Potential Similarities in Temporal Lobe Epilepsy and Alzheimer’s Disease: From Clinic to Pathology

Mechanisms Involved in Epileptogenesis in Alzheimer's Disease and Their Therapeutic Implications

Epilepsy and Alzheimer's disease (AD) incidence increases with age. There are reciprocal relationships between epilepsy and AD. Epilepsy is a risk factor for AD and, in turn, AD is an independent risk factor for developing epilepsy in old age, and abnormal AD biomarkers in PET and/or CSF are frequently found in late-onset epilepsies of unknown etiology. Accordingly, epilepsy and AD share pathophysiological processes, including neuronal hyperexcitability and an early excitatory-inhibitory dysregulation, leading to dysfunction in the inhibitory GABAergic and excitatory glutamatergic systems. Moreover, both β-amyloid and tau protein aggregates, the anatomopathological hallmarks of AD, have proepileptic effects. Finally, these aggregates have been found in the resection material of refractory temporal lobe epilepsies, suggesting that epilepsy leads to amyloid and tau aggregates. Some epileptic syndromes, such as medial temporal lobe epilepsy, share structural and functional neuroimaging findings with AD, leading to overlapping symptomatology, such as episodic memory deficits and toxic synergistic effects. In this respect, the existence of epileptiform activity and electroclinical seizures in AD appears to accelerate the progression of cognitive decline, and the presence of cognitive decline is much more prevalent in epileptic patients than in elderly patients without epilepsy. Notwithstanding their clinical significance, the diagnosis of clinical seizures in AD is a challenge. Most are focal and manifest with an altered level of consciousness without motor symptoms, and are often interpreted as cognitive fluctuations. Finally, despite the frequent association of epilepsy and AD dementia, there is a lack of clinical trials to guide the use of antiseizure medications (ASMs). There is also a potential role for ASMs to be used as disease-modifying drugs in AD.

Beyond Seizure Control: Treating Comorbidities in Epilepsy via Targeting of the P2X7 Receptor

Epilepsy is one of the most common chronic diseases of the central nervous system (CNS). Treatment of epilepsy remains, however, a clinical challenge with over 30% of patients not responding to current pharmacological interventions. Complicating management of treatment, epilepsy comes with multiple comorbidities, thereby further reducing the quality of life of patients. Increasing evidence suggests purinergic signalling via extracellularly released ATP as shared pathological mechanisms across numerous brain diseases. Once released, ATP activates specific purinergic receptors, including the ionotropic P2X7 receptor (P2X7R). Among brain diseases, the P2X7R has attracted particular attention as a therapeutic target. The P2X7R is an important driver of inflammation, and its activation requires high levels of extracellular ATP to be reached under pathological conditions. Suggesting the therapeutic potential of drugs targeting the P2X7R for epilepsy, P2X7R expression increases following status epilepticus and during epilepsy, and P2X7R antagonism modulates seizure severity and epilepsy development. P2X7R antagonism has, however, also been shown to be effective in treating conditions most commonly associated with epilepsy such as psychiatric disorders and cognitive deficits, which suggests that P2X7R antagonisms may provide benefits beyond seizure control. This review summarizes the evidence suggesting drugs targeting the P2X7R as a novel treatment strategy for epilepsy with a particular focus of its potential impact on epilepsy-associated comorbidities.

Dementia with non-convulsive seizures: a case report

Non-convulsive seizures (NCSs) are highly treatable, but appropriate management is usually delayed because of inaccurate diagnoses as a result of variable clinical presentations, including an altered mental state. It is difficult to detect NCSs in patients with dementia. We report a case of NCS superimposed on cognitive decline caused by Alzheimer’s dementia. The patient’s history was carefully recorded. An electroencephalogram was recorded with sphenoidal electrodes, which showed epileptiform discharges in the right mesial temporal lobe and focal, sharply contoured, slow wave activity in the left fronto-temporal area, suggesting an epileptic origin contributing to the patient’s cognitive decline. After treatment with antiepileptic drugs, the patient’s cognitive functioning gradually improved. An accurate diagnosis of NCS relies on performing a detailed inventory of a patient’s history, thorough physical and neurological examinations, and electroencephalogram recordings. In patients with cognitive decline, testing for NCS should always be included in the differential diagnosis of cognitive impairment, even in the case of dementia. Early administration of antiepileptic drug therapy is the mainstay treatment for reversing the condition and for preventing prolonged insults from neurological sequelae.

The Association of Periodontitis and Alzheimer's Disease: How to Hit Two Birds with One Stone

Alzheimer's disease (AD) is the leading cause of cognitive impairment in the elderly. Recent evidence suggests that preventive interventional trials could significantly reduce the risk for development of dementia. Periodontitis is the most common dental disease characterized by chronic inflammation and loss of alveolar bone and perialveolar attachment of teeth. Growing number of studies propose a potential link between periodontitis and neurodegeneration. In the first part of the paper, we overview case-control studies analyzing the prevalence of periodontitis among AD patients and healthy controls. Second, we survey observational libraries and cross-sectional studies investigating the risk of cognitive decline in patients with periodontitis. Next, we describe the current view on the mechanism of periodontitis linked neural damage, highlighting bacterial invasion of neural tissue from dental plaques, and periodontitis induced systemic inflammation resulting in a neuroinflammatory process. Later, we summarize reports connecting the four most common periodontal pathogens to AD pathology. Finally, we provide a practical guide for further prevalence and interventional studies on the management of cognitively high-risk patients with and without periodontitis. In this section, we highlight strategies for risk control, patient information, dental evaluation, reporting protocol and dental procedures in the clinical management of patients with a risk for periodontitis and with diagnosed periodontitis. In conclusion, our review summarizes the current view on the association between AD and periodontitis and provides a research and intervention strategy for harmonized interventional trials and for further case-control or cross-sectional studies.

Deep Feature Selection and Causal Analysis of Alzheimer's Disease

Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer’s disease (AD). However, it is also well known that DCNNs are “black boxes” owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the development of AD and causal paths from genetic variants to AD via image mediation. The proposed algorithm is applied to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset with diffusion tensor imaging (DTI) in 151 subjects (51 AD and 100 non-AD) who were measured at four time points of baseline, 6 months, 12 months, and 24 months. The algorithm identified brain regions underlying AD consisting of the temporal lobes (including the hippocampus) and the ventricular system.