Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application.

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Mapping morphological cortical networks with joint probability distributions from multiple morphological features.

Morphological features sourced from structural magnetic resonance imaging can be used to infer human brain connectivity. Although integrating different morphological features may theoretically be beneficial for obtaining more precise morphological connectivity networks (MCNs), the empirical evidence to support this supposition is scarce. Moreover, the incorporation of different morphological features remains an open question. In this study, we proposed a method to construct cortical MCNs based on multiple morphological features. Specifically, we adopted a multi-dimensional kernel density estimation algorithm to fit regional joint probability distributions (PDs) from different combinations of four morphological features, and estimated inter-regional similarity in the joint PDs via Jensen-Shannon divergence. We evaluated the method by comparing the resultant MCNs with those built based on different single morphological features in terms of topological organization, test-retest reliability, biological plausibility, and behavioral and cognitive relevance. We found that, compared to MCNs built based on different single morphological features, MCNs derived from multiple morphological features displayed less segregated, but more integrated network architecture and different hubs, had higher test-retest reliability, encompassed larger proportions of inter-hemispheric edges and edges between brain regions within the same cytoarchitectonic class, and explained more inter-individual variance in behavior and cognition. These findings were largely reproducible when different brain atlases were used for cortical parcellation. Further analysis of macaque MCNs revealed weak, but significant correlations with axonal connectivity from tract-tracing, independent of the number of morphological features. Altogether, this paper proposes a new method for integrating different morphological features, which will be beneficial for constructing MCNs.

A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects.

Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback-Leibler divergence > Jensen-Shannon divergence; proportional: Jensen-Shannon divergence > Kullback-Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.

Effect of 1 Year of Qigong Exercise on Cognitive Function Among Older Chinese Adults at Risk of Cognitive Decline: A Cluster Randomized Controlled Trial.

Background: The rapidly aging Chinese population is showing an increase in age-related illnesses, including mild cognitive impairment and Alzheimer disease. The best types of physical activity for the improvement of cognition remain unknown. This study aimed to compare the effectiveness of a tailored qigong exercise with that of stretching exercise in the maintenance of cognitive abilities in Chinese elders at risk of cognitive decline. Methods: Seventy-four community-dwelling adults aged ≥60 years were screened for eligibility. Using a randomized control group design, participants with scores ≥19 on the Chinese version of the Montreal Cognitive Assessment-Basic (MoCA) were allocated to a 1-year qigong intervention (n = 33) and a stretching control exercise group (n = 33). The primary outcome was the MoCA score, as a measure of global cognitive function, and secondary outcomes were globe cognition and five domain scores on the Chinese version of the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). The MoCA and RBANS were administered at baseline and 1 year after intervention to assess the effect of the exercises on cognitive decline. Results: Twenty-five of 33 (75.8%) participants in the qigong group and 26 of 33 (78.8%) participants in the control group completed the 1-year exercise programs. A bivariate test revealed strong correlation between MoCA and RBANS total scores after the intervention (r = 0.517, p < 0.01). Generalized estimating equations revealed a lower risk of progression of cognitive decline at 1 year in the qigong group than in the control group (odds ratio, 0.314; 95% confidence interval, 0.103-0.961; p = 0.04). Two-way repeated-measures ANOVA followed by post hoc t tests with Bonferroni corrections indicated that MoCA and RBANS scores were significantly higher in the qigong group than in the control group (MoCA and RBANS global cognition, memory, visuospatial/constructional ability, and language, all p < 0.01), with the exception of RBANS attention score (p > 0.05). Conclusions: One year of qigong practice was significantly superior to stretching exercise not only for the prevention of cognitive decline progression, but also for the improvement of several cognitive functions, among older Chinese adults at risk of cognitive decline.

[Evaluation of three-dimensional conformal radiotherapy based on dose distribution].

In this paper, we briefly introduce 3 methods of evaluation for a treatment plan, and mainly discuss the criteria of the plan's optimization based on dose distribution. The elaboratively-designed treatment plan is capable of having the tumor receive higher dose level while the normal tissues and organs receive minimum dose level.