Temporal lobe epilepsy affects spatial organization of entorhinal cortex connectivity

Machine learning localization to identify the epileptogenic side in mesial temporal lobe epilepsy

BACKGROUND

Mesial temporal sclerosis (MTS) is the most common pathology associated with drug-resistant mesial temporal lobe epilepsy (mTLE) in adults. Most atrophic hippocampi can be identified using MRI based on standard epilepsy protocols; however, difficulties can arise in cases where sclerotic changes in the hippocampus are subtle or non-epilepsy-specific protocols have been implemented. In such cases, quantitative methods, such as T1-weighted axial series MRIs, are valuable additional tools to complement epilepsy-specific protocols. In the current study, we applied machine learning (ML) techniques to the analysis of brain regions of interest (ROIs), including the hippocampus, thalamus, and cortical areas, to enhance the accuracy of lesion lateralization in MRI.

METHODS

This study included 104 patients diagnosed with mTLE, including 55 with lesions on the right side and 49 with lesions on the left side. FreeSurfer software was used to extract features from high-resolution T1-weighted axial brain MRI scans for use in computing lateralization indices (LI) for various brain regions. After using feature selection to pinpoint critical ROIs, the corresponding LI values were used as parameters in training the ML model.

RESULTS

The proposed ML model demonstrated exceptional performance in the lateralization of mTLE, achieving test accuracy of 92.38 % with an AUROC of 0.97.

CONCLUSION

This study demonstrated the efficacy of ML in detecting instances of MTS from thin-slice T1 images. The proposed method provides valuable insights for surgical planning and treatment. Nonetheless, additional research will be required to enhance the robustness of the model and rigorously validate its effectiveness and applicability in clinical settings.

Overexpression of KCNN4 channels in principal neurons produces an anti-seizure effect without reducing their coding ability

Gene therapy offers a potential alternative to the surgical treatment of epilepsy, which affects millions of people and is pharmacoresistant in ~30% of cases. Aimed at reducing the excitability of principal neurons, the engineered expression of K+ channels has been proposed as a treatment due to the outstanding ability of K+ channels to hyperpolarize neurons. However, the effects of K+ channel overexpression on cell physiology remain to be investigated. Here we report an adeno-associated virus (AAV) vector designed to reduce epileptiform activity specifically in excitatory pyramidal neurons by expressing the human Ca2+-gated K+ channel KCNN4 (KCa3.1). Electrophysiological and pharmacological experiments in acute brain slices showed that KCNN4-transduced cells exhibited a Ca2+-dependent slow afterhyperpolarization that significantly decreased the ability of KCNN4-positive neurons to generate high-frequency spike trains without affecting their lower-frequency coding ability and action potential shapes. Antiepileptic activity tests showed potent suppression of pharmacologically induced seizures in vitro at both single cell and local field potential levels with decreased spiking during ictal discharges. Taken together, our findings strongly suggest that the AAV-based expression of the KCNN4 channel in excitatory neurons is a promising therapeutic intervention as gene therapy for epilepsy.

Nonlinear Causal Discovery for High-Dimensional Deterministic Data

Nonlinear causal discovery with high-dimensional data where each variable is multidimensional plays a significant role in many scientific disciplines, such as social network analysis. Previous work majorly focuses on exploiting asymmetry in the causal and anticausal directions between two high-dimensional variables (a cause-effect pair). Although there exist some works that concentrate on the causal order identification between multiple variables, i.e., more than two high-dimensional variables, they do not validate the consistency of methods through theoretical analysis on multiple-variable data. In particular, based on the asymmetry for the cause-effect pair, if model assumptions for any pair of the data are violated, the asymmetry condition will not hold, resulting in the deduction of incorrect order identification. Thus, in this article, we propose a causal functional model, namely high-dimensional deterministic model (HDDM), to identify the causal orderings among multiple high-dimensional variables. We derive two candidates' selection rules to alleviate the inconvenient effects resulted from the violated-assumption pairs. The corresponding theoretical justification is provided as well. With these theoretical results, we develop a method to infer causal orderings for nonlinear multiple-variable data. Simulations on synthetic data and real-world data are conducted to verify the efficacy of our proposed method. Since we focus on deterministic relations in our method, we also verify the robustness of the noises in simulations.

[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.

A comparative study of pre-alpha islands in the entorhinal cortex from selected primates and in lissencephaly

The entorhinal cortex (EC) is the main interface between the sensory association areas of the neocortex and the hippocampus. It is crucial for the evaluation and processing of sensory data for long-term memory consolidation, and shows damage in many brain diseases, e.g., neurodegenerative diseases, such as Alzheimer's disease and developmental disorders. The pre-alpha layer of the EC in humans (layer II) displays a remarkable distribution of neurons in islands. These cellular islands give rise to a portion of the perforant path - the major reciprocal data stream for neocortical information into the hippocampal formation. However, the functional relevance of the morphological appearance of the pre-alpha layer in cellular islands and the precise timing of their initial appearance during primate evolution are largely unknown. Here, we conducted a comparative study of the EC from 38 non-human primates and Homo sapiens and found a strong relationship between gyrification index (GI) and the presence of the pre-alpha cellular islands. The formation of cellular islands also correlated wih brain and body weight as well as neopallial volume. In the two human lissencephalic cases, the cellular islands in the pre-alpha layer were lacking. These findings emphasize the relationship between cortical folding and island formation in the entorhinal cortex from an evolutionary perspective, and suggest a role in the pathomechanism of developmental brain disorders. This article is protected by copyright. All rights reserved.

Normal and Abnormal Sharp Wave Ripples in the Hippocampal-Entorhinal Cortex System: Implications for Memory Consolidation, Alzheimer's Disease, and Temporal Lobe Epilepsy

The appearance of hippocampal sharp wave ripples (SWRs) is an electrophysiological biomarker for episodic memory encoding and behavioral planning. Disturbed SWRs are considered a sign of neural network dysfunction that may provide insights into the structural connectivity changes associated with cognitive impairment in early-stage Alzheimer's disease (AD) and temporal lobe epilepsy (TLE). SWRs originating from hippocampus have been extensively studied during spatial navigation in rodents, and more recent studies have investigated SWRs in the hippocampal-entorhinal cortex (HPC-EC) system during a variety of other memory-guided behaviors. Understanding how SWR disruption impairs memory function, especially episodic memory, could aid in the development of more efficacious therapeutics for AD and TLE. In this review, we first provide an overview of the reciprocal association between AD and TLE, and then focus on the functions of HPC-EC system SWRs in episodic memory consolidation. It is posited that these waveforms reflect rapid network interactions among excitatory projection neurons and local interneurons and that these waves may contribute to synaptic plasticity underlying memory consolidation. Further, SWRs appear altered or ectopic in AD and TLE. These waveforms may thus provide clues to understanding disease pathogenesis and may even serve as biomarkers for early-stage disease progression and treatment response.

Test-retest reliability of high angular resolution diffusion imaging acquisition within medial temporal lobe connections assessed via tract based spatial statistics, probabilistic tractography and a novel graph theory metric

This study examined the reliability of high angular resolution diffusion tensor imaging (HARDI) data collected on a single individual across several sessions using the same scanner. HARDI data was acquired for one healthy adult male at the same time of day on ten separate days across a one-month period. Environmental factors (e.g. temperature) were controlled across scanning sessions. Tract Based Spatial Statistics (TBSS) was used to assess session-to-session variability in measures of diffusion, fractional anisotropy (FA) and mean diffusivity (MD). To address reliability within specific structures of the medial temporal lobe (MTL; the focus of an ongoing investigation), probabilistic tractography segmented the Entorhinal cortex (ERc) based on connections with Hippocampus (HC), Perirhinal (PRc) and Parahippocampal (PHc) cortices. Streamline tractography generated edge weight (EW) metrics for the aforementioned ERc connections and, as comparison regions, connections between left and right rostral and caudal anterior cingulate cortex (ACC). Coefficients of variation (CoV) were derived for the surface area and volumes of these ERc connectivity-defined regions (CDR) and for EW across all ten scans, expecting that scan-to-scan reliability would yield low CoVs. TBSS revealed no significant variation in FA or MD across scanning sessions. Probabilistic tractography successfully reproduced histologically-verified adjacent medial temporal lobe circuits. Tractography-derived metrics displayed larger ranges of scanner-to-scanner variability. Connections involving HC displayed greater variability than metrics of connection between other investigated regions. By confirming the test retest reliability of HARDI data acquisition, support for the validity of significant results derived from diffusion data can be obtained.