Individual differences in mind and behavior are believed to reflect the functional variability of the human brain. Due to the lack of a large-scale longitudinal dataset, the full landscape of variability within and between individual functional connectomes is largely unknown. We collected 300 resting-state functional magnetic resonance imaging (rfMRI) datasets from 30 healthy participants who were scanned every three days for one month. With these data, both intra- and inter-individual variability of six common rfMRI metrics, as well as their test-retest reliability, were estimated across multiple spatial scales. Global metrics were more dynamic than local regional metrics. Cognitive components involving working memory, inhibition, attention, language and related neural networks exhibited high intra-individual variability. In contrast, inter-individual variability demonstrated a more complex picture across the multiple scales of metrics. Limbic, default, frontoparietal and visual networks and their related cognitive components were more differentiable than somatomotor and attention networks across the participants. Analyzing both intra- and inter-individual variability revealed a set of high-resolution maps on test-retest reliability of the multi-scale connectomic metrics. These findings represent the first collection of individual differences in multi-scale and multi-metric characterization of the human functional connectomes in-vivo, serving as normal references for the field to guide the use of common functional metrics in rfMRI-based applications.

Objective: Accurate staging is of great importance in treatment selection for patients with nasopharyngeal carcinoma (NPC). The aims of this study were to construct radiomic models of NPC staging based on positron emission tomography (PET) and magnetic resonance (MR) images and to investigate the correlation between metabolic parameters and radiomic features. Methods: A total of 100 consecutive cases of NPC (70 in training and 30 in the testing cohort) with undifferentiated carcinoma confirmed pathologically were recruited. Metabolic parameters of the local lesions of NPC were measured. A total of 396 radiomic features based on PET and MRI images were calculated [including histogram, Haralick, shape factor, gray level co-occurrence matrix (GLCM), and run length matrix (RLM)] and selected [using maximum relevance and minimum redundancy (mRMR) and least shrinkage and selection operator (LASSO)], respectively. The logistic regression models were established according to these features. Finally, the relationship between the metabolic parameters and radiomic features was analyzed. Results: We selected the nine most relevant radiomic features (six from MR images and three from PET images) from local NPC lesions. In the PET model, the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and the specificity of the training group were 0.84, 0.75, 0.90, and 0.69, respectively. In the MR model, those metrics were 0.85, 0.83, 0.75, and 0.86, respectively. Pearson's correlation analysis showed that the metabolic parameters had different degrees of correlation with the selected radiomic features. Conclusion: The PET and MR radiomic models were helpful in the diagnosis of NPC staging. There were correlations between the metabolic parameters and radiomic features of primary NPC based on PET/MR. In the future, PET/MR-based radiomic Feng et al. NPC Radiomics Based on PET/MR Imaging models, with further improvement and validation, can be a more useful and economical tool for predicting local invasion and distant metastasis of NPC.

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Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

PurposeNeurovascular compression (NVC) is considered as the main factor leading to the classical trigeminal neuralgia (CTN), and a part of idiopathic TN (ITN) may be caused by NVC (ITN-nvc). This study aimed to explore the risk factors for unilateral CTN or ITN-nvc (UC-ITN), which have bilateral NVC, using machine learning (ML).MethodsA total of 89 patients with UC-ITN were recruited prospectively. According to whether there was NVC on the unaffected side, patients with UC-ITN were divided into two groups. All patients underwent a magnetic resonance imaging (MRI) scan. The bilateral cisternal segment of the trigeminal nerve was manually delineated, which avoided the offending vessel (Ofv), and the features were extracted. Dimensionality reduction, feature selection, model construction, and model evaluation were performed step-by-step.ResultsFour textural features with greater weight were selected in patients with UC-ITN without NVC on the unaffected side. For UC-ITN patients with NVC on the unaffected side, six textural features with greater weight were selected. The textural features (rad_score) showed significant differences between the affected and unaffected sides (p < 0.05). The nomogram model had optimal diagnostic power, and the area under the curve (AUC) in the training and validation cohorts was 0.76 and 0.77, respectively. The Ofv and rad_score were the risk factors for UC-ITN according to nomogram.ConclusionBesides NVC, the texture features of trigeminal-nerve cisternal segment and Ofv were also the risk factors for UC-ITN. These findings provided a basis for further exploration of the microscopic etiology of UC-ITN.