Present status and future challenges of electroencephalography- and magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia

ARTICLE IN PRESS

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Brain Research Bulletin xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Brain Research Bulletin journal homepage: www.elsevier.com/locate/brainresbull

Review

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Present status and future challenges of electroencephalography- and magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia

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Slavianka Moyanova a,∗ , Rick Dijkhuizen b,c a

Neuropharmacology Unit, IRCCS – Institute of Neurology NEUROMED, Pozzilli, Italy Biomedical MR Imaging & Spectroscopy Group, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands c Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Clark Center, E150, 318 Campus Drive, Stanford, CA 94305-5427, United States b

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a r t i c l e

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a b s t r a c t

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Article history: Received 30 August 2013 Received in revised form 7 January 2014 Accepted 14 January 2014 Available online xxx

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Keywords: Focal models of stroke Rodents EEG MR imaging

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Contents

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Animal models are useful tools for better understanding the mechanisms underlying neurological deterioration after an ischemic insult as well as subsequent evolution of changes and recovery of functions. In response to the updated requirements for preclinical investigations of stroke to include relevant functional measurement techniques and biomarker endpoints, we here review the state of knowledge on application of some translational electrophysiological and neuroimaging methods, and in particular, electroencephalography monitoring and magnetic resonance imaging in rodent models of ischemic stroke. This may lead to improvement of diagnostic methods and identification of new therapeutic targets, which would considerably advance the translational value of preclinical stroke research. © 2014 Published by Elsevier Inc.

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Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . EEG in preclinical stroke studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. EEG in rodents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. EEG in rodent stroke models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1. Early ischemia-induced EEG suppression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2. EEG changes after reperfusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3. EEG changes in the contralateral hemisphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3. Magnetic resonance imaging in preclinical stroke studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. MRI of acute stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Functional MRI of brain reorganization after stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Combination of EEG and MRI in preclinical stroke studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Combined EEG and MRI acutely after stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Combined EEG and fMRI after stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations: BOLD, blood oxygenation level-dependent contrast; CBF, cerebral blood flow; CT, computed tomography; DWI, diffusion-weighted imaging; EEG, electroencephalography, electroencephalogram; Et-1, Endothelin-1; fMRI, functional magnetic resonance imaging; MCAO, middle cerebral artery occlusion; MRI, magnetic resonance imaging; PET, positron emission tomography; PWI, perfusion-weighted imaging; rs-fMRI, resting state functional magnetic resonance imaging. ∗ Corresponding author at: IRCCS NEUROMED, Località Camerelle, 86077 Pozzilli (Is), Italy. Tel.: +390865929609; fax: +390865927575. E-mail addresses: [email protected], [email protected] (S. Moyanova). 0361-9230/$ – see front matter © 2014 Published by Elsevier Inc. http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

Please cite this article in press as: Moyanova, S., Dijkhuizen, R., Present status and future challenges of electroencephalographyand magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia. Brain Res. Bull. (2014), http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

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1. Introduction

Much of the medical progress of understanding the dynamics 43 of diseases, their underlying mechanisms and the development of 44 therapeutic strategies has come from animal studies. Preclinical 45 investigations with animal models of stroke are highly valuable 46 for gaining a comprehensive understanding of brain activity and 47 functions, for characterizing ischemic tissue fate, and for offering 48 new possibilities for reinstatement of lost functions (Braeuninger 49 and Kleinschnitz, 2009). In ischemic stroke in humans, throm50 bosis or embolism occludes a major cerebral artery, most often 51 the middle cerebral artery (MCA), causing a focal cerebral infarct. 52 Therefore, this brain vessel is the artery mostly targeted to induce 53 focal cerebral ischemia by its occlusion (MCA-occlusion (MCAO) 54 models) in preclinical studies, which have demonstrated that the 55 ischemia-induced changes closely resemble those seen in ischemic 56 stroke patients. The principal mechanisms of ischemia, includ57 ing complex cellular and molecular cascades, are well established 58 and have been reviewed in detail elsewhere (e.g., Lipton, 1999; 59 Xing et al., 2012). Occlusion of the MCA may be performed in 60 animals by intraluminal insertion of a pro-thrombotic agent or a 61 filament, ligation, cauterization, photothrombosis or extraluminal 62 application of the potent vasoconstrictory peptide endothelin-1 63 (Et-1). Detailed characteristics, advantages and disadvantages of 64 the different models of MCAO, and various output measures to char65 acterize the consequences of ischemia in these models have been 66 described elsewhere (Carmichael, 2005; Durukan and Tatlisumak, 67 2007; Braeuninger and Kleinschnitz, 2009; Howells et al., 2010; 68 Macrae, 2011; Liu and McCullough, 2011; Roulston et al., 2012). 69 Functional behavioral tests are an essential part of preclin70 ical research to assess the animal’s functional status after stroke. 71 Behavioral tests that have been used in animal stroke research 72 have been extensively described in previous reviews (e.g., DeVries 73 et al., 2001; Schallert, 2006). These tests have for instance been 74 used to measure unilateral MCAO-induced sensorimotor deficits 75 in contralateral fore- and hindlimbs, including paralysis or weak76 ness, loss of muscle tone and coordination, and lack of response to 77 sensory or proprioceptive stimuli. These behavioral impairments 78 correspond with loss of function in cerebral areas that are sup79 plied by the MCA, i.e., the somatosensory cortex, most of the 80 motor cortex, lateral and medial segments of the caudate-putamen, 81 and the pyramidal tract. There is conflicting evidence regarding a 82 correlation between deficits in sensorimotor functions and the vol83 ume of MCAO-induced hemispheric infarction (see references in 84 Hunter et al., 1998). It is believed that impairment of specific func85 tional regions better predicts functional outcome than total infarct 86 volume (DeVries et al., 2001). Therefore, for effective and compre87 hensive examining of consequences of cerebral ischemia as well as 88 for preclinical drug testing, behavioral endpoints should ideally be 89 complemented with neural correlates that can explain functional 90 output. 91 Nowadays, a bulk of techniques and approaches exists to 92 measure functional changes in the brain after stroke. Electroen93 cephalographic (EEG) and magnetic resonance imaging (MRI) 94 techniques are among the most widely applied in clinical and 95 preclinical research. Despite a long history of EEG application in 96 clinical practice and the important role it has played in studying 97 cerebrovascular diseases (reviews of Faught, 1993; Jordan, 2004; 98 Foreman and Claassen, 2012), relatively few preclinical studies 99 have adopted EEG measures as quantitative surrogate markers 100 for brain dysfunction in stroke models (Table 1). Besides the 101 accumulated knowledge about the complex electrophysiological 102 processes triggered by ischemia at cellular level (Astrup et al., 1981; ´ 2008), we are not aware of any recent 103 Hossmann, 1983; Krnjevic, 104 review on changes in the gross intrinsic bioelectrical activity, i.e., 105 Q2 EEG, recorded in preclinical models of stroke. On the contrary, 42

a large number of reviews have been published on application, benefits and pitfalls of various MRI techniques in animal models of stroke (Dijkhuizen and Nicolay, 2003; Farr and Wegener, 2010; Denic et al., 2011; Hoehn et al., 2001; Hoehn, 2011; Duong, 2012; Obenaus and Ashwal, 2012). Interestingly, electrophysiological (EEG, local field potentials, unit neuronal activity) and functional MRI (fMRI) measures may be combined in the same experimental setting (Logothetis et al., 2001), which can significantly contribute to improved understanding of (changes in) brain function in health and disease. This has been demonstrated in studies on epilepsy, sleep and cognition (e.g., Duyn, 2012), and recently also after stroke in human patients (Dubovik et al., 2012) or in experimental animal models (van Meer et al., 2012). The purpose of this literature review is to summarize findings of studies using EEG and MRI measurements in animal models of focal cerebral ischemia, and to speculate on future directions in this field.

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2. EEG in preclinical stroke studies

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2.1. EEG in rodents

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The physiological basis and technical aspects of EEG have been Q3 largely reviewed in the literature (e.g., Schaul, 1998; Amzica, 2010). In animal experiments, electrodes can be placed on calvarium (epidurally or subdurally) for recording of electrocorticogram, or inserted into brain structures for recording of local field potentials or electrosubcorticogram. These recordings reflect gross electrical activity emanating from summated extracellular excitatory and inhibitory postsynaptic currents in dendrites of cortical pyramidal cells (e.g., Speckmann and Elger, 1999). The acronym EEG will be used to indicate electro(sub) corticogram, local field potentials and EEG for the remainder of this article, although it is recognized that differences do exist between data obtained from each of these measurements. The EEG in healthy animals, including rodents, exhibits a spectrum of oscillation frequencies and various patterns mostly dependent on the functional state during the different phases of the ultradian cycles of sleep and wakefulness. For example, low-frequency and high-voltage oscillations dominate the slow wave sleep pattern, while during waking state the EEG is asynchronous and with low-voltage oscillations. The intrinsic (background) EEG activity recorded in normal conditions when the animal is awake, quiet and immobile (i.e., in a “resting state”), is regulated by a homeostatic system, involving dynamic interactions among anatomically dispersed brain regions of different brain structures, such as brainstem, thalamus, limbic areas, and cortex, which play an important role in a wide variety of behavioral functions. EEG activity in animal studies has conventionally been described in terms of a set of frequency bands, usually defined as delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (12–30 Hz). The distribution of the fast Fourier transformation spectrum among these four frequency bands normally is 25–45%, 40%, 12–15% and 3–20%, respectively, in the total frequency band (usually from 1 to 30–32 Hz), as measured in animal studies (e.g., Lu et al., 2001; Zhang et al., 2013). A bulk of algorithms have been developed to process the EEG signals, extracting a variety of EEG features in order to obtain reliable alerting indices of possible cerebral pathology. Some of the quantitative EEG .approaches and spectral parameters include: fast Q4 Fourier transformation of the EEG signal; absolute power spectral density across the total frequency band; percentage distribution of total power into delta, theta, alpha and beta frequency bands (relative power); alpha-to-delta ratio (Zhang et al., 2013) or ratio between alpha + beta and delta + theta powers (frequency index,

Please cite this article in press as: Moyanova, S., Dijkhuizen, R., Present status and future challenges of electroencephalographyand magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia. Brain Res. Bull. (2014), http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

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Table 1

Q6 Summary of EEG studies in rodent MCAO models of strokea . Model of MCAO, duration of occlusion. Anesthesia for MCAO model.

