Functional Magnetic Resonance Imaging Correlates of Fatigue in Multiple Sclerosis

NeuroImage 15, 559 –567 (2002) doi:10.1006/nimg.2001.1011, available online at http://www.idealibrary.com on

Functional Magnetic Resonance Imaging Correlates of Fatigue in Multiple Sclerosis M. Filippi, M. A. Rocca, B. Colombo,* A. Falini,† M. Codella, G. Scotti,† and G. Comi* Neuroimaging Research Unit, Department of Neuroscience, *Department of Neurology, and †Department of Neuroradiology, Scientific Institute and University Ospedale San Raffaele, 20132 Milan, Italy Received July 30, 2001; published online January 22, 2002

Although fatigue is a common and troublesome symptom of multiple sclerosis (MS), its pathogenesis is poorly understood. In this study, we used functional magnetic resonance imaging (fMRI) to test whether a different pattern of movement-associated cortical and subcortical activations might contribute to the development of fatigue in patients with MS. We obtained fMRI during the execution of a simple motor task with completely normally functioning hands from 15 MS patients with fatigue (F), 14 MS patients without fatigue (NF), and 15 sex- and age-matched healthy volunteers. F and NF MS patients were also matched for major clinical and MRI variables. FMRI data were analyzed using statistical parametric mapping. In all patients, severity of fatigue was rated using the Fatigue Severity Scale (FSS). Compared to healthy subjects, MS patients showed more significant activations of the contralateral primary somatomotor cortex, the contralateral ascending limb of the Sylvian fissure, the contralateral intraparietal sulcus (IPS), the contralateral supplementary motor area, and the ipsilateral and contralateral cingulate motor area (CMA). Compared to F MS patients, NF patients showed more significant activations of the ipsilateral cerebellar hemisphere, the ipsilateral rolandic operculum, the ipsilateral precuneus, the contralateral thalamus, and the contralateral middle frontal gyrus. In contrast, F MS patients had a more significant activation of the contralateral CMA. Significant inverse correlations were found between FSS scores and relative activations of the contralateral IPS (r ⴝ ⴚ0.63), ipsilateral rolandic operculum (r ⴝ ⴚ0.61), and thalamus (r ⴝ ⴚ0.62). This study provides additional evidence that fatigue in MS is related to impaired interactions between functionally related cortical and subcortical areas. It also suggests that fMRI might be a valuable tool to monitor the efficacy of treatment aimed at reducing MS-related fatigue. ©

2002 Elsevier Science (USA)

INTRODUCTION Although fatigue is a relatively common and troublesome symptom of multiple sclerosis (MS) (Krupp et al.,

1988), its pathophysiology has not been fully elucidated yet. Nevertheless, electrophysiological (Sheean et al., 1997; Leocani et al., 2001) and positron emission tomography (PET) (Roelcke et al., 1997) studies have provided converging evidence of a central origin of fatigue in MS. Using PET and [ 18F]fluorodeoxyglucose, Roelcke et al. (1997) have shown reduced glucose metabolism in the frontal lobe and basal ganglia of MS patients with fatigue. Using transcranial magnetic stimulation (Sheean et al., 1997) and EEG-based technology (Leocani et al., 2001), additional evidence for frontal lobe cortical dysfunction in MS patients with fatigue has been collected. However, assessing the functional correlates of MS-related fatigue using electrophysiology or PET is not without limitations. On the one hand, electrophysiological data are recorded with surface electrodes and, as a consequence, they cannot exactly define the cortical localization of potential generation and do not provide accurate information about the functional status of subcortical structures. On the other hand, PET is a relatively invasive procedure, and this might limit its application in serial studies. Functional magnetic resonance imaging (fMRI) is an entirely noninvasive technique which depends on the blood oxygenation level-dependent (BOLD) effect to show cortical areas with changes of deoxyhemoglobin concentration subsequent to variation in neuronal activity during task performance compared to rest (Ogawa et al., 1993). Recent fMRI work in MS has suggested that functional cortical and subcortical changes can have an adaptive role in limiting the clinical deficits secondary to MS tissue injury (Lee et al., 2000; Reddy et al., 2000a,b; Rocca et al., 2002). Against this background and considering that electrophysiological cortical dysfunction has been detected even during a simple motor task in MS patients with fatigue (Leocani et al., 2001), we postulated that the extent of movement-associated cortical and subcortical activation should be reduced in MS patients with fatigue compared to matched MS patients without fatigue. To this end, we used fMRI and a general search method (Friston et al., 1995) to define the pattern of brain

559 ©

1053-8119/02 $35.00 2002 Elsevier Science (USA) All rights reserved.

560

FILIPPI ET AL.

TABLE 1 Functional Assessment of Right Upper Limbs of MS Patients with and without Fatigue and Healthy Volunteers Healthy volunteers

NF MS patients

F MS patients

19.8 (3.2) 3.6 (0.4)

20.5 (2.5) 3.5 (0.5)

20.2 (2.7) 3.4 (0.7)

Mean time to complete the 9HPT (SD) (s) Mean maximum finger-tapping rate (SD) (/s) Note. NF, nonfatigued; F, fatigued; 9HPT, 9-hole peg test.

activation in MS patients with fatigue during the performance of a simple motor task. We also compared it with those of nonfatigued MS patients and healthy volunteers and correlated fMRI changes with the clinical severity of fatigue. PATIENTS AND METHODS Patients We studied 29 right-handed patients (20 women and 9 men) with clinically definite MS (Poser et al., 1983) and a relapsing–remitting (RR) course (Lublin et al., 1996). To be included patients had to have: (1) no clinical relapses for at least 6 months prior to study entry; (2) no other major medical conditions and no substance abuse; (3) no concomitant therapy with antidepressant, psychoactive, steroid, and other immunomodulant/immunosuppressive drugs; (4) no complaint of mood or sleep disorders and no evidence of depression [the Montgomery and Asberg Depression Rating Scale (Montgomery and Asberg, 1979) was administered to all subjects and all of them had to have a score ⱕ16); (5) no or only modest overall neurological impairment, defined as an Expanded Disability Status Scale (EDSS) score (Kurtzke et al., 1983) of 2.0 or less; and (6) no previous symptoms and completely normal functioning of the right upper limb. These selection criteria were used to minimize the effect of possible confounding factors (such as the presence of moderate to severe disability) and to avoid differences in task performance between patients and controls during fMRI acquisition. In all patients, fatigue was assessed and scored within 24 – 48 h from MRI acquisition by a single physician, unaware of MRI and fMRI results, according to the Fatigue Severity Scale (FSS) (Krupp et al., 1989). This is a nine-statement interview with a 7-point-scale response per statement. We calculated a global score by summing up the values obtained at each individual item of the scale. Patients who had an FSS score of 25 (mean ⫹ 2 SD from our normative data obtained from 46 sex- and age-matched healthy volunteers) or higher were considered fatigued (F), while those with an FSS score lower than 25 were considered nonfatigued (NF). There were 15 F patients [mean age (SD) 39.3 (8.2)

years; median (range) disease duration 7 (1– 40) years; median (range) EDSS score 1.0 (0.0 –1.0); mean (SD) FSS score 39.5 (7.1)] and 14 NF patients [mean age (SD) 37.6 (6.6) years; median (range) disease duration 6.5 (2–10) years; median (range) EDSS score 1.0 (0.0 – 1.0); mean (SD) FSS score 19.3 (5.2)]. The only significant difference between the two groups of patients was that of the FSS scores (P ⬍ 0.0001). Fifteen sex- and age-matched right-handed healthy volunteers with no previous history of neurological dysfunction and a normal neurological exam served as control (9 women and 6 men, mean age 38.6 years, range 21–54 years). All subjects were assessed clinically by a single neurologist, who was unaware of the MRI and fMRI results. Local ethical committee approval and written informed consent from all subjects were obtained prior to study initiation. Functional Assessment Motor functional assessments were performed for all the subjects at the time of MRI acquisition using the maximum finger-tapping frequency and the nine-hole peg test (Herndon, 1997). The mean of two trials for each subject is reported for each of these functional scales (Table 1). Finger-tapping rate and time to complete the nine-hole peg test did not differ significantly between the three groups studied and all values from MS patients were within 1 SD of the mean values obtained from the control group. Experimental Design Using a block design (ABAB), in which periods of activation were alternated with periods of rest, the subjects were scanned while performing a simple motor task consisting of repetitive flexion– extension of the last four fingers of the right hand moving together. The movements were paced by a metronome at a 1-Hz frequency. Patients and controls were trained before performing the experiments. The subjects were instructed to keep their eyes closed during fMRI acquisition and were monitored visually during scanning to ensure accurate task performance and to check for additional movements (e.g., mirror movements).

561

FMRI CORRELATES OF FATIGUE IN MULTIPLE SCLEROSIS

TABLE 2 Activation Sites in Healthy Subjects and MS Patients (with and without Fatigue) during Task Performance (Random-Effect Analysis) Healthy subjects

NF MS patients

Talairach coordinates Activation site R cerebellum L cerebellum L thalamus L insula R insula L SII R SII Bilateral CMA L SMC R SMC Bilateral SMA R IFG L IFG R precentral gyrus

x

y

z

18, ⫺52, ⫺24 ⫺24, ⫺60, ⫺26 ⫺12, ⫺18, 4 ⫺40, 2, 2 54, 12, ⫺4 ⫺58, ⫺22, 20 64, ⫺22, 24 2, 6, 40 ⫺40, ⫺16, 58 50, ⫺32, 50 ⫺2, ⫺6, 50 58, 6, 34 ⫺56, 4, 28 48, ⫺6, 46

F MS patients

Talairach coordinates t value 18.4 4.9 6.6 8.4 5.0 8.8 8.9 9.8 15.8 9.5 9.8 6.4 7.0 6.4

x

y

z

22, ⫺56, ⫺26 ⫺26, ⫺56, ⫺30 ⫺12, ⫺20, 4 ⫺48, 2, ⫺2 42, 6, 0 ⫺58, ⫺22, 16 60, ⫺20, 12 0, 6, 36 ⫺42, ⫺18, 56 50, ⫺36, 48 ⫺2, ⫺12, 52 58, 8, 20 ⫺56, 10, 10 40, ⫺8, 58

Talairach coordinates t value 10.3 10.2 9.2 5.4 6.4 7.8 5.4 9.1 15.5 8.0 9.5 7.5 5.3 7.4

x

y

z

22, ⫺52, ⫺26 ⫺22, ⫺64, ⫺20 ⫺12, ⫺22, 6 ⫺40, 2, 2 44, 12, ⫺6 ⫺48, ⫺26, 12 58, ⫺26, 18 ⫺8, 46, 12 ⫺34, ⫺26, 52 58, 26, 18 ⫺4, ⫺8, 44 50, 8, 0 60, 2, 0 —

t value 9.7 6.3 5.3 6.4 6.9 6.6 7.8 8.9 13.7 7.8 10.9 5.7 4.7 —

Note. NF, nonfatigued; F, fatigued; SMC, sensorimotor cortex; SMA, supplementary motor area; CMA, cingulate motor area; SII, upper bank Sylvian fissure; IFG, inferior frontal gyrus. See text for further details.

FMRI Acquisition Brain MRI scans were obtained using a 1.5-T machine (Vision, Siemens, Erlangen, Germany). Sagittal T1-weighted images were acquired to define the anterior–posterior commissural (AC-PC) plane. Functional MR images were acquired using a T2*-weighted echoplanar imaging sequence (TE ⫽ 66 ms, flip angle 90°, matrix size 128 ⫻ 128, field of view (FOV) 256 ⫻ 256 mm, TR ⫽ 5.5 s). Twenty-four axial slices, parallel to the AC-PC plane, with a thickness of 5 mm, covering the whole brain were acquired during each measurement. Shimming was performed for the entire brain using an auto-shim routine, which yielded satisfactory magnetic field homogeneity. Structural MRI Acquisition and Analysis On the same occasion and using the same magnet, the following sequences were acquired from all subjects: (a) dual-echo turbo spin echo (TR ⫽ 3300, first echo TE ⫽ 16, second echo TE ⫽ 98, echo train length 5, slice thickness 5 mm, FOV 192 ⫻ 256 mm, matrix size 190 ⫻ 256) and (b) T1-weighted conventional spin echo (TR ⫽ 768, TE ⫽ 15, slice thickness 5 mm, FOV 192 ⫻ 256 mm, matrix size 190 ⫻ 256). Volumes of lesions seen on dual-echo scans were measured by an experienced observer, unaware of the fMRI results, using a semiautomated segmentation technique based on local thresholding (Rovaris et al., 1997). Brain volumes were measured on T1-weighted scans by the same observer using a highly reproducible segmentation technique, which has been extensively described

elsewhere (Rovaris et al., 2000). Differences among groups in terms of dual-echo lesion load and brain volume were assessed using a Student t test for unpaired data. Correlations between structural MRI metrics and EDSS were investigated using the Spearman rank correlation coefficient. FMRI Analysis All image postprocessing was performed on an independent computer workstation (Sun Sparcstation, Sun Microsystems, Mountain View, CA). FMRI data were analyzed using the statistical parametric mapping (SPM99) software developed by Friston et al. (1995). Prior to statistical analysis, all images were realigned to the first one to correct for subject motion, spatially normalized into the stereotaxic space of Talairach and Tournoux (1988), and smoothed with a 10-mm, 3D Gaussian filter. Changes in BOLD contrast associated with the performance of the motor task were assessed on a pixel-by-pixel basis, using the general linear model (Friston et al., 1995) and the theory of Gaussian fields (Worsley and Friston, 1995). Specific effects were tested by applying appropriate linear contrasts. Significant hemodynamic changes for each contrast were assessed using t statistical parametric maps. This approach, known as fixed-effect analysis, allows only the assessment of the mean effect for individual subjects, and the results cannot be generalized to the whole population studied. The comparisons between groups using such an approach are unsatisfactory because

562

FILIPPI ET AL.

