BLM is a recipient of EPSRC PhD studentship and received a travel grant to University of Eastern Finland from the School of Experimental Psychology, University of Bristol. MJK is funded by the Elizabeth Blackwell Institute and by the Wellcome Trust international strategic support fund [ISSF2: 105612/Z/14/Z]. KTJ and OHJG are funded by Academy of Finland, UEF-Brain strategic funding from University of Eastern Finland and by Biocenter Finland. The work was supported by The Dunhill Medical Trust [grant number R385/1114].

Animal procedures were conducted according to European Community Council Directives 86/609/EEC guidelines and approved by the Animal Care and Use Committee of the University of Eastern Finland, Kuopio, Finland.
1. Animal Model
<ol>
	<li>Anesthetize male Wistar rats weighing 300 - 400 g with isoflurane in N2/O2 flow (70%/30%) through a facemask for the duration of the operation and MRI experiments. Induce anesthesia in ventilated hood. Maintain isoflurane levels between 1.5 and 2.4%.
	<ol>
		<li>Monitor depth of anesthesia during MRI from breathing frequency via a pneumatic pillow under the torso. Lack of response to pinch reflex is taken as a sign of sufficient depth for surgical anesthesia. Use isoflurane scavenger attached to a magnet bore.</li>
	</ol>
	</li>
	<li>Perform permanent middle cerebral artery occlusion (MCAO) to induce focal ischemic stroke. Use the intraluminal thread model for MCAO and carry out the operation according to methods given by Longa et al.22.
	<ol>
		<li>Leave the occluding thread (silicon-PTFE tempered monofil filament, diameter 0.22 mm) in place for the duration of the MRI experiment.</li>
	</ol>
	</li>
	<li>Analyze arterial blood gases and pH using a blood analyzer.
	<ol>
		<li>During the MRI, monitor the breathing rate with a pneumatic pillow placed under the torso and the rectal temperature using a rectal temperature monitoring system. Maintain a core temperature close to 37 &#176;C using a water heating pad under the torso.</li>
		<li>Immediately after MCAO, secure the rat in a cradle at the center of the magnet bore using a rat head holder. Inject 2 mL of saline intraperitoneally before transferring the rats into the magnet bore.</li>
	</ol>
	</li>
</ol>
2. MRI
<ol>
	<li>Acquire MRI data using a 9.4T/31 cm (with the 12-cm gradient insert) horizontal magnet interfaced to a console equipped with an actively decoupled linear volume transmitter and quadrature receiver coil pair.</li>
	<li>Scan each rat for up to 5 h post MCAO. At hourly intervals (60, 120, 180, 240 min post MCAO), acquire 12 congruently sampled (0.5 mm slice-gap, slice thickness = 1 mm, field of view = 2.56 cm x 2.56 cm) coronal slices of the trace of diffusion tensor (2.2.1.), Carr-Purcell-Meiboom-Gill T2 (2.2.2.) and Fast Low Angle Shot T1 (2.2.3.).
	<ol>
		<li>Obtain the trace of the diffusion tensor images (Dav= 1/3 trace [D]) with three bipolar gradients along each axis (the duration of the diffusion gradient = 5 ms, the diffusion time = 15 ms) and three b-values (0, 400 and 1400 s/mm-2s), where, &#916; = 15 ms, &#8706; = 5 ms, echo time (TE) = 36 ms, repetition time (TR) = 4000 ms and acquisition time = 7.36 min.</li>
		<li>Obtain the Carr-Purcell-Meiboom-Gill T2 sequence with 12 echoes for T2 quantification, where echo-spacing = 10 ms, TR = 2000 ms and acquisition time = 4.20 min.</li>
		<li>Obtain the Fast Low Angle Shot (FLASH) for T1, where the time from inversion to the first FLASH sequence (T10) is 7.58 ms, with 600 ms increments of 10 inversions up to 5407.58 ms, TR = 5.5 ms, time between inversion pulses (Trelax) = 10 s and acquisition time = 8.20 min.</li>
	</ol>
	</li>
</ol>
3. Image Processing
<ol>
	<li>Computation of relaxometry and ADC maps: Compute qT2, qT1 and ADC maps using Matlab functions provided on University of Bristol website [DOI:10.5523/bris.1bjytiabmtwqx2kodgbzkwso0k], for which the input is a file path to the location of the MRI data.
	<ol>
		<li>For T2 data, apply Hamming filtering in k-space prior to reconstruction of images (or in image domain by convolution, with equivalent results but computationally less efficient). Compute qT2 maps by taking the logarithm of each time series and solving on a voxel-wise basis by linear least-squares (a&#160;bi-exponential fit can also be performed to the T2 decays, but the voxel-wise F-tests tests revealed that voxels within the image for which additional parameters could not be justified).</li>
		<li>For T1 data, apply Hamming filtering in k-space prior to reconstruction of images. Perform T1 fitting according to methods given in reference 23. To deal with the problem of unknown sign (due to the use of magnitude images), the lowest-intensity point may be either excluded, or estimated in the course of fitting with similar results.</li>
		<li>For diffusion-weighted data, apply Hamming filtering by convolution in image domain (this is more straightforward due to the segmented k-space trajectory). Fit ADC maps by the method in13.</li>
	</ol>
	</li>
	<li>Identification of ischemic tissue
	<ol>
		<li>Identify ischemic tissue on reciprocal Dav images (1/Dav) as this provides clear contrast for lesion identification. To generate ischemic volumes of interest (VOI), define ischemic tissue as voxels with values one median absolute deviation above the median value of the whole-brain 1/Dav distribution. To identify homologous regions in the non-ischemic hemisphere, reflect the ischemic VOI about the vertical axis. Manually adjust non-ischemic VOIs to avoid including voxels containing cerebrospinal fluid.</li>
		<li>In order to determine the relationship of qT1 and qT2 with time post MCAO, for each rat and time-point, load the ischemic and non-ischemic VOIs onto qT1 and qT2 maps. Extract mean relaxation times and calculate the percentage difference in qT1 and qT2 between hemispheres (&#916;T1 and &#916;T2) using the following equation: 
		<img alt="Equation" src="/files/ftp_upload/55277/55277eq1.jpg" /> 
		Where Tx is the chosen parameter, qT1 or qT2. <img alt="Equation" src="/files/ftp_upload/55277/55277eq2.jpg" />refers to the mean relaxation time of the ischemic VOI and <img alt="Equation" src="/files/ftp_upload/55277/55277eq3.jpg" /> the mean relaxation time in the non-ischemic VOI. The non-ischemic VOI should be used so that each rat serves as its own control.</li>
		<li>Use the following criteria to identify voxels with elevated qT1 and qT2: any voxels within the ischemic VOI with relaxation times exceeding the median relaxation time of the qT1 or qT2 distribution in the non-ischemic VOI by more than one half-width at half-maximum (HWMH). These criteria mean relaxation times must be in the 95th percentile or higher to be classified as &#39;high&#39;. Use of the median relaxation time of the non-ischemic VOI allows each rat to serve as its own control.</li>
		<li>To picture the spatial distribution of relaxation time changes within regions of decreased diffusion, identify and color code voxels with elevated qT1 or qT2 as well as voxels with both elevated qT1 and qT2 termed &#39;qT1 and qT2 overlap&#39;.</li>
		<li>To determine the size of the lesion according to qT1 and qT2, compute the parameter f (as introduced by Knight et al. 18) from MRI data acquired for each rat and time-point. f1 and f2 represent the number of voxels with high qT1 or qT2 (respectively) as a percentage of the size of the ischemic VOI.
		<ol>
			<li>Use the following equation to calculate f1 and f2: 
			<img alt="Equation" src="/files/ftp_upload/55277/55277eq4.jpg" /> 
