Detection of metabolic and perfusion changes in the hippocampus and temporal lobe regions in patients with temporal lobe epilepsy (TLE) via hybrid 18F-FDG PET/MRI

Detection of metabolic and perfusion changes in the hippocampus and temporal lobe regions in patients with temporal lobe epilepsy (TLE) via hybrid 18F-FDG PET/MRI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Detection of metabolic and perfusion changes in the hippocampus and temporal lobe regions in patients with temporal lobe epilepsy (TLE) via hybrid 18 F-FDG PET/MRI Maher Mohamad Rajab Arnous, Afnan Ahmed Mohamed Al-Asbahi, Liu Fang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5440001/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: To investigate and compare metabolic and perfusion alterations in temporal lobe epilepsy (TLE) patients via hybrid 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI). Methods: Twenty-one TLE patients (15 with left-sided TLE (LTLE) and 6 with right-sided TLE (RTLE)) who underwent brain 18 F-FDG PET/MRI, and eight healthy controls (Hc) who had 18 F-FDG PET/MRI for health examination, were included. Brain regions were segmented based on the automated anatomical labeling (AAL) template, and the hippocampus and temporal lobe were isolated for further analysis. Left and right sides of these structures were analyzed separately. Accordingly, the maximum standardized uptake value (SUV max ), mean standardized uptake value (SUV mean ) and cerebral blood flow (CBF) were compared between the two sides via paired t test. Asymmetry indexes (AI) were calculated and statistically compared between the TLE patients and Hc, along with PET and Arterial spin labeling (ASL)-derived AI. Results: LTLE patients showed significant asymmetrical differences in SUV max , SUV mean, and CBF within the hippocampus region (p<0.01). In RTLE patients, only SUV mean showed significant asymmetrical in both the hippocampus (p=0.009) and temporal lobe (p=0.018). The PET-derived AI in the hippocampus nearly doubled in the TLE group compared to Hc group. Similarly, ASL-derived AI in the hippocampus also increased (7.22% vs 3.86%) in the TLE group compared to Hc group (p=0.051). In the temporal lobe, both PET and ASL-derived AIs increased in the TLE group; however, these increases were not statistically significant (p=0.260, p=0.364). In the hippocampus, a significant difference existed for the AI between PET and ASL (p=0.001), while the temporal lobe showed a significant correlation for the AI between PET and ASL (r=0.49, p=0.024). Conclusion: TLE patients exhibited distinct patterns of brain metabolism and perfusion between LTLE and RTLE. And the AIs derived from PET was more accurate than those of ASL in detecting abnormalities in the hippocampus. Meanwhile, metabolism and perfusion in TLE patients differed significantly in the hippocampus, while revealing a correlation in the temporal lobe. Temporal lobe epilepsy hippocampus PET MR 18F- fluorodeoxyglucose and perfusion Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Temporal lobe epilepsy (TLE) is the most common seizure-related illness, affecting approximately 50 million people worldwide ( 1 ). It is usually diagnosed in the first two decades of life and is not gender related. TLE is a type of epilepsy that originates mainly in the temporal lobes of the brain, which are responsible for memory, emotions, and language processing, and in the hippocampus lobes, which play a crucial role in memory formation and retrieval ( 2 ). Patients with TLE are at great risk of memory impairment, anxiety and depression, with adult-onset TLE patients exhibiting the most affected memory ( 3 ). Although pharmacotherapy is an effective treatment option for the vast majority of epileptic patients, approximately 30% of patients develop pharmacologically resistant epilepsy ( 4 ). Characterized by frequent and recurring seizures, TLE is the most common type of pharmacoresistant epilepsy to antiepileptic drugs (AEDs) ( 5 ). Identification of the etiology and location of TLE is highly important but highly difficult due to many complicated factors that play a role in seizure onset and development. Multimodal neuroimaging, including high-resolution MRI, 18 F- FDG PET, and perfusion single-photon emission computed tomography (SPECT), as well as neurophysiological testing, including electroencephalography (EEG), are used as part of the presurgical evaluation for patients with focal epilepsy ( 6 ). 18 F-FDG PET is useful for differentiating lesions in lesional TLE patients before surgery ( 7 ), and may independently indicate the purpose for surgical resection when MRI findings are normal. This method is a well-established tool with 85–90% sensitivity for diagnosing hypometabolic TLE foci ( 8 ). In TLE patients, PET/MR has proven valuable for accurate presurgical diagnostic workup ( 9 ), limiting radiation dose, sedation duration, and sedation events. Accurate seizure focal localization is essential to limit surgical resection size, preserve healthy brain tissue, and achieve seizure control ( 10 ). Arterial spin labeling (ASL) is a non-invasive perfusion MRI imaging technique that evaluates cerebral blood flow (CBF) by magnetically labeling of inflowing blood. This approach can also be helpful in non-invasively identification of interictal hypoperfusion on the focal side in TLE patients and the identification of possible epileptogenic foci ( 11 ). Furthermore, an increasing number of studies have revealed that ASL can be used to detect seizure foci ( 12 , 13 ). Understanding the relationship between 18 F-FDG PET and ASL-MRI is essential for physicians to improve their knowledge of metabolic and hemodynamic changes that occur in TLE patients. This knowledge can aid in identifying the epileptogenic focus and assist treatment planning. Hybrid PET/MR imaging is an emerging technology that facilitates the rapid succession of simultaneous acquisition of PET and MR images. Only a small number of studies have been published thus far on the application of hybrid PET/MR imaging evaluating refractory focal epilepsy ( 14 ). The ability to obtain data in a single session reduces the number of examinations required for the patient, which is advantageous given the large number of assessments involved in epilepsy surgery planning ( 15 – 17 ). Furthermore, advancements in PET/MR imaging now allow for the simultaneous acquisition of FDG-PET and ASL perfusion data, leading to a more accurate assessment of metabolic detection and perfusion changes in TLE patients. The present study aimed to detect the metabolism and perfusion changes in the TLE patients using the hybrid 18 F-FDG PET/MRI, and to further compare the relationship between metabolism and perfusion among RTLE (n = 6) and LTLE (n = 15) patients as well as healthy controls (HCs; n = 8) via hybrid PET/MR brain imaging. Materials and methods 1.1 Ethics statement This research was performed under the guidelines approved by the Ethics Committee of Union Hospital. All of the patients and healthy volunteers who were part of the study consented to ASL and 18 F-FDG PET hybrid PET/MR brain imaging. Parental consent, or consent from a surrogate, was obtained if the child needed parental consent. 1.2 Participants A retrospective analysis was conducted on twenty-one patients with TLE (including fifteen with LTLE and six with RTLE), between June 1, 2018, and January 20, 2023 (mean age: 26 years, range: 8–55 years). The diagnosis was made by two experts in clinical neuroimaging, with considering the clinical symptoms, EEG and PET/MRI results. These patients had undergone simultaneous hybrid PET/MRI brain scans and were collected from our PET center. In addition, eight healthy individual who also underwent simultaneous hybrid PET/MRI brain imaging for health examination were also included. All 21 patients and 8 Hc underwent the same protocol: 18 F-FDG-PET and ASL scans, which were acquired concurrently with the hybrid PET/MR scanner. Consecutive patients with a diagnosis of TLE registered at our PET center were enrolled. Patients were divided into two groups: LTLE and RTLE. The inclusion criteria were as follows: 1) had hippocampal and temporal lobe-related TLE and 2) had simultaneous imaging via ASL and 18 F-FDG PET with a hybrid PET/MR brain scan. The exclusion criteria were other types of epilepsy and a lack of ASL scans (Fig. 1 ). 1.3 Image Acquisition and Reconstruction: All patients underwent a static 18 F-FDG PET/MRI scan with the following clinical scan protocol: Blood glucose levels were assessed immediately before FDG injection to exclude hyperglycemic patients whose serum glucose levels exceed 200 mg/dL (those with a serum glucose level less than 8 mmol/L). Patients fasted for at least 6 hours. All patients received an intravenous injection of 18 F-FDG (3.7 MBq/kg). The MRI sequences were scanned during PET acquisition, including the 3D T1-weighted and ASL sequence with 3D pseudocontinuous arterial spin labeling. The parameters were as follows: 3D T1 sequence, three-dimensional gradient-echo sequence; flip angle = 12°; time of echo [TE]/time of repetition [TR] = 2.6/6.9 ms; bandwidth = 50 KHz; FOV = 24 cm × 24 cm; matrix = 384 × 384; ASL sequence, TR = 4809 ms; TE = 10.7 ms; post labeling delay = 2025 ms; field of view (FOV) = 240×40 mm; number of excitations (NEX) = 3; spiral readout of 8 arms×512 samples; 32×4.0 mm axial sections with whole-brain coverage. Brain images were acquired with the patient in the supine position. The images were acquired using a General Electric (GE) SIGNA TOF PET/MR system (GE Healthcare, Milwaukee, WI, USA). 18 F-FDG PET reconstruction was performed with the TOF OSEM algorithm, and the parameters were as follows: FOV = 30 cm × 30 cm, matrix = 192 × 192, filter cutoff = 3.0 mm, subsets = 28, Iterations = 3. The PET attenuation correction was atlas-based MRI attenuation correction combined with the zero-echo-time sequence. The duration was approximately 15 minutes for each task. 1.4 Brain segmentation and qualification: Automatic brain segmentation was based on an atlas template from the automated anatomical labeling ( 18 ) shown in Fig. 2 . To construct a unique atlas template and optimize PET segmentation accuracy, we registered the atlas template to subject images (RATSI). According to the 3D T1 images, we segmented the brain based on the Anatomical Automatic Labeling (AAL) template, and each region of interest (ROI) was resampled to have 1-mm isotropic voxels. The region-specific volume of each ROI was calculated by counting the voxels in regions defined in the AAL template, which is in the Montreal Neurological Institute (MNI) reference space (Fig. 2 ). The AAL template contained annotations for 116 brain areas, while only four regions, including the left and right temporal lobe and the left and right hippocampi, were extracted according to their anatomical locations. The segmented hippocampal and temporal lobe regions were used for analysis. Our study first compared the extracted values of SUVmax, SUVmean, and rCBF between the segmented left and right temporal regions (TL/TR) and used the Hc group as a reference. Furthermore, we calculated the AI of the two match groups, ASL and 18 F-FDG PET, in the TLE and Hc groups. Additionally, hippocampal and temporal lobe AI comparisons were performed for TLE patients, which were inferred from interregional 18 F-FDG PET uptake and ASL perfusion correlations. For all patients, four quantitative metrics, SUV max and SUV mean from FDG-PET and mean rCBF from ASL, were measured for subsequent analysis. The AI was calculated from the mean rCBF ( 19 ) and SUV mean ( 20 ) values identified in each region using the following formulas: $$\:\text{A}\text{I}=2\text{*}\frac{{mean\:value}_{left}-{mean\:value}_{\:right}}{{mean\:value}_{left}+{mean\:value}_{right}}*100\%$$ 1.5 Statistics: The left and right measurements, including the SUV max , SUV mean and rCBF in the hippocampus and temporal lobe, were compared statistically by two-tailed paired t test. The AI was compared between the TLE patients and Hc groups to distinguish 18 F-FDG PET metabolism from ASL perfusion in the hippocampus and temporal lobe. To understand the possible mechanism of AI values for 18 F-FDG PET metabolism and ASL perfusion within epileptic foci in the TLE group, Pearson's correlation and a paired t test were applied to analyze the correlation and difference, respectively, between 18 F-FDG PET and ASL in the hippocampus and temporal lobe. The P < 0.05 was considered to indicate statistical significance. The Pearson correlations are described as follows: 0.0–0.2 = slight; 0.2–0.4 = fair; 0.4–0.6 = moderate; 0.6–0.8 = substantial; 0.8–1.0 = almost perfect ( 21 ). All statistical analyses were performed by SPSS (version24, IBM). Results Table 1. The list of SUV max , SUV mean and CBF values in the hippocampus and temporal lobes, including the mean values and standard deviation. The mean value and standard deviation (SD) of the metrics, including the SUV max and SUV mean from PET and CBF in the left and right hippocampus and temporal lobes for the three groups, LTLE, RTLE and Hc are summarized in Table 1. The corresponding paired t test results between the left and right sides were also shown. All the metrics showed significant asymmetrical differences in the hippocampus (p=0.001), but not in the temporal lobe (P>0.05) in LTLE patients. For the RTLE, the SUV mean in the hippocampus and temporal lobe, the CBF in the temporal lobe showed significant asymmetrical differences (P=0.009, 0.018, 0.034). For the healthy group, the SUV mean in the hippocampus was significantly different, but the difference in the Hc group was very small (6.33 vs. 6.77). In both subgroups, the ipsilateral mean of the SUV mean and rCBF in the hippocampus and temporal lobe were lower than those in the contralateral hemisphere and there were significant differences in the hippocampus LTLE (P= 0.001 and P= 0.001) and temporal lobes RTLE (P= 0.018 and P= 0.034) between two groups. In comparing the left and right sided of hippocampal LTLE and RTLE to control Hc groups, there was ipsilateral and contralateral significance difference of SUV max LTLE compared to control Hc (P= 0.001 and P=0.001, respectively). In addition, there was a significant difference in the ipsilateral SUV mean between LTLE and RTLE patients compared to control Hc (P= 0.018 and P=0.006, respectively), while a significant difference in the contralateral perfusion of rCBF LTLE was found compared to control Hc (P= 0.016). In comparing the left and right sides of temporal lobes LTLE and RTLE to control Hc groups, there was ipsilateral and contralateral significance difference of SUV max in both LTLE and RTLE and SUV mean LTLE compared to control Hc, while an ipsilateral significant difference of SUV mean RTLE compared to control Hc (P= 0.019). In addition, a significant difference in contralateral perfusion was found between the rCBF LTLE group and the control group (P= 0.01). Table 2. Comparisons between the TLE group and healthy control group, including the results for PET and ASL in the hippocampus and temporal lobe. Table 2 lists the mean value and SDs of AIs for the hippocampus and temporal lobe determined via PET and ASL. The corresponding statistical comparisons between the TLE and Hc groups are also shown. All the mean values for the AI increased in the TLE group compared with those in the Hc group, especially the AI from PET which increased nearly twofold. However, only the difference in the AI from the PET in the hippocampus was significant (p=0.005). The AI from ASL in the hippocampus also increased notably (7.22% vs. 3.86%) in the TLE group compared with that in the Hc group, although the difference was not statistically significant (p = 0.051). In the temporal lobe, the AIs from both PET and ASL increased in the TLE group, although the difference was not statistically significant (p > 0.05). Figure 3 shows the boxplots used to compare the AIs between the TLE patients and Hc individuals. The difference from PET was much greater than that from ASL. Table 3. The AI correlation between PET and ASL signals in the hippocampus and temporal lobe. Correlations were assessed by the Pearson correlation coefficient, and difference were calculated by paired t tests. Table 3 lists the results of reflecting the correlation and difference in the AIs between PET and ASL in the hippocampus and temporal lobe. In the hippocampus, a significant difference in the AI was shown between the PET and ASL, calculated by paired t tests; while in the temporal lobe, a strong correlation was shown between the PET and ASL (r=0.49) and the correlation was statistically significant (p=0.024). Figure 4 shows a comparison of the AIs between PET and ASL by boxplots in both the hippocampus and temporal lobe, and the mean AI for PET was still greater than that for ASL, in both hippocampal and temporal lobe. Discussion Simultaneous PET/MR brain imaging was found to be helpful in identifying regional abnormalities and localizing seizure focus in TLE patients ( 10 , 22 , 23 ). Our study showed the importance of the consistency, clinical use and relationship of simultaneous hybrid PET/MR brain imaging of rCBF perfusion and 18 F-FDG PET metabolism for the temporal lobe in the context of TLE to characterize a potential epileptogenic zone. This approach enabled more accurate comparisons and superior results since the data were collected under the same physiological and pathophysiological conditions in a single center experience, clinically analyzed hippocampal and temporal lobe epilepsy in both right- and left-sided TLE patients, and to enhance the impact of our study, a group of Hc was used as a reference group. LTLE causes significant changes in metabolism and perfusion in the hippocampus, whereas the temporal lobe remains largely unaltered. In contrast, RTLE can be identified by altered metabolism in both the hippocampus and the temporal lobe, with no noticeable changes in perfusion. This lateralization may be attributed to the differences in brain metabolic patterns between both sides. Previous research has documented the phenomenon of a contralateral epileptogenic effect, whereby “Mirror” foci can develop in the contralateral temporal lobe of TLE patients ( 24 ). These “Mirror” foci may exist preoperatively or appear latent after the surgical removal of the primary epileptogenic focus. Their presence explains the appearance of a simultaneous decrease in metabolic activity in both temporal lobes. In this study, we found that LTLE and RTLE patients have different patterns of brain metabolism. For instance, due to their distinctive hemispheric structure, morphological and functional variations in LTLE and RTLE patients have been observed and reported in previous studies ( 25 , 26 ). Ono SE et al. ( 27 ) suggested that TLE should not be considered a unilateral, local, or focal insult. Instead, it deserves to be viewed as a multifactorial and network pathology that may involve several brain structures. Our findings showed a reduction of 18 F-FDG PET metabolism in LTLE patients, mainly in the hippocampal lobe. In RTLE patients, in addition to the reduction in 18 F-FDG PET metabolism in the hippocampus, there is more diffuse network dysfunction, causing a decrease in 18 F-FDG PET metabolism in the temporal lobe. Previous clinical studies have reported similar findings in patients with TLE ( 28 , 29 ). LTLE appeared to cause more extensive network impairment than RTLE in some studies ( 30 , 31 ), whereas other research has shown that the opposite is true ( 32 ). Haneef Z ( 30 ), investigated hippocampal network changes in TLE patients to assess for the complete distribution of abnormalities. Our study revealed that the observed difference in patient data, including SUV max , SUV mean , and rCBF, were significantly greater between the affected and contralateral hippocampal lobes in LTLE patients (Table 1 ) than in Hc individuals. This increased statistical significance may have medical implications in the diagnosis and treatment of TLE. It aids in identifying regions of abnormal metabolic activity in the brain, which can then be used to guide therapy decisions. Reduced metabolic activity in the affected hippocampus may promote the long-term benefit of surgical resection of the hippocampus with low rates of postoperative consequences ( 33 ). PET image analysis in clinical settings often requires identifying a ROI followed by determining the standardized uptake value (SUV) and AI ( 34 ). Our study evaluated TLE with Hc and discovered a significant difference, notably in the hippocampus, which is represented in a significant change in AI. These findings are consistent with previous studies and indicate a strong correlation between glucose metabolism (measured by PET) and ASL perfusion in TLE patients ( 35 ). Other research investigations have revealed a significant correlation between ASL data and 18F-FDG PET in TLE patients, with these areas displaying consistent results from quantitative evaluation ( 36 ). Based on our findings, the AI obtained from both 18F-FDG PET and ASL may have comparable clinical utility. Overall, AI is an effective technique to investigate variances in brain function. In the setting of TLE, AI can help identify perfusion variations in temporal lobe activity that may be related to seizure activity, hippocampal abnormalities, or network modifications. Wang et al. ( 37 ), investigated the relationship between ASL and 18F-FDG PET scans acquired simultaneously, in a study of 12 individuals with unilateral TLE. Based on PET/MR acquisition, their findings suggest that ASL might be a more reliable and complementary method for TLE lateralization than PET. However, their study was limited to a small sample size and the analysis focused only on the hippocampus, a single segment implicated in TLE, for both ASL and PET. Our quantitative analysis of PET activity may complement qualitative visual analysis. While visual analysis was more sensitive in detecting left-hemispheric hypometabolism, quantitative analysis performed equally well in both the left and right hemispheres ( 38 ). The use of non-invasive simultaneous PET/MR brain imaging for epileptic zone localization may reduce the need on more expensive stereo electroencephalography (SEEG) examinations and improve diagnostic characterization TLE patients. For patients who are not eligible for surgical treatment, simultaneous PET/MR can provide insights into the physiological mechanism off drugs resistance, impacting the development of novel medical treatment alternatives ( 36 ). Using AI data, we found a significant correlation (r = 0.49, p = 0.024) between metabolism and perfusion in the temporal lobe TLE patients. These findings are similar with recent research by Boscolo GI et al. (r = 0.49, p < 0.0005) ( 39 ) and Wang YH et al. (r = 0.74, p = 0.006) ( 37 ). These results are consistent with the findings of our investigation. AI was used to analyze asymmetry in symmetrical parts of the brain in order to indicate changes in cerebral regions. When the AI was used to statistically compare cerebral perfusion determined by ASL and glucose metabolism determined by 18 F-FDG PET, our data revealed overall perfusion/metabolism concordance in the temporal lobe (Fig. 4 ). Our findings matched previously reported data, correlating well PET with ASL ( 40 ). On the other hand, AI PET does not correlate well with AI ASL in the hippocampus due to limitations in imaging the skull base, including the hippocampus, because of the reduced signal-to-noise ratio (SNR) of ASL perfusion. PET scanning, unlike ASL, directly evaluates radiotracer concentration in the hippocampus, bypassing bone and tissue attenuation ( 41 , 42 ). While both PET and ASL can be used to diagnose TLE, PET-derived AI detects hippocampal abnormalities more accurately than ASL-derived AI. The superior sensitivity of PET-derived AI arises from its specificity for detecting metabolic changes, higher SNR and direct targeting of the epileptic focus, which make PET-derived AI of the hippocampus the preferred indicator for the diagnosis of TLE patients. Our findings indicate that both PET-derived and ASL-derived AI lack sensitivity in the temporal lobes (Fig. 4 , Table 3 ). Previous research has shown the superiority of AI-based analysis of 18 F-FDG PET following asymmetry correction, compared to conventional visual analysis ( 43 ). This is because AI can quantify the subtle differences in activity or perfusion between the left and right hemispheres, which is particularly valuable for diseases like TLE, Stroke, Parkinson’s disease and Alzheimer’s disease, all characterized by hemispheric imbalances ( 44 , 45 ). AI provides a non-invasive and objective method for assessing brain function, enabling repeated measurements to monitor disease progression and treatment response over time. Due to the limitations of our study, we need to be careful when interpreting our findings. A small sample size was obtained since our cohort was small. A larger sample size could help characterize the relationship between 18 F-FDG PET metabolism and ASL perfusion better and provide more accurate findings in TLE patients. Another limitation is the focus on the diagnostic aspect of TLE without its association with relevant clinical symptoms. Future studies should involve collaboration with other departments. Conclusion TLE patients showed distinctive patterns of brain metabolism and perfusion, with variations observed between LTLE and RTLE. The AIs derived from PET was more accurate than those of ASL in detecting abnormalities in the hippocampus. For LTLE, metabolic changes occurred mainly in the hippocampus, whereas RTLE patients revealed metabolic changes in both the hippocampus and the temporal lobe. Meanwhile, in TLE patients, metabolism and perfusion in the hippocampus differed significantly, while these findings were correlated in the temporal lobe. Abbreviations AAL Anatomical Automatic Labeling AEDs Antiepileptic drugs AI Asymmetry indexes ASL Arterial spin labeling CBF Cerebral blood flow EEG Electroencephalography FDG 18 F-fluorodeoxyglucose FOV Field of view GE General Electric Hc Healthy controls LTLE Left-sided TLE MNI Montreal Neurological Institute MRI Magnetic resonance imaging NEX Number of excitations PET Positron emission tomography RATSI Register atlas template to subject images ROI Region of interest RTLE Right-sided TLE SD Standard deviation SEEG Stereo electroencephalography SNR Signal-to-noise ratio SPECT Single-photon emission computed tomography SUV Standardized uptake value SUV max Maximum standardized uptake value SUV mean Mean standardized uptake value TE Time of echo TLE Temporal lobe epilepsy TR Time of repetition Declarations Compliance with Ethical Standards This article does not contain any experiments with animals. All procedures involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the Clinical Research Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (NO. 20200290). Written informed consent was obtained from the parents in the study. Data Availability The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors have no relevant financial or non-financial interests to disclose. The author Xiaoli Lan is an Associate Editor in this journal. Author Contributions Ruan W and Lan X contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ruan W, Arnous M, Al-Asbahi A, Fang Liu, Yongkang Gai, Hua Shu, Xun Sun, Ling Yang. The first draft of the manuscript was written by Arnous M, Al-Asbahi A, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding National Natural Science Foundation of China (82372081); Hubei Province Science and Technology Innovation Team ([2022] No. 72); STI2030-Major Projects (2022ZD0211800) References Stanescu L, Ishak GE, Khanna PC, Biyyam DR, Shaw DW, Parisi MT. FDG PET of the brain in pediatric patients: imaging spectrum with MR imaging correlation. Radiographics. 