Frequency dependent whole-brain coactivation patterns analysis in Alzheimer's disease.

Background: The brain in resting state has complex dynamic properties and shows frequency dependent characteristics. The frequency-dependent whole-brain dynamic changes of resting state across the scans have been ignored in Alzheimer's disease (AD). Objective: Coactivation pattern (CAP) analysis can identify different brain states. This paper aimed to investigate the dynamic characteristics of frequency dependent whole-brain CAPs in AD. Methods: We utilized a multiband CAP approach to model the state space and study brain dynamics in both AD and NC. The correlation between the dynamic characteristics and the subjects' clinical index was further analyzed. Results: The results showed similar CAP patterns at different frequency bands, but the occurrence of patterns was different. In addition, CAPs associated with the default mode network (DMN) and the ventral/dorsal visual network (dorsal/ventral VN) were altered significantly between the AD and NC groups. This study also found the correlation between the altered dynamic characteristics of frequency dependent CAPs and the patients' clinical Mini-Mental State Examination assessment scale scores. Conclusion: This study revealed that while similar CAP spatial patterns appear in different frequency bands, their dynamic characteristics in subbands vary. In addition, delineating subbands was more helpful in distinguishing AD from NC in terms of CAP.

Criticality in transient behavior of coupled oscillator system toward chimera and synchronization.

Chimera states in spatiotemporal dynamical systems have been investigated in physical, chemical, and biological systems, while how the system is steering toward different final destinies upon spatially localized perturbation is still unknown. Through a systematic numerical analysis of the evolution of the spatiotemporal patterns of multi-chimera states, we uncover a critical behavior of the system in transient time toward either chimera or synchronization as the final stable state. We measure the critical values and the transient time of chimeras with different numbers of clusters. Then, based on an adequate verification, we fit and analyze the distribution of the transient time, which obeys power-law variation process with the increase in perturbation strengths. Moreover, the comparison between different clusters exhibits an interesting phenomenon, thus we find that the critical value of odd and even clusters will alternatively converge into a certain value from two sides, respectively, implying that this critical behavior can be modeled and enabling the articulation of a phenomenological model.

Triple-network analysis of Alzheimer's disease based on the energy landscape.

Introduction: Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Methods: Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. Results: AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. Discussion: The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.

Unsupervised cluster analysis reveals distinct subgroups in healthy population with different exercise responses of cardiorespiratory fitness.

Background: Considerable attention has been paid to interindividual differences in the cardiorespiratory fitness (CRF) response to exercise. However, the complex multifactorial nature of CRF response variability poses a significant challenge to our understanding of this issue. We aimed to explore whether unsupervised clustering can take advantage of large amounts of clinical data and identify latent subgroups with different CRF exercise responses within a healthy population. Methods: 252 healthy participants (99 men, 153 women; 36.8 ± 13.4 yr) completed moderate endurance training on 3 days/week for 4 months, with exercise intensity prescribed based on anaerobic threshold (AT). Detailed clinical measures, including resting vital signs, ECG, cardiorespiratory parameters, echocardiography, heart rate variability, spirometry and laboratory data, were obtained before and after the exercise intervention. Baseline phenotypic variables that were significantly correlated with CRF exercise response were identified and subjected to selection steps, leaving 10 minimally redundant variables, including age, BMI, maximal oxygen uptake (VO2max), maximal heart rate, VO2 at AT as a percentage of VO2max, minute ventilation at AT, interventricular septal thickness of end-systole, E velocity, root mean square of heart rate variability, and hematocrit. Agglomerative hierarchical clustering was performed on these variables to detect latent subgroups that may be associated with different CRF exercise responses. Results: Unsupervised clustering revealed two mutually exclusive groups with distinct baseline phenotypes and CRF exercise responses. The two groups differed markedly in baseline characteristics, initial fitness, echocardiographic measurements, laboratory values, and heart rate variability parameters. A significant improvement in CRF following the 16-week endurance training, expressed by the absolute change in VO2max, was observed only in one of the two groups (3.42 ± 0.4 vs 0.58 ± 0.65 ml⋅kg-1∙min-1, P = 0.002). Assuming a minimal clinically important difference of 3.5 ml⋅kg-1∙min-1 in VO2max, the proportion of population response was 56.1% and 13.9% for group 1 and group 2, respectively (P<0.001). Although group 1 exhibited no significant improvement in CRF at group level, a significant decrease in diastolic blood pressure (70.4 ± 7.8 vs 68.7 ± 7.2 mm Hg, P = 0.027) was observed. Conclusions: Unsupervised learning based on dense phenotypic characteristics identified meaningful subgroups within a healthy population with different CRF responses following standardized aerobic training. Our model could serve as a useful tool for clinicians to develop personalized exercise prescriptions and optimize training effects.