The Use of Artificial Intelligence in the Management of Intracranial Aneurysms

The use of artificial intelligence (AI) has potential benefits in the management of intracranial aneurysms. Early detection of intracranial aneurysms is critical due to their high risk of complications such as rupture, vasospasm, and ischemia with highly impact on morbidity and mortality. The main findings suggest that AI can improve the accuracy of aneurysm detection, rupture risk prediction, and assist neurointervention in planning and performing procedures. This chapter discusses the potential for AI to improve patient care by enabling earlier diagnosis and timely treatment, reducing medical errors, costs, morbidity, and mortality. However, further validation of AI-based applications is necessary in a real-world clinical setting

Low-Intensity Pulsed Ultrasound Stimulation Modulates the Nonlinear Dynamics of Local Field Potentials in Temporal Lobe Epilepsy

Low-intensity pulsed ultrasound stimulation (LIPUS) can inhibit seizures associated with temporal lobe epilepsy (TLE), which is the most common epileptic syndrome in adults and accounts for more than half of the cases of intractable epilepsy. Electroencephalography (EEG) signal analysis is an important method for studying epilepsy. The nonlinear dynamics of epileptic EEG signals can be used as biomarkers for the prediction and diagnosis of epilepsy. However, how ultrasound modulates the nonlinear dynamic characteristics of EEG signals in TLE is still unclear. Here, we used low-intensity pulsed ultrasound to stimulate the CA3 region of kainite (KA)-induced TLE mice, simultaneously recorded local field potentials (LFP) in the stimulation regions before, during, and after LIPUS. The nonlinear characteristics, including complexity, approximate entropy of different frequency bands, and Lyapunov exponent of the LFP, were calculated. Compared with the control group, the experimental group showed that LIPUS inhibited TLE seizure and the complexity, approximate entropy of the delta (0.5–4 Hz) and theta (4–8 Hz) frequency bands, and Lyapunov exponent of the LFP significantly increased in response to ultrasound stimulation. The values before ultrasound stimulation were higher ∼1.87 (complexity), ∼1.39 (approximate entropy of delta frequency bands), ∼1.13 (approximate entropy of theta frequency bands) and ∼1.46 times (Lyapunov exponent) than that after ultrasound stimulation (p < 0.05). The above results demonstrated that LIPUS can alter nonlinear dynamic characteristics and provide a basis for the application of ultrasound stimulation in the treatment of epilepsy

Classifying epilepsy pragmatically: Past, present, and future

The classification of epilepsy is essential for people with epilepsy and their families, healthcare providers, physicians and researchers. The International League Against Epilepsy proposed updated seizure and epilepsy classifications in 2017, while another four-dimensional epilepsy classification was updated in 2019. An Integrated Epilepsy Classification system was proposed in 2020. Existing classifications, however, lack consideration of important pragmatic factors relevant to the day-to-day life of people with epilepsy and stakeholders. Despite promising developments, consideration of comorbidities in brain development, genetic causes, and environmental triggers of epilepsy remains largely user-dependent in existing classifications. Demographics of epilepsy have changed over time, while existing classification schemes exhibit caveats. A pragmatic classification scheme should incorporate these factors to provide a nuanced classification. Validation across disparate contexts will ensure widespread applicability and ease of use. A team-based approach may simplify communication between healthcare personnel, while an individual-centred perspective may empower people with epilepsy. Together, incorporating these elements into a modern but pragmatic classification scheme may ensure optimal care for people with epilepsy by emphasising cohesiveness among its myriad users. Technological advancements such as 7T MRI, next-generation sequencing, and artificial intelligence may affect future classification efforts

Detection of Pathological HFO Using Supervised Machine Learning and iEEG Data

Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures

Role of Interictal Rhythmic Activity of Focal Cortical Dysplasia in Intracranial EEG

Fokální kortikální dysplázie (FCD) je onemocnění kortikálního vývoje mozku a je jednou z možných příčin ložiskové farmakorezistentní epilepsie. Pacienti trpící farmakorezistentní epilepsií mohou profitovat z chirurgické léčby, jež spočívá v odstranění dysplastické tkáně. Optimální rozsah resekce je předoperačně plánován zejména pomocí monitorace iEEG s ohledem na podtypu FCD. Přesný podtyp FCD lze v současné době diagnostikovat až na základě pooperačního histologického rozboru. Cílem práce bylo ověřit možnost určení konkrétního podtypu FCD pomocí specifické aktivity delta-brushes v intrakraniálních EEG (iEEG) záznamech. Pomocí technik cross-frequency coupling (CFC) byly identifikovány, parametrizovány a kvantifikovány delta-brushes v iEEG záznamech 46 pacientů s FCD podtypy I a II. Výskyt delta-brushes byl porovnáván v závislosti na fázích cirkadiálního rytmu a klinicky definovaných epileptických zónách s ohledem na schopnost lokalizovat epileptogenní zónu. V práci byly testovány tři CFC metody, a to poměr výšek (HR – Hights Ratio), výkonově spektrální hustota (PSD – Power Spectral Density) a korelace signálu k obálce (ESC – Envelope to Signal Correlation). Tyto metody prokázaly schopnost částečně lokalizovat epileptogenní zóny na základě delta-brushes. Sensitivita ani specificita signifikantně nepotvrdily rozdíl mezi FCD typu I a II, a to ani v závislosti na záznamech spánku či bdění. Z výsledků vyplývá, že CFC technika identifikace delta-brushes není dostatečně specifická pro stratifikaci FCD podtypů, přestože výskyt delta-brushes se překrývá s epileptogenními zónami. Nicméně sensitivita ani specificita CFC technik identifikace delta-brushes není postačující k jejich vymezení.Focal cortical dysplasia (FCD) is a disease of cortical brain development and is one of the possible causes of focal drug-resistant epilepsy. Patients with drug-resistant epilepsy may benefit from surgical treatment that involves the resection of dysplastic tissue. The optimal extent of resection is planned preoperatively, especially with iEEG monitoring based on knowledge of the FCD subtype. The exact subtype of FCD can currently be diagnosed only based on postoperative histological analysis. The aim of the work was to verify the possibility of determining a specific subtype of FCD using the specific activity of delta-brushes in intracranial EEG (iEEG). Delta-brushes were identified, parameterized and quantified using cross-frequency coupling (CFC) techniques in iEEG records of 46 patients with FCD subtypes I and II. The incidence of delta-brushes was compared depending on the phase of the circadian rhythm and the clinically defined epileptic zones with respect to the ability to localize the epileptogenic zone. Three CFC methods were tested, namely the Hights Ratio (HR), Power Spectral Density (PSD) and Envelope to Signal Correlation (ESC). These methods have demonstrated the ability to partially localize epileptogenic zones based on delta-brushes. The results show that the CFC technique of delta-brushes identification is not specific enough for the stratification of FCD subtypes, although the occurrence of delta-brushes overlaps with epileptogenic zones. However, neither the sensitivity nor specificity of delta-brushes from CFC identification techniques is sufficient to define them