FIG. 1. Brain patterns of cortical activations on a rendered brain in right-handed healthy subjects (A, C, and E) and MS patients (B, D, and F) during the performance of a simple motor task with their clinically unimpaired and fully normally functioning right hands. Ipsilateral (⽥) and contralateral (*) primary ssnsorimotor cortex, supplementary motor area (}), ipsilateral (ƒ) and contralateral (⫹) SII, ipsilateral (␶) and contralateral (F) inferior frontal gyrus, ipsilateral middle frontal gyrus (⬍⬍), ipsilateral (⫻) and contralateral (⽤) insula and rolandic operculum, ipsilateral precentral gyrus (⬍), and ipsilateral cerebellum (⵩).

group differences may be heavily affected by interpatient variability, rather than reflect systematic differences between populations. Therefore, after the firstlevel analysis, a second-level analysis, known as random-effect analysis (Friston et al., 1999), was also performed. In other words, this two-stage approach consisted of collapsing data for each subject into a single image parameterizing the effect of interest

(within-subject modeling) and assessing these images across subjects using a simple between-subject model (one-sample t test was used to assess the activations of individual groups, ANOVA and two-sample t test were used for comparisons between groups). Using the same approach and linear regression analysis (Friston et al., 1999), we also evaluated the correlation of BOLD changes with FSS scores, dual-echo lesion load, and

FMRI CORRELATES OF FATIGUE IN MULTIPLE SCLEROSIS

563

FIG. 2. Relative cortical activations of right-handed nonfatigued MS patients during the performance of a simple motor task with their clinically unimpaired and fully normally functioning right hands in comparison to right-handed fatigued MS patients performing the same task. (A) Ipsilateral cerebellum. (B) Contralateral thalamus and ipsilateral rolandic operculum. (C) Contralateral middle frontal gyrus and ipsilateral precuneus. FIG. 3. Relative cortical activation of the contralateral cingulate motor area in fatigued MS patients during the performance of a simple motor task with their clinically unimpaired and fully normally functioning right hands in comparison to right-handed nonfatigued MS patients performing the same task. (A) Axial view. (B) Sagittal view.

brain volume. We report activations below a threshold of P ⬍ 0.05 corrected for multiple comparisons. RESULTS Structural MRI All healthy volunteers had normal brain MRI dualecho scans and a mean brain volume of 1178 ml (range

1020 –1287 ml). In MS patients, the mean dual-echo lesion volume was 8.1 ml (range 1.1– 41.7 ml) and the mean brain volume was 1149 ml (range 1001–1273 ml). There was no significant difference in brain volume between healthy subjects and MS patients. No significant differences in dual-echo lesion load and brain volume were found between F and NF MS patients. No significant correlation was found between the FSS

564

FILIPPI ET AL.

score and the dual-echo lesion load, as well as between the FSS score and the brain volume. Functional MRI All subjects performed the task correctly and no additional movements were observed during fMRI acquisition. In Table 2, the brain areas with significant activations detected during performance of the task by healthy volunteers and MS patients are reported along with the corresponding coordinates of the main foci of activation and t values. Figure 1 shows the activated areas in healthy volunteers and MS patients on a rendered brain. Compared to healthy subjects, MS patients showed more significant relative activations of the contralateral ascending limb of the Sylvian fissure (Talairach coordinates ⫺46, ⫺28, 14; corrected P value ⫽ 0.009); in a region that corresponds to SII (Fink et al., 1997), the cingulate motor area (CMA) (Talairach coordinates 2, ⫺8, 36; corrected P value ⫽ 0.01), bilaterally; in the contralateral intraparietal sulcus (IPS) (Talairach coordinates ⫺40, ⫺64, 36; corrected P value ⫽ 0.01); in the contralateral primary sensorimotor cortex (SMC) (Talairach coordinates ⫺42, ⫺26, 54; corrected P value ⫽ 0.01); and in the contralateral supplementary motor area (SMA) (Talairach coordinates ⫺6, ⫺22, 50; corrected P value ⫽ 0.01). Compared to F MS patients, NF MS patients showed more significant relative activations of the ipsilateral cerebellar hemisphere (Talairach coordinates 26, ⫺64, ⫺26; corrected P value ⫽ 0.004), the ipsilateral rolandic operculum (Talairach coordinates 52, ⫺6, 14; corrected P value ⫽ 0.001), the ipsilateral precuneus (Talairach coordinates 10, ⫺52, 50; corrected P value ⬍ 0.001), the contralateral thalamus (Talairach coordinates ⫺16, ⫺14, 12; corrected P value ⬍ 0.001), and the contralateral middle frontal gyrus (Talairach coordinates ⫺30, ⫺2, 50; corrected P value ⫽ 0.003) (Fig. 2). On the contrary, F MS patients had a more significant relative activation of the contralateral CMA (Talairach coordinates ⫺8, 46, 12; corrected P value ⫽ 0.001) than those without fatigue (Fig. 3). Healthy volunteers had more significant relative activations of the ipsilateral inferior frontal gyrus (Talairach coordinates 60, 18, 24; corrected P value ⫽ 0.01) and the contralateral thalamus (Talairach coordinates ⫺8, ⫺12, 2; corrected P value ⫽ 0.001) than F MS patients. For those areas that showed significantly different relative activations at group analysis, we also evaluated and compared (Student t test for unpaired data) the extent of their activations using cluster analysis on a patient-by-patient basis. Compared to healthy volunteers, MS patients had significantly larger activations of the contralateral primary SMC (2311 vs 1719; P ⫽ 0.03) and of the SMA (485 vs 281; P ⫽ 0.05). Compared to F MS patients, NF MS patients had significantly

larger activations of the ipsilateral cerebellar hemisphere (1463 vs 703; P ⫽ 0.02) and the contralateral thalamus (308 vs 30; P ⫽ 0.02). Correlations between fMRI Findings and FSS In MS patients, significant correlations were found between FSS scores and the activity in the contralateral IPS (r ⫽ ⫺0.63, P ⬍ 0.0001), thalamus (r ⫽ ⫺0.62, P ⬍ 0.0001) (Fig. 4), and ipsilateral rolandic operculum (r ⫽ ⫺0.61, P ⬍ 0.0001). No correlation was found between fMRI findings and dual-echo lesion load, as well as between fMRI findings and brain volume. DISCUSSION In this study, we used fMRI to improve our understanding of the functional substrates of MS-related fatigue. To this end, we carefully selected a group F MS patients in order to avoid or minimize any potential confounding factor in the assessment of fatigue and fMRI results. We also used a general search method to analyze fMRI data in order to obtain information about activation of cortical and subcortical structures with the potential to be associated with the genesis of fatigue in MS. Data from F MS patients were compared with those of two control groups, one comprising NF MS patients comparable to the study group in terms of clinical and MRI variables and the other age- and sex-matched healthy volunteers. In agreement with others (Lee et al., 2000; Reddy et al., 2000a), we found increased SMA activation in our patients. Since efferents from the SMA project directly to the brain stem and the cervical cord, increased SMA activation may represent recruitment of motor pathways that can function in parallel with the injured contralateral corticospinal tract (Martino and Strick, 1987). Previous fMRI studies have also shown increased ipsilateral SMC activation in patients with either RR MS or secondary progressive MS (Lee et al., 2000; Reddy et al., 2000a). Increased ipsilateral SMC activation was not observed in the present study, in which, instead, an increased activation of the contralateral SMC was detected. This discrepancy might be explained by the different clinical characteristics of the patients studied, since previous studies (Lee et al., 2000; Reddy et al., 2000a) recruited patients with more disabling MS than those of the present study. We also found that cortical functional changes in nondisabled MS patients are not limited to the SMC and the SMA, but involve a more widespread sensorimotor network including the CMA, the SII, and the IPS. All these areas are considered crucial in motor programming and execution (Grafton et al., 1992; Rao et al., 1993; Paus et al., 1993; Jenkins et al., 1994; Schlaug et al., 1994; Disbrow et al., 2000), and in normal subjects their activation has been found to be related to presen-

FMRI CORRELATES OF FATIGUE IN MULTIPLE SCLEROSIS

FIG. 4. Correlation between relative activation of the contralateral thalamus and Fatigue Severity Scale scores in patients with MS. Note that the values of some subjects are negative because they have been scaled to the mean value of the fMRI scans of each individual (i.e., values are mean centered).

tation of new motor tasks and perhaps reflects relative task difficulty (Grafton et al., 1992; Rao et al., 1993; Paus et al., 1993; Jenkins et al., 1994; Schlaug et al., 1994). Since increased activation of these areas was found in our patients when performing a simple motor task, we speculate that this enhanced activation of sensorimotor areas might be an additional mechanism contributing to the limitation of the functional consequences of brain damage in MS (Lee et al., 2000; Reddy et al., 2000a). The main result of this study is, however, the demonstration of a distinct pattern of movement-related brain activation in MS patients with fatigue. MS patients with fatigue showed a significantly lower activation of several cortical and subcortical areas devoted to motor planning and execution in comparison with patients without fatigue. In addition, we also found a strong inverse correlation between the extent of fatigue and the relative activations of areas related to motor programming and execution, including the contralateral thalamus. Interestingly, the thalamus is an important relay station of the complex reentrant circuitry that links the motor and the prefrontal cortices to the basal ganglia and which is part of the feedback loops of the limbic system able to modulate the cortical motor output (Chaudhuri and Behan, 2000). Thus, our findings provide additional evidence that MS-related fatigue could be secondary to disruption or dysfunction of corticosubcortical circuits. They also fit with the metabolic changes found in the frontal cortex and basal

565

ganglia of F MS patients (Roelke et al., 1997) and with electrophysiological findings (Sandroni et al., 1992; Sheean et al., 1997; Leocani et al., 2001). Previous electrophysiological studies have indeed suggested that the functioning of the primary motor cortex as well as the conduction along the primary afferent or efferent pathways (Sandroni et al., 1992; Sheean et al., 1997) is normal in MS patients complaining of fatigue, whereas abnormalities of cortico-cortical and corticosubcortical circuitries involved in motor planning and execution have been detected (Sheean et al., 1997; Leocani et al., 2001). This study has also shown that MS patients with fatigue have a larger and more significant activation of the anterior cingulate cortex compared to those without fatigue. This cortical area is involved in attentional tasks and subserves several executive functions (Vogt et al., 1992). It is also active during early stages of motor learning (Rao et al., 1993; Jenkins et al., 1994) or motor planning (Paus et al., 1993). Although this is speculative and needs to be confirmed by other studies, the increased activation of the anterior cingulate cortex in MS patients with fatigue during the performance of a simple motor task might be viewed as a compensatory mechanism operating in individuals who have a much higher perceived effort for the executed task. We are aware that changes of the anterior cingulate cortex activity have also been related to depressive disorders (Brody et al., 2001). Nevertheless, since depression has been carefully excluded in our patients, it seems unlikely that the increased activation of the anterior cingulate cortex found in fatigued MS patients might be attributable to their mood status. Although the most likely explanation for the distinct pattern of the functional cortical/subcortical changes we and others (Roelcke et al., 1997) found is the presence of MS lesions in the white matter tracts connecting functionally related areas, we found neither a difference in dual-echo lesion load between F and NF MS patients nor a correlation between lesion extent and relative fMRI activations. This finding confirms and extends those of previous studies, which were also unable to show significant differences of conventional MRI metrics between these two groups of patients or any significant correlation of fatigue severity with the overall amount of T2 and enhancing lesions (van der Werf et al., 1998; Bakshi et al., 1999; Mainero et al., 1999; Colombo et al., 2000). This disappointing lack of a correlation between MS-related fatigue and conventional MRI findings is likely due to the known limitations of conventional MRI, including its inability to quantify the extent of tissue damage within and outside white matter T2-visible lesions (Filippi, 2001a) and its poor sensitivity for the detection of MS-related gray matter pathology (Filippi, 2001b). In conclusion, although this study has to be regarded as preliminary since it is based on a relatively small

566

FILIPPI ET AL.

sample of patients, it provides additional evidence that fatigue in MS might be related to impaired interactions between functionally related cortical and subcortical areas. Considering the ability of fMRI to provide information about the activity of cortical and subcortical gray matter structures, its excellent spatial resolution, and the fact that fMRI is entirely noninvasive and, as a consequence, can be obtained serially, this study also suggests that fMRI might be a valuable tool to monitor objectively the efficacy of treatment aimed at reducing MS-related fatigue. ACKNOWLEDGMENTS This study was in part supported by a grant from the Italian Ministry of Health (Contract ICS030.5/RF00.79) and a grant by the Armenise–Harvard Foundation.

