Sustained prefrontal activation during ataxic gait: A compensatory mechanism for ataxic stroke?

www.elsevier.com/locate/ynimg NeuroImage 37 (2007) 1338 – 1345

Sustained prefrontal activation during ataxic gait: A compensatory mechanism for ataxic stroke? Masahito Mihara,a,b,⁎ Ichiro Miyai,a Megumi Hatakenaka,a Kisou Kubota,a and Saburo Sakodab a

Neurorehabilitation Research Institute, Morinomiya Hospital, Japan Department of Neurology, Osaka University Graduate School of Medicine, Japan

b

Received 14 March 2007; revised 15 May 2007; accepted 5 June 2007 Available online 29 June 2007

There is accumulated evidence that cortical reorganization plays an important role in motor recovery after supratentorial stroke. However neural mechanisms underlying functional recovery of ataxia after infratentorial stroke remain unclear. We investigated cortical activations during ataxic gait in patients with infratentorial stroke to test the hypothesis that cerebral cortices were involved in compensatory mechanisms for ataxic gait. Twelve patients with infratentorial stroke (mean duration ± S.D. from the onset: 88.3 ± 44.8 days) and 11 agematched healthy subjects participated in this study. All patients had predominant ataxia without severe hemiparesis. We measured cortical activation as assessed by task-related increase of oxygenated hemoglobin during gait on a treadmill using functional near-infrared spectroscopy. Task consisted of three repetitions of gait period alternated with rest period. In controls, cortical activations in the lateral and medial prefrontal cortex during the acceleration phase tended to be attenuated during the steady phase of the gait period while these activations were sustained throughout the gait period in ataxic patients. Repeated measures ANOVA for cortical activation revealed significant interactions (p b 0.005) between phase (acceleration/steady) and group (control/stroke) in the medial and lateral prefrontal regions. These results suggest that sustained prefrontal activation during ataxic gait might be relevant to compensatory mechanisms for ataxic gait after infratentorial stroke. © 2007 Elsevier Inc. All rights reserved.

Introduction Recent advances in functional neuroimaging have enabled investigation of neural mechanisms underlying functional recovery after stroke, and there is accumulated evidence that functional reorganization of intact cortices plays a crucial role in motor recovery of the affected upper and lower extremities after supratentorial stroke (Calautti and Baron, 2003; Chollet et al., 1991; Cramer et al., 1997). ⁎ Corresponding author. Neurorehabilitation Research Institute, Morinomiya Hospital, 2-1-88 Morinomiya, Jyoto-ku, Osaka, Osaka, 536-0025, Japan. Fax: +81 6 6969 8001. E-mail address: [email protected] (M. Mihara). Available online on ScienceDirect (www.sciencedirect.com). 1053-8119/$ - see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2007.06.014

Human bipedal walking requires complex visuo-sensorimotor coordination. Like other animals, human locomotion is controlled by multiple neural systems hierarchically distributed throughout the central nervous system (CNS) including the spinal cord, brainstem, cerebellum, basal ganglia, and motor cortex (Grillner and Wallen, 2004). Based on evidence from both animal and human studies, the cerebellum and brainstem contribute much to controlling equilibrium and interlimb coordination during locomotion (Morton and Bastian, 2004; Thach and Bastian, 2004). From the clinical point of view, previous studies showed that patients after infratentorial stroke tended to make good recovery (Kelly et al., 2001; Tohgi et al., 1993), but mechanisms of functional recovery remain unclear and only few studies investigated the neural mechanisms for functional recovery of ataxia after infratentorial stroke involving the cerebellum and brainstem (Kinomoto et al., 2003). Anatomical studies revealed tight connections between the cerebellum and cerebral cortices, especially the frontal lobe (Schmahmann, 2000; Schmahmann and Pandya, 1997). Such cortico-cerebellar system plays an important role in motor learning and relearning (Doyon et al., 2003). Indeed functional recovery after cerebellar stroke was affected by supratentorial white matter lesions that might disrupt the functional connectivity (Grips et al., 2005). Therefore, we hypothesized that cerebral cortices were involved in compensatory mechanisms for ataxic gait after infratentorial stroke. To test this hypothesis, we compared cortical activation patterns during gait on a treadmill in patients with ataxia after infratentorial stroke with those in healthy control subjects. In this study, cortical activation was assessed by using functional near-infrared spectroscopy (fNIRS), since fNIRS is suitable for evaluation of cortical activation during dynamic movements such as locomotion (Jobsis, 1977; Miyai et al., 2001; Suzuki et al., 2004). Methods Subjects Twelve patients who had previously suffered an infratentorial stroke (age range: 12–74 years, mean ± S.D.: 52.7 ± 16.9 years) and

