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Quantitative MR imaging of the neocortex
Neuroimag Clin N Am 14 (2004) 425 – 436
Quantitative MR imaging of the neocortex Andrea Bernasconi, MD Department of Neurology and McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal H3A 2B4, Quebec, Canada
MR imaging has a major impact on the presurgical evaluation of patients with medically intractable epilepsy by defining cerebral structural damage and in delineating the extent of the epileptogenic zone (ie, the site of seizure onset). In general, patients with lesional epileptic syndromes are likely to experience a reduction in seizure frequency after epilepsy surgery. MR imaging provides the ability to vary image acquisition and postprocessing parameters to study different types of anatomic properties. Techniques such as the volumetric acquisition with thin contiguous slices have increased the ability of MR imaging to display brain anatomy and allowed better visual inspection of epileptogenic brain lesions. Currently, new image processing techniques provide a further improvement by allowing a quantitative and objective analysis, which opens new prospects in structural brain imaging. This article provides an overview on quantitative image analysis methods that have been applied to the study of patients with neocortical epilepsy, in particular those undergoing surgical treatment of medically intractable partial seizures.
The role of quantitative structural MR imaging in neocortical epilepsy Focal epilepsy syndromes are usually separated into three major groups: lesional epilepsy, mesial temporal lobe epilepsy (TLE), and neocortical epilepsy [1]. The last group includes patients with lateral temporal neocortical or extratemporal seizures. This division has clear practical implications when dealing
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with patients being considered for surgical treatment. Typically, surgical treatment in patients with TLE related to hippocampal atrophy and in those with other lesional syndromes consists in removing the primary epileptogenic pathology as determined by MR imaging. In neocortical epilepsy, MR imaging has been confirmed to be a reliable indicator of foreign-tissue pathology, such as tumors, vascular malformations, encephalomalacia, and malformations of cortical development (MCD) [2]. The close relation between structural lesions identified by MR imaging and the epileptogenic zone has been demonstrated by the favorable surgical outcome in patients with lesions [3 – 8]. MCD are increasingly recognized as an important cause of intractable focal epilepsy. Although MR imaging has made it possible to detect MCD in some patients, the number of cases treated surgically is far less and the prognosis poorer than in patients operated for other types of lesions, mainly because identification of many of these malformations on visual inspection of conventional MR imaging is difficult due to their subtlety and the complexity of the brain’s convolution. Furthermore, there is evidence that the extent of histologic and epileptogenic abnormalities in MCD may go beyond the lesion that is visible on conventional MR imaging. In a large percentage of patients with medically refractory neocortical epilepsy, conventional structural MR imaging is unfortunately unremarkable. Epilepsy surgery in the absence of a lesion is presently one of the greatest clinical challenges because the criteria to localize the epileptic focus, and consequently a potential surgical target, are unclear. When these ‘‘MR imaging – negative’’ patients are operated, outcome is known to be worse than when a lesion is present on MR imaging [9,10]. MR imaging – nega-
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tive neocortical epilepsies are those forms that are usually considered cryptogenic unless histologic findings demonstrate the presence of subtle MCD, such as microdysgenesis or gliosis [6,11 – 13]. Features commonly accepted to represent microdysgenesis include neuronal clustering, cortical dyslamination, abnormal cortical myelinated fibers, and an excess of neurons in cortical layer I and the subcortical white matter [14]. Many of these subtle changes may have no macroscopic correlation and may not be directly visualized on MR imaging. However, postprocessing of highresolution MR images can offer surrogate markers of tissue integrity and neuronal connectivity that provide indication of an underlying subtle structural abnormality. In the author’s experience, T1-weighted 3D gradient-echo technique, which provides exquisite anatomic details and can be reconstructed to obtain high-quality volume imaging, provides the best substrate for digital image processing. Contrary to visual MR imaging inspection, which is intrinsically subjective, image processing provides quantitative analysis of digital images and therefore objective MR imaging analysis, which is likely to be of great aid in structural brain imaging. Image processing methods are becoming increasingly sophisticated and the tendency is to develop as much as automation as possible. In epilepsy, the purpose of quantitative structural MR imaging of the neocortex is to identify brain abnormalities overlapping with the epileptogenic zone and to detect other potentially epileptogenic areas, either in correspondence or distant to the main lesion that may not be readily recognizable by visual analysis alone. The following methods will be discussed in this article: image preprocessing and tissue segmentation, voxel-based morphometry, shape analysis, cortical thickness measurements, and texture analysis.
Tissue classification and segmentation Tissue classification refers to the differentiation of voxels within an MR image into classes, such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Because these classes have sufficiently different gray-level intensities on the MR image histogram, tissue classification algorithms are usually based on the absolute MR intensity from one or more image types. When more than one tissue class is present in a voxel (partial volume effect), based on a set of predefined rules, the classification algorithms attributes this voxel to a single class. Tissue segmentation deals with the identification of anatomic structures and provides the basis for the
generation of volumetric data. Currently, most volumetric MR imaging protocols in epilepsy are based on manual segmentation of various limbic structures, including the hippocampus, amygdala, and entorhinal cortex [15 – 17]. Hippocampal atrophy, as determined by volumetric MR imaging, has been demonstrated to be a reliable in vivo indicator of mesial temporal sclerosis [18]. Similarly, volumetric measurements provide a mean of estimating the amount of tissue in neocortical areas. Studies in TLE have been based mostly on manual labeling and have shown atrophy of either temporal neocortical GM, WM, or both [17,19 – 22]. Manual segmentation is time consuming and may suffer from interobserver variations in the labeling strategy, which can potentially affect reproducibility of findings. These issues are particularly relevant when dealing with the segmentation of neocortical areas, because the visual identification of the anatomic boundaries is more complex than in the mesial temporal lobe or other deep brain structures, such as the thalamus [23]. However, computer-based segmentation of neocortical structures is demanding and faces difficulties related to anatomic variability, nonhomogeneity of pixel intensity values in a single tissue or structure, and the presence of blurred boundaries between various tissue types. Most segmentation methods applied so far to epilepsy combine automated segmentation with some level of operator intervention, particularly in defining anatomic landmarks [24]. Recently, there has been a considerable proliferation of increasingly sophisticated methods that aim to provide fully automated analytical tools, overcoming the bias of interactive methods. An exhaustive list is beyond the scope of this article. These methods make use of several strategies, such as artificial neural networks [25] and 3D model-based segmentation techniques [26 – 32]. Only some of them have been validated by comparison of automatic segmentation with the results obtained by interactive expert segmentation [27,33 – 35]. Therefore, visual inspection of results is still a necessity for quality control despite full automation. Regional cortical GM and WM have been assessed using block analysis [36]. In this technique, the segmented images of each cerebral hemisphere are divided into a number of equal slices (blocks) in an anterior-posterior axis, allowing measurement of interhemispheric volume ratios between blocks. This method is highly dependent on the quality of the image segmentation and provides crude anatomic information. In a study of patients with MCD, widespread changes were seen in apparently normal areas distant to the evident areas of dysgenesis [36,37]. In
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Fig. 1. MR imaging segmentation using automatic nonlinear image matching and anatomical labeling in a patient with unilateral polymicrogyria. (A) The lesion is shown by black arrows on a T1-weighted MR imaging axial slice. (B) The brain is segmented into frontal gray (red) and white (green) matter, parietal gray (pink) and white (yellow) matter, and occipital gray (navy blue) and white (sky blue) matter.
TLE, block analysis allowed correlating widespread changes with unsuccessful surgery in patients undergoing standardized temporal resections [38]. To date, the application of automatic segmentations methods has been limited and results have provided only lobar volumes without differentiation between GM and WM compartments [21]. With the refinement of segmentation techniques, future work should aim for separate analysis of GM and WM of individual brain regions (ie, specific gyri), allowing analysis of the whole brain (Fig. 1). Volumetric neocortical measurements may provide an objective method to evaluate the extent of resection and its relation to surgical results [13,39]. Preliminary data have shown that quantitative lobar volumetric measurements in patients with nonlesional neocortical epilepsy may be a predictor of improved postsurgical seizure control [13].
Voxel-based morphometry Brain pathology in epilepsy causes not only volume reduction but also changes in water relaxation behavior, which are manifested by gray-level variation. Voxel-based morphometry (VBM) is a powerful tool for analyzing subtle differences in signal intensity [40]. VBM involves a voxel-wise comparison of the local ‘‘concentration’’ of some property between two groups of subjects. VBM heavily relies on an accurate image coregistration process to conform all brains to the same orientation and size before comparing different MR imaging volumes at a voxel level. Any differences due to registration errors will
result in added noise to the statistical analysis. VBM involves three main steps. After tissue classification, images are smoothed so that each voxel contains the average signal around the voxel, often referred as tissue ‘‘density’’ or ‘‘concentration’’ [40]. Voxel-wise parametric statistical tests are then used to compare the smoothed concentration maps from the groups, and correction for multiple comparisons is made [41]. Many neuroimaging laboratories have the capability to perform VBM using the SPM package [40]. As in other image processing methods, results of VBM depend on the quality of image registration, segmentation, classification, and intensity nonuniformity correction. The effects of nonlinear image registration to further conform all individuals to a common reference volume at a local level may modify the results on VBM. The size of the kernel used for smoothing the images is an important factor for interpreting results and should be comparable to the size of the expected regional differences between groups. For example, a kernel size of 14 mm would be larger than the expected hippocampal atrophy in TLE. VBM has previously been used to demonstrate GM abnormalities in patients with TLE and hippocampal atrophy [42,43], juvenile myoclonic epilepsy [44], and MCD [45,46]. In TLE, VBM has produced conflicting results showing significant clusters of decreased GM intensity in the epileptogenic hippocampus in some studies [42,43] and no abnormalities in others [46]. However, VBM was able to show consistently across different studies neocortical GM reduction, either lateralized or not to the epileptic focus, demonstrating abnormalities beyond the visu-
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Fig. 2. Voxel-based morphometry in patients with right temporal lobe epilepsy. Statistically significant peaks of gray matter decrease in the (A) right hippocampus, (B) the left superior temporal cortex, (C) bilateral frontal areas, and (D) thalami. Statistically significant peaks of white matter decrease in the callosum and the right temporo-polar area (E), the right entorhinal, and perirhinal areas (F). Statistical maps are superimposed on an average template of 152 healthy controls for anatomical reference (on coronal and axial maps, right is right on the image; sagittal maps of the right hemisphere are presented).
alized lesions. The author examined 85 patients with medically intractable TLE and unilateral hippocampal atrophy and compared them to 47 healthy individuals. The study revealed that GM pathology in TLE extends beyond the hippocampus, involving other limbic areas, such as the cingulum and the thalamus, as well as extralimbic areas, particularly the frontal lobe. WM reduction was found only ipsilateral to the seizure focus, including the temporopolar, entorhinal, and perirhinal areas (Fig. 2). In relation to duration of epilepsy, GM reduction was observed in the primary epileptogenic areas, such as the hippocampus and the parahippocampal region as well as extratemporal areas, in particular the frontal lobe. This pattern of structural changes is suggestive of disconnection involving preferentially frontolimbic pathways in TLE. The pertinence of these widespread changes in individual cases with respect to clinical parameters, such as outcome after surgery, remains to be evaluated. Previous studies have also reported the presence of GM increase in TLE and juvenile myoclonic epilepsy [42,46,47]. The pathologic correlates of an increase in GM concentration found in these studies are unclear and have been tentatively attributed to microdysgenesis. By performing voxel-wise analysis of GM and WM, we were able to observe that in TLE, areas of increased GM coincided with those of decreased WM. Therefore, it is conceivable that previous observations of increased GM were second-
ary to a relative displacement of the GM due to atrophy of underlying WM. In TLE, it has also been shown that extrahippocampal GM abnormalities may be correlated with duration of epilepsy, suggesting secondary brain damage [43].
Cortical and sulcal morphometry Though extensive manual segmentation of the cortical mantle on MR imaging is prohibitive, reliable automatic assessment of the cerebral cortex remains a challenging undertaking due to its convoluted nature. However, measuring cortical thickness in an accurate fashion has the potential to provide maps of changes across the cortical ribbon with a meaningful metric of change, namely thickness loss or gain measured in millimeters. There have been few studies attempting to measure cortical thickness from MR imaging data [48 – 55]. The most common approach used involves selecting a point on the WM surface and finding the closest point on the GM surface [48,51,55]. Few studies have validated the accuracy of these techniques by direct comparisons with manual measures on postmortem brains [53]. Cortical thickness in patients with TLE using deformable models was recently evaluated [51]. Preliminary data in 32 patients with
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Fig. 3. Cortical thickness measurement in patients with left temporal lobe epilepsy. Various areas of decrease in cortical thickness are seen outside the temporal lobes, in the left and right frontal, central, and left insular cortices. Lateral views of (A) right and (B) left hemisphere, and top view of the brain (C) are shown (right is right on the image).
left TLE [56] compared with 51 healthy controls showed a decreased cortical thickness of the ipsilateral entorhinal cortex and bilateral decrease in thickness in frontal and central areas (Fig. 3). On visual analysis of conventional MR imaging, sulcal and gyral abnormalities are sometimes noted in patients with epilepsy. These are characterized by a spectrum of changes ranging from clefts of various depth [57] to broad gyri, shallow or deep sulci, or gyral simplification [58,59]. These abnormalities, which are thought to represent an underlying pathologic cortical cytoarchitecture [60], have been reported mainly in patients with MCD, but are also found in MR imaging-negative cases [61]. Most studies have relied on visual MR imaging inspection to identify gross abnormalities in gyration. The high variability of the branching patterns of the major sulci and the large number of secondary gyri make reliable quantification of the cortical gyral and sulcal pattern a difficult task, and this may explain why there has been virtually no work done in this area of research. Models of structure and shape have the potential to identify cortical characteristics that are not easily available through visual observation. Measurements of GM and WM surface area and gyral index of curvature have shown subtle abnormalities in the neocortex of TLE patients not evident on visual MR imaging inspection [19]. Similarly, fractal analysis has been applied in an attempt to detect simplification of gyral patterns. Results indicated that, compared with a group of normal controls, a subset of patients with frontal lobe epilepsy [62] and cryptogenic epilepsy [63] present with some degree of gyral simplification as indicated by abnormally low fractal dimension (an index of contour complexity) of the GM-WM interface and the WM surface. In another study, Sisodiya et al [37] found disproportion between surface areas derivatives of the GM-WM interface and volumetric
data from block analysis in some patients with MCD, suggesting widespread disorganization of the cerebral hemispheres in patients with cerebral dysgenesis, possibly due to abnormal connectivity. The results of these studies illustrate the potential of quantitative sulcal and gyral analysis for detecting anomalous cortical morphology, particularly if extended into 3D. Automated or semiautomated methods for statistical models of sulci rely on graphs constructed from 3D point sets [64,65], on ribbons used to model the space between opposite sides of a sulcus [66] or on curves located on the outer cortical surface [67]. These methods allow intersubject sulcal shape comparisons of the major sulci [68] and can quantify various geometric characteristics, such as length, volume, and curvature [69]. It is conceivable that results from cortical and sulcal morphometry may increase sensitivity in detecting subtle localized of sulcal and gyral pattern alterations in epileptic brains. Because these abnormalities may represent a marker for the site of seizure onset and of an underlying subtle MCD, they may be useful to direct invasive EEG monitoring in MR imaging-negative patients. However, correlation studies with electrophysiologic and histopathologic data will be essential in clarifying the relationship between these changes and the epileptic foci.
Computer-based models of focal cortical dysplasia Focal cortical dysplasia (FCD) [70] is the most common form of MCD in patients with pharmacologically intractable focal epilepsy referred for presurgical evaluation. On T1-weighted MR imaging, FCD is mainly characterized by variable degrees of cortical thickening, a poorly defined transition between GM and WM, and by a hyperintense signal
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within the dysplastic lesion with respect to normal cortex. As discussed previously, the inherent complexity of the brain’s convolutional pattern makes the visual identification of FCD sometimes difficult. Therefore, the author sought to develop a computerbased method to assist in the detection of FCD based on its in vivo pathologic characteristics. The initial approach was to apply morphologic and first-order texture models of the characteristics of FCD on T1weighted MR imaging [71]. Texture analysis of digital images provides statistical methods capable of identifying the relationship between neighboring pixels over a region of interest, often considering the probability that certain intensities are found in specific spatial locations with respect to each other [72].
The following voxel-wise operators were applied to the 3D T1-weighted MR imaging for each patient: (1) a gray-matter run-length operator to model GM thickness; (2) an absolute gradient operator to quantify the blurring of the GM/WM interface; and (3) an operator to model hyperintense T1 signal within GM through intensity measurement of a voxel relative to the threshold intensity between GM and WM (termed ‘‘relative intensity’’). To maximize visibility of FCD lesions, the three feature maps were combined into a composite map (Fig. 4). Results in 16 patients in whom FCD had been histologically proven at surgery showed that the composite maps yielded a significantly increased sensitivity of FCD lesion detection relative to conventional MR imaging (88% versus
Fig. 4. Computer-based models of focal cortical dysplasia. (A) T1-weighted MR image axial slice of a focal cortical dysplasia in the left parietal lobe (left is left on the image). (B) The panel in the center shows a portion of the T1-weighted MR image including the lesion. On the left, a small region of cortex and adjacent white matter (WM) are schematically shown in 3D and magnified, each cube representing a voxel. To model cortical thickening, each individual voxel in the T1-weighted MR image is used as the starting point (yellow dot) for gray matter (GM) run-length coding (red arrows). The GM thickness map of the same axial slice is shown on the right. The increased cortical thickness is represented by the brighter intensities in the color map. (C) To model the blurring between GM and WM, the absolute gradient of gray level intensities is calculated. In regions of normal transition between GM and WM (magnified region on the left), the gradient is expected to be steep. On the other hand, in regions of GM-WM blurring (magnified on the right), the gradient is less steep. The GM-WM blurring is represented by the darker intensity in the GM-WM transition zone in the gradient map. (D) The hyperintense signal within the lesions is modeled by calculating the difference between the intensity of each voxel and the intensity at the GM-WM boundary. The hyperintense voxels are shown on the relative intensity map on the right. (E) The ratio map (GM thickness relative intensity/gradient) enhances the contrast and maximizes lesion visibility. (Adapted from Bernasconi A, Antel S, Collins DL, Bernasconi N, Olivier A, Dubeau F, et al. Texture analysis and morphological processing of MRI assist detection of focal cortical dysplasia in extratemporal partial epilepsy. Annals of Neurology 2001;49:770 – 5; with permission.)
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Fig. 5. Second-order texture analysis. (A) An example of a focal cortical dysplasia in the right central area (black arrow) is shown on a T1-weighted MRI axial slice (right is right on the image). Second-order texture maps are shown in the other panels: (B) angular second momentum, (C) difference entropy, and (D) contrast.
Fig. 6. Automatic classification of focal cortical dysplasia. Three examples are shown. Left column: T1-weighted MR image. Center column: MR image with manual lesion label (blue). Right column: MR image with lesion identified automatically by the classifier (yellow).
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50%) by maintaining high specificity. This work demonstrated for that the application of computerbased FCD models could significantly improve the sensitivity of lesion detection. After demonstrating that our method could be improved by incorporating more sophisticated cortical thickness models [73], we implemented an automated classifier prototype [74]. The computational models of MR imaging characteristics of FCD presented, which provide visually discernable information, were combined with a set of second-order texture features that quantify lessavailable information regarding tissue organization (Fig. 5). The classifier correctly identified FCD lesions in 15 of 18 patients (83%). Representative examples are shown in Fig. 6. The classifier improved upon previous techniques by providing an automated and reliable approach to FCD lesion detection. In five patients the classifier identified lesional areas that did not colocalize with the main FCD lesion. Retrospective visual analysis of the individual feature maps input revealed that these lesional clusters exhibited a pattern of texture features similar to the known FCD lesions. However, EEG
data from these regions did not exhibit any epileptic abnormality or patterns found to be associated with FCD [75], and retrospective visual analysis of these regions on conventional MR imaging was also not suggestive of FCD pathology. Furthermore, no clinical characteristics that would differentiate these five patients from the remaining were found. Yet the absence of any false positives in control subjects combined with reports of diffuse [70] or nonfocal [76] cortical involvement in FCD suggests that these clusters may indicate abnormal regions that are otherwise undetectable by way of conventional means. Clinical relevance of quantitative MR imaging of the neocortex By providing reliable and objective lesion detection, quantitative MR imaging of the neocortex may complement clinical decision making and contribute to the understanding of the biologic basis of poor seizure outcome after surgery. This is illustrated by the case of a 30-year-old patient with longstanding sensorimotor seizures of
Fig. 7. Example of a patient with medically intractable frontal lobe epilepsy (see text for details). T1-weighted MR image on the left and texture ratio maps on the right. (A) Preoperative MR image was reported as normal. The ratio map shows an area of hyperintense signal in the right prae-central region (right is left on the figure). (B) Postoperative texture ratio map shows the same area of hyperintense signal located anterior to the surgical resection, indicating high reproducibility of the method.
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the left arm followed by episodes of secondarily generalization. Scalp EEG recording showed an active interictal epileptic abnormality in the right frontocentral area (F4 and C4). The MR imaging was reported as normal. The ratio map showed an area of hyperintense signal in the right precentral area (Fig. 7A). The patient underwent a corticectomy in 1999 involving the right postcentral area with no clinical improvement. Histopathologic examination of the resected tissue revealed only a slight myelin loss. Postoperative texture ratio map consistently showed an area of hyperintense signal in the right precentral area (Fig. 7B) located anterior to the resection. Because of the persistence of seizures, the patient underwent an investigation with stereotactic implanted intracerebral depth electrodes. This examination showed that seizures originated from the prefrontal area, the motor hand area, and the supplementary motor area. An extension of the resection is planned to include the right frontocentral area, part of the SMA, and the second frontal gyrus. This observation demonstrates the high degree of reproducibility of the quantitative MR imaging image analysis technique and its potential as complement information in clinical decision making.
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these abnormalities. Follow-up studies, particularly in patients with refractory focal neocortical epilepsy and normal MR imaging, will be also needed to determine whether these MR imaging abnormalities are a predictor of outcome following epilepsy surgery. Besides the technical aspect, the clinical input will remain essential in deciding which features to investigate and which technique may be the most appropriate to answer specific questions. The new techniques discussed are performed on standard clinical MR imaging data but identify lesions that are not detectable using conventional means of analysis. Thus, despite the advanced postprocessing involved, they offer a substantial cost benefit.
Acknowledgments Thanks to Neda Bernasconi for her invaluable contributions. The data presented here are the result of collaborations with S. Antel, S. Duchesne, J. Lerch, D.L. Collins, and A. Evans. The author is grateful to the Canadian Institute of Health Research (CIHR), the Savoy Foundation for Epilepsy, and the Scottish Rite Charitable Foundation for funding research in this area.
Future directions Increasingly powerful computing facilities and sophisticated image processing, combined with the excellent tissue contrast obtained using MR imaging protocols, should continue to improve accurate definition of neocortical abnormalities outside the range of visual detection. Although the need for consistency and great attention to detail presently limit to some extent the application of many quantitative structural MR imaging of the neocortex to centers with research facilities, it is conceivable that some of the methods discussed in this article will be usable in routine clinical practice in the near future. These methods have been applied mainly to population-based studies, allowing a more global understanding of the disease. In the future, however, they should be tailored to provide information in individual patients making their impact more relevant at a clinical level. The evaluation of extent and severity of microdysgenesis in apparently cryptogenic cases and subtle abnormalities associated with obvious pathology is probably the biggest challenge for the future, mainly because the MR imaging signature of these changes are still unclear. Correlative studies with electrophysiologic data and, when possible, with histology will be essential in determining the clinical significance of
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Quantitative MR imaging of the neocortex Andrea Bernasconi, MD Department of Neurology and McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal H3A 2B4, Quebec, Canada
MR imaging has a major impact on the presurgical evaluation of patients with medically intractable epilepsy by defining cerebral structural damage and in delineating the extent of the epileptogenic zone (ie, the site of seizure onset). In general, patients with lesional epileptic syndromes are likely to experience a reduction in seizure frequency after epilepsy surgery. MR imaging provides the ability to vary image acquisition and postprocessing parameters to study different types of anatomic properties. Techniques such as the volumetric acquisition with thin contiguous slices have increased the ability of MR imaging to display brain anatomy and allowed better visual inspection of epileptogenic brain lesions. Currently, new image processing techniques provide a further improvement by allowing a quantitative and objective analysis, which opens new prospects in structural brain imaging. This article provides an overview on quantitative image analysis methods that have been applied to the study of patients with neocortical epilepsy, in particular those undergoing surgical treatment of medically intractable partial seizures.
The role of quantitative structural MR imaging in neocortical epilepsy Focal epilepsy syndromes are usually separated into three major groups: lesional epilepsy, mesial temporal lobe epilepsy (TLE), and neocortical epilepsy [1]. The last group includes patients with lateral temporal neocortical or extratemporal seizures. This division has clear practical implications when dealing
E-mail address: [email protected]
with patients being considered for surgical treatment. Typically, surgical treatment in patients with TLE related to hippocampal atrophy and in those with other lesional syndromes consists in removing the primary epileptogenic pathology as determined by MR imaging. In neocortical epilepsy, MR imaging has been confirmed to be a reliable indicator of foreign-tissue pathology, such as tumors, vascular malformations, encephalomalacia, and malformations of cortical development (MCD) [2]. The close relation between structural lesions identified by MR imaging and the epileptogenic zone has been demonstrated by the favorable surgical outcome in patients with lesions [3 – 8]. MCD are increasingly recognized as an important cause of intractable focal epilepsy. Although MR imaging has made it possible to detect MCD in some patients, the number of cases treated surgically is far less and the prognosis poorer than in patients operated for other types of lesions, mainly because identification of many of these malformations on visual inspection of conventional MR imaging is difficult due to their subtlety and the complexity of the brain’s convolution. Furthermore, there is evidence that the extent of histologic and epileptogenic abnormalities in MCD may go beyond the lesion that is visible on conventional MR imaging. In a large percentage of patients with medically refractory neocortical epilepsy, conventional structural MR imaging is unfortunately unremarkable. Epilepsy surgery in the absence of a lesion is presently one of the greatest clinical challenges because the criteria to localize the epileptic focus, and consequently a potential surgical target, are unclear. When these ‘‘MR imaging – negative’’ patients are operated, outcome is known to be worse than when a lesion is present on MR imaging [9,10]. MR imaging – nega-
1052-5149/04/$ – see front matter D 2004 Elsevier Inc. All rights reserved. doi:10.1016/j.nic.2004.04.013
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tive neocortical epilepsies are those forms that are usually considered cryptogenic unless histologic findings demonstrate the presence of subtle MCD, such as microdysgenesis or gliosis [6,11 – 13]. Features commonly accepted to represent microdysgenesis include neuronal clustering, cortical dyslamination, abnormal cortical myelinated fibers, and an excess of neurons in cortical layer I and the subcortical white matter [14]. Many of these subtle changes may have no macroscopic correlation and may not be directly visualized on MR imaging. However, postprocessing of highresolution MR images can offer surrogate markers of tissue integrity and neuronal connectivity that provide indication of an underlying subtle structural abnormality. In the author’s experience, T1-weighted 3D gradient-echo technique, which provides exquisite anatomic details and can be reconstructed to obtain high-quality volume imaging, provides the best substrate for digital image processing. Contrary to visual MR imaging inspection, which is intrinsically subjective, image processing provides quantitative analysis of digital images and therefore objective MR imaging analysis, which is likely to be of great aid in structural brain imaging. Image processing methods are becoming increasingly sophisticated and the tendency is to develop as much as automation as possible. In epilepsy, the purpose of quantitative structural MR imaging of the neocortex is to identify brain abnormalities overlapping with the epileptogenic zone and to detect other potentially epileptogenic areas, either in correspondence or distant to the main lesion that may not be readily recognizable by visual analysis alone. The following methods will be discussed in this article: image preprocessing and tissue segmentation, voxel-based morphometry, shape analysis, cortical thickness measurements, and texture analysis.
Tissue classification and segmentation Tissue classification refers to the differentiation of voxels within an MR image into classes, such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). Because these classes have sufficiently different gray-level intensities on the MR image histogram, tissue classification algorithms are usually based on the absolute MR intensity from one or more image types. When more than one tissue class is present in a voxel (partial volume effect), based on a set of predefined rules, the classification algorithms attributes this voxel to a single class. Tissue segmentation deals with the identification of anatomic structures and provides the basis for the
generation of volumetric data. Currently, most volumetric MR imaging protocols in epilepsy are based on manual segmentation of various limbic structures, including the hippocampus, amygdala, and entorhinal cortex [15 – 17]. Hippocampal atrophy, as determined by volumetric MR imaging, has been demonstrated to be a reliable in vivo indicator of mesial temporal sclerosis [18]. Similarly, volumetric measurements provide a mean of estimating the amount of tissue in neocortical areas. Studies in TLE have been based mostly on manual labeling and have shown atrophy of either temporal neocortical GM, WM, or both [17,19 – 22]. Manual segmentation is time consuming and may suffer from interobserver variations in the labeling strategy, which can potentially affect reproducibility of findings. These issues are particularly relevant when dealing with the segmentation of neocortical areas, because the visual identification of the anatomic boundaries is more complex than in the mesial temporal lobe or other deep brain structures, such as the thalamus [23]. However, computer-based segmentation of neocortical structures is demanding and faces difficulties related to anatomic variability, nonhomogeneity of pixel intensity values in a single tissue or structure, and the presence of blurred boundaries between various tissue types. Most segmentation methods applied so far to epilepsy combine automated segmentation with some level of operator intervention, particularly in defining anatomic landmarks [24]. Recently, there has been a considerable proliferation of increasingly sophisticated methods that aim to provide fully automated analytical tools, overcoming the bias of interactive methods. An exhaustive list is beyond the scope of this article. These methods make use of several strategies, such as artificial neural networks [25] and 3D model-based segmentation techniques [26 – 32]. Only some of them have been validated by comparison of automatic segmentation with the results obtained by interactive expert segmentation [27,33 – 35]. Therefore, visual inspection of results is still a necessity for quality control despite full automation. Regional cortical GM and WM have been assessed using block analysis [36]. In this technique, the segmented images of each cerebral hemisphere are divided into a number of equal slices (blocks) in an anterior-posterior axis, allowing measurement of interhemispheric volume ratios between blocks. This method is highly dependent on the quality of the image segmentation and provides crude anatomic information. In a study of patients with MCD, widespread changes were seen in apparently normal areas distant to the evident areas of dysgenesis [36,37]. In
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Fig. 1. MR imaging segmentation using automatic nonlinear image matching and anatomical labeling in a patient with unilateral polymicrogyria. (A) The lesion is shown by black arrows on a T1-weighted MR imaging axial slice. (B) The brain is segmented into frontal gray (red) and white (green) matter, parietal gray (pink) and white (yellow) matter, and occipital gray (navy blue) and white (sky blue) matter.
TLE, block analysis allowed correlating widespread changes with unsuccessful surgery in patients undergoing standardized temporal resections [38]. To date, the application of automatic segmentations methods has been limited and results have provided only lobar volumes without differentiation between GM and WM compartments [21]. With the refinement of segmentation techniques, future work should aim for separate analysis of GM and WM of individual brain regions (ie, specific gyri), allowing analysis of the whole brain (Fig. 1). Volumetric neocortical measurements may provide an objective method to evaluate the extent of resection and its relation to surgical results [13,39]. Preliminary data have shown that quantitative lobar volumetric measurements in patients with nonlesional neocortical epilepsy may be a predictor of improved postsurgical seizure control [13].
Voxel-based morphometry Brain pathology in epilepsy causes not only volume reduction but also changes in water relaxation behavior, which are manifested by gray-level variation. Voxel-based morphometry (VBM) is a powerful tool for analyzing subtle differences in signal intensity [40]. VBM involves a voxel-wise comparison of the local ‘‘concentration’’ of some property between two groups of subjects. VBM heavily relies on an accurate image coregistration process to conform all brains to the same orientation and size before comparing different MR imaging volumes at a voxel level. Any differences due to registration errors will
result in added noise to the statistical analysis. VBM involves three main steps. After tissue classification, images are smoothed so that each voxel contains the average signal around the voxel, often referred as tissue ‘‘density’’ or ‘‘concentration’’ [40]. Voxel-wise parametric statistical tests are then used to compare the smoothed concentration maps from the groups, and correction for multiple comparisons is made [41]. Many neuroimaging laboratories have the capability to perform VBM using the SPM package [40]. As in other image processing methods, results of VBM depend on the quality of image registration, segmentation, classification, and intensity nonuniformity correction. The effects of nonlinear image registration to further conform all individuals to a common reference volume at a local level may modify the results on VBM. The size of the kernel used for smoothing the images is an important factor for interpreting results and should be comparable to the size of the expected regional differences between groups. For example, a kernel size of 14 mm would be larger than the expected hippocampal atrophy in TLE. VBM has previously been used to demonstrate GM abnormalities in patients with TLE and hippocampal atrophy [42,43], juvenile myoclonic epilepsy [44], and MCD [45,46]. In TLE, VBM has produced conflicting results showing significant clusters of decreased GM intensity in the epileptogenic hippocampus in some studies [42,43] and no abnormalities in others [46]. However, VBM was able to show consistently across different studies neocortical GM reduction, either lateralized or not to the epileptic focus, demonstrating abnormalities beyond the visu-
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Fig. 2. Voxel-based morphometry in patients with right temporal lobe epilepsy. Statistically significant peaks of gray matter decrease in the (A) right hippocampus, (B) the left superior temporal cortex, (C) bilateral frontal areas, and (D) thalami. Statistically significant peaks of white matter decrease in the callosum and the right temporo-polar area (E), the right entorhinal, and perirhinal areas (F). Statistical maps are superimposed on an average template of 152 healthy controls for anatomical reference (on coronal and axial maps, right is right on the image; sagittal maps of the right hemisphere are presented).
alized lesions. The author examined 85 patients with medically intractable TLE and unilateral hippocampal atrophy and compared them to 47 healthy individuals. The study revealed that GM pathology in TLE extends beyond the hippocampus, involving other limbic areas, such as the cingulum and the thalamus, as well as extralimbic areas, particularly the frontal lobe. WM reduction was found only ipsilateral to the seizure focus, including the temporopolar, entorhinal, and perirhinal areas (Fig. 2). In relation to duration of epilepsy, GM reduction was observed in the primary epileptogenic areas, such as the hippocampus and the parahippocampal region as well as extratemporal areas, in particular the frontal lobe. This pattern of structural changes is suggestive of disconnection involving preferentially frontolimbic pathways in TLE. The pertinence of these widespread changes in individual cases with respect to clinical parameters, such as outcome after surgery, remains to be evaluated. Previous studies have also reported the presence of GM increase in TLE and juvenile myoclonic epilepsy [42,46,47]. The pathologic correlates of an increase in GM concentration found in these studies are unclear and have been tentatively attributed to microdysgenesis. By performing voxel-wise analysis of GM and WM, we were able to observe that in TLE, areas of increased GM coincided with those of decreased WM. Therefore, it is conceivable that previous observations of increased GM were second-
ary to a relative displacement of the GM due to atrophy of underlying WM. In TLE, it has also been shown that extrahippocampal GM abnormalities may be correlated with duration of epilepsy, suggesting secondary brain damage [43].
Cortical and sulcal morphometry Though extensive manual segmentation of the cortical mantle on MR imaging is prohibitive, reliable automatic assessment of the cerebral cortex remains a challenging undertaking due to its convoluted nature. However, measuring cortical thickness in an accurate fashion has the potential to provide maps of changes across the cortical ribbon with a meaningful metric of change, namely thickness loss or gain measured in millimeters. There have been few studies attempting to measure cortical thickness from MR imaging data [48 – 55]. The most common approach used involves selecting a point on the WM surface and finding the closest point on the GM surface [48,51,55]. Few studies have validated the accuracy of these techniques by direct comparisons with manual measures on postmortem brains [53]. Cortical thickness in patients with TLE using deformable models was recently evaluated [51]. Preliminary data in 32 patients with
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Fig. 3. Cortical thickness measurement in patients with left temporal lobe epilepsy. Various areas of decrease in cortical thickness are seen outside the temporal lobes, in the left and right frontal, central, and left insular cortices. Lateral views of (A) right and (B) left hemisphere, and top view of the brain (C) are shown (right is right on the image).
left TLE [56] compared with 51 healthy controls showed a decreased cortical thickness of the ipsilateral entorhinal cortex and bilateral decrease in thickness in frontal and central areas (Fig. 3). On visual analysis of conventional MR imaging, sulcal and gyral abnormalities are sometimes noted in patients with epilepsy. These are characterized by a spectrum of changes ranging from clefts of various depth [57] to broad gyri, shallow or deep sulci, or gyral simplification [58,59]. These abnormalities, which are thought to represent an underlying pathologic cortical cytoarchitecture [60], have been reported mainly in patients with MCD, but are also found in MR imaging-negative cases [61]. Most studies have relied on visual MR imaging inspection to identify gross abnormalities in gyration. The high variability of the branching patterns of the major sulci and the large number of secondary gyri make reliable quantification of the cortical gyral and sulcal pattern a difficult task, and this may explain why there has been virtually no work done in this area of research. Models of structure and shape have the potential to identify cortical characteristics that are not easily available through visual observation. Measurements of GM and WM surface area and gyral index of curvature have shown subtle abnormalities in the neocortex of TLE patients not evident on visual MR imaging inspection [19]. Similarly, fractal analysis has been applied in an attempt to detect simplification of gyral patterns. Results indicated that, compared with a group of normal controls, a subset of patients with frontal lobe epilepsy [62] and cryptogenic epilepsy [63] present with some degree of gyral simplification as indicated by abnormally low fractal dimension (an index of contour complexity) of the GM-WM interface and the WM surface. In another study, Sisodiya et al [37] found disproportion between surface areas derivatives of the GM-WM interface and volumetric
data from block analysis in some patients with MCD, suggesting widespread disorganization of the cerebral hemispheres in patients with cerebral dysgenesis, possibly due to abnormal connectivity. The results of these studies illustrate the potential of quantitative sulcal and gyral analysis for detecting anomalous cortical morphology, particularly if extended into 3D. Automated or semiautomated methods for statistical models of sulci rely on graphs constructed from 3D point sets [64,65], on ribbons used to model the space between opposite sides of a sulcus [66] or on curves located on the outer cortical surface [67]. These methods allow intersubject sulcal shape comparisons of the major sulci [68] and can quantify various geometric characteristics, such as length, volume, and curvature [69]. It is conceivable that results from cortical and sulcal morphometry may increase sensitivity in detecting subtle localized of sulcal and gyral pattern alterations in epileptic brains. Because these abnormalities may represent a marker for the site of seizure onset and of an underlying subtle MCD, they may be useful to direct invasive EEG monitoring in MR imaging-negative patients. However, correlation studies with electrophysiologic and histopathologic data will be essential in clarifying the relationship between these changes and the epileptic foci.
Computer-based models of focal cortical dysplasia Focal cortical dysplasia (FCD) [70] is the most common form of MCD in patients with pharmacologically intractable focal epilepsy referred for presurgical evaluation. On T1-weighted MR imaging, FCD is mainly characterized by variable degrees of cortical thickening, a poorly defined transition between GM and WM, and by a hyperintense signal
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within the dysplastic lesion with respect to normal cortex. As discussed previously, the inherent complexity of the brain’s convolutional pattern makes the visual identification of FCD sometimes difficult. Therefore, the author sought to develop a computerbased method to assist in the detection of FCD based on its in vivo pathologic characteristics. The initial approach was to apply morphologic and first-order texture models of the characteristics of FCD on T1weighted MR imaging [71]. Texture analysis of digital images provides statistical methods capable of identifying the relationship between neighboring pixels over a region of interest, often considering the probability that certain intensities are found in specific spatial locations with respect to each other [72].
The following voxel-wise operators were applied to the 3D T1-weighted MR imaging for each patient: (1) a gray-matter run-length operator to model GM thickness; (2) an absolute gradient operator to quantify the blurring of the GM/WM interface; and (3) an operator to model hyperintense T1 signal within GM through intensity measurement of a voxel relative to the threshold intensity between GM and WM (termed ‘‘relative intensity’’). To maximize visibility of FCD lesions, the three feature maps were combined into a composite map (Fig. 4). Results in 16 patients in whom FCD had been histologically proven at surgery showed that the composite maps yielded a significantly increased sensitivity of FCD lesion detection relative to conventional MR imaging (88% versus
Fig. 4. Computer-based models of focal cortical dysplasia. (A) T1-weighted MR image axial slice of a focal cortical dysplasia in the left parietal lobe (left is left on the image). (B) The panel in the center shows a portion of the T1-weighted MR image including the lesion. On the left, a small region of cortex and adjacent white matter (WM) are schematically shown in 3D and magnified, each cube representing a voxel. To model cortical thickening, each individual voxel in the T1-weighted MR image is used as the starting point (yellow dot) for gray matter (GM) run-length coding (red arrows). The GM thickness map of the same axial slice is shown on the right. The increased cortical thickness is represented by the brighter intensities in the color map. (C) To model the blurring between GM and WM, the absolute gradient of gray level intensities is calculated. In regions of normal transition between GM and WM (magnified region on the left), the gradient is expected to be steep. On the other hand, in regions of GM-WM blurring (magnified on the right), the gradient is less steep. The GM-WM blurring is represented by the darker intensity in the GM-WM transition zone in the gradient map. (D) The hyperintense signal within the lesions is modeled by calculating the difference between the intensity of each voxel and the intensity at the GM-WM boundary. The hyperintense voxels are shown on the relative intensity map on the right. (E) The ratio map (GM thickness relative intensity/gradient) enhances the contrast and maximizes lesion visibility. (Adapted from Bernasconi A, Antel S, Collins DL, Bernasconi N, Olivier A, Dubeau F, et al. Texture analysis and morphological processing of MRI assist detection of focal cortical dysplasia in extratemporal partial epilepsy. Annals of Neurology 2001;49:770 – 5; with permission.)
