Introduction Sleep has an important role in learning and memory (1). It has been reported that insufficient sleep could lead to neurocognitive deficits in humans (2). Rapid eye movement (REM) sleep is believed to be critical for memory consolidation (3). REM sleep deprivation (SD) has been widely employed to investigate the mechanisms underlying sleep and learning, however, mostly restricted to behavioral or histological studies (4). DTI is emerging as a powerful tool for probing neural plasticity. Its noninvasive nature allows the exploration of plasticity network between various brain regions simultaneously and longitudinally. This study aims to assess the microstructural plasticity using DTI, which may provide insights into the plasticity network changes that accompanies SD in vivo. Methods MRI Protocols: Nine male Sprague-Dawley rats (~8 weeks old) were scanned twice using a 7T Bruker scanner before and after SD treatment. Animals were kept warm under circulating water at 37C with respiratory monitoring. Diffusion-weighted images were acquired using a SE 4-shot EPI sequence with 30 diffusion gradient directions. Five additional images with b-value=0 (B0 images) were also acquired. The imaging parameters were: TR/TE=3000/31.62ms, δ/Δ=5/17ms, NEX=4, FOV=3.2x3.2cm, acq matrix=128x128 (zero-filled to 256×256), slice thickness=1mm (0.2mm gap), b-value=1000s/mm. Sleep deprivation (5): A 72-hour REM-SD was employed using the multiple small platform technique. Five platforms with each 6cm diameter were placed in the middle of a water tank. Platforms were spaced 9cm apart so that rats could easily move between them but could not lie across any two. The water reached up to ~2 cm below the surface of the platform. Food and water were available ad libitum. All treatments lasted 72 hr under a 12 hr day/night cycle. Video monitoring was performed throughout REM-SD and was used for later behavioral validation. Data Analysis: Each rat brain volume was normalized to a custom mean B0 template generated from all animals. Voxel-wise paired t-test was performed between preand post-SD DTI index maps, the resulting significant voxels were projected onto each individual animal’s DTI index maps at each time point for quantitation comparisons. The normalization and statistical procedures were performed using SPM5. Results FIG.1 illustrates the significant voxels with decreased axial diffusivity between preand post-SD in the voxel-wise paired t-test analysis, where altogether 203 voxels were bilaterally identified in various locations in hippocampus in three continuous slices, and 236 voxels were bilaterally identified in cortex. FIG.2 shows the preand post-SD DTI index measurements from the resulting voxels pointed in FIG.1 for hippocampus and cortex. In hippocampus, a consistently significant decrease was observed in all diffusivity measurements, whereas FA exhibited little change. In cortex, axial diffusivity decreased more than radial diffusivity, significant decrease was found in axial, mean diffusivity and FA. Discussions and Conclusions The main finding in this study is the significant diffusivity decrease identified in the hippocampus and cortex using voxel-wise paired t-test. The significant voxels observed in hippocampus were located in multiple slices in separated clusters. They were possibly located within specific layers, such as dentate gyrus and CA1, which are believed to be sensitive to SD (5). Our high resolution manganese enhanced MRI study results also indicated that neuronal activity in specific layers of hippocampus such as dentate gyrus was exceptionally susceptible to SD (data submitted in another abstract). Previous studies reported that REM-SD dramatically impaired hippocampal-dependent learning, which is demonstrated by neuron loss as well as synaptic remodeling in cortex and specific layers of hippocampus (5-7). As little is known about how these cellular factors affect DTI indices particularly in gray matter regions, evidences from probing axonal plasticity and neurite density in rat hippocampus using DTI and DWI (8, 9) have provided some insights into the structural basis underlying the DTI measurements. On the other hand, it is possible that the physiological consequences of severe sleep loss, such as increased metabolic rate, weight loss and hypothermia (10) may also affect the DTI measurements. However, the REM-SD protocol employed in this study was mild compared to the chronic total SD model where severe physiological alterations were observed (10). Although the exact biological processes underlying the DTI changes post SD remain to be elucidated, this study demonstrated that DTI is a sensitive and non-invasive in vivo tool that can provide insights into the microstructural plasticity in specific regions during REM-SD. References [1] P. Maquet, Science, 2001. [2] N. Goel, et al., Semin Neurol, 2009. [3] J. M. Siegel, Science, 2001. [4] C. L. Patti, et al., Sleep, 2010. [5] C. M. McDermott, et al., J Neurosci, 2003. [6] M. G. Frank, et al., Neuron, 2001. [7] R. Guzman-Marin, et al., Sleep, 2008. [8] T. Laitinen, et al., Neuroimage, 2010. [9] S. N. Jespersen, et al., Neuroimage, 2010. [10] A. Rechtschaffen, et al., Behav Brain Res, 1995. FIG.2 DTI index quantitation and comparisons between preand post-SD in the voxels indicated in FIG.1 (n=9). Twotailed paired t-test was performed. * p<0.05, ** p<0.005, FIG.1 Voxels showing significant axial diffusivity decrease between preand post-SD with threshold p<0.05. Statistical map was overlaid on the average FA map from all animals (n=9). Significant voxels were extracted for DTI indices quantitation in hippocampus (blue arrows, 203 voxels) and cortex (yellow arrows, 236 voxels). Color bar indicates T score of the statistical map.

