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DOI: 10.1148/radiol.2241011005
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(Radiology 2002;224:184-192.)
© RSNA, 2002

Neuroradiology

Multiple Sclerosis: Low-Frequency Temporal Blood Oxygen Level–Dependent Fluctuations Indicate Reduced Functional Connectivity—Initial Results1

Mark J. Lowe, PhD, Micheal D. Phillips, MD, Joseph T. Lurito, MD, PhD, David Mattson, MD, PhD, Mario Dzemidzic, PhD and Vincent P. Mathews, MD

1 From the Departments of Radiology (M.J.L., M.D.P., J.T.L., M.D., V.P.M.) and Neurology (D.M.), Indiana University School of Medicine, CL 157, 541 Clinical Dr, Indianapolis, IN 46202-5111. Received June 6, 2001; revision requested July 23; final revision received January 4, 2002; accepted January 15. Supported in part by a grant from the Whitaker Foundation. Address correspondence to M.J.L. (e-mail: mjlowe@iupui.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
PURPOSE: To study the correlation of low-frequency blood oxygenation level–dependent (BOLD) fluctuations on magnetic resonance (MR) images obtained of the left- and right-hemisphere primary motor regions in healthy control subjects and patients with multiple sclerosis (MS).

MATERIALS AND METHODS: Sixteen healthy volunteers and 20 patients with MS underwent MR imaging with a 1.5-T imager by using a protocol designed to monitor low-frequency BOLD fluctuations. Data for low-frequency BOLD fluctuations were acquired with subjects at rest and during continuous performance of a bilateral finger-tapping task. These data were low-pass filtered (<0.08 Hz), and cross correlations of all acquired pixels to a region of interest in the left precentral gyrus were calculated. Confidence levels were calculated from the cross correlations. The fraction of pixels in the right precentral gyrus above a confidence level of 95% for correlation with the left precentral gyrus was calculated for each subject.

RESULTS: A plot of the fraction of the right precentral gyrus with high correlation with the left precentral gyrus for the finger-tapping state versus the resting state showed a clear discrimination between patients with MS and control subjects. Compared with control subjects, patients with MS generally had a smaller fraction of the pixels in the right precentral gyrus above the confidence level. This finding indicates that our method results in greater than 60% sensitivity and 100% specificity for discriminating patients with MS from control subjects. No significant correlation was found between clinical measures of MS disease and correlations of low-frequency BOLD fluctuations between left and right precentral gyri.

CONCLUSION: On the basis of the connectivity measure of low-frequency BOLD fluctuations, patients with MS exhibited lower functional connectivity between right- and left-hemisphere primary motor cortices when compared with that in control subjects.

© RSNA, 2002

Index terms: Brain, MR, 10.121411, 10.121412, 10.121417 • Brain, white matter • Magnetic resonance (MR), magnetization transfer, 10.121412, 10.121417 • Sclerosis, multiple, 10.871


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Spontaneous low-frequency oscillations in the regional cerebral blood flow and oxygenation in animals have been observed with many different detection techniques (1). The oscillations have been seen with laser Doppler flow (2,3), fluororeflectometry (4,5), fluorescence video microscopy (6), and polarographic measurement of brain tissue (7,8).

Biswal et al (9) showed that blood oxygen level–dependent (BOLD) magnetic resonance (MR) time series data contain spontaneous low-frequency fluctuations that appear to be synchronous between the right and left primary motor cortices. BOLD MR imaging allows the monitoring of hemodynamic fluctuations across the entire brain. This monitoring has allowed the study of low-frequency fluctuations over distance scales considered large compared with those normally relevant to studies mentioned previously. This advantage of spatial coverage with BOLD MR imaging led to the observation of synchronous fluctuations in the low-frequency domain between many brain regions with similar function (9–14). Findings in a recent study (13) also showed that these correlations are present between many different regions of cerebral cortex that are part of distributed neuronal networks involved in common tasks and that these correlations depend on the state of the network.

