Stroke is the third most frequent cause of acquired adult disability [1]. Despite there being a statistically significant reduction in the rates of incidence, mortality, and disability-adjusted life years (DALYs) from 1990 to 2013, the absolute number of people affected by stroke has increased significantly [2]. Stroke recovery is heterogeneous: it is estimated that 25–74% of the 50 million stroke survivors require assistance or are dependent for activities of daily living (ADL) after stroke [3]; approximately 14% of the stroke survivors achieve full recovery of ADLs, between 25% and 50% require some assistance, and approximately half experience long-term dependency [4]. As a consequence, the absolute number of DALYs due to ischemic stroke (more than 47 million) is dramatically high. Additionally, stroke should no longer be regarded as a disease of the elderly, as two-thirds of all strokes occur among persons aged below 70 years.
With stroke, the life of an individual undergoes a complete change – and the quality of life is significantly affected by decisions made in the initial period.
Prediction of outcome after ischemic stroke therefore is important for setting realistic and attainable treatment goals, informing clients and their relatives properly, facilitating discharge planning, and anticipating possible consequences for home adjustments and community support [5]. Table 4.1 summarizes the approaches for the prediction of outcome after ischemic stroke with stepwise increasing complexity of clinical data and addition of various imaging results.
Table 4.1 Attempts for prediction of outcome after ischemic stroke: application of increasing complexity of clinical data and addition of results from imaging studies
| Clinical data: | |
|---|---|
| Individual variables | Age, initial neurological status, arm paresis, ability to walk, pre-stroke independence, previous stroke (Veerbeek et al. 2011 [6]) |
| Scores | National Institutes of Health Stroke Score (NIHSS) (Weimar et al. 2004 [7], Kwakkel et al. 2010 [8]) |
| Indices | Age and NIHSS (König et al. 2008 [9]). Barthel Index (Granger et al. 1979 [10]) Functional Independence Measure (Alexander et al. 1994 [11]) |
| Models | – including age, upper limb paralysis, NIHSS, urinary catheter, oxygen administration (Muscari et al. 2011 [12]) – including age, pre-stroke independence, arm paresis, ability to walk, stroke severity score (Reid et al. 2010 [13]) – including age, sex, pre-stroke disability, dysarthria, urinary incontinence, limb deficit (Tilling et al. 2001 [14]) |
| Complex model: ASTRAL | Age, NIHSS, time from symptom onset to admission, stroke-related visual field deficit, acute blood glucose value, level of consciousness (Ntaios et al. 2012 [15]) |
| Addition of imaging modalities* | Volume of infarct on CT or MRI, hemorrhagic transformation or intracerebral hemorrhage on CT or MRI, location and size of infarct: ASPECTS Dense middle cerebral artery sign on CT volume of early irreversible damage: DWI Affected fiber tracts: DTI |
| Addition of data on blood supply | Vessel occlusion on angiography, CT or MR angiography Collateral flow on CTA or MRA Perfusion in tissue: pCT, PWI Mismatch/penumbra: PW/DWI |
| Addition of functional imaging | fMRI at rest and during activation by specific tasks, PET at rest and during activation by specific tasks |
* For references, see text.
Several systemic reviews have discussed the relationship of standardized measures to various aspects of stroke outcome and recovery, including quantification of neurologic deficits, functional outcome, and quality of life [16]. A systematic review of prognostic studies [6] indicated that age and motor weakness were important predictive variables of outcome in addition to stroke severity; however, gender and presence of vascular risk factors were not. Employing simple models, a modestly large percentage of patients could be correctly classified with regard to survival and functional recovery (70.4% and 72.9% [9]) and to the severity of impairment on the BI (severe vs. mild neurologic deficits, AUC 0.789–0.808 depending on time of assessment 2–5 days [8]). The addition of more clinical variables in a relatively simple model improved prediction accuracy slightly (83.9% [12]). The complex model based on an integer score from age, severity of stroke at admission by National Institutes of Health Stroke Scale (NIHSS), time from stroke onset to admission, range of visual fields, acute glucose value, and level of consciousness reached an AUC of 0.850 in the original population and of 0.903 in a stroke population pooled from three centers [15] and was superior to prediction by experienced physicians (3 months mRS:286.5 vs. 56.8% [17]). Especially the changes on the NIHSS and of symptoms and signs of traditional Chinese medicine during the first 5 days after stroke predicted 90-day outcome [18]. As motoric functions and walking are in the center of rehabilitative activities several studies concentrated on prediction of recovery of these modalities and developed special models for this application [19–21]. Chances for improvement of post-stroke aphasia can be estimated from the performance in word repetition from a language screening task supporting the importance of perception and motor production for recovery of language function [22]. Additionally, studies of evoked potentials are helpful to assess the potential for functional recovery and to select patients who can benefit from targeted rehabilitative procedures [23]. Recently it was shown that also the causative classification of stroke has validity for prediction of recovery [24].