			Where <img alt="Equation" src="/files/ftp_upload/55277/55277eq5.jpg" /> refers to the relaxation time (qT1 or qT2), <img alt="Equation" src="/files/ftp_upload/55277/55277eq6.jpg" />refers to the number of &#39;high&#39; relaxation time voxels in the ischemic VOI, <img alt="Equation" src="/files/ftp_upload/55277/55277eq7.jpg" /> is the number of &#39;low&#39; relaxation time voxels in the ischemic VOI and <img alt="Equation" src="/files/ftp_upload/55277/55277eq8.jpg" />, the total number of voxels within the ischemic VOI. Criteria for identifying voxels with elevated qT1 and qT2 are outlined in section 3.2.3. &#39;Low&#39; voxels are voxels with relaxation times less than the median qT1 or qT2 of the non-ischemic VOI by one HWHM. The subtraction of <img alt="Equation" src="/files/ftp_upload/55277/55277eq7.jpg" /> allows for decreases in relaxation times due to ischemia or other pathologies 17.&#160;</li>
			<li>Determine the extent of &#39;qT1&#160;and qT2&#160;overlap&#39; by calculating the volume of overlapping elevated qT1&#160;and qT2&#160;as a percentage of whole brain volumes, hereby referred to as &#39;VOverlap&#39;. Use the following equation: 
			<img alt="Equation" src="/files/ftp_upload/55277/55277eq9.jpg" /> 
			Where, <img alt="Equation" src="/files/ftp_upload/55277/55277eq10.jpg" /> refers to the number of voxels within the ischemic VOI with both &#39;high&#39; qT1 and &#39;high&#39; qT2 and <img alt="Equation" src="/files/ftp_upload/55277/55277eq11.jpg" /> represents the total number of voxels in the whole rat brain. Determine the number of voxels in the rat brain by manually creating a VOI around the whole brain on qT2 relaxometry maps.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
4. Verification of Ischemic Lesion with Triphenyletrazolium Chloride (TTC)
<ol>
	<li>Immediately after decapitation, carefully extract the rat brain from the skull. Complete this procedure within 10 min from the time the rat was decapitated.</li>
	<li>Store brains in refrigerated 0.01 M phosphate buffered saline (PBS) before using a rat brain slicer matrix to section the brain into serial 1 mm-thick coronal slices.</li>
	<li>After sectioning, incubate each brain slice in 20 mL PBS containing TTC at 37 &#176;C for 30 min in the dark, as recommended in 24. Although 1% TTC concentration is acceptable, use 0.5% for improved contrast.
	<ol>
		<li>Cover the containers of the sections in foil to keep it dark.</li>
	</ol>
	</li>
	<li>After incubation, remove the TTC solution using a pipette and wash slices in three changes of PBS.</li>
	<li>Immediately photograph slices using a standard light microscope and a digital camera.</li>
</ol>
5. Statistical Analysis
<ol>
	<li>Carry out statistical analysis using Matlab and statistical software.</li>
	<li>Determining the relationship of MR parameters with time
	<ol>
		<li>Perform Pearson&#39;s correlations on pooled rat data to determine the relationship of &#916;T1, &#916;T2, f1 and f2 andVOverlap with time post MCAO.</li>
		<li>For parameters that show a significant linear relationship (p &#60;0.05), perform linear least square regression to determine whether stroke onset time can be predicted by quantifying the parameter of interest. Use the root mean square error (RMSE) to assess the accuracy of onset time estimates.</li>
	</ol>
	</li>
	<li>Quantification of lesion size
	<ol>
		<li>To compare lesion sizes according to different qMRI parameters, conduct one-way related ANOVAs and Fisher&#39;s least significant difference post-hoc on the average number of voxels in the ischemic VOI and the average number of voxels with high qT1 and qT2. Differences are considered significant at p &#60;0.05. If sphericity assumptions are not met per Mauchly&#39;s sphericity test, correct degrees of freedom and significance values according to Greenhouse Geisser estimates.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The current protocol for estimation of stroke onset time in rats uses quantitative diffusion and relaxation time MRI data rather than signal intensities of respective weighted MR contrast images19. Recent evidence points to inferior performance of image intensities in estimating stroke onset time 14,25. In the &#39;diffusion-positive&#39; stroke lesion our MRI protocol provides stroke onset times from qT1 and qT2 MRI data with an accuracy of half an hour or so. It is a general trend that qT2 data outperforms that of qT1. The best accuracy for onset time determination is obtained from the volume of overlapping elevated qT1 and qT2 (Voverlap).
The images in Figure 1 demonstrate that while the reduced diffusion coefficient appears rather uniform, regions with abnormal qT1 and qT2 are heterogeneously scattered within the ischemic lesion. This finding is in accordance with previous observations and is likely due to different sensitivities of these qMRI parameters to pathophysiological changes caused by ischemia 6. This suggests qMRI parameters may be informative of tissue status and supports notions that DWI over-estimates ischemic damage 26. Indeed, recent preclinical evidence points toward the heterogeneity of ischemic damage within diffusion defined lesions 27. Thus, the combination of diffusion, qT1 and qT2 potentially provides information on stroke onset time and tissue status, both of which are clinically useful for treatment decisions regarding patients with unknown onset.
Voverlap and f2 gave the most accurate estimates of stroke onset time. The benefit of quantifying relaxation times is that unlike signal intensities they are insensitive to inherent variations caused by technical factors such as magnetic field inhomogeneities and proton density 6, including the expected magnetic field variation within the ischemic lesion 18. Reduced uncertainty associated with onset time estimates of qT1 and f1 are likely due to the aforementioned bi-phasic response of qT1 to ischemia, which contributes to the shallow slope of the time-dependent qT1 change 8,15,16. The MRI data shown (Figure 2) are in accordance with previous works 13,14, in that the time courses of relaxation time differences between the ischemic and contralateral non-ischemic brain are adequately described by linear functions. It is, however, important to note that the underpinning hydrodynamic changes due to ischemia are not linear 1,18.
The current MRI protocol for stroke time&#160;is demonstrated in rats subjected to permanent ischemia using the Longa et al. procedure 22. In our experience, the Longa et al. procedure fails to induce MCAO in 10-20% of rats, however, as ADC is used to verify presence of ischemia, the experiments can be terminated prematurely. Failure to induce MCAO is often due to imperfect occluder thread. A further factor resulting in experimental failures is that MCAO is a severe procedure causing death of up to 20% of rats during a prolonged MRI session.
The stroke onset timing protocol applies only to permanent ischemia. In rat focal ischemia with reperfusion, the relationship between Dav and qT1 or qT2 will dissociate as Dav&#160;recovers, but may not for qT1 and qT2&#160;depending on duration of ischemia prior to reperfusion 8,28. Additionally, the evolution of ischemic damage is likely to be more variable in stroke patients due to individual differences in factors affecting microcirculation such as age and co-morbidities (e.g., diabetes, hypertension, heart disease). These factors will inevitably influence the time dependence of f1, f2 and Voverlap in human strokes and thus requires investigation in clinical settings.
To conclude, qMRI parameters provide estimates of stroke onset time. Voverlap and f2 provide the most accurate estimates and may also be informative of tissue status. qMRI could therefore be clinically beneficial in terms of aiding treatment decisions for patients with unknown onset time. An issue to be considered here is that the gray-to-white-matter ratio in rat brain is much higher than in humans, and hydrodynamics in these brain tissue types may vary 18. Nevertheless, further investigation into the time dependence of f2, Voverlap and qT2 in hyper acute stroke patients is warranted.

Brain tissue is particularly vulnerable to ischemia due to the high dependence of the oxidative phosphorylation for ATP synthesis and limited energy reserves. Ischemia results in subtle time-dependent ionic changes in intracellular and extracellular spaces that lead to redistribution of brain water pools, release of excitotoxic neurotransmitters and ultimately initiation of destructive processes 1. In focal ischemia, tissue damage spreads beyond the initial core if blood flow is not restored within a certain time frame 2. The time of stroke onset is currently one of the key criteria in clinical decisions for the pharmacotherapy of ischemic stroke, including recanalization by thrombolytic agents 3. Consequently, many patients are automatically ineligible for thrombolytic therapy due to unknown symptom onset time, owing to the stroke occurring during sleep (&#39;wake-up stroke&#39;), lack of witness, or being unaware of symptoms 4,5. A procedure that determines the time of stroke onset is therefore required so that such patients may be considered for thrombolysis.
MRI probes water in vivo. Dynamics of which are severely perturbed by acute ischemic energy failure 6. Most notably, the diffusion of water governed by translational (thermal) motion of water molecules is reduced in the early moments of ischemia due to the energy failure 7. This in turn results in anoxic depolarization of neural cells 8. Diffusion MRI (DWI) has become a gold standard diagnostic imaging modality for acute stroke 9. The DWI signal increases rapidly in response to ischemia allowing ischemic tissue to be identified but does not show any time-dependency during the first few hours of ischemic stroke 10. Likewise, quantitative measures of water diffusion such as the apparent diffusion coefficient (ADC) or the trace of the diffusion tensor (Dav) decrease rapidly in ischemic tissue, but show no relationship with time from stroke onset in animal stroke models 10 and patients 11.
Quantitative MRI (qMRI) relaxation parameters, qT1, qT2 and qT1&#961;, are governed by rotational motion and the exchange of water hydrogen atoms and show complex time-dependent changes in brain parenchyma following ischemic energy failure 6. Such time-dependent changes enabled stroke onset time to be estimated in patients 12 and animal models of ischemia 13,14,15. In rat focal stroke, qT1&#961; increases almost instantaneously after ischemia onset and continues linearly for at least 6 hours 13,14. qT1 relaxation times also increase in a time-dependent fashion in ischemic brain tissue which can be described by two time constants: an initial fast phase followed by a slow phase lasting for hours 8,16. Because of this biphasic increase, the use of qT1 in stroke timing may be more complicated than that of qT1&#961; MRI 15. qT2 relaxation times also show a bi-phasic change in rat focal stroke, whereby there is an initial shortening within the first hour, followed by a linear increase with time 13. The initial shortening can be explained by two parallel running factors including: (i) the buildup of deoxyhemoglobin resulting in the so-called &#39;negative blood oxygenation level dependent effect&#39; and (ii), the shift of extracellular water into the intracellular space 17,18. The time-dependent increase in qT2 is likely due to cytotoxic and/or vasogenic edema with subsequent breakdown of intracellular macromolecular structures 18. Both qT1&#961; and qT2 data provide accurate estimates of stroke onset time in preclinical models 14. qT2 12 and T2-weighted signal intensities 19,20 have also been exploited for stroke onset time estimation in clinical settings.
In addition to hemispheric differences in quantitative relaxation times, the spatial distribution of elevated relaxation times within the ischemic region may also serve as surrogates for stroke onset time 14. In rat models of stroke, regions with elevated qT1&#961;, qT2 and qT1 relaxation times are initially smaller than the diffusion defined ischemic lesion but increase with time 14,15,21. Hence quantification of the spatial distribution of elevated relaxation times as a percentage of the size of the ischemic lesion also enables stroke onset time to be estimated 14,15. Here, we describe the protocol to determine stroke onset time in a rat model of stroke using qMRI parameters.

