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BackgroundDynamic functional network connectivity (dFNC) of the brain has attracted considerable attention recently. Many approaches have been suggested to study dFNC with sliding window Pearson correlation (SWPC) being the most well-known. SWPC needs a relatively large sample size to reach a robust estimation but using large window sizes prevents us to detect rapid changes in dFNC.New methodHere we first calculate the gradients of each time series pair and use the magnitude of these gradients to calculate weighted average of shared trajectory (WAST) as a new estimator for dFNC.ResultsUsing WAST to compare healthy control and schizophrenia patients using a large dataset, we show disconnectivity between different regions associated with schizophrenia. In addition, WAST results reveals patients with schizophrenia stay longer in a connectivity state with negative connectivity between motor and sensory regions than do healthy controls.Comparison with existing methodsWe compare WAST with SWPC and multiplication of temporal derivatives (MTD) using different simulation scenarios. We show that WAST enables us to detect very rapid changes in dFNC (undetected by SWPC) while MTD performance is generally lower.ConclusionsAs large window sizes are unable to detect short states, using shorter window size is desirable if the estimator is robust enough. We provide evidence that WAST requires fewer samples (compared to SWPC) to reach a robust estimation. As a result, we were able to identify rapidly varying dFNC patterns undetected by SWPC while still being able to robustly estimate slower dFNC patterns.

There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.

The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.

The analysis of time-varying activity and connectivity patterns (i.e., the chronnectome) using resting-state magnetic resonance imaging has become an important part of ongoing neuroscience discussions. The majority of previous work has focused on variations of temporal coupling among fