Antiseizure Medications Normalize Electroencephalographic Functional Connectivity and Power in Children With Benign Epilepsy With Centrotemporal Spikes.

BACKGROUND: The decision to treat children with benign epilepsy with centrotemporal spikes (BECTS) using antiseizure medications (ASM) is controversial. Our goal is to compare the effect of ASM treatment on the alteration of electroencephalographic (EEG) functional connectivity and power across four frequency bands in children with BECTS. METHODS: Children with BECTS with two-year follow-up were retrospectively divided into ASM versus non-ASM groups. The network properties of the EEGs as based on network-based statistic and graph theory were evaluated by the following indices: global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands (delta, theta, alpha, and beta). EEG power including absolute power (AP) and relative power (RP) was analyzed in four frequency bands. RESULTS: In children with BECTS with ASM treatment, there was no significant change in EEG connectivity across all bands before and after two years of ASM. In children with BECTS without ASM treatment, there was a significant increase of global efficiency, clustering coefficient, and nodal strength but not the betweenness centrality in the delta band after two years of follow-up. A decrease in AP in the delta and theta bands and a decrease in RP in the theta band were found in the ASM group after two years of treatment. CONCLUSIONS: Our results suggest that ASM may play a role in modulating the development of increasing overall brain connectivity and in downregulating overt synaptic activity, but not intrinsic focal connectivity, in the early years of BECTS. The changes in the EEG power indicate that ASM significantly normalized slow-wave band power.

Glucose Metabolism of Hippocampal Subfields in Medial Temporal Lobe Epilepsy.

PURPOSE: Reduced glucose metabolism in the hippocampus is commonly observed in cases of medial temporal lobe epilepsy (MTLE) with hippocampal sclerosis (HS). Glucose metabolism among the various hippocampal subfields has not been thoroughly investigated. PATIENTS AND METHODS: This study examined 29 patients (18 females; 15-58 years) diagnosed with HS who underwent surgery for drug-resistant epilepsy. FreeSurfer 7.1.1 was used in the processing of MRI data and 18 F-FDG PET scans to derive volumetric data and the FDG SUVr in the whole hippocampus and hippocampal subfields, including the CA1, CA2-4, granule cell and molecular layer of the dentate gyrus (GC-ML-DG), and subiculum. Asymmetries in the volume and SUVr between the 2 sides from the subfields of the hippocampus were defined in terms of an asymmetry index. Comparisons of the asymmetry index among these regions were performed. The correlations between asymmetry index values and postoperative outcomes and presurgical neuropsychological test results were also evaluated. RESULT: The CA1, CA2-4, subiculum, GC-ML-DG, and whole hippocampus presented reductions in volume and hypometabolism ipsilateral to MTLE. Asymmetries in volume and SUVr were significantly less pronounced in the CA1 and subiculum than in the CA2-4 or GC-ML-DG. Postoperative seizure outcomes were not correlated with the asymmetry index for volume or SUVr in any hippocampal subfield. In cases of left MTLE, scores of immediate logical memory and delayed logical memory were positively correlated with the asymmetry index for SUVr in the following subfields: CA1 ( R = 0.829, P = 0.021; R = 0.770, P = 0.043), CA2-4 ( R = 0.825, P = 0.022; R = 0.894, P = 0.007), subiculum ( R = 0.882, P = 0.009; R = 0.853, P = 0.015), GC-ML-DG ( R = 0.850, P = 0.015; R = 0.796, P = 0.032), and whole hippocampus ( R = 0.841, P = 0.018; R = 0.822, P = 0.023). In cases of right MTLE, the scores for delayed face memory were positively correlated with the asymmetry index for SUVr in the subiculum ( R = 0.935, P = 0.006). CONCLUSIONS: In cases of HS, changes in glucose metabolism levels varied among the hippocampal subfields. Asymmetries in glucose metabolism among the CA-1, CA2-4, subiculum, and GC-ML-DG subregions were correlated with scores for verbal memory among patients with left MTLE. Asymmetric glucose metabolism in the subiculum was also correlated with visual memory scores among patients with right MTLE.

