Diminished neural network dynamics in amnestic mild cognitive impairment

Autor: Benjamin M. Hampstead, Emily C. Grossner, Einat K. Brenner, Nicholas Gilbert, Rachel A. Bernier, Frank G. Hillary, Krishnankutty Sathian
Rok vydání: 2018
Předmět:
Male
medicine.medical_specialty
Perseveration
Neuropsychological Tests
Audiology
behavioral disciplines and activities
Article
050105 experimental psychology
03 medical and health sciences
0302 clinical medicine
Physiology (medical)
mental disorders
Image Processing
Computer-Assisted
medicine
Humans
Dementia
Cognitive Dysfunction
0501 psychology and cognitive sciences
Cognitive impairment
Default mode network
Aged
Aged
80 and over
Brain Mapping
Principal Component Analysis
Artificial neural network
medicine.diagnostic_test
General Neuroscience
05 social sciences
Disease progression
Brain
Middle Aged
medicine.disease
Magnetic Resonance Imaging
Neural network analysis
Oxygen
Neuropsychology and Physiological Psychology
Female
Amnesia
Nerve Net
medicine.symptom
Mental Status Schedule
Psychology
Functional magnetic resonance imaging
030217 neurology & neurosurgery
Zdroj: International Journal of Psychophysiology. 130:63-72
ISSN: 0167-8760
DOI: 10.1016/j.ijpsycho.2018.05.001
Popis: Mild cognitive impairment (MCI) is widely regarded as an intermediate stage between typical aging and dementia, with nearly 50% of patients with amnestic MCI (aMCI) converting to Alzheimer's dementia (AD) within 30 months of follow-up (Fischer et al., 2007). The growing literature using resting-state functional magnetic resonance imaging reveals both increased and decreased connectivity in individuals with MCI and connectivity loss between the anterior and posterior components of the default mode network (DMN) throughout the course of the disease progression (Hillary et al., 2015; Sheline & Raichle, 2013; Tijms et al., 2013). In this paper, we use dynamic connectivity modeling and graph theory to identify unique brain “states,” or temporal patterns of connectivity across distributed networks, to distinguish individuals with aMCI from healthy older adults (HOAs). We enrolled 44 individuals diagnosed with aMCI and 33 HOAs of comparable age and education. Our results indicated that individuals with aMCI spent significantly more time in one state in particular, whereas neural network analysis in the HOA sample revealed approximately equivalent representation across four distinct states. Among individuals with aMCI, spending a higher proportion of time in the dominant state relative to a state where participants exhibited high cost (a measure combining connectivity and distance), predicted better language performance and less perseveration. This is the first report to examine neural network dynamics in individuals with aMCI.
Databáze: OpenAIRE
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