Thesis
Reference
Advanced magnetic resonance imaging techniques in diagnostic neuroradiology
HALLER, Sven
Abstract
Advanced magnetic resonance imaging (MRI) techniques in neuroimaging allow the assessment of a wide range of different parameters, including structural analyses of grey matter, white matter and iron deposition as well as functional analyses of neuronal activations.
These techniques have substantially extended our knowledge of brain physiology in healthy volunteers and brain pathophysiology in a wide range of diseases. The number of publications implementing these techniques has continuously increased in the last years, paralleled by substantial improvements of MRI hardware, MR sequences, data post-processing and statistical analyses. Despite all these progress, the transfer of these advanced neuroimaging techniques into daylily clinical neuroradiology has been minimal. There are two main causes.
First, theses technique become more complex, hence they are simply not applicable in clinical routine. Second, most analyses lead to group level differences, which are difficult to transfer to the diagnosis of individual patients. In agreement with the current neuroradiological approach, the thesis is divided into a structural and [...]
HALLER, Sven. Advanced magnetic resonance imaging techniques in diagnostic neuroradiology. Thèse de privat-docent : Univ. Genève, 2010
DOI : 10.13097/archive-ouverte/unige:12836
Available at:
http://archive-ouverte.unige.ch/unige:12836
Disclaimer: layout of this document may differ from the published version.
A DVANCED M AGNETIC R ESONANCE I MAGING TECHNIQUES IN D IAGNOSTIC N EURORADIOLOGY
Thèse d’habilitation au titre de Privat-Docent à la Faculté de Médecine de Genève
Sven Haller
Service neuro-diagnostique et neuro-interventionnel DISIM Hôpitaux Universitaires de Genève
Table of contents
Summary ... 3!
Abbreviations... 5!
1. Introduction ... 6!
2. MRI predictive markers in schizophrenia and mild cognitive impairment... 8!
2.1 Analysis of cortical thickness asymmetry in first episode psychosis and at risk mental state ...9!
2.2 Support vector machine analysis of diffusion tensor imaging data in mild cognitive impairment ...11!
2.3 Analysis of cerebral microhemorrhages and support vector machine analysis of iron deposition in mild cognitive impairment ...15!
3. fMRI investigations in clinical neurology ... 19!
3.1 BOLD ceiling fMRI...21!
3.2. MRI assessment of cerebrovascular reserve...22!
4. Perspectives: Clinical application of real-time fMRI neurofeedback.. 25!
6. Attachments ... 30!
6.1 Can cortical thickness asymmetry analysis contribute to detection of at-risk mental state and first-episode psychosis? A pilot study ...30!
6.2 Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data...41!
6.3 Susceptibility weighted imaging (SWI) assessment of cerebral microbleeds and iron deposition in mild cognitive impairment ...55!
6.4 Mapping continuous neuronal activation without an ON-OFF paradigm: initial results of BOLD ceiling fMRI...66!
6.5 Reduced Cerebrovascular Reserve at CO2 BOLD MR Imaging Is Associated with Increased Risk of Periinterventional Ischemic Lesions during Carotid Endarterectomy or Stent Placement: Preliminary Results ...74!
6.6 Real-time fMRI feedback training may improve chronic tinnitus ...83!
7. References ... 92!
Summary
Advanced magnetic resonance imaging (MRI) techniques in neuroimaging allow the assessment of a wide range of different parameters, including structural analyses of grey matter, white matter and iron deposition as well as functional analyses of neuronal activations. These techniques have substantially extended our knowledge of brain physiology in healthy volunteers and brain pathophysiology in a wide range of diseases. The number of publications implementing these techniques has continuously increased in the last years, paralleled by substantial improvements of MRI hardware, MR sequences, data post-processing and statistical analyses.
Despite all these progress, the transfer of these advanced neuroimaging techniques into daylily clinical neuroradiology has been minimal. There are two main causes. First, theses technique become more complex, hence they are simply not applicable in clinical routine. Second, most analyses lead to group level differences, which are difficult to transfer to the diagnosis of individual patients.
In agreement with the current neuroradiological approach, the thesis is divided into a structural and a functional part. In the first part of this thesis, three structural studies will be discussed that are tailored to the early diagnosis of individual patients with neuropsychiatric conditions such as first episode psychosis and progressive mild cognitive impairment. In the second part, modified and newly developed functional magnetic resonance (fMRI) techniques tailored for clinical applications will be discussed. BOLD ceiling fMRI allows the assessment of continuous neuronal activations such as continuous tinnitus, which cannot be assessed directly with “standard” fMRI.
CO2BOLD allows the non-invasive assessment of the cerebral perfusion reserve at the level of the brain parenchyma, which allowed detecting patients at risk for the development of new peri-interventinal ischemic lesions in patients with high-grade stenosis of the internal carotid artery. Notably, this technique can be used to assess the cerebral autoregulation in dementia.
This method can thus be combined with the developed morphometric methods described in the first part, to obtain an even more precise individual assessment of structural and functional brain parameters at the individual level in clinical neuroradiology.
Abbreviations
AD Alzheimer disease
BOLD blood oxygenation level dependent (effect) CO2 carbon dioxide
DTI diffusion tensor imaging FA fractional anisotropy
fMRI functional magnetic resonance imaging MCI mild cognitive impairment
MRI magnetic resonance imaging MVPA multi vector pattern analysis
rtfMRI real-time functional magnetic resonance imaging SVM support vector machine
SWI susceptibility weighted imaging TBSS tract based spatial statistics
1. Introduction
There has been a tremendous development in advanced neuroimaging techniques in the last years, in particular in the domain of magnetic resonance imaging (MRI). This can be attributed to the fact that MRI combines a number of beneficial features including non-invasiveness, absence of radiation and very high soft tissue contrast, which in concert make MRI a nearly ideal technique for advanced neuroimaging. Moreover, MRI permits an assessment along various distinct domains that include brain morphometry of grey matter including voxel-based morphometry (Ashburner and Friston, 2000) and cortical thickness measurements (Fischl and Dale, 2000), brain morphometry of white matter in particular using diffusion tensor imaging (DTI) (Le Bihan et al., 1991; Le Bihan et al., 2001) and assessment of neuronal brain functioning using functional MRI (fMRI) (Ogawa et al., 1992). These techniques have revolutionized our understanding of brain physiology in healthy volunteers and brain pathophysiology in a wide range of diseases.
Despite this fundamental progress in both methodology and neuroscientific knowledge, there is almost no transfer of these new techniques into daily neuroradiological clinical routine. This might be attributed to two main causes.
First, the data processing becomes more and more difficult and time- consuming, requiring highly specialized experts, and is therefore simply impractical in clinical routine workup. Second, the vast majority of these studies concern group differences that are highly interesting from a scientific perspective but very difficult to interpret at an individual level.
In this thesis, the use of advanced neuroimaging studies in neuropsychiatric and clinical neurology will be discussed across four experimental paradigms:
(a) systematic analysis of brain morphometry for early diagnosis of individuals at high risk for first episode psychosis (Haller et al., 2009) and mild cognitive impairment (MCI) (Haller et al., 2010a; Haller et al., 2010a), (b) blood oxygenation level dependent (BOLD) ceiling fMRI to assess continuous neuronal activations (Haller et al., 2006), (c) carbon dioxide (CO2) based assessment of cerebrovascular perfusion reserve in cerebrovascular disease (Haller et al., 2008) and (d) clinical application of real-time fMRI neurofeedback to improve chronic tinnitus (Haller et al., 2010b).
2. MRI predictive markers in schizophrenia and mild cognitive impairment
The basic concept of brain morphometry is to detect systematic changes in brain structure derived from MRI studies between two or more groups of patients and controls. The most commonly implemented approach of such brain morphometry studies is the assessment of grey matter based on T1 weighted images. Less frequently, white matter changes are assessed typically using diffusion tensor imaging (DTI) (Le Bihan et al., 1991; Le Bihan et al., 2001). The analyses can further be classified into manual region of interest analyses, semi-manual volume segmentation e.g. of the hippocampal volume (Apostolova et al., 2006), or automatic voxel-based morphometry (VBM) analyses, which compare the local concentration of grey matter (Ashburner and Friston, 2000), for example (Karas et al., 2004; Ferreira et al., 2009). Such analyses may detect differences in brain morphometry between groups. While these group level analyses are highly interesting from a scientific perspective, these group results cannot be transferred into clinical diagnostic neuroradiology. For example in the domain of Alzheimer disease (AD), the current daily neuroradiological practise still is largely limited to the exclusion of other brain pathologies such as hemorrhage or brain tumors. The marked atrophy confined to the hippocampal formation represents a very late sign of AD with low sensitivity and specificity (Petrella et al., 2003), not contributing to an early diagnosis at the individual level. The fundamental assumption of using brain morphometry for early diagnosis at an individual level is that systematic patterns of changes in morphometry exist in the MRI
data at an early stage, which are not detectable by visual inspection yet require tailored, computer-aided analyses.
2.1 Analysis of cortical thickness asymmetry in first episode psychosis and at risk mental state
The aim of our study “Can cortical thickness asymmetry analysis contribute to detection of at-risk mental state and first-episode psychosis? a pilot study”
(Haller et al., 2009) was to systematically analyze cortical thickness derived from 3D T1w MRI images for the early diagnosis of first episode psychosis and at risk mental state subjects at the individual level. In order to obtain an early diagnosis, we needed to obtain a high sensitivity. We therefore analyzed cortical thickness and not the more commonly used VMB derived cortical density, because cortical thickness appears to have higher sensitivity and specificity than VBM, as demonstrated in normal ageing (Hutton et al., 2009).
A direct comparison of these methods is not yet available in schizophrenia, still it is plausible that cortical thickness might be more sensitive than VBM also in this domain. Additionally, cortical thickness may more closely reflect cytoarchitectural abnormalities than cortical volume (Thompson et al., 2003;
Yoon et al., 2007).
A fundamental problem for systematic analyses of brain morphometry is the substantial inter-individual variation even in healthy controls that can reach up to 15% for cortical thickness (Haug, 1987). This may be more pronounced than the expected variation related to disease, in particular in early stages of a disease. To overcome this limitation, we assumed less intra-individual variation, and therefore analyzed cortical thickness asymmetry of
corresponding anatomic regions between both hemispheres. To further improve the sensitivity of the data analysis, we did not assess the absolute cortical thickness asymmetry yet the normalized variation in cortical thickness asymmetry. To further increase the sensitivity, we combined the analysis across several regions. We can assume that some regions will better discriminate between groups than others, and some regions might not discriminate at all. On the one hand inclusion of all regions would consequently decrease the sensitivity, because non-discriminative regions would be included. On the other hand, the use of too few regions would also decrease the sensitivity because discriminative information would be excluded from the analysis. We used a jackknife technique to identify the most discriminative regions. In principle, this method tests the discrimination between groups by randomly excluding regions from the comparison. This method can determine the degree of discrimination for each region. In our data, seven regions of the Talairach standard space (Talairach and Tournoux, 1988) were strongly discriminative, while the remaining 34 regions only weakly discriminated between groups. For the final analysis, these seven discriminative regions were combined to obtain a high sensitivity. The final result of this newly developed processing chain consequently is one single parameter per subject. This non-invasive and operator-independent parameter discriminated first episode psychosis from healthy controls (sensitivity 70.0%, specificity 85.0%) and showed a trend towards the discrimination of at risk mental state individuals from healthy controls (sensitivity 75.0%, specificity 65.0%).
2.2 Support vector machine analysis of diffusion tensor imaging data in mild cognitive impairment
The study “Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data” (Haller et al., 2010a) aimed to individually diagnose subjects with mild cognitive impairment (MCI) based on neuroimaging parameters at baseline investigation. MCI was originally conceived as a functionally non- disabling amnestic disorder that has been later on expanded to include essentially any form of cognitive complaint (Petersen, 2004). MCI might be a precursor or prodromal stage of Alzheimer Disease (AD), yet not all MCI evolve to AD or decline at identical rates and a significant proportion of cases remain stable for several years or even improve (Petersen and Negash, 2008). In our study, MCI subjects performed extensive neuropsychological testing at baseline and again after one year. If the subjects had significant deterioration of cognitive functions after one years, they were considered as progressive MCI, otherwise as stable MCI. An early and individual diagnosis of AD might be beneficial to detect subjects who might benefit from treatment, or select subjects for pharmaceutical trials. We thus aimed in particular to individually distinguish between stable and progressive MCI individuals. To obtain an individual classification, we used a support vector machine (SVM) (Noble, 2006). The SVM (Noble, 2006) analysis for individual classification is fundamentally different from the commonly used voxel-wise analysis. Such voxel-wise analyses are univariate tests, which separately analyze each included voxel between two (or more) groups, resulting in for example one t- value per voxel. Given the multiple tests, it is necessary to implement as a
second step a correction for multiple comparisons. Such voxel-wise analyses can be used to investigate the spatial distribution of differences between groups at the group level, yet do not directly permit a classification of individual subjects. In contrast, SVM analyses are multivariate tools that originate from a field called “machine learning” or multi voxel pattern analysis (MVPA), a branch of artificial intelligence. The aim is to identify patterns that allow discrimination between groups. There is only one resulting parameter per subject, hence there is no need for corrections for multiple comparisons.
SVM analyses allow a classification at the individual level, yet the spatial information is only of minor importance. Given the fundamental differences of the SVM analysis, there are no resulting t- or p-values as in the more
“standard” statistical analyses discussed above.
Figure 1
Figure 1 illustrates the basic principle of support vector machine (SVM) analyses. In a simple case of two groups, a patient (x) and a control group (o) and only 2 features, the distribution can be illustrated as x-y plot (A). In this basic example, there are many possible lines that discriminate between both groups (B). The strength of the SVM analysis is to identify the optimum separation between groups (C).
The basic principle of an SVM is briefly discussed. One part of the data (or one dataset) is needed to train the classifier. In the easiest case of only two features (or voxels), one might imagine a X-Y scatterplot in which ideally one group is dominantly located for example in the right upper part, while the other group is dominantly located in the left lower part. The SVM then tries to identify a boundary that discriminates between the two groups. The strength of the SVM is that this method optimizes this boundary to obtain the best possible discrimination between groups. In the simple case of only two features, this boundary is a line. In higher dimensional space as in our data, this boundary is a so-called hyerplane. Once the SVM is trained with the training data, it is tested how accurate this SVM classifier can be generalized and classify another test dataset. We can assume that not all of voxels of the entire brain will discriminate between groups. One disadvantage of the SVM is that all included voxels (or features in the terminology of classifiers) are considered equally important. The inclusion of non-discriminative features will result in overlapping representations between groups, hence reducing the accuracy of the classifier. In the example of only two features discussed above, one might image overlapping clouds for the two groups. On the other hand, the exclusion of discriminative features also decreases the performance of the SVM classifier, since discriminative information is not available. To address this issue, our analysis included two steps. In a first step, a feature selection “Relieff” algorithm (Kononenko et al., 1997) was used to identify the most discriminative voxels implementing 10 fold cross validation. In principle, this feature selection estimates how strong each feature discriminates between two groups. The more discriminative a feature, the higher the
resulting weights. It is thus possible to sort the features according to their discriminative properties. Five data sets were created that contained the 100, 250, 500, 750 and 1000 most discriminative features. In a second step, these datasets were analyzed analysed using the SVM algorithm “sequential minimal optimization” SMO (Platt, 1999) with a radial basis function (RBF) kernel. A ten fold cross validation approach was used. This means that the dataset was divided into 10 parts. 9 parts were used for training, and one part was used for testing. This procedure is repeated 10 times, hence each subject is once in the test dataset. The reported accuracy values are the average of 10 repetitions of this procedure. The SVM classification of our cases was
made using the WEKA software package
(http://www.cs.waikato.ac.nz/ml/weka). This is a very powerful and versatile freely available software package, which includes numerous features for classification analyses. The whole procedure was performed twice and separately for the analysis of HC versus MCI and for sMCI versus pMCI.
Consequently, there is no potential interference of the HC selection on the performance of the discrimination between sMCI and pMCI.