Electronic Posters: Neuroimaging - Computer 111: Imaging of Psychiatric Disorders: no. 4342

Introduction Quantitative diffusion tensor imaging (DTI) is now widely used to probe the microstructural changes in neural tissues [1,2], where its absolute quantitation accuracy and reproducibility become essential. However, quantitation of DTI indices in vivo can be confounded by the presence of cerebral vasculature and blood perfusion [3]. This study aims to quantitatively examine the effect of cerebral hemodynamic changes on DTI indices by using a hypercapnia model. Methods Hypercapnia Paradigms: 6 female adult Sprague-Dawley rats were anesthetized using isoflurane (3% induction and 1.5-2% maintenance) via a nose cone. 48 continuous DTI acquisitions including 6 pairs of normocapnia (OFF)/hypercapnia (ON) were performed. Each pair included 5 DTI acquisitions for normocapnia (OFF) with room air inhalation and 3 DTI acquisitions for hypercapnia (ON) with 5% CO2/air inhalation delivered into the nose cone [4]. Respiration rate, heart rate, arterial oxygen saturation and rectal temperature were monitored throughout the experiments. MRI Protocols: All MRI measurements were acquired using a 7T Bruker scanner. In vivo Diffusion-weighted (DW) images were acquired with a SE 2-shot EPI sequence with 6 diffusion gradient directions. Five additional images with bvalue=0 (B0 images) were also acquired, yielding a scan time of 85 seconds per DTI acquisition. The imaging parameters were: TR/TE=2500/31ms, δ/Δ=5/17ms, FOV=4.5x4.5cm, acq matrix= 96x96 (zero-filled to 256×256), slice thickness =1mm (0.2mm gap), b-value of 1000 and 300s/mm. Data Analysis: RAW data were first co-registered within individual animal using AIR5.2.5. FA, mean diffusivity, axial and radial diffusivities were calculated from DWIs with two b-values, 0 versus 1000 or 300 s/mm respectively. Whole brain area in each animal was first defined based on the mean of 48 FA maps, and then the brain was segmented based on FA and MD into gray matter (GM, MD<1.6 ms/ms, 0.03<FA<0.31), white matter (WM, MD<1.6 ms/ms, FA>0.31) and CSF (MD>1.6 ms/ms). These regional masks were used to quantify the regional changes of various DTI indices. Results Fig.1 shows the average physiologic recordings in all acquisition sessions. Representative GM, WM regional masks are illustrated in Fig.2. Fig.3 shows that the MD histogram shifted to higher diffusivity during hypercapnia. Fig. 4 illustrates the average MD time courses computed with b value of 1000 and 300s/mm in all animals. For whole brain, the peak-to-peak percentage increases were measured as 1.56±0.49% and 3.21±0.72% for b=1000 and 300s/mm, respectively. Regionally, MD increases were found to 1.58±0.49% in GM and 1.69±0.58% in WM (Fig.5). In addition, FA was observed to generally decrease during hypercapnia in both GM and WM (Fig. 6). Discussions and Conclusions Our data showed both MD, axial and radial diffusivities change globally and regionally to respond to hypercapnia, suggesting that vasculature alterations can affect in vivo DTI quantitation. The effect of hemodynamic alterations on DTI was more pronounced at lower b because of increased pseudo diffusion effect of blood perfusion. In this study, FA was found to decrease during hypercapnia likely because of the more pronounced pseudo diffusion effect that was associated with relatively random capillary vasculature. These findings indicated that alterations in physiologic conditions, vascular characteristics and hemodynamic regulations can affect the absolute quantitation of various DTI indices in vivo. Therefore, caution must be taken in designing experiments and interpreting DTI indices.

