Brain Advance Access originally published online on October 26, 2005
Brain 2006 129(2):527-537; doi:10.1093/brain/awh670
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Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis
1 Centre for Functional Magnetic Resonance Imaging of the Brain, The John Radcliffe Hospital and 2 Department of Neurology, The Radcliffe Infirmary, Oxford, UK
Correspondence to: Professor Paul Matthews, Centre for Functional Magnetic Resonance Imaging of the Brain, The John Radcliffe Hospital, Headington, Oxford OX3 9DU, UK E-mail: paul{at}fmrib.ox.ac.uk
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Cognitive dysfunction (affecting particularly attention and working memory) occurs early in patients with multiple sclerosis. Previous studies have focused on identifying potentially adaptive functional reorganization through recruitment of new brain regions that could limit expression of these deficits. However, lesion studies remind us that functional specializations in the brain make certain brain regions necessary for a given task. We therefore have asked whether altered functional interactions between regions normally recruited provide an alternative adaptive mechanism with multiple sclerosis pathology. We used a version of the n-back task to probe working memory in patients with early multiple sclerosis. We applied a functional connectivity analysis to test whether relationships between relative activations in different brain regions change in potentially adaptive ways with multiple sclerosis. We studied 21 patients with relapsing-remitting multiple sclerosis and 16 age- and sex-matched healthy controls with 3T functional MRI. The two groups performed equally well on the task. Task-related activations were found in similar regions for patients and controls. However, patients showed relatively reduced activation in the superior frontal and anterior cingulate gyri (P > 0.01). Patients also showed a variable, but generally substantially smaller increase in activation than healthy controls with greater task complexity, depending on the specific brain region assessed (P < 0.001). Functional connectivity analysis defined further differences not apparent from the univariate contrast of the task-associated activation patterns. Control subjects showed significantly greater correlations between right dorsolateral prefrontal and superior frontal/anterior cingulate activations (P < 0.05). Patients showed correlations between activations in the right and left prefrontal cortices, although this relationship was not significant in healthy controls (P < 0.05). We interpret these results as showing that, while cognitive processing in the task appears to be performed using similar brain regions in patients and controls, the patients have reduced functional reserve for cognition relevant to memory. Functional connectivity analysis suggests that altered inter-hemispheric interactions between dorsal and lateral prefrontal regions may provide an adaptive mechanism that could limit clinical expression of the disease distinct from recruitment of novel processing regions. Together, these results suggest that therapeutic enhancement of the coherence of interactions between brain regions normally recruited (functional enhancement), as well as recruitment of alternative areas or use of complementary cognitive strategies (both forms of adaptive functional change), may limit expression of cognitive impairments in multiple sclerosis.
Key Words: fMRI; multiple sclerosis; cognition
Abbreviations: fMRI = functional MRI; ROI = region of interest; VWM = verbal working memory
Received April 19, 2005. Revised August 29, 2005. Accepted September 28, 2005.
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Introduction |
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Cognitive dysfunction (estimated to affect between 30 and 70% of patients) is now appreciated to be a characteristic feature of multiple sclerosis (Rao et al., 1991
). The most commonly identified functional impairments affect attention, speed of information processing and memory (Achiron and Barak, 2003).
Some functional imaging studies already have attempted to define changes in brain activity during memory tests in patients with multiple sclerosis relative to healthy controls. Using the paced auditory serial addition task, Mainero et al. (2004a) defined differences in activity in several regions of the brain, including areas involved in the primary task (inferior and middle frontal and superior and middle temporal gyri and the inferior parietal cortex). In a memory recall task there was overlap between regions recruited to a greater extent in patients who performed well on the task relative to healthy controls and the primary task-associated activations (Mainero et al., 2004a). Similar to findings for Alzheimer's disease, in which subjects in an early preclinical phase show relatively increased prefrontal cortical activation with memory deficits (Bookheimer et al., 2000), the findings in multiple sclerosis were interpreted as evidence for increased activity in relevant areas that is able to functionally compensate for injury associated with progression of the disease. Correlations between activation changes and T2-hyperintense lesion burden were consistent with this. However, increased brain activity is not expected generally with progression of the disease. Previous PET and SPECT studies have emphasized reduced cerebral blood flow and metabolism in patients with established multiple sclerosis (Brooks et al., 1984; Paulesu et al., 1996; Sun et al., 1998). A recent report using arterial spin labelling confirms that grey matter hypoperfusion is associated generally with secondary progressive and primary progressive multiple sclerosis (Rashid et al., 2004). These latter studies suggest that patients with multiple sclerosis may have limited cognitive ‘functional reserve’, the ability to match brain activity to cognitive demands.
