Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET

Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Metabolic connectivity is associated with seizure outcome in surgically treated temporal lobe epilepsies: A 18F-FDG PET seed correlation analysis

In temporal lobe epilepsy (TLE), the epileptogenic focus is focal and unilateral in the majority of patients. A key characteristic of focal TLE is the presence of subclinical epileptiform activity in both the ictal and contralateral "healthy" hemisphere. Such interictal activity is clinically important, as it may reflect the spread of pathology, potentially leading to secondary epileptogenesis. The role played by white matter pathways in this process is unknown. We compared three interhemispheric white matter tracts (anterior commissure, fornix, and tapetum) to determine the pathway most associated with the presence of contralateral interictal spikes. Forty patients with unilateral left or right TLE were categorized based on the presence or absence of contralateral interictal spikes. Analyses of variance (ANOVAs) were run on diffusion properties from each tract. The analyses revealed that patients with left TLE and with bilateral interictal spikes had lower fractional anisotropy (FA) and higher mean diffusivity (MD) in the tapetum. Patients with right TLE did not show this effect. No significant associations with bilateral activity were observed for the other tracts. Blood oxygen level-dependent (BOLD) functional connectivity data revealed that homotopic lateral, not mesial, temporal areas were reliably correlated in bilateral patients, independent of ictal side. Our results indicate that, among the tracts investigated, only the tapetum was associated with contralateral epileptiform activity, implicating this structure in seizures and possible secondary epileptogenesis. We describe two mechanisms that might explain this association (the interruption of inhibitory signals or the toxic effect of carrying epileptiform signals toward the healthy hemisphere), but also acknowledge other rival factors that may be at work. We also report that patients with TLE with bilateral spikes had increased lateral bitemporal lobe connectivity. Our current results can be seen as bringing together important functional and structural data to elucidate the basis of contralateral interictal activity in focal, unilateral epilepsy. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here.

Over the past two decades, the development of functional imaging methods has greatly promoted our understanding on the changes of neurons following neurodegenerative disorders, such as Parkinson's disease (PD). The application of a spatial covariance analysis on 18F-FDG PET imaging has led to the identification of a distinctive disease-related metabolic pattern. This pattern has proven to be useful in clinical diagnosis, disease progression monitoring as well as assessment of the neuronal changes before and after clinical treatment. It may potentially serve as an objective biomarker on disease progression monitoring, assessment, histological and functional evaluation of related diseases. PD is one of the most common neurodegenerative disorders in the elderly. It is characterized by progressive loss of dopamine neurons in the substantia nigra pars compacta. Throughout the course of disease, the most obvious symptoms are movement-related, such as resting tremor, muscle rigidity, hypokinesia and postural instability (Worth, 2013). Currently, a definite diagnosis of PD is made by clinical evaluation with at least 2 years of follow-up (Hughes et al., 2002; Bhidayasiri and Reichmann, 2013), due to the overlap of motor symptoms between early PD and atypical parkinsonism including multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). However, this classic diagnostic criterion does not benefit the early diagnosis of disease. The prognostic outcome and treatment option are substantially different between PD and atypical parkinsonism. Thus it is critical to develop biomarkers for earlier and more accurate diagnosis of PD. Generally, appropriate diagnostic biomarker for PD ought to cover several key characteristics: (i) minimal invasiveness to detect the biomarker in easily accessible body tissue or fluids, (ii) excellent sensitivity to explore the patients with PD, (iii) high specificity to prevent false-positive results in PD-free individuals, and (iv) robustness against potential affecting factors. A PD-related spatial covariance pattern (PDRP) with quantifiable expression on 18F-FDG PET imaging has been gradually detected using a spatial covariance method during the last two decades and it has been demonstrated to be the right diagnostic biomarker for PD (Eidelberg et al., 1994). PDRP has proven not only to be effective in early discrimination of PD from atypical parkinsonian disorders, but also to be able to assess the disease progression and treatment response. Thus it is considered as a multifunctional biomarker. In this review, we aim to provide an overview of the development in pattern-based biomarker for PD.

