetylaspartylglutamate. Spectra acquired with TE = 135 ms were only used in vivo to assess the presence of lactate. In some cases, spectra were too broad to allow an accurate signal quantification and were therefore not included in the results. 2.4.2. In vitro relaxation time determination In vitro T1 values were determined using the equation derived for progressive saturation for the STEAM pulse sequence: h i 1 SZ ðTRÞ ¼  S0 eTM=T1 1  eTD=T 1 2

ð1Þ

where S Z(TR) is the signal for a specific TR, S 0 the signal at equilibrium magnetization and TD = TR  TE/2  TM [24]. The correction for the transverse relaxation (T2) process was unnecessary because the TE value was sufficiently small relative to T2. S Z(TR) for each metabolite was plotted as a function of the time delay (TD), and the profiles were fitted using a two-parameter Levenberg– Marquardt non-linear regression using Microcal Origin 5.0 (Microcal Software, Northampton, MA). In order to probe the exponential nature of T1, the approximated monoexponential fit and the exact biexponential fit of the data sets were used. The exact procedure was retained because of a better fit of data points. In vitro T2 values were determined using the equation derived for exponential decay for the PRESS pulse sequence: h  SZ ðTEÞ ¼ S0 1  ð1  f Þet2=T 1  f ð1  f ÞeðTEt1Þ=T 1  i þ f 2 eTE=T1 eðTRTEÞ=T 1 eTE=T2 ð2Þ where S Z(TE) is the signal for a specific TE, S 0 the signal at equilibrium magnetization, t1 the time between the 90pulse to the first 180- pulse (t1 = 7936 As for TE = 30 ms and 9552 As for TE > 40 ms), t2 the time between the first echo to the second 180- pulse and TE = 2t1 + 2t2 [23]. A correction for partial saturation was applied because the TR value was not sufficiently long relative to T1. It was assumed that refocusing pulses were perfect ( f = 1) [30] and signal loss from diffusive motion outside the ROI while increasing TE was neglected. S Z(TE) for each metabolite

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Fig. 1. Voxel locations in each plane for magnetic resonance spectroscopy acquisitions in the (A) pons, (B) basal ganglia, (C) frontal lobe, (D) cerebellar hemisphere and (E) vermis.

was plotted as a function of the TE, and the profiles were fitted using a one-parameter mono-exponential decay using Microcal Origin 5.0. 2.4.3. Longitudinal and transverse relaxation correction The average previously published in vivo T1 and T2 values (T1vivo, T2vivo) and the in vitro T1 and T2 values

measured in this study (T1vitro, T2vitro) were used to deterQ mine the in vivo and in vitro relaxation correction factors, S Z(T1vivo, T2vivo) and S Z(T1vitro, T2vitro), for each metabolite in each brain region (Table 2). Eq. (2) was used with t1 = 7936 As and assuming that refocusing pulses were perfect. Relaxation effect variations associated with the temperature difference between phantom and human sub-

M. Viau et al. / Brain Research 1049 (2005) 191 – 202 Table 2 In vivo and in vitro longitudinal and transverse relaxation times (ms) used to calculate relaxation correction factors (S z (T1, T2)) of MRS metabolitesa Region Pons

T1 T2

Basal ganglia

T1 T2

Frontal

T1 T2

Cerebellar hemisphere

T1 T2 T1

Vermis

T2 Phantom

T1 T2

a b c d

Cho

Cr

mI

NAA

References

1115 294 (50) 1400 (500) 284 (23) 1340 (80) 239 (4) 1220 (160) 307 1500 (150) 374 (77) 280 (60) 179 (10)

1465 185 (34) 1300 (400) 183 (17) 1710 (110) 169 (3)

1035

1330 330 (46) 1300 (300) 272 (22) 1590 (100) 300 (8)

[5]b [26]

1330 (180) 274 1500 (150) 217 (26) 530 (80) 271 (14)

900 (90)c

1370 (130)

1250 (180) 1850 (185)

430 (100)

1420 (150) 383 1700 (170) 341 (82) 600 (110) 412 (16)

195

where the in vitro metabolite concentration (C vitro) is corrected for the difference in MRS signal (S vivo /S vitro), in ROI volume (V vitro/(V vivo(1  f CSF))), in temperature (T vivo/ T vitro) and in T1 and T2 relaxation (S Z(T1vitro , T2vitro )/ S Z(T1vivo , T2vivo )) [11]. 2.5. Statistical analysis

[8] [35]d [5] [6] [13] [19] [15] [26] This work This work

Standard deviation values are given in parentheses. Average T1 value from parietal white and occipital gray matter. T1 value from occipital gray matter [5]. Linear correction applied for static magnetic field difference.

