Disrupted White Matter Integrity and Structural Brain Networks in Temporal Lobe Epilepsy With and Without Interictal Psychosis

Background: Despite the importance of psychosis as a comorbidity of temporal lobe epilepsy (TLE), the underlying neural mechanisms are still unclear. We aimed to investigate abnormalities specific to psychosis in TLE, using diffusion MRI parameters and graph-theoretical network analysis. Material and Methods: We recruited 49 patients with TLE (20 with and 29 without interictal schizophrenia-like psychosis) and 42 age-/gender-matched healthy controls. We performed 3-tesla MRI scans including 3D T1-weighted imaging and diffusion tensor imaging in all participants. Among the three groups, fractional anisotropy (FA), mean diffusivity (MD), and global network metrics were compared by analyses of covariance. Regional connectivity strength was compared by network-based statistics. Results: Compared to controls, TLE patients showed significant temporal and extra-temporal changes in FA, and MD, which were more severe and widespread in patients with than without psychosis. We observed distinct differences between TLE patients with and without psychosis in the anterior thalamic radiation (ATR), inferior fronto-occipital fasciculus (IFOF), and inferior longitudinal fasciculus (ILF). Similarly, for network metrics, global, and local efficiency and increased path length were significantly reduced in TLE patients compared to controls, but with more severe changes in TLE with psychosis than without psychosis. Network-based statistics detected significant differences between TLE with and without psychosis mainly involving the left limbic and prefrontal areas. Conclusion: TLE patients with interictal schizophrenia-like psychosis showed more widespread and severe white matter impairment, involving the ATR, IFOF and ILF, as well as disrupted network connectivity, particularly in the left limbic and prefrontal cortex, than patients without psychosis

Neuroimaging-based brain-age prediction in diverse forms of epilepsy: a signature of psychosis and beyond

Epilepsy is a diverse brain disorder, and the pathophysiology of its various forms and comorbidities is largely unknown. A recent machine learning method enables us to estimate an individual’s “brain-age” from MRI; this brain-age prediction is expected as a novel individual biomarker of neuropsychiatric disorders. The aims of this study were to estimate the brain-age for various categories of epilepsy and to evaluate clinical discrimination by brain-age for (1) the effect of psychosis on temporal lobe epilepsy (TLE), (2) psychogenic nonepileptic seizures (PNESs) from MRI-negative epilepsies, and (3) progressive myoclonic epilepsy (PME) from juvenile myoclonic epilepsy (JME). In total, 1196 T1-weighted MRI scans from healthy controls (HCs) were used to build a brain-age prediction model with support vector regression. Using the model, we calculated the brain-predicted age difference (brain-PAD: predicted age—chronological age) of the HCs and 318 patients with epilepsy. We compared the brain-PAD values based on the research questions. As a result, all categories of patients except for extra-temporal lobe focal epilepsy showed a significant increase in brain-PAD. TLE with hippocampal sclerosis presented a significantly higher brain-PAD than several other categories. The mean brain-PAD in TLE with interictal psychosis was 10.9 years, which was significantly higher than TLE without psychosis (5.3 years). PNES showed a comparable mean brain-PAD (10.6 years) to that of epilepsy patients. PME had a higher brain-PAD than JME (22.0 vs. 9.3 years). In conclusion, neuroimaging-based brain-age prediction can provide novel insight into or clinical usefulness for the diverse symptoms of epileps

Japanese multicenter database of healthy controls for [123I]FP-CIT SPECT

Purpose: The aim of this multicenter trial was to generate a [¹²³I]FP-CIT SPECT database of healthy controls from the common SPECT systems available in Japan. Methods: This study included 510 sets of SPECT data from 256 healthy controls (116 men and 140 women; age range, 30–83 years) acquired from eight different centers. Images were reconstructed without attenuation or scatter correction (NOACNOSC), with only attenuation correction using the Chang method (ChangACNOSC) or X-ray CT (CTACNOSC), and with both scatter and attenuation correction using the Chang method (ChangACSC) or X-ray CT (CTACSC). These SPECT images were analyzed using the Southampton method. The outcome measure was the specific binding ratio (SBR) in the striatum. These striatal SBRs were calibrated from prior experiments using a striatal phantom. Results: The original SBRs gradually decreased in the order of ChangACSC, CTACSC, ChangACNOSC, CTACNOSC, and NOACNOSC. The SBRs for NOACNOSC were 46% lower than those for ChangACSC. In contrast, the calibrated SBRs were almost equal under no scatter correction (NOSC) conditions. A significant effect of age was found, with an SBR decline rate of 6.3% per decade. In the 30–39 age group, SBRs were 12.2% higher in women than in men, but this increase declined with age and was absent in the 70–79 age group. Conclusions: This study provided a large-scale quantitative database of [¹²³I]FP-CIT SPECT scans from different scanners in healthy controls across a wide age range and with balanced sex representation. The phantom calibration effectively harmonizes SPECT data from different SPECT systems under NOSC conditions. The data collected in this study may serve as a reference database