Our objective was to determine the pattern and extent of generalized and focal neocortical atrophy that develops in patients with epilepsy and the factors associated with such changes. As part of a prospective, longitudinal follow-up study of 122 patients with chronic epilepsy, 68 newly diagnosed patients, and 90 controls, serial magnetic resonance imaging scans were obtained 3.5 years apart. Image subtraction was used to identify diffuse and focal neocortical change that was quantified with a regional brain atlas and a fully automated segmentation algorithm. New focal or generalized neocortical volume losses were identified in 54% of patients with chronic epilepsy, 39% of newly diagnosed patients and 24% of controls. Patients with chronic epilepsy were significantly more likely to develop neocortical atrophy than control subjects. The increased risk of cerebral atrophy in epilepsy was not related to a history of documented seizures. Risk factors for neocortical atrophy were age and multiple antiepileptic drug exposure. Focal and generalized neocortical atrophy commonly develops in chronic epilepsy. Neocortical changes seen in a quarter of our control group over 3.5 years were likely to reflect physiological changes. Our results show that ongoing cerebral atrophy may be widespread and remote from the putative epileptic focus, possibly reflecting extensive networks and interconnections between cortical regions.
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http://dx.doi.org/10.1002/ana.10463 | DOI Listing |
Neurology
February 2025
Department of Pharmacology and Toxicology, University of Arizona, Tucson.
Background And Objectives: Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder influenced by genetic and environmental factors. Conditions such as type 2 diabetes (T2D), cardiovascular disease, obesity, depression, and obstructive sleep apnea (OSA) increase AD risk and progression. This study aimed to examine the genetic predisposition to these conditions and their effect on AD pathophysiology, risk, and progression.
View Article and Find Full Text PDFMol Psychiatry
December 2024
Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología Clínica, Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Universidad de Sevilla/CSIC/CIBERNED, Sevilla, Spain.
Cortical hypometabolism on FDG-PET is a well-established neuroimaging biomarker of cognitive impairment in Parkinson's disease (PD), but its pathophysiologic origins are incompletely understood. Cholinergic basal forebrain (cBF) degeneration is a prominent pathological feature of PD-related cognitive impairment and may contribute to cortical hypometabolism through cholinergic denervation of cortical projection areas. Here, we investigated in-vivo associations between subregional cBF volumes on 3T-MRI, cortical hypometabolism on [F]FDG-PET, and cognitive deficits in a cohort of 95 PD participants with varying degrees of cognitive impairment.
View Article and Find Full Text PDFNeuroimage
January 2025
Department of Otolaryngology, Head and Neck, University of Tübingen, Tübingen 72076, Germany. Electronic address:
The slowing and reduction of auditory responses in the brain are recognized side effects of increased pure tone thresholds, impaired speech recognition, and aging. However, it remains controversial whether central slowing is primarily linked to brain processes as atrophy, or is also associated with the slowing of temporal neural processing from the periphery. Here we analyzed electroencephalogram (EEG) responses that most likely reflect medial geniculate body (MGB) responses to passive listening of phonemes in 80 subjects ranging in age from 18 to 76 years, in whom the peripheral auditory responses had been analyzed in detail (Schirmer et al.
View Article and Find Full Text PDFGeroscience
November 2024
Department of Neuroscience, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
Identifying early predictors of cognitive decline and at-risk individuals is essential for timely intervention and prevention of dementia. This study aimed to detect neurobiological changes and factors related to cognitive performance in the Metropolit 1953 Danish male birth cohort. We analyzed data from 582 participants, aged 57-68 years, using machine learning techniques to group cognitive trajectories into four clusters differentiating high- and low-performing groups.
View Article and Find Full Text PDFBrain
November 2024
Department of Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan.
The clinical manifestation of Parkinson's disease exhibits significant heterogeneity in the prevalence of non-motor symptoms and the rate of progression of motor symptoms, suggesting that Parkinson's disease can be classified into distinct subtypes. In this study, we aimed to explore this heterogeneity by identifying a set of subtypes with distinct patterns of spatiotemporal trajectories of neurodegeneration. We applied Subtype and Stage Inference (SuStaIn), an unsupervised machine learning algorithm that combined disease progression modelling with clustering methods, to cortical and subcortical neurodegeneration visible on 3 T structural MRI of a large cross-sectional sample of 504 patients and 279 healthy controls.
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