Group homogeneities were evaluated with one-way ANOVA, followed by Tukey’s contrasts if necessary. Metabolite ratios were subjected to ANOVAs with repeated measures on the factor brain region (R) with five levels (pons, basal ganglia, frontal lobe, cerebellar hemisphere, vermis) and with non-repeated factor diagnosis (D) with five levels (controls, ARSACS, FRDA, SCA2, SCA6). In cases where sphericity was not assumed (Mauchly’s test), the Greenhouse – Geisser adjustment was used. Because no interaction (R * D) was found, t tests with a Bonferroni correction were used on R and Tukey contrasts were used on D when necessary. The heterogeneity of two diagnostic groups (D) for the age at examination (A) was assessed by ANCOVA. Absolute metabolite comparisons on the factor D were made using a MANOVA followed by ANOVAs and Tukey contrasts. Pearson correlations were evaluated

jects and variations between normal and ataxic human brain were neglected. 2.4.4. Segmentation Voxel localization on T2-weighted images was done on an HP Advantage Workstation 4.1 using the volume viewer application (GE Medical Systems, Milwaukee, WI). The fields of view (FOVs) for all images were normalized to 512
1024. Marked images for VOI locations were transferred to a SUN workstation (Sun Microsystems, Palo Alto, CA) and using MEDx 3.4.1 software (Sensor Systems, Sterling, VA), voxels were reconstituted followed by a segmentation using a threshold fixed at 550 over 1024 shades of gray (Fig. 2). A histogram of pixel intensity from the segmented voxel allows pixel quantification under and above the threshold reflecting the relative amount of brain tissue and cerebrospinal fluid (CSF). The CSF fraction in each voxel (f CSF) was used to correct absolute metabolite concentrations for the partial volume effect due to the CSF [40]. 2.4.5. Absolute concentration determinations Absolute metabolite concentrations in vivo (C vivo) were calculated using the following equation: Cvivo ¼ Cvitro



Svivo Vvitro Tvivo

Svitro Vvivo
ð1  fCSF Þ Tvitro

SZ ðT 1vitro ; T 2vitro Þ SZ ðT 1vivo ; T 2vivo Þ

ð3Þ

Fig. 2. Representation of the segmentation threshold for (A) a sagittal plane of the pons and the vermis and (B) an axial plane of the frontal voxel.

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Table 3 Average metabolite ratios within each brain region for control subjects and ataxia patients Region

Metabolite ratio

Controls (n = 20)

ARSACS (n = 4)

FRDA (n = 4)

SCA2 (n = 8)

SCA3 (n = 1)

SCA6 (n = 2)

SCA8 (n = 1)

Pons

Cho/Cr Glx/Cr mI/Cr NAA/Cr Cho/Cr Glx/Cr mI/Cr NAA/Cr Cho/Cr Glx/Cr mI/Cr NAA/Cr Cho/Cr Glx/Cr mI/Cr NAA/Cr Cho/Cr Glx/Cr mI/Cr NAA/Cr

0.60 2.64 1.24 2.38 0.26 2.25 0.65 1.05 0.33 2.28 0.82 1.53 0.28 1.65 0.74 1.15 0.25 1.50 0.67 0.96

0.50 2.70 1.29 2.03 0.50 2.28 1.14 0.87 0.34 1.51 1.11 1.23 0.25 1.31 0.69 0.79 0.23 1.33 0.80 0.84

0.53 2.04 1.47 1.75 0.33 1.14 2.04 1.20 0.33 2.27 1.22 1.47 0.26 1.74 0.82 1.21 0.24 1.81 0.76 0.82

0.47 2.09 1.62 1.41 0.32 2.25 0.98 1.07 0.34 2.03 0.98 1.32 0.21 1.26 0.80 0.58 0.21 1.29 0.74 0.65

