Книги по МРТ КТ на английском языке / MR Imaging in White Matter Diseases of the Brain and Spinal Cord - K Sartor Massimo Filippi Nicola De Stefano Vincent Dou

BOLD signal

b

visual

auditory

time

Fig. 8.12. Block design fMRI experiment showing an overlapping paradigm consisting of visual and auditory stimulation epochs. The visual stimulus consisted of a flickering checkerboard (8 Hz frequency) shown for 30 s alternated with 30-s display of a fixation cross. The auditory stimulus consisted of recorded speaking voices for 45 s alternated with 45 s of rest. A combined analysis was applied to reveal the visual (yellow–red) and auditory (green–blue) processing areas of the brain. (Data provided by the FMRIB Centre, University of Oxford, with permission.)

It is also possible to design tasks that modulate a particular functional process, such that it can be detected via a parametric analysis. An example might be to perform sustained periods of motor activity (hand flexion, finger opposition, etc.), but with each stimulus epoch performed at a different level of task difficulty. The analysis of the data then seeks those areas of the brain that show a graded response to the task level.

8.4.2

Event-Related Designs

It is not always appropriate or physiologically feasible to sustain certain stimulus types for an extended period. Such an example might be the application of a brief painful stimulus. Further, there may be a need to introduce an element of unpredictability into the stimulus presentation, for example to avoid

confounds of an anticipatory response. In these cases one approach that can be adopted is the use of a socalled “event-related” design (Buckner 1998). This is shown schematically in Fig. 8.11b, in which a series of brief stimuli are presented, perhaps at regular intervals or perhaps at random intervals. It is implicit that a fast imaging sequence be used (2–3 s volume-to- volume interval), since the haemodynamic responses to be characterized are also relatively brief (6–12 s). The data are then analysed for brain areas that show a time-course signature that correlates with the predicted haemodynamic response (also shown in Fig. 8.11b). It should be noted that event-related designs are more sensitive to the details of the haemodynamic response model used (see below) than are block designs. In this sense block designs are more robust, since a sustained cognitive state can be modelled, and hence a sustained (and simple) haemodynamic response can be assumed. In contrast, analysis of event-related designs may produce poor results if an inaccurate haemodynamic response function is assumed. This may be all the more the case if the interval between successive stimuli becomes short, or if widely distributed brain areas are of interest, in which case it may be necessary to model a set of area-specific haemodynamic response functions. Nevertheless, event-related designs have proved their use when short-duration stimuli are required.

8.5

Data Analysis Strategies

With the advent of well-packaged analysis programs, and with the introduction of scanners with on-board fMRI analysis, it is becoming less essential to have comprehensive knowledge regarding analytical methods for processing fMRI data. Nevertheless, it should be a high priority to any serious clinician or researcher to appreciate what is being performed within the “black box”. Only a brief summary is provided here, but a more lengthy discussion of the details of fMRI data analysis can be found in the book by Jezzard et al. (2001).

8.5.1

Pre-Statistics Procedures

In most fMRI analysis packages a number of steps are first taken to improve the quality of the underlying data, or to otherwise “encourage” the data to conform

to the set of assumptions behind a particular statistical model. One important first step is to attempt to correct the time-course data for motion artefacts associated with subject movement. Usually this requires an algorithm that performs a rigid body transformation in order to register successive volumes. Many sites also perform a slice timing correction that attempts to account for the fact that the different slices that comprise a volume are not actually collected simultaneously.

The other principal processing step that is often applied to the data is to perform filtering operations that attempt to improve the conformity of the data to assumptions that are subsequently used during statistical inference. In particular, many packages apply a spatial blurring in order to improve the confidence that the images have a particular point spread function (i.e. a known spatial correlation). Typically the spatial filter that is applied is only marginally wider than the digital (pixel) resolution itself. But in some cases more severe filtering is applied. The advantage of substantial spatial filtering is that the effective signal-to-noise ratio of the data can be significantly improved. However, this is at the expense of blurring the raw data to a lower resolution. A related step that may be applied is to filter the data in the time domain. This is done by selecting the time-course from each pixel in turn and applying a specific filter that suppresses undesirable features in the time series. Typically a band-pass filter is used that suppresses both low temporal fluctuations, that can arise from gradually evolving scanner instabilities or residual effects from subject motion, and high temporal frequencies that may be assumed to be noise (since the haemodynamic response function associated with “real” activations has a certain latency and width).

A number of final pre-statistics procedures may also be applied, such as intensity normalization of each volume (such that the total integrated intensity is the same in each volume). This is appropriate provided that the anticipated brain areas are small relative to the volume of brain covered.

8.5.2

Time-Course Modelling

Once the data have been preprocessed and are ready for final analysis a model of the expected fMRI timecourse must be generated (although one can use model-free analysis methods as an alternative). In the case of a simple block design paradigm it may be sufficient simply to model the predicted time-course

as a box car function that is “1” during periods of stimulus (or task A) and “0” during rest (or task B). An improvement to this model is to convolve the box car function with an estimate of the instantaneous haemodynamic response function (Friston et al. 1994). Such a function represents the response of the MRI signal (and hence vasculature that causes the change in MRI signal) to a brief stimulus event. This approach is used in the field of engineering when one wishes to predict the output signal from a modulating system or device when given an arbitrary input signal. This can be accomplished if one knows the output response function resulting from an instantaneous input signal, and provided one can assume that the modulating system or device acts as a linear system. This latter assumption is not always strictly true in the case of the fMRI signal, but may often be applied regardless, particularly in the case of block stimuli or widely spaced brief stimuli. Examples of the predicted fMRI time-course to a blocked and event-related experimental design are shown in Fig. 8.11a and 8.11b, respectively. Once generated, this time course model can be used as the basis for testing against each pixel time series in the raw fMRI data.

8.5.3

Statistical Inference

The ultimate aim of statistical inference is to identify those pixels whose time-course matches the modelled haemodynamic time-course, and to assign a statistical confidence to this identification, given the level of noise present. The most common approach is to adopt a general linear model (Friston et al. 1995), in which the observed data are modelled as:

where y(t) is the array of observed data (a 1D vector of signal intensities representing a given pixel’s timecourse); x(t) is the haemodynamic model (also sometimes known as the explanatory variable) describing the expected signal time-course resulting from the stimulus paradigm; β is the parameter estimate for x(t) (in other words it is the scalar that must be multiplied by x(t) in order to fit the data); c is a constant, and represents the signal amplitude during the baseline condition; and e is an error term, that should consist of white Gaussian-distributed random noise if the fit is good. If the paradigm consisted of two different stimuli (for example in the case of

the visual and auditory stimulus data set shown in Fig. 8.12) then a second parameter estimate and haemodynamic model can be introduced as β2 * x2(t). Note also that more sophisticated questions can be asked within this framework by generating new explanatory variables, such as whether the presence of two overlapping stimuli results in an fMRI signal that is greater than the sum of their signals if the stimuli are presented separately.

Once the entire data set has been fitted, one can produce a T statistic for each pixel location, based on the ratio between the size of the parameter estimate (β) and the size of its standard error. Once a T statistic has been calculated one can transform into a P (probability) or Z statistic image. The only remaining step is to set a threshold level at which to accept or reject pixels that pass or fail the statistical test.Typically probability thresholds of P<0.01 are selected, after suitable correction has been made for the number of statistical tests that have been performed.

8.6

Spinal Cord fMRI

Advances in the treatments of spinal cord injury and disease have improved outcome in recent years. In addition, active research is currently being undertaken to explore the possibility of promoting axonal regeneration (Horner and Gage 2000). The effective use of these treatments will require the accurate monitoring of the anatomical and functional characteristics of the spinal cord within and around the injury site. The application of fMRI to the spinal cord (spinal fMRI) as a non-invasive mapping technique of neural function is therefore potentially of great significance.

One of the earliest studies employed a unilateral hand-closing paradigm at 1.5 T to demonstrate signal change of 4.8% in the ventral grey matter of the ipsilateral seventh cervical cord segment (Yoshizawa et al. 1996). Less consistent activation was also noted in the dorsal grey horn and contralateral spinal cord, and was explained by the presence of interneuronal connections.

Subsequently, substantial human spinal fMRI work has been conducted by Stroman et al. They have concentrated on cervical cord imaging and sensory stimulation paradigms. Cervical cord BOLD-related signal changes were initially demonstrated at 3 T (Stroman et al. 1999) and then at 1.5 T (Stroman and Ryner 2001; Stroman et al. 2001b) looking at

sensory and motor-related activations. At 1.5 T GE and SE EPI were compared and found to impart similar activation signal changes (about 4–5%) although theoretically GE EPI should be expected to provide much larger signal changes than SE EPI. The authors explained this by alluding to anatomical differences between brain tissue and spinal cord. For example, only small vessels lie within the spinal cord parenchyma and these vessels tend to be radially orientated. The vessel size and orientation would maximize the magnetic field susceptibility gradients and increase the expected BOLD contrast. A further factor is that spinal cord structure,unlike the cerebral cortex, is not tortuous and is more likely to contain homogeneous grey matter and consequently be less susceptible to partial volume effects (Stroman and Ryner 2001).

Studies by Stroman et al. have also demonstrated that there may be a second contrast mechanism that manifests with SE EPI sequences at very short echo times (Stroman et al. 2001a; Stroman et al. 2002a; Stroman et al. 2003). This is because the estimated fractional signal changes during activation in SE EPI fMRI have values of about 2.5% when extrapolated to a TE of zero. This non-zero intercept has been interpreted as perhaps arising from a proton density increase within the extravascular space and has been termed “signal enhancement by extravascular protons” or SEEP. Alternatively, this effect may be related to blood volume increases.

Further work has demonstrated a dermatomal distribution of sensory stimulation (using a cold stimulus) within the cervical cord in a study of 13 healthy volunteers. By using a “clustering” technique that congregates active pixels based upon their intensity time-courses the authors were able to discriminate true activations from false ones (Stroman et al. 2002b). Lumbar spinal fMRI has also been developed in studies using temperature stimulation paradigms and has shown in healthy subjects a relationship between the magnitude of the fMRI response within the dorsal lumbar cord and the degree of noxious stimulation (Stroman et al. 2002c). In patients with spinal cord injuries, fMRI responses have also been demonstrated with lumbar sensory stimulation, even when the patients had no subjective sensations themselves. This activity tends to be localized to the contralateral side of the cord (Stroman et al. 2002c; Stroman et al. 2004).

8.7

Clinical Applications in White Matter Disorders

FMRI has many clinical and research applications. Several examples of its use in helping to understand demyelinating disease and schizophrenia are outlined below. One of its most challenging roles is to explore cortical plasticity in neurological disease.

8.7.1

Cortical Plasticity

Functional improvement is often seen following acute brain injury; however certain aspects of recovery cannot be explained by structural mechanisms such as the resolution of inflammation or oedema. FMRI has presented the opportunity to measure changes relating to interactions at the level of large neuronal populations that follow neural insult (cortical plasticity). The term cortical plasticity has been defined as the “reorganization of distributed patterns of normal task-associated brain activity that accompany action, perception, and cognition and that compensate impaired function resulting from disease or brain injury” (Frackowiak et al. 1997). Plasticity in neuroscience research may also relate to lower levels of neural organization and may include axonal/neuronal mechanisms, for example reflecting changes in sodium channel expression (Waxman 2001) or dendritic arborization (Jones and Schallert 1992), and synaptic mechanisms, for example indicating changes in synaptic density, distribution or strength (Jacobs and Donoghue 1991).

Although the capacity for cortical plasticity is at its greatest during the early years of CNS development, it appears to persist throughout life. The behavioural effects of cortical reorganization following CNS insult may be divided into different categories which describe, for example, adaptive and maladaptive processes. Adaptive cortical reorganization implies that the redistribution of neural processing in some way contributes to the mechanisms involved in clinical recovery or helps to maintain a degree of clinical function in the presence of structural damage.At least four forms of adaptive plasticity have been suggested (Grafman and Litvan 1999): (1) homologous area adaptation – implies that the damaged brain region can be compensated for by transferring the neural operations to other unaffected brain modules (usually in the homologous region of the opposite hemisphere); (2) cross-modal reassignment—occurs

when cortical modules usually devoted to processing particular sensory inputs, now accept inputs from another sensory modality; (3) map expansion—is the enlargement of a functional cortical region in response to frequent stimulus exposure or following adjacent cortical injury; and (4) compensatory masquerade – is the novel allocation of a cognitive strategy to perform a task that otherwise would depend on another cognitive process which is now impaired.