S. J. Fan, M. M. Cheung, A. Y. Ding, F. Y. Lee, Z. W. Qiao, J. Yang, and E. X. Wu Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, Medical Imaging Center of the First Affiliated Hospital, School of Medicine of Xi'an Jiaotong University, Xi'an, Shanxi Province, China, People's Republic of

I. Y. Zhou, A. Y. Ding, Q. Li, F. Y. Lee, S. J. Fan, K. C. Chan, G. M. McAlonan, and E. X. Wu Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China, People's Republic of, Department of Psychiatry, The University of Hong Kong, Centre for Reproduction Growth and Development, The University of Hong Kong

Introduction Diffusion Tensor Imaging (DTI) offers a valuable in vivo tool to characterize water diffusion behavior in biological tissues, particularly brain tissues. To such principle, it can be predicted that quantification of diffusivity might be interfered by the presence of cerebral vasculature. However, knowledge about the degree of influencing of vasculature effects towards diffusion indices is limited. In this study, we employed a hypercapnia model which will cause passive dilation of blood vessels and increase blood flow by inhaling 5% carbon dioxide (CO2) [1]. In vivo DTI experiment was performed and cerebrovascular response was confirmed by BOLD [2]. Thus, the correlation of blood flow and diffusivity values were investigated to provide substantial information on applying in vivo DTI for evaluating brain tissue as well as exogenic vascular regulations. Methods Stimulation Paradigms: Normal adult Sprague-Dawley rats (N=7) were kept warm at normal temperature and inhaled isoflurane anesthesia (3% induction and 1.5-2% maintenance). Each scan included 48 continuous DTI acquisitions which were segmented into 6 couples of OFF/ON periods by manually switching room air (OFF) or 5% CO2 (ON) circulation into the animal mouth cone at certain time points. Each couple of OFF/ON period included 5 DTI acquisitions for normocapnia (OFF) with room air inhalation and 3 DTI acquisitions for hypercapnia (ON) with 5% CO2/air inhalation. MRI Protocols: All MRI measurements were acquired using a 7T Bruker scanner. In vivo Diffusion-weighted (DW) images were acquired with a spin echo 2-shot EPI readout sequence with encoding scheme of 6 gradient directions. 5 additional Images with b-value = 0 (B0 images) were also acquired [3, 4], yielding a scan time of 81.4 seconds per DTI acquisition. The imaging parameters were: TR/TE=3700/29.97ms, δ/Δ=5/17ms, FOV=5 x 5cm, acq matrix = 96 x 96, slice thickness = 1mm (0.5mm gap), b-value of 1000s/mm. Monitoring of respiration rate, heart rate, arterial oxygen saturation and rectal temperature were performed by animal probe (SA-Instruments, Stony Brook, NY) throughout the experiments and were shown in Fig.1. Data Analysis: All the DTI derived parametric maps were analyzed using the STIMULATE software package (STIMULATE, Center for Magnetic Resonance Research, University of Minnesota). Percentage changes of mean diffusivity, axial diffusivity, radial diffusivity and FA were calculated from activated voxels generated by fixing the correlation threshold at 0.2 and were averaged across stimulations and animals. The activated voxels in whole brain included for each parametric map analysis were shown in percentage in Table 1. When averaging the DTI derived values for normocapnia and hypercapnia conditions, the first 3 trials of each OFF period were considered to be recovery time, and the first trial of each ON period was considered to be activation rise time [5, 6]. Therefore 2 trials of OFF periods and 2 trials of ON periods were used to evaluate the signal changes between normocapnia and hypercapnia as shown in Table 1. Voxel-based two-sample t-test analysis was also performed on mean diffusivity and B0 with threshold of p<0.05 using SPM5 (FIL, UCL, London, UK). Results and Discussions The statistical dependence of trace (Fig.2A) and B0 value (Fig.2B) between normoxia/hypercapnia has shown strong evidence of diffusivity change accompanied with BOLD effect which indicated the elevation of both CBV and CBF caused by hemodynamic activation under hypercapnic challenge [5]. And the