0.55 2.09 1.54 1.47 0.22 2.02 0.39 1.07 0.35 2.64 0.84 1.68 0.23 1.44 0.68 0.78 0.23 1.67 0.62 0.63

0.57 2.34 1.32 1.86 0.27 2.28 0.73 1.15 0.35 2.01 0.89 1.47 0.29 1.011 0.72 0.75 0.24 1.001 0.83 0.75

0.41 1.78 1.31 1.75 0.13 1.76 0.29 0.91 0.36 2.11 0.91 1.31 0.27 1.18 1.67 0.66 0.24 1.62 1.14 0.59

Basal ganglia

Frontal lobe

Cerebellar hemisphere

Vermis

(0.20) (0.73) (0.43) (0.79) (0.04)1 (0.58)6 (0.33)2 (0.40) (0.06) (0.42) (0.11) (0.19) (0.05) (0.32)3 (0.14) (0.27) (0.04) (0.30) (0.11) (0.27)

(0.09) (1.28) (0.35) (0.57) (0.41) (0.54)2 (0.27)1 (0.60) (0.03) (0.27) (0.23) (0.05) (0.05) (0.20) (0.04) (0.06) (0.02) (0.12) (0.17) (0.09)

(0.16) (0.46)1 (0.30) (0.58) (0.05) (0.87) (0.30) (0.77) (0.07) (0.52)2 (0.49) (0.24)1 (0.02) (0.45) (0.18) (0.46) (0.04) (0.36) (0.14) (0.08)

(0.08) (0.32) (0.52) (0.28) (0.07)1 (0.50)3 (0.37)2 (0.54) (0.05) (0.34) (0.30) (0.15) (0.06) (0.31)1 (0.20) (0.17) (0.04) (0.17) (0.09) (0.09)

Standard deviation values are given in parentheses, the number of missing observations is indicated in exponent.

between independent parameters. Statistical significance was set at P < 0.05.

3. Results A homogeneity analysis was performed on the different diagnostic groups used in this study. Control subjects were found to be younger (F D(4,33) = 12.54; P < 0.001) relative to ARSACS (P = 0.008), FRDA (P = 0.005) and SCA2

patients (P < 0.001) at the moment of examination. Patient groups were homogeneous in age and duration of symptoms at examination (F D(3,14) = 1.08; P = 0.4). The metabolite ratios Cho/Cr, Glx/Cr, mI/Cr and NAA/ Cr and absolute concentrations of Cho, Cr, mI and NAA in five cerebral regions of control subjects and ataxia patients are presented in Tables 3 and 4, respectively. Many regional within-factors metabolite ratio differences were found for control subjects and ataxia patients, as presented in Fig. 3. Significant differences were found

Table 4 Average absolute metabolite concentrations (mM) in each brain region for each group of ataxia patients Region

Metabolites

Controls (n = 20)

ARSACS (n = 4)

FRDA (n = 4)

SCA2 (n = 8)

SCA3 (n = 1)

SCA6 (n = 2)

SCA8 (n = 1)

Pons

[Cho] [Cr] [mI] [NAA] [Cho] [Cr] [mI] [NAA] [Cho] [Cr] [mI] [NAA] [Cho] [Cr] [mI] [NAA] [Cho] [Cr] [mI] [NAA]

4.12 11.13 9.87 19.72 1.45 6.18 3.24 6.79 1.67 6.92 4.81 9.43 4.27 18.12 13.19 20.65 3.16 14.98 11.30 14.67

4.40 13.27 11.94 20.22 1.81 5.02 4.43 6.46 2.01 7.92 7.35 8.72 4.89 23.01 15.88 18.47 3.27 16.22 15.37 14.15

3.85 12.07 12.40 17.50 1.77 6.17 4.35 6.72 1.47 6.05 6.10 7.45 3.41 15.64 12.06 17.52 2.06 9.91 8.72 8.42

4.36 14.25 16.87 15.72 1.72 4.93 4.06 5.13 1.74 6.98 5.65 8.13 3.72 20.98 16.85 12.22 2.99 17.17 14.72 11.70

3.88 10.61 12.17 12.56 1.22 6.27 2.05 6.48 1.88 7.17 5.14 10.81 4.66 23.33 15.86 18.50 2.91 14.68 10.51 9.65