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			<td>Magnetic Field Strength</td>
			<td>Operation Frequency</td>
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		<tr>
			<td>MRI scanner</td>
			<td>Agilent, Santa Clara, CA, USA</td>
			<td>9.4T</td>
			<td>400.13 MHz</td>
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			<td>Linear volume transmit RF-coil</td>
			<td>RAPID Biomedical, Rimpar, Germany</td>
			<td>-</td>
			<td>400.13MHz</td>
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		<tr>
			<td>Actively decoupled receive coil</td>
			<td>RAPID Biomedical, Rimpar, Germany</td>
			<td>-</td>
			<td>400.13MHz</td>
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			<td>Rat head holder</td>
			<td>RAPID Biomedical, Rimpar, Germany</td>
			<td></td>
			<td></td>
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			<td>i-Stat handheld blood-gas analyzer</td>
			<td>i-Stat Co, East Windsor, NJ, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pneumatic pillow breathing rate monitor</td>
			<td>SA Instruments Inc, Stony Brook, NY, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Rodent rectal temperarure moniring device</td>
			<td>SA Instruments Inc, Stony Brook, NY, USA</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Name</td>
			<td>Company</td>
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			<td>Chemicals</td>
			<td></td>
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			<td></td>
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			<td>Isoflurane: Attane Vet 1000mg/g</td>
			<td>Piramal Healthcare UK Ltd, Northumberland, UK</td>
			<td></td>
			<td></td>
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			<td>2,3,5-Triphenyltetrazolium cholide=TTC</td>
			<td>Sigma-Aldrich, Gillinham, Dorset, UK</td>
			<td></td>
			<td></td>
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a magnetic resonance imaging protocol for stroke onset time estimation in permanent cerebral ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T1 and T2 MRI relaxation times (qT1 and qT2) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT1 and qT2 within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT1 and qT2 relaxation times and the location and volume of abnormal qT1 and qT2 voxels within the lesion. Uncertainties associated with onset time estimates of qT1 and qT2 MRI data vary from &plusmn; 25 min to &plusmn; 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT1 and qT2 lesion volumes, termed 'Voverlap' (&plusmn; 25 min) or by quantifying hemispheric differences in qT2 relaxation times only (&plusmn; 28 min). Overall, qT2 derived parameters outperform those from qT1. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