Use of tasks with graded difficulty to assess the ability of brain regions to increase activity with increasing task demands offers a parametric approach to testing this notion directly. In their initial work, Wishart et al. (2004) used 1- and 2-back tasks in a small group of multiple sclerosis patients to test for memory task-related increases in brain activity. They found that patients showed relatively less increased activity than healthy controls in regions associated with increasing task difficulty. Although interpretation of this study is limited by the lack of direct measurement of performance during the functional MRI (fMRI) task, these results suggest that early functional impairments in patients may be reflected as a reduced ability to recruit relevant brain regions.
However, it is important to appreciate the complexities of interpreting differences in patterns of activation across the brains of subjects with pathology relative to healthy controls. First, fMRI identifies brain regions in which activity is associated with task performance, not those that are necessary (Johansen-Berg et al., 2002). Secondly, alternative strategies for performance of a task can be associated with differences in patterns of activation without being able to be interpreted in a simple way as adaptive (Cifelli and Matthews, 2002). Finally, pathology may affect more fundamental processes, such as those for perception or action, that also can be associated with differences in brain activity and potentially may not be adequately controlled in the contrast (Price and Friston, 1997). Thus, interpretation of evidence for functional reorganization (particularly for cognitive tasks) is complex.
Here we present data from an n-back working memory task over three levels of task difficulty in order to parametrically assess changes in brain activity in multiple sclerosis patients relative to healthy controls. We reasoned that if adaptive functional reorganization contributes to limiting clinical expression of pathology affecting cognition then differences between patients and controls should be manifested even in early stages of the disease. We therefore chose to study a group of relapsing-remitting multiple sclerosis patients who do not clinically express memory deficits. Study of a patient group without clinically evident cognitive deficits also removes the confound of performance differences. We hypothesized that adaptive changes may be manifested as altered functional interactions between brain regions, as well as differences between the regions recruited. We therefore extended our work with a functional connectivity analysis to test for multivariate differences in relations between activities in brain regions normally recruited for the task.
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Subjects
A total of 21 right-handed patients (6 men, 15 women; median age 39 years, range 22–55 years) with clinically definite multiple sclerosis (relapsing-remitting or relapsing-progressive; median duration 6 years, range 1–20 years) according to the Poser criteria (Poser et al., 1983
) performed a verbal working memory (VWM) paradigm whilst undergoing fMRI scanning (Table 1). Disability was assessed with Kurtzke Expanded Disability Status Scale (EDSS) (median EDSS 2, range 0–6) (Kurtzke, 1983) at the time of the fMRI scanning by experienced neurologists (A.C., Z.C.). None of the patients had clinically evident visual deficits, impairment of right-hand movement, or current or previous symptoms related to altered cognition (memory, language, calculation or attentional deficits) on the basis of clinical histories obtained during prior routine follow-up or directed questioning at the initial interview for this study. None were on disease-modifying therapy. Subjects with a history of psychological and psychiatric problems, epilepsy, learning disability, alcoholism, head injury, episodes of loss of consciousness or severe ongoing stressors (such as bereavement, divorce and financial problems) were excluded. As controls, 16 right-handed, age-matched healthy subjects were also studied (6 men, 10 women; median age 39 years, range 23–51 years). All subjects were advised to abstain from smoking and drinking caffeine-containing beverages for 4–6 h before the scan. Written consent was obtained from all subjects. Ethical approval for this study was given by Oxford Regional Ethics Committee.
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Sessions
The subjects were asked to attend the imaging centre on two separate sessions, which were at least 1 day, but less than a week, apart from each other. During the first session the subjects were screened for exclusion criteria, as stated above, and were invited to read typewritten instructions of the paradigm without any time limit. They then were asked whether they understood the task and any further questions were answered. At this stage the examiner suggested a strategy to carry out the task more efficiently: subvocally repeating the letters of the paradigm as they were presented, in groups of 2, 3 or 4 (according to the condition type for n = 1, 2 or 3, respectively). A training paradigm that is different but of a general design identical to that for the fMRI task was then administered with a desktop PC. After completion of the paradigm, the National Adult Reading Test (Nelson, 1991
) and a hospital anxiety depression questionnaire (Zigmond and Snaith, 1983) were administered.
During the second session the subject was first reminded of the rules of the task and of the strategy, and then imaged while performing the task inside the scanner. The total duration of the imaging session varied between 60 and 90 min. After scanning, all patients underwent a full neurological examination. Finally the Hospital Anxiety and Depression scale (Bjelland et al., 2002) was administered to patients and controls, and the Fatigue Severity Scale (Krupp et al., 1988, 1989) to the patients only.
The paradigm used was a sequential letter task that has been previously used in several neuroimaging studies of VWM (Braver et al., 1997; Cohen et al., 1997; Nystrom et al., 2000; Honey et al., 2002). Subjects viewed the stimuli on a PC video display unit for the behavioural session and on a back-projection screen at the foot of the MRI couch during functional scanning. The screen was easily seen using prism glasses, which also allowed use of corrective lenses in case of refractive deficits.
Paradigm
Subjects were presented with a sequence of English alphabet consonants in either lower or upper case (subtending an angle of 6°) appearing one at a time. In each session the task involved one control condition and three memory conditions of varying difficulty. Each task block lasted for 1 min with four blocks per condition arranged in a pseudo-random order. At the start of each block the subjects would see 0-, 1-, 2- or 3-back displayed to indicate which task they would perform for that full block. In the 0-back task, a target letter was shown under the word 0-back at the start and the subject was required to press a button every time that letter appears, regardless of the case. For the other three memory conditions a button press was required whenever any letter was repeated (upper or lower case) the appropriate number of times (n = 1, 2 or 3) further down the sequence. For example, in the 1-back condition the button was pressed when the same letter appeared immediately, but in the 3-back condition it was pressed only if it appeared three letters further on. Each task condition was controlled for the number of target letters, and the letters were presented every 2 s. The number of responses correctly made for each task was recorded.
Imaging
All scans were performed using a 3.0 tesla whole body scanner with a Varian Inova console and a quadrature birdcage radiofrequency head coil. An echoplanar imaging sequence was used to acquire the fMRI data [24 x 8 mm coronal slices, echo time (TE) = 30 ms, repetition time (TR) = 3000 ms, field of view = 192 x 256 mm, matrix 64 x 64]. A T1-weighted [64 x 3 mm coronal slices, TR = 15 ms, TE = 6.9 ms] and a T2-weighted [34 x 5 mm coronal slices, TR = 5000 ms, TE = 65 ms] anatomical scan was also acquired for each subject.
Data analysis
Lesion volume quantification was measured manually using Jim, Version 3 (kindly provided in a demonstration package by Dr Mark Horsfield, Xinapse Software, Leicester University). This software uses a semi-automated threshold outlining approach to identifying lesion borders and allowed the total volume of lesions for each subject to be estimated from T2-weighted images. The observer was blinded to other results. The mean intra-rater variability was 3.5% for use of the software. A regional analysis was performed in bilateral white matter of the medial halves of the prefrontal cortex (defined posteriorly by the central sulcus and inferiorly by the sylvian fissure). Cingulate cortex cross-sectional area in the coronal plane was measured using the same software by outlining the edge of the cingulate cortex, averaged over three separate slices at the level of the anterior commissure. These values were normalized for brain volume by the intracranial width at the same level, giving a ratio in units of millimetre.