5.01 13.15 13.04 19.88 1.16 4.87 2.97 5.16 1.78 6.76 5.12 8.93 3.92 16.03 11.61 12.04 2.61 12.69 12.23 9.81

3.45 12.49 12.22 17.64 0.84 7.44 1.84 6.56 1.65 6.28 4.83 7.34 3.15 13.58 22.77 9.17 2.24 11.06 14.56 6.75

Basal ganglia

Frontal lobe

Cerebellar hemisphere

Vermis

(0.86) (3.71) (3.23) (4.04) (0.40)1 (1.11)1 (1.26)2 (1.09)2 (0.29) (0.87) (0.64) (1.29) (0.81) (3.71) (2.62) (4.33) (0.54) (3.79) (2.18) (4.53)

(1.54) (4.64) (3.55) (2.82) (0.68) (1.66) (1.09)1 (1.60)1 (0.22) (0.80) (0.79) (1.20) (0.90) (1.35) (1.61) (1.87) (1.06) (4.06) (7.18) (3.38)

(0.73) (5.48) (2.92) (5.13) (0.56) (2.02) (1.76) (2.22) (0.21) (0.80) (2.03) (1.42) (1.16) (5.64) (1.27) (1.37) (0.92) (3.68) (3.90) (3.30)

(0.48) (2.72) (4.95) (1.45) (1.16) (1.57)1 (0.79)2 (1.29) (0.25) (1.41) (1.12) (1.06) (0.91) (2.75) (4.17) (3.39) (0.26) (4.94) (5.39) (4.74)

Standard deviation values are given in parentheses, the number of missing observations is indicated in exponent.

M. Viau et al. / Brain Research 1049 (2005) 191 – 202

197

Fig. 3. Comparison of within-factors metabolite ratios between the pons, basal ganglia, frontal lobe, cerebellar hemisphere and vermis of control subjects and ataxia patients, irrespective of their diagnostic. Statistically significant differences are indicated by arrows and P values.

for Cho/Cr (F R(2.19,67.85) = 25.09; P < 0.001), Glx/Cr (F R(2.23,31.21) = 9.15; P = 0.01), mI/Cr (F R(2.34,65.47) = 25.59; P < 0.001) and NAA/Cr (F R(1.79,49.97) = 23.65; P < 0.001), and these differences were irrespective of their diagnostic group because no interaction was present (F R * D > 0.05). Between-factors metabolite ratio comparisons showed no differences of Cho/Cr (F D(4,31) = 1.46; P = 0.24) and Glx/Cr (F D(4.14) = 1.13; P = 0.36) in any of the examined regions. Differences observed for the other metabolite ratios are presented in Fig. 4. The mI/Cr ratio was increased (F D(4,28) = 5.35; P = 0.002) in all regions of SCA2 patients relative to control subjects. The NAA/Cr ratio was decreased (F D(4,28) = 8.62; P < 0.001) in all regions for SCA2 patients relative to control subjects with the exception of the cerebellar hemisphere. The variation of NAA/Cr ratio in the cerebellar hemisphere was controlled for the heterogeneity of the age at examination between the control and SCA2 groups. A covariance analysis was used because no difference was found between the two correlations (FA * D(1,24) = 1.46; P = 0.24). For an age of 31.9 years (FA(1,25) = 3.78; P = 0.063), the estimated mean of NAA/Cr for the control group was 1.07,

and the estimated mean for the SCA2 group was 0.77. Therefore, the difference in NAA/Cr in the cerebellar hemisphere between the control and SCA2 groups was not significant (F D(1,25) = 3.09; P = 0.091). No lactate was present in any region as assessed from spectra acquired with TE = 135 ms. Atrophy levels (f CSF) within the studied voxels used to compute absolute concentrations are presented in Fig. 5. Between-factors atrophy levels were different in the pons (F R(4,33) = 8.04; P < 0.001) and in the vermis (F R(4,33) = 6.95; P < 0.001). Absolute concentrations of Cho, Cr, mI and NAA are presented in Table 4. Between-factors absolute concentration differences are presented in Fig. 4. [Cho] was decreased in the vermis (F D(4,33) = 3.16; P = 0.026) of FRDA patients relative to control subjects. [Cr] was decreased in the cerebellar hemisphere of FRDA patients (F D(4,33) = 3.42; P = 0.019) relative to ARSACS patients. [mI] was increased in the pons (F D(4,33) = 5.26; P = 0.002) and in the cerebellar hemisphere (F D(4,33) = 3.66; P = 0.014) in SCA2 patients respective to control subjects. [mI] was also increased in the frontal lobe

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M. Viau et al. / Brain Research 1049 (2005) 191 – 202

Fig. 4. Comparison of between-factors metabolite ratios for control subjects and different types of ataxia patients. Statistically significant differences are indicated by arrows and P values.

(F D(4,33) = 6.53; P = 0.001) of ARSACS patients relative to control subjects. [NAA] was decreased in the frontal lobe (F D(4,33) = 3.05; P = 0.031) of FRDA patients relative to controls. [NAA] was decreased in the cerebellar hemisphere (F D(4,33) = 8.29; P < 0.001) of SCA2 and SCA6 patients compared to controls.

A number of statistically significant Pearson correlations were found for control subjects, SCA2, ARSACS and FRDA patients. Table 5 presents the within-regions correlations for ataxia patients involving metabolite ratios, atrophy levels (f CSF) and parameters such as the age at examination or at the onset of symptoms, the duration of

Fig. 5. Comparison of atrophy levels ( f CSF) in voxels of the pons (P), frontal lobe (FL), cerebellar hemisphere (CH) and vermis (V) between control subjects and ataxia patients of different types. Statistically significant differences are indicated by arrows and P values. The mean (g), minimum and maximum ( ), 25th and 75th percentile values are illustrated.

M. Viau et al. / Brain Research 1049 (2005) 191 – 202 Table 5 Correlations between measured parameters in ataxia patients Region

Parameter 1

Parameter 2

SCA2 Basal ganglia

Cho/Cr

Vermis

[mI] NAA/Cr mI/Cr

Age at onset of symptoms Age at examination Glx/Cr NAA/Cr NAA/Cr mI/Cr NAA/Cr NAA/Cr Symptoms duration Symptoms duration NAA/Cr No. of repeats on large allele Symptoms duration No. of repeats on large allele Age at examination Symptoms duration Atrophy ( f CSF)

ARSACS Basal ganglia Frontal lobe Pons

[mI] Glx/Cr mI/Cr

FRDA Basal ganglia

[Cho]

NAA/Cr Cerebellar hemisphere Frontal lobe

Pons

Cho/Cr Cho/Cr Glx/Cr Cho/Cr Cho/Cr mI/Cr [Cho] [NAA] Cho/Cr Cho/Cr Cho/Cr [Cho]

Glx/Cr Pons

Vermis

[Cho] [Cho] [Cr] [NAA] [NAA] Glx/Cr

r

P

n

0.765

0.045

7

0.784

0.037

7

0.899 0.779 0.893 0.792 0.838 0.826 0.709 0.769 0.841 0.759

0.006 0.023 0.007 0.019 0.009 0.011 0.048 0.025 0.009 0.048

7 8 7 8 8 8 8 8 8 7

0.799 0.733

0.017 0.042

8 7

0.750 0.724 0.810

0.032 0.042 0.015

8 8 8

Symptoms duration Atrophy ( f CSF) Atrophy ( f CSF)

0.998 1 0.999

0.037 <0.001 <0.001

3 3 4

No. of repeats on large allele No. of repeats on small allele Age at examination Symptoms duration No. of repeats on large allele Age at examination Symptoms duration Atrophy ( f CSF)

0.984

0.016

4

0.983

0.017

4

0.989 0.978 0.966

0.011 0.022 0.033

4 4 4

0.991 0.982 0.998

0.009 0.018 <0.001

4 4 4

symptoms and the number of repeats on the large and small alleles.