Across rats the blood gas profiles were as follows: SO2 95.8 &#177; 3.2%, PaCO2 51.6 &#177; 2.9 mmHg, and pH 7.30 &#177; 0.04.
Typical Dav, qT2 and qT1 images from a central slice of a representative rat at 4 time points post MCAO are shown in the first 3 panels of Figure 1a. Images in the other panels of Figure 1 show the automatically detected ischemic lesion in red and regions within the ischemic lesion with elevated qT1, qT2 and regions with Voverlap are shown in green. Up to 2 h post-MCAO, regions with high qT1 within the ischemic lesion were significantly larger than high qT2 (p &#60;0.01), but converged with time (Figure 1). The ischemic lesion by Dav was also larger than regions of high qT1 (p &#60;0.05) and qT2 (p &#60;0.05) in the first two hours.
The time dependencies of qMRI parameters are shown in Figure 2. All qMRI parameters were significant predictors of time post MCAO (&#916;T1:&#160;R2 = 0.71, &#916;T2:&#160;R2 = 0.75, f1: R2 = 0.53, f2: R2 = 0.82, Voverlap: R2 = 0.87). Based on the RMSE for each parameter, uncertainties associated with estimates of time since stroke onset were &#177; 37 min for &#916;T1, &#177; 28 min for &#916;T2, &#177; 47 min for f1, &#177;34 min for f2, and &#177;25 min for Voverlap. Thus, Voverlap gave the most accurate estimate of time since stroke onset.
TTC staining of brains samples around 6 h after MCAO verified irreversible ischemic damage predominantly in grey matter (Figure 1d).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/55277/55277fig1.jpg" /> 
Figure 1: Changes in qMRI parameters due to ischemic stroke in an example rat. (a) shows example qMRI images over a 4 h period of ischemia. The first four columns show Dav maps, qT2 maps, qT1 maps and T2 weighted images respectively. Remaining columns show the Dav maps with various representations of the automatically segmented lesions. The automatically detected Dav lesion is shown in column 5 in red. In column 6 the Dav lesion is shown in red with voxels with high qT2 shown in green. In column 7 the Dav lesion is shown in red with high qT1 voxels shown in green. In column 8 the Dav lesion is shown in red with voxels with both elevated qT1 and qT2 (Voverlap) shown in green. (b) shows the distribution of qT2 in the Dav lesion as a function of time post MCAO, as well as the non-lesion qT2 distribution at time zero. (c) shows the corresponding qT1 distributions, the adjacent legend pertaining to both panels (b) and (c). (d) shows a TTC-stained brain slice after the animal was sacrificed at 6 h post-MCAO. <a href="http://cloudflare.jove.com/files/ftp_upload/55277/55277fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/55277/55277fig2.jpg" /> 
Figure 2: The relationships between time post-MCAO and qMRI parameters relevant to the timing of ischemia. (a) shows f1, (b) the f2, (c), Voverlap, (d),&#916;T1 and (e), &#916;T2. Best fit for each parameter (solid red line) and RMSE bars (solid black lines) are shown. Dotted lines represent each of the indivudual 5 rats subjected to MCAO. <a href="http://cloudflare.jove.com/files/ftp_upload/55277/55277fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia at JoVE.com

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke.

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat ...

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14.0K Views.  University of Bristol. A protocol for stroke onset time estimation in a rat model of stroke exploiting quantitative magnetic resonance imaging (qMRI) parameters is described. The procedure exploits diffusion MRI for delineation of the acute stroke lesion and quantitative T1 and T2 (qT1 and qT2) relaxation times for timing of stroke. 