Metabolic Brain Network and Surgical Outcome in Temporal Lobe Epilepsy: A Graph Theoretical Study Based on 18F-fluorodeoxyglucose PET

Compromised Memory Function in Schizophrenia and Temporal Lobe Epilepsy

The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emission tomography (18F-FDG PET) at lateralizing seizure foci and diagnosing patients with unilateral temporal lobe epilepsy (TLE). Seventeen patients with unilateral TLE (11 right TLE and 6 left TLE) were retrospectively selected for semi-quantitative 18F-FDG PET analysis. Twenty-three control subjects with a Mini Mental State Examination (MMSE) score of 29 or greater were selected for comparison. Globally averaged standardized uptake value (gSUVmean) was computed for each temporal lobe. Lateralization indices (LI) and the absolute value of lateralization indices (|LI|) were calculated to assess the degree of asymmetry in each subject. Logistic regression analyses were performed at a probability cutoff of 0.5 to classify TLE patients as left or right TLE and to discriminate patients from control subjects. Receiver operating characteristic (ROC) curves were generated to evaluate the utility of LI and |LI| as classification predictors. The Bland Altman test was used to evaluate the reproducibility of the measurements. There was a statistically significant difference in gSUVmean computed LI between left and right TLE patients (P<0.01). There was no statistically significant difference in |LI| between the patient and control groups (P=0.22). Logistic regression revealed that 82% of TLE patients were lateralized correctly using LI as the sole predictor. The area under the ROC curve (AUC) was 0.80. Logistic regression using |LI| on the combined patient/control population showed a diagnostic accuracy of 65% and an AUC of 0.44. Bland Altman analysis revealed an intra-observer reproducibility of 96% and an inter-observer reproducibility of 96% and 91% on successive trials. We conclude that gSUVmean computed LI is a reliable and reproducible measure for predicting seizure lateralization in unilateral TLE patients. However, gSUVmean computed |LI| does not appear to be particularly effective at diagnosing TLE patients from control subjects. Further studies with more patients should investigate other machine learning techniques that combine gSUVmean with other diagnostic predictors.

Altered topological properties and their relationship to cognitive functions in unilateral temporal lobe epilepsy

Objective: To identify brain metabolic network features for temporal lobe epilepsy (TLE) subtype classification and surgical prognosis prediction using machine learning algorithms, thereby supporting clinical decision-making for TLE subtyping and outcome assessment. Methods: ¹⁸F-FDG PET images from 137 patients with drug-resistant TLE treated at Xiangya Hospital's Comprehensive Epilepsy Center from January 2016 to June 2021 were retrospectively analyzed as the training cohort. Network connectivity data were derived using Kullback-Leibler divergence similarity estimation (KLSE), yielding 6 902 network attributes alongside relevant demographic and clinical features. Eight machine learning models (including decision tree and random forest) were trained. The resulting models classified TLE subtypes and were validated using ¹⁸F-FDG PET metabolic network data from an independent cohort of 92 drug-resistant TLE patients (from July 2021 to August 2023). Decision curve analysis was used to select the most clinically practical model for predicting the surgical prognosis of 138 temporal lobe epilepsy patients, including 105 with mesial TLE (76 in the training group and 29 in the independent test group) and 33 with neocortical TLE (23 in the training group and 10 in the independent test group). Results: There were 84 males and 53 females in the training group, with an age of (22.0±8.0) years; in the independent test group, there were 45 males and 47 females, with an age of (24.2±12.8) years. The area under the receiver operating characteristic curve(AUC) of the 8 machine learning models in the training group ranged from 0.904 to 0.985; the AUC in the independent test group ranged from 0.859 to 0.946. According to the comparison of the performance of the above models, it was found that the prediction result of the random forest model was the most accurate and stable [AUC 0.985 (95%CI: 0.985-0.986), accuracy 0.998(95%CI: 0.995-1.000), sensitivity 0.950 (95%CI: 0.898-1.000), specificity 1.000 (95%CI: 1.000-1.000)]. For patients with mesial temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.838 (95%: 0.753-0.923), and the accuracy was 0.838 (95%CI: 0.836-0.841); the AUC in the independent test group reached 0.783(95%CI: 0.549-1.000), with an accuracy of 0.793 (95%CI: 0.782-0.804). For patients with neocortical temporal lobe epilepsy who underwent surgery, the AUC for predicting surgical prognosis in the training group was 0.962(95%CI: 0.881-1.000), and the accuracy was 0.957 (95%CI: 0.953-0.960); while the AUC in the independent test group also reached 0.800 (95%CI: 0.408-1.000), with an accuracy of 0.900 (95%CI: 0.882-0.918). Conclusion: Machine learning models incorporating metabolic network features extracted from ¹⁸F-FDG PET data effectively support TLE subtype classification and surgical prognosis assessment.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Differences in graph theory functional connectivity in left and right temporal lobe epilepsy