2013;33(5):1279-303. Wiltgen BJ, Zhou M, Cai Y, Balaji J, Karlsson MG, Parivash SN, et al. The hippocampus plays a selective role in the retrieval of detailed contextual memories. Current biology : CB. 2010;20(15):1336-44. Novak A, Vizjak K, Rakusa M. Cognitive Impairment in People with Epilepsy. 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Asymmetrical hippocampal connectivity in mesial temporal lobe epilepsy: evidence from resting state fMRI. BMC Neuroscience. 2010;11(1):66. Steiger BK, Muller AM, Spirig E, Toller G, Jokeit H. Mesial temporal lobe epilepsy diminishes functional connectivity during emotion perception. Epilepsy research. 2017;134:33-40. Pereira Dalio MTR, Velasco TR, Feitosa IDF, Assirati Junior JA, Carlotti Junior CG, Leite JP, et al. Long-Term Outcome of Temporal Lobe Epilepsy Surgery in 621 Patients With Hippocampal Sclerosis: Clinical and Surgical Prognostic Factors. Frontiers in neurology. 2022;13:833293. Ohta Y, Nariai T, Ishii K, Ishiwata K, Mishina M, Senda M, et al. Voxel- and ROI-based statistical analyses of PET parameters for guidance in the surgical treatment of intractable mesial temporal lobe epilepsy. Annals of nuclear medicine. 2008;22(6):495-503. Sone D, Maikusa N, Sato N, Kimura Y, Ota M, Matsuda H. Similar and Differing Distributions Between (18)F-FDG-PET and Arterial Spin Labeling Imaging in Temporal Lobe Epilepsy. Frontiers in neurology. 2019;10:318. Storti SF, Boscolo Galazzo I, Del Felice A, Pizzini FB, Arcaro C, Formaggio E, et al. Combining ESI, ASL and PET for quantitative assessment of drug-resistant focal epilepsy. NeuroImage. 2014;102 Pt 1:49-59. Wang YH, An Y, Fan XT, Lu J, Ren LK, Wei PH, et al. Comparison between simultaneously acquired arterial spin labeling and (18)F-FDG PET in mesial temporal lobe epilepsy assisted by a PET/MR system and SEEG. NeuroImage Clinical. 2018;19:824-30. Kumar A, Juhász C, Asano E, Sood S, Muzik O, Chugani HT. Objective detection of epileptic foci by 18F-FDG PET in children undergoing epilepsy surgery. Journal of nuclear medicine : official publication, Society of Nuclear Medicine. 2010;51(12):1901-7. Boscolo Galazzo I, Mattoli MV, Pizzini FB, De Vita E, Barnes A, Duncan JS, et al. Cerebral metabolism and perfusion in MR-negative individuals with refractory focal epilepsy assessed by simultaneous acquisition of (18)F-FDG PET and arterial spin labeling. NeuroImage Clinical. 2016;11:648-57. Boscolo Galazzo I, Mattoli MV, Pizzini FB, De Vita E, Barnes A, Duncan JS, et al. Cerebral metabolism and perfusion in MR-negative individuals with refractory focal epilepsy assessed by simultaneous acquisition of 18F-FDG PET and arterial spin labeling. NeuroImage: Clinical. 2016;11:648-57. Bi S, Yan S, Chen Z, Cui B, Yang H, Lu J. Comparison of ASL and 18F-FDG PET in Evaluating Alzheimer's Disease and Mild Cognitive Impairment using Integrated PET/MRI. Journal of Nuclear Medicine. 2023;64(supplement 1):P420-P. Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, et al. Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR American journal of neuroradiology. 2008;29(4):688-93. Aslam S, Damodaran N, Rajeshkannan R, Sarma M, Gopinath S, Pillai A. Asymmetry index in anatomically symmetrized FDG-PET for improved epileptogenic focus detection in pharmacoresistant epilepsy. Journal of neurosurgery. 2023;138(3):828-36. Zhao X, Kang H, Zhou Z, Hu Y, Li J, Li S, et al. Interhemispheric functional connectivity asymmetry is distinctly affected in left and right mesial temporal lobe epilepsy. Brain and behavior. 2022;12(3):e2484. Lubben N, Ensink E, Coetzee GA, Labrie V. The enigma and implications of brain hemispheric asymmetry in neurodegenerative diseases. Brain communications. 2021;3(3):fcab211. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5440001","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379211919,"identity":"85dcf47d-4665-49f8-9576-8580aecc136f","order_by":0,"name":"Maher Mohamad Rajab Arnous","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCA0DM2MDAwMbefODABwYQg1gtfDzHEg/OAGlhJlaLnESO8WEekAghLXy3D7Bu+LnDJp+N51jCYZtf2+T5mBkYP3zMwa1F8lwC283eM2mWbUC/HM7tu23YxszALDlzG24tBmcY2G7wth02ANuS23ObEaiFjZmXgJabf9v+G7BJ5Bgctuy5bU+Ultu8bQcgWhh+3E4kqEXyDGPbbdm2ZLDDDvY23E5uY2ZsxusXvjPMx26+bbMzkG9vPvzhx5/btvPbmw9++IhHCyRS4Ow2dBHC4A8pikfBKBgFo2CkAAAUPlcQt2j1AgAAAABJRU5ErkJggg==","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Maher","middleName":"Mohamad Rajab","lastName":"Arnous","suffix":""},{"id":379211921,"identity":"e97f93bb-b367-4568-ad82-3cc300d9f3a9","order_by":1,"name":"Afnan Ahmed Mohamed Al-Asbahi","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Afnan","middleName":"Ahmed Mohamed","lastName":"Al-Asbahi","suffix":""},{"id":379211923,"identity":"fe3371e2-1278-4673-adf4-4439e830beb3","order_by":2,"name":"Liu Fang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Fang","suffix":""},{"id":379211925,"identity":"990cfbd4-f9f8-4d66-bafa-3723171c523c","order_by":3,"name":"Gaigy Yongkang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Gaigy","middleName":"","lastName":"Yongkang","suffix":""},{"id":379211926,"identity":"0bd3ceff-7ec9-4592-9a14-ed2be4ca092a","order_by":4,"name":"Shu Hua","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Hua","suffix":""},{"id":379211928,"identity":"bd383b1a-f69d-44b2-b21b-e27d5d20d7d1","order_by":5,"name":"Sun Xun","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Xun","suffix":""},{"id":379211930,"identity":"8de04904-390a-41ff-938c-c8c8c3f57cc0","order_by":6,"name":"Ling Yang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Yang","suffix":""},{"id":379211931,"identity":"af066405-8724-4e8c-972e-5614e8235ad9","order_by":7,"name":"Xiaoli Lan","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Lan","suffix":""},{"id":379211932,"identity":"509a5933-b2cb-4e29-8802-1553a95cad9d","order_by":8,"name":"Ruan Weiwei","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ruan","middleName":"","lastName":"Weiwei","suffix":""}],"badges":[],"createdAt":"2024-11-12 13:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5440001/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5440001/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70479207,"identity":"7aef73f7-6c53-496f-a79b-6a233313283e","added_by":"auto","created_at":"2024-12-03 14:41:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82143,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of all patients with epilepsy registered at our PET Center, from June 2018 to January 2023. (TLE: temporal lobe epilepsy; ASL: arterial spin labeling)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5440001/v1/3e30fc196f8c336acfe83615.png"},{"id":70479209,"identity":"e0dec99e-dadc-4c2e-b6b8-a46cac995cca","added_by":"auto","created_at":"2024-12-03 14:41:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164834,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram displaying images analysis process, including preprocessing, extracting the hippocampus and temporal lobe based on the AAL template, and calculating the SUV\u003csub\u003emax\u003c/sub\u003e and SUV\u003csub\u003emean\u003c/sub\u003e for the PET images and the CBF for the ASL images. The asymmetry indices (AIs) for the left and right lobe were calculated accordingly.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5440001/v1/4433a5512ee4e450a7a654d6.png"},{"id":70479694,"identity":"c000ce48-cb6a-4331-b898-79348a93131f","added_by":"auto","created_at":"2024-12-03 14:49:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77087,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the AIs between the TLE group (red) and healthy group (blue). The first row of plots represents the AI in the hippocampus for the PET (left) and ASL (right) images. The second row of plots represents the AIs in the temporal lobe for PET and ASL on both left and right sides, respectively. A significant difference existed only for the AIs calculated from PET images of the hippocampus between the TLE group and the healthy group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5440001/v1/72eca0406ee78e451e3cbc4b.png"},{"id":70479210,"identity":"d0ed3c0a-dab2-4a68-bdc1-25063b373dd1","added_by":"auto","created_at":"2024-12-03 14:41:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":85510,"visible":true,"origin":"","legend":"\u003cp\u003eThe AIs from the TLE group were used to compare the difference and correlation between the \u003csup\u003e18\u003c/sup\u003eF-FDG PET and ASL in both the hippocampus and temporal lobe. The box diagram reflects the differences, and the scatter diagram reflects the correlation between the PET and ASL data.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5440001/v1/ed3e9a81ce3d68e14d345ce3.png"},{"id":77423260,"identity":"7fd58571-1c8c-4999-8755-0deeaf8e01af","added_by":"auto","created_at":"2025-02-28 12:38:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1377625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5440001/v1/7ad16fc5-c8bd-4c0d-922d-78049448ca54.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDetection of metabolic and perfusion changes in the hippocampus and temporal lobe regions in patients with temporal lobe epilepsy (TLE) via hybrid \u003csup\u003e18\u003c/sup\u003eF-FDG PET/MRI\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTemporal lobe epilepsy (TLE) is the most common seizure-related illness, affecting approximately 50\u0026nbsp;million people worldwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is usually diagnosed in the first two decades of life and is not gender related. TLE is a type of epilepsy that originates mainly in the temporal lobes of the brain, which are responsible for memory, emotions, and language processing, and in the hippocampus lobes, which play a crucial role in memory formation and retrieval (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Patients with TLE are at great risk of memory impairment, anxiety and depression, with adult-onset TLE patients exhibiting the most affected memory (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Although pharmacotherapy is an effective treatment option for the vast majority of epileptic patients, approximately 30% of patients develop pharmacologically resistant epilepsy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Characterized by frequent and recurring seizures, TLE is the most common type of pharmacoresistant epilepsy to antiepileptic drugs (AEDs) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Identification of the etiology and location of TLE is highly important but highly difficult due to many complicated factors that play a role in seizure onset and development.\u003c/p\u003e \u003cp\u003eMultimodal neuroimaging, including high-resolution MRI, \u003csup\u003e18\u003c/sup\u003eF- FDG PET, and perfusion single-photon emission computed tomography (SPECT), as well as neurophysiological testing, including electroencephalography (EEG), are used as part of the presurgical evaluation for patients with focal epilepsy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). \u003csup\u003e18\u003c/sup\u003eF-FDG PET is useful for differentiating lesions in lesional TLE patients before surgery (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), and may independently indicate the purpose for surgical resection when MRI findings are normal. This method is a well-established tool with 85\u0026ndash;90% sensitivity for diagnosing hypometabolic TLE foci (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In TLE patients, PET/MR has proven valuable for accurate presurgical diagnostic workup (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), limiting radiation dose, sedation duration, and sedation events. Accurate seizure focal localization is essential to limit surgical resection size, preserve healthy brain tissue, and achieve seizure control (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eArterial spin labeling (ASL) is a non-invasive perfusion MRI imaging technique that evaluates cerebral blood flow (CBF) by magnetically labeling of inflowing blood. This approach can also be helpful in non-invasively identification of interictal hypoperfusion on the focal side in TLE patients and the identification of possible epileptogenic foci (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Furthermore, an increasing number of studies have revealed that ASL can be used to detect seizure foci (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Understanding the relationship between \u003csup\u003e18\u003c/sup\u003eF-FDG PET and ASL-MRI is essential for physicians to improve their knowledge of metabolic and hemodynamic changes that occur in TLE patients. This knowledge can aid in identifying the epileptogenic focus and assist treatment planning.\u003c/p\u003e \u003cp\u003eHybrid PET/MR imaging is an emerging technology that facilitates the rapid succession of simultaneous acquisition of PET and MR images. Only a small number of studies have been published thus far on the application of hybrid PET/MR imaging evaluating refractory focal epilepsy (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The ability to obtain data in a single session reduces the number of examinations required for the patient, which is advantageous given the large number of assessments involved in epilepsy surgery planning (\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, advancements in PET/MR imaging now allow for the simultaneous acquisition of FDG-PET and ASL perfusion data, leading to a more accurate assessment of metabolic detection and perfusion changes in TLE patients.\u003c/p\u003e \u003cp\u003eThe present study aimed to detect the metabolism and perfusion changes in the TLE patients using the hybrid \u003csup\u003e18\u003c/sup\u003eF-FDG PET/MRI, and to further compare the relationship between metabolism and perfusion among RTLE (n\u0026thinsp;=\u0026thinsp;6) and LTLE (n\u0026thinsp;=\u0026thinsp;15) patients as well as healthy controls (HCs; n\u0026thinsp;=\u0026thinsp;8) via hybrid PET/MR brain imaging.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Ethics statement\u003c/h2\u003e \u003cp\u003e This research was performed under the guidelines approved by the Ethics Committee of Union Hospital. All of the patients and healthy volunteers who were part of the study consented to ASL and \u003csup\u003e18\u003c/sup\u003eF-FDG PET hybrid PET/MR brain imaging. Parental consent, or consent from a surrogate, was obtained if the child needed parental consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.2 Participants\u003c/h3\u003e\n\u003cp\u003eA retrospective analysis was conducted on twenty-one patients with TLE (including fifteen with LTLE and six with RTLE), between June 1, 2018, and January 20, 2023 (mean age: 26 years, range: 8\u0026ndash;55 years). The diagnosis was made by two experts in clinical neuroimaging, with considering the clinical symptoms, EEG and PET/MRI results. These patients had undergone simultaneous hybrid PET/MRI brain scans and were collected from our PET center. In addition, eight healthy individual who also underwent simultaneous hybrid PET/MRI brain imaging for health examination were also included. All 21 patients and 8 Hc underwent the same protocol: \u003csup\u003e18\u003c/sup\u003eF-FDG-PET and ASL scans, which were acquired concurrently with the hybrid PET/MR scanner. Consecutive patients with a diagnosis of TLE registered at our PET center were enrolled. Patients were divided into two groups: LTLE and RTLE. The inclusion criteria were as follows: 1) had hippocampal and temporal lobe-related TLE and 2) had simultaneous imaging via ASL and \u003csup\u003e18\u003c/sup\u003eF-FDG PET with a hybrid PET/MR brain scan. The exclusion criteria were other types of epilepsy and a lack of ASL scans (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e1.3 Image Acquisition and Reconstruction:\u003c/h3\u003e\n\u003cp\u003eAll patients underwent a static \u003csup\u003e18\u003c/sup\u003eF-FDG PET/MRI scan with the following clinical scan protocol: Blood glucose levels were assessed immediately before FDG injection to exclude hyperglycemic patients whose serum glucose levels exceed 200 mg/dL (those with a serum glucose level less than 8 mmol/L). Patients fasted for at least 6 hours. All patients received an intravenous injection of \u003csup\u003e18\u003c/sup\u003eF-FDG (3.7 MBq/kg). The MRI sequences were scanned during PET acquisition, including the 3D T1-weighted and ASL sequence with 3D pseudocontinuous arterial spin labeling. The parameters were as follows: 3D T1 sequence, three-dimensional gradient-echo sequence; flip angle\u0026thinsp;=\u0026thinsp;12\u0026deg;; time of echo [TE]/time of repetition [TR]\u0026thinsp;=\u0026thinsp;2.6/6.9 ms; bandwidth\u0026thinsp;=\u0026thinsp;50 KHz; FOV\u0026thinsp;=\u0026thinsp;24 cm \u0026times; 24 cm; matrix\u0026thinsp;=\u0026thinsp;384 \u0026times; 384; ASL sequence, TR\u0026thinsp;=\u0026thinsp;4809 ms; TE\u0026thinsp;=\u0026thinsp;10.7 ms; post labeling delay\u0026thinsp;=\u0026thinsp;2025 ms; field of view (FOV)\u0026thinsp;=\u0026thinsp;240\u0026times;40 mm; number of excitations (NEX)\u0026thinsp;=\u0026thinsp;3; spiral readout of 8 arms\u0026times;512 samples; 32\u0026times;4.0 mm axial sections with whole-brain coverage. Brain images were acquired with the patient in the supine position. The images were acquired using a General Electric (GE) SIGNA TOF PET/MR system (GE Healthcare, Milwaukee, WI, USA). \u003csup\u003e18\u003c/sup\u003eF-FDG PET reconstruction was performed with the TOF OSEM algorithm, and the parameters were as follows: FOV\u0026thinsp;=\u0026thinsp;30 cm \u0026times; 30 cm, matrix\u0026thinsp;=\u0026thinsp;192 \u0026times; 192, filter cutoff\u0026thinsp;=\u0026thinsp;3.0 mm, subsets\u0026thinsp;=\u0026thinsp;28, Iterations\u0026thinsp;=\u0026thinsp;3. The PET attenuation correction was atlas-based MRI attenuation correction combined with the zero-echo-time sequence. The duration was approximately 15 minutes for each task.