It has been argued that naturalistic conditions in FMRI studies provide a useful paradigm for investigating perception and cognition through a synchronization measure, inter-subject correlation (ISC). However, one analytical stumbling block has been the fact that the ISC values associated with each single subject are not independent, and our previous paper (Chen et al., 2016) used simulations and analyses of real data to show that the methodologies adopted in the literature do not have the proper control for false positives. In the same paper, we proposed nonparametric subject-wise bootstrapping and permutation testing techniques for one and two groups, respectively, which account for the correlation structure, and these greatly outperformed the prior methods in controlling the false positive rate (FPR); that is, subject-wise bootstrapping (SWB) worked relatively well for both cases with one and two groups, and subject-wise permutation (SWP) testing was virtually ideal for group comparisons. Here we seek to explicate and adopt a parametric approach through linear mixed-effects (LME) modeling for studying the ISC values, building on the previous correlation framework, with the benefit that the LME platform offers wider adaptability, more powerful interpretations, and quality control checking capability than nonparametric methods. We describe both theoretical and practical issues involved in the modeling and the manner in which LME with crossed random effects (CRE) modeling is applied. A data-doubling step further allows us to conveniently track the subject index, and achieve easy implementations. We pit the LME approach against the best nonparametric methods, and find that the LME framework achieves proper control for false positives. The new LME methodologies are shown to be both efficient and robust, and they will be added as an additional option and settings in an existing open source program, 3dLME, in AFNI (http://afni.nimh.nih.gov).

Ginseng (the root of Panax ginseng C.A. MEYER, Araliaceae) is frequently taken orally, as a crude substance, as a traditional medicine in Asian countries. The major components of ginseng are ginsenosides, which contain an aglycone with a dammarane skeleton.1,2) These ginsenosides have been reported to exhibit various biological activities, including anti-inflammatory action and antitumor effects (inhibition of tumor-induced angiogenesis and the prevention of tumor invasion and metastasis). [3][4][5] The pharmacological actions of these ginsenosides have been explained by their biotransformation by human intestinal bacteria. [6][7][8] For example, protopanaxadiol ginsenosides are transformed to 20-O-b-D-glucopyranosyl-20(S)-protopanaxadiol (compound K) by human intestinal bacteria. The metabolite compound K induces an antimetastatic or anticarcinogenic effect by blocking tumor invasion or preventing chromosomal aberration and tumorigenesis.5) Ginsenosides Re and Rg1 are also transformed to ginsenoside Rh1 or 20(S)-protopanaxatriol, which have exhibited potent antiallergic and antiinflammatory effects. [9][10][11][12] However, the antiiflammatory effect of protopanaxadiol ginsenosides, such as ginsenoside Rb1, and compound K, has not been studied.Therefore, we isolated ginsenoside Rb1 from ginseng and its metabolite compound K and investigated the antiinflammatory effect of ginsenoside Rb1 and its metabolite compound K (Fig. 1), using RAW264.7 cell induced by lipopolysaccharide (LPS). The RAW 264.7 cells were purchased from the Korean Cell Line Bank (Seoul, Korea). MATERIALS AND METHODS Materials Isolation of Ginsenoside Rb1 and Its Metabolite Compound K by Human Intestinal Microflora GinsenosideRb1 and compound K were isolated from fermented Ginseng according to the previously published method.2,13) Ginsenoside Rb1 (2 g) from a BuOH extract of white ginseng (Kyung Dong Market, Seoul, Korea), was isolated by silica gel column chromatography using CHCl 3 -MeOH-H 2 O (10 : 3 : 1, lower layer), according to the previously reported methods.Fresh human feces (5 g) were suspended in TS broth, centrifuged at 500ϫg for 10 min, and the resulting supernatant * To whom correspondence should be addressed. Hoegi, Dongdaemun-ku, Seoul 130-701, Korea: and b Korea Food Research Institute; San 46-1, Baekhyun, Bundang-Ku, Seoungnam-Shi 463-420, Korea. Received August 25, 2004; accepted December 2, 2004 In this study, the antiinflammatory activities of ginsenoside Rb1, which is a main constituent of the root of Panax ginseng (Araliaceae), and of its metabolite compound K, as produced by human intestinal bacteria, on lipopolysaccharide (LPS)-induced RAW264.7 cells were investigated. Compound K potently inhibited the production of NO and prostaglandin E2 in LPS-induced RAW 264.7 cells, with IC 50 values of 0.012 and 0.004 mM, respectively. Compound K also reduced the expression levels of the inducible NO synthase (iNOS) and COX-2 proteins and inhibited the activation of NF-kB, a nuclear transcription factor. Compound K inhibite...

BackgroundMobile mental-health trackers are mobile phone apps that gather self-reported mental-health ratings from users. They have received great attention from clinicians as tools to screen for depression in individual patients. While several apps that ask simple questions using face emoticons have been developed, there has been no study examining the validity of their screening performance.ObjectiveIn this study, we (1) evaluate the potential of a mobile mental-health tracker that uses three daily mental-health ratings (sleep satisfaction, mood, and anxiety) as indicators for depression, (2) discuss three approaches to data processing (ratio, average, and frequency) for generating indicator variables, and (3) examine the impact of adherence on reporting using a mobile mental-health tracker and accuracy in depression screening.MethodsWe analyzed 5792 sets of daily mental-health ratings collected from 78 breast cancer patients over a 48-week period. Using the Patient Health Questionnaire-9 (PHQ-9) as the measure of true depression status, we conducted a random-effect logistic panel regression and receiver operating characteristic (ROC) analysis to evaluate the screening performance of the mobile mental-health tracker. In addition, we classified patients into two subgroups based on their adherence level (higher adherence and lower adherence) using a k-means clustering algorithm and compared the screening accuracy between the two groups.ResultsWith the ratio approach, the area under the ROC curve (AUC) is 0.8012, indicating that the performance of depression screening using daily mental-health ratings gathered via mobile mental-health trackers is comparable to the results of PHQ-9 tests. Also, the AUC is significantly higher (P=.002) for the higher adherence group (AUC=0.8524) than for the lower adherence group (AUC=0.7234). This result shows that adherence to self-reporting is associated with a higher accuracy of depression screening.ConclusionsOur results support the potential of a mobile mental-health tracker as a tool for screening for depression in practice. Also, this study provides clinicians with a guideline for generating indicator variables from daily mental-health ratings. Furthermore, our results provide empirical evidence for the critical role of adherence to self-reporting, which represents crucial information for both doctors and patients.