4. Discussion In this study, metabolite ratios and absolute metabolite concentrations were determined by MRS in five brain and cerebellar regions for 20 ataxia patients belonging to six different subtypes and compared to a group of control subjects. A number of within- and between-factors differences were found between brain regions and diagnostic groups, and a statistical analysis of the differences was performed for groups with more than one patient. Pearson correlations were done in groups of four or more subjects. In groups with only one patient, SCA3 and SCA8, only qualitative analyses could be performed. The low number of patients is attributable to the low prevalence of these

199

diseases and the difficulty to convince such patients to participate given their condition. Indeed, most previously reported studies on ataxic patients were performed on smaller numbers of patients [37]. The control group was younger than the ARSACS, FRDA and SCA2 patient groups. Nevertheless, the comparison between these groups of different ages remains valid since metabolic profiles of normal brains are known to remain constant between 16 to 60 years [10]. Our data showed no correlation between metabolite values and age at examination in the control group except for NAA/Cr in the cerebellar hemisphere. The Cho/Cr and NAA/Cr ratios were higher in the pons in comparison to the cerebellar hemisphere and vermis (Fig. 3), as previously reported [26]. Our data agree with a reported decrease of NAA/Cr in the vermis for SCA2 patients, but they did not show any Cho/Cr differences in SCA2 patients compared to control subjects and SCA6 patients nor a presence of lactate for SCA2 patients that could permit a differentiation from SCA6 [2]. The NAA/Cr ratio was not decreased in the pons and cerebellar hemisphere in FRDA patients in disagreement with a previous report [27]. The reported absolute concentration ranges for Cho, Cr and NAA in normal humans were, respectively, 2.2 – 7.7 mM, 4.3 –13.7 mM and 8.3 –23.8 mM for the pons, 3.1– 6.0 mM, 6.7 –20.4 mM and 12.6 –32.6 mM for basal ganglia, 1.4 – 4.7 mM, 5.0– 15.6 mM and 7.7 – 24.4 mM for the frontal lobe, 1.4– 2.5 mM, 5.8 –9.1 mM and 6.5 –11.0 mM for the cerebellar hemisphere and 1.8– 3.0 mM, 6.8 – 13.3 mM and 7.3 –14.3 mM for the vermis [9,21,22,26,34]. Our metabolite concentrations are not within the reported range for the basal ganglia, the cerebellar hemisphere and the vermis. When our metabolite concentrations differ from those previously published, all metabolites in that region differ. This may be caused by methodological differences between our study and the reported studies. First, some studies did not take into account the partial volume effect due to CSF within voxels, and this can lead to significantly lower metabolite concentrations. Second, differences may be caused by the different in vivo relaxation time values used to compute absolute concentrations. Some studies measured the in vivo relaxation times based on the signal of a particular peak [8,15], while others measured an average relaxation time for all resonances of the metabolite [5,13]. Finally, calibration methods differed, and relaxation processes were not always taken into account. Due to these methodological differences, comparisons of absolute metabolite concentrations in specific brain regions were not performed. Although a direct comparison with reported absolute metabolite concentrations is not possible, the variations of these concentrations between SCA2 patients and control subjects can be compared. Our measured variations agreed with those published except for [Cr] for which we found an increase in the pons and cerebellar hemisphere, while decreases of these concentrations were reported [18]. Metabolite ratios are more robust data than absolute concentrations because they do not depend on other