Analysis of the fMRI data was carried out using FMRI Expert Analysis Tool, version 5 (FEAT) (www.fmrib.ox.ac.uk/fsl). The following prestatistics processing steps were applied: motion correction using MCFLIRT (Jenkinson and Smith, 2001), spatial smoothing using a Gaussian kernel of full width half maximum (FWHM) 5 mm, and mean-based intensity normalization of all volumes by a constant factor and high-pass filtering (Gaussian-weighted LSF straight line fitting, with sigma = 200.0 s). Statistical analysis was carried out using FMRIB's Improved Linear Model (FILM) with local autocorrelation correction (Woolrich et al., 2001). All probability values reported are corrected for multiple comparisons. The statistical images generated were related to the brain anatomy of each subject by registration with the individual T1-weighted structural scan.
To identify brain activation during the VWM task in patients and controls, analysis of mean activation at each level of task difficulty was performed. For patients and then for control subjects separately, the three task conditions (1-, 2- and 3-back) were contrasted with the 0-back condition. The data was then analysed for areas showing increasing activation with increasing task difficulty by applying a linear contrast from 1-back through 3-back. Two ‘between-group’ analyses were performed, (i) patients–controls and (ii) controls–patients, for this linear contrast.
All group analyses were performed using FMRIB's Local Analysis of Mixed Effects (FLAME) model (Behrens et al., 2003) with Z (Gaussianized T) statistic images thresholded using clusters determined by Z > 3.5 and a (corrected) cluster significance of P = 0.01 (Worsley et al., 1992; Friston et al., 1994; Forman et al., 1995). The high-resolution T1-weighted images from the subjects were co-registered into a standard space (Montreal Neurological Institute 152 Brain) and the group thresholded Z statistic images overlain. In the between-groups analysis, the T1-weighted images were averaged to produce a mean structural image on which the thresholded Z statistic image was overlaid. This allowed assessment of activation areas in terms of anatomical landmarks as well as reporting the co-ordinates of peak activations within each anatomically defined area.
Brain regions showing task-difficulty dependant increases in activation in controls as identified from the thresholded Z statistic group mean image of the linear contrast in controls were divided into ‘masks’. These masks were then applied as regions of interest on the functional image of each individual during 1-, 2- and 3-back tasks, allowing a calculation of mean activation per subject in each region at each level of difficulty.
Functional connectivity
To observe the interactions between different regions involved in the VWM task, a measure of functional connectivity was calculated. There are several approaches that have been suggested for functional connectivity analysis of fMRI data (Penny et al., 2004; Ramnani et al., 2004). We have adopted an approach relying on few assumptions, to minimize the potential bias in contrasts between the patient and control populations. Our analysis assesses multivariate correlations between incremental changes in brain activation with increasing task difficulty.
The analysis was confined to regions that demonstrated a monotonic increase in activity with task difficulty and corresponded to the same regions used above in the region-of-interest (ROI) analysis. Within each ROI the difference in mean signal from 1-back (the lowest level of difficulty) to 3-back (the highest level of difficulty) was calculated to determine the relative activation increase corresponding to increasing task demand.
The correlations between activation changes in directly anatomically connected regions were calculated for each of the multiple sclerosis patients and for the healthy control subjects. To do this, each region was considered separately as being dependent on activity across all of the other regions of interest. A linear regression of signal changes from anatomically connected areas on the signal change in each defined dependent region was performed. For example, signal change in the cingulate, superior medial frontal, left prefrontal and right parietal regions was applied to the linear regression analysis of signal change in the right prefrontal region. To account for co-linearity between regions, the independent contribution of each region was assessed by the partial correlation co-efficient. The significance of each correlation was tested against the null hypothesis that there was no contribution as part of the linear regression (SPSS 12.0.1). A corrected, two-tailed P < 0.05 was considered statistically significant. Any correlations that were found to be significant in only patients or control subjects were indicated and a direct comparison between the groups was performed using a Fisher's Zr transformation (Rao, 1973). The Z value from this transform was compared against a normal distribution to obtain a P value. Together, this approach allows identification of regions of potential functional connectivity, and also regions where the magnitude of connectivity differs between groups.
