Diagnostic Performance of MRI Volumetry in Epilepsy Patients With Hippocampal Sclerosis Supported Through a Random Forest Automatic Classification Algorithm.

Introduction: Several methods offer free volumetry services for MR data that adequately quantify volume differences in the hippocampus and its subregions. These methods are frequently used to assist in clinical diagnosis of suspected hippocampal sclerosis in temporal lobe epilepsy. A strong association between severity of histopathological anomalies and hippocampal volumes was reported using MR volumetry with a higher diagnostic yield than visual examination alone. Interpretation of volumetry results is challenging due to inherent methodological differences and to the reported variability of hippocampal volume. Furthermore, normal morphometric differences are recognized in diverse populations that may need consideration. To address this concern, we highlighted procedural discrepancies including atlas definition and computation of total intracranial volume that may impact volumetry results. We aimed to quantify diagnostic performance and to propose reference values for hippocampal volume from two well-established techniques: FreeSurfer v.06 and volBrain-HIPS. Methods: Volumetry measures were calculated using clinical T1 MRI from a local population of 61 healthy controls and 57 epilepsy patients with confirmed unilateral hippocampal sclerosis. We further validated the results by a state-of-the-art machine learning classification algorithm (Random Forest) computing accuracy and feature relevance to distinguish between patients and controls. This validation process was performed using the FreeSurfer dataset alone, considering morphometric values not only from the hippocampus but also from additional non-hippocampal brain regions that could be potentially relevant for group classification. Mean reference values and 95% confidence intervals were calculated for left and right hippocampi along with hippocampal asymmetry degree to test diagnostic accuracy. Results: Both methods showed excellent classification performance (AUC:> 0.914) with noticeable differences in absolute (cm3) and normalized volumes. Hippocampal asymmetry was the most accurate discriminator from all estimates (AUC:1~0.97). Similar results were achieved in the validation test with an automatic classifier (AUC:>0.960), disclosing hippocampal structures as the most relevant features for group differentiation among other brain regions. Conclusion: We calculated reference volumetry values from two commonly used methods to accurately identify patients with temporal epilepsy and hippocampal sclerosis. Validation with an automatic classifier confirmed the principal role of the hippocampus and its subregions for diagnosis.

A multidimensional and multi-feature framework for cardiac interoception.

Interoception (the sensing of inner-body signals) is a multi-faceted construct with major relevance for basic and clinical neuroscience research. However, the neurocognitive signatures of this domain (cutting across behavioral, electrophysiological, and fMRI connectivity levels) are rarely reported in convergent or systematic fashion. Additionally, various controversies in the field might reflect the caveats of standard interoceptive accuracy (IA) indexes, mainly based on heartbeat detection (HBD) tasks. Here we profit from a novel IA index (md) to provide a convergent multidimensional and multi-feature approach to cardiac interoception. We found that outcomes from our IA-md index are associated with -and predicted by- canonical markers of interoception, including the hd-EEG-derived heart-evoked potential (HEP), fMRI functional connectivity within interoceptive hubs (insular, somatosensory, and frontal networks), and socio-emotional skills. Importantly, these associations proved more robust than those involving current IA indexes. Furthermore, this pattern of results persisted when taking into consideration confounding variables (gender, age, years of education, and executive functioning). This work has relevant theoretical and clinical implications concerning the characterization of cardiac interoception and its assessment in heterogeneous samples, such as those composed of neuropsychiatric patients.

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging.

INTRODUCTION: Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. METHODS: We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier. RESULTS: Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). DISCUSSION: This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

Nuevos conceptos en neuromodulación cerebral: rol de DTI y estimulación de tractos / New concepts in brain neuromodulation: role of DTI and tract stimulation

Introducción: La estimulación cerebral profunda es una técnica difundida y validada para eltratamiento de múltiples dolencias neurológicas y psiquiátricas, entre ellas el temblor esencial. Objetivo: Evaluar si existe un correlato clínico-anatómico, para un paciente con TE, entre la mejor estimulación lograda y los tractos involucrados. Para esto se realiza una descripción de la técnica utilizada, incluyendo el procesamiento de imágenes necesario. Material y métodos: Se presenta el caso de un paciente de 53 años de edad, con una historia de 23 años de temblor esencial progresivo e incapacitante. Se realizó un implante de DBS bilateral en Vim. Se realizó un post procesamiento de imágenes con un método desarrollado por nuestro equipo a través del cual se permitió evaluar gráficamente el área de estimulación cerebral y sus relaciones con los tractos implicados en la patología (dento-rubro-talámico, haz piramidal y haz lemniscal). Resultados: El paciente presentó una mejoría del 55% medido por escala de temblor de Tolosa. Se obtuvo una correlación anatómica y funcional de lo esperado según imágenes y la respuesta clínica del paciente. Se constataron efectos adversos cuando la estimulación implicaba fibras del haz piramidal y lemniscal, presentando los mejores efectos clínicos cuando el haz dento-rubro-talámico era influenciado por el área de acción del campo eléctrico. Conclusiones: En este reporte mostramos la aplicabilidad de DTI y tractografía para explicar los efectos de la programación de los pacientes con estimulación cerebral profunda.

Introduction: Deep brain stimulation is a widespread and validated technique for the treatment of multiple neurological and psychiatric disorders, including essential tremor. Objective: To evaluate if there is a clinical-anatomical correlate, for a patient with essential tremor, between the best stimulation achieved and the tracts involved. For this, a description of the technique used is made, including the necessary image processing. Methods: We present the case of a 53-year-old patient with a 23-year history of progressive and disabling essential tremor. A bilateral DBS implant was performed on Vim. We performed a post-processing of images with a method developed by our team through which we were able to graphically evaluate the area of brain stimulation and its relationships with the tracts involved in the pathology (dento-rubro-thalamic tract, pyramidal tract and lemniscal tract). Conclusions: In this report we showed the applicability of DTI and tractography to explain the clinical effects of the programming features in patients with deep brain stimulation.

Looking for Alzheimer's Disease morphometric signatures using machine learning techniques.

BACKGROUND: We present our results in the International challenge for automated prediction of MCI from MRI data. We evaluate the performance of MRI-based neuromorphometrics features (nMF) in the classification of Healthy Controls (HC), Mild Cognitive Impairment (MCI), converters MCI (cMCI) and Alzheimer's Disease (AD) patients. NEW METHODS: We propose to segregate participants in three groups according to Mini Mental State Examination score (MMSEs), searching for the main nMF in each group. Then we use them to develop a Multi Classifier System (MCS). We compare the MCS against a single classifier scheme using both MMSEs+nMF and nMF only. We repeat this comparison using three state-of-the-art classification algorithms. RESULTS: The MCS showed the best performance on both Accuracy and Area Under the Receiver Operating Curve (AUC) in comparison with single classifiers. The multiclass AUC for the MCS classification on Test Dataset were 0.83 for HC, 0.76 for cMCI, 0.65 for MCI and 0.95 for AD. Furthermore, MCS's optimum accuracy on Neurodegenerative Disease (ND) detection (AD+cMCI vs MCI+HC) was 81.0% (AUC=0.88), while the single classifiers got 71.3% (AUC=0.86) and 63.1% (AUC=0.79) for MMSEs+nMF and only nMF respectively. COMPARISON WITH EXISTING METHOD: The proposed MCS showed a better performance than using all nMF into a single state-of-the-art classifier. CONCLUSIONS: These findings suggest that using cognitive scoring, e.g. MMSEs, in the design of a Multi Classifier System improves performance by allowing a better selection of MRI-based features.