REFERENCES Bakshi, R., Miletich, R. S., Henschel, K., Shaikh, Z. A., Janardhan, V., Wasay, M., Stengel, L. M., Ekes, R., and Kinkel, P. R. 1999. Fatigue in multiple sclerosis: Cross-sectional correlation with brain MRI findings in 71 patients. Neurology 53: 1151–1153. Brody, A. L., Barsom, M. W., Bota, R. G., and Saxena, S. 2001. Prefrontal–subcortical and limbic circuit mediation of major depressive disorder. Semin. Clin. Neuropsychiatry 6: 102–112. Chaudhuri, A., and Behan, P. O. 2000. Fatigue and basal ganglia. J. Neurol. Sci. 179: 34 – 42. Colombo, B., Martinelli, F., Rossi, P., Rovaris, M., Maderna, L., Filippi, M., and Comi, G. 2000. MRI and motor evoked potential findings in non-disabled multiple sclerosis patients with and without symptoms of fatigue. J. Neurol. 247: 506 –509. Disbrow, E., Roberts, T., and Krubitzer, L. 2000. Somatotopic organization of cortical fields in the lateral sulcus of Homo sapiens: Evidence for SII and PV. J. Comp. Neurol. 418: 1–21. Filippi, M. 2001a. Linking structural, metabolic and functional changes in multiple sclerosis. Eur. J. Neurol. 8: 291–297. Filippi, M. 2001b. Multiple sclerosis: A white matter disease with associated gray matter damage. J. Neurol. Sci. 185: 3– 4. Fink, G. R., Frackowiak, R. S., Pietrzyk, U., and Passingham, R. E. 1997. Multiple nonprimary motor areas in the human cortex. J. Neurophysiol. 77: 2164 –2174. Friston, K. J., Holmes, A. P., Poline, J. B., Grasby, P. J., Williams, S. C., Frackowiak, R. S., and Turner, R. 1995. Analysis of fMRI time-series revisited. NeuroImage 2: 45–53. Friston, K. J., Holmes, A. P., Price, C. J., Buchel, C., and Worsley, K. J. 1999. Multisubject fMRI studies and conjunction analyses. NeuroImage 10: 385–396. Grafton, S. T., Mazziotta, J. C., Woods, R. P., and Phelps, M. E. 1992. Human functional anatomy of visually guided finger movements. Brain 115: 565–587. Herndon, R. M. 1997. Handbook of Neurologic Rating Scales. Demos Vermande, New York. Jenkins, I. H., Brooks, D. J., Nixon, P. D., Frackowiak, R. S., and Passingham, R. E. 1994. Motor sequence learning: A study with positron emission tomography. J. Neurosci. 14: 3775–3790. Krupp, L. B., Alvarez, L. A., La Rocca, N. G., and Scheinberg, L. C. 1988. Fatigue in multiple sclerosis. Arch. Neurol. 45: 435– 437. Krupp, L. B., La Rocca, N. G., Muir-Nash, J., and Steinberg, A. D. 1989. The fatigue severity scale. Application to patients with mul-