The underlying basis of the contrast mechanism that causes these temporally correlated fluctuations is not understood. Biswal et al (11) showed that the correlations between given homologous brain regions are reversibly diminished in hypercapnia, which implied that the effect is modulated by blood flow similar to that in BOLD contrast. This result indicated that the fluctuations are secondary to neuronal activity, but the neuronal basis of these correlations remains obscure.

For a number of reasons, multiple sclerosis (MS) is uniquely suited as a human model to evaluate potential tests of functional connectivity. MS, a common disease, is the most common idiopathic demyelinating disorder in humans. There is an extensive body of data that demonstrates clear changes in neuronal transmission with MS. The slowing of neuronal transmission by using visual, brainstem, and somatosensory evoked potentials is typical in cases of MS. Pathologically, extensive focal areas of white matter demyelination characterize MS. This finding tends to affect the periventricular and callosal white matter (15–19) by making it well suited as a model to test methods for determining the degree of interhemispheric functional connection.

The purpose of our study was to determine the correlation between low-frequency BOLD fluctuations on MR images of primary motor regions in the left and right hemispheres in healthy control subjects and patients with MS. Our hypothesis is that if the synchrony of low-frequency BOLD fluctuations is neuronally mediated, the correlations between low-frequency BOLD fluctuations should be reduced in a population with impaired white matter pathways.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
DISCUSSION
 REFERENCES
 
From November 1999 through May 2001, 20 patients (six men and 14 women; age range, 26–69 years; mean age, 46 years) with varying degrees of MS were selected consecutively from patients with MS who presented at the institutional MS clinic. The 20 patients underwent MR imaging with a protocol designed to monitor low-frequency BOLD fluctuations in the primary motor cortices of the left and right hemispheres. Inclusion criteria were clinically definite MS on the basis of the Poser criteria, ability to provide consent, and willingness to participate. Subjects were excluded if they did not meet the inclusion criteria or if contraindications to MR imaging were present (eg, pacemakers, aneurysm clips, implanted devices). An experienced neurologist (D.M.) examined all patients with MS; the clinical measures included the Expanded Disability Status Score (EDSS) (20).

Sixteen control subjects (five men and 11 women; age range, 21–45 years; mean age, 33 years) underwent MR imaging with the identical protocol. Healthy volunteers were selected from the institutional school of medicine. Inclusion criteria were no history of neurologic illness, ability to provide consent, and willingness to participate. Volunteers were excluded if they did not meet the inclusion criteria or if contraindications to MR imaging were present. Our institutional review board approved the study, and informed consent was obtained from all patients and volunteers.

The imaging protocol consisted of acquisition of MR images designed to obtain imaging correlates of the disease state and dynamic BOLD MR images to assess the synchrony of low-frequency BOLD fluctuations. The protocol consisted of T2-weighted MR imaging of the whole brain, gradient-echo MR imaging with magnetization transfer (MT) contrast of the region of the corpus callosum, and dynamic BOLD gradient-echo echo-planar MR imaging of bilateral primary and subcortical motor regions.

Imaging Protocol
The MR imaging protocol for each subject consisted of three anatomic and three dynamic MR studies. The three anatomic studies included the following: anatomic image 1, whole-brain T1-weighted three-dimensional spoiled gradient-echo sequence (repetition time msec/echo time msec of 12/35, 30° flip angle, 124 transverse sections, 1.0–1.2-mm section thickness, 24 x 24-cm field of view, 256 x 128 matrix, 32-kHz receiver bandwidth); anatomic image 2, whole-brain dual-echo two-dimensional fast spin-echo sequence (2,500/18, 90; 40–50 transverse sections; 3-mm-thick sections; 0-mm intersection gap; 24 x 24-cm field of view; echo train length of eight; 256 x 192 matrix; 64-kHz receiver bandwidth); and anatomic image 3, callosal two-dimensional MT-prepared gradient-echo sequence (106/5, 10 sagittal sections [through the corpus callosum], 3-mm-thick sections, 0-mm intersection gap, 24 x 24-cm field of view, 256 x 128 matrix, 32-kHz receiver bandwidth, 2-kHz MT frequency offset). The first seven patients with MS did not undergo MT imaging owing to a technical problem with the pulse sequence.

The three dynamic studies included the following: dynamic image 1, resting state, gradient-echo echo-planar sequence (250/50, 300° flip angle, two transverse sections, 7-mm-thick sections, 24 x 24-cm field of view, 256 x 128 matrix, 125-kHz receiver bandwidth, 1,100 volume repetitions; dynamic image 2, continuous finger tapping, same pulse sequence as that used for dynamic image 1; and dynamic image 3, block interleaved 32-second rest 32-second finger tapping, gradient-echo echo-planar sequence (2,000/50, 90° flip angle, 15 transverse sections, 7-mm-thick sections, 2-mm intersection gap, 24 x 24-cm field of view, 64 x 64 matrix, 125-kHz receiver bandwidth, 160 volume repetitions).

Findings in a previous study (12) demonstrated that the dynamic MR image obtained in the resting state could be used to show high correlation between low-frequency fluctuations in the primary motor cortices in the right and left hemispheres. Findings in a subsequent study (13) showed that the dynamic MR image obtained during continuous performance of a task that involves the regions of interest, such as the motor cortex, may depict more correlations than are seen on the resting state MR image. Bilateral finger tapping was chosen to activate the motor network in both hemispheres. Our hypothesis was that one or both of these MR images might reveal reduced synchrony in low-frequency BOLD fluctuations between regions with transcallosal connections in patients with MS with a large lesion burden in the corpus callosum.

Data Analysis
The imaging protocol was designed so that data were obtained that allowed assessment of callosal lesion volume and measurement of low-frequency BOLD fluctuations in cortical and subcortical motor regions during rest and continuous finger tapping.

MT image analysis.—Images from the MT imaging series (anatomic image 3) were analyzed to determine the lesion burden within the corpus callosum by calculating MT ratios with commercially available software (Cheshire; Hayden Image, Boulder, Colo). Ten 5-mm-thick MR images were obtained contiguously in the sagittal plane centered on the midline corpus callosum. Initial MR images were segmented to exclude the bony calvarium and extracranial contents. MT ratios were then calculated and expressed as a percentage on a pixel-by-pixel basis with the following formula: MT ratio = [1 - (S1/S0)] x 100, where S0 is the signal produced without the MT pulse during the imaging sequence and S1 is the signal obtained with the MT pulse. MT ratios were divided into 100 bins and expressed in terms of a histogram. To normalize for differences in brain size, the number of pixels within each bin was divided by the segmented intracranial volume; the subsequent number was multiplied by 1,000.

Normalized histograms were then analyzed (M.D.P.), in an automated fashion with the software, for the value of the peak height of the histogram and for the position of the peak. Peak is defined as the number of pixels in the histogram bin that contains the greatest number of pixels, and the position of the peak is defined as the MT ratio associated with that bin. Histogram analysis was performed for the entire imaged brain volume and for a finger-tapping volume of interest selected to contain crossing motor fibers within the corpus callosum.

Findings in studies (21–25) suggest that fibers connecting the primary motor cortices run through the middle portion of the corpus callosum (the posterior portion of the anterior half and the anterior portion of the posterior half of the corpus callosum). Therefore, to increase the sensitivity for detecting lesions in crossing motor fibers, a volume of interest that contained these fibers and excluded the remainder of the brain was selected for histogram analysis of the MT ratio. The volume of interest that contained these fibers was selected by using a midline section as a guide to divide the corpus callosum into three parts: anterior, middle, and posterior portions. The middle portion consisted of a 90° arc of tissue that centered on the middle of the corpus callosum. This arc of tissue was constructed by drawing a set of intersecting lines, as described in Figure 1.



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  		Figure 1. MT-prepared 5-mm-thick sagittal gradient-echo MR images (106/5 with flip angle of 12°). To select the volume of the middle portion of the corpus callosum, two lines were drawn so that one was tangential to the inferior splenium and genu and the other passed through the mamillary bodies while perpendicularly intersecting the first line. A 90° arc was then centered on the vertical line to form the anterior and posterior boundaries of the midline volume of interest.