Vascular compactness of unruptured brain arteriovenous malformation predicts risk of hemorrhage after stereotactic radiosurgery.

The aim of the study was to investigate whether morphology (i.e. compact/diffuse) of brain arteriovenous malformations (bAVMs) correlates with the incidence of hemorrhagic events in patients receiving Stereotactic Radiosurgery (SRS) for unruptured bAVMs. This retrospective study included 262 adult patients with unruptured bAVMs who underwent upfront SRS. Hemorrhagic events were defined as evidence of blood on CT or MRI. The morphology of bAVMs was evaluated using automated segmentation which calculated the proportion of vessel, brain tissue, and cerebrospinal fluid in bAVMs on T2-weighted MRI. Compactness index, defined as the ratio of vessel to brain tissue, categorized bAVMs into compact and diffuse types based on the optimal cutoff. Cox proportional hazard model was used to identify the independent factors for post-SRS hemorrhage. The median clinical follow-ups was 62.1 months. Post-SRS hemorrhage occurred in 13 (5.0%) patients and one of them had two bleeds, resulting in an annual bleeding rate of 0.8%. Multivariable analysis revealed bAVM morphology (compact versus diffuse), bAVM volume, and prescribed margin dose were significant predictors. The post-SRS hemorrhage rate increased with larger bAVM volume only among the diffuse nidi (1.7 versus 14.9 versus 30.6 hemorrhage per 1000 person-years in bAVM volume < 20 cm3 versus 20-40 cm3 versus > 40 cm3; p = 0.022). The significantly higher post-SRS hemorrhage rate of Spetzler-Martin grade IV-V compared with grade I-III bAVMs (20.0 versus 3.3 hemorrhages per 1000 person-years; p = 0.001) mainly originated from the diffuse bAVMs rather than the compact subgroup (30.9 versus 4.8 hemorrhages per 1000 person-years; p = 0.035). Compact and smaller bAVMs, with higher prescribed margin dose harbor lower risks of post-SRS hemorrhage. The post-SRS hemorrhage rate exceeded 2.2% annually within the diffuse and large (> 40 cm3) bAVMs and the diffuse Spetzler-Martin IV-V bAVMs. These findings may help guide patient selection of SRS for the unruptured bAVMs.

Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network.

PURPOSE: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. METHODS: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. RESULTS: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. CONCLUSION: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions.

Morphological and metabolic asymmetries of the thalamic subregions in temporal lobe epilepsy predict cognitive functions.

Both morphological and metabolic imaging were used to determine how asymmetrical changes of thalamic subregions are involved in cognition in temporal lobe epilepsy (TLE). We retrospectively recruited 24 left-TLE and 15 right-TLE patients. Six thalamic subnuclei were segmented by magnetic resonance imaging, and then co-registered onto Positron emission tomography images. We calculated the asymmetrical indexes of the volumes and normalized standard uptake value ratio (SUVR) of the entire and individual thalamic subnuclei. The SUVR of ipsilateral subnuclei were extensively and prominently decreased compared with the volume loss. The posterior and medial subnuclei had persistently lower SUVR in both TLE cases. Processing speed is the cognitive function most related to the metabolic asymmetry. It negatively correlated with the metabolic asymmetrical indexes of subregions in left-TLE, while positively correlated with the subnuclei volume asymmetrical indexes in right-TLE. Epilepsy duration negatively correlated with the volume asymmetry of most thalamic subregions in left-TLE and the SUVR asymmetry of ventral and intralaminar subnuclei in right-TLE. Preserved metabolic activity of contralateral thalamic subregions is the key to maintain the processing speed in both TLEs. R-TLE had relatively preserved volume of the ipsilateral thalamic volume, while L-TLE had relatively decline of volume and metabolism in posterior subnucleus.

Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression.

BACKGROUND: The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype. METHOD: This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC). RESULTS: We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders. CONCLUSIONS: The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.

Identification of tophi in ultrasound imaging based on transfer learning and clinical practice.

