Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology

Serum SIRT3 levels in epilepsy patients and its association with clinical outcomes and severity: A prospective observational study

Objective

In this prospective observational study, we aimed to investigate the serum levels of sirtuin (SIRT)3 in epilepsy patients and its association with the severity of the disease.

Methods

This prospective observational study included 203 patients with symptomatic epilepsy and 100 healthy controls who visited our hospital from November 2019 to November 2022. The severity of the disease in epilepsy patients was assessed using the National Hospital Seizure Severity Scale (NHS3). We used enzyme-linked immunosorbent assay to measure the serum levels of SIRT3, interleukin (IL)-6, IL-1β, tumor necrosis factor-alpha, and C-reactive protein in all patients. In addition, the cognitive function of all study participants was evaluated using the Mini-Mental State Examination and the Montreal Cognitive Assessment (MOCA). All data were analyzed using SPSS 25.0 software.

Results

The MOCA scores of the epilepsy patients were significantly lower compared to the healthy volunteers (P < 0.05). The serum SIRT3 levels were decreased significantly in patients with refractory epilepsy (183.16 ± 17.22 pg/mL) compared to non-refractory epilepsy patients (199.00 ± 18.68 pg/mL). In addition, serum SIRT3 levels were negatively correlated with the inflammatory factors IL-6 (Pearson's correlation -0.221, P = 0.002) and NHS score (Pearson's correlation -0.272, P < 0.001) of epilepsy patients, while positively correlated with MOCA scores (Pearson's correlation 0.166, P = 0.018). Furthermore, the receiver operating characteristic curve demonstrated that serum SIRT3 could be used to diagnose epilepsy, as well as refractory epilepsy. Finally, logistic regression analysis showed that SIRT3 (OR = 1.028, 95%CI: 1.003-1.054, P = 0.028), IL-6 (OR = 0.666, 95%CI: 0.554-0.800, P < 0.001), IL-1β (OR = 0.750, 95%CI: 0.630-0.894, P = 0.001), and NHS3 (OR = 0.555, 95%CI: 0.435-0.706, P < 0.001) were risk factors for refractory epilepsy.

Conclusion

In conclusion, our findings demonstrated that serum SIRT3 levels were significantly decreased in epilepsy patients and further decreased in patients with refractory epilepsy. This study might provide new therapeutic targets and comprehensive treatment strategies for epilepsy patients.

Cognitive decline in adult-onset temporal lobe epilepsy: Insights from aetiology

PURPOSE

To identify patients with adult-onset temporal lobe epilepsy (TLE) at risk of developing cognitive decline. Detecting which patients, aetiologies, or factors are most closely related with memory decline would allow us to identify patients that would eventually benefit from more specific treatment.

METHODS

Single centre, retrospective analysis of a prospectively followed-up cohort study, including all patients with the diagnosis of adult-onset TLE during 2013, with a minimum follow-up of five years. Memory and cognitive decline were analysed at 5 years and at last follow-up.

RESULTS

Of 89 initially selected patients, 71 were included. After 5 years, 11/71 (15.5%) patients suffered cognitive decline, of which 1/71 (4%) developed dementia. At last follow-up (range 65-596 m) a total of 34/71 (47.8%) patients were diagnosed with cognitive decline, specifically either memory decline or dementia. Cognitive decline at 5 years was related to: 1. Age at onset: 62.65 years (SD 9.04) in the group with cognitive decline vs 50.33 y. (SD 13.02 in the group without cognitive decline; p=0.004); 2. Onset as status epilepticus (3/6 in patients with memory decline vs 8/65 in patients without cognitive decline; p=0.04); 3. Immune aetiology: 42% compared with unknown (10%) and structural (10%) aetiologies; p=0.036; 4. Hippocampal sclerosis on MRI: 5/11 patients with cognitive decline vs 9/51 patients without cognitive decline; p=0.035. Cognitive decline was not related to seizure frequency, sex, or age (p=0.78; p=0.40; p=0.95, respectively).

CONCLUSIONS

Older age at epilepsy onset, onset as status epilepticus, immune aetiology, and hippocampal sclerosis are risk factors for developing cognitive decline in patients with adult-onset temporal lobe epilepsy.

Comparing neuropsychological functioning in Turkish patients with right and left temporal lobe epilepsy

We compared neuropsychiatric evaluations in temporal lobe epilepsy according to the lateralized hemisphere. Forty-one (68.3%) left-sided temporal lobe epilepsy (LTLE) were compared to 19 right-sided temporal lobe epilepsy (RTLE) (31.7%) (p < 0.001). RTLE mean age was 37 (22-46) years, and LTLE mean age 38 was (30-42). RTLE disease duration was 10 (6-20) years, and LTLE was 22 (10-33) (p < 0.013). Gender (man/woman) for RTLE was 7/12, and for LTLE was 18/23. LTLE scored poorer on the Wechsler Memory Scale (WMS)-III's Mental Control Months-error, WMS-V's "Forward Number Range" and "Backward Number Range" than RTLE (p < 0.017, p < 0.023, p < 0.004). There were differences between hemispheres for "Number of Items Remembered with a Hint" and "Total number of Recalled Items" (WMS-IV) (p < 0.038, p < 0.045). LTLE had lower scores in the Verbal Fluency -K-A-S letters words and WAIS (Wechsler Adult Intelligence Scale) similarity than RTLE (p < 0.019, p < 0.024, p < 0.033, p < 0.026). Oktem and Boston-number of Self-Named Items Tests were poorer in LTLE than RTLE (p < 0.05, p < 0.043). Mental Control Months-error (WMS-III), "Total Number of Recalled Items", "Number of Items Remembered with Hint" (WMS-IV), forward and backward number range (WMS-V), Oktem, Verbal Fluency -K,-A,-S letters words, WAIS similarity, and Boston-number of Self-Named Items tests, can help identify lateralization, particularly in LTLE.