Statistics
A non-parametric, Kruskal–Wallis test was used to test for differences in behavioural performance. Using a repeated measures analysis, the effects of group (patient or control), task difficulty (1-, 2- or 3-back), region and their interactions on mean activations were determined. The analysis considered mean signal changes in six regions of interest (bilateral dorsolateral prefrontal cortices, bilateral parietal cortices, anterior cingulate and superior medial frontal gyri). Pearson's correlation was used to assess any relationship of lesion volume or cingulate cross-sectional area with functional activation measures. All statistical analyses were performed using SPSS for Windows (version 12.0.1).
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Results |
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Patients and healthy controls showed similar performance on the n-back task
A comparable level of performance on the n-back task was found between the relapsing-remitting multiple sclerosis patient and control groups during the fMRI study (Fig. 1). Both patients and healthy volunteers demonstrated a similar decline in performance with increasing task difficulty.
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Patients and healthy controls activated similar brain regions during the n-back task
In the control group, the task specific activation was found in regions corresponding to medial frontal areas and bilaterally in posterior parietal cortices, inferior frontal gyri and dorsolateral prefrontal cortices (Z > 3.5, corrected P < 0.01). These areas were identified at each level of task difficulty (Fig. 2A–C and Table 2). Regions identified with the main effect of task also showed significant monotonic increases with task difficulty, modelled as a linear increase in activity in a second level analysis from n = 1–3 (Z > 3.5, corrected P < 0.01) (Fig. 2D and Table 2).
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Patients showed similar areas of activation to the control group at each level of task difficulty (Z > 3.5, P < 0.01) (Table 2). No significant differences were found for a simple contrast between patient and control groups for the main effect of task at n = 1, 2 or 3.
Patients had reduced increments of brain activity with increasing task difficulty: evidence for impaired functional reserve
To test the hypothesis that there may be impairment in functional reserve in patients, we tested for activity related changes in activation for the n-back task. Significant monotonic increases in activation were found with greater task difficulty (across n = 1–3) in patients (Z < 3.5, P < 0.01) (Table 2). A mixed-effects analysis contrasting patients with controls showed significant between-group differences, however. Controls showed significantly greater increases in activation in the superior medial frontal gyrus than patients (Z > 3.5, P < 0.01) (Fig. 3B). There were no areas in which patients showed greater incremental activation than controls.
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Exploration of parametric changes in brain activity with increasing task difficulty using a ROI analysis
To better understand these group analysis results, the monotonically increasing activation changes with increasing task difficulty were assessed in each subject for regions of interest including the medial superior frontal gyrus and the anterior cingulate cortex. In the control group, 13 out of 16 subjects showed significant activation in these regions, while only 8 out of 21 and 3 out of 21 multiple sclerosis patients showed significant relative activation in the anterior cingulate or in the superior frontal gyrus, respectively (Chi-square, P < 0.001). No significant difference in cranial size normalized cross-sectional area of the anterior cingulate (measured at the same level) was found between the patients (mean, 2.17 ± 0.56 mm) and healthy controls (mean, 2.27 ± 0.35 mm) to account for the differences in activation. No correlation between cross-sectional area and activation was found for patients in the multiple sclerosis group (r = –0.17, P = 0.46) (Fig. 4).
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The ROI analysis was extended to assess increases in activity associated with increasing task difficulty across each ROI defined in the main effect of task. The mean relative intensity for each of the regions (Table 2) was calculated at each level of task difficulty for each subject across n = 1, 2 or 3 relative to the 0-back task. Patients showed consistently lower increases in activation with increasing task difficulty relative to healthy controls in all regions, with a significant main effect of the task (Fig. 5B) (corrected P < 0.001). There was also a significant interaction between region and group for the increase in activation with increasing difficulty (P < 0.001).
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Activation changes in patients were not related to T2-lesion load or disease duration
There was no significant correlation between either the global T2-hyperintense lesion load or the T2-lesion volume in the medial frontal cortex and activations for the individual contrasts for main effect or the monotonic increase in activation with increasing task difficulty.
Evidence for potentially adaptive, altered functional interactions between brain regions in patients during the n-back task
To test the hypothesis that changes in interactions between brain regions might mediate adaptive functional changes with disease progression, a functional connectivity analysis was performed as a multivariate test for group differences in correlations between regional activities with increasing task difficulty. Individual correlations between activity-changes in anatomically directly connected regions were identified (Table 3). We then tested for differences in these correlations between the healthy controls and patients. The control group showed a significant correlation between the right dorsolateral prefrontal cortex and the superior frontal gyrus (corrected P < 0.05), which was not found in the patient group (Fig. 5B). Patients showed a significant correlation between activity changes in the right and left dorsolateral prefrontal regions (corrected P < 0.05), which was not demonstrated in the control subjects (Fig. 5C).
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Discussion |
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Reduced functional reserve for cognition in patients with multiple sclerosis
The first major finding of our study was that, relative to healthy control subjects, patients with multiple sclerosis had reduced increments in functional activation with increasing difficulty of a VWM task, a task assessing neuropsychological domains commonly impaired in multiple sclerosis. Decreased activation for patients was found despite similar performance on the task for patients and controls. This therefore reflects changes in brain processing for the task and can be interpreted as an index of reduced functional reserve for cognition (Lazeron et al., 2004
). The differences between healthy controls and the patients reflect the consequences of pathology; although, in terms of cognition, this was subclinical at the stage that the patients were studied. The findings are consistent with previous metabolic imaging studies suggesting relative brain hypometabolism in patients with multiple sclerosis (Brooks et al., 1984; Paulesu et al., 1996; Sun et al., 1998; Rashid et al., 2004). Differences in activation could be due to differences in cognitive strategy. However, this is unlikely. All subjects were limited from using spatial cues by including both upper and lowercase letters as targets, forcing reliance on verbal cues. All subjects also were trained in a specific strategy involving subvocalization of letters throughout the paradigm. The notion that subjects used a common cognitive strategy for the task is supported by the observation that functional activation patterns for the main effect of task were similar for both patients and control subjects.
Although some previous studies have identified potentially adaptive recruitment of novel brain regions in patients with multiple sclerosis during cognitive tasks (Penner et al., 2003; Mainero et al., 2004a), our study emphasized that controls and patients both activated anatomically similar regions. Consistent with prior studies of VWM, activations in our study were most significant in the posterior parietal, lateral prefrontal and medial prefrontal cortices. Our observations are not without precedent. Lazeron et al. (2004) failed to find novel regions of brain recruitment with a Tower of London task (although the fMRI contrast was not controlled for differences in task performance). The failure to identify novel adaptively recruited brain regions in this study may be explained in part by differences in the tasks used and differences in the organization of the distributed networks responsible for cognition related to task performance. We speculate that perhaps also significant, however, is the finding that performance in the patient and control groups was well-matched in our study and that cognitive strategy was controlled by training subjects with a preferred strategy. Control for the motor responses required for the task also may have limited differences, as patients with multiple sclerosis show altered patterns of motor-related activation at an early stage in the disease (Reddy et al., 2000; Pantano et al., 2002).
Patients show most consistent relative functional impairment in mesial prefrontal polymodal cortex
A second major finding of our study was that, while differences between patients and controls in task related activation increases were widespread, the most consistent difference in task-related activation between patients and controls was found in the superior frontal and anterior cingulate gyri. Both these regions include polymodal neocortex involved in processing cognitive functions including attention. Demands for coherence of input for this polymodal processing may make function of this region particularly vulnerable to pathology from multiple sclerosis. It also is possible that this region is somewhat selectively involved by neocortical pathology with multiple sclerosis. Bo et al. (2003) had described a particularly high neocortical lesion load in the anterior cingulate. However, the relative selectivity of differences in this region may simply be quantitative. It is possible that a large group of healthy controls might confer greater sensitivity to detection of a wider range of changes in the patient group.