Gout is a common metabolic disorder characterized by deposits of monosodium urate monohydrate crystals (tophi) in soft tissue, triggering intense and acute arthritis with intolerable pain as well as articular and periarticular inflammation. Tophi can also promote chronic inflammatory and erosive arthritis. 2015 ACR/EULAR Gout Classification criteria include clinical, laboratory, and imaging findings, where cases of gout are indicated by a threshold score of ≥ 8. Some imaging-related findings, such as a double contour sign in ultrasound, urate in dual-energy computed tomography, or radiographic gout-related erosion, generate a score of up to 4. Clearly, the diagnosis of gout is largely assisted by imaging findings; however, dual-energy computed tomography is expensive and exposes the patient to high levels of radiation. Although musculoskeletal ultrasound is non-invasive and inexpensive, the reliability of the results depends on expert experience. In the current study, we applied transfer learning to train a convolutional neural network for the identification of tophi in ultrasound images. The accuracy of predictions varied with the convolutional neural network model, as follows: InceptionV3 (0.871 ± 0.020), ResNet101 (0.913 ± 0.015), and VGG19 (0.918 ± 0.020). The sensitivity was as follows: InceptionV3 (0.507 ± 0.060), ResNet101 (0.680 ± 0.056), and VGG19 (0.747 ± 0.056). The precision was as follows: InceptionV3 (0.767 ± 0.091), ResNet101 (0.863 ± 0.098), and VGG19 (0.825 ± 0.062). Our results demonstrate that it is possible to retrain deep convolutional neural networks to identify the patterns of tophi in ultrasound images with a high degree of accuracy.

Association Between Pseudoprogression of Vestibular Schwannoma After Radiosurgery and Radiological Features of Solid and Cystic Components.

BACKGROUND AND OBJECTIVES: The pathophysiology of vestibular schwannoma (VS) pseudoprogression after Gamma Knife radiosurgery (GKRS) remains unclear. Radiological features in pretreatment magnetic resonance images may help predict VS pseudoprogression. This study used VS radiological features quantified using an automated segmentation algorithm to predict pseudoprogression after GKRS treatment. METHODS: This is a retrospective study comprising 330 patients with VS who received GKRS. After image preprocessing and T2W/contrast-enhanced T1-weighted image (CET1W) image generation, with fuzzy C-means clustering, VSs were segmented into solid and cystic components and classified as solid and cystic. Relevant radiological features were then extracted. The response to GKRS was classified into "nonpseudoprogression" and "pseudoprogression/fluctuation". The Z test for two proportions was used to compare solid and cystic VS for the likelihood of pseudoprogression/fluctuation. Logistic regression was used to assess the correlation between clinical variables and radiological features and response to GKRS. RESULTS: The likelihood of pseudoprogression/fluctuation after GKRS was significantly higher for solid VS compared with cystic VS (55% vs 31%, P < .001). For the entire VS cohort, multivariable logistic regression revealed that a lower mean tumor signal intensity (SI) in T2W/CET1W images was associated with pseudoprogression/fluctuation after GKRS ( P = .001). For the solid VS subgroup, a lower mean tumor SI in T2W/CET1W images ( P = .035) was associated with pseudoprogression/fluctuation after GKRS. For the cystic VS subgroup, a lower mean SI of the cystic component in T2W/CET1W images ( P = .040) was associated with pseudoprogression/fluctuation after GKRS. CONCLUSION: Pseudoprogression is more likely to occur in solid VS compared with cystic VS. Quantitative radiological features in pretreatment magnetic resonance images were associated with pseudoprogression after GKRS. In T2W/CET1W images, solid VS with a lower mean tumor SI and cystic VS with a lower mean SI of cystic component were more likely to have pseudoprogression after GKRS. These radiological features can help predict the likelihood of pseudoprogression after GKRS.

Connectome-based predictive modeling for functional recovery of acute ischemic stroke.

Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.

Application of artificial intelligence to stereotactic radiosurgery for intracranial lesions: detection, segmentation, and outcome prediction.