 
The volume of interest was then made (M.D.P.), as described in Figure 2, to approximate the course of crossing motor fibers from one primary motor region through the corpus callosum to the contralateral motor cortex. MT ratios from this volume of interest were calculated on a pixel-by-pixel basis, and histograms were constructed with the method used for the whole-brain MR ratio images. In this case, however, histograms were normalized on the basis of the volume measurement of the volume of interest (mean volume, 45 cm3; volume range, 32–56 cm3). Again, histograms were analyzed for the peak height and the position of the peak height.



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  		Figure 2. Anatomic images 1-10. The midline volume of interest was determined on images 5 and 6 by using the cingulate gyrus as the superior border and the inferior aspect of the corpus callosum as the inferior border. This volume of interest was then projected on images 4 and 7, 5 mm posterior and 5 mm superior to its initial position; on images 3 and 8, 10 mm posterior and 10 mm superior; on images 2 and 9, 15 mm posterior and 15 mm superior; and on images 1 and 10, 20 mm posterior and 20 mm superior. The final volume of interest (white box) was designed to approximate the course of callosal fibers that connect the primary motor cortices.

 
Correlation analysis of low-frequency BOLD fluctuations.—A simple approach was adopted to detect very specific deficits between the primary motor regions of the right and left hemispheres. The low-frequency BOLD fluctuations were analyzed in two steps. First, regions of interest were determined on the basis of results at functional MR imaging (dynamic image 3). Second, the fraction of pixels in the motor region of the right hemisphere with statistically significant (P < .05) correlation with the pixels in the motor region of the left hemisphere was calculated.

Determination of regions of interest for correlation analysis of low-frequency BOLD fluctuations.—The whole-brain three-dimensional T1-weighted image (anatomic image 1) was used to identify the most likely region that contained primary motor cortex for finger tapping. Two transverse sections were identified; the superior section contained the cortical motor regions, and the inferior section contained the subcortical regions. The superior section was located approximately 1 cm above the superior margin of the cingulate gyrus, and the inferior section was located at the middle portion of the globus pallidus. These sections were used to depict the resting state and continuous finger tapping (dynamic images 1 and 2, respectively), although only the superior section was used in data analysis for this study. The block-interleaved functional MR image (dynamic image 3) was analyzed with a previously described least-squares method (26) to confirm appropriate selection of section location and to determine the precise regions of motor cortex in the prescribed section of images 1 and 2.

Once the primary motor regions were identified on dynamic image 3, regions of interest were drawn around the peak-activated area (Fig 3) in the primary and supplementary motor areas of each hemisphere.



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  		Figure 3. MR image (500/10) of the superior transverse section acquired depicts the regions of interest (black boxes) selected for correlation analysis in the right precentral gyrus (rPCG) and left precentral gyrus (lPCG). Red-to-yellow areas, which indicate regions of peak activation seen on dynamic image 3, were used to identify the cortical motor regions. R = right, L = left, 3.5 <- t -> 10 = color range from red to yellow.

 
All region-of-interest locations were reviewed by an experienced neuroradiologist (J.T.L., V.P.M., M.D.P.). These cortical regions of interest had approximate dimensions of 11.25 x 11.25 x 7 mm (ie, 3 x 3 x 1 pixel), and they were used in correlation analysis of images obtained in the resting state and during continuous finger tapping.

Correlation of low-frequency BOLD fluctuations in the motor cortices in the right and left hemispheres.—The hypothesis that is the basis for analysis of data for low-frequency BOLD fluctuations is that fluctuations in the data are correlated in regions that are functionally associated. To restrict the correlation with low-frequency fluctuations, the time series for each pixel were first low-pass filtered with a cutoff frequency of 0.08 Hz. This cutoff frequency is the same used in previous studies of such fluctuations (11–13). Then, we calculated the correlation coefficient (cc) between all pixels in the acquired sections and the pixels in the region of interest in the left precentral gyrus as follows:

where r is the reference time series from the left precentral gyrus, S is the signal for the given pixel, and and are the temporal average of reference and current pixel signal levels, respectively. The summation was performed over all N time points.