Investigating the Impact of Epilepsy on Cognitive Function: A Narrative Review

It has been noted that people who have epilepsy have an increased propensity for cognitive dysfunction. We explored 25 relevant articles on PubMed and Cochrane Library after implementing inclusion criteria. Different factors have been postulated and studied that may cause cognitive dysfunction in these patients; structural brain abnormalities, polypharmacy of antiepileptic medication, and neuropsychiatric disorders are the most common causes. Cognitive assessments such as Montreal Cognitive Assessment (MOCA) and Mini-Mental State Exam (MMSE) are the mainstay tools used to diagnose the degree of cognitive decline, and alterations in EEG (electroencephalogram) parameters have also been noted in people with cognitive decline. The mechanisms and treatments for cognitive decline are still being studied, while attention has also been directed toward preventive and predictive methods. Early detection and treatment of cognitive impairment can help minimize its impact on the patient's quality of life. Regular cognitive assessments are essential for epileptic patients, particularly those on multiple antiepileptic drugs. While proper management of epilepsy and related comorbidities would reduce cognitive decline and improve the overall quality of life for people with epilepsy.

[Structural brain changes in patients with temporal lobe epilepsy and comorbid depression]

OBJECTIVE

To assess the morphological features of the brain structures in patients with temporal lobe epilepsy and comorbid depression.

MATERIAL AND METHODS

From 1 January 2017 to 31 December 2020, we studied 80 patients with temporal lobe epilepsy (aged 18-60 years, 38 of whom had comorbid depression) and 48 healthy subjects of comparable age. Magnetic resonance imaging (MRI) of the brain was performed using the epilepsy protocol in a scanner with a magnetic field strength of 1.5 T. Focal temporal lobe epilepsy was diagnosed by neurologists (epileptologists) specialising in epilepsy according to the International League Against Epilepsy (ILAE) classification of epilepsy. Psychiatrists assessed the presence and severity of depressive disorders by clinical interview and by participants' scores on the Beck Depression Inventory (BDI-II). MRI data were processed using FreeSurfer 6.0 software to determine volumes of subcortical structures and thicknesses of cortical structures. At the group level, analysis of covariance with Holm-Bonferroni correction was used as the statistical method.

RESULTS

Morphometric analysis revealed a significant decrease in the volume of the thalamus bilaterally and the brain stem and an increase in the volume of the choroid plexus in the left hemisphere, as well as a significant decrease in the thickness of the entorhinal cortex, temporal pole and isthmus of the cingulate gyrus in the left hemisphere and middle temporal gyrus and inferior temporal gyrus in the right hemisphere in patients with epilepsy compared to healthy controls. No association was found between the presence of depression and significant structural changes on MRI.

CONCLUSION

The data obtained suggest an effect of temporal lobe epilepsy, but not comorbid depression, on the morphology of brain structures.

BRAINCODE for Cognitive Impairment Diagnosis in Older Adults: Designing a Case-Control Pilot Study

An early, extensive, accurate, and cost-effective clinical diagnosis of neurocognitive disorders will have advantages for older people and their families, but also for the health and care systems sustainability and performance. BRAINCODE is a technology that assesses cognitive impairment in older people, differentiating normal from pathologic brain condition, based in an EEG biomarkers evaluation. This paper will address BRAINCODE's pilot design, which intends to validate its efficacy, to provide guidelines for future studies and to allow its integration on the SHAPES platform. It is expected that BRAINCODE confirms a regular clinical diagnosis and neuropsychologic tests to discriminate 'normal' from pathologic cognitive decline and differentiates mild cognitive impairment from dementia in older adults with/without subjective cognitive complains.

Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review

Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging. There have been some reviews on machine learning and epilepsy before, and they mainly focused on electrophysiological signals such as electroencephalography (EEG) and stereo electroencephalography (SEEG), while neglecting the potential of neuroimaging in epilepsy research. Neuroimaging has its important advantages in confirming the range of the epileptic region, which is essential in presurgical evaluation and assessment after surgery. However, it is difficult for EEG to locate the accurate epilepsy lesion region in the brain. In this review, we emphasize the interaction between neuroimaging and machine learning in the context of epilepsy diagnosis and prognosis. We start with an overview of epilepsy and typical neuroimaging modalities used in epilepsy clinics, MRI, DWI, fMRI, and PET. Then, we elaborate two approaches in applying machine learning methods to neuroimaging data: (i) the conventional machine learning approach combining manual feature engineering and classifiers, (ii) the deep learning approach, such as the convolutional neural networks and autoencoders. Subsequently, the application of machine learning on epilepsy neuroimaging, such as segmentation, localization, and lateralization tasks, as well as tasks directly related to diagnosis and prognosis are looked into in detail. Finally, we discuss the current achievements, challenges, and potential future directions in this field, hoping to pave the way for computer-aided diagnosis and prognosis of epilepsy.

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection

Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.