BACKGROUND: Rapid evolution of artificial intelligence (AI) prompted its wide application in healthcare systems. Stereotactic radiosurgery served as a good candidate for AI model development and achieved encouraging result in recent years. This article aimed at demonstrating current AI application in radiosurgery. METHODS: Literatures published in PubMed during 2010-2022, discussing AI application in stereotactic radiosurgery were reviewed. RESULTS: AI algorithms, especially machine learning/deep learning models, have been administered to different aspect of stereotactic radiosurgery. Spontaneous tumor detection and automated lesion delineation or segmentation were two of the promising application, which could be further extended to longitudinal treatment follow-up. Outcome prediction utilized machine learning algorithms with radiomic-based analysis was another well-established application. CONCLUSIONS: Stereotactic radiosurgery has taken a lead role in AI development. Current achievement, limitation, and further investigation was summarized in this article.

Compactness index: a radiosurgery outcome predictor for patients with unruptured brain arteriovenous malformations.

OBJECTIVE: The goal of the study was to define and quantify brain arteriovenous malformation (bAVM) compactness and to assess its effect on outcomes after Gamma Knife radiosurgery (GKRS) for unruptured bAVMs. METHODS: Unsupervised machine learning with fuzzy c-means clustering was used to differentiate the tissue constituents of bAVMs on T2-weighted MR images. The percentages of vessel, brain, and CSF were quantified. The proposed compactness index, defined as the ratio of vasculature tissue to brain tissue, categorized bAVM morphology into compact, intermediate, and diffuse types according to the tertiles of this index. The outcomes of interest were complete obliteration and radiation-induced changes (RICs). RESULTS: A total of 209 unruptured bAVMs treated with GKRS were retrospectively included. The median imaging and clinical follow-up periods were 49.2 and 72.3 months, respectively. One hundred seventy-three bAVMs (82.8%) achieved complete obliteration after a median latency period of 43.3 months. The rates of RIC and permanent RIC were 76.1% and 3.8%, respectively. Post-GKRS hemorrhage occurred in 14 patients (6.7%), resulting in an annual bleeding risk of 1.0%. Compact bAVM, smaller bAVM volume, and exclusively superficial venous drainage were independent predictors of complete obliteration. Diffuse bAVM morphology, larger bAVM volume, and higher margin dose were independently associated with RICs. CONCLUSIONS: The compactness index quantitatively describes the compactness of unruptured bAVMs. Moreover, compact bAVMs may have a higher obliteration rate and a smaller risk of RICs than diffuse bAVMs. This finding could help guide decision-making regarding GKRS treatment for patients with unruptured bAVMs.

Graph Theory-Based Electroencephalographic Connectivity via Phase-Locking Value and Its Association with Ketogenic Diet Responsiveness in Patients with Focal Onset Seizures.

Ketogenic diets (KDs) are a promising alternative therapy for pediatric refractory epilepsy. Several predictors of KD responsiveness have been identified, including biochemical parameters, seizure types, and electroencephalography (EEG) examinations. We hypothesized that graph theory-based EEG functional connectivity could explain KD responses in patients presenting focal onset seizure (FOS). A total of 17 patients aged 0-30 years old with focal onset seizures (FOS) were recruited as a study group between January 2015 and July 2021. Twenty age-matched children presenting headache with no intracranial complications nor other medical issues were enrolled as a control group. Data were obtained at baseline and at 12 months after initiating KD therapy (KDT) using the child behavior checklist (CBCL) and brain functional connectivity parameters based on phase-locking value from 19 scalp EEG signals, including nodal strength, global efficiency, clustering coefficient, and betweenness centrality. Compared with age-matched controls, patients presenting FOS with right or bilateral EEG lateralization presented higher baseline functional connectivity, including parameters such as global efficiency, mean cluster coefficient and mean nodal strength in the delta and beta frequency bands. In patients presenting FOS with right or bilateral EEG lateralization, the global efficiency of functional connectivity parameters in the delta and theta frequency bands was significantly lower at 12 months after KDT treatment than before KDT. Those patients also presented a significantly lower mean clustering coefficient and mean nodal strength in the theta frequency band at 12 months after KDT treatment. Changes in brain functional connectivity were positively correlated with social problems, attention, and behavioral scores based on CBCL assessments completed by parents. This study provides evidence that KDT might be beneficial in the treatment of patients with FOS. Graph theoretic analysis revealed that the observed effects were related to decreased functional connectivity, particularly in terms of global efficiency. Our findings related to brain connectivity revealed lateralization to the right (non-dominant) hemisphere; however, we were unable to define the underlying mechanism. Our data revealed that in addition to altered brain connectivity, KDT improved the patient's behavior and emotional state.

