Identifying canonical and replicable multi-scale intrinsic connectivity networks in 100k+ resting-state fMRI datasets.

Despite the known benefits of data-driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi-spatial-scale canonical intrinsic connectivity network (ICN) templates via the use of multi-model-order independent component analysis (ICA). We also study the feasibility of estimating subject-specific ICNs via spatially constrained ICA. The results show that the subject-level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large-scale ICNs require less data to achieve specific levels of (within- and between-subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject-level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within-subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.

Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping.

Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.

Screening the antifungal activities of monoterpenes and their isomers against Candida species.

AIM: Increasing the occurrence of non-albicans Candida species with intrinsic or acquired resistance to antifungals as well as the emergence of multidrug Candida species coupled with the limited antifungal agents challenges the treatment of candidiasis. Consequently, a class of secondary metabolites of plants exhibiting decent antifungal activity. Therefore, this study aimed to evaluate the antifungal potential of various monoterpenes including Carvone, Limonene, Pinene, Menthone, Menthol, Camphor, Thujone, Citronellol, and Piperitone against standard and clinical isolates of Candida. METHODS AND RESULTS: Minimum inhibitory concentration (MIC) and minimum fungicidal concentration (MFC) of compounds were determined, using the broth Microdilution method based on M27-A3 protocol documented by clinical laboratory standard institute (CLSI). Amongst the tested monoterpenes, oxygenated terpenoids showed strong antifungal activity. Specifically, alcoholic terpenoids such as (±)-Citronellol possess more efficacy than the corresponding ketonic ones with MICs ranging from 0·03 to 2·00 µl ml-1 (0·16-10·80 mmol l-1 ). Among the examined yeasts, Candida tropicalis was the most susceptible species to (±)-Citronellol. Moreover, the examined monoterpenes successfully inhibited the growth of fluconazole-resistant Candida species. Moreover, statistical analysis showed no statistically significant difference between the (+) and (-) isomers, except for (±)-α-Pienene and (±) Menthone (ρ-value < 0·05). CONCLUSION: Among the tested monoterpenes, (±)-Citronellol was the most potent compounds followed by (+)-α-Pinene and Menthol. Considering the significant antifungal activity of the examined monoterpenes, they could be used in controlling or treating candidiasis. Those potent antifungal monoterpenes with GRAS status in addition to their pleasant taste and odour make them appropriate additive or preservative compounds in food and cosmetics products. Furthermore, these data might help researchers to predict EOs antifungal activities, after determining its constituents. SIGNIFICANCE AND IMPACT OF THE STUDY: This study provides new information about the antifungal activities of monoterpenes and their isomers presented widely in essential oils. Screening results against pathogenic yeasts confirm the correlation between the chemical structure of tested monoterpenes and their antifungal effects. The present findings might be helpful to anticipate the antifungal activity of essential oils.

Hydroalcoholic extract of Carum carvi L. in oral mucositis: a clinical trial in male golden hamsters.

OBJECTIVES: Several studies have attempted to prevent or improve oral mucositis (OM) but have not produced a qualified treatment yet. This study evaluates the effects of Carum carvi L. (caraway) hydroalcoholic extract (CHE) as one of the traditional medicinal plants in 5-fluorouracil (5-FU)-induced OM in golden hamsters. MATERIALS AND METHODS: OM was induced in 54 male golden hamsters by 5-FU and cheek pouch scratching. Starting from day 12, 500 and 1000 mg kg(-1) per day topical CHE were administered. Pouch histopathology score, malondialdehyde and reduced glutathione contents, and activity of myeloperoxidase plus microbial cultures of cheek pouch, antimicrobial properties of CHE, and essential oil constituents were evaluated. RESULTS: Lower histopathology score (0, 1, and 2) and malondialdehyde level, higher reduced glutathione level and activities of myeloperoxidase were detected in 1000 and 500 mg kg(-1) per day topical CHE and control groups, respectively (P < 0.001). The CHE was more potent against Staphylococcus epidermidis and Streptococcus intermedius. γ-Terpinene (37.2%) was identified as the main constituent of essential oil. CONCLUSION: The use of CHE in topical form may be associated with reduced intensity of OM. This may be due to appropriate antibacterial activity and terpinene contents.

A method for estimating and characterizing explicitly nonlinear dynamic functional network connectivity in resting-state fMRI data.

The past 10 years have seen an explosion of approaches that focus on the study of time-resolved change in functional connectivity (FC). FC characterization among networks at a whole-brain level is frequently termed functional network connectivity (FNC). Time-resolved or dynamic functional network connectivity (dFNC) focuses on the estimation of transient, recurring, whole-brain patterns of FNC. While most approaches in this area have attempted to capture dynamic linear correlation, we are particularly interested in whether explicitly nonlinear relationships, above and beyond linear, are present and contain unique information. This study thus proposes an approach to assess explicitly nonlinear dynamic functional network connectivity (EN dFNC) derived from the relationship among independent component analysis time courses. Linear relationships were removed at each time point to evaluate, typically ignored, explicitly nonlinear dFNC using normalized mutual information (NMI). Simulations showed the proposed method estimated explicitly nonlinearity over time, even within relatively short windows of data. We then, applied our approach on 151 schizophrenia patients, and 163 healthy controls fMRI data and found three unique, highly structured, mostly long-range, functional states that also showed significant group differences. In particular, explicitly nonlinear relationships tend to be more widespread than linear ones. Results also highlighted a state with long range connections to the visual domain, which were significantly reduced in schizophrenia. Overall, this work suggests that quantifying EN dFNC may provide a complementary and potentially valuable tool for studying brain function by exposing relevant variation that is typically ignored.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk.

Background: Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. Methods: We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. Results: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. Conclusions: Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape.

Multimodal data fusion of cortical-subcortical morphology and functional network connectivity in psychotic spectrum disorder.

Multiple authors have noted overlapping symptoms and alterations across clinical, anatomical, and functional brain features in schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder (BPI). However, regarding brain features, few studies have approached this line of inquiry using analytical techniques optimally designed to extract the shared features across anatomical and functional information in a simultaneous manner. Univariate studies of anatomical or functional alterations across these disorders can be limited and run the risk of omitting small but potentially crucial overlapping or joint neuroanatomical (e.g., structural images) and functional features (e.g., fMRI-based features) which may serve as informative clinical indicators of across multiple diagnostic categories. To address this limitation, we paired an unsupervised multimodal canonical correlation analysis (mCCA) together with joint independent component analysis (jICA) to identify linked spatial gray matter (GM), resting-state functional network connectivity (FNC), and white matter fractional anisotropy (FA) features across these diagnostic categories. We then calculated associations between the identified linked features and trans-diagnostic behavioral measures (MATRICs Consensus Cognitive Battery, MCCB). Component number 4 of the 13 identified displayed a statistically significant relationship with overall MCCB scores across GM, resting-state FNC, and FA. These linked modalities of component 4 consisted primarily of positive correlations within subcortical structures including the caudate and putamen in the GM maps with overall MCCB, sparse negative correlations within subcortical and cortical connection tracts (e.g., corticospinal tract, superior longitudinal fasciculus) in the FA maps with overall MCCB, and negative relationships with MCCB values and loading parameters with FNC matrices displaying increased FNC in subcortical-cortical regions with auditory, somatomotor, and visual regions.