The study aims to explore the contribution of the hippocampal formation to the retained language-comprehension network in patients with unilateral mesial temporal lobe epilepsy (TLE). We performed a functional magnetic resonance (MRI) study based on a language comprehension paradigm in 45 right-handed patients with unilateral mesial TLE and 35 healthy control subjects. Activations in the hippocampal formations in both hemispheres were analyzed for each subject as well as for groups of left TLE, right TLE, and controls. In sum, 82% of TLE patients displayed hippocampal activations. A significant difference in hippocampal activation between left and right TLE was found: Right TLE patients showed increased activity in the left hippocampal formation compared with left TLE patients. In contrast, patients with left TLE did not show increased activity in the right hippocampal formation compared with right TLE patients. In comparison with a healthy control group, right TLE patients activated the left hippocampal formation to a greater extent, whereas patients with left TLE did not activate the right hippocampal formation to a greater degree. These findings point to an increased involvement of the left hippocampal formation during a language-comprehension task in right TLE patients. In contrast, left TLE in right-handed patients seems not associated with an enhanced involvement of the right hippocampal formation in retained language comprehension. These findings suggest that effective language comprehension in right-handed subjects with TLE depends on the involvement of the left hippocampal formation and underline the risks of postoperative language decline in patients with left TLE.

Impaired cerebral blood flow networks in temporal lobe epilepsy with hippocampal sclerosis: A graph theoretical approach.

Neuropsychology/Language/Behavior: Adult

To categorize and clinically characterize subtypes of brain structural connectivity patterns in unilateral temporal lobe epilepsy (TLE). Voxel based morphometry (VBM) and surfaced based morphometry (SBM) analysis were used to detect brain structural alterations associated with TLE from MRI data. Principal component analysis (PCA) was performed to identify subtypes of brain structural connectivity patterns. Correlation analysis was used to explore associations between PC scores and clinical characteristics. A total of 59 patients with TLE and 100 healthy adults were included in this study. Widespread cortical atrophy was shown in both left and right TLE (P < 0.05, FWE corrected). Six principal components (PCs) that explained more than 70% of the variance were extracted for left and right TLE, reflecting patterns of brain structural connectivity. PCs representing perisylvian connectivity were positively correlated with verbal IQ (left TLE: r = 0.696, P < 0.001; right TLE: r = 0.484, P = 0.012) and total IQ (left TLE r = 0.608, P < 0.001) and negatively correlated with disease duration (r = -0.448, P = 0.009). In left TLE, the PC in the ipsilateral mesial temporal region was negatively correlated with age at onset (r = -0.382, P = 0.028). In right TLE, the PC representing the default mode network was negatively correlated with number of antiepileptic drugs (r = -0.407, P = 0.039). This study categorized subtypes of unilateral TLE based on brain structural connectivity patterns. Findings may provide insight into seizure pathways, the pathophysiology of epilepsy, including comorbidities such as cognitive impairment, and help predict treatment outcomes.