Resting-State EEG Functional Connectivity in Children with Rolandic Spikes with or without Clinical Seizures.

Alterations in dynamic brain network function are increasingly recognized in epilepsy. Benign childhood epilepsy with centrotemporal spikes (BECTS), or benign rolandic seizures, is the most common idiopathic focal epilepsy in children. In this study, we analyzed EEG functional connectivity (FC) among children with rolandic spikes with or without clinical seizures as compared to controls, to investigate the relationship between FC and clinical parameters in children with rolandic spikes. The FC analysis based on graph theory and network-based statistics in different frequency bands evaluated global efficiency, clustering coefficient, betweenness centrality, and nodal strength in four frequency bands. Similar to BECTS patients with seizures, children with rolandic spikes without seizures had significantly increased global efficiency, mean clustering coefficient, mean nodal strength, and connectivity strength, specifically in the theta frequency band at almost all proportional thresholds, compared with age-matched controls. Decreased mean betweenness centrality was only present in BECTS patients with seizures. Age at seizure onset was significantly positively associated with the strength of EEG-FC. The decreased function of betweenness centrality was only presented in BECTS patients with clinical seizures, suggesting weaker local connectivity may lower the seizure threshold. These findings may affect treatment policy in children with rolandic spikes.

Strategic infarct location for post-stroke seizure.

Post-stroke seizure (PSS) can have a strong negative impact on functional recovery after stroke. Researchers have identified numerous risk factors of PSS; however, the relationship between infarction location and PSS remains unclear. We recruited patients who presented with an acute cerebral infarction between 2012 and 2017 and suffered from seizures within 1 year after stroke (PSS group). PSS group was subgrouped into early-PSS and late-PSS groups based on the interval between seizure and stroke. We also recruited an equal number of acute cerebral infarction patients without post-stroke seizures during the follow-up period (Non-PSS group). All brain MRIs from the two groups were processed, whereupon normalized infarct maps from the PSS and Non-PSS groups were compared via voxel- and volumetric-based analyses. A total of 132 subjects were enrolled in the study, including PSS (n = 66, consisting of 31 early-PSS and 35 late-PSS) and Non-PSS (n = 66) patients. No significant differences were observed between the two groups in terms of stroke lateralization or severity. Image analysis revealed that the volume of infarction was larger in the PSS group than in the Non-PSS group; however, the difference did not reach the level of significance. Unlike the Non-PSS group, the PSS group presented hot spots over the left central region, left superior parietal lobule, and right frontal operculum. We observed differences between the distribution of hot spots among patients with early-PSS and those with late-PSS. We found that some brain regions were significantly associated with the development of PSS after ischemic stroke, and these regions differed between cases of early and late PSS. It appears that the location of infarction could help clinicians assess the risk of PSS in specific post-stroke stages.

Left Frontotemporal Region Plays a Key Role in Letter Fluency Task-Evoked Activation and Functional Connectivity in Normal Subjects: A Functional Near-Infrared Spectroscopy Study.

