MRI derived hippocampal asymmetry identifies hippocampal sclerosis in epilepsy surgical specimens

Subtle Hippocampal Subfield Volumetric Alterations in Clinically Diagnosed MRI-Negative Temporal Lobe Epilepsy.

Introduction Approximately 20-30% of temporal lobe epilepsy (TLE) cases present with no visible abnormalities on conventional magnetic resonance imaging (MRI). When interictal electroencephalography (EEG) recordings are also persistently normal, the diagnosis and lateralization become major clinical challenges. We aimed to investigate whether automated hippocampal subfield volumetry could detect subtle structural alterations in these "electro-clinically silent" MRI-negative cases and to correlate these findings with seizure semiology. Methods We analyzed 24 patients with clinically diagnosed MRI-negative/EEG-negative TLE and 26 age-matched healthy controls. Despite normal visual inspection on 1.5-T MRI, hippocampal subfield volumes were extracted using an automated segmentation pipeline (volBrain). Seizure semiology was systematically categorized to provide clinical-anatomical correlation. Results TLE patients exhibited a significant and selective volume reduction in the right CA4-dentate gyrus (CA4-DG) subfield compared to healthy controls (p=0.003), even after adjusting for age, sex, and total intracranial volume (p=0.002). No statistically significant differences were observed in other hippocampal subfields (CA1, CA2/3, or subiculum), indicating a highly localized pattern of structural alteration. Conclusion These results imply that selective CA4-DG atrophy is a defining characteristic of the MRI-negative TLE phenotype, thereby corroborating the "dentate gate" failure hypothesis. Quantitative volumetry provides a crucial diagnostic tool in settings with limited resources, where advanced techniques like 3-T MRI or invasive monitoring are not accessible. This objective structural marker, when integrated with comprehensive semiological analysis, improves diagnostic accuracy and facilitates early clinical intervention in difficult non-lesional TLE cases.

The Role of MRI Volumetric Assessment of Hippocampus and its Parts in Early Diagnosis of Mesial Temporal Lobe Epilepsy in Epileptic Children. A case-control study.

Hippocampal microstructural alterations in temporal lobe epilepsy with hippocampal sclerosis assessed using diffusion kurtosis imaging.

strategy for temporal lobe epilepsy with dual pathology and incomplete evidence from EEG and neuroimaging

Structural Brain Biomarkers for Identifying Progressive Amnestic Mild Cognitive Impairment

Objective. To identify the structural features of the brain in patients with amnestic mild cognitive impairment (aMCI) compared to healthy individuals, as well as to detect structural markers of progression to dementia in aMCI subgroups with different clinical outcomes. Materials and Methods. 41 patients with aMCI (mean age 71.5{+/-}9.5 years, 33 females) who underwent clinical assessment during 1 year follow-up, and 38 healthy controls (mean age 63.7{+/-}11.7 years, 28 females) were included. MRI scans were acquired, and the cortical thickness in both hemispheres, as well as the volumes of subcortical structures and hippocampal subfields, were analyzed. Results. Patients with aMCI as a whole, as well as aMCI subgroups with different clinical outcomes, showed significantly smaller bilateral hippocampal volumes and volumes of several hippocampal subfields than healthy controls. In addition, patients who were subsequently diagnosed with Alzheimer's disease dementia exhibited lower cortical gray matter thickness and smaller bilateral amygdala volumes than the healthy controls. Several subcortical structures and hippocampal subregions remained preserved in patients with aMCI, including those with unfavorable clinical outcomes. Conclusion. The study revealed structural brain abnormalities in aMCI, characterized by reduced hippocampal and hippocampal subfield volumes, as well as amygdala volume reduction, while other subcortical structures were relatively preserved. Furthermore, reduced cortical gray matter thickness may serve as a potential prognostic marker for conversion to dementia in patients with aMCI.

Impact of Variability in Brain Volume Measurements from MRI scans in Multi-Centre Neurodegeneration Assessments

Dementia is characterised by the cumulative loss of cognitive and emotional abilities to extents that it disrupts everyday life. Dementia-causing diseases can induce structural and chemical changes in the brain leading to neuronal loss and brain volume shrinkage, and have a prolonged onset period which can go unnoticed for years before significant symptoms manifest or a diagnosis can be made. Thus, an early and accurate diagnosis for dementia and its subtypes is crucial to deliver appropriate patient treatment and care, but that is an enormous challenge. Current gold standards for diagnosis involve neuroimaging techniques such as Magnetic Resonance Imaging (MRI) to measure volumes in regions of the brain repeatedly over time, where hippocampal shrinkage is indicative of Alzheimer’s dementia. Brain volumetry typically involves segmentation algorithms to isolate the relevant areas of the brain. Rapid advancements in artificial intelligence (AI) have led to a plethora of segmentation methods, with many vendors providing AI tools in their medical imaging instrument software. AI is a powerful tool for clinicians to analyse data, though it lacks transparency and compounds the ‘black box’ issues. Critical information regarding the data pre- and post- processing, in addition to details of the AI model implementation, hyper-parameters, training data, et cetera, are not typically available. Moreover, these aspects can change over time with no warning to the users through software updates. There is a lack of standardisation between the segmentation algorithms resulting in variation between AI outputs and, highlighted in the work reported here, the measured brain volumes from MRI data. We investigate the differences between brain volumes extracted from the same patient scan at different centres to examine variability in the measured volumes. We use the Alzheimer’s Disease Neuroimaging Initiative data consisting of imaging data acquired under a standardised protocol curated at one site, with data processing and analysis occurring at several independent sites. We find a low degree of concordance between measurements of a patient’s hippocampal volumes between centres, with differences between centres exceeding thresholds for mild, moderate and severe dementia from Vijayakumar et al, ISRN Radiology [2003]. Comparing hippocampal volumes between three centres, we find that there are many cases where patients were diagnosed as cognitively normal or with mild cognitive impairment but had more than a 5 % or 12 % difference in volume between centres. This indicates that a patient evaluated at different centres risks receiving different diagnoses between the centres. This difference risks patients not being diagnosed and treated in a timely manner due to poor measurement of hippocampal volumes. Results indicate that hippocampal volume measurement between centres suffers inconsistencies due to a lack of standardisation in image post-processing, segmentation, and extraction pipelines at each centre. Our work illustrates that when neurodegeneration in a patient is evaluated in different centres based on an MRI scan, there can be significant differences in the hippocampal volume measurement that can hinder accurate and timely clinical intervention; highlighting an urgent need for standardisation of imaging pipelines to ensure consistency in diagnosis, treatment and assuring patient safety.