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Table 1 Clinical characteristics of the ataxic patients Age (years)

M/F Gait speed Location of lesion (km/h)

Stroke subtype

Signs and symptoms

Duration from MMSE onset (days)

Ischemic

lt limb/truncal ataxia, dysarthria

83

30

Ischemic

29

Pt. 1

37

M

2.0

Pt. 2

43

F

0.4

Pt. 3 Pt. 4

46 53

M M

1.0 1.0

lt cerebellar hemisphere, lt middle cerebellar peduncle bil cerebellar hemisphere, midbrain, pontine tegmentum lt lateral medulla rt. cerebellar hemisphere

Pt. 5

55

M

1.0

lt cerebellar hemisphere

Hemorrhagic

Pt. 6

58

M

0.4

rt. pontine tegmentum

Hemorrhagic

Pt. Pt. Pt. Pt. Pt. Pt.

63 67 54 70 74 12

M M M M M M

1.0 0.8 1.6 0.5 1.6 3.0

lt cerebellar hemisphere lt cerebellar hemisphere lt lateral medulla lt cerebellar hemisphere rt pontine tegmentum lt cerebellar hemisphere

Ischemic Hemorrhagic Ischemic Hemorrhagic Ischemic Hemorrhagic

bil limb/truncal ataxia, 80 lt hemiparesis, Rt. numbness Vertigo, lt limb ataxia, lt numbness 70 rt. limb/truncal ataxia, dysarthria, 157 diplopia, lt hemiparesis, rt. Numbness lt limb/truncal ataxia, 42 Dysarthria, diplopia Nystagmus, rt. limb/truncal ataxia, 86 dysarthria lt limb/truncal ataxia 70 Vertigo, rt. limb/truncal ataxia 21 lt limb/truncal ataxia, vertigo 50 lt limb/truncal ataxia 145 lt limb/truncal ataxia, dysarthria 95 lt limb/truncal ataxia, dysarthria 160

–

–

7 8 9 10 11 12

Mean ± S.D. 52.7 ± 16.9

1.19 ± 0.8 ⁎ –

Ischemic Hemorrhagic

88.3 ± 44.8

30 28 30 29 30 30 30 30 30 Not examined 29.6 ± 0.7

Pt: patient, rt: right, lt: left, bil: bilateral, M/F: Male/Female, MMSE: Mini-Mental State Examination score (the maximum score is 30). ⁎ p b 0.05 to control subjects.

11 healthy subjects (age range: 30–70 years, mean ± S.D.: 42.6 ± 11.6 years) participated in this study. There was no significant difference in age between the two groups. All patients predominantly showed ataxic symptoms including ataxic gait, ataxic dysarthria, hand clumsiness and dizziness. All of them needed help to walk at the onset, but they regained ability to walk before participating in this study. Motor paresis was much less apparent since all patients had muscle strength more than 4/5 in MRC rating scale and were scored more than 80/100 in Fugl-Meyer motor scale (Fugl-Meyer et al., 1975). Somatosensory system was almost intact except two of them complained mild numbness of the face or

limbs. They showed no cognitive dysfunction including aphasia, amnesia and dementia. Clinical characteristics of the patients are shown in Table 1. The study protocol was approved by the local ethical review board and written informed consent was obtained from each subject. Task for fNIRS All subjects were required to perform locomotion task on a treadmill (Model-3200; SportsArt Ind., WA). In healthy controls, treadmill speed was set at their usual walking speed (3.0–5.0 km/h,

Fig. 1. Task designs for fNIRS. Task designs for fNIRS in control subjects (A) and ataxic patients with infratentorial stroke (B). Controls performed the locomotor task consisting of 15-s rest periods before and after 60-s gait period for three repetitions. Patients performed the task consisting of 15-s rest periods before and after 30-s gait period for three repetitions. Rest data were sampled from 6-s period just before the treadmill was started. Data from the acceleration phase of the locomotion period were sampled from 4 to 10 s after treadmill was started in both groups, and data from the steady phase were from 30 to 36 s in controls and 24 to 30 s inpatients to match the interval between the beginning of the steady phase and the start point of sampling.

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Fig. 2. Location of the optodes and the representative data of task dependent-changes in hemoglobin oxygenation. Schema for the location of the optodes was shown (A). Twenty-eight optodes consisting of 12 light-source fibers (red circles) and 16 detectors (blue circles) were placed on a scalp covering the frontoparietal cortical surface. This figure indicates the anatomical location exposed onto the normalized brain surface and presumed cortical region covered by each channel (Suzuki et al., 2004). The representative data of changes in hemoglobin oxygenation during the locomotor task in a control subject and a stroke patient with ataxia were shown (B). Red lines indicate oxyHb concentration (mMol/l × cm), blue lines indicate deoxyHb and green lines indicate totalHb. Black lines represent treadmill speed and three vertical bars indicate time periods from which the data were obtained for further quantitative analyses. Blue, orange and purple bars indicate the rest period, acceleration phase and steady phase of the gait period, respectively.

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mean 3.5 km/h), and the task consisted of 60-s gait period alternated with 30-s rest period (15 s before and after the gait period) for three repetitions (Fig. 1A). In stroke patients, because of the impaired gait, treadmill speed was eventually set at lower speed than in controls but as fast as they could walk comfortably (0.4–3.0 km/h, mean 1.19 km/h), and the task consisted of 30-s gait period alternated with 30-s rest period for three repetitions (Fig. 1B). During the rest period, subjects were instructed to stand still on the treadmill. The treadmill reached 1 km/h at 4.5 s, and 3 km/h at 13 s after starting the gait period. After it reached the target speed, treadmill speed was kept steady until the end of the gait period. For safety reason, patients with stroke wore parachute jackets attached to a body weight support apparatus to be prevented from falling, although no weight support was applied during the locomotion task. fNIRS The details of the fNIRS system using continuous wave laser diodes with wavelength of 780, 805 and 830 nm (OMM-3000; Shimadzu, Kyoto, Japan) were described previously (Miyai et al., 2001; Suzuki et al., 2004). We used a 42-channel system with 28 optodes, consisting of 12 light-source fibers and 16 detectors. Interoptode distance was set at 3.0 cm and we located optodes to

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cover the area of 13 × 15 cm skull surface in the fronto-parietal region as shown in Fig. 2A. As described previously (Suzuki et al., 2004), the anterior commissure line was estimated to be located between the second and the third raw of 42 square grids of NIRS channels. Thus the left and right lateral prefrontal cortex (PFC) were covered by channels 1, 2, 8, 9 and 29, 30, 36, 37 (Picard and Strick, 2001), the medial PFC by channels 15, 16, 22, 23, supplementary motor area (SMA) by channels 17, 18, 24, 25 and medial primary sensorimotor cortex (SMC) by channels 19, 20, 26, 27. Data analyses Because of uncertainty about differential pathlength factor, measured data were not absolute values of hemoglobin concentration on cortical surface (Hoshi, 2003). Since no specific value of differential pathlength factor could be adopted from previous studies (Van der Zee et al., 1992; Duncan et al., 1996), the scale unit of the measured value was concentration multiplied by unknown pathlength (mMol/l × cm). We used changes of oxygenated hemoglobin (oxyHb) concentration rather than those of deoxygenated hemoglobin (deoxyHb) for assessment of regional cortical activation, since previous studies showed that oxyHb was the most sensitive marker for task-related hemodynamic changes

Fig. 3. The cortical mapping of gait in the patients and controls. The representative cortical mappings of locomotion tasks based on changes in oxyHb levels from a stroke patient with ataxia (A) and from a control subject (B) are shown. The scales indicate the color coordinates of concentration changes. Both groups showed activations in the medial sensorimotor, premotor and prefrontal cortices in the acceleration phase. During the steady phase, the patient showed sustained cortical activation, whereas cortical activation tended to decrease in the control subject.