statistical significance concentrated on the cortex and subcortical grey matter in brain also suggested that typical regions which contained abundant vasculatures might be more susceptible to hemodynamic changes [7]. Furthermore, the averaged stimulus-induced diffusivity changes shown in Fig.3 demonstrated that axial diffusivity, which was used to interpret the principal diffusion direction in microstructures, had a further increase up to 10% when compared with radial and mean diffusivity. As illustrated in Table 1, the activated voxels, which exhibited high correlation between hypercapnic challenges and diffusion quantification changes, accounted for about one third of the whole brain area. In addition, different trends but substantial FA value changes were observed in different parts of brain. These quantification alterations correlated with CO2 manipulations significantly, hence might be contributed by the cerebral vascular smooth muscle response to the increased partial pressure of CO2 which would enhance the ion exchange in local acidic environment and resulted in passive vessel dilation as well as decreased resistance of cerebral arterial smooth muscle [5]. It is also noteworthy that isoflurane, which was used for anesthesia in this study, is a potent cerebrovasodilator that increases basal CBF. Thus it may reduce the hypercapnia-evoked hemodynamic effects as well as diffusivity changes [6]. Conclusions As observed from this in vivo study, changes occur in all parametric DTI maps at activated voxels in response to hypercapnia induced cerebrovascular changes, suggesting that vascular factors may interfere with in vivo DTI characterization of neural tissues in normal anesthetized animals. Our data implied that these alterations could lead to change in different diffusivity index to a variable extent, but more apparent changes may be observed in axial direction. Different activated voxel localizations in brain exhibited opposite trends in FA quantification corresponding to hypercapnic stimulation. Consequently, hemodynamic challenges can potentially affect the DTI quantification of tissue microscopic diffusivities and possibly lead to contamination towards pathological alterations. When exogenic vascular regulations happened and small voxel size evaluations were performed for in vivo DTI, these quantification interferences would become more complicated and problematic. Therefore, cautions must be taken when interpreting DTI parameters in vivo. References [1] L. L. Latour, et al. Magn Reson Med, vol. 48, pp. 478-86, 2002. [2] M.E. Brevard, et al. Magn Reson Imaging, 2003 21(9):995-1001. [3] D. K. Jones, et al. Magn Reson Med, vol. 42, pp. 515-25, 1999. [4] S. Skare, et al. J Magn Reson, vol. 147, pp. 340-52, 2000. [5] M. E. Brevard, et al. Magn Reson Imaging, vol. 21, pp. 995-1001, 2003. [6] J. Lu, et al. Neuroimage, vol. 45, pp. 1126-1134, 2009. [7] H. R. Weiss, et al. Circ Res, vol. 51, pp. 494-503, 1982. Diffusivity change % Activated voxels %

Introduction Diffusion-weighted (DW) signal dependence on b-value in neural tissues deviates from monoexponential decay. As a result, numerous models have been proposed to characterize such non-monoexponential decay. Diffusion kurtosis imaging (DKI) is a 4 order diffusion analysis that characterizes the restricted non-Gaussian diffusion (1). Stretched-exponential model describes the diffusion process with a continuous distribution of apparent diffusion coefficients (ADC) (2). In these two models, two free fitting parameters are used, which is one less than in the biexponential model (3). In addition, both models make no assumption on the number of compartments. Thus, compared to monoexponential and bioexponential models, kurtosis and stretched-exponential models may provide potentially more robust and meaningful fitting of DW signals observed in neural tissue that has inherently complex cellular microstructures. This study aimed to evaluate the performance of these two models in describing the experimental DW signals obtained from rodent brains in vivo. Methods In vivo experiments were performed on 3 normal adult SD rats using a 7T Bruker scanner. DW images were acquired with a respiration-gated SE 4-shot EPI with encoding scheme of 30 gradient directions and 5 b0 using: TE/TR=32.3/3000ms, δ/Δ=7/17ms, image resolution=313x313x1000μm , 5 b-values of 500, 1000, 1500, 2000 and 2500s/mm, and NEX=4. DWI data was fitted to ( ) ( ) K D b bD S b S 2 2 6 1 exp ) 0 ( ) ( + − ⋅ = (1,4) [1]

