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

Abstract

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 epilepsy.

T1-weighted MRI-driven Brain Age Estimation in Alzheimer’s Disease and Parkinson’s Disease

“…First, voxel-wise brain age frameworks require a very large number of T1w MRI scans from HCs in order to build a robust prediction model. To overcome this aspect, the voxel-wise brain age frameworks use the MRI scans from different sites [14,34,41]. This point might be considered as a potential weakness, because it was shown that MRI measurements are influenced by scanner characteristics and imaging protocol [42,43].…”

<p>Widely Impaired White Matter Integrity and Altered Structural Brain Networks in Psychogenic Non-Epileptic Seizures</p>

“…These areas are also related to normal aging during young adulthood to middle age, 34 which may explain the faster brain aging in patients with PNES. 5 Interestingly, FA in the corpus callosum is the area most associated with the aging process. 35 Because we found a severe FA reduction mainly in the deep white matter, including the corpus callosum (Figure 1), these FA changes may indicate a strong link between PNES and abnormal brain aging.…”

“…We recently reported an abnormal brain aging process in Japanese patients with PNES based on T1-weighted morphological neuroimaging. 5 On the other hand, diffusion tensor imaging (DTI) has conventionally been used to evaluate white matter tracts through microstructural parameters such as fractional anisotropy (FA) and mean diffusivity (MD), 6 which might provide additional information on the brain in PNES. Recent technical advances have also enabled us to evaluate structural connectivity and brain networks.…”

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

“…Several neuroimaging studies tried to clarify the pathological changes of psychosis in epilepsy (8). A few studies using structural MRI suggested hippocampal tail atrophy (9) as well as abnormal aging (10) in TLE patients with psychosis. However, according to a recent systematic review (8), the results of morphological studies are not consistent, and little is known about other advanced imaging modalities.…”