SVMs were recently successfully applied to MRI data and discriminated between AD versus controls, with accuracies of 89% (Kloppel et al., 2008) and 94.5% (Magnin et al., 2009) and between stable versus progressive MCI with accuracies of 75% (Plant et al., 2010), 81.5% (Misra et al., 2009) and 85% (Fan et al., 2008). All of these studies used VBM preprocessed T1w images, i.e. grey matter concentration. In contrast to these studies, we analyzed white matter derived from diffusion tensor imaging (DTI). DTI is a MRI technique that allows for the interrogation of the microstructural integrity
of white matter. We focused on fractional anisotropy (FA) is estimated from diffusion tensor imaging (DTI) (Le Bihan et al., 2001) and correlates with the integrity of white matter fiber bundles (Thomalla et al., 2004). Although cortico-cortical disconnection is thought to be a main determinant of clinically overt dementia ((Morrison and Hof, 2007)), only a few studies explored the structural changes occurring in white matter (WM) in MCI. We implemented the recently introduced advanced preprocessing of DTI data Tract-Based Spatial Statistics (TBSS), an improved voxel-based technique (Damoiseaux et al., 2009; Liu et al., 2009; Bosch et al., 2010). The elegance of this method is that the individual FA values are projected onto a group average white matter
“skeleton” representing the major white matter tracts. This improves a number of problems in the spatial registration of individual subjects into a common standard space, which is fundamental for later systematic analyses. This TBSS analysis was crossed with a SVM analysis, resulting in excellent individual classification accuracies of up to 91% for the comparison of controls versus MCI, and more importantly up to 98% for the comparison of stable versus progressive MCI.
2.3 Analysis of cerebral microhemorrhages and support vector machine analysis of iron deposition in mild cognitive impairment
Recent neuroimaging studies postulated that mild cognitive impairment (MCI) conversion to dementia might be predicted by the occurrence of cerebral microhemorrhages (CMHs) and accumulation of iron in subcortical nuclei. The study “Susceptibility weighted imaging (SWI) assessment of cerebral microbleeds and iron deposition in mild cognitive impairment” (Haller et al.,
2010a) included the same patient collectives as the study “Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data” (Haller et al., 2010a) discussed above. In this study, we analyzed susceptibility weighted imaging (SWI). SWI is a recently introduced imaging technique that combines phase and magnitude images (Haacke et al., 2004) resulting in a novel image contrast, which is particularly sensitive to both blood products, venous vasculature (Reichenbach et al., 1997) and iron content (Ogg et al., 1999;
Kirsch et al., 2009). This study focused on two different aspects. The first concerns cerebral microhemorrhages, which occur with a nearly random distribution, and were visually analyzed. MCI subjects had more cerebral microhemorrhages at baseline compared to controls, yet the number of cerebral microhemorrhages did not predict subsequent cognitive decline. The second focus was iron deposition, which typically occurs at defined anatomic locations, notably in the basal ganglia. To analyze the SWI images in a systematic way, a spatial registration was needed equivalent to the discussion above. The available spatial registration tools however originate from the domains of fMRI, VBM or DTI analyses. SWI images have a substantially different image contrast, and these available spatial registration tools therefore unlikely provide accurate results. To overcome this limitation, we used the optimized spatial registration of DTI images, which were acquired in the same imaging sessions, and applied these non-linear spatial registration results to the SWI data. The resulting average SWI image of the entire group depicts remarkably clearly the deep basal structures of the brain, for example the red nucleus or substantia nigra. We could demonstrate that MCI was
associated with increased iron concentration compared to healthy controls in the right pallidum and substantia nigra, but decreased iron concentration in the right red nucleus. Similar to the study above, we then used a support vector machine to discriminate between healthy controls versus MCI, and between stable versus progressive MCI. Only the SVM analysis, yet not the more classical region of interest analysis, of SWI at baseline discriminated between stable and progressive MCI an accuracy of 85%. The lower accuracy rates for this SWI-derived analysis of iron deposition compared to the analysis of DTI-derived white matter discussed above is consistent with a later modification in iron content compared to the white matter changes occurring in MCI. Nevertheless, these results point to existing changes in iron concentration very early in the degenerative disease progress, which is present already in MCI subjects even when they remain cognitively stable over a short period of time.
Figure 2
Figure 2 summarizes the morphological differences between mild cognitive impairment (MCI) versus control subjects. Decreased fractional anisotropy (FA) in white matter in MCI compared to controls are present in a distributed network, most pronounced in the ventral part of the corpus callosum (A). MCI subjects had increased iron concentration in the right pallidum and thalamus, and a trend towards decreased iron concentration in bilateral red nucleus (B).
3. fMRI investigations in clinical neurology
Functional magnetic resonance imaging (fMRI) is an advanced imaging method, which allows the assessment of brain activations (Ogawa et al., 1992). This method combines several attractive properties, notably it is non- invasive, does not require irradiation or contrast agents, and there are no known side-effects. The combination of these properties makes it a very powerful neuroimaging tool not only for patient studies, but also for studies of healthy volunteers. This is reflected in an increasing number of fMRI studies indexed in Pubmed, from less than 10 in 1992 to over 2500 in 2008 (Kaiser et al., 2009). This impressive and increasing number of publications in the domain of fMRI is paralleled by substantial improvements in MR hardware, acquisition techniques, data processing and statistical analyses. Despite this fundamental progress, the situation is similar to the advanced analysis of brain morphometry discussed above in that there is no transfer of these advanced neuroimaging techniques into daily clinical neuroradiology.
The next sections will discuss two projects aiming to obtain such a transfer.
The concept of BOLD ceiling fMRI could make it possible to extend the spectrum of fMRI to be applicable also to chronic or continuous neuronal activations as present in numerous diseases such as tinnitus. The concept of CO2BOLD based on the application carbon dioxide (CO2), a potent vasodilator allows for assessing the cerebrovascular perfusion reserve in the course of cerebrovascular diseases. Moreover, initial results in the domain of mild cognitive impairment and dementia show that this technique of CO2BOLD might also be used to assess cerebovascular autoregulation in the
course of these diseases. This later observation opens the perspective to combine brain morphometry and functional MRI to ultimately obtain an earlier and more specific diagnosis in the domain of dementia using multimodal advance neuroimaging techniques.
Figure 3
Figure 3 illustrates the principle of CO2BOLD. In the beginning, there is a baseline period, in which the vessels are at rest. Then, an increased CO2 concentration mixed in synthetic air is applied via a nasal cannula. This CO2 induces a vasodilation and a resulting blood oxygenation level dependent (BOLD) response. In contrast to the BOLD response known from functional MRI, the CO2 induced BOLD response affects the entire brain. After a wash-in period, a plateau is observed. After the stop of CO2 application, the signal returns to baseline after a wash-out period. The amplitude
between CO2 and baseline period reflects the ability of the cerebral vessels to perform a vasodilation, hence it reflects the magnitude of the cerebrovascular reserve (CVR).
3.1 BOLD ceiling fMRI
Functional magnetic resonance imaging (fMRI) maps brain activations due to a local vascular response to neuronal activation and the resulting blood oxygenation level dependent (BOLD) contrast (Ogawa et al., 1992). Ever since the introduction of fMRI in the early 1990s, alternations between activation (ON) and baseline (OFF) periods have remained the basic principle of standard fMRI (Amaro and Barker, 2006). Brain activation maps can be generated from local task-induced BOLD response differences using this ON OFF paradigm design (Friston et al., 1995). One limitation of this “standard”
fMRI is that it cannot (directly) map continuous neuronal activations, where ON-OFF paradigms are unachievable. The aim of our study “Mapping continuous neuronal activation without an ON-OFF paradigm: initial results of BOLD ceiling fMRI” was to overcome this limitation in the example of continuous auditory stimulation, as a model for tinnitus. The basic concept of BOLD ceiling resides in the assumption of a limitation or ceiling of the maximum possible BOLD response (Bruhn et al., 1994; Buxton and Frank, 1997), because the capability of cerebral vessel dilation is limited (Bayliss, 1902). Compared to standard fMRI, we introduced the application of a potent vasodilator. In contrast to local task-induced BOLD activations, vasodilatative compounds such as CO2 (Cohen et al., 2002) or acetazolamide (Vorstrup et al., 1984) evoke a global BOLD response in the entire brain. We aimed to exploit this putative BOLD ceiling for mapping of continuous neuronal activations. We hypothesized that inactive brain areas are in the BOLD
baseline state and present the full drug-induced !BOLD amplitude. In contrast, we postulate a reduction of this !BOLD in continuously active brain areas due to the pre-existing local task-induced BOLD response. We successfully demonstrated this principle in the example of continuous auditory stimulation simulating tinnitus, yet this concept can be transferred numerous other, in particular in clinical, conditions including continuous hallucinations, chronic pain chronic pain or depression with continuous activations, which are not applicable to “standard” fMRI ON OFF paradigms.
3.2. MRI assessment of cerebrovascular reserve
The recently introduced method of CO2BOLD, derived from functional MRI, allows a non-invasive and operator-independent assessment of the cerebral perfusion reserve (CVR). The CVR is the ability of the cerebral vessels to dilate from their resting state to a maximal dilation. The basic principle of CO2BOLD resides in the application of vasoactive agents such as carbon dioxide (CO2) (Rostrup et al., 1994) or acetazolamide (Bruhn et al., 1994), which induce a blood oxygen level–dependent (BOLD) response (Ogawa et al., 1992) that can be detected with T2* weighted images in particular.
Recently, this CO2BOLD technique detected impaired CVR in patients with severe ICA stenosis and occlusion (Ziyeh et al., 2005). We used this method of CO2BOLD to assess CVR in patients with high-grade stenosis of the internal carotid artery before and after carotid endarterectomy or stenting in the study “Reduced Cerebrovascular Reserve at CO2 BOLD MR Imaging Is Associated with Increased Risk of Periinterventional Ischemic Lesions during Carotid Endarterectomy or Stent Placement: Preliminary Results” (Haller et
al., 2008). We could demonstrate that the reduced CVR in the dependent territory of the internal carotid artery normalized after carotid intervention.
Additionally, there was an overshooting or luxury perfusion reserve in the contra lateral hemisphere after carotid intervention, probably related to the development of collateral circuits in the long-standing carotid stenosis which are no longer necessary after carotid intervention. Importantly, this method detected subjects at high risk for new periinterventional ischemic events. The assessment of CVR has in our perspective two major advantages. First, CO2BOLD directly assesses CVR in the brain parenchyma taking into account collateral flow. The stenosis grade, in contrast, cannot assess collateral flow. In our series, only CVR assessment yet not the stenosis grade of the internal carotid artery allowed to detect subjects at high risk for periinterventional ischemic events. Second, this method might be more sensitive than the assessment of the cerebral perfusion per se because only after the CVR is exhausted, the cerebral perfusion will be reduced. Inversely it is unlikely that the perfusion is already reduced despite a still normal CVR.
Consequently, CVR assessment might allow earlier detection of disease compared to perfusion.
This method of CO2BOLD is very simple and fast, without specific demands on the MRI equipment. We think that this is fundamental prerequisite for a potential clinical application not only in patients with carotid stenosis, but also in other diseases. A current project, which is supported by the VELUX foundation, uses this CO2BOLD method to achieve an early diagnosis of dementia, in particular Alzheimer Dementia. This functional assessment is
possible within only a few additional minutes - without additional examinations, providing a single measurement session for the patients to assess structural AND functional information. This would shorten the overall measurement time thereby increasing patient comfort and reduce the exposure to radioactive tracers that is necessary for positron emission tomography (PET), the current reference standard for functional assessment in dementia (Herholz et al., 2007; Foster et al., 2008). Because the patient does not have to perform a specific cognitive task such as a working memory task in functional MRI (Sperling, 2007), the CO2 based functional assessment of the CVR avoids potential confounds related to variations in task compliance depending on the cognitive status of the patient (Haller and Bartsch, 2009). In summary, the presented CVR assessment is simple and robust and consequently principally applicable in clinical routine MRI assessment. A preliminary analysis of this ongoing project suggests that CO2BOLD might indeed discriminate between different types of dementia (Haller et al., 2010b).
4. Perspectives: Clinical application of real-time fMRI neurofeedback All of the previously summarized studies addressed the use of advanced neuroimaging MRI techniques as diagnostic tools. In contrast, the following method might eventually modify specific brain activations by means of biofeedback, and might represent a novel “therapeutic” neuroimaging technique. Biofeedback was first demonstrated for the autonomous nervous system in the 1950s and learning voluntary control over heart rate and skin conductance (Miller, 1975). This basic principle was recently transferred to the domain of real-time functional magnetic resonance imaging (rtfMRI), and allows learning voluntary control over specific brain areas by means of operant conditioning (Weiskopf et al., 2003). The few currently available rtfMRI neurofeedback studies in healthy volunteers demonstrated the following fundamental properties of the voluntary control of brain activations.
First, the neurofeedback effect is specific and spatially highly localized in the brain (Weiskopf et al., 2003). Second, a gradual up and down regulation is possible. Third, the neurofeedback effect persists over time (Yoo et al., 2008).