Potentially adaptive, altered functional connectivity between medial and lateral prefrontal cortex in patients with multiple sclerosis
Despite the evidence for reduced neurophysiological functional reserve, multiple sclerosis patients were able to maintain working memory performance (as assessed by the n-back task) similar to controls, suggesting potentially compensatory increases in efficiency of use or of interactions between interacting regions. Consistent with this, our third major finding was of altered task-related functional connectivity in patients compared with controls. Previous work concentrating on consequences of axonal injury in the white matter in patients with multiple sclerosis has emphasized disruption of frontal connectivity as an expression of pathology. Neuropsychological deficits with multiple sclerosis have been interpreted in terms of disconnection models. However, analysis of functional connectivity suggests a more complex situation. Based on an effective connectivity analysis of PASAT working memory task, Audoin et al. (2003) recently reported that different interactions show either decreased or increased connectivity in patients with multiple sclerosis. This suggests that with lower susceptibility to injury or pathological involvement, some pathways show enhanced function to compensate (at least partially) for impaired functional connectivity within a processing network.
There are several approaches to measuring functional connectivity (Ramnani et al., 2004). For our study, we have used a highly data-driven approach constrained only by basic anatomical information limiting the possible connections to those for which there is evidence for large direct tracts. An advantage of this approach is that it has less potential model-based bias. We took care in the correlation analysis also to factor out co-linearities.
Not unsurprisingly, healthy controls showed significantly stronger functional connectivity between the superior medial frontal region and the right prefrontal cortex. Patients showed significantly reduced activation of the medial frontal regions relative to the controls. The functional connectivity and univariate activation analyses together therefore suggest functional pathology limiting interactions between the lateral and medial prefrontal cortex in the patients. However, the functional connectivity analysis also defined changes in patients that could not be inferred directly from a univariate analysis: patients showed relatively increased functional connectivity between the right and left prefrontal cortices, a functional relationship not significant in healthy control subjects. Recruitment of homologous regions in the two hemispheres as a compensatory mechanism is consistent with observations in the motor system for patients with multiple sclerosis, in whom increased recruitment of premotor cortex ipsilateral to the hand moved is among the most consistent differences in multiple sclerosis patients relative to healthy controls (Cifelli and Matthews, 2002). We speculate that the increased functional connectivity between the two lateral prefrontal cortices is an adaptive mechanism that contributes to limiting expression of pathology with this cognitive task.
A new strategic focus for cognitive therapies in multiple sclerosis?
In summary, our results have shown that even early in the progression of disease and before clinical expression of neuropsychological deficits, multiple sclerosis patients have evidence for reduced cognitive functional reserve by fMRI. The consistency of fMRI changes in the patients was striking, suggesting that it provides a sensitive measure of disease-related change. Associated with the primary activation changes are differences in functional connectivity involving increased direct interactions between lateral prefrontal cortices in the two hemispheres. This suggests unmasking of a direct inter-hemispheric pathway (not used in healthy control subjects) that may in part compensate for relative dysfunction in medial prefrontal regions. We speculate that the reduced functional reserve in patients will lead to failure of processing with increasing cognitive demands in the patients at lower levels of task demands than for healthy controls. As a functional expression of pathology, we expect that it may provide a measure sensitive to change with an increasing burden of disease over time.
It now is of importance to extend this work by testing for the relation between these neurophysiological findings and neurophysiological deficits induced by graded cognitive loads in a neuropsychologically well-characterized population. Strategies to enhance the functional reserve and limit its rate of loss or to enhance potentially compensatory functional connectivity provide new targets for therapy in multiple sclerosis. While these may appear novel, in important respects this may simply be an updating of context for drugs previously considered as approaches for enhancing conduction or prolonging neuronal summation times (Mainero et al., 2004b).
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Acknowledgements |
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P.M.M. is grateful to the MRC (UK) for personal and for core support of the FMRIB Centre. P.M.M. and J.P. jointly thank the Multiple Sclerosis Society of Great Britain and Northern Ireland for support of multiple sclerosis studies. The authors thank Dr Christian Beckman, FMRIB Centre, Oxford, for advice concerning the functional connectivity analysis.
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