The resulting correlation coefficient is not distributed according to the null hypothesis (12) because of spatial correlations present in the data and widely reported spatial variations in spectral density that result in variation in degrees of freedom after temporal filtering. Correction is made by fitting the central part of the distribution to the known theoretic correlation coefficient distribution so that a proper confidence level can be calculated.

For each subject in our study, regions of interest were defined around peak-activated regions in right precentral gyrus. Pixels in this region of interest were examined to determine the fraction of pixels that exceeded a 95% confidence level for significant correlation with low-frequency BOLD fluctuations in the region of interest of the left precentral gyrus. The fraction of pixels in the right precentral gyrus with significant correlation with the pixels in the left precentral gyrus was calculated for the resting-state and continuous finger-tapping data for all subjects.

Data selection criteria.—The criteria used in selecting the final data reported included the following: (a) strong right handedness (one left-handed control subject and one left-handed patient were mistakenly included in the study population), (b) no evidence of motion on any of the dynamic MR images (six studies had evidence of gross head motion on one of the images), and (c) visualization of the primary motor cortices and supplementary motor area in the left and right hemispheres at functional MR imaging (dynamic image 3) (activation of these structures was not visualized on images in three studies, presumably owing to inappropriate selection of the section position).

The final data set included data for 13 of the 20 patients with MS (nine women and four men; age range, 26–69 years; mean age, 46 years) and 12 of the 16 healthy control subjects (seven women and five men; age range, 22–41 years; mean age, 32 years).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Clinical Measures and Callosal MT Ratio
Callosal MT ratios for the final study population are shown in Figure 4. The distribution of callosal MT ratios for patients with MS was generally lower than that for the control subjects, although Figure 4 exhibits a large overlap. Figures 5 and 6 show that there is a low correlation between the MT ratios for white matter (callosum and periventricular white matter) and the EDSS (r = -0.262, P < .411 [two-tailed test]) and between the callosal MT ratios and the EDSS (r = -0.056, P < .86 [two-tailed test]), respectively.



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  		Figure 4. Graphs depict the distribution of MT ratio (MTR) for 12 control subjects and 12 patients with MS measured in the (A) entire corpus callosum and periventricular white matter and (B) in a region of interest in the corpus callosum (see Materials and Methods for the description of the region of interest). Data are shown for all patients for whom MTR data were collected.

 


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  		Figure 5. Plot of MT ratio (MTR) measured in the corpus callosum and surrounding periventricular white matter versus EDSS for 12 patients with MS. No significant correlation is observed between the two variables. Data are shown for all patients for whom MTR data were collected.

 


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  		Figure 6. Plot of MT ratio (MTR) measured in the corpus callosum versus EDSS for 12 patients with MS. No significant correlation was observed between the two variables. Data are shown for all patients for whom MTR data were collected.

 
Connectivity Measures for Low-Frequency BOLD Fluctuations in the Motor Cortex
The distribution of the percentage of pixels in the right precentral gyrus that are over the 95% confidence level shows a large overlap for patients and control subjects when either the resting state or continuous finger-tapping data are considered alone (Fig 7). However, when the fraction of pixels in the right precentral gyrus above the threshold for continuous finger tapping versus the resting state are plotted for each subject, there is a difference between patients with MS and control subjects (Fig 8).



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  		Figure 7. Graphs depict the distribution (A) in the resting state and (B) with continuous finger tapping of the fraction of pixels in the right precentral gyrus that are above a 95% confidence level for correlation with low-frequency BOLD fluctuations in the left precentral gyrus.

 


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  		Figure 8. Plots in (left) patients with MS and (right) control subjects depict the fraction of pixels in a region of interest in the right precentral gyrus (PCG) that are above a 95% confidence level for correlation with low-frequency BOLD fluctuations in a region of interest in the left precentral gyrus during continuous finger tapping versus the resting state. Regions of interest were defined on the basis of results in a motor study with functional MR imaging. The regions below the dashed lines contain data for more than 60% of the patients and for none of the control subjects.

