Anterior temporal lobectomy is an effective treatment for drug-resistant epilepsy of temporal origin, although new language impairment may develop after surgery. Since correlations between functional connectivity (FC) MRI of the language network and verbal-IQ performance before surgery have recently been reported, we investigated the existence of correlations between the preoperative FC of the language network and post-operative verbal-IQ decline. FC between nodes of the language network of the two hemispheres (Interhemispheric-FC) and within nodes of the left hemisphere (LH-FC) and language lateralization indexes were estimated in five right-handed patients with non-tumoral left temporal lobe epilepsy undergoing anterior temporal lobectomy. Correlations between preoperative FC measures and lateralization indexes, and the post-operative (12 months) neuropsychological verbal-IQ decline were investigated. Verbal-IQ decline was inversely correlated with the degree of left lateralization and directly correlated with the strength of Interhemispheric-FC. No significant correlation was found between LH-FC and post-operative verbal-IQ change. The results from this limited number of patients suggest that a stronger preoperative connectivity between homologue regions, associated with the absence of a definite hemispheric lateralization, appears to be an unfavorable prognostic biomarker.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4202851PMC
http://dx.doi.org/10.15274/NRJ-2014-10031DOI Listing

Publication Analysis

Top Keywords

language network
12
verbal-iq decline
12
functional connectivity
8
connectivity mri
8
temporal lobe
8
lobe epilepsy
8
anterior temporal
8
temporal lobectomy
8
correlations preoperative
8
post-operative verbal-iq
8

Similar Publications

Social networks are a battlefield for political propaganda. Protected by the anonymity of the internet, political actors use computational propaganda to influence the masses. Their methods include the use of synchronized or individual bots, multiple accounts operated by one social media management tool, or different manipulations of search engines and social network algorithms, all aiming to promote their ideology.

View Article and Find Full Text PDF

Post-stroke aphasia is a network disorder characterized by language impairments and aberrant network activation. While patients with post-stroke aphasia recover over time, the dynamics of the underlying changes in the brain remain elusive. Neuroimaging work demonstrated that language recovery is a heterogeneous process, characterized by varying activation levels in several regions of the left-hemispheric language network and the domain-general bilateral multiple-demand network.

View Article and Find Full Text PDF

Background And Purpose: Asymptomatic carotid stenosis (ACS) is an independent risk factor for ischemic stroke and vascular cognitive impairment, affecting cognitive function across multiple domains. This study aimed to explore differences in static and dynamic intrinsic functional connectivity and temporal dynamics between patients with ACS and those without carotid stenosis.

Methods: We recruited 30 patients with unilateral moderate-to-severe (stenosis ≥ 50%) ACS and 30 demographically-matched healthy controls.

View Article and Find Full Text PDF

Evaluating simulated teaching audio for teacher trainees using RAG and local LLMs.

Sci Rep

January 2025

Office for the Advancement of Educational Information, Chengdu Normal University, Chengdu, 610000, China.

In the training of teacher students, simulated teaching is a key method for enhancing teaching skills. However, traditional evaluations of simulated teaching typically rely on direct teacher involvement and guidance, increasing teachers' workload and limiting the opportunities for teacher students to practice independently. This paper introduces a Retrieval-Augmented Generation (RAG) framework constructed using various open-source tools (such as FastChat for model inference and Whisper for speech-to-text) combined with a local large language model (LLM) for audio analysis of simulated teaching.

View Article and Find Full Text PDF

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!