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(Miyai et al., 2001; Miyai et al., 2002; Wolf et al., 2002; Strangman et al., 2002). After the data from three repetitive periods were averaged, we calculated “ΔoxyHb during the gait period − ΔoxyHb during the rest period” in each channel. The rest data were obtained from 6 s just before the gait period, as indicated by dotted arrows in Fig. 1. The gait data were obtained from two phases of the gait period, acceleration and steady phases. From the acceleration phase, we obtained data for cortical activation related to adjusting walking speed to increasing treadmill speed. Considering several seconds' delay between the beginning of neural activity and the peak of related hemodynamic response (Jasdzewski et al., 2003), we collected the data from 6 s between the 4th second and 10th second after starting the treadmill in both controls and stroke patients, as indicated by thick arrows in Fig. 1. Data for cortical activation related to controlling gait at a steady speed were obtained from the steady phase. Since the treadmill reached the target speed earlier in stroke patients than controls, the data for the steady phase were collected from 6 s between the 30th and 36th second after starting the treadmill in controls and between the 24th and 30th second in patients, as indicated by thin arrows in Fig. 1. Statistical analyses We used one sample t-test to 0 to detect the significant cortical activation, and three-way repeated measures ANOVA with phase (acceleration/steady) as a within-subject factor and group (control/

stroke) and region of interest (left PFC/right PFC/medial PFC/ SMA/medial SMC) or group and side of region (left/right) as a between-subject factor. To investigate whether ataxic patients showed different activation pattern in specific cortical regions or specific hemisphere, the model included the interaction among phase, group and region of interest, or interaction among phase, group and side. To compare the effect of region of interest, we performed two-way repeated measures ANOVA with phase as a within-subject factor and group as a between-subject factor in each cortical region. The unpaired t-test was used to detect differences in regional activation of acceleration and steady phase, age and treadmill speed between two subject groups. Statistical significance was set at p b 0.05. Results The locomotion task was tolerated well by both patients and controls, and no one complained of excessive effort. The representative data of the averaged time course of the oxyHb, deoxyHb, and totalHb levels in each region of interest are shown in Fig. 2B. Task-related changes in the deoxyHb level were less evident than the oxyHb, consistent with the results from previous literature (Miyai et al., 2001; Suzuki et al., 2004). The cortical mappings based on task-related increase of oxyHb are shown in Fig. 3. These data suggested that controls and patients showed distinct cortical activation patterns. Cortical activation was obvious in the medial SMC, SMA and PFC during the acceleration phase in both controls and patients. In the steady phase, it attenuated in

Fig. 4. Mean regional changes of oxyHb concentration during the acceleration and steady phase of the gait period in controls and patients. There were significant interactions between period (acceleration/steady) × groups (control/stroke) in the left PFC (F1,84 = 30.133, p b 0.001), medial PFC (F1,86 = 9.986, p b 0.005) and right PFC (F1,84 = 20.350, p b 0.001). See text for details. Error bars represent 2 standard errors. ⁎: p b 0.05, ⁎⁎: p b 0.005, ⁎⁎⁎: p b 0.001 for significant difference to zero. †: p b 0.05, ††: p b 0.005, †††: p b 0.001 for significant difference between controls and ataxic patients. §: p b 0.005, §§: p b 0.001 for significant interaction between subject group (control/ataxic) and gait phase (acceleration/steady). PFC: the prefrontal cortex, SMA: the supplementary motor area, SMC: the sensorimotor cortex.