Species Recovery time (time of sacrifice)

EEG measure, analysis.

Time sessions of EEG recording. Anesthesia during EEG recording. Main findings

References

tMCAO filament 120 min Anesthesia-ketamine

Rats (SD) 3 days

EEG amplitude

Longa et al., 1989

tMCAO Et-1 infusion (60 pmol) near to MCA Conscious rats tMCAO filament -Proximal: 60, 120, 180 min -Distal: 60 min Anesthesia-chloral hydrate tMCAO filament 90 min Anesthesia-halothane

Rats (W) 14 days

FFT power spectral analysis, absolute PSD relative to baseline power before MCAO

Rats (W) 24 h

EEG amplitude, root mean square

After 2 h-occlusion of MCA and 2 h after reperfusion (anesthetized rats-ketamine). Bilateral decrease in the EEG amplitude after right MCAO, more in the ischemic hemisphere (∼25%). The amplitude did not recover significantly after reperfusion. Before and at 1d, 3–7d, 8–10d and 11–14d (freely-moving rats). In S1FL penumbral region - 35% reduction of alpha-like and faster beta EEG waves and 45% augmentation of slow delta waves at 3–7d after Et-1. Before and at 1 h, 2 h, 3 h and 24 h (anesthesia -urethane). EEG suppression of about 52% vs. pre-ischemic values within 10 min of ischemia.

Rats (SD) 7 days

EEG amplitude (% of baseline)

tMCAO filament 120 min Anesthesiahalothane tMCAO filament 120 min Anesthesiahalothane

Rats (SD) 1or 3 days

FFT power spectral analysis

Rats (SD) --------24 h or 3 days

FFT power spectral analysis

tMCAO filament 120 min Anesthesia halothane

Rats (SD) 3 days

FFT power spectral analysis. EEG recovery as % of EEG power in pre-MCAO

pMCAO filament tMCAO filament 120 min Anesthesia halothane

Rats (SD) 7 days

Visual EEG and relative FFT power spectral analysis (of total power). Topo-graphic EEG brain mapping

tMCAO filament 120 min Anesthesia equithesin

Rats (SD) 3 days

FFT power spectral analysis FI

pMCAO electro-coagulation Anesthesia

Rats (SD) 6–8 h

FFT power spectral analysis

Moyanova et al., 1998

Bolay and Dalkara, 1998

Continuously-before, during and until 2 h after reperfusion -anesthetized rats (halothane). Ipsilateral EEG amplitude in S1BF decreases to ∼40% of baseline with a delay of ∼4 s after the onset of CBF reduction. After reperfusion, slow recovery to ∼60–70% of baseline. Before MCAO and at 2 h, 24 h and 72 h -anesthetized rats (halothane). EEG power suppression>80% at 2 h after MCAO, used as an inclusion criterion. No recovery by 24 h.

Schmid-Elsaesser et al., 1999

Before, during MCAO (anesthetized ratshalothane) and at 24 h and 72 h (freely moving rats). A drop in EEG power at 2 h (immediately after MCAO) used as confirmation of a successful occlusion. At 24 h - EEG recovery of 23% (compared to pre-MCAO) and increase in delta power. Positive correlation between the reduction in infarct volume and improved neurological status. Before, during MCAO in anesthetized rats (halothane) and then at 2 h, 24 h and 72 h after MCAO (freely moving rats). 70% drop in EEG power at 2 h after occlusion compared with pre-occlusion amplitude, used as confirmation of a successful occlusion. By 72 h–9% recovery compared to the EEG drop at 2 h. At 0.5 h, 1 h, 2 h, 3 h, and 6 h after MCAO and each day up to 7d (freely-moving rats). In pMCAO model–early nearly isoelectric EEG in the ipsilateral ischemic core (temporal region). By 2 h–sharp waves and/or spike-wave complexes parietally. Within 24 h - diffuse slowing and pronounced increase of polymorphic delta. In tMCAO rats - worsening of the EEG abnormalities ipsilaterally after reperfusion. Increase of delta, beta and rhythmic alpha activity by 7d in the contralateral cortex. Before MCAO, during occlusion and reperfusion. (anesthetized rats - equithesin) and then at days 1, 2 and 3 (freely-moving rats). Within a few minutes after MCAO markedly decrease of EEG amplitude in the ischemic hemisphere. On days 1, 2 and 3 decrease in FI, i.e., a shift toward slow EEG frequencies in the ischemic hemisphere. Continuously for 6–8 h (anesthetized rats -halothane) reduction in EEG power across all frequency ranges 1–3 h after pMCAO in the ischemic ipsilateral cortex. Coinciding with diminishing of CBF (30% of baseline) and increase of glutamate release (1400%).

Williams et al., 2000

Phillips et al., 2000

Bao et al., 2001

Lu et al., 2001

Frigeni et al., 2001

Guyot et al., 2001

Please cite this article in press as: Moyanova, S., Dijkhuizen, R., Present status and future challenges of electroencephalographyand magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia. Brain Res. Bull. (2014), http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

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Species Recovery time (time of sacrifice)

EEG measure, analysis.

Time sessions of EEG recording. Anesthesia during EEG recording. Main findings

References

(1) pMCAO (2) tMCAO filament 120 min Anesthesia halothane

Rats (SD) 3 days

Visual and FFT spectral power analysis

Hartings et al., 2003

tMCAO filament 60 or 120 min Anesthesiahalothane

Rats (SD) 3 days

FFT power spectral analysis

tMCAO filament 120 min Anesthesiahalothane tMCAO filament 90 min Anesthesiaisoflurane

Rats (SD) 12 mo

Video-EEG monitoring

Rats (W) 4 h or 24 h

FFT spectral power analysis 8–12.5 Hz band

tMCAO infusion of Et-1 (120 pmol) near to MCA Anesthesiahalothane tMCAO/CCA craniotomy wire around the distal MCA 180 min Anesthesia chlorale hydrate

Rats (SD) 6 mo and 12 mo

Intermittent video-EEG monitoring

Rats (LE) 6 mo

FFT spectral power analysis

tMCAO filament 50 min Anesthesiachloral hydrate

Rats (SD) 3 days

FFT spectral power (AR), relative of the total EEG power.

tMCAO Et-1 infusion (60 pmol) near to MCA Conscious rats

Rats (W) 14 days

FFT spectral power, absolute PSD values relative to baseline values before MCAO

pMCAO electro-cauterization Anesthesia-ketamine and xylazine

Rats (SH) 1, 7 or 28 days

FFT power analysis, absolute PSD values, relative to baseline values before MCAO

Continuous EEG recording before and during the first 6 h after MCAO and then at 24 h or 72 h after MCAO in freely-moving rats. More than 60% drop in the EEG power at 30 min post-MCAO (used as an inclusion criterion). Appearance of NCS (rhythmic spiking of 1–3 Hz), PLED and IRDA 1 h after MCAO and then up to 3d both in permanent and transient models, but in tMCAO model, these EEG abnormalities ceased after reperfusion. Before, during MCAO, at 1 h (immediately after halothane), and at 6 h, 24 h, 48 h and 72 h. (nonanesthetized rats). A drop in EEG power at 1 h (except delta band) used as confirmation of a successful occlusion. Frontal, parietal and occipital electrodes bilaterally. NCS during the first hour of occlusion, attenuated during reperfusion, when PDA appeared. Delta power increase correlates with neurological deficits and infarct volume. Beginning from 10 weeks after MCAO -continuous (1-week) EEG recording at 3, 7, and 12 months after MCAO (freely-moving rats). No spontaneous EEG seizures detected up to one year. EEG in penumbral region before and up to 280 min after MCAO in anesthetized rats (isoflurane). EEG power suppression immediately after MCAO to 30% of baseline, recovering to 50% at 90 min, no full recovering up to 180 min. 2 h after MCAO and then at 2 mo, 4 mo, 6 mo and 12 mo (freely-moving rats). Spiking in some animals, but epilepsy developed in only 1 rat (out of 35) during 6 or 12 months of monitoring. Within 6 months after MCAO (freely-moving rats) Control rats: spontaneous focal and generalized SWDs. Ischemic rats: the ictal SWDs (absence seizures accompanied with motor arrest of the animals) are less frequent and shorter duration compared with non-ischemic rats, and the EEG power in 7–15 Hz frequency band decreases compared with controls. No evidence of stroke-induced epileptogenesis. Before, during MCAO surgery and first 1 h of reperfusion (anesthetized rats) and then at 6 h, 12 h, 24 h, 48 h post-reperfusion (freely-moving rats). Severe voltage depression and diffused EEG slowing as earlier as 1 min after MCAO ipsilaterally in striatum and hippocampus. Anesthesia and MCAO provoked a 2.5-fold increase in delta power in all regions. Decrease in theta power. Before and at 1 h, 4 h, d1, d3, d7, d 14–freely-moving rats. In penumbral S1FL region: EEG drops by ∼20% below the pre-ischemic power at 1 h and 4 h. Then, an increase in power of the slow delta waves (PDA). No complete resolution of the spectral EEG profile until day 14 after Et-1. Before and at d1, d 3, d 5, d 7, d 14, d 21, d 28 after MCAO (freely-moving rats). Marked slowing of the EEG: increase in delta frequency band power and reduction in alpha frequency band power from 1–28d.