Fourth, rtfMRI neurofeedback may influence behaviour, for example some aspects of linguistic processing (emotional prosodic) improved after voluntary up-regulation of the right-hemispheric analogue of Broca’s area (Rota et al., 2009). The ability to learn voluntary control over specific brain areas implies that rtfMRI neurofeedback might be applied to numerous diseases. Currently, there are only two clinical applications of rtfMRI neurofeedback. One study demonstrated a positive effect in chronic pain (deCharms et al., 2005). Our pilot study entitled “Real-time fMRI feedback training may improve chronic tinnitus” demonstrated a positive effect in tinnitus (Haller and Veit, 2009). We
chose tinnitus since it has been shown that this disorder is associated with over-activation within the auditory network (Muhlnickel et al., 1998; Andersson et al., 2000; Kleinjung et al., 2005). Additionally, repetitive transcranial magnetic stimulation (rTMS) over the auditory area, that temporarily disrupts neuronal activations, may alleviate tinnitus symptoms (Kleinjung et al., 2005;
Plewnia et al., 2007; Rossi et al., 2007). The combination of these two observations suggested that voluntary down-regulation of the hyper activation in the auditory area by means of rtfMRI neurofeedback may also improve tinnitus. The method of rtfMRI neurofeedback has numerous other potential applications including depression, epilepsy, and addictive behaviours.
Figure 3
Figure 3 illustrates the principle of real-time fMRI neurofeedback. While the subject is in the MRI scanner, the brain fMRI signal is transferred and analyzed in (near) real time in a dedicated software. The current signal within a region of interest is then projected back to the subject. By means of trial and error, the subjects may learn individual
strategies to voluntarily regulate the spontaneous activity within the target region by means of operant conditioning.
5. Conclusions
Morphometric changes associated with psychiatric disorders are usually subtle, in particular in early stages of the disease progress. In combination with the substantial variability in brain anatomy, it is extremely difficult to obtain an early and individual diagnosis of these disorders based on established standard neuroimaging techniques and visual image analysis.
Consequently, the main role of the standard neuroimaging is the exclusion of so-called organic causes, such as chronic subdural hematomas. Advanced neuroimaging techniques in combination with complex computer-aided data analyses provided fascinating insights in altered brain morphometry in a variety of psychiatric illnesses. However, the vast majority of these studies are however group comparisons, and it is not possible to transfer these group results to obtain an individual diagnosis in clinical routine. The investigations summarized in the first part of this thesis show tailored data analysis strategies to obtain an individual and early detection based on cortical thickness (in the domain of psychosis), white matter or iron deposition (both in the domain of dementia). The analyses are deliberately optimized and chosen with respect to potential clinical applications. Ultimately, one should keep in mind that these techniques are not limited to psychosis or dementia, but may be applied to a wide variety of psychiatric conditions.
The second part of the thesis addresses the use of advanced functional neuroimaging techniques in clinical neurology. The application of CO2 in functional imaging allows the investigation of continuous neuronal activations in BOLD ceiling fMRI. Such continuous activations like tinnitus cannot be
assessed in using standard fMRI, which needs ON-OFF designs. Moreover, CO2 can be used to assess the cerebrovascular perfusion reserve, which provided an additional functional parameter and identified patients at risk for new peri-interventional ischemic events during endarterectomy or stenting of symptomatic stenoses of the internal carotid artery. Both methods are again tailored to be clinically applicable.
The final third part of this thesis completes the diagnostic perspective developed in the first two parts by proposing the therapeutic use of real-time fMRI neurofeedback to selectively modify brain activity. This method improved chronic tinnitus in a pilot study.
Importantly, all the MRI methods described in the context of this work are tailored to be potentially applicable in clinical work-up, and have the potential to provide additional and complimentary information compared to the established neuroimaging methods. In the near future, these different techniques might be combined to increase the diagnostic accuracy in clinical neuroradiology.
6. Attachments
6.1 Can cortical thickness asymmetry analysis contribute to detection of at-risk mental state and first-episode psychosis? A pilot study
6.2 Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data
6.3 Susceptibility weighted imaging (SWI) assessment of cerebral microbleeds and iron deposition in mild cognitive impairment
6.4 Mapping continuous neuronal activation without an ON-OFF paradigm: initial results of BOLD ceiling fMRI
6.5 Reduced Cerebrovascular Reserve at CO2 BOLD MR Imaging Is Associated with Increased Risk of Periinterventional Ischemic Lesions during Carotid Endarterectomy or Stent Placement: Preliminary Results
6.6 Real-time fMRI feedback training may improve chronic tinnitus
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