INTRODUCTION: Neonatal hypoxic-ischemic (HI) encephalopathy is a major cause of brain damage in infants, and may result in periventricular white matter injury and chronic neurological dysfunctions (1,2). Although infants with HI injuries frequently present cerebral visual impairments upon unilateral posterior cerebral lesions (3), our previous functional MRI (fMRI) study demonstrated the residual visual functions in the subcortical adult rat brain of both hemispheres after severe neonatal HI injury to the entire ipsilesional visual cortex (4). In order to understand the structural-functional relationship of such visual deficit and plasticity, this study employed diffusion tensor imaging (DTI) to determine the long-term outcomes of microstructural integrity along the visual pathways after severe neonatal HI injury. MATERIALS AND METHODS: Animal Preparation: Sprague-Dawley rats (12-16 g, N=14) were divided into two groups. In the HI group (n=7), the ipsilesional visual cortex was damaged after unilateral ligation of the left common carotid artery at postnatal day (P) 7, followed by hypoxia at 36-37C for 2 hours. The other 7 animals were untreated and acted as controls. T1WI, T2WI and DTI were performed to all animals at P60. MRI Protocol: All MRI measurements were acquired utilizing a 7 T Bruker scanner. T1WI and T2WI were acquired using 2D RARE pulse sequences. For DTI, 4-shot SE-EPI diffusion weighted images were acquired with FOV = 32x32 mm, MTX = 128 x 128, slice thickness = 1 mm, number of slices = 15, TR/TE = 3000/28 ms, b = 0 and 1000 s/mm and 30 diffusion directions. Data Analysis: DTI parameters, including FA, λ//, λ┴ and diffusion trace value were obtained using DTIStudio v2.30 after co-registration. As more than 98.5% of the axons of rat retinal ganglion cells decussate to the contralateral posterior visual components at the optic chiasm (5), DTI parameters along the visual pathway projected from the ipsilateral eye (ipsilateral optic nerve, and contralateral anterior and posterior optic tract) were compared to the contralateral eye (contralateral optic nerve, and ipsilateral anterior and posterior optic tract) in the same group. DTI parameters on the same sides were also compared between groups. RESULTS: In Figure 1, a porencephalic cyst was presented in the HI-injured animals by hyperintensity in diffusion trace map covering the ipsilesional hemisphere, including the visual cortex. The HI group exhibited a reduced FA in the ipsilesional prechiasmatic optic nerve (solid arrows) and the contralesional anterior (dashed arrows) and posterior (arrowheads) optic tracts, whereby the ipsilesional posterior optic tract (open arrows) appeared to be displaced by the porencephalic cyst (asterisks), and was identified as a dorsoventrally-oriented fiber bundle in the color-encoded FA directionality map. Quantitative analyses in Figure 2 showed that, compared to age-matched normal brains, a significantly lower FA but higher λ//, λ┴ and diffusion trace value was observed in the ipsilesional posterior optic tract in the HI brains, whereas significantly lower FA but mildly lower λ// and higher λ┴ and trace was observed in the ipsilesional prechiasmatic optic nerve and contralesional anterior and posterior optic tracts. Along the visual pathway projected from the ipsilesional eye, the differences in DTI metrics between normal and HI brains were the largest in the ipsilesional optic nerve and the smallest in the contralesional posterior optic tract, whereas along the pathway from the contralesional eye, such differences were the largest in the ipsilesional posterior optic tract and the smallest in the contralesional optic nerve. The maximum decrease in FA along the visual pathway projected from the ipsilesional eye (44.6% in ipsilesional prechiasmatic optic nerve) was also