Williams and Tortella, 2002 Williams et al., 2003

Karhunen et al., 2003

Lei et al., 2004

Karhunen et al., 2006

Kelly et al., 2006

Zhang et al., 2006

Moyanova et al., 2007 Moyanova et al., 2008

Lämmer et al., 2011

Please cite this article in press as: Moyanova, S., Dijkhuizen, R., Present status and future challenges of electroencephalographyand magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia. Brain Res. Bull. (2014), http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

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Table 1 (Continued) Model of MCAO, duration of occlusion. Anesthesia for MCAO model.

Species Recovery time (time of sacrifice)

EEG measure, analysis.

Time sessions of EEG recording. Anesthesia during EEG recording. Main findings

References

tMCAO Et-1 infusion (150 pmol)) near to MCA Conscious rats

Rats (W) 14 days

FFT power, absolute PSD values relative to the EEG power suppression values at 15 min post-ischemia

Moyanova et al., 2011

tMCAO filament Anesthesia-ketamine and xylazine tMCAO filament 90 min Anesthesiachlorale-hydrate

Rats (CF)

FFT and PSD analysis

Rats (SD) 168 h

FFT power analysis, relative of total power AR method; ADR

tMCAO filament 60 min Anesthesia

Rats

FFT spectral PSD analysis

tMCAO Et-1 infusion (150 pmol)) near to MCA Conscious rats

Rats (W, two age groups: 3–4 mo and 12 mo) 14 days or 8 m

FFT spectral analysis, PSD. Asymmetry ipsilateral/contralateral EEG in S1FL, S1BF, V1 (pdBSI)

Before and at 15 min, 1-4.25 h, and at d 1, d3, d7, d14 (freely-moving rats). An initial suppression of 20–32% of EEG power at 15–60 min after Et-1 and appearing of slow EEG waves ipsilaterally 1–3d post-ischemia. Various initial EEG effects and degrees of recovery in four cortical areas compared with the drop at 15 min. Before and after MCAO in freely-moving rats. Decrease in EEG power (all frequency bands) in fronto-parietal, temporal and occipital regions. Before and at 3, 6, 12, 24, 48, 72, 96, 120, 144, 168 h (freely-moving rats). Reduction of the EEG amplitude to 44.3 ± 5.9% from the baseline after MCAO and recovered to 64.4 ± 4.4% from the baseline after reperfusion. Occurrence of PDAs at 3–6 h. Pronounced increase in delta waves at 24 and 48 h. Decrease in alpha power at 12–72 h. Reduction of ADR at 3 h and gradual recovering at 168 h. which correlated with the functional recovery measured with somatosensory tests. Before MCAO (anesthesia) and at day 8 after MCAO (freely moving rats). Frontal, parietal, temporal and occipital cortical areas. Pronounced increase in delta and theta EEG activity in fronto-parietal, occipital regions and temporal regions, while alpha and beta rhythms were highly depressed Before and at 15 min, 1 h, 4 h, and 1d, 3d, 7d, 14d; and from 1–8 month after Et-1 (freely-moving rats). Early (15 min) suppression of EEG ipsilaterally, increase in EEG power contralaterally in S1FL and in S1BF. Increase in EEG asymmetry (pdBSI) only in the acute phase of ischemia (15 min, 1 h) in young rats and up to the 14th day in 12-month-old rats.

Paul et al., 2012

Zhang et al., 2013

Bhattacharya et al., 2013

Moyanova et al., 2013

a Included are in-vivo studies involving permanent (pMCAO) or transient MCAO (tMCAO) with an intraluminal suture (filament) or clot (embolic) under anesthesia, and Et-1 model with stereotaxically intracerebral infusion of endothelin-1 (Et-1) near the MCA in conscious rats. Only results on spontaneous EEG in untreated (drug-free) and normothermic rats are included. Studies with partial occlusion of MCA (cortical cauterization of its branches) are not included. Spreading depression, evoked potentials and magnetoencephalography (MEG) studies were not considered. Abbreviations for time are: min for minute, h for hour, wk for week, and mo for month; For rat species: CF (Charles-Foster); F344 (Fisher), LE (Long–Evans) SD (SpragueDawley), W (Wistar), and W-K (Wistar-Kyoto). Other abbreviations: ADR–ratio alpha/delta EEG power; AR–autoregressive (spectral estmation); FFT–fast Fourier transformation; PSD–power spectral density; S1FL–somatosensory cortex (forelimb area); S1BF–somatosensory cortex (barrel field); FI–frequency index (ratio alpha + beta/delta + theta EEG powers); NCS–non-convulsive rhythmic spiking of 1–3 Hz, pdBSI–power density brain symmetry index; PSD–power spectral density; PDA–polymorphic delta activity; PLED–periodic lateralized epileptic discharge; IRDA–intermittent rhythmic delta activity; SWDs–spike-wave discharges; VI–primary visual cortex.

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Frigeni et al., 2001); acute delta change index (Finnigan et al., 2004), regional attenuation without delta (Schneider and Jordan, 2005), spectral density derived asymmetry index (van Putten et al., 2004); autoregressive and multivariate autoregressive and numerous nonlinear methods, based on estimators derived from the chaos theory, e.g., wavelet transform- and entropy-based EEG methods (reviewed in Thakor and Tong, 2004). It should be mentioned that EEG measurements in rodents are limited by several factors, such as (1) restricted number of EEG electrodes for recording because of small rodents’ head dimensions; (2) ambiguity of correct placement of the reference electrode; (3) poor source anatomical localization in epidural EEG recordings; (4) intensive amount of time required for the analysis of signal characteris, etc. Nevertheless, EEG measures are highly sensitive to dynamics of functional changes in brain tissue, and provide indispensable, sensitive and affordable means for detecting neuronal dysfunction, and EEG retains a useful place in the evaluation of processes induced by cerebral ischemia.

2.2. EEG in rodent stroke models EEG measurements in rodent stroke models have resulted in important findings that have contributed to the elucidation of mechanisms of functional consequences of focal cerebral ischemia, as summarized in Table 1. The EEG findings cited in this table have been acquired from different rodent MCAO models, from different brain regions, and at various time points after stroke. The poststroke time points of EEG recordings in these studies vary from hyperacute (<1 h), acute (1–24 h), subacute (1–10 days), to intermediate and chronic (>10 days) (according to stages defined by Andrews (1991)). The conventional approach in EEG investigations in preclinical stroke models is to delineate power in the EEG spectral bands, and draw relevant inferences from frequency-dependent changes in the EEG oscillations. For example, after transient MCAO, the balance of delta, theta, alpha and beta powers has been shown to be altered (85%, 7%, 5% and 3% of the total EEG power, respectively) during

