Each study has a usefulness score out of 100 (rubric version 4.0). Tap a score to see what makes it up.
Highest usefulness score first. Studies without a score come last.
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Gait impairment is a prominent falls risk factor and a prevalent feature among older adults with cognitive impairment.
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Brain extraction is an important step in the analysis of brain images.
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The science of large-scale brain networks offers a powerful paradigm for investigating cognitive and affective dysfunction in psychiatric and neurological disorders.
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While resting state functional connectivity has been shown to decrease in patients with mild and/or moderate Alzheimer's disease, it is not yet known how functional connectivity changes in patients as the disease progresses.
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Using resting state (RS) functional magnetic resonance imaging (fMRI), the connectivity patterns of the default mode (DMN), frontoparietal, executive, and salience networks were explored in 13 Alzheimer's disease (AD) patients, 12 amnestic mild cognitive impairment (aMCI) patients, and 13 healthy controls.
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This review focuses on resting-state functional connectivity, a functional MRI technique which allows the study of spontaneous brain activity generated under resting conditions.
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36/100
Cognitive reserve explains why those with higher IQ, education, occupational attainment, or participation in leisure activities evidence less severe clinical or cognitive changes in the presence of age-related or Alzheimer's disease pathology.
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Although beta-amyloid (Aβ) deposition is a characteristic feature of Alzheimer's disease (AD), this pathology is commonly found in elderly normal controls (NC).
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Microinfarcts are common, and persons with multiple cortical microinfarcts have higher odds of dementia.
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Recent research on Alzheimer's disease (AD) has shown that the decline of cognitive and memory functions is accompanied by a disrupted neuronal connectivity characterized by white matter (WM) degeneration.
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Our objectives were to assess the frequency of behavioural changes in patients with amyotrophic lateral sclerosis (ALS) and to compare the clinical profile of ALS patients with those with behavioural variant frontotemporal dementia (bvFTD).
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This paper reviews recent progress in the diagnosis of Alzheimer's disease (AD) from electroencephalograms (EEG).
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In recent years, there has been an explosion of studies on network modeling of brain connectivity.
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Magnetic resonance imaging (MRI) patterns were examined together with cerebrospinal fluid (CSF) biomarkers in serial scans of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants with mild cognitive impairment (MCI).
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Machine learning methods pool diverse information to perform computer-assisted diagnosis and predict future clinical decline.
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Our brain is a network. It consists of spatially distributed, but functionally linked regions that continuously share information with each other.
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Functional magnetic resonance imaging (fMRI) studies have observed hyperactivity in the hippocampal region in individuals with Mild Cognitive Impairment (MCI).
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The hallmark clinical symptom of early Alzheimer's disease (AD) is episodic memory impairment.
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The early identification of brain anatomy deviating from the normal pattern of growth and atrophy, such as in Alzheimer's disease (AD), has the potential to improve clinical outcomes through early intervention.
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Visual impairment is a common symptom of Alzheimer's disease (AD).
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36/100
Dementia is a complex human disease. The incidence of dementia among the elderly population is rising rapidly worldwide.
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Recent developments in the statistical physics of complex networks have been translated to neuroimaging data in an effort to enhance our understanding of human brain structural and functional networks.
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It is commonly assumed that functional brain connectivity reflects structural brain connectivity.
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Recent neuroimaging studies have demonstrated that both remembering the past and simulating the future activate a core neural network including the medial temporal lobes.
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The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging.