Neuroanatomical and psychological considerations in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy and is associated with a variety of structural and psychological alterations. Recently, there has been renewed interest in using brain tissue resected during epilepsy surgery, in particular `non-epileptic¿ brain samples with normal histology that can be found alongside epileptic tissue in the same epileptic patients ¿ with the aim being to study the normal human brain organization using a variety of methods. An important limitation is that different medical characteristics of the patients may modify the brain tissue. Thus, to better determine how `normal¿ the resected tissue is, it is fundamental to know certain clinical, anatomical and psychological characteristics of the patients. Unfortunately, this information is frequently not fully available for the patient from which the resected tissue has been obtained ¿ or is not fully appreciated by the neuroscientists analyzing the brain samples, who are not necessarily experts in epilepsy. In order to present the full picture of TLE in a way that would be accessible to multiple communities (e.g., basic researchers in neuroscience, neurologists, neurosurgeons and psychologists), we have reviewed 34 TLE patients, who were selected due to the availability of detailed clinical, anatomical, and psychological information for each of the patients. Our aim was to convey the full complexity of the disorder, its putative anatomical substrates, and the wide range of individual variability, with a view toward: (1) emphasizing the importance of considering critical patient information when using brain samples for basic research and (2) gaining a better understanding of normal and abnormal brain functioning. In agreement with a large number of previous reports, this study (1) reinforces the notion of substantial individual variability among epileptic patients, and (2) highlights the common but overlooked psychopathological alterations that occur even in patients who become ¿seizure-free¿ after surgery. The first point is based on pre- and post-surgical comparisons of patients with hippocampal sclerosis and patients with normal-looking hippocampus in neuropsychological evaluations. The second emerges from our extensive battery of personality and projective tests, in a two-way comparison of these two types of patients with regard to pre- and post-surgical performance.This work was supported by grants from the following entities: Grant PID2021-127924NB-I00 funded by MCIN/AEI/10.13039/501100011033; Centro de Investigación en Red sobre Enfermedades Neurodegenerativas (CIBERNED, CB06/05/0066); and CSIC Interdisciplinary Thematic Platform (PTI) Cajal Blue Brain (PTI-BLUEBRAIN; Spain). RA was supported by ANDIA grant #0011-3947-2021-000023 from the Gobierno de Navarra

Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery

Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking in to account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery

Neuropathological findings in a patient with epilepsy and the Parry-Romberg syndrome

The Parry-Romberg syndrome is an unusual disorder frequently associated with epilepsy. The origin of this disease, and the cause of epilepsy, are unknown. This study is the first reported case of the Parry-Romberg syndrome, with intractable temporal lobe epilepsy, in which detailed microanatomic analyses have been performed on resected brain tissue obtained after surgical intervention. Standard histopathologic methods and correlative light and electron microscopy, combined with immunocytochemical techniques, were used to study in detail the synaptic microorganization of the resected hippocampal formation. After surgery, the patient was seizure free (follow-up period of 4 years and 7 months). The resected temporal lobe showed a variety of dramatic microanatomic alterations (small groups of ectopic cells, neuronal loss, gliosis, and activated microglial cells) in mesial structures, including the entorhinal cortex, subiculum, and dentate gyrus. At the electronmicroscopic level, we found that in the dentate gyms, the number of synapses in the cell-sparse region adjacent to the ectopic mass of neurons was almost twice that found in the molecular and polymorph cell layers, indicating the intrusion of neuritic processes and synapse formation. In addition, the symmetrical axosomatic synapses characteristically found on granule cells, which are likely derived from γ-aminobutyric acid (GABA)ergic inhibitory basket cells, were not observed. The complete seizure relief after surgery suggests that the pacemaker region(s) of seizure activity were within the resected tissue. However, we do not know which of the multiple neuropathologic findings reported here were the primary cause of seizure activity. Nevertheless, the changes found in the dentate gyrus circuitry appear to be among the most important alterations that would lead to epilepsy.Sin financiación3.271 JCR (2001) Q1, 15/136 Clinical NeurologyUE

Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

<div><p>Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.</p></div

Summary of the clinical data from the epileptic patients and the surgical outcome.

<p>f: female, gen: secondarily generalized, L: Left, m: male, PC: partial complex seizures, PS: partial simple seizures, R: right, Engel scale for surgical outcome: class I seizure-free, class II rare seizures and class III worthwhile improvement.</p

Estimated classification performance using LOOCV validation.

<p>Only features available before surgery were included in this performance analysis. The x-axis reflects the size of the subset of features retained. A) The upper chart shows the estimated accuracy; whereas, B) the lower chart shows the associated area under the ROC curve. Note that the features for a given point on the x-axis can differ depending on the classifier used (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0062819#pone-0062819-t003" target="_blank">Table 3</a> for the respective feature subsets).</p

Set of predictive variables and their associated values. Cardinalities of each value are shown between brackets. Missing values are also indicated.

<p>Set of predictive variables and their associated values. Cardinalities of each value are shown between brackets. Missing values are also indicated.</p

Features and associated p-values obtained from a Wilcoxon signed-rank test comparing the values of each feature with the Engel output. in order of increasing p-value.

†<p>indicates statistical significance at a 95% confidence level. Features are listed.</p

Number of times that features were included in the different intermediate subsets selected by the race search feature selection for over 1,000 dataset resamplings.

<p>Number of times that features were included in the different intermediate subsets selected by the race search feature selection for over 1,000 dataset resamplings.</p

Variables ordered according to their frequency of selection during resampling and variable subset selection (occ: occurrence). Three different rankings are displayed - one for each of the classifiers used during the search.

<p>Variables ordered according to their frequency of selection during resampling and variable subset selection (occ: occurrence). Three different rankings are displayed - one for each of the classifiers used during the search.</p