Letter fluency task (LFT) is a tool that measures memory, executive function, and language function but lacks a definite cutoff value to define abnormalities. We used the optical signals of functional near-infrared spectroscopy (fNIRS) to study the differences in power and connectivity between the high-functioning and low-functioning participants while performing three successive LFTs, as well as the relationships between the brain network/power and LFT performance. We found that the most differentiating factor between these two groups was network topology rather than activation power. The high-functional group (7 men and 10 women) displayed higher left intra-hemispheric global efficiency, nodal strength, and shorter characteristic path length in the first section. They then demonstrated a higher power over the left Broca's area than the right corresponding area in the latter two sections. The low-LFT group (9 men and 11 women) displayed less left-lateralized connectivity and activation power. LFT performance was only related to the network topology rather than the power values, which was only presented in the low-functioning group in the second section. The direct correlation between power and connectivity primarily existed in the inter-hemispheric network, with the timing relationship also seeming to be present. In conclusion, the high-functioning group presented more prominent left-lateralized intra-hemispheric network connectivity and power activation, particularly in the Broca's area. The low-functioning group seemed to prefer using other networks, like the inter-hemispheric, rather than having a single focus on left intra-hemispheric connectivity. The network topology seemed to better reflect the LFT performance than did the power values.

Clinical characteristics and long-term outcome of cerebral cavernous malformations-related epilepsy.

OBJECTIVE: Cerebral cavernous malformations (CCMs) present variably, and epileptic seizures are the most common symptom. The factors contributing to cavernoma-related epilepsy (CRE) and drug resistance remain inconclusive. The outcomes of CRE after different treatment modalities have not yet been fully addressed. This study aimed to characterize the clinical features of patients with CRE and the long-term seizure outcomes of medical and surgical treatment strategies. METHODS: This was a retrospective cohort of 135 patients with CCM who were diagnosed in 2007-2011 and followed up for 93.6 months on average. The patients were divided into drug-resistant epilepsy (DRE; n = 29), non-DRE (n = 45), and no epilepsy (NE; n = 61). RESULTS: Temporal CCM was the factor most strongly associated with the development of both CRE and DRE. The majority of patients with single temporal CCMs had CRE (86.8%, n = 33), and 50% had DRE, whereas only 14.7% (n = 5) with a nontemporal supratentorial CCM had DRE (p < .05). The most common lesion site in the DRE group was the mesiotemporal lobe (50%). Multiple CCMs were more frequently observed in the CRE (29.2%) than the NE (11.5%) group (p < .05). In patients with CRE, multiple lesions were associated with a higher rebleeding rate (odds ratio = 11.1), particularly in those with DRE (odds ratio = 15.4). The majority of patients who underwent resective surgery for DRE (76.5%, n = 13) achieved International League Against Epilepsy Class I and II seizure outcomes even after a long disease course. SIGNIFICANCE: Temporal CCM not only predisposes to CRE but also is a major risk factor for drug resistance. The mesiotemporal lobe is the most epileptogenic zone. Multiple CCMs are another risk factor for CRE and increase the rebleeding risk in these patients. Surgical resection could provide beneficial long-term seizure outcomes in patients with DRE.

Structural connectivity in children after total corpus callosotomy.

OBJECTIVES: To investigate structural connectivity after total callosotomy. METHODS: Deterministic fiber tracking (tractography) of whole brain white matter was performed on 13 epilepsy patients pre- and post-callosotomy. The analysis of structural connectivity was based on graph theory and network-based analysis with a focus on the inter- and intrahemispheric networks. Clinical demographic data including seizure patterns and outcomes were scored for the identification of correlations. RESULTS: After total callosotomy, structural interhemispheric networks were significantly interrupted. Specific changes were observed in the structural intrahemispheric networks in both hemispheres: 3 edges presented with significant decreases in the left hemisphere, whereas 2 edges presented with significant decreases in the right hemisphere. No global changes were observed in the network density, average weighted strength, average characteristic path length, or global efficiency of intrahemispheric networks. The intrahemispheric hubs and nodal efficiency were minimal changed after callosotomy. CONCLUSION: While there was a significant decrease in structural interhemispheric connectivity post-callosotomy, we observed synchronously decremented changes of intrahemispheric edges in each hemisphere. This study suggests that white matter maintains the structural connectivity intrahemispherically although functional connectivity recovered after total callosotomy.

Added Value of Computed Tomography Virtual Intravascular Endoscopy in the Evaluation of Coronary Arteries with Stents or Plaques.

Coronary computed tomography angiography (CCTA) is a widely used imaging modality for diagnosing coronary artery disease (CAD) but is limited by a high false positive rate when evaluating coronary arteries with stents and heavy calcifications. Virtual intravascular endoscopy (VIE) images generated from CCTA can be used to qualitatively assess the vascular lumen and might be helpful for overcoming this challenge. In this study, one hundred subjects with coronary stents underwent both CCTA and invasive coronary angiography (ICA). A total of 902 vessel segments were analyzed using CCTA and VIE. The vessel segments were first analyzed on CCTA alone. Then, using VIE, the segments were classified qualitatively as either negative or positive for in-stent restenosis (ISR) or CAD. These results were compared, using ICA as the reference, to determine the added diagnostic value of VIE. Of the 902 analyzed vessel segments, CCTA/VIE had sensitivity, specificity, accuracy, positive predictive value, and negative predictive value (shown in %) of 93.9/90.2, 96.2/98.2, 96.0/97.7, 70.0/83.1, and 99.4/99.0, respectively, in diagnosing ISR or CAD, with significantly improved specificity (p = 0.025), accuracy (p = 0.046), and positive predictive value (p = 0.047). VIE can be a helpful addition to CCTA when evaluating coronary arteries.

Automated Cerebral Infarct Detection on Computed Tomography Images Based on Deep Learning.

The limited accuracy of cerebral infarct detection on CT images caused by the low contrast of CT hinders the desirable application of CT as a first-line diagnostic modality for screening of cerebral infarct. This research was aimed at utilizing convolutional neural network to enhance the accuracy of automated cerebral infarct detection on CT images. The CT images underwent a series of preprocessing steps mainly to enhance the contrast inside the parenchyma, adjust the orientation, spatially normalize the images to the CT template, and create a t-score map for each patient. The input format of the convolutional neural network was the t-score matrix of a 16 × 16-pixel patch. Non-infarcted and infarcted patches were selected from the t-score maps, on which data augmentation was conducted to generate more patches for training and testing the proposed convolutional neural network. The convolutional neural network attained a 93.9% patch-wise detection accuracy in the test set. The proposed method offers prompt and accurate cerebral infarct detection on CT images. It renders a frontline detection modality of ischemic stroke on an emergent or regular basis.

Machine Learning Quantitative Analysis of FDG PET Images of Medial Temporal Lobe Epilepsy Patients.

PURPOSE: 18F-FDG PET is widely used in epilepsy surgery. We established a robust quantitative algorithm for the lateralization of epileptogenic foci and examined the value of machine learning of 18F-FDG PET data in medial temporal lobe epilepsy (MTLE) patients. PATIENTS AND METHODS: We retrospectively reviewed patients who underwent surgery for MTLE. Three clinicians identified the side of MTLE epileptogenesis by visual inspection. The surgical side was set as the epileptogenic side. Two parcellation paradigms and corresponding atlases (Automated Anatomical Labeling and FreeSurfer aparc + aseg) were used to extract the normalized PET uptake of the regions of interest (ROIs). The lateralization index of the MTLE-associated regions in either hemisphere was calculated. The lateralization indices of each ROI were subjected for machine learning to establish the model for classifying the side of MTLE epileptogenesis. RESULT: Ninety-three patients were enrolled for training and validation, and another 11 patients were used for testing. The hit rate of lateralization by visual analysis was 75.3%. Among the 23 patients whose MTLE side of epileptogenesis was incorrectly determined or for whom no conclusion was reached by visual analysis, the Automated Anatomical Labeling and aparc + aseg parcellated the associated ROIs on the correctly lateralized MTLE side in 100.0% and 82.6%. In the testing set, lateralization accuracy was 100% in the 2 paradigms. CONCLUSIONS: Visual analysis of 18F-FDG PET to lateralize MTLE epileptogenesis showed a lower hit rate compared with machine-assisted interpretation. While reviewing 18F-FDG PET images of MTLE patients, considering the regions associated with MTLE resulted in better performance than limiting analysis to hippocampal regions.