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controls but persisted in patients. Quantitative analyses of group data are shown in Fig. 4. Ataxic patients and controls showed significant activation during both the acceleration and steady phases in the medial SMC, SMA and medial and lateral PFCs, except in the right lateral PFC of the controls during the steady phase. During the steady phase, the cortical activation in ataxic patients was significantly higher in the SMA (t76 = − 3.923, p b 0.001), left PFC (t86 = −4.784, p b 0.001), right PFC (t84 = − 5.231, p b 0.001) and medial PFC (t86 = − 3.241, p b 0.005). Ataxic patients also showed higher activation during the acceleration phase in the SMA (t76 = −2.713, p b 0.01) and right PFC (t84 = −2.075, p b 0.05). In the medial SMC, the cortical activation in the ataxic patients was comparable to the controls throughout the gait period. Further, repeated measures ANOVA revealed a significant interaction among phase, group and region of interest (F1,408 = 2.863, p b 0.05), but there was no significant interaction among phase, group and side (F1,414 = 0.762, p = 0.383). Two-way repeated measures ANOVA revealed a significant interaction between group and phase in the left PFC (F1,84 = 30.133, p b 0.001), right PFC (F1,84 = 20.350, p b 0.001) and medial PFC (F1,86 = 9.986, p b 0.005), but there were no significant interactions in the medial PFC, SMA and medial SMC. These results suggest that serial changes of cortical activation patterns during sustained walking on the treadmill are different between the two groups. In control subjects, activation in the lateral and medial PFCs increased during the acceleration phase and decreased during the steady phase. On the contrary, in ataxic patients with stroke, cortical activation in the lateral and medial PFCs was persistent throughout the gait period. Discussion The present study revealed different cortical activation patterns during sustained gait on a treadmill between control subjects and ataxic patients with infratentorial stroke. Altered cortical activations during ataxic gait might be explained by distinct roles of the infra- and supratentorial structures in locomotor control (Drew et al., 2004) and functional linkage between these structures. Although both the infra- and supratentorial structures are considered to regulate locomotion through the putative central pattern generator in the spinal cord (Dimitrijevic et al., 1998), these contribute to different aspects of locomotor control. The infratentorial regions including the reticular nuclei and the medial cerebellum are regarded as the main structures for controlling the automated locomotion and the muscle tones during gait (Drew et al., 2004; Armstrong, 1988; Matsuyama et al., 2004; Mori et al., 2004). In contrast, the supratentorial structures are predominantly involved in adjusting locomotor performance to altered environment (Armstrong, 1988; Matsuyama et al., 2004; Takakusaki et al., 2004). In human bipedal locomotion, the cerebral cortex appeared to be less involved in executing automated gait on a flat surface than in adjusting locomotor performance. For instance, studies using fMRI and PET reported decreased cortical activation during motor imagery of automated locomotion such as walking at a steady speed (Malouin et al., 2003; Jahn et al., 2004). Our previous fNIRS studies also revealed that the medial prefrontal activation was prominent while healthy subjects were adjusting locomotion to higher treadmill speed (Suzuki et al., 2004). These results indicate that the cortical demand for controlling gait is reduced

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during the steady phase of locomotion in control subjects, since the infaratentorial structures engaged in the automated locomotion are intact. By contrast, the ataxic patients suffering from infratentorial stroke are likely to have impaired control for automated locomotion. Increased postural sway, difficulty in multi-joint adjustment during locomotion causing substantial variability in lower limb movements, and reduced walking speed are reported in ataxic patients (Morton and Bastian, 2004; Earhart and Bastian, 2001; Morton and Bastian, 2003). Such substantial variability of limb movements may require more attention and intention to control lower limb movements during gait. Since the medial and lateral PFCs has been reported to be involved in adjusting locomotion (Suzuki et al., 2004), anticipation of the foot movements (Sahyoun et al., 2004) and intentional control of action including locomotion (Malouin et al., 2003; Jahn et al., 2004; Pochon et al., 2001; Lau et al., 2004), sustained activities in the medial and lateral PFCs in ataxic patients might reflect the intentional control for impaired automated locomotion. Thus, our findings are in accordance with the hypothesis that impaired control of the automated locomotion in ataxic patients is compensated by recruitment of frontal cortices in the course of gait recovery, especially the PFCs. The hypothesis is also supported by the clinical observation that functional recovery after cerebellar infarction is affected by arteriosclerotic changes in the supratentorial white matters (Grips et al., 2005). Another possible explanation for the sustained prefrontal activation may be the frontal disinhibition after cerebellar damage (Tanaka et al., 2003). But this hypothesis seems to be inconsistent with the results showing different cortical activation patterns between the acceleration and steady phase. The higher cortical activation of ataxic patients in the steady phase but not in the steady phase might support the frontal compensation for the ataxic gait rather then disinhibition. There are several limitations in our study and care must be taken to interpret the results. First, because of substantial gait disturbance, we had to set the treadmill speed lower and duration of the locomotion task shorter in patients than in controls. The lower gait speed itself could modify cortical activation patterns and gait performance since cortical activation was enhanced by imagery for slower gait in healthy subjects (Jahn et al., 2004) and gait performance tended to improve during walking at faster speed in stroke patients (Lamontagne and Fung, 2004). To minimize these problems we set the treadmill speed at which both controls and patients can walk most comfortably, and the treadmill speeds in stroke patients were similar to those for their routine gait training. Secondly, fNIRS cannot measure activations in the cerebellum and deep brain structures including the basal ganglia and brainstem. However we believe that cortical monitoring during gait can give a new insight into the mechanisms underlying human locomotor control and locomotor recovery after infratentorial stroke since the cerebellum and basal ganglia have functional and anatomical connections with the cerebral cortex as discussed above. Thirdly, relatively small number of subjects might have reduced statistical power, although we could detect consistent cortical activation patterns. Finally, we could not correlate gait parameters with the activation patterns. It remains unclear how severity of ataxia influenced the cortical activation patterns during gait. Further studies to investigate longitudinal changes of cortical activation patterns are needed to clarify these questions.

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Conclusion In healthy subjects, cortical demands for controlling gait increased during the acceleration phase of walking on the treadmill compared to the steady phase. Patients presenting with ataxic gait after infratentorial stroke showed sustained cortical activation in the prefrontal cortices even during walking at a steady speed. These findings may be associated with compensatory mechanisms for the damage of locomotor control systems in the cerebellum and brainstem. Acknowledgments We really appreciated the technical assistance of Mie Arita for the fNIRS measurement. This study is supported by a Grant-in-Aid for “the Research Committee for Ataxic Diseases” of the Research on Measures for Intractable Diseases, Funds for Comprehensive Research on Aging and Health from the Ministry of Health, Labour and Welfare, Japan, and a Grant from Japan Cardiovascular Research Foundation. References Armstrong, D.M., 1988. The supraspinal control of mammalian locomotion. J. Physiol. 405, 1–37. Calautti, C., Baron, J.C., 2003. Functional neuroimaging studies of motor recovery after stroke in adults: a review. Stroke 34, 1553–1566. Chollet, F., DiPiero, V., Wise, R.J., Brooks, D.J., Dolan, R.J., Frackowiak, R.S., 1991. The functional anatomy of motor recovery after stroke in humans: a study with positron emission tomography. Ann. Neurol. 29, 63–71. Cramer, S.C., Nelles, G., Benson, R.R., Kaplan, J.D., Parker, R.A., Kwong, K.K., Kennedy, D.N., Finklestein, S.P., Rosen, B.R., 1997. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke 28, 2518–2527. Dimitrijevic, M.R., Gerasimenko, Y., Pinter, M.M., 1998. Evidence for a spinal central pattern generator in humans. Ann. N. Y. Acad. Sci. 860, 360–376. Doyon, J., Penhume, V., Ungerleider, L.G., 2003. Distinct contribution of the cortico-striatal and cortico-cerebellar systems to motor skill learning. Neuropsychologia 41, 252–262. Drew, T., Prentice, S., Schepens, B., 2004. Cortical and brainstem control of locomotion. Prog. Brain Res. 143, 251–261. Duncan, A., Meek, J.H., Clemence, M., Elwell, C.E., Fallon, P., Tyszczuk, L., Cope, M., Delpy, D.T., 1996. Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy. Pediatr. Res. 39, 889–894. Earhart, G.M., Bastian, A.J., 2001. Selection and coordination of human locomotor forms following cerebellar damage. J. Neurophysiol. 85, 759–769. Fugl-Meyer, A.R., Jaasko, L., Leyman, I., Olsson, S., Steglind, S., 1975. The post-stroke hemiplegic patient. 1. A method for evaluation of physical performance. Scand. J. Rehabil. Med. 7, 13–31. Grillner, S., Wallen, P., 2004. Innate versus learned movements—A false dichotomy? Prog. Brain Res. 143, 3–12. Grips, E., Sedlaczek, O., Bazner, H., Fritzinger, M., Daffertshofer, M., Hennerici, M., 2005. Supratentorial age-related white matter changes predict outcome in cerebellar stroke. Stroke 36, 1988–1993. Hoshi, Y., 2003. Functional near-infrared optical imaging: utility and limitations in human brain mapping. Psychophysiology 40, 511–520. Jahn, K., Deutschlander, A., Stephan, T., Strupp, M., Wiesmann, M., Brandt, T., 2004. Brain activation patterns during imagined stance and locomotion in functional magnetic resonance imaging. NeuroImage 22, 1722–1731.

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