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parameters. However, absolute concentrations give supplementary information and avoid problems of ratio normalization, in which a change in the denominator without a change in the numerator may result in an artefactual group difference [25] or, in the opposite situation, could mask a group difference. Absolute quantification allowed us to investigate whether a change in metabolite ratio is due to a variation of the numerator, a variation of the denominator or both. For example, in the current study, the decreased [Cho] metabolite concentrations in vermis in FRDA patients relative to controls subjects were evident from absolute concentrations but not from ratios over Cr because [Cr] was also decreased. Findings of this type demonstrate the appropriateness of absolute concentration measurements. All metabolite quantifications (ratios and absolute concentrations) were done using LCModel software which takes into account the varying acquisition conditions (TG, R1 and R2) by scaling each data set by a factor inversely proportional to the total gain for a given ROI. No correction was applied for RF coil inhomogeneities. Absolute metabolite quantification required partial volume effect corrections since ataxias are characterized by significant atrophy, the CSF volume in the ROI being too important to be neglected. A systematic error could arise from the choice of the threshold which is user-dependent. Other errors could be caused by B0 inhomogeneity and associated image distortions. Based on our experience, we believe that these errors are relatively small. Corrections for the incomplete longitudinal relaxation (T1) and for signal loss from transverse relaxation (T2) were necessary to evaluate absolute metabolite concentrations. The longitudinal correction could have been neglected if sufficiently long TR values (when TR > 5T1) had been used. However, this would have required much longer examination times which would have been unacceptable for patients. The transverse correction also could have been neglected at TE = 30 ms, but since we worked with a phantom containing GdDTPA (Magnevist), T2vivo and T2vitro were too different (Table 2) to neglect this correction. In order to apply these corrections, we chose the calibration phantom method because a well established procedure existed for 1.5 T GE data [31,40]. However, the external referencing method relies on the homogeneity of the static magnetic field at the position of the measured volume [11]. The in vitro relaxation times are subject to systematic errors due to ignoring the transverse relaxation and from the diffusion signal loss, for T1vitro and T2vitro respectively, from the assumption they remain constant from 20 -C to 37 -C and from the choice of the exponential model used. They are also associated with a significant variance that is typical of spectroscopic relaxation measurements (Table 2). Our T2vitro obtained at 1.5 T are following the same pattern (Cho: 183 ms > Cr: 259 ms > NAA: 452 ms) and are of similar magnitude as those obtained at 3.0 T [20]. Relaxation time variations between normal subjects and ataxic patients were neglected because of insufficient data

for each ataxia type and the studied regions. Reported T2 values in the pons and cerebellar hemisphere of SCA2 patients are unchanged relative to control subjects, while for FRDA patients, T2 values are unchanged and decreased in the basal ganglia and dentate nucleus respectively [18,39]. T1 measurements in ataxic patients could not have been performed in a reasonable time, and two point T2 determinations from our measures at TE = 30 ms and TE = 135 ms were not sufficiently reliable to be used. Moreover, as these errors can be expected to be similar for all patients, differences in metabolite levels remain valid. The atrophy levels are related to the fraction of CSF (f CSF) within the examined ROI, which implies that the atrophy found is not the atrophy of the entire brain region. The fact that ROI localization procedures is manual implies that they are prone to inter-subject variations. Nevertheless, considering the fact it is a standardized procedure based on 3D ROI representation (such as in Fig. 1), the relation between f CSF and atrophy for the studied region remains valid. The severe atrophy of the pons (Fig. 5), characteristic of SCA2 patients, is observed, while the reported vermis atrophy of ARSACS and SCA6 patients is also significant [37]. Pearson correlations (Table 5) could be informative of the underlying physiopathology of the studied ataxia. In SCA2 patients, the atrophy is correlated positively with mI/ Cr and negatively with NAA/Cr. These correlations, combined to increased mI/Cr and decreased NAA/Cr in the studied regions for SCA2 patients, are suggestive of gliosis since mI and Cr concentrations are higher in astrocytes than in neurons [4,17,36] and NAA is a neuronal marker. The correlations are also useful to investigate whether a difference between two groups that are inhomogeneous for one parameter is caused by that same parameter. In our case, a decrease of NAA/Cr was found in the cerebellar hemisphere of SCA2 patients relative to control subjects while displaying an inverse correlation with the age at examination in control subjects. Since the control group was younger than the SCA2 group, it is possible that the decreased NAA/Cr in the cerebellar hemisphere is not related to the pathology but to the age difference between the two groups at examination. In order to probe this possibility, we re-examined the data using a covariance analysis and found no difference between the two groups when controlled for the age at examination. Although the number of patients in each ataxia group was very small, the results of this study suggest that MRI and MRS could be useful to characterize some types of ataxias. MRS provides parameters susceptible to orient the diagnosis of ataxia, while the correlations among these parameters and the underlying pathology remain to be explored.

Acknowledgments This work was funded by the Canadian Institutes of Health Research (CIHR), Canada’s Research-Based Phar-

M. Viau et al. / Brain Research 1049 (2005) 191 – 202

maceutical Companies — Health Research Foundation (Rx&D-HRF) (M.V.) and the Radiology Department of the Universite´ de Montre´al. Miguel Chagnon, M.Sc., from the Department of Mathematics and Statistics of the Universite´ de Montre´al and the MR research team of the Hoˆpital SaintLuc are gratefully acknowledged.

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