Neuroimaging modalities are able to measure the extent of damage to brain tissue and to indicate alternative functional networks and thereby may help to assess functional outcome and to predict the efficacy of rehabilitation in individual patients.
Structural Imaging
Computed Tomography
Computed tomography (CT) and magnetic resonance imaging (MRI) are the most important procedures for diagnosis and management of acute stroke (Chapter 3). The most widely used imaging procedure in acute stroke is CT especially for differentiation between hemorrhagic and ischemic stroke, for localization of the lesion, and for decision-making regarding administration of potentially risky stroke therapies as thrombolysis. Initial infarct volume determined within 72 hours of ischemic stroke onset was an independent predictor of outcome at 90 days, along with age and NIHSS score [25].The prognosis for stroke recovery is also related to the site of ischemic brain injury: strokes in the insular region have been associated with increased mortality [26]. In a prospective study of patients with acute ischemic stroke anterior chorioidal infarcts had intermediate long-term prognosis between lacunar infarcts and large artery territory hemispheric infarcts [27]. Lesions located in the internal capsule demonstrated a worse prognosis for recovery of hand motor function at 1 year than strokes in the corona radiata or motor cortex [28]. Evidence of brain edema predicts poor outcome after non-lacunar ischemic stroke [29].
As a measure for quantifying ischemic changes on CT the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) was developed which evaluates the extent and location of ischemic changes in 10 regions within the territory of the middle cerebral artery (MCA) [30]. ASPECTS has been found to be better reproducible than the one-third MCA rule and can help to predict functional outcome on mRS at 3 months post-stroke and to select patients for acute intravascular treatment [31]. In combination with age and the severity of neurologic deficits a subacute ASPECTS of better than 5 had a significant predictive value of greater functional independence at 3 months (R2 = 0.701) and 1 year post-stroke (R2 = 0.528) [32]. In another large study initial lesion volume was found to be a strong and independent predictor of stroke outcome in a statistical regression model that also accounts for age and NIHSS. By including the lesion volume as an additive predictive factor the fraction of unexplained variability could be reduced by 15% [25] (Figure 4.1). As a consequence, the inclusion of lesion size in predictive models of outcome will improve stratification of samples and increase power for effect detection in trials of acute therapy and of rehabilitative strategies in ischemic stroke. The evaluation of stroke volume and localization when combined to the NIHSS showed potential predictive value, which might be further improved by several biomarkers, namely the S100 calcium binding protein B, C-reactive protein, matrix metalloproteinases, and cerebral natriuretic peptide [33].
Figure 4.1 Probability of outcome (mRS and NIHSS) dependent on baseline parameters in patients with ischemic stroke. (A) Probability of mRS outcome dependent on baseline parameters in patients with ischemic stroke. mRS is modeled as an ordinal variable using logistic regression analysis. The 7 points on the mRS scale are color-coded. The height of each color segment corresponds to the probability of mRS outcome at the x-axis values. (B) Probability of outcome on the NIHSS at day 90. NIHSS, age, and lesion volume (log-transformed) are modeled using knotted splines. The x-axis of lesion volume is a log-scale, but non-transformed values (cm3) are given as labels. Dashed lines in B indicate the 95% CI. mRS indicates modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale.
The predictive value of complex clinical models as ASTRAL [15] can be improved by addition of dense middle cerebral artery sign from CT [34]. This score could predict outcome after thrombolysis with high accuracy (AVC 0.84) and was better than the prediction by experienced physicians (mRS 5-6 at 3 months: 80.4% vs. 40.4% [17]).
Clinical scores combined with findings on CT scans can predict outcome after 3 months or after thrombolysis.
Magnetic Resonance Imaging
High-resolution MRI reproducibly identifies even small stroke lesions, but relating the size of lesions to clinical impairment and functional outcome is difficult since especially small lesions of the subcortical white matter or the brainstem can produce disproportionate clinical disturbances [35]. The affection of the corticospinal (CS) tract by the ischemic lesion is a particularly important factor limiting motoric recovery [36].
Severe white matter disease may also be an independent predictor of poor functional outcome [37].
Hemorrhagic transformation (HT), visualized on non-contrast CT or T2*-weighted MRI sequences is a biomarker of potentially poor outcome. Gradient Echo Sequences MRI is significantly more sensitive to HT than CT or other MRI sequences [38]. Rating scales incorporating the extent of hemorrhage along with measures of neurologic deterioration have shown that the presence of HT, particularly when considered “symptomatic ICH,” is predictive of poor functional outcome. However, compared to other predictive factors, the contribution of symptomatic ICH may be smaller [39]. Of interest, even “asymptomatic HT” appears to be a predictor of poor outcome [40].
Diffusion-weighted imaging (DWI) provides an early, distinct, and sensitive measure of both the size and location of ischemic brain lesions. The lesion can be outlined early by DWI, which is sensitive to the movement of water molecules within the tissue and reduced by cytotoxic edema in early ischemia. In patients with non-lacunar strokes in the arterior circulation, lesion volume assessed by DWI in addition to age and NIHSS score was an independent predictor of outcome separating patients with a final BI above or below 85 [41]. DWI lesion volume significantly increased power of prediction models, but this effect was not large enough to be clinically important in another analysis [42]. However, the likelihood to achieve excellent neurological outcome diminishes substantially with growth in DWI infarct volume in the first 5 days after ischemic stroke of mild to moderate severity [43].
Diffusion tensor imaging (DTI) permits to visualize white matter pathways in the brain and was used especially to demonstrate damage to the CS tract which is associated with motoric impairment in chronic stroke patients [44]. DTI measures may also be used to predict outcome. The initial fiber number ratio calculated for the affected CS tract normalized to the contralateral unaffected side predicted motor outcome after 1 year; in a multivariate analysis initial fiber number ratio was an independent predictor of motor outcome (p = 0.031), improving prediction compared with using only initial Fugl-Meyer score, age, and stroke volume (p = 0.026) [45]. DTI lesions visible within 3 weeks after stroke were associated with motor deficits at 3 months in supratentorial stroke patients with severe motor involvement [46]. Extent of damage to the CS tract following a corona radiata infarct assessed 7–30 days after a stroke was related to motor function of the affected hand 6 months later [47, 48] (Figure 4.2). The damage to the pyramidal tract assessed by fractional anisotropy in DTI progressively decreased in the medulla as well as in proximal portions 1–12 weeks after pontine infarction, and these anterograde and retrograde degenerations were accompanied by deterioration in the clinical scales [49]. The prediction of motor impairment and recovery was improved if not merely the pyramidal tract but also alternative motor fibers were included in the classification of damage [36]. Damage to the posterior limb of the internal capsule within 12 hours of symptom onset correlated well with motor impairment at 30 and 90 days; the sensitivity and specificity of the DTI parameters were superior to lesion volume in the corona radiata or the cortex and to baseline clinical scores [50]. A random effects model developed using the results from 11 selected studies revealed that the DTI parameter fractional anisotropy is a significant predictor of upper limb motor recovery after ischemic stroke [51].
Figure 4.2 Diffusion tensor imaging: prediction of motor outcome by CS tract integrity. Classification of diffusion tensor tractography: (A) T2-weighted MR images, (B) coronal images of diffusion tensor tractography (DTT), (C) combined axial (at the infarct level) and coronal images of DTT, and (D) axial images (at the primary motor cortex level) of DTT. Motricity index (MI) for hand distribution at 6 months from the time of stroke onset. The MI distribution was significantly uneven (Pearson’s chi-squared test; p 0.003) and was significantly influenced by the diffusion tensor tractography type (Kruskal–Wallis test, p 0.0002). MBC modified Brunnstrom classification.
Efficiency of rehabilitative therapy was related to DTI parameters of individual tracts and tract combinations and may indicate a patient’s individual recovery potential and the optimal rehabilitative intervention [52]. Additionally, gains from treatment were related to the degree of injury to specific motor tracts (descending from primary motor cortex, supplementary motor area, dorsal premotor cortex, and ventral premotor cortex, respectively), and the damage to these tracts had a greater impact on the therapeutic effect than infarct volume or baseline clinical status [53]. Damage to the posterior limb of the internal capsule within 12 hours of symptoms onset correlated well with motor impairment at 30 and 90 days; the sensitivity and specificity of the DTI parameters were superior to lesion volume in the corona radiata or the cortex and to baseline clinical scores [50].
Non-motor pathways can also be studied and their damage related to higher brain function, e.g. language performance [54]. Lower fractional anisotropy values in the superior longitudinal and arcuate fasciculi of the left hemisphere were correlated with decreased ability to repeat spoken language, lower FA values in the arcuate fasciculus were associated with comprehension deficits; these relationships were independent of the degree of damage to cortical areas [55]. The outcome of aphasia was improved in the patients whose left FA could be reconstructed [56].
All these data stress that the connectivity in networks as assessed by DTI is more important for outcome and recovery than the extent of the primary structural lesion. However, despite all these promising results structural neuroimaging neither provides information on the cause of the ischemic lesion and compensatory mechanisms, nor whether or how surviving tissues are working [57]. The individual markers for structural integrity – CT, MRI, and DTI – are not sufficient to reliably predict post-stroke recovery. Their validity might be improved by adding functional biomarkers, e.g. functional MRI and transcranial magnetic stimulation, in a combination of biomarkers [58]. The functional connectivity between cortical and subcortical components of neural networks determines the capacity for reorganization and recovery. The studies of these measures require modalities for physiologic, molecular, and functional imaging.
High-resolution MRI reproducibly identifies even small stroke lesions, but relating the size of lesions to clinical impairment and functional outcome is difficult since small lesions can produce disproportionate clinical disturbances. The connectivity in networks as assessed by DTI is more important for outcome and recovery than the extent of the primary structural lesion.
Assessment of Brain Blood Supply and Cerebral Perfusion
The cause of ischemic stroke is the reduction of tissue blood flow below a critical threshold for a critical period of time (Chapter 1). Usually this shortage in blood supply is due to the occlusion of the feeding vessel and the insufficiency of collateral perfusion. Occlusion of a large intracranial vessel, such as basilar, internal carotid, and middle cerebral artery, is associated with higher mortality and more severe permanent deficits and therefore the pathologic vascular state can be expected to contribute predictive value to models of stroke outcome. Retrospective studies of patients undergoing conventional angiography found that basilar and internal carotid artery occlusions had the highest NIHSS scores [59], whereas normal angiograms predict a good prognosis. In a prospective study results from CT angiography performed within 24 hours of symptoms onset were related to outcome after 6 months [60] (Figure 4.3). Larger vessel occlusion significantly increased 6 months mortality (4.5-fold increase) and was negatively correlated to good outcome (mRS ≤ 2; 3-fold reduction). In a multivariate analysis the presence of basilar and internal carotid occlusions independently predicted outcome in addition to age and NIHSS. Inclusion of information from CT angiography contributed significantly more to outcome prediction than the ASPECTS score [61, 62]. Evidence of large-vessel occlusion (LVO), therefore, is crucial for improving outcome by early endovascular interventions. Stroke outcome prediction was improved when CTA results were combined with the NIHSS (STOP Stroke study [61]).
Figure 4.3 Large-vessel occlusion within NIHSS strata and probability of good outcome (A) and mortality (B): Influence of LVO within NIHSS strata on probability of good outcome (A) and mortality (B).
The vascular occlusion and its eventual recanalization are decisive for the evolution of tissue damage and for the clinical deficits, but the final size of an infarct is also influenced by the extent and quality of collateral circulation to the affected brain area. The presence of robust collateral flow is best visualized by conventional angiography and has been linked to improved clinical outcomes and reduced infarct volumes; in cases receiving thrombolysis collateralization was a significant univariate predictor in addition to occlusion type and recanalization [63, 64]. CT angiography as a non-invasive alternative has better spatial resolution than transcranial Doppler or MR angiography and can depict leptomeningeal collaterals. Rapid recruitment of sufficient collaterals was related to favorable outcome, whereas patients with diminished sylvian and leptomeningeal collaterals had a greater risk of worsening [65]. Univariate analysis identified the grade of leptomeningeal vascularity as an independent predictor of good outcome [66]. Early CTP ASPECTS leptomeningeal collaterals in the M5 (parietal) region were independently associated with good functional outcome; when M5 collaterals score was added to the NIHSS a better prediction value was achieved (area under the curve: 0.752, p<0.001) [67].
The validity of perfusion parameters obtained by CT or MRI for prediction of long-term outcome has not been accurately established, but some data indicate a weak relationship of PWI lesion size early after the ictus [68] as well as perfusion CT-mismatch [69] and mRS 3 months after the stroke confirming early results of the relationship between cerebral blood flow measured with 133 xenon [70] or with 99m Technetium labeled hexamethylpropyleneamine [71] and final outcome. Several studies have suggested that ASPECTS applied to CTP is more accurate in predicting outcome compared to NCCT ASPECTS [72, 73]. Location-weighted CTP analysis may be a valuable tool for predicting motor improvement and language improvement [74, 75]. For multimodal MRI, addition of both DWI and MTT lesion volumes to NIHSS information is superior to NIHSS alone in predicting outcomes [76, 77]. In one study cerebral blood volume defined by CTP ASPECTS was the best predictor of clinical outcome in acute ischemic stroke as it recognizes the infarct; it was better than CTP mismatch implying that the extent of the core is the main determinant of outcome irrespective of penumbra site [78]. CBV-ASPECTS was also a significant predictor of clinical outcome in patients with acute ischemic stroke treated with mechanical thrombectomy [79].