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Fig. 5. Second-order texture analysis. (A) An example of a focal cortical dysplasia in the right central area (black arrow) is shown on a T1-weighted MRI axial slice (right is right on the image). Second-order texture maps are shown in the other panels: (B) angular second momentum, (C) difference entropy, and (D) contrast.
Fig. 6. Automatic classification of focal cortical dysplasia. Three examples are shown. Left column: T1-weighted MR image. Center column: MR image with manual lesion label (blue). Right column: MR image with lesion identified automatically by the classifier (yellow).
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50%) by maintaining high specificity. This work demonstrated for that the application of computerbased FCD models could significantly improve the sensitivity of lesion detection. After demonstrating that our method could be improved by incorporating more sophisticated cortical thickness models [73], we implemented an automated classifier prototype [74]. The computational models of MR imaging characteristics of FCD presented, which provide visually discernable information, were combined with a set of second-order texture features that quantify lessavailable information regarding tissue organization (Fig. 5). The classifier correctly identified FCD lesions in 15 of 18 patients (83%). Representative examples are shown in Fig. 6. The classifier improved upon previous techniques by providing an automated and reliable approach to FCD lesion detection. In five patients the classifier identified lesional areas that did not colocalize with the main FCD lesion. Retrospective visual analysis of the individual feature maps input revealed that these lesional clusters exhibited a pattern of texture features similar to the known FCD lesions. However, EEG
data from these regions did not exhibit any epileptic abnormality or patterns found to be associated with FCD [75], and retrospective visual analysis of these regions on conventional MR imaging was also not suggestive of FCD pathology. Furthermore, no clinical characteristics that would differentiate these five patients from the remaining were found. Yet the absence of any false positives in control subjects combined with reports of diffuse [70] or nonfocal [76] cortical involvement in FCD suggests that these clusters may indicate abnormal regions that are otherwise undetectable by way of conventional means. Clinical relevance of quantitative MR imaging of the neocortex By providing reliable and objective lesion detection, quantitative MR imaging of the neocortex may complement clinical decision making and contribute to the understanding of the biologic basis of poor seizure outcome after surgery. This is illustrated by the case of a 30-year-old patient with longstanding sensorimotor seizures of
Fig. 7. Example of a patient with medically intractable frontal lobe epilepsy (see text for details). T1-weighted MR image on the left and texture ratio maps on the right. (A) Preoperative MR image was reported as normal. The ratio map shows an area of hyperintense signal in the right prae-central region (right is left on the figure). (B) Postoperative texture ratio map shows the same area of hyperintense signal located anterior to the surgical resection, indicating high reproducibility of the method.
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the left arm followed by episodes of secondarily generalization. Scalp EEG recording showed an active interictal epileptic abnormality in the right frontocentral area (F4 and C4). The MR imaging was reported as normal. The ratio map showed an area of hyperintense signal in the right precentral area (Fig. 7A). The patient underwent a corticectomy in 1999 involving the right postcentral area with no clinical improvement. Histopathologic examination of the resected tissue revealed only a slight myelin loss. Postoperative texture ratio map consistently showed an area of hyperintense signal in the right precentral area (Fig. 7B) located anterior to the resection. Because of the persistence of seizures, the patient underwent an investigation with stereotactic implanted intracerebral depth electrodes. This examination showed that seizures originated from the prefrontal area, the motor hand area, and the supplementary motor area. An extension of the resection is planned to include the right frontocentral area, part of the SMA, and the second frontal gyrus. This observation demonstrates the high degree of reproducibility of the quantitative MR imaging image analysis technique and its potential as complement information in clinical decision making.
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these abnormalities. Follow-up studies, particularly in patients with refractory focal neocortical epilepsy and normal MR imaging, will be also needed to determine whether these MR imaging abnormalities are a predictor of outcome following epilepsy surgery. Besides the technical aspect, the clinical input will remain essential in deciding which features to investigate and which technique may be the most appropriate to answer specific questions. The new techniques discussed are performed on standard clinical MR imaging data but identify lesions that are not detectable using conventional means of analysis. Thus, despite the advanced postprocessing involved, they offer a substantial cost benefit.
Acknowledgments Thanks to Neda Bernasconi for her invaluable contributions. The data presented here are the result of collaborations with S. Antel, S. Duchesne, J. Lerch, D.L. Collins, and A. Evans. The author is grateful to the Canadian Institute of Health Research (CIHR), the Savoy Foundation for Epilepsy, and the Scottish Rite Charitable Foundation for funding research in this area.
Future directions Increasingly powerful computing facilities and sophisticated image processing, combined with the excellent tissue contrast obtained using MR imaging protocols, should continue to improve accurate definition of neocortical abnormalities outside the range of visual detection. Although the need for consistency and great attention to detail presently limit to some extent the application of many quantitative structural MR imaging of the neocortex to centers with research facilities, it is conceivable that some of the methods discussed in this article will be usable in routine clinical practice in the near future. These methods have been applied mainly to population-based studies, allowing a more global understanding of the disease. In the future, however, they should be tailored to provide information in individual patients making their impact more relevant at a clinical level. The evaluation of extent and severity of microdysgenesis in apparently cryptogenic cases and subtle abnormalities associated with obvious pathology is probably the biggest challenge for the future, mainly because the MR imaging signature of these changes are still unclear. Correlative studies with electrophysiologic data and, when possible, with histology will be essential in determining the clinical significance of
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