 
No significant correlations were observed between connectivity measures of low-frequency BOLD fluctuations and MT ratios.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
REFERENCES
 
Clinical evaluation remains the standard of reference for determining the severity of MS. The EDSS is the measurement tool that is used most widely for following the clinical progression of MS (20). The EDSS measures combined impairment and disability by incorporating scores of functional status scales with the patient’s ability to walk, use the upper limbs, communicate, and swallow. The EDSS is easy to administer and is the standard for physical evaluation in most clinical and imaging trials in patients with MS. However, the EDSS is imperfect. It is biased toward motor function, particularly ambulation, and is relatively insensitive to clinical changes that do not impair gait (27,28). Although there are numerous alternative and supplemental measures to the EDSS, none of them combines the excellence in validity, reliability, and responsiveness to disease progression necessary for widespread adoption as a single measurement of clinical status (29). Therefore, the EDSS was used in this study.

MS has been studied extensively with MR imaging. The characteristic imaging appearance of MS is of multiple areas of hyperintensity on T2-weighted MR images that depict primarily the corpus callosum and periventricular white matter (30–33). Although lesion burden on T2-weighted images has been studied extensively with a wide variety of automated and semiautomated techniques for assessment of lesion volume, there is no clear relationship between the extent of lesions depicted on T2-weighted MR images and the degree of clinical impairment in patients with MS (34–37). Several reasons may account for this apparent lack of correlation. Studies of lesion burden have largely focused on brain lesions, but much of the clinical deficit may be due to spinal cord lesions. Clinical assessments with the EDSS are biased for motor deficits, but brain lesions also affect the nonmotor brain regions (34–37). Additionally, findings in MT imaging studies of MS suggest that normal-appearing white matter may contain lesions on T2-weighted MR images (38–42).

In MT imaging, which was developed by Wolff and Balaban (43), an off-resonance radio-frequency pulse is used to saturate water protons tightly bound to macromolecules. These saturated protons then exchange with protons in the free water pool, which produces a net loss in signal. The MT effect is more prominent in tissues with highly organized macromolecules (44), such as myelinated white matter (38). The reduction in signal due to the MT effect can be expressed quantitatively in terms of the MT ratio. MS lesions seen on T1-weighted, gadolinium-enhanced T1-weighted, and T2-weighted MR images demonstrate decreased MT ratios (38,45–49). More interestingly, the normal-appearing white matter on T2-weighted MR images in patients with MS demonstrates decreased MT ratios compared with those in control subjects (38–42). This finding demonstrates that the MS lesion burden that is not detected at conventional MR imaging may be the imaging correlate of microscopic lesions seen in normal-appearing white matter at pathologic examination (38–42,50,51). Changes in MT ratios correlate with standard imaging measures of lesion burden and, more important, with clinical examination findings (52–57). MT ratios calculated on a pixel-by-pixel basis and expressed as histograms are sensitive robust user-independent tools for measuring whole-brain and regional lesion burden in patients with MS. Such patients demonstrate a decrease in the peak height of the MT ratio at histogram analysis, as well as movement of the peak to lower MT ratios (48,54,55,58–62).

While the distribution of callosal and whole-brain MT ratio peak height for patients with MS in our study was generally lower than that for the control subjects, there was a large overlap in MT ratio distribution between the two. In addition, there was low correlation of MT ratio with EDSS in our study. This observation in the case of callosal MT ratio is consistent with that in other studies of regional MT ratio assessment (56,63–65) in which only portions of the brain were analyzed. Because clinical measures such as the EDSS are global measures, they should be compared with more comprehensive imaging measures. In previous studies, a significant correlation was found between whole-brain volumetric MT ratio analysis and physical disability (55,65–67), but all had large study populations. The present study was designed to test the relationship of lesion burden to loss of low-frequency BOLD fluctuations. Hence, a relatively small number of patients was used, and the study was focused on a limited type of motor performance, finger tapping. Development of quantitative behavioral measures of the specific motor task used to generate functional MR imaging activation could increase the specificity of our clinical measures.