larger than the contralateral one (24.1% in ipsilesional posterior optic tract). DISCUSSION AND CONCLUSION: The larger maximum decrease in FA along the visual pathway projected from the ipsilesional eye than the contralateral one was in parallel with the larger reduction in BOLD signal increase in the contralesional superior colliculus than the ipsilesional one in the same groups of animals in our previous fMRI study (4), indicative of smaller hemodynamic responses in more disorganized tissues. On the other hand, it is believed that the ipsilateral posterior optic tract experienced primary white matter lesion, which was known for FA reduction, increased average diffusivity, and increased λ// relative to secondary fiber loss resulted from decrease in FA and λ//, and increase in λ┴ and trace in the ipsilesional optic nerve and contralesional anterior and posterior optic tracts (6). Previous studies showed that visual cortex damage caused trans-synaptic degeneration of the thalamus and the retinal ganglion cells. However, the survivors continued to transmit visual information via the remaining routes to the superior colliculus of the midbrain (7), which could be enhanced as the result of extensive training (8). Our results on the long-term outcome of the remaining visual pathways after neonatal brain injury are potentially important in determining and improving the functional consequences of brain lesions after most compensatory and reparative phases have been passed.

INTRODUCTION Sleep plays a key role in facilitating learning and memory consolidation [1, 2]. Insufficient sleep, with a high prevalence in society today, affects alertness and neurocognitive process in a negative manner [3]. Several studies have shown that deprivation of sleep can result in memory deficits and impaired cognitive performance in humans [4, 5]. The neurobiological alterations underlying these behavioral deficits, especially in regions related to learning and memory such as hippocampus and in the structures involved in alertness and attention such as thalamus, are of interest. Proton MRS (H MRS) can be used to assess the metabolic changes in living brain [6] and thus could provide biochemical evidence underlying the neural process, such as psychiatric disorders [7]. In this study, we aim to use in vivo H MRS to investigate the metabolic changes induced by sleep deprivation (SD) in hippocampus and thalamus. MATERIALS AND METHODS Animal Preparation: Male Sprague-Dawley rats (~8 weeks old, N=9) were subjected to rapid eye movement (REM) sleep deprivation (SD) and MR scanned before and after the treatment. Sleep deprivation [8]: In the multiple smallplatform technique employed, five platforms with each 6cm diameter were placed in the middle of a water tank. Platforms were spaced 9cm apart so that rats could easily move between them but could not lie across any two. The water reached up to ~2 cm below the surface of the platform. Food and water were available ad libitum. All SD treatments lasted 72 hr under a 12h day/night cycle. Video monitoring was performed throughout the training and was used for later sleep deprivation validation. MRI Protocols: All MR measurements were performed on a 7 T Bruker MRI scanner using a quadrature surface coil. Under inhaled isoflurane anaesthesia, the animal was kept warm under circulating water at 37 C with respiratory monitoring. RARE T2-weighted anatomical images were acquired for voxel localization in H MRS. After localized shimming with FieldMap, H MRS was performed using a PRESS sequence combined with outer volume suppression (OVS) and with TR/TE=2500/20ms, 2048 data points and 256 averages. A 2×4×2 mm voxel was placed over the left hippocampus and another 3×3×3 mm voxel was centered at the left thalamus. Data Analysis: MR spectra were processed with jMRUIv4.0 software using simulated metabolites in NMR-SCOPE as prior knowledge. The raw data was apodized with a 15-Hz Gaussian filter and phase-corrected. The residual water signal was filtered out with HLSVD algorithm. Various ratios of metabolites, NAA:Cr, Cho:Cr, Glu:Cr, Lac:Cr, m-Ins:Cr and Tau:Cr were statistically evaluated using