Video: A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral line-widths and allows high-resolution spectra to be obtained from extracts, intact cells, cell cultures, and more importantly intact tissue to investigate relationships between metabolites and cellular processes. In vivo HRMAS 1H-MRS studies have yet to be reported in the live fruit fly Drosophila melanogaster. Drosophila, as a simpler genetic organism, allows the multiple biological functions and various evolutionarily conserved signaling pathways to be examined at the whole organism level and it is a useful model for investigating genetics and physiology. To this end, we developed and implemented an in vivo HRMAS 1H-MRS method to investigate live Drosophila at 14.1 T. Here, we outline an HRMAS 1H-MRS protocol for the molecular characterization of Drosophila with a conventional MR spectrometer equipped with an HRMAS probe. This technique is a novel, in vivo, non-destructive Drosophila metabolite measurement approach, which enables the identification of disease biomarkers and thus may contribute to novel therapeutic development.

Magnetic Resonance Spectroscopy of live Drosophila melanogaster using Magic Angle Spinning

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High-Resolution Magic Angle Spinning (HRMAS) proton magnetic resonance spectroscopy (1H-MRS) is a novel non-destructive technique that improves spectral ...

Multiple-mouse Neuroanatomical Magnetic Resonance Imaging

The field of mouse phenotyping with magnetic resonance imaging (MRI) is rapidly growing, motivated by the need for improved tools for characterizing and evaluating mouse models of human disease. MRI is an excellent modality for investigating genetically altered animals. It is capable of whole brain coverage, can be used in vivo, and provides multiple contrast mechanisms for investigating different aspects of neuranatomy and physiology. The advent of high-field scanners along with the ability to scan multiple mice simultaneously allows for rapid phenotyping of novel mutations.
Effective mouse MRI studies require attention to many aspects of experiment design. In this article, we will describe general methods to acquire quality images for mouse phenotyping using a system that images mice concurrently in shielded transmit/receive radio frequency (RF) coils in a common magnet (Bock et al., 2003). We focus particularly on anatomical phenotyping, an important and accessible application that has shown a high potential for impact in many mouse models at our imaging centre. Before we can provide the detailed steps to acquire such images, there are important practical considerations for both in vivo brain imaging (Dazai et al., 2004) and ex vivo brain imaging (Spring et al., 2007) that should be noted. These are discussed below.

Magnetic resonance imaging (MRI) has become an increasingly popular tool for examining the phenotype of genetically altered mice. This article illustrates the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI.

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon measuring activity on the surface of cerebral cortex rather than in subcortical regions such as midbrain and brainstem. Subcortical fMRI must overcome two challenges: spatial resolution and physiological noise. Here we describe an optimized set of techniques developed to perform high-resolution fMRI in human SC, a structure on the dorsal surface of the midbrain; the methods can also be used to image other brainstem and subcortical structures.
High-resolution (1.2 mm voxels) fMRI of the SC requires a non-conventional approach. The desired spatial sampling is obtained using a multi-shot (interleaved) spiral acquisition1. Since, T2* of SC tissue is longer than in cortex, a correspondingly longer echo time (TE ~ 40 msec) is used to maximize functional contrast. To cover the full extent of the SC, 8-10 slices are obtained. For each session a structural anatomy with the same slice prescription as the fMRI is also obtained, which is used to align the functional data to a high-resolution reference volume.
In a separate session, for each subject, we create a high-resolution (0.7 mm sampling) reference volume using a T1-weighted sequence that gives good tissue contrast. In the reference volume, the midbrain region is segmented using the ITK-SNAP software application2. This segmentation is used to create a 3D surface representation of the midbrain that is both smooth and accurate3. The surface vertices and normals are used to create a map of depth from the midbrain surface within the tissue4.
Functional data is transformed into the coordinate system of the segmented reference volume. Depth associations of the voxels enable the averaging of fMRI time series data within specified depth ranges to improve signal quality. Data is rendered on the 3D surface for visualization.
In our lab we use this technique for measuring topographic maps of visual stimulation and covert and overt visual attention within the SC1. As an example, we demonstrate the topographic representation of polar angle to visual stimulation in SC.

This article describes techniques to perform high-resolution functional magnetic resonance imaging with 1.2 mm sampling in human midbrain and subcortical structures using a 3T scanner. Use of these techniques to resolve topographic maps of visual stimulation in the human superior colliculus (SC) is given as an example.

Functional MRI (fMRI) is a widely used tool for non-invasively measuring correlates of human brain activity. However, its use has mostly been focused upon ...

The Use of Magnetic Resonance Spectroscopy as a Tool for the Measurement of Bi-hemispheric Transcranial Electric Stimulation Effects on Primary Motor Cortex Metabolism

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of neurological and psychiatric disorders such as stroke and depression. Yet, the mechanisms underlying its ability to modulate brain excitability to improve clinical symptoms remains poorly understood 33. To help improve this understanding, proton magnetic resonance spectroscopy (1H-MRS) can be used as it allows the in vivo quantification of brain metabolites such as &#947;-aminobutyric acid (GABA) and glutamate in a region-specific manner 41. In fact, a recent study demonstrated that 1H-MRS is indeed a powerful means to better understand the effects of tDCS on neurotransmitter concentration 34. This article aims to describe the complete protocol for combining tDCS (NeuroConn MR compatible stimulator) with 1H-MRS at 3 T using a MEGA-PRESS sequence. We will describe the impact of a protocol that has shown great promise for the treatment of motor dysfunctions after stroke, which consists of bilateral stimulation of primary motor cortices 27,30,31. Methodological factors to consider and possible modifications to the protocol are also discussed.