Low-frequency BOLD fluctuations have been studied in patients with agenesis of the corpus callosum (68) but have not been previously studied in patients with MS with callosal lesions, to our knowledge. We believe that subjects with MS offer the best human model for white matter disconnection. Unlike subjects with agenesis of the corpus callosum, there are large populations of patients with MS. White pathways in patients with MS can be assumed to have been initially healthy anatomically (ie, healthy pattern of connections that are injured by the disease process). Patients with agenesis of the corpus callosum, however, never had healthy interhemispheric connections.

The lack of significant correlation between MT ratio peak value measures and reduced synchrony of low-frequency BOLD fluctuations is likely owing to the relatively large volume of interest selected to encompass the crossing motor fibers in the MT ratio analysis. The volume of interest was relatively large due to the variability of the position for the crossing fibers reported in the literature (20–25). Although selection of a large volume of interest virtually guarantees the inclusion of the crossing fibers, it also ensures the inclusion of white matter outside the desired white matter tract. This inclusion lowers the specificity of the MT ratio measurement, which may have led to the poor correlation with measurements of low-frequency BOLD fluctuations. Additionally, only the midline portion of the crossing fibers was imaged with the MT imaging sequence used in this study; therefore, MS lesions that were located laterally in the subcortical portions of the crossing fibers were missed with our protocol. This miss reduced the sensitivity to lesions in the pathway, which may have resulted in reduced low-frequency BOLD fluctuations.

In future studies, investigators could attempt to increase this specificity by targeting only white matter tracts that connect the precentral gyrus, perhaps by using white matter tractography with diffusion tensor imaging (69–73).

The authors recognize the difference in ages between patients with MS and control subjects. The results of the present study, however, are unlikely to be affected by this discrepancy. Findings in several studies suggest that there is relatively little change in the integrity of white matter over the age range of our subjects. Diffusion analysis of healthy brain white matter shows no important changes until the 6th decade of life (74,75), and no changes in MT ratio (76) have been seen with aging. These findings suggest relatively stable white matter with little loss over the age range of our subjects.

Evaluation of the low-frequency BOLD fluctuations data in both the resting state and during finger tapping allowed us to differentiate patients with MS from control subjects. Our control subjects had at least one of the low-frequency BOLD fluctuations images with relatively high connectivity between right and left hemisphere precentral gyri, but most patients with MS had a low connectivity measure for both images. While they do not completely discriminate between these two groups, the measures shown in Figure 8 represent the first functional imaging measures with the potential to discriminate patients with MS independent of clinical measures. It is also strong evidence in support of our hypothesis that low-frequency BOLD fluctuations correlations reflect intact neuronal connections.

In conclusion, patients with MS exhibited lower synchrony of low-frequency BOLD fluctuations between right- and left-hemisphere primary motor cortices when compared with control subjects. With the assumption that MS constitutes a model of impaired white matter integrity, our results constitute the first evidence, to our knowledge, that the high correlations between low-frequency BOLD fluctuations observed by many researchers between functionally connected cortices are mediated by neuronal connections. In addition, the use of functional MR imaging of low-frequency BOLD fluctuations to assess white matter integrity and function represents a new and fundamentally different approach to the imaging analysis of MS and other white matter diseases. Such a test of functional connectivity could be a central component of future research and clinical studies in MS and other neurologic diseases that result in impaired white matter connectivity.


   ACKNOWLEDGMENTS
 
The authors thank the technologist staff of the Indiana University School of Medicine MR Imaging Facility for their valuable assistance in performing this study.


   FOOTNOTES
 
Abbreviations: BOLD = blood oxygen level–dependent, EDSS = Expanded Disability Status Score, MS = multiple sclerosis, MT = magnetization transfer

Author contributions: Guarantor of integrity of entire study, M.J.L.; study concepts, M.J.L., M.D.P., J.T.L., V.P.M., D.M.; study design, all authors; literature research, M.J.L., M.D.P.; clinical studies, M.J.L., M.D.P., J.T.L., D.M.; data acquisition, M.J.L., M.D.; data analysis/interpretation, M.J.L., M.D.P., M.D.; statistical analysis, M.J.L., M.D., M.D.P.; manuscript preparation, M.J.L., M.D.P., V.P.M.; manuscript definition of intellectual content, editing, revision/review, and final version approval, all authors.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 

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