Abstract
Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01∼0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01∼0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01∼0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.
Highlights
Vascular dementia (VaD), called multi-infarct dementia, occurs when cells in the brain are deprived of oxygen
We obtained accuracy rates of 56.4%, 38.46%, and 58.9% corresponding to the slow-5, slow-4, and whole-band, respectively. This indicates that the determination accuracy rates when the labels were randomized were approximately random, indicating that the 100% accuracy in the classification result shown in Fig. 3 reflects actual group differences in the Resting-state functional connectivity (rsFC) patterns between the VaD patients and the controls
The reliability of frequency-specific spatiotemporal structures has been tested [15]. Consistent with these previous studies, our study indicated that brain spontaneous BOLD fluctuations exhibit frequency-specific spatiotemporal patterns in whole-brain functional networks derived from BOLD-based resting state BOLD-fMRI (rsfMRI) datasets
Summary
Vascular dementia (VaD), called multi-infarct dementia, occurs when cells in the brain are deprived of oxygen. A network of blood vessels (the vascular system) supplies the brain with oxygen. If a blockage occurs in the vascular system, or if the system is diseased, blood may be prevented from reaching the brain. VaD is one of the most common types of dementia, ranking second only to Alzheimer’s disease (AD) [3]. It is characterized by a sudden onset followed by a progressive decline in language, memory, and other cognitive functions. Studies suggested that VaD is associated with a specific neuropsychological dysfunction modality that can be compared to AD [4] and other dementias [5]. The diagnostic criteria of VaD have been continuously refined [6], more reliable, applicable diagnostic modalities continue to be urgently needed for clinical practice and research purposes
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