Penumbral imaging and its role in selecting patients for reperfusion therapies is discussed in a recent review [80]. However, a number of prediction scores have been developed and applied in cohorts undergoing reperfusion therapies. The effect of therapy with rtPA was related to clinical data and findings in imaging [81], and a special combined score (DRAGON) could predict poor and good outcome (AUC 0.82 and 0.84 respectively) [82, 83]. A five-item scale including infarct volume was able to predict the outcome after iv-tPA treatment in the DEFUSE cohort with 83% sensitivity and 86% specificity [84]. Reperfusion demonstrated on perfusion-weighted (PW)-MRI was associated with good clinical outcome [85–88]. Even late reperfusion seen in PWI after embolectomy predicted improved outcome [89]. A score combining age, glucose, NIHSS, and ASPECTS of ≥ 5 predicted poor outcome after intra-arterial therapy [90].
CT angiography contributes to outcome prediction, can provide evidence of large-vessel occlusion for early endovascular interventions, and visualizes collateral circulation to the affected brain area.
Role of Functional Imaging in Stroke Patients
The functional deficit after a focal brain lesion is determined by the localization and the extent of the tissue damage; recovery depends on the adaptive plasticity of the undamaged brain, especially the cerebral cortex, and of the non-affected elements of the functional network. Since destroyed tissue usually cannot be replaced in the adult human brain, improvement or recovery of neurological deficits can be achieved only by reactivation of functionally disturbed but morphologically preserved areas or by recruitment of alternative pathways within the functional network. This activation of alternative pathways may be accompanied by the development of different strategies to deal with the new functional-anatomical situation at the behavioral level. Additionally, the sprouting of fibers from surviving neurons and the formation of new synapses could play a role in long-term recovery. These compensatory mechanisms are expressed in altered patterns of blood flow or metabolism at rest and during activation within the functional network involved in a special task, and therefore functional imaging tools can be applied successfully for studying physiological correlates of plasticity and recovery non-invasively after localized brain damage. The observed patterns depend on the site, the extent, and also the type and the dynamics of the development of the lesion; they change over time and thereby are related to the course and the recovery of a deficit. The visualization of disturbed interaction in functional networks and of their reorganization in the recovery after focal brain damage is the domain of functional imaging modalities such as PET and functional magnetic resonance imaging (fMRI).
For the analysis of the relationship between disturbed function and altered brain activity studies can be designed in several ways: measurement at rest, comparing location and extent to deficit and outcome (eventually with follow-up); measurement during activation tasks, comparing changes in activation patterns to functional performance; and measurement at rest and during activation tasks early and later in the course of disease (e.g. after stroke) to demonstrate recruiting and compensatory mechanisms in the functional network responsible for complete or partial recovery of disturbed functions. Only a few studies have been performed applying this last and most complete design together with extensive testing for the evaluation of the quality of performance finally achieved.
A large amount of data has been collected over the past years with functional imaging of changes in activation patterns related to recovery of disturbed function after stroke [91–97].
The visualization of disturbed interaction in functional networks and of their reorganization in the recovery after focal brain damage is the domain of functional imaging modalities such as PET and functional magnetic resonance imaging (fMRI).
The Principle of Functional and Activation Studies Using Positron Emission Tomography
The energy demand of the brain is very high and relies almost entirely on the oxidative metabolism of glucose (see Chapter 1). Mapping of neuronal activity in the brain can be primarily achieved by quantitation of the regional cerebral metabolic rate for glucose (rCMRGlc), as introduced for autoradiographic experimental studies by Sokoloff et al. [98] and adapted for positron emission tomography (PET) in humans by Reivich et al. [99]. The cerebral metabolic rate for glucose (CMRGlc) can be quantified with PET using 2-[18F]fluoro-2-deoxyglucose (FDG) and a modification of the three-compartment model equation developed for autoradiography by Sokoloff et al. [98]. Like glucose, FDG is transported across the blood–brain barrier and into brain cells, where it is phosphorylated by hexokinase. However, FDG-6-phosphate cannot be metabolized to its respective fructose-6-phosphate analog, and does not diffuse out of the cells in significant amounts. The distribution of the radioactivity accumulated in the brain remains quite stable between 30 and 50 minutes after intravenous tracer injection, thus permitting multiple intercalated scans. Using (1) the local radioactivity concentration measured with PET during this steady-state period, (2) the concentration–time course of tracer in arterial plasma, (3) plasma glucose concentration, and (4) a lumped constant correcting for the differing behavior in brain of FDG and glucose, CMRGlc can be computed pixel by pixel according to an optimized operational equation [100]. The resulting pseudocolor-coded images reflect all effects on cerebral glucose metabolism. Because of its robustness with regard to procedure and model assumptions, the FDG method has been employed in many PET studies, including prediction of recovery after stroke [101].
Almost all commonly applied methods for the quantitative imaging of CBF are based on the principle of diffusible tracer exchange. Using 15O-labeled water administered either directly by intravenous bolus injection or by the inhalation of 15O-labeled carbon dioxide, which is converted into water by carbonic anhydrase in the lungs, CBF can be estimated from steady-state distribution or from the radioactivity concentration–time curves in arterial plasma and brain. Typical measuring times range between 40 seconds and 2 minutes, and, because of the short biological half-life of the radiotracers, repeat studies can be performed [102, 103].
Various PET methods have been developed for determining the cerebral metabolic rate for oxygen (CMRO2), using continuous [103] or single-breath inhalation [104] of air containing trace amounts of 15O-labeled molecular oxygen. All require the concurrent estimation or paired measurement of CBF in order to convert the measured oxygen extraction fractions (OEFs) into images of CMRO2 as given by the product of arterial oxygen concentration, local OEF, and local CBF. Because 15O has a short half-life (123 seconds), an on-site cyclotron is necessary; this and other methodological complexities limit the use of CMRO2 as a measure of brain function. Application of this method for detection of penumbra tissue is described in Chapter 1.
Functional activation studies as they are used now rely primarily on the hemodynamic response, assuming a close association between energy metabolism and blood flow. Whereas it is well documented that increases in blood flow and glucose consumption are closely coupled during neuronal activation, the increase in oxygen consumption is considerably delayed, leading to a decreased oxygen extraction fraction (OEF) during activation [105]. PET detects and, if required, can quantify changes in CBF and CMRGlc accompanying different activation states of brain tissue. The regional values of CBF or CMRGlc represent the brain activity due to a specific state, task, or stimulus in comparison to the resting condition, and color-coded maps can be analyzed or correlated to morphological images. Due to the radioactivity of the necessary tracers, activation studies with PET are limited to a maximum of 12 doses of 15O-labeled tracers, e.g. 12 flow scans, or two doses of 18F-labeled tracers, e.g. two metabolic scans. Especially for studies of glucose consumption, the time to metabolic equilibrium (20–40 minutes) must be taken into consideration, as well as the time interval between measurements required for isotope decay (HT for 18F 108 minutes, for 15O 2 minutes).
PET used to quantify the regional concentration of these tracers relies on the labeling of the compounds with short-lived cyclotron-produced radioisotopes (e.g. 15O, 11C, 13N, 18F) which are characterized by a unique decay scheme. A positron, i.e. a positively charged particle of the mass of an electron, is emitted from a labeled probe molecule (Figure 4.4). Following emission from the atomic nucleus, the positron takes a path marked by multiple collisions with ambient electrons. Approximately 1–3 mm from its origin, it has lost so much energy that it combines with an electron, resulting in the annihilation of the two oppositely charged particles by the emission at an angle of 180° ± 0.5° of two 511 keV (kilo electron volt) photons that are recorded as coincident events, using pairs of uncollimated (convergent) detectors facing each other. Therefore, the origin of the photons can be localized directly to the straight line between these coincidence detectors. State-of-the-art PET scanners are equipped with thousands of detectors arranged in up to 24 rings, simultaneously scanning 47 slices of <5 mm thickness. Pseudocolor-coded tomographic images of the radioactivity distribution are then reconstructed from the many projected coincidence counts by a computer, using CT-like algorithms and reliable scatter and attenuation corrections. Typical in-plane resolution (full width at half-maximum) is <5 mm; 3D data accumulation and reconstruction permits imaging of the brain in any selected plane or view.
Figure 4.4 Principle of positron emission tomography. For description, see text.
Functional Magnetic Resonance Imaging (fMRI)
fMRI measures signals that depend on the differential magnetic properties of oxygenated and deoxygenated hemoglobin, termed the blood-oxygen-level-dependent (BOLD) signal, which gives an estimate of changes in oxygen availability [106]. This means that mainly the amount of deoxyhemoglobin in small blood vessels is recorded, which depends on the flow of well-oxygenated arterial blood (CBF), on the outflow of O2 to the tissue (CMRO2), and on the cerebral blood volume (CBV) [107]. The magnitude of these changes in signal intensity relative to the resting conditions is color-coded to produce fMRI images that map changes in brain function, which can be superimposed on the anatomical image. This results in a spatial resolution of fMRI of 1–3 mm with a temporal resolution of approximately 10 seconds. As fMRI does not involve ionizing radiation and thus is also used without limitation in healthy subjects, and allows more rapid signal acquisition and more flexible experimental set-ups, it has become the dominant technique for functional imaging. There are some advantages of PET, however – physiologically specific measures, better quantitation, better signal-to-noise ratio, fewer artifacts, actual activated and reference values – which support its continued use especially in complex clinical situations and in combination with special stimulating techniques, such as transcranial magnetic stimulation (TMS).
Functional MRI (fMRI) detects changes in brain function by measuring differences in magnetic properties of hemoglobin depending on the blood oxygen level.
Motor and Somatosensory Deficits
Motor function may be impaired by damage to a widely distributed network, involving multiple cortical representations and complex fiber tracts. The degree of motor impairment and the potential for recovery depends on the site of the lesion, the combination of lesions in cortical areas and in fiber tracts, and the involvement of deep gray structures, e.g. the basal ganglia, thalamus, and brainstem. The patterns of altered metabolism and blood flow and the patterns of activation after stimuli or during motor tasks are manifold and reflect the site and extent of the lesion, but they are also dependent on the paradigm of stimulus or task. With severe motor impairment, patients cannot carry out complex or even simple motor tasks, and the activation paradigm must be restricted to passive movement or imagination of motor performance.
Motor recovery is not rapid during the first month after stroke and reaches a plateau within 3 months; activities of daily living do not improve beyond 6 months post-stroke [108]. The motor recovery plateau can be predicted for individual patients by combining clinical measures with an objective evaluation of descending motor pathway integrity [109]: assessment of shoulder abduction and finger extension is combined with transcranial magnetic stimulation to test the functional integrity of the cortico-motor pathway and MRI to detect the extent of damage to the posterior limb of the internal capsule. The algorithm used predicted an individual patient’s potential to make complete, notable, or limited recovery, or no recovery of upper limit functions within 3 months measured with the Action Research Arm Test (ARAT) [108].
The diverging experimental conditions make the interpretation and comparison of different studies extremely difficult, and might help explain the lack of a clear concept of “neuronal plasticity” applicable to recovery from motor stroke (reviews in [92, 94, 95, 110, 111]). A recent review concluded that “motor recovery after stroke depends on a variety of mechanisms including perilesional motor reorganization, use of motor pathways in subcortical structures, use of collateral pathways in the ipsilateral hemisphere, or use of collateral pathways in the contralateral hemisphere, or possibly the development of entirely new motor networks” [92]. A combination of structural and functional imaging methods improves monitoring and predicting hand-motor outcome after stroke: lesions are mapped by T1-weighted imaging: DWI with DTI measures structural connectivity as well as intactness of CS tract, resting stroke fMRI assesses functional connectivity between different regions of a network; activation fMRI demonstrates regions involved in a function even when alternative pathways are used due to a damage in the primary centers [112]; this combination of biomarkers permits to classify patients into different subgroups with regard to probable outcome and may help to select specific strategies for rehabilitation.
In most fMRI or PET studies involving active or passive movements, a widespread network of neurons was activated in both hemispheres. The areas included frontal and parietal cortices, and sometimes the basal ganglia and cerebellum. In particular, (ipsilateral) premotor cortex, supplementary motor area (SMA), anterior parts of the insula/frontal operculum, and bilateral inferior parietal cortices are often activated (Figure 4.5). These results suggest that sensorimotor functions are represented in extended, variable, probably parallel processing, bilateral networks [96, 113]. Whereas changes in both the damaged and the undamaged hemisphere can be observed, ipsilateral activation of motor cortex is consistently found to be stronger for movement of the paretic fingers after recovery from stroke, whereas movements of the unaffected hand (as in normal subjects) were accompanied mainly by activation of the contralateral cerebral cortex. In addition to stronger intensity, the spatial extent of activation in motor cortex was enlarged, and activation on the ipsilateral side was also seen in premotor and insular cortex. These results indicate that recruitment of ipsilateral cortices plays a role in recovery: the higher the activation in the ipsilesional M1(BA4p), S1, and insula, the better the recovery 1 year after stroke [114]; patients who activated the posterior primary motor cortex early after stroke had a better recovery of hand function (Figure 4.6).
Figure 4.5 Brain activity for hand grip compared to rest for individual subjects with CS damage. These fMRI studies demonstrate that increasing CS damage leads to a shift in the pattern of activation from the primary to the secondary motor system.
Figure 4.6 Prognostic value of MRI in recovery of hand function: (A) Areas where the intensity of activation 20 days after stroke (E1) correlates with finger tapping motor performance at E3 (1 year after stroke). Activations are overlaid on a healthy brain. The lesioned side is on the left of the image (radiological convention). (B) Corresponding plots of the positive correlations between the individual β values and finger tapping performance. The β values of the 10 healthy subjects at E1 are also given for the same coordinates. (With permission from Loubinoux et al. 2007 [114].)