This article aims to describe a basic protocol for combining transcranial direct current stimulation (tDCS) with proton magnetic resonance spectroscopy (1H-MRS) measurements to investigate the effects of bilateral stimulation on primary motor cortex metabolism.

Transcranial direct current stimulation (tDCS) is a neuromodulation technique that has been increasingly used over the past decade in the treatment of ...

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The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has been heavily studied. While many imaging studies treat the hippocampus as a single unitary neuroanatomical structure, it is, in fact, composed of several subfields that have a complex three-dimensional geometry. As such, it is known that these subfields perform specialized functions and are differentially affected through the course of different disease states. Magnetic resonance (MR) imaging can be used as a powerful tool to interrogate the morphology of the hippocampus and its subfields. Many groups use advanced imaging software and hardware (&#62;3T) to image the subfields; however this type of technology may not be readily available in most research and clinical imaging centers. To address this need, this manuscript provides a detailed step-by-step protocol for segmenting the full anterior-posterior length of the hippocampus and its subfields: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus (DG), strata radiatum/lacunosum/moleculare (SR/SL/SM), and subiculum. This protocol has been applied to five subjects (3F, 2M; age 29-57, avg. 37). Protocol reliability is assessed by resegmenting either the right or left hippocampus of each subject and computing the overlap using the Dice&#39;s kappa metric. Mean Dice&#39;s kappa (range) across the five subjects are: whole hippocampus, 0.91 (0.90-0.92); CA1, 0.78 (0.77-0.79); CA2/CA3, 0.64 (0.56-0.73); CA4/dentate gyrus, 0.83 (0.81-0.85); strata radiatum/lacunosum/moleculare, 0.71 (0.68-0.73); and subiculum 0.75 (0.72-0.78). The segmentation protocol presented here provides other laboratories with a reliable method to study the hippocampus and hippocampal subfields in vivo using commonly available MR tools.

High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging

The goal of this manuscript is to study the hippocampus and hippocampal subfields using MRI. The manuscript describes a protocol for segmenting the hippocampus and five hippocampal substructures: cornu ammonis (CA) 1, CA2/CA3, CA4/dentate gyrus, strata radiatum/lacunosum/moleculare, and subiculum.

The human hippocampus has been broadly studied in the context of memory and normal brain function and its role in different neuropsychiatric disorders has ...

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The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of structural MRI in vivo is ultimately limited by physiological noise, including pulsation, respiration and head movement. Although imaging hardware continues to improve, it is still difficult to resolve structures on the millimeter scale. For example, the primary visual sensory pathways synapse at the lateral geniculate nucleus (LGN), a visual relay and control nucleus in the thalamus that normally is organized into six interleaved monocular layers. Neuroimaging studies have not been able to reliably distinguish these layers due their small size that are less than 1 mm thick.
The resolving limit of structural MRI, in a postmortem brain was tested using multiple images averaged over a long duration (~24 h). The purpose was to test whether it was possible to resolve the individual layers of the LGN in the absence of physiological noise. A proton density (PD)1 weighted pulse sequence was used with varying resolution and other parameters to determine the minimum number of images necessary to be registered and averaged to reliably distinguish the LGN and other subcortical regions. The results were also compared to images acquired in living human brains. In vivo subjects were scanned in order to determine the additional effects of physiological noise on the minimum number of PD scans needed to differentiate subcortical structures, useful in clinical applications.

High-resolution Structural Magnetic Resonance Imaging of the Human Subcortex In Vivo and Postmortem

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The focus of this study was to test the resolution limits of structural MRI of a postmortem brain compared to living human brains. The resolution of ...

Utilizing 3D Printing Technology to Merge MRI with Histology: A Protocol for Brain Sectioning

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume three-dimensionally. While MRI is an extremely sensitive tool for detecting tissue abnormalities, association of signal changes with an underlying pathological process is usually not straightforward. In the central nervous system, for example, inflammation, demyelination, axonal damage, gliosis, and neuronal death may all induce similar findings on MRI. As such, interpretation of MRI scans depends on the context, and radiological-histopathological correlation is therefore of the utmost importance. Unfortunately, traditional pathological sectioning of brain tissue is often imprecise and inconsistent, thus complicating the comparison between histology sections and MRI. This article presents novel methodology for accurately sectioning primate brain tissues and thus allowing precise matching between histology and MRI. The detailed protocol described in this article will assist investigators in applying this method, which relies on the creation of 3D printed brain slicers. Slightly modified, it can be easily implemented for brains of other species, including humans.

The overall goal of this protocol is to accurately align magnetic resonance imaging (MRI) image volumes with histology sections via the creation of customized 3D-printed brain holders and slicer boxes.

Magnetic resonance imaging (MRI) allows for the delineation between normal and abnormal tissue on a macroscopic scale, sampling an entire tissue volume ...

Evaluation of Bioenergetic Function in Cerebral Vascular Endothelial Cells

The integrity of the blood-brain-barrier (BBB) is critical to prevent brain injury. Cerebral vascular endothelial (CVE) cells are one of the cell types that comprise the BBB; these cells have a very high-energy demand, which requires optimal mitochondrial function. In the case of disease or injury, the mitochondrial function in these cells can be altered, resulting in disease or&#160;the opening of the BBB. In this manuscript, we introduce a method to measure mitochondrial function in CVE cells by using whole, intact cells and a bioanalyzer. A mito-stress assay is used to challenge the cells that have been perturbed, either physically or chemically, and evaluate their bioenergetic function. Additionally, this method also provides a useful way to screen new therapeutics that have direct effects on mitochondrial function. We have optimized the cell density necessary to yield oxygen consumption rates that allow for the calculation of a variety of mitochondrial parameters, including ATP production, maximal respiration, and spare capacity. We also show the sensitivity of the assay by demonstrating that the introduction of the&#160;microRNA, miR-34a, leads to a pronounced and detectable decrease in mitochondrial activity. While the data shown in this paper is optimized for the bEnd.3 cell line, we have also optimized the protocol for primary CVE cells, further suggesting the utility in preclinical and clinical models.

Endothelial cell mitochondria are critical to maintain blood-brain-barrier integrity. We introduce a protocol to measure bioenergetic function in cerebral vascular endothelial cells.

The integrity of the blood-brain-barrier (BBB) is critical to prevent brain injury. Cerebral vascular endothelial (CVE) cells are one of the cell types ...

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We describe the development of a reproducible, child-friendly motor response inhibition task suitable for online Transcranial Magnetic Stimulation (TMS) characterization of primary motor cortex (M1) excitability and inhibition. Motor response inhibition prevents unwanted actions and is abnormal in several neuropsychiatric conditions. TMS is a non-invasive technology that can quantify M1 excitability and inhibition using single- and paired-pulse protocols and can be precisely timed to study cortical physiology with high temporal resolution. We modified the original Slater-Hammel (S-H) stop signal task to create a &#34;racecar&#34; version with TMS pulses time-locked to intra-trial events. This task is self-paced, with each trial initiating after a button push to move the racecar towards the 800 ms target. GO trials require a finger-lift to stop the racecar just before this target. Interspersed randomly are STOP trials (25%) during which the dynamically adjusted stop signal prompts subjects to prevent finger-lift. For GO trials, TMS pulses were delivered at 650 ms after trial onset; whereas, for STOP trials, the TMS pulses occurred 150 ms after the stop signal. The timings of the TMS pulses were decided based on electroencephalography (EEG) studies showing event-related changes in these time ranges during stop signal tasks. This task was studied in 3 blocks at two study sites (n=38) and we recorded behavioral performance and event-related motor-evoked potentials (MEP). Regression modelling was used to analyze MEP amplitudes using age as a covariate with multiple independent variables (sex, study site, block, TMS pulse condition [single- vs. paired-pulse], trial condition [GO, successful STOP, failed STOP]). The analysis showed that TMS pulse condition (p&#60;0.0001) and its interaction with trial condition (p=0.009) were significant. Future applications for this online S-H/TMS paradigm include the addition of simultaneous EEG acquisition to measure TMS-evoked EEG potentials. A potential limitation is that in children, the TMS pulse sound could&#160;affect behavioral task performance.

We describe an experimental procedure to quantify excitability and inhibition of primary motor cortex during a motor response inhibition task by using Transcranial Magnetic Stimulation throughout the course of a Stop Signal Task.

We describe the development of a reproducible, child-friendly motor response inhibition task suitable for online Transcranial Magnetic Stimulation (TMS) ...

Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic resonance imaging (MRI) plays an important role in both confirming the diagnosis and evaluating the degree of compression. However, the anatomical detail provided by conventional MRI is not sufficient to accurately estimate neuronal damage and/or assess the possibility of neuronal recovery in chronic spinal cord compression patients. In contrast, diffusion tensor imaging (DTI) can provide quantitative results according to the detection of water molecule diffusion in tissues. In the present study, we develop a methodological framework to illustrate the application of DTI in chronic spinal cord compression disease. DTI fractional anisotropy (FA), apparent diffusion coefficients (ADCs), and eigenvector values are useful for visualizing microstructural pathological changes in the spinal cord. Decreased FA and increases in ADCs and eigenvector values were observed in chronic spinal cord compression patients compared to healthy controls. DTI could help surgeons understand spinal cord injury severity and provide important information regarding prognosis and neural functional recovery. In conclusion, this protocol provides a sensitive, detailed, and noninvasive tool to evaluate spinal cord compression.

Here, we present a protocol for the application of diffusion tensor imaging parameters to evaluate spinal cord compression.

Chronic spinal cord compression is the most common cause of spinal cord impairment in patients with nontraumatic spinal cord damage. Conventional magnetic ...