Cendes, Fernando

Abstract The diagnosis of predementia stages indicates an increased risk of progression to dementia. The Amyloid, Tau, and Neurodegeneration AT(N) classification considers measurements of altered proteins and the presence of neurodegeneration to classify the risk groups regarding the pathophysiology of Alzheimer's disease (AD). The cognitive and anatomical characteristics of the patients in the predementia stage according to the AT(N) classification are not fully understood. To investigate whether there are differences in the clinical and anatomical profiles among older adults in the predementia stage according to the ATN classification, and to investigate the associations involving cognition and cortical thickness and subcortical volume in the AT(N) groups. In total, 72 older adults with subjective cognitive decline and mild cognitive impairment were allocated to groups according to the AT(N) classification (AD continuum: n = 37; suspected non-AD pathophysiology: n = 8; normal biomarkers: n = 27). The participants were investigated through cognitive tests, magnetic resonance imaging scans, and cerebrospinal fluid analyses. We used multivariate and univariate analyses with post-hoc testing to verify differences among groups. In addition, linear regressions were performed to verify the interactions involving cognition and gray matter metrics. The suspected non-AD pathophysiology group showed worse performance in attention/executive function than the AD continuum and normal biomarkers groups (p = 0.04). However, the ATN classification groups did not differ in terms of cortical thickness (p > 0.05). In addition, in the AD continuum group, memory was associated with left fusiform gyrus thickness (p = 0.000 uncorrected; r = 0.238). Cognition, but not gray matter metrics, differs among AT(N) classification groups. Memory is associated with cortical thickness in patients with positive amyloid Beta (AD continuum).

Abstract This paper provides a practical guide to applying the updated seizure classification in clinical settings. The updated classification, published by the International League Against Epilepsy in 2025, builds on the operational classification introduced in 2017. It aims to enhance clarity, clinical relevance, and consistency in seizure terminology across various healthcare settings. The classification system distinguishes between four main seizure classes: focal, generalized, unknown whether focal or generalized, and unclassified. The basic version allows for application even in resource‐limited or primary care environments, while the expanded version offers detailed semiological descriptors and supports advanced diagnostic and surgical decision‐making. Consciousness, defined through awareness (recall) and responsiveness, is now considered a classifier. The paper also highlights the importance of distinguishing observable from non‐observable features and integrates semiology into the expanded classification with attention to temporal sequence and somatotopic detail. Generalized seizures are categorized into seizure types having a direct impact on syndrome diagnosis and treatment decisions. Epileptic spasms are given special attention because of their unique presentation and the urgency of early intervention, particularly in infants. To support clinical implementation, this paper includes a comprehensive table of semiological descriptors, definitions of generalized seizure types, and a series of real‐world case vignettes illustrating the application of the updated seizure classification. Supplementary figures and videos further support the educational aims of the paper. This practical guide is intended for healthcare professionals managing patients with epilepsy, providing a clear, structured approach to seizure classification that is adaptable to varying levels of clinical expertise and diagnostic resources.

Abstract The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic–clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource‐limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

AbstractCorrectly diagnosing and classifying seizures and epilepsies is paramount to ensure the delivery of optimal care to patients with epilepsy. Focal seizures, defined as those that originate within networks limited to one hemisphere, are primarily subdivided into focal aware, focal impaired awareness, and focal to bilateral tonic–clonic seizures. Focal epilepsies account for most epilepsy cases both in children and adults. In children, focal epilepsies are typically subdivided in three groups: self‐limited focal epilepsy syndromes (e.g., self‐limited epilepsy with centrotemporal spikes), focal epilepsy of unknown cause but which do not meet criteria for a self‐limited focal epilepsy syndrome, and focal epilepsy of known cause (e.g., structural lesions—developmental or acquired). In adults, focal epilepsies are often acquired and may be caused by a structural lesion such as stroke, infection and traumatic brain injury, or brain tumors, vascular malformations, metabolic disorders, autoimmune, and/or genetic causes. In addition to seizure semiology, neuroimaging, neurophysiology, and neuropathology constitute the cornerstones of a diagnostic evaluation. Patients with focal epilepsy who become drug‐resistant should promptly undergo assessment in an epilepsy center. After excluding pseudo‐resistance, these patients should be considered for presurgical evaluation as a means to identify the location and extent of the epileptogenic zone and assess their candidacy for a surgical procedure. The goal of this seminar in epileptology is to summarize clinically relevant information concerning focal epilepsies. This contributes to the ILAE's mission to ensure that worldwide healthcare professionals, patients, and caregivers continue to have access to high‐quality educational resources concerning epilepsy.