Task-oriented arm training increased activation bilaterally in the inferior parietal area, in premotor areas, and in the contralateral sensorimotor cortex, suggesting an improved functional brain reorganization in the bilateral sensory and motor systems [115]. Similar results were obtained by fMRI, by which an evolution of the activation in the sensorimotor cortex from early contralesional activity to late ipsilateral activity was found [116], suggesting a dynamic bihemispheric reorganization of motor networks during recovery from hemiparesis. It was also shown that the over-activation observed a few weeks after a stroke diminishes over time, suggesting compensatory mechanisms appearing even late in the course [117]. Ipsilateral cortical recruitment seems to be a compensatory cortical process related to the lesion of the contralateral primary motor cortex; this process of compensatory recruitment will persist if the primary motor cortex is permanently damaged. Newly learned movements after focal cortical injury are represented over larger cortical territories, an effect which is dependent on the intensity of rehabilitative training. It is of importance that the unaffected hemisphere actually inhibits the generation of a voluntary movement by the paretic hand [118]. This effect of transcallosal inhibition can be reduced by repetitive transcranial magnetic stimulation (rTMS) [119, 120]. Recovery from infarction is also accompanied by substantial changes in the activity of the proprioceptive systems of the paretic and non-paretic limb, reflecting an interhemispheric shift of attention to proprioceptive stimuli associated with recovery [121].
During recovery from hemiparesis, a dynamic bihemispheric reorganization of motor networks takes place. fMRI and PET studies can display the compensatory cortical processes and show the importance of transcallosal inhibition.
Post-Stroke Aphasia
Studies of glucose metabolism in aphasia after stroke have shown metabolic disturbances in the ipsilateral hemisphere caused by the lesion and in the contralateral hemisphere caused by functional deactivation (diaschisis) (review in [111]). In right-handed individuals with language dominance in the left hemisphere, the left temporo-parietal region, in particular the angular gyrus, supramarginal gyrus, and lateral and transverse superior temporal gyrus are the most frequently and consistently impaired, and the degree of impairment is related to the severity of aphasia. The functional disturbance as measured by rCMRGlc in speech-relevant brain regions early after stroke is predictive of the eventual outcome of aphasia, but also the metabolism in the hemisphere outside the infarct was significantly related to outcome of post-stroke aphasia, a finding supporting previous results of a significant correlation of CMRGlu outside the infarct with functional recovery [101]. Additionally, the functionality of the bihemispheric network has a significant impact on outcome: although the brain recruits right-hemispheric regions for speech processing when the left-hemispheric centers are impaired, outcome studies reveal that this strategy is significantly less effective than repair of the speech-relevant network in adults. That the quality of recovery is mainly dependent on undamaged portions of the language network in the left hemisphere and to a lesser extent on homologous right hemisphere areas can be deduced from activation studies in the course after post-stroke aphasia [122]. The differences in improvement of speech deficits were reflected in different patterns of activation in the course after stroke (Figure 4.7): the subcortical and frontal groups improved substantially and activated the right inferior frontal gyrus and the right superior temporal gyrus (STG) at baseline and regained regional left STG activation at follow-up. The temporal group improved only in word comprehension; it activated the left Broca area and supplementary motor areas at baseline and the precentral gyrus bilaterally as well as the right STG at follow-up, but could not reactivate the left STG. These results were confirmed in comparable studies [123–125].
Figure 4.7 Activation patterns in patients with left hemispheric stroke 2 and 8 weeks after stroke. In the case of subcortical and frontal infarction, the left temporal areas are reactivated correlating to better recovery of language function.
Studies of glucose metabolism in aphasia after stroke have shown metabolic disturbances in the ipsilateral hemisphere caused by the lesion and contralateral hemisphere caused by functional deactivation (diaschisis).
Combination of rTMS with Activated Imaging
rTMS is a non-invasive procedure to create electric currents in discrete brain areas which, depending on frequency, intensity, and duration, can lead to transient increases and decreases in excitability of the affected cortex. Low frequencies of rTMS (below 5 Hz) can suppress excitability of the cortex, while higher-frequency stimulation (5–20 Hz) leads to an increase in cortical excitability [126]. Increases in relative cerebral blood volume in contralateral homologous language regions during overt propositional speech fMRI in chronic, non-fluent aphasia patients indicated over-activation of right language homologues. This right hemisphere over-activation may represent a maladaptive strategy and can be interpreted as a result of decreased transcallosal inhibition due to damage of the specialized and lateralized speech areas. TMS studies with blockade of this contralateral over-activation by series of 1 Hz rTMS [127] have reported improved picture-naming ability in chronic non-fluent aphasia patients. Collateral ipsilateral as well as transcallosal contralateral inhibition can be demonstrated by simultaneous rTMS and PET activation studies [128]: at rest, rTMS decreased blood flow ipsilaterally and contralaterally. During verb generation, rCBF was decreased during rTMS ipsilaterally under the coil, but increased ipsilaterally outside the coil and in the contralateral homologous area (Figure 4.8). The effect of rTMS was accompanied by a prolongation of reaction time latencies to verbal stimuli.