two-tailed paired student’s t-tests between before and after SD treatment with p < 0.05 considered as significant. RESULTS Fig. 1 illustrates the typical localization of the voxels for H MRS measurement of rat hippocampus and thalamus. For each region of interest, H MRS spectra before and after SD were averaged from all rats and shown in Fig.2. Reduction of Nacetylaspartate (NAA) level can be clearly observed in the hippocampal spectrum after SD compared to that of before SD. The statistical evaluation (Fig. 3) of the metabolite signal with respect to creatine (Cr) peak revealed that, besides the distinct higher hippocampal NAA level (p<0.001), glutamate (Glu) signal was also significantly lower (p<0.01) in hippocampus after SD. Meanwhile, Glu:Cr significantly increased (p<0.05) in thalamus after SD. Lactate (Lac) level decreased (p<0.05) in thalamus after SD. DISCUSSION AND CONCLUSION Reduction of NAA, a marker of neuronal density, integrity and health [6], indicates neuronal loss and cellular dysfunction [9]. Previous histological study showed that the cell proliferation in the dentate gyrus of hippocampus was suppressed by prolonged (72hr) SD. Therefore, the reduced hippocampal NAA:Cr after SD can be due to neuronal loss. Moreover, using the same SD paradigm, neurophysiological study found severely reduced neuronal excitability in CA1 area of hippocampus [8]. It indicates that, along with neuronal loss, the NAA decrease observed after SD could also result from neuron cell dysfunction. Furthermore, the reduced cellular excitability in hippocampus can be reflected by the reduction of Glu, an amino acid acting as excitatory neurotransmitter in the brain, observed in this study. In thalamus, glucose metabolism decreases after SD [10]. Therefore, Lac, an end product of anaerobic glycolysis, was found to decrease in thalamus after SD. Our finding of Glu:Cr increase in thalamus is in line with previous ex vivo measurement in cats [11], possibly arising from increased glutamine synthetase [12]. In conclusion, the metabolic alterations elicited by sleep deprivation in rat brain are documented in this study using in vivo H MRS, providing neurochemical evidence of the behavioral deficits associated with sleep deprivation. REFERENCES [1]Maquet P. Science 2001;294:1048-52. [2] Siegel J. M. Science 2001;294:105863. [3]Durmer J. S. and D. F. Dinges Semin Neurol 2005;25:117-29. [4]Chee M. W. and Y. M. Chuah Proc Natl Acad Sci U S A 2007;104:9487-92. [5]Goel N., Semin Neurol 2009;29:320-39. [6]Choi J. K., NMR Biomed 2007;20:216-37. [7]Lyoo I. K. and P. F. Renshaw Biol Psychiatry 2002;51:195-207. [8]McDermott C. M., J Neurosci 2003;23:9687-95. [9]Siegmund A., Biol Psychiatry 2009;65:258-62. [10]Thomas M., J Sleep Res 2000;9:335-52. [11]Micic D., Nature 1967;215:169-70. [12]Sallanon-Moulin M., Brain Res Mol Brain Res 1994;22:113-20. Fig.3 Comparisons of metabolite ratios before and after exposure to sleep deprivation (SD) in hippocampus and thalamus. Paired t-tests were performed with * p<0.05, ** p<0.01, ***p<0.001. Fig.2 Averaged H MRS spectra of all rats (N=9) before and after sleep deprivation (SD) in hippocampus and thalamus. Fig.1 Localization of the voxels for H MRS measurement in hippocampus and thalamus.

INTRODUCTION: Divalent manganese ion (Mn), as a calcium analog, has been introduced as a valuable cellular contrast agent for tracing neuronal pathways, for the enhancement of neural architecture and for brain function [1]. Several previous studies have reported the use of manganese-enhanced MRI (MEMRI) to detect neurodegeneration during the acute-phase of neonatal hypoxic-ischemic (H-I) cerebral insult [2, 3]. However, significant recovery is known to occur weeks after the initial H-I insult and last for at least 6 months [4], indicating that the regenerative processes toward the functional restitution continues late after the H-I insult when the infarct lesion stabilizes. These processes include hyper proliferation of astrocytes [5], neurogenesis [6] and regeneration of synapses [7]. This study aims to employ in vivo MEMRI to investigate the cellular alterations at the late stage after H-I insult.