The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients' cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted,and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.
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http://dx.doi.org/10.3389/fnagi.2023.1073039 | DOI Listing |
iScience
February 2025
Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, MA, USA.
Neurodevelopmental impairments associated with congenital heart disease (CHD) may arise from perturbations in brain developmental pathways, including the formation of sulcal patterns. While genetic factors contribute to sulcal features, the association of noncoding variants (ncDNVs) with sulcal patterns in people with CHD remains poorly understood. Leveraging deep learning models, we examined the predicted impact of ncDNVs on gene regulatory signals.
View Article and Find Full Text PDFBrain Inj
January 2025
Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
Introduction: Magnetic resonance imaging (MRI) has revolutionized our capacity to examine brain alterations in traumatic brain injury (TBI). However, little is known about the level of implementation of MRI techniques in clinical practice in TBI and associated obstacles.
Methods: A diverse set of health professionals completed 19 multiple choice and free text survey questions.
J Neural Eng
January 2025
Shanghai Dianji University, shnaghai, Shanghai, Shanghai, 201306, CHINA.
Objective: Among all BCI paradigms, motion imagery (MI) has gained favor among researchers because it allows users to control external devices by imagining movements rather than actually performing actions. This property holds important promise for clinical applications, especially in areas such as stroke rehabilitation. Electroencephalogram (EEG) signals and functional near-infrared spectroscopy (fNIRS) signals are two of the more popular neuroimaging techniques for obtaining MI signals from the brain.
View Article and Find Full Text PDFBrain
January 2025
Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA.
Although the pathophysiology of migraine involves a complex ensemble of peripheral and central nervous system changes that remain incompletely understood, the activation and sensitization of the trigeminovascular system is believed to play a major role. However, non-invasive, in vivo neuroimaging studies investigating the underlying neural mechanisms of trigeminal system abnormalities in human migraine patients are limited. Here, we studied 60 patients with migraine (55 females, mean age ± SD: 36.
View Article and Find Full Text PDFMach Learn Clin Neuroimaging (2024)
December 2024
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called ntrastive Learning-based ph Generalized nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis.
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