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the acute phase of ischemia, resulting in significant decrease of the alpha-to-delta ratio (Williams et al., 2003; Zhang et al., 2013). The most frequently reported EEG changes during the subacute and chronic phases of transient MCAO have been an increase in delta and a reduction in alpha and beta EEG power across the entire ipsilateral hemisphere (Moyanova et al., 1998, 2007, 2008, 2013; Lu et al., 2001; Zhang et al., 2013). The loss of fast EEG rhythms and appearance of focal slow wave activity are supposed to be due to dysfunctional or pathological neural tissue and impaired network communication in affected cortical/subcortical regions (Gloor et al., 1977). For assessment of stroke-induced EEG changes and recovery, the whole spectrum EEG power have been compared to that before MCAO (Schmid-Elsaesser et al., 1999; Lei et al., 2004; Paul et al., 2012; Zhang et al., 2013), during MCAO (Phillips et al., 2000), or during the EEG suppression hyperacutely after MCAO (Williams et al., 2000; Moyanova et al., 2011). Abnormal EEG patterns (characterized in details in the clinical stroke literature, e.g., Andraus and Alves-Leon, 2011) that have been reported in EEG studies in rodent MCAO models include: polymorphic slow-wave delta activity; intermittent rhythmic delta activity consisting of readily identifiable brief (<10 s) rhythmic large-amplitude bursts in the 4–7 Hz range; non-convulsive seizures, electrographically defined as rhythmic discharges with spike components of 1–4 Hz frequency; occasional rhythmic spikeand-wave or polyspike discharges, not accompanied by motor convulsions; periodic lateralized epileptic discharge, consisting of interictal spikes; and sharp or slow waves recurring with a variable period of 1–8 s (Williams et al., 2000, 2003; Williams and Tortella, 2002; Hartings et al., 2003) (Table 1). Rodents have been shown to exhibit non-convulsive seizures predominately during the first hour after filament MCAO (Williams et al., 2000), and their appearance might attribute to the abrupt mechanical occlusion of MCA, because such abnormal EEG patterns have not been observed in the Et-1 MCAO model (Moyanova et al., 2007), where the occlusion by Et-1 is gradual. The high variability in the size and spatial extent of cerebral ischemic lesions in various focal cerebral ischemia models affects functional output measures including the EEG pattern. Rodent strain, vessel anatomy, degree and duration of arterial occlusion, are among the variables that determine the extent of ischemic injury and the brain’s functional status in rodent MCAO models (Table 1). Different EEG patterns have been recorded after transient proximal MCAO (Bolay and Dalkara, 1998; Williams et al., 2000, 2003; Williams and Tortella, 2002; Lu et al., 2001; Bao et al., 2001; Frigeni et al., 2001; Hartings et al., 2003; Karhunen et al., 2003; Lei et al., 2004; Zhang et al., 2006; Paul et al., 2012; Zhang et al., 2013; Bhattacharya et al., 2013); permanent proximal MCAO (Lu et al., 2001; Guyot et al., 2001; Hartings et al., 2003; Lämmer et al., 2011); permanent distal MCAO (Bolay and Dalkara, 1998; Kelly et al., 2006), and transient MCAO induced by Et-1 (Moyanova et al., 1998, 2007, 2008, 2011, 2013; Karhunen et al., 2006), as summarized in Table 1. Quite different EEG results have been reported in transient and permanent occlusion models, for example, the polymorphic delta EEG activity has been found to be less expressed after transient MCAO model as compared to the permanent MCAO model (Lu et al., 2001; Hartings et al., 2003). The localization of the recording electrodes with respect to the site of ischemic injury critically affects the EEG pattern. Attempts to differentiate between EEG parameters recorded from areas representing the ischemic core, penumbra, and remote regions have been made in only a few studies. For example, quasi-topographic mapping of EEG spectral values from several cortical regions after permanent or transient MCAO in rats has demonstrated that abnormal EEG patterns were spatiotemporally different: while the cortical parietal region (peri-infarct) was dominated by sharp waves and/or spike-wave complexes, the EEG in temporal cortical

regions (ischemic core) was severely depressed at 24 h after MCAO (Lu et al., 2001). Interestingly, diffuse polymorphic delta activity has been reported to appear predominantly in the peri-infarct parietal cortical region after transient MCAO and to a lesser degree in the temporal cortical region representing the ischemic core (Williams et al., 2003). In another study, suppression of the EEG power during the hyperacute phase after Et-1 infusion near to the MCA was found to be greater in ischemic core regions (barrel field of the somatosensory cortex, and granular field of the insular cortex) than in the penumbra (forelimb region of the somatosensory cortex) and outside the lesion territory (primary visual cortex) (Moyanova et al., 2011). In the same study, the suppression of the EEG amplitude was shown to recover first in the region outside of the infarct, then in the penumbral region, and much later or not at all in the ischemic core. EEG recording may not be limited to cortical tissue, as studies in animal models also allow intracerebral insertion of electrodes in subcortical structures. In stroke models in which subcortical brain regions are part of the ischemic territory, e.g. after intraluminal filament occlusion of the proximal part of the MCA, subcortical EEG recordings have demonstrated ipsilaterally a drop in EEG amplitude as earlier as one minute after MCAO and a significant increase in delta and theta power (Zhang et al., 2006). Permanent or temporary occlusion of the MCA at its distal part through a subtemporal craniotomy induces primarily cortical infarction (Brint et al., 1988). Despite the craniotomy, long-term EEG recordings are feasible, as has been shown by Kelly and coworkers who recorded EEG from frontal and parietal cortical areas in freely-moving rats for 6 months after MCAO (Kelly et al., 2006, see Table 1 for the results). Using this model, Bolay and Dalkara (1998) demonstrated with electrophysiological measurement of electrocorticograms and somatosensory- and motor-evoked potentials that cortex and caudate-putamen were differently affected by the vessel occlusion, and that motor dysfunctions after recirculation might be due to persistent transmission failure at cortical synapses and blockade of electrical impulses at the subcortical level during ischemia. The two main EEG characteristics in relation to ischemiainduced alterations are the suppression of all EEG waves during the hyperacute phase (from several minutes to 1–2 h), and late EEG slowing during subacute to semichronic phases of ischemia (1–10 days). The next two subsections will discuss specific early and late EEG patterns in response to ischemia-reperfusion in more detail. 2.2.1. Early ischemia-induced EEG suppression The electrical function of the brain depends critically on the CBF and oxidative metabolism of glucose, delivered to the brain tissue via the arterial blood supply. Occlusion of the MCA leads to reduction of CBF in the brain territory supplied by the artery. The critical need of sufficient oxygen delivery leads to failure of synaptic transmission, which has been proposed to account for electric silence in the ischemic core and penumbra (references in: Hofmeijer and van Putten, 2012) and thus, for the severe reduction in the EEG amplitude observed during the hyperacute phase of ischemia in MCA-occluded rodents (Table 1). Specific electrical changes accompanying the progressive neuronal dysfunction have been applied to determine the thresholds of CBF after ischemia as follows (Astrup et al., 1981; Heiss, 1983; Hossmann, 1983): 20 ml/100 g/min–decreased amplitude of somatosensory evoked potentials; 18 ml/100 g/min–abnormal EEG, loss of fast frequencies; 15 ml/100 g/min–increases in slow frequencies, loss of postsynaptic evoked responses; 12 ml/100 g/min–EEG flat, loss of presynaptic evoked responses. Significant reduction in the EEG power one hour after permanent MCAO has been found to correlate with >30% CBF reduction as measured by combined laser Doppler flowmetry adjacent to the EEG electrode (Guyot et al., 2001). In

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rodent MCAO models, the degree of the EEG amplitude reduction in the hyperacute phase of ischemia was shown to be between 20% and 80% dependent on the model used and on the duration of the vessel occlusion (Bolay and Dalkara, 1998; Schmid-Elsaesser et al., 1999; Phillips et al., 2000; Williams et al., 2000; Hartings et al., 2003; Lei et al., 2004; Moyanova et al., 2007, 2011). Therefore, some investigators have confirmed success of MCAO in rodents based on the EEG suppression over the ipsilateral somatosensory and motor cortical areas during the hyperacute phase of ischemia (Phillips et al., 2000; Williams et al., 2000; Hartings et al., 2003). This criterion has been confirmed by the observation that rats without significantly decreased EEG amplitude in the ipsilateral hemisphere during or immediately after MCAO, revealed no ischemic tissue damage a few days later (Frigeni et al., 2001). The potential of (hyper) acute EEG changes to predict on stroke outcome has not been fully explored and requires further investigations.

“transhemispheric diaschisis” has been termed to describe cortical changes in excitability and metabolism observed in contralateral hemisphere following unilateral stroke (Andrews, 1991). EEG studies that describe post-stroke signal changes in the contralateral hemisphere are sparse. An increase in EEG delta activities has been found in the contralateral sensorimotor cortical areas at 1–7 days after the MCAO in rodents (Lu et al., 2001; Williams et al., 2003; Hartings et al., 2003; Moyanova et al., 2013). In addition, an increase in EEG asymmetry (spectral density-derived brain asymmetry index) between ipsilateral and contralateral EEG power, has been shown to coincide with increase in behavioral asymmetry (Moyanova et al., 2013). Improved knowledge of bioelectrical activity patterns in the unaffected hemisphere would help to understand its putative role in functional compensation and recovery after unilateral stroke.