However, cortical reorganization may not necessarily contribute to recovery of clinical function. It may also reflect the recruitment of unusual distributed neural networks purely as a ‘stress’ response to CNS injury implying that it can be non-adaptive or coincidental.

Indeed, plastic changes in response to injury may also have deleterious behavioural effects resulting in functional loss rather than gain. This form of plasticity is termed maladaptive and is,for example,thought to account for phantom limb pain following amputation (Lotze et al. 2001).

8.7.2

Functional MRI in Demyelinating Disease

Several studies have been performed to investigate how the brain possibly adapts to neurological damage in demyelinating disease. These studies have mostly employed experimental paradigms for the visual and the motor systems and can be categorized into those that address (1) clinically isolated syndromes, e.g. optic neuritis, (2) relapsing remitting multiple sclerosis (MS), (3) secondary progressive MS, and (4) primary progressive MS. Further treatment of this subject can be found in Chapter 14 of this book.

8.7.3

Functional MRI in Schizophrenia

Schizophrenia is a debilitating psychiatric disorder characterized by the presence of certain types of ‘positive’ symptoms such as delusions, hallucinations and thought disorder (Andreasen 1995). These ‘positive’ symptoms are usually complemented by ‘negative’ symptoms such as psychomotor retardation and affective flattening. In addition, a significant role for cognitive dysfunction has become increasingly recognized in helping to understand the clinical deficits of schizophrenia. The cognitive deficits are related to but distinct from negative symptoms and appear to significantly influence clinical outcome in schizo-

phrenia (Sharma and Antonova 2003). FMRI has been used to investigate the neural correlates associated with both positive symptoms, negative symptoms and cognitive dysfunction over recent years. Some examples of its contributions are described below.

Auditory hallucinations constitute a prominent feature of positive symptoms and their impact has been explored in several studies. The results have suggested processing abnormalities in primary auditory and association cortices and have also implicated subcortical and limbic structures. Reduced activation in response to auditory stimulation has been found in auditory areas resulting in the concept that auditory hallucinations compete with external auditory stimuli (David et al. 1996; Woodruff et al. 1997).

A novel methodological technique was implemented by Shergill et al. to measure neural activity associated with auditory hallucinations without requiring subjects to signal when the hallucinations occurred. They discovered that auditory hallucinations are associated with activation in a distributed network of cortical and subcortical areas that include the inferior frontal/insular, anterior cingulate, and temporal cortex bilaterally (with greater responses on the right), the right thalamus and inferior colliculus, and the left hippocampus and parahippocampal cortex (Shergill et al. 2000). A further study by the same group investigated the notion of auditory hallucinations as a disorder of the self-monitoring of ‘inner speech’. Eight schizophrenic subjects with prominent auditory hallucinations were compared with eight comparison subjects using a paradigm that experimentally varied the generation rate of inner speech. When the rate was increased, the schizophrenia patients showed a relatively attenuated response within the right temporal, parietal, parahippocampal and cerebellar cortices, areas previously implicated in verbal self-monitoring (Shergill et al. 2003).

Abnormalities in the integration of auditory and visual language perception are also felt to correlate with psychotic symptoms. Surguladze et al. (2001) performed three experiments each on seven healthy volunteers and fourteen schizophrenia patients, seven of whom were actively psychotic. The tasks comprised listening to auditory speech, silent lipreading (visual speech perception) and perception of meaningless lip movements (visual non-speech). Patients overall demonstrated lower activation in temporal regions for the lip-reading task, compared with healthy controls. They also activated fewer posterior (occipitotemporal) and more anterior (frontal, insular and striatal) areas than controls, accounted

for largely by the psychotic subgroup. This suggests that schizophrenia patients with psychotic symptoms activate polysensory regions in response to visually ambiguous stimuli (non-speech) (Surguladze et al. 2001).

Investigations into cognitive dysfunction and negative symptoms can be difficult to interpret,especially with studies on working memory. This is because their interpretation can be confounded by impaired working memory function in schizophrenia patients (Sharma 2003). In spite of this, a functional abnormality of the dorsolateral prefrontal cortex (DLPFC) appears to play a significant role in complex cognitive dysfunction. Most studies have demonstrated ‘hypofrontality’ or reduced activation of the DLPFC in response to complex cognitive tasks when compared with controls (Barch et al. 2001; Barch et al. 2002; MacDonald and Carter 2003; Perlstein et al. 2001; Riehemann et al. 2001). Tasks that do not require complex processing have also demonstrated reduced DLPFC activation in schizophrenia patients relative to controls (Rubia et al. 2001).

Some studies have not observed ‘hypofrontality’. Honey et al. controlled for task performance by selecting patients with normal scores on a relatively low-load verbal memory task (Honey et al. 2002). Although no differences were found between schizophrenia patients and controls, the control group exhibited positive correlations between reaction time and the fMRI response within the posterior parietal cortex. This correlation was not found in the schizophrenia group,perhaps implying that‘hypofrontality’ occurs only when the ability of the DLPFC to respond to a working memory task is exceeded by the cognitive load.

A few other studies have reported greater DLPFC activation in schizophrenia patients with complex cognitive tasks (Callicott et al. 2000; Manoach et al. 2000; Ramsey et al. 2002). Ramsey et al., in particular, investigated the role of antipsychotic medication and controlled for cognitive performance with a logical reasoning task (Ramsey et al. 2002). Task performance was modestly reduced in the patient group. In patients on medication, the fMRI activity did not differ significantly from controls after correction for task performance. However, in medication-naive patients, the performance-corrected fMRI activity was significantly elevated within the DLPFC.These results point to increased neural recruitment with untreated schizophrenia during logical reasoning. The mixed results presented above indicate that the function(s) of the DLPFC in schizophrenia remain to be elucidated and require further research.

Knowledge about the precise neuropathological and neurophysiological substrates of schizophrenia is also incomplete. Structural findings have included enlargement of the lateral and third ventricles (Daniel et al. 1991; Lawrie and Abukmeil 1998) and reductions in grey and white matter volume and density. Grey matter abnormalities have been found within the limbic system, thalamus, medial frontal, temporal, precuneate, posterior cingulate and insular areas (Andreasen 1997;Gaser et al.1999; Hulshoff Pol et al. 2001; Wright et al. 2000).White matter abnormalities have been detected in the frontal lobes (Paillere-Martinot et al. 2001), left posterior periventricular area (Sowell et al. 2000), temporal lobes (Sigmundsson et al. 2001) and the corpus callosum (Hulshoff Pol et al. 2004).

It has been proposed that the clinical abnormalities arise from interregional aberrant connectivity or ‘disconnectivity’ (Friston 1998; Friston and Frith 1995). This hypothesis may explain the symptoms and cognitive deficits in schizophrenia and is generally felt to have some truth, but its precise nature is unknown; for example the spatial scale of the disconnectivity may be present at the synaptic or dendritic level rather than at the level of the cortical region. However, in future, by combining MR measures of function (with fMRI) and of structure (for example, with diffusion tensor imaging) it may be possible to explore the relationships between effective connectivity and anatomical connectivity in schizophrenia and thus contribute to a more complete understanding of its pathophysiology.

8.8 Conclusion

This chapter attempts to describe the principles of functional MRI, and also includes a brief discussion of applications of fMRI to white matter disorders. Conventional fMRI is only able to access haemodynamic, and hence metabolic, information from the grey matter. As such, the information that can be obtained on white matter disorders is derived from the effect that a given disorder has on the participating grey matter networks. Nevertheless, preliminary work has been done to improve our understanding of the evolution and treatment of various white matter disorders, most notably in multiple sclerosis.

One probable evolution that will occur in the field of neuroimaging is the combining of information from several different contrast mechanisms and mo-

dalities. In the context of MRI contrast it is becoming clear that diffusion-weighted information, and in particular diffusion tractography, will reveal rich information on white matter tracts, and that this information can be combined with standard fMRI contrast of the grey matter processing units. Figure 8.13 shows an example of combined fMRI and diffusion tractography imaging, indicating how structural and functional information can be combined. Beyond MRI there is the prospect of combined electroencephalography (EEG) and fMRI, in order to improve the temporal resolution of the information available. Likewise the spatial resolution of fMRI may be used to constrain the location of sources when analysing magnetoencephalography (MEG) data.

Acknowledgements

P.J. gratefully acknowledges the support of the UK Medical Research Council and the Dunhill Medical Trust. A.T. gratefully acknowledges support from the Brain Research Trust.

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