Cognitive functioning depends on intact brain networks that can be assessed with functional magnetic resonance imaging (fMRI) techniques. We hypothesized that cognitive decrements in type 1 diabetes mellitus (T1DM) are associated with alterations in resting-state neural connectivity and that these changes vary according to the degree of microangiopathy. T1DM patients with (MA(+): n = 49) and without (MA(-): n = 52) microangiopathy were compared with 48 healthy control subjects. All completed a neuropsychological assessment and resting-state fMRI. Networks were identified using multisubject independent component analysis; specific group differences within each network were analyzed using the dual-regression method, corrected for confounding factors and multiple comparisons. Relative to control subjects, MA(-) patients showed increased connectivity in networks involved in motor and visual processes, whereas MA(+) patients showed decreased connectivity in networks involving attention, working memory, auditory and language processing, and motor and visual processes. Better information-processing speed and general cognitive ability were related to increased degree of connectivity. T1DM is associated with a functional reorganization of neural networks that varies, dependent on the presence or absence of microangiopathy.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3379683PMC
http://dx.doi.org/10.2337/db11-1358DOI Listing

Publication Analysis

Top Keywords

brain networks
8
t1dm associated
8
control subjects
8
connectivity networks
8
motor visual
8
visual processes
8
networks
6
resting-state brain
4
networks type
4
type diabetic
4

Similar Publications

Graph convolution network-based eeg signal analysis: a review.

Med Biol Eng Comput

January 2025

School of Control Science and Engineering, Tiangong University, Tianjin, 300387, China.

With the advancement of artificial intelligence technology, more and more effective methods are being used to identify and classify Electroencephalography (EEG) signals to address challenges in healthcare and brain-computer interface fields. The applications and major achievements of Graph Convolution Network (GCN) techniques in EEG signal analysis are reviewed in this paper. Through an exhaustive search of the published literature, a module-by-module discussion is carried out for the first time to address the current research status of GCN.

View Article and Find Full Text PDF

. To develop an augmentation method that simulates cone-beam computed tomography (CBCT) related motion artifacts, which can be used to generate training-data to increase the performance of artificial intelligence models dedicated to auto-contouring tasks.The augmentation technique generates data that simulates artifacts typically present in CBCT imaging.

View Article and Find Full Text PDF

Background And Objective: MicroRNAs (miRNAs) are implicated in cancer by exerting roles in tumor growth, metastasis, and even drug resistance. The general trends of miRNA research in diverse cancers are not fully understood. In this work, miRNA-related research in colorectal cancer, prostate cancer, leukemia, and brain tumors was analyzed in search of key research trends with clinical potential.

View Article and Find Full Text PDF

Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance.

View Article and Find Full Text PDF

Introduction: Resting state-fMRI, provides a sensitive method for detecting changes in brain functional integrity, both with respect to regional oxygenated blood flow and whole network connectivity. The primary goal of this report was to examine alterations in functional connectivity in collegiate American football players after a season of repetitive head impact exposure.

Methods: Collegiate football players completed a rs-fMRI at pre-season and 1 week into post-season.

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!