\u003c/p\u003e\n\u003ch3\u003e1.4 Brain segmentation and qualification:\u003c/h3\u003e\n\u003cp\u003eAutomatic brain segmentation was based on an atlas template from the automated anatomical labeling (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. To construct a unique atlas template and optimize PET segmentation accuracy, we registered the atlas template to subject images (RATSI). According to the 3D T1 images, we segmented the brain based on the Anatomical Automatic Labeling (AAL) template, and each region of interest (ROI) was resampled to have 1-mm isotropic voxels. The region-specific volume of each ROI was calculated by counting the voxels in regions defined in the AAL template, which is in the Montreal Neurological Institute (MNI) reference space (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The AAL template contained annotations for 116 brain areas, while only four regions, including the left and right temporal lobe and the left and right hippocampi, were extracted according to their anatomical locations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe segmented hippocampal and temporal lobe regions were used for analysis. Our study first compared the extracted values of SUVmax, SUVmean, and rCBF between the segmented left and right temporal regions (TL/TR) and used the Hc group as a reference. Furthermore, we calculated the AI of the two match groups, ASL and \u003csup\u003e18\u003c/sup\u003eF-FDG PET, in the TLE and Hc groups. Additionally, hippocampal and temporal lobe AI comparisons were performed for TLE patients, which were inferred from interregional \u003csup\u003e18\u003c/sup\u003eF-FDG PET uptake and ASL perfusion correlations.\u003c/p\u003e \u003cp\u003eFor all patients, four quantitative metrics, SUV\u003csub\u003emax\u003c/sub\u003e and SUV\u003csub\u003emean\u003c/sub\u003e from FDG-PET and mean rCBF from ASL, were measured for subsequent analysis. The AI was calculated from the mean rCBF (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and SUV\u003csub\u003emean\u003c/sub\u003e (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) values identified in each region using the following formulas:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{A}\\text{I}=2\\text{*}\\frac{{mean\\:value}_{left}-{mean\\:value}_{\\:right}}{{mean\\:value}_{left}+{mean\\:value}_{right}}*100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e1.5 Statistics:\u003c/h3\u003e\n\u003cp\u003eThe left and right measurements, including the SUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003emean\u003c/sub\u003e and rCBF in the hippocampus and temporal lobe, were compared statistically by two-tailed paired t test. The AI was compared between the TLE patients and Hc groups to distinguish \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism from ASL perfusion in the hippocampus and temporal lobe. To understand the possible mechanism of AI values for \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism and ASL perfusion within epileptic foci in the TLE group, Pearson's correlation and a paired t test were applied to analyze the correlation and difference, respectively, between \u003csup\u003e18\u003c/sup\u003eF-FDG PET and ASL in the hippocampus and temporal lobe. The P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance. The Pearson correlations are described as follows: 0.0\u0026ndash;0.2\u0026thinsp;=\u0026thinsp;slight; 0.2\u0026ndash;0.4\u0026thinsp;=\u0026thinsp;fair; 0.4\u0026ndash;0.6\u0026thinsp;=\u0026thinsp;moderate; 0.6\u0026ndash;0.8\u0026thinsp;=\u0026thinsp;substantial; 0.8\u0026ndash;1.0\u0026thinsp;=\u0026thinsp;almost perfect (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). All statistical analyses were performed by SPSS (version24, IBM).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e The list of SUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003emean\u003c/sub\u003e and CBF values in the hippocampus and temporal lobes, including the mean values and standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe mean value and standard deviation (SD) of the metrics, including the SUV\u003csub\u003emax\u003c/sub\u003e and SUV\u003csub\u003emean\u003c/sub\u003e from PET and CBF in the left and right hippocampus and temporal lobes for the three groups, LTLE, RTLE and Hc are summarized in Table 1. The corresponding paired t test results between the left and right sides were also shown. All the metrics showed significant asymmetrical differences\u0026nbsp;in the hippocampus (p=0.001), but not in the temporal lobe (P\u0026gt;0.05) in LTLE patients. For the RTLE, the SUV\u003csub\u003emean\u003c/sub\u003e in the hippocampus and temporal lobe, the CBF in the temporal lobe showed significant asymmetrical\u0026nbsp;differences (P=0.009, 0.018, 0.034). For the healthy group, the SUV\u003csub\u003emean\u003c/sub\u003e in the hippocampus was significantly different, but the\u0026nbsp;difference in the Hc group was very small (6.33 vs. 6.77). In both subgroups, the ipsilateral mean of the SUV\u003csub\u003emean\u003c/sub\u003e and rCBF in the hippocampus and temporal lobe were lower than those in the contralateral hemisphere and there were significant differences in the hippocampus LTLE (P= 0.001 and P= 0.001) and temporal lobes RTLE (P= 0.018 and P= 0.034) between two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;In comparing the left and right sided of hippocampal LTLE and RTLE to control Hc groups, there was ipsilateral and contralateral significance difference of SUV\u003csub\u003emax\u0026nbsp;\u003c/sub\u003eLTLE compared to control Hc (P= 0.001 and P=0.001, respectively). In addition, there was a significant difference in the ipsilateral SUV\u003csub\u003emean\u0026nbsp;\u003c/sub\u003ebetween\u003csub\u003e\u0026nbsp;\u003c/sub\u003eLTLE and RTLE patients compared to control Hc (P= 0.018 and P=0.006, respectively), while a significant difference in the contralateral perfusion of rCBF LTLE was found compared to control Hc (P= 0.016).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;In comparing the left and right sides of temporal lobes LTLE and RTLE to control Hc groups, there was ipsilateral and contralateral significance difference of SUV\u003csub\u003emax\u0026nbsp;\u003c/sub\u003ein both\u003csub\u003e\u0026nbsp;\u003c/sub\u003eLTLE and RTLE and SUV\u003csub\u003emean\u003c/sub\u003e LTLE compared to control Hc, while an ipsilateral significant difference of SUV\u003csub\u003emean\u0026nbsp;\u003c/sub\u003eRTLE compared to control Hc (P= 0.019). In addition, a significant difference in contralateral perfusion was found between the rCBF LTLE group and the control group (P= 0.01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Comparisons between the TLE group and healthy control group, including the results for PET and ASL in the hippocampus and temporal lobe.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 lists the mean value and SDs of AIs for the hippocampus and temporal lobe determined via PET and ASL. The corresponding statistical comparisons between the TLE and Hc groups are also shown. All the mean values for the AI increased in the TLE group compared with those in the Hc group, especially the AI from PET which increased nearly twofold. However, only the difference in the AI from the PET in the hippocampus was significant (p=0.005). The AI from ASL in the hippocampus also increased notably (7.22% vs. 3.86%) in the TLE group compared with that in the Hc group, although the difference was not statistically significant (p = 0.051). In the temporal lobe, the AIs from both PET and ASL increased in the TLE group, although the difference was not statistically significant (p \u0026gt; 0.05). Figure 3 shows the boxplots used to compare the AIs between the TLE patients and Hc individuals. The difference from PET was much greater than that from ASL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e The AI correlation between PET and ASL signals in the hippocampus and temporal lobe. Correlations were assessed by the Pearson correlation coefficient, and difference were calculated by paired t tests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Table 3 lists the results of reflecting the correlation and difference in the AIs between PET and ASL in the hippocampus and temporal lobe. In the hippocampus, a significant difference in the AI was shown between the PET and ASL, calculated by paired t tests; while in the temporal lobe, a strong correlation was shown between the PET and ASL (r=0.49) and the correlation was statistically significant (p=0.024). Figure 4 shows a comparison of the AIs between PET and ASL by boxplots in both the hippocampus and temporal lobe, and the mean AI for PET was still greater than that for ASL, in both hippocampal and temporal lobe.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSimultaneous PET/MR brain imaging was found to be helpful in identifying regional abnormalities and localizing seizure focus in TLE patients (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our study showed the importance of the consistency, clinical use and relationship of simultaneous hybrid PET/MR brain imaging of rCBF perfusion and \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism for the temporal lobe in the context of TLE to characterize a potential epileptogenic zone. This approach enabled more accurate comparisons and superior results since the data were collected under the same physiological and pathophysiological conditions in a single center experience, clinically analyzed hippocampal and temporal lobe epilepsy in both right- and left-sided TLE patients, and to enhance the impact of our study, a group of Hc was used as a reference group.\u003c/p\u003e \u003cp\u003eLTLE causes significant changes in metabolism and perfusion in the hippocampus, whereas the temporal lobe remains largely unaltered. In contrast, RTLE can be identified by altered metabolism in both the hippocampus and the temporal lobe, with no noticeable changes in perfusion. This lateralization may be attributed to the differences in brain metabolic patterns between both sides. Previous research has documented the phenomenon of a contralateral epileptogenic effect, whereby \u0026ldquo;Mirror\u0026rdquo; foci can develop in the contralateral temporal lobe of TLE patients (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). These \u0026ldquo;Mirror\u0026rdquo; foci may exist preoperatively or appear latent after the surgical removal of the primary epileptogenic focus. Their presence explains the appearance of a simultaneous decrease in metabolic activity in both temporal lobes.\u003c/p\u003e \u003cp\u003eIn this study, we found that LTLE and RTLE patients have different patterns of brain metabolism. For instance, due to their distinctive hemispheric structure, morphological and functional variations in LTLE and RTLE patients have been observed and reported in previous studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Ono SE et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) suggested that TLE should not be considered a unilateral, local, or focal insult. Instead, it deserves to be viewed as a multifactorial and network pathology that may involve several brain structures. Our findings showed a reduction of \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism in LTLE patients, mainly in the hippocampal lobe. In RTLE patients, in addition to the reduction in \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism in the hippocampus, there is more diffuse network dysfunction, causing a decrease in \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism in the temporal lobe. Previous clinical studies have reported similar findings in patients with TLE (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). LTLE appeared to cause more extensive network impairment than RTLE in some studies (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), whereas other research has shown that the opposite is true (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Haneef Z (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), investigated hippocampal network changes in TLE patients to assess for the complete distribution of abnormalities.\u003c/p\u003e \u003cp\u003eOur study revealed that the observed difference in patient data, including SUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003emean\u003c/sub\u003e, and rCBF, were significantly greater between the affected and contralateral hippocampal lobes in LTLE patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) than in Hc individuals. This increased statistical significance may have medical implications in the diagnosis and treatment of TLE. It aids in identifying regions of abnormal metabolic activity in the brain, which can then be used to guide therapy decisions. Reduced metabolic activity in the affected hippocampus may promote the long-term benefit of surgical resection of the hippocampus with low rates of postoperative consequences (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePET image analysis in clinical settings often requires identifying a ROI followed by determining the standardized uptake value (SUV) and AI (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Our study evaluated TLE with Hc and discovered a significant difference, notably in the hippocampus, which is represented in a significant change in AI. These findings are consistent with previous studies and indicate a strong correlation between glucose metabolism (measured by PET) and ASL perfusion in TLE patients (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Other research investigations have revealed a significant correlation between ASL data and 18F-FDG PET in TLE patients, with these areas displaying consistent results from quantitative evaluation (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Based on our findings, the AI obtained from both 18F-FDG PET and ASL may have comparable clinical utility. Overall, AI is an effective technique to investigate variances in brain function. In the setting of TLE, AI can help identify perfusion variations in temporal lobe activity that may be related to seizure activity, hippocampal abnormalities, or network modifications.\u003c/p\u003e \u003cp\u003eWang et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), investigated the relationship between ASL and 18F-FDG PET scans acquired simultaneously, in a study of 12 individuals with unilateral TLE. Based on PET/MR acquisition, their findings suggest that ASL might be a more reliable and complementary method for TLE lateralization than PET. However, their study was limited to a small sample size and the analysis focused only on the hippocampus, a single segment implicated in TLE, for both ASL and PET.\u003c/p\u003e \u003cp\u003eOur quantitative analysis of PET activity may complement qualitative visual analysis. While visual analysis was more sensitive in detecting left-hemispheric hypometabolism, quantitative analysis performed equally well in both the left and right hemispheres (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). The use of non-invasive simultaneous PET/MR brain imaging for epileptic zone localization may reduce the need on more expensive stereo electroencephalography (SEEG) examinations and improve diagnostic characterization TLE patients. For patients who are not eligible for surgical treatment, simultaneous PET/MR can provide insights into the physiological mechanism off drugs resistance, impacting the development of novel medical treatment alternatives (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Using AI data, we found a significant correlation (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;=\u0026thinsp;0.024) between metabolism and perfusion in the temporal lobe TLE patients. These findings are similar with recent research by Boscolo GI et al. (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0005) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) and Wang YH et al. (r\u0026thinsp;=\u0026thinsp;0.74, p\u0026thinsp;=\u0026thinsp;0.006) (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). These results are consistent with the findings of our investigation.\u003c/p\u003e \u003cp\u003eAI was used to analyze asymmetry in symmetrical parts of the brain in order to indicate changes in cerebral regions. When the AI was used to statistically compare cerebral perfusion determined by ASL and glucose metabolism determined by \u003csup\u003e18\u003c/sup\u003eF-FDG PET, our data revealed overall perfusion/metabolism concordance in the temporal lobe (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our findings matched previously reported data, correlating well PET with ASL (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). On the other hand, AI PET does not correlate well with AI ASL in the hippocampus due to limitations in imaging the skull base, including the hippocampus, because of the reduced signal-to-noise ratio (SNR) of ASL perfusion. PET scanning, unlike ASL, directly evaluates radiotracer concentration in the hippocampus, bypassing bone and tissue attenuation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). While both PET and ASL can be used to diagnose TLE, PET-derived AI detects hippocampal abnormalities more accurately than ASL-derived AI. The superior sensitivity of PET-derived AI arises from its specificity for detecting metabolic changes, higher SNR and direct targeting of the epileptic focus, which make PET-derived AI of the hippocampus the preferred indicator for the diagnosis of TLE patients. Our findings indicate that both PET-derived and ASL-derived AI lack sensitivity in the temporal lobes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious research has shown the superiority of AI-based analysis of \u003csup\u003e18\u003c/sup\u003eF-FDG PET following asymmetry correction, compared to conventional visual analysis (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). This is because AI can quantify the subtle differences in activity or perfusion between the left and right hemispheres, which is particularly valuable for diseases like TLE, Stroke, Parkinson\u0026rsquo;s disease and Alzheimer\u0026rsquo;s disease, all characterized by hemispheric imbalances (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). AI provides a non-invasive and objective method for assessing brain function, enabling repeated measurements to monitor disease progression and treatment response over time.\u003c/p\u003e \u003cp\u003eDue to the limitations of our study, we need to be careful when interpreting our findings. A small sample size was obtained since our cohort was small. A larger sample size could help characterize the relationship between \u003csup\u003e18\u003c/sup\u003eF-FDG PET metabolism and ASL perfusion better and provide more accurate findings in TLE patients. Another limitation is the focus on the diagnostic aspect of TLE without its association with relevant clinical symptoms. Future studies should involve collaboration with other departments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTLE patients showed distinctive patterns of brain metabolism and perfusion, with variations observed between LTLE and RTLE. The AIs derived from PET was more accurate than those of ASL in detecting abnormalities in the hippocampus. For LTLE, metabolic changes occurred mainly in the hippocampus, whereas RTLE patients revealed metabolic changes in both the hippocampus and the temporal lobe. Meanwhile, in TLE patients, metabolism and perfusion in the hippocampus differed significantly, while these findings were correlated in the temporal lobe.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAAL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eAnatomical Automatic Labeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAEDs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eAntiepileptic drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eAsymmetry indexes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eArterial spin labeling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eCerebral blood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eElectroencephalography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eField of view\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eGeneral Electric\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eHealthy controls\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLTLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eLeft-sided TLE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eMontreal Neurological Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMRI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNEX\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eNumber of excitations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003ePositron emission tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRATSI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eRegister atlas template to subject images\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eROI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eRegion of interest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRTLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eRight-sided TLE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eStereo electroencephalography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eSignal-to-noise ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSPECT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eSingle-photon emission computed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eStandardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eMaximum standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSUV\u003csub\u003emean\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eMean standardized uptake value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eTime of echo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTLE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eTemporal lobe epilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6751%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85.3249%;\"\u003e\n \u003cp\u003eTime of repetition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any experiments with animals. All procedures involving human participants were carried out in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the Clinical Research Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (NO. 20200290). Written informed consent was obtained from the parents in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. The author Xiaoli Lan is an Associate Editor in this journal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRuan W and Lan X contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ruan W, Arnous M, Al-Asbahi A, Fang Liu, Yongkang Gai, Hua Shu, Xun Sun, Ling Yang. The first draft of the manuscript was written by Arnous M, Al-Asbahi A, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (82372081); Hubei Province Science and Technology Innovation Team ([2022] No. 72); STI2030-Major Projects (2022ZD0211800)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eStanescu L, Ishak GE, Khanna PC, Biyyam DR, Shaw DW, Parisi MT. FDG PET of the brain in pediatric patients: imaging spectrum with MR imaging correlation. Radiographics. 2013;33(5):1279-303.\u003c/li\u003e\n \u003cli\u003eWiltgen BJ, Zhou M, Cai Y, Balaji J, Karlsson MG, Parivash SN, et al. The hippocampus plays a selective role in the retrieval of detailed contextual memories. Current biology : CB. 2010;20(15):1336-44.\u003c/li\u003e\n \u003cli\u003eNovak A, Vizjak K, Rakusa M. Cognitive Impairment in People with Epilepsy. 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Brain communications. 2021;3(3):fcab211.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Temporal lobe epilepsy, hippocampus, PET MR, 18F- fluorodeoxyglucose and perfusion ","lastPublishedDoi":"10.21203/rs.3.rs-5440001/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5440001/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To investigate and compare metabolic and perfusion alterations in temporal lobe epilepsy (TLE) patients via hybrid \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Twenty-one TLE patients (15 with left-sided TLE (LTLE) and 6 with right-sided TLE (RTLE)) who underwent brain \u003csup\u003e18\u003c/sup\u003eF-FDG PET/MRI, and eight healthy controls (Hc) who had \u003csup\u003e18\u003c/sup\u003eF-FDG PET/MRI for health examination, were included. Brain regions were segmented based on the automated anatomical labeling (AAL) template, and the hippocampus and temporal lobe were isolated for further analysis. Left and right sides of these structures were analyzed separately. Accordingly, the maximum standardized uptake value (SUV\u003csub\u003emax\u003c/sub\u003e), mean standardized uptake value (SUV\u003csub\u003emean\u003c/sub\u003e) and cerebral blood flow (CBF) were compared between the two sides via paired t test. Asymmetry indexes (AI) were calculated and statistically compared between the TLE patients and Hc, along with PET and Arterial spin labeling (ASL)-derived AI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e LTLE patients showed significant asymmetrical differences in SUV\u003csub\u003emax\u003c/sub\u003e, SUV\u003csub\u003emean,\u003c/sub\u003e and CBF within the hippocampus region (p\u0026lt;0.01). In RTLE patients, only SUV\u003csub\u003emean\u003c/sub\u003e showed significant asymmetrical in both the hippocampus (p=0.009) and temporal lobe (p=0.018). The PET-derived AI in the hippocampus nearly doubled in the TLE group compared to Hc group. Similarly, ASL-derived AI in the hippocampus also increased (7.22% vs 3.86%) in the TLE group compared to Hc group (p=0.051). In the temporal lobe, both PET and ASL-derived AIs increased in the TLE group; however, these increases were not statistically significant (p=0.260, p=0.364). In the hippocampus, a significant difference existed for the AI between PET and ASL (p=0.001), while the temporal lobe showed a significant correlation for the AI between PET and ASL (r=0.49, p=0.024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e TLE patients exhibited distinct patterns of brain metabolism and perfusion between LTLE and RTLE. \u0026nbsp;And the AIs derived from PET was more accurate than those of ASL in detecting abnormalities in the hippocampus. Meanwhile, metabolism and perfusion in TLE patients differed significantly in the hippocampus, while revealing a correlation in the temporal lobe.\u003c/p\u003e","manuscriptTitle":"Detection of metabolic and perfusion changes in the hippocampus and temporal lobe regions in patients with temporal lobe epilepsy (TLE) via hybrid 18F-FDG PET/MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-03 14:41:06","doi":"10.21203/rs.3.rs-5440001/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eed489f9-1974-44af-9e8b-1b884a1ae118","owner":[],"postedDate":"December 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-28T12:38:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-03 14:41:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5440001","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5440001","identity":"rs-5440001","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}