tiple sclerosis and systemic lupus erythematosus. Arch. Neurol. 46: 1121–1123. Kurtzke, J. F. 1983. Rating neurological impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology 33: 1444 –1452. Lee, M., Reddy, H., Johansen-Berg, H., Pendlebury, S., Jenkinson, M., Smith, S., Palace, J., and Matthews, P. M. 2000. The motor cortex shows adaptive functional changes to brain injury from multiple sclerosis. Ann. Neurol. 47: 606 – 613. Leocani, L., Colombo, B., Magnani, G., Martinelli-Boneschi, F., Cursi, M., Rossi, P., Martinelli, V., and Comi, G. 2001. Fatigue in multiple sclerosis is associated with abnormal cortical activation to voluntary movement—EEG evidence. NeuroImage 13: 1186 – 1192. Lublin, F. D., Reingold, S. C., and the National Multiple Sclerosis Society (U.S.A.) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. 1996. Defining the clinical course of multiple sclerosis: Results of an international survey. Neurology 46: 907–911. Mainero, C., Faroni, J., Gasperini, C., Filippi, M., Giugni, E., Ciccarelli, O., Rovaris, M., Bastianello, S., Comi, G., and Pozzilli, C. 1999. Fatigue and magnetic resonance imaging activity in multiple sclerosis. J. Neurol. 246: 454 – 458. Martino, A. M., and Strick, P. L. 1987. Corticospinal projections originate from the arcuate premotor area. Brain Res. 404: 307– 312. Montgomery, S. A., and Asberg, M. 1979. A new depression scale designed to be sensitive to change. Br. J. Psychiatry 134: 382–389. Ogawa, S., Menon, R. S., Tank, D. W., Kim, S. G., Merkle, H., Ellermann, J. M., and Ugurbil, K. 1993. Functional brain mapping by blood oxygenation level-dependent contrast magnetic resonance imaging. A comparison of signal characteristics with a biophysical model. Biophys. J. 64: 803– 812. Paus, T., Petrides, M., Evans, A. C., and Meyer, E. 1993. Role of the human anterior cingulate cortex in the control of oculomotor, manual, and speech responses: A positron emission tomography study. J. Neurophysiol. 70: 453– 469. Poser, C. M., Paty, D. W., Scheinberg, L., McDonald, W. I., Davis, F. A., Ebers, G. C., Johnson, K. P., Sibley, W. A., Silberberg, D. H., and Tourtellotte, W. W. 1983. New diagnostic criteria for multiple sclerosis: Guidelines for research protocols. Ann. Neurol. 13: 227– 231. Rao, S. M., Binder, J. R., Bandettini, P. A., Hammeke, T. A., Yetkin, F. Z., Jesmanowicz, A., Lisk, L. M., Morris, G. L., Mueller, W. M., and Estkowski, L. D. 1993. Functional magnetic resonance imaging of complex human movements. Neurology 43: 2311–2318. Reddy, H., Narayanan, S., Arnoutelis, R., Jenkinson, M., Antel, J., and Matthews, P. M. 2000a. Evidence for adaptive functional changes in the cerebral cortex with axonal injury from multiple sclerosis. Brain 123: 2314 –2320. Reddy, H., Narayanan, S., Matthews, P. M., Hoge, R. D., Pike, G. B., Duquette, P., Antel, J., and Arnold, D. L. 2000b. Relating axonal injury to functional recovery in MS. Neurology 54: 236 –239. Rocca, M. A., Matthews, P. M., Caputo, D., Ghezzi, A., Falini, A., Scotti, G., Comi, G., and Filippi, M. 2002. Evidence for widespread movement-associated functional MRI changes in patients with PPMS. Neurology, in press. Roelcke, U., Kappos, L., Lechner-Scott, J., Brunnschweiler, H., Huber, S., Ammann, W., Plohmann, A., Dellas, S., Maguire, R. P., Missimer, J., Radu, E. W., Steck, A., and Leenders, K. L. 1997. Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: A 18F-fluorodeoxyglucose positron emission tomography study. Neurology 48: 1566 – 1571.

FMRI CORRELATES OF FATIGUE IN MULTIPLE SCLEROSIS Rovaris, M., Filippi, M., Calori, G., Colombo, B., Campi, A., Rodegher, M., and Comi, G. 1997. Intra-observer reproducibility in measuring new putative MR markers of demyelination and axonal loss in multiple sclerosis: A comparison with conventional T2weighted images. J. Neurol. 244: 266 –270. Rovaris, M., Inglese, M., van Schijndel, R. A., Sormani, M. P., Rodegher, M., Comi, G., and Filippi, M. 2000. Sensitivity and reproducibility of volume change measurements of different brain portions on magnetic resonance imaging in patients with multiple sclerosis. J. Neurol. 247: 960 –965. Sandroni, P., Walker, C., and Starr, A. 1992. ‘Fatigue’ in patients with multiple sclerosis. Motor pathway conduction and eventrelated potentials. Arch. Neurol. 49: 517–524. Schlaug, G., Knorr, U., and Seitz, R. 1994. Inter-subject variability of cerebral activations in acquiring a motor skill: A study with positron emission tomography. Exp. Brain Res. 98: 523–534.

567

Sheean, G. L., Murray, N. M., Rothwell, J. C., Miller, D. H., and Thompson, A. J. 1997. An electrophysiological study of the mechanism of fatigue in multiple sclerosis. Brain 120: 299 –315. Talairach, J., and Tournoux, P. 1988. Co-planar Stereotaxic Atlas of the Human Brain. Thieme, New York. van der Werf, S. P., Jongen, P. J., Lycklama a Nijeholt, G. J., Barkhof, F., Hommes, O. R., and Bleijenberg, G. 1998. Fatigue in multiple sclerosis: Interrelations between fatigue complaints, cerebral MRI abnormalities and neurological disability. J. Neurol. Sci. 160: 164 –170. Vogt, B. A., Finch, D. M., and Olson, C. R. 1992. Functional heterogeneity in cingulate cortex: The anterior executive and posterior evaluative regions. Cereb. Cortex 2: 435– 443. Worsley, K. J., and Friston, K. J. 1995. Analysis of fMRI time-series revisited—Again. NeuroImage 2: 173–181.