2.2.2. EEG changes after reperfusion In the transient filament MCAO model, the re-establishment of blood flow following the mechanical retraction of the filament is abrupt, which may lead to hyperperfusion. Although reperfusion restores supply of oxygen and nutrients to previously ischemic brain tissue, reperfusion may also exacerbate ischemic injury by generation of reactive oxygen species, leading to so-called “reperfusion injury” (Aronowski et al., 1997; Pan et al., 2007). The effects of reperfusion on brain EEG oscillations have been incompletely characterized. Some EEG abnormalities (e.g., nonconvulsive rhythmic spikes), which were present during 2 h MCAO, were shown to disappear after reperfusion, while other abnormal signals, such as polymorphic and intermittent rhythmic delta activities, remained, which the authors associated with reperfusion injury (Hartings et al., 2003). In another study, polymorphic delta activity in periinfarct regions of the ipsilateral parietal cortex was shown to increase following reperfusion after MCAO (Lu et al., 2001). In the Et-1 MCAO model, reperfusion is spontaneous and gradual, because the vasoconstrictory effect of Et-1 gradually wears off. The duration of the perfusion deficit in this model ranges from 80 min (Robinson et al., 1991) to 7–16 h (Biernaskie et al., 2001) depending on the dose of Et-1 and the site of CBF measurement. Gradual reperfusion as observed in the Et-1 model is believed to closely resemble the situation in human stroke (Biernaskie et al., 2001; Roulston et al., 2012) than the mechanically induced reperfusion in filament MCAO models. The only abnormal EEG pattern recorded during the subacute reperfusion phase after Et-1 MCAO was the polymorphic delta activity (Moyanova et al., 1998), while non-convulsive abnormal EEG patterns related to reperfusion have been observed in the filament MCAO model (Hartings et al., 2003, Table 1). Prolonged longitudinal EEG studies after stroke could give information on temporal evolution of ischemia-induced consequences and progression of recovery processes. Long-term EEG monitoring during the chronic phase of stroke (>10 days) in animal MCAO models (Table 1: Moyanova et al., 1998, 2007, 2008, 2011, 2013; Phillips et al., 2000; Karhunen et al., 2003; 2006; Kelly et al., 2006; Lämmer et al., 2011; Zhang et al., 2013), has demonstrated different time courses of EEG impairments and recovery depending on the model and recording site. The findings of gradual restoration of EEG patterns are in line with the concept of functional brain plasticity and neuronal network reorganization, which may be further studied by combined and serial multifunctional assessment of behavioral, electrophysiological and imaging outputs.

3. Magnetic resonance imaging in preclinical stroke studies

413

3.1. MRI of acute stroke

414

2.2.3. EEG changes in the contralateral hemisphere Focal brain ischemia has consequences for the whole brain. Partial damage of a neuronal network may also functionally impair remote regions, as well as induce compensatory remodeling of connected intact areas (Rema and Ebner, 2003). For example,

Various imaging strategies are available in clinical and preclinical settings to assess structural and functional injury after stroke, including positron emission tomography (PET), computed tomography (CT), single photon emission computed tomography (SPECT) and MRI (e.g., Baird and Warach, 1998; Kilpatrick et al., 2001; Dijkhuizen et al., 2012). CT is the most widely available neuroimaging modality in clinics and is typically used to detect the presence of early subtle acute ischemic signs, to rule out hemorrhage and/or angiographically detect the vascular occlusion site. Despite its practicality for acute stroke diagnosis, CT, however, may not directly detect ischemic tissue injury until at least 6 h after stroke onset (Baird and Warach, 1998). Recent advances with dynamic CT perfusion have enabled the assessment of the hemodynamic state of the ischemic brain tissue in stroke patients (Eastwood et al., 2003), potentially providing a correlate of the ischemic penumbra. Although CT is not frequently utilized in experimental stroke studies, CT perfusion has also been successfully applied in rats (Nikolova et al., 2009), and new CT techniques have recently been introduced, such as microCT and nano-CT (Langheinrich et al., 2010; Hayasaka et al., 2012), which may be capable to evaluate dynamic and evolving processes after experimental stroke in more detail. PET allows quantitative measurement of cerebral blood flow and metabolism in human patients as well as animal models, and remains the imaging gold standard for identification of potentially salvageable tissue, i.e. the ischemic penumbra (Baron, 1999; Heiss, 2000; Virdee et al., 2012). Nevertheless, this technique is usually not applied in clinical routine due to complicated procedures, invasive methodology, complex logistics, limited access and exposure to radioactivity. Throughout the world, MRI is increasingly used for clinical stroke diagnosis, because of its noninvasiveness and versatility. Furthermore, translational studies in scientific laboratories have more frequently involved MRI than PET and CT. Despite the advantages of MRI, it should be noted that the equipment is expensive, massive, immobile and not uniformly available in laboratories and clinics. MRI does not expose the organism to potentially harmful ionizing radiation and thus is easy to serially repeat. It can be used to characterize the evolution of the stroke lesion “in vivo” by monitoring edema, perfusion, and vascular morphology (see Table 2 for applications in animal models of stroke). Infarcted tissue with extracellular edema can be identified with T2 -weighted MRI methods, which is a robust means to detect ischemic infarctions at subacute to chronic stages. Diffusion-weighted imaging

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Table 2 Summary of selected MRI studies in rodent MCAO models of strokea . Model of MCAO, duration of occlusion

Species, time of imaging

(MR) imaging technique

Main findings

Reference

tMCAO filament 1 h, 2 h tMCAO filament 4h pMCAO electro-coagulation

Rats (SD) 30, 60, 90 min after MCAO. (2 h) Rats (SD) 1, 24, 72 h, 1wk after MCAO.

DWI, PWI, T2 w

DWI predicted infarct size.

Minematsu et al., 1992

DWI, PWI

Quast et al., 1993

Rats (SD) 7h

DWI, T1 w, T2 w

tMCAO filament 30 min, 2 h

Rats (W) Immediately after MCAO and then at 10 min intervals for up to 7 h.

pMCAO filament

Rats (W) 6, 18, 28, 39, 61, 83 and 105 min after MCAO.

pMCAO filament

Rats (W) Before and every 30 min after MCAO.

In-bore, DWI, autoradiography bioluminescence and fluorescence imaging for ATP, glucose, lactate and pH maps. In-bore, DWI, auto-radiography, fluorescence imaging for ATP, pH. In-bore, DWI, T1 w, T2 w, PWI-ASL.

Severest perfusion deficit (the core) at 1 h after MCAO surrounded by a zone of low perfusion (penumbra) and vasogenic edema–over 72 h. At 7 h after MCAO: a reduction of ADC, which reflected the region of injury, breakdown of energy metabolism and tissue acidosis. Signal intensity in DW images began to rise within 12–14 min after MCAO. The size of the infarct core during the initial 2 h of vascular occlusion increased more rapidly than that of the penumbra.

tMCAO filament 30, 60 min, 2.5 h

Rats (SD) 0.5 h, 1.5 h, 2.5 h, and 1, 2, 7d after reperfusion.

DWI, T1 w, T2 w

tMCAO filament 10, 30 min

Rats (SD) During MCAO, immediately after reperfusion, and at 0.5, 1, 1.5, 12, 24, 48 and 72 h after reperfusion. Rats (F344) 9d

DWI, PWI, T2 w

pMCAO electrocoagulation pMCAO filament

T2 w, DWI, PWI

pMCAO electrocoagulation tMCAO filament 2h

Rats (SD) 3 and 14d 1, 3, 14d

DWI, T1 w, T2 w, multistep algorithm to incorporate the multiple MRI parameters T2 w, DWI and contrast-enhanced CBV-weighted (f)MRI.

tMCAO filament 5, 15, 30, 60 min pMCAO tMCAO filament 15 min

Rats (F344) 60 min after reperfusion

CBV-weighted phMRI (bicucullin)

Rats (SD) 3h 30, 90, 180 min 24 h

DWI, PWI-ASL, pixel-by-pixel, ISODATA BOLD -CO2 challenge

pMCAO tMCAO filament 60 min tMCAO filament 2h

Rats (SD), 24 h

DWI, PWI-ASL

Rats (SD) 2wks

fMRI, CBV, T2 w, DTI

Rats (SD) 180 min Before and at 5, 15, 30, 60, 90, 120, 150, 180 min, 1, 7d after stroke

DWI, PWI BOLD-CO2 ASL, T2 w

pMCAO filament and embolic tMCAO

Rats (W) Acute(4–8 h) Subacute (16-24 h) Chronic (48–168 h)

Gradual decrease of ADC within the ischemic territory from the periphery (corona with normal energy metabolism but severe tissue acidosis) to the center (core). Combined analysis of T2 (decrease) and T1 (increase) in first few minutes distinguished between severely affected and moderately affected (border) regions. Delayed recurrence of the DWI secondary lesions during reperfusion after 30 and 60 min of ischemia. After 2.5 h occlusion: BBB damage associated with vasogenic edema. Secondary DWI hyperintensity and T2 WI abnormalities at 12 h. The resolution of DWI lesions does not indicate salvage of brain tissue.

Back et al., 1994

Kohno et al., 1995a,b

Hoehn-Berlage et al., 1995

Calamante et al., 1999a

Neumann-Haefelin et al., 2000

Li et al., 2000

Infarct volumes increased over the first 48 h, ADC decreased for more than 3 days, recovered at 4–8 days and increased by day 9. The histological scoring correlated with the multiparametric imaging data.

Rudin et al., 2001

Limb dysfunction after stroke was related to loss of brain activation in the ipsilesional sensorimotor cortex. Restoration of function was associated with biphasic recruitment of peri- and contralesional functional fields in the brain: at 3d–extensive activation-induced responses in the contralesional hemisphere, at 14d–reduced involvement of the contralesional hemisphere, the focus of activation returned to the ipsilesional cortex. Even a short ischemic event of 15 min duration significantly reduced the phMRI response in caudate putamen 60 min after reperfusion. Spatiotemporal progression of acute ischemic brain injury. A significant “perfusion-diffusion mismatch” up to 2 h. In ischemic hemisphere three clusters: normal, core and at risk diffusion–perfusion mismatch. Estimation of CO2 -based BOLD changes in the ‘perfusion–diffusion mismatch’. A substantial mismatch between ADC- and CBF-derived lesion volumes up to 60 min after MCAO.

Dijkhuizen et al., 2001, 2003

After stroke, animals showed variable degrees of fMRI activation in ipsilesional cortex, the extent of which did not correlate with structural damage as measured using other imaging techniques. Substantial differences in acute ischemic lesion evolution between embolic and mechanical (filament) occlusion of MCA. Persistent tissue dysfunction after the recovery of the CBF/ADC lesion.

Kim et al., 2005

Jacobs et al., 2001

Reese et al., 2002

Shen et al., 2004, 2005

Meng et al., 2004

Henninger et al., 2006, 2007

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Table 2 (Continued) Model of MCAO, duration of occlusion

Species, time of imaging

(MR) imaging technique

Main findings

Reference

tMCAO filament 60 min

Rats (W) 1–10wks

T1 w, T2 w

Wegener et al., 2006

tMCAO filament 20 min

Rats (SD) Before, during MCAO, at 30–180 min, 1, 7, 21d or at 15–180 min and 24 h after reperfusion. Rats (SD) 30, 60, 90, 120, 180 min after MCAO, immediately after reperfusion and at 24 h

DWI, PWI, T2 w, fMRIBOLD-CO2 and forepaw stimulation

tMCAO filament 90 min

Rats (W) 1 or 2wks after stroke, before-2d and 4d after tracer injection.

MEMRI

tMCAO filament 60 min pMCAO tMCAO filament 90 min tMCAO filament 65 min

Rats (W) 2d,1, 2, 4, 7wks after MCAO

fMRI-BOLD

Rats (W) 70, 150 min, 4 h

T1 w, T2 w, DWI

The temporal profile of relaxation time changes over the chronic time period allowed discrimination of the lesion development into selective neuronal death or pannecrosis. Ipsilesional fMRI responses were significantly impaired after ischemia and did not fully normalize at 3 and 24 h and 21d after MCAO. Only fMRI to forepaw stimulation was capable of identifying dysfunctional neural tissue. Substantial salvage of mismatch tissue after reperfusion and substantial permanent recovery of initial core pixels with early reperfusion. Severity of CBF reduction during ischemia determined the tissue fate. At 2wk–loss of connectivity between areas of sensorimotor network (thalamus, cortex, caudate putamen). The plateau stage of functional recovery was associated with restoration of ipsilateral sensorimotor pathways and enhanced interhemispheric connectivity. Functional recovery is an activation reemergence in the original representation field without plastic reorganization. T1 was superior for detecting early ischemic changes, which were not detected with T2 or ADC.

T1 w, T2 w, APTR, PWI-ASL, DTI

The fate of the tissue can be predicted more certainly by using combined MRI parameters.

Jokivarsi et al., 2010

tMCAO filament 90 min

Rats (W), in-bore imaging 0–60 min during MCAO, 90–120 min after removal of the occluding device Rats (W) before–2d, and 3, 7, 21, 70d after stroke.

T2 w, MEMRI, rs-fMRI (BOLD)

van Meer et al., 2010a,b, 2011, 2012

pMCAO filament

Rats (SD) immediately after stroke

BOLD–O2 challenge, PWI-ASL DWI

tMCAO filament 90 min

Rats (W) during: hyperacute (2–3 h), acute (1–3d), subacute (4–14d), chronic (4–8 wks) phases of ischemia Rats (SD) 7d

DWI, DTI

Acute deterioration and subsequent retrieval of interhemispheric functional connectivity within the sensorimotor system correlated significantly with the evolution of sensorimotor functional scores. Increase in neuroanatomical and functional intrahemispheric connectivity in the contralesional cortex. Significant decrease in interhemispheric functional connectivity. Cortical network remodeling (reinstatement of interhemispheric neuronal signal synchronization) was accompanied by recovery of initially disrupted structural integrity in ipsilesional corticospinal tract. The results support the utility of MRI and O2 challenge to detect viable penumbral tissue following stroke. DTI indices revealed different individual patterns reflecting different facades and phases of tissue injury.

tMCAO filament 35 and 95 min

pMCAO tMCAO filament 45 min tMCAO filament

tMCAO filament

DWI, T2 w

Sicard et al., 2006

Bardutzky et al., 2007

van der Zijden et al., 2007, van der Zijden et al., 2008

Weber et al., 2008

Kaur et al., 2009

Robertson et al., 2011a,b Pitkonen et al., 2012

PWI-ASL, DWI, T2 w, DKI

Mean diffusion kurtosis contrast persisted 1–7 day post-occlusion.

Hui et al., 2012

Rats (SD) before, at 30, 60 min, 2, 6, 12, 24 h after MCAO

Rs-fMRI (BOLD), MRA, T2 w, DWI

Yao et al., 2012

Rats (W) before. At 7, 14, 21, 28d after MCAO

CBV-weighted MRI

Following MCAO, the signal intensities of abnormal amplitude of LFFs increased and migrated from the ischemic core to the edge of lesion. Persistently decreased MRI vessel density

Boehm-Sturm et al., 2013

a The empirical studies listed in this Table are a selection and by no means represent a complete overview of this specific field of research. Included are in-vivo studies involving permanent (pMCAO) or transient MCAO (tMCAO) with an intraluminal suture (filament) or clot (embolic) under anesthesia. Abbreviations for time are: min for minute, h for hour, wk for week, and mo for month; For rat species: CF (Charles-Foster); F344 (Fisher), LE (Long–Evans) SD (SpragueDawley), W (Wistar), and W-K (Wistar-Kyoto). Other abbreviations: ADC– apparent diffusion coefficient; APTR–amide proton transfer ratio; ASL–arterial spin labeling; CBV–cerebral blood volume; DKI–diffusional kurtosis imaging; DTI –diffusion tensor imaging; phMRI–pharmacological MRI; ISODATA–automated iterative-self-organizing-data-analysis algorithm; MEMRI–manganeseenhanced magnetic resonance imaging; MRA–magnetic resonance angiography; T1 w–T1 -weighted imaging; T2 w–T2 -weighted imaging.

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(DWI) and perfusion-weighted imaging (PWI) allow detection of acute ischemia-induced cerebral changes within minutes after stroke. Dynamic susceptibility contrast-enhanced imaging is the most widely used PWI approach, which is based on dynamic imaging of the first passage of an intravascularly injected exogenous paramagnetic MRI contrast agent (Calamante et al., 1999b). Alternatively, an arterial spin-labeling technique, which measures perfusion based on signal from endogenous arterial blood water (Detre et al., 1994), can be used to quantify CBF loss after stroke. Perfusion imaging methods are used to estimate the hemodynamic status of ischemic tissue, whereas flow in larger vessels can be assessed using MR angiography. DWI allows detection of disturbances of cellular water homeostasis, which is one of the first elements of the pathophysiological cascade leading to ischemic tissue injury (Sevick et al., 1992). DWI contrast is based on tissue water’s apparent diffusion coefficient, from which the location and volume of early ischemic tissue changes may be determined. For a detailed description of the principles and applications of these methods we can refer to many previous reviews (Hoehn et al., 2001; Dijkhuizen and Nicolay, 2003; Farr and Wegener, 2010; Denic et al., 2011; Duong, 2012; Obenaus and Ashwal, 2012). Translational stroke research with MRI may significantly contribute to improvements in stroke diagnostics and therapeutics. Numerous studies with animal models of stroke have been performed in search of neuroprotective and neurorestorative therapies that may help stroke victims (e.g., review by Howells et al., 2012). Unfortunately, however, translation of neuroprotective benefits from the laboratory bench to the emergency room has not really been successful (Sutherland et al., 2012). To determine whether treatment can be beneficial, there is a need for an objective means of assigning tissue status at the time of ischemic stroke diagnosis, in other words, a need for advanced diagnostics that can identify the presence of salvageable tissue at risk of infarction. This refers to the concept of the ischemic penumbra. Originally, the ischemic penumbra was defined on an electrophysiological basis as brain tissue with impaired electrical activity but preserved cellular metabolism and viability (Astrup et al., 1981). The differentiation of “salvageable, penumbra” versus “nonsalvageable, core” in ischemic tissue may allow optimal selection of patients who could benefit from acute therapeutic intervention. Therefore, over the years, a variety of MRI strategies have been proposed by which regional CBF, and tissue status can be measured and analyzed to identify the ischemic penumbra, and to trace its evolution towards irreversible injury or its spontaneous or therapeutically induced resolution. For example, the so-called “perfusion-diffusion mismatch” (area with abnormal PWI signal and normal DWI signal) has been introduced in the clinic to define salvageable tissue at risk of infarction (Schlaug et al., 1999). PWI–DWI mismatch regions have also been identified in animal stroke models (Meng et al., 2004; Henninger et al., 2006). Many studies have used PWI and DWI in transient MCAO models to assess early and late ischemia/reperfusion-induced changes in the brain (Table 2), which has contributed to our current knowledge of distinct traits of stroke pathophysiology such as cytotoxic edema development, prolonged hyperemia, secondary tissue injury after reperfusion, etc. (see reviews by Baird and Warach, 1998; Dijkhuizen and Nicolay, 2003; Duong, 2012). The evolution of the PWI–DWI mismatch region has been reported to differ between experimental models (Henninger et al., 2006) and animal strains (Bardutzky et al., 2007), and several studies suggest that the PWI–DWI mismatch may not be adequate to predict the outcome of ischemic brain tissue, and uncertainty remains about the significance of perfusion and diffusion changes and their relationship (see review by Wardlaw, 2010). Recently introduced predictive algorithms that combine and weigh various tissue and perfusion parameters obtained with MRI could significantly improve the sensitivity and specificity of identification of potentially salvageable

tissue after stroke (Ostergaard et al., 2009; Wu et al., 2010). Promising methodological developments have been successfully tested and applied in preclinical studies in animal stroke models (Bouts et al., 2013), and await further evaluation in human stroke patients. 3.2. Functional MRI of brain reorganization after stroke Structural MRI methods enable construction of relatively static tissue maps and provide anatomical and basic (patho) physiological information. They are based on the fact that different types of tissue (e.g. skull, gray matter, white matter, cerebrospinal fluid) have different MRI-detectable biophysical properties, which may change under pathological conditions. On the contrary, functional imaging methods allow construction of dynamic maps of processes and responses in brain tissue. These methods are based on the fact that neural activity leads to local physiological changes in the brain. The most widely applied functional imaging technique is fMRI. The physical principles and application of fMRI after stroke have been described in previous reviews (Dijkhuizen and Nicolay, 2003; Weber et al., 2006; Dijkhuizen et al., 2012). Neural activity increases oxygen consumption, which triggers an increase in blood flow to that region and a change in local tissue oxygenation. This has been termed the BOLD effect (for blood oxygenation leveldependent contrast; Ogawa et al., 1990). Neuronal activity induces a BOLD effect with a characteristic time course, which is known as the hemodynamic response function. FMRI enables acquisition of functional activation maps with a relatively good combination of temporal (up to subsecond) and spatial resolution (up to submillimeter), and is gaining acceptance as a research method for assessing spatiotemporal changes in brain activity in relation to pathophysiology and behavioral recovery in neurological disorders. The use of fMRI in animal models has been particularly advantageous in preclinical and translational studies of stroke recovery (Dijkhuizen et al., 2001, 2003; Markus et al., 2005; Kim et al., 2005; Weber et al., 2008). FMRI studies may involve imaging during task- or stimulusrelated activation paradigms, or imaging without any presentation of stimuli, i.e. resting state fMRI (rs-fMRI), which can be applied to assess the integrity of functional areas and networks in poststroke human or animal brain (Grefkes and Fink, 2011; Carter et al., 2012; Dijkhuizen et al., 2012). Stimulus-related fMRI activation studies in animal stroke models have relied on techniques that are weighted by cerebral blood volume responses (Dijkhuizen et al., 2001, 2003) or BOLD responses (Kim et al., 2005; Weber et al., 2008). The cerebral blood volume-weighted fMRI measurements provide high contrast-to-noise ratio, but require injection of an intravascular contrast agent. BOLD fMRI is noninvasive and has been used in the vast majority of human fMRI studies. A recent development in rs-fMRI, which assesses spatial correla- Q5 tions of spontaneous fMRI signals within neural networks, has allowed measurement of intrinsic functional connectivity in the brain. The principle of rs-fMRI is based on detection of synchronizations of BOLD low frequency fluctuations of less than 0.1 Hz. Assessment of spatiotemporal signal correlations, for example with independent component analysis, allows identification of functionally connected brain networks. Rapid current developments in acquisition and analysis methods are continuing to improve the accuracy, reproducibility and utility of rs-fMRI (van den Heuvel and Hulshoff Pol, 2010; Smith, 2012). During the last years, a bulk of rs-fMRI studies have appeared, which reported on functional connectivity patterns and neural network topology in humans, non-human primates (Beckmann et al., 2005; Moeller et al., 2009) and rodents (Hutchison et al., 2010). These studies have for instance demonstrated the existence of a default mode network and several bilaterally organized networks that correspond with functionally homologous hemispheric brain areas. The examination of

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functional connectivity in brain disorders, such as stroke, may be particularly relevant because it could provide new and important insights into functional brain reorganization in relation to loss and recovery of functions (Carter et al., 2012). Studies in a rodent stroke model have shown that functional connectivities in structurally intact brain regions are disrupted after focal cerebral ischemia, but can gradually recovery, reflective of functional network remodeling (van Meer et al., 2010a,b, 2011, 2012). Importantly, connectivity changes within the bilateral sensorimotor cortical network were shown to be correlated with changes in sensorimotor function, suggesting a tight interrelationship between functional network connectivity and behavioral output. Clinical experience has shown that a large proportion of stroke patients experience at least some degree of spontaneous recovery. Resolution of neurological deficits after stroke may occur through changes in the structure, function, or connectivity of surviving neurons. These processes are believed to develop during subacute to chronic stages. Most stroke studies in patients and animal models, however, have typically been performed during the first hours or days after stroke (Table 2). To better understand the evolution of tissue injury and the impact of neuroprotective and neurorestorative therapeutic interventions, it is important to investigate the temporal profiles of structural and functional brain parameters, e.g., obtained from imaging studies, for longer time periods. Recent longitudinal MRI measurements, including rs-fMRI, diffusion tensor imaging and contrast-enhanced vascular MRI, after MCAO in rats have already provided exciting new insights into brain plasticity processes like alterations in functional network topologies, white matter remodeling and revascularization (Seevinck et al., 2010; van Meer et al., 2012; Boehm-Sturm et al., 2013). Importantly, parenchymal, vascular and functional parameters can be studied with multiparametric MRI in a single experimental session. Longitudinal structural, functional and vascular MRI studies could help to elucidate mechanisms that underlie the reversibility or irreversibility of functionally impaired tissue after ischemia. Furthermore, it can aid in clarifying to what extent improved performance following therapy is due to recovery of the affected brain area and/or to functional reorganization of peri-lesional and/or remote (contralesional) neuronal networks in the brain (Dijkhuizen et al., 2001, 2003; Sicard et al., 2006; Weber et al., 2008; Jiang et al., 2010; Johnston et al., 2012). To summarize, continued methodological advances in brain imaging with MRI are likely to extend and deepen our understanding of cerebrovascular disorders, e.g., ischemic stroke. Integration of neuroimaging approaches into experimental setups would further illuminate pathophysiology, provide guidance for early or late interventions, facilitate prediction of stroke outcome and treatment response, and could lead to clinically applicable protocols for the development, screening and monitoring of therapeutic strategies. This may be effectively achieved in multiparametric studies in animal models of stroke by fusion of modern structural, molecular and functional imaging methods with electrophysiological techniques.

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Valuable data in terms of quality and quantity can be obtained by the combination of complementary techniques such as EEG and MRI. The EEG signal is limited by low spatial resolution, whereas MRI can provide relatively high spatial resolution (within millimeters). On the other hand, EEG monitoring provides superb temporal resolution (within milliseconds), while MRI is limited by relative temporal insensitivity. Thus, the combined use of structural or

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functional brain imaging (e.g., MRI) with electrophysiological (e.g., EEG) techniques enables detection of signals with high temporal resolution in spatially well characterized regions (Horwitz and Poeppel, 2002). Rationales for combining EEG and MRI in a single experimental setting to study stroke include: (1) association of different physiological phenomena reflecting brain activity, i.e., hemodynamic neuronal activation responses (detected with functional MRI) and electric potentials directly generated by neuronal activity (detected with EEG); (2) correlation of early pathophysiological responses to reduced CBF, such as hyperacute EEG abnormalities and DWI signal changes; (3) anatomical localization (from MRI) of electrophysiological signals (from EEG). In humans, the combination of electrophysiological recording and imaging has successfully demonstrated that the foci of abnormal EEG delta waves correlate well with areas of cerebral infarct identified by CT, PET and MRI (Nuwer et al., 1987; Murri et al., 1998; Nagata et al., 1989; Finnigan et al., 2004; Lu et al., 2007). EEG recording within an MRI scanner allows direct correlation of stroke-induced EEG abnormalities with structural or functional MRI changes. However, the recording of EEG within the high magnetic field of a MR scanner is associated with significant technical problems (Krakow et al., 2000). Due to methodological progress in this field, technical obstacles can be overcome and simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular in research on epilepsy and cognitive function in humans. In stroke research, combined EEG and MRI may elucidate the role of distinct EEG oscillation patterns in relation to underlying structural damage. As an example, simultaneous acquisition of MRI and EEG data has helped to understand the anatomic-functional basis of asymmetry in frontal intermittent delta activity in stroke patients (Accolla et al., 2011). Until now, however, there are only a few papers that have reported on simultaneous use of MRI and EEG in animal models of cerebral ischemia. Early studies, in which EEG served to directly ascertain successful MCAO, have demonstrated that early (6–14 min after MCAO) EEG slowing and flattening coincided with increases of the DWI signal (Kohno et al., 1995a,b; Hoehn-Berlage et al., 1995). We speculate that incorporation of EEG recordings into MRI-based assessment of the diffusion–perfusion mismatch model, could contribute to improved differentiation of the penumbra from oligemia or the ischemic core. Such evaluation of an EEG–DWI–PWI mismatch may enable identification of preserved neuronal synaptic function within and around the DWI-detected tissue lesion, which could improve the accuracy of diagnosis, outcome prediction and treatment selection. 4.2. Combined EEG and fMRI after stroke In the last years, the combination of fMRI with EEG recordings has been increasingly used to study the underlying mechanisms of functional processing in a non-invasive manner combining hemodynamic with neuronal measurements (Shibasaki, 2008). Combining EEG with fMRI in the same experimental setting is an exciting challenge in neuroscience, still however with limited application in research on neurological disorders like stroke. Combined EEG-fMRI approaches could potentially achieve sufficient spatial and temporal resolution to verify effective functional connectivity between different brain regions. Using this approach in healthy anesthetized rats, a correlation between resting-state functional connectivity in bilateral primary somatosensory cortices and power coherence in slow delta EEG band has been demonstrated (Lu et al., 2007). Simultaneous use of rs-fMRI and EEG in animal stroke models may help to appropriately assess and interpret disturbances in temporally correlated signals and phase shifts within widespread neural networks. In a rat MCAO model using a

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protocol with parallel rs-fMRI and EEG measurements (performed not simultaneously, but in separate groups of animals) it has been shown that subacutely after stroke interhemispheric synchronization is significantly reduced for low-frequency BOLD signals as well as low-frequency EEG delta wave signals, indicative of loss of functional connectivity between bilaterally homologous regions (van Meer et al., 2012). An important consideration in the use of EEG and fMRI in animal studies is the effect of anesthesia on the functional output signals. EEG changes such as replacement of fast alpha and beta rhythms by slow delta and theta rhythms, occurrence of burst suppression pattern, or isoelectricity have been reported after anesthesia (e.g. Otto, 2008). These effects may bias the effects of stroke induction on the EEG output, as was shown by Zhang et al. (2006) using chloral hydrate anesthesia. Anesthesia may also affect neurovascular coupling and as such influence (rs-)fMRI signals, and different effects have been observed with different types of anesthetics (Williams et al., 2010). Nevertheless, several studies have demonstrated that (rs-)fMRI is feasible in lightly anesthetized rodents, using medetomidine, ␣-chloralose or isoflurane, allowing robust detection of activation responses and functional connectivity (Masamoto et al., 2009; Wang et al., 2011). Further studies with multimodal approaches (fusion of fMRI and EEG) may aid in further understanding the reorganization of neuronal networks during the recovery process after stroke. A methodological protocol to evaluate a number of problems emerging from the particulars of using rodents in simultaneous fMRI and EEG recording, including a mini-cap for recording with arrays of electrodes, has recently been introduced (Sumiyoshi et al., 2011), and subsequently used in rats under mild hypoxic conditions (Sumiyoshi et al., 2012). We belief that with advent of new techniques, which will overcome many technical problems of simultaneous EEG–MRI (Gutberlet, 2010), there will be a jump in experimental research to characterize the impact of a stroke-induced lesion on local and remote signaling and connectivity within and between neuronal networks. This will expand our understanding of disturbed brain function and recovery mechanisms after stroke in relation to natural history as well as therapeutic interventions, which could be translated to clinical stroke units and rehabilitation hospitals.

5. Conclusions EEG is playing an increasingly important role in clinics for analyzing brain activities and diagnosing brain diseases including stroke. Despite availability of high-technology instrumentation (PET, MRI) to unravel the intricacies of cerebral blood flow and metabolism, EEG provides a valuable and affordable tool in the evaluation of processes induced by cerebral ischemia, especially in general laboratory settings. The usefulness and promise of MRI techniques for routine clinical application are enormous, especially regarding its potential to predict the clinical course of stroke or medication efficacy. Although much of the early success in clinical practice has been based on preclinical investigations, the situation is nowadays often opposite in the field of imaging and electrophysiology: large investments have been made to improve quantitative EEG and neuroimaging techniques for use in the clinics, but innovative and sophisticated techniques and approaches are not always available in the preclinical laboratories. Nevertheless, implementation of new multimodal and multiparametric imaging techniques in preclinical investigations with animal models, would significantly contribute to elucidation of mechanisms underlying brain impairment and recovery, and aid in optimization of diagnostic methods. Significant progress has already been made in multiparametric MRI to delineate and monitor potentially salvageable ischemic tissue which is at risk of irreversible damage without therapeutic

intervention, which may be exploited in translational studies on promising efficacious medications. Integration of multimodal MRI and EEG techniques in a single study setting would allow comprehensive characterization of structural and functional tissue status, which would improve identification of salvageable or remodeling tissue and may provide unique and predictive biomarkers of functional brain tissue status. Furthermore, it can lead to new insights into intrinsic mechanisms of recovery, which may be exploited for the development and monitoring of neuroreparative interventions. Conflict of interest statement The authors have no conflicts of interest to declare. Acknowledgements European Union’s Seventh Framework Programme (FP7/20072013) under grant agreements no. (201024 and no. 202213 (European Stroke Network). The authors apologize to those colleagues whose work could not be cited because of space considerations. References Accolla, E.A., Kaplan, T.W., Maeder-Ingvar, M., Jukolila, S., Rossetti, A.O., 2011. Clinical correlates of frontal intermittent rhythmic delta activity (FIRDA). Clin. Neurophysiol. 122, 27–31. Amzica, F., Lopes da Silva, F.H., 2010. Cellular substrates of brain rhythms. In: Niedermeyer, E., Schomer, D.L., Lopes da Silva, F.H. (Eds.), Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. , sixth ed. Wolters Kluwer Health/Loppincott Williams & Wilkins, Philadelphia, pp. 33–64. Andraus, M.E.C., Alves-Leon, S.V., 2011. Non-epileptiform EEG abnormalities. Arq. Neuropsiquiatr. 69, 829–835. Andrews, R.J., 1991. Transhemispheric diaschisis. A review and comment. Stroke 22, 943–949. Aronowski, J., Strong, R., Grotta, J.C., 1997. Reperfusion injury: demonstration of brain damage produced by reperfusion after transient focal ischemia in rats. J. Cereb. Blood Flow Metab. 17, 1048–1056. Astrup, J., Siesjo, B.K., Symon, L., 1981. Thresholds in cerebral ischemia–the ischemic penumbra. Stroke 12, 723–725. Back, T., Hoehn-Berlage, M., Kohno, K., Hossmann, K.A., 1994. Diffusion nuclear magnetic resonance imaging in experimental stroke. Correlation with cerebral metabolites. Stroke 25, 494–500. Baird, E., Warach, S., 1998. Magnetic resonance imaging of acute stroke. J. Cereb. Blood Flow Metab. 18, 583–609. Bao, W.L., Williams, A.J., Faden, A.I., Tortella, F.C., 2001. Selective mGluR5 receptor antagonist or agonist provides neuroprotection in a rat model of focal cerebral ischemia. Brain Res. 922, 173–179. Bardutzky, J., Shen, Q., Henninger, N., Schwab, S., Duong, T.Q., Fisher, M., 2007. Characterizing tissue fate after transient cerebral ischemia of varying duration using quantitative diffusion and perfusion imaging. Stroke 38, 1336–1344. Baron, J.C., 1999. Mapping the ischaemic penumbra with PET: implications for acute stroke treatment. Cerebrovasc. Dis. 9, 193–201. Beckmann, F., DeLuca, M., Devlin, J.T., Smith, S.M., 2005. Investigations into restingstate connectivity using independent component analysis. Phil. Trans. R. Soc. B 360, 1001–1013. Bhattacharya, P., Pandey, A.K., Paul, S., Patnaik, R., 2013. Does piroxicam really protect ischemic neurons and influence neuronal firing in cerebral ischemia? An exploration towards therapeutics. Med. Hypoth. 81, 429–435. Biernaskie, J., Corbett, D., Peeling, J., Wells, J., Lei, H., 2001. A serial MR study of cerebral blood flow changes and lesion development following endothelin-1induced ischemia in rats. Magn. Reson. Med. 46, 827–830. Boehm-Sturm, P., Farr, T.D., Adamczak, J., Jikeli, J.F., Mengler, L., Wiedermann, D., Kallur, T., Kiselev, V., Hoehn, M., 2013. Vascular changes after stroke in the rat: a longitudinal study using optimized magnetic resonance imaging. Contrast Media Mol. Imaging 8, 383–392. Bolay, H., Dalkara, T., 1998. Mechanisms of motor dysfunction after transient MCA occlusion: persistent transmission failure in cortical synapses is a major determinant. Stroke 29, 1988–1994. Bouts, M.J., Tiebosch, I.A., van der Toorn, A., Viergever, M.A., Wu, O., Dijkhuizen, R.M., 2013. Early identification of potentially salvageable tissue with MRI-based predictive algorithms after experimental ischemic stroke. J. Cereb. Blood Flow Metab. 33, 1075–1082. Braeuninger, S., Kleinschnitz, C., 2009. Rodent models of focal cerebral ischemia: procedural pitfalls and translational problems. Exp. Transl. Stroke Med. 1, 1–11. Brint, S., Jacewicz, M., Kiessling, M., Tanabe, J., Pulsinelli, W., 1988. Focal brain ischemia in the rats: methods for reproducible neocortical infarction using

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Please cite this article in press as: Moyanova, S., Dijkhuizen, R., Present status and future challenges of electroencephalographyand magnetic resonance imaging-based monitoring in preclinical models of focal cerebral ischemia. Brain Res. Bull. (2014), http://dx.doi.org/10.1016/j.brainresbull.2014.01.003

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