AbstractFocal cortical dysplasia (FCD) type II is a highly epileptogenic developmental malformation and a common cause of surgically treated drug-resistant epilepsy. While clinical observations suggest frequent occurrence in the frontal lobe, mechanisms for such propensity remain unexplored. Here, we hypothesized that cortex-wide spatial associations of FCD distribution with cortical cytoarchitecture, gene expression and organizational axes may offer complementary insights into processes that predispose given cortical regions to harbour FCD.We mapped the cortex-wide MRI distribution of FCDs in 337 patients collected from 13 sites worldwide. We then determined its associations with (i) cytoarchitectural features using histological atlases by Von Economo and Koskinas and BigBrain; (ii) whole-brain gene expression and spatiotemporal dynamics from prenatal to adulthood stages using the Allen Human Brain Atlas and PsychENCODE BrainSpan; and (iii) macroscale developmental axes of cortical organization.FCD lesions were preferentially located in the prefrontal and fronto-limbic cortices typified by low neuron density, large soma and thick grey matter. Transcriptomic associations with FCD distribution uncovered a prenatal component related to neuroglial proliferation and differentiation, likely accounting for the dysplastic makeup, and a postnatal component related to synaptogenesis and circuit organization, possibly contributing to circuit-level hyperexcitability. FCD distribution showed a strong association with the anterior region of the antero-posterior axis derived from heritability analysis of interregional structural covariance of cortical thickness, but not with structural and functional hierarchical axes. Reliability of all results was confirmed through resampling techniques.Multimodal associations with cytoarchitecture, gene expression and axes of cortical organization indicate that prenatal neurogenesis and postnatal synaptogenesis may be key points of developmental vulnerability of the frontal lobe to FCD. Concordant with a causal role of atypical neuroglial proliferation and growth, our results indicate that FCD-vulnerable cortices display properties indicative of earlier termination of neurogenesis and initiation of cell growth. They also suggest a potential contribution of aberrant postnatal synaptogenesis and circuit development to FCD epileptogenicity.

Abstract Objective Anterior temporal lobectomy (ATL) is a widely performed and successful intervention for drug‐resistant temporal lobe epilepsy (TLE). However, up to one third of patients experience seizure recurrence within 1 year after ATL. Despite the extensive literature on presurgical electroencephalography (EEG) and magnetic resonance imaging (MRI) abnormalities to prognosticate seizure freedom following ATL, the value of quantitative analysis of visually reviewed normal interictal EEG in such prognostication remains unclear. In this retrospective multicenter study, we investigate whether machine learning analysis of normal interictal scalp EEG studies can inform the prediction of postoperative seizure freedom outcomes in patients who have undergone ATL. Methods We analyzed normal presurgical scalp EEG recordings from 41 Mayo Clinic (MC) and 23 Cleveland Clinic (CC) patients. We used an unbiased automated algorithm to extract eyes closed awake epochs from scalp EEG studies that were free of any epileptiform activity and then extracted spectral EEG features representing (a) spectral power and (b) interhemispheric spectral coherence in frequencies between 1 and 25 Hz across several brain regions. We analyzed the differences between the seizure‐free and non–seizure‐free patients and employed a Naïve Bayes classifier using multiple spectral features to predict surgery outcomes. We trained the classifier using a leave‐one‐patient‐out cross‐validation scheme within the MC data set and then tested using the out‐of‐sample CC data set. Finally, we compared the predictive performance of normal scalp EEG‐derived features against MRI abnormalities. Results We found that several spectral power and coherence features showed significant differences correlated with surgical outcomes and that they were most pronounced in the 10–25 Hz range. The Naïve Bayes classification based on those features predicted 1‐year seizure freedom following ATL with area under the curve (AUC) values of 0.78 and 0.76 for the MC and CC data sets, respectively. Subsequent analyses revealed that (a) interhemispheric spectral coherence features in the 10–25 Hz range provided better predictability than other combinations and (b) normal scalp EEG‐derived features provided superior and potentially distinct predictive value when compared with MRI abnormalities (>10% higher F1 score). Significance These results support that quantitative analysis of even a normal presurgical scalp EEG may help prognosticate seizure freedom following ATL in patients with drug‐resistant TLE. Although the mechanism for this result is not known, the scalp EEG spectral and coherence properties predicting seizure freedom may represent activity arising from the neocortex or the networks responsible for temporal lobe seizure generation within vs outside the margins of an ATL.

Abstract Aims The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems‐level analysis. Methods Imaging‐based cortical structural maps from a large‐scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell‐type deconvolution, differential expression analysis and cell‐type enrichment analyses were used to identify differences in cell‐type distribution. These differences were followed up in post‐mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell‐type‐specific depletion was used in a murine model of acquired epilepsy. Results We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post‐mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non‐spatial memory test seen in epileptic mice not depleted of microglia. Conclusions These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Abstract Objective This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom. Methods We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step‐down procedure was used to sequentially eliminate the least‐informative variables from the model until the change in the concordance index (c‐index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure‐freedom prediction. Results Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism‐corrected c‐index of 0.65, whereas the c‐index of the model without EEG data was 0.59. The presence of focal to bilateral tonic‐clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models’ interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes. Significance This study highlights the value of scalp EEG, particularly the significance of IEDs, in predicting surgical outcome. The nomogram delivers an individualized prediction of postoperative outcome, and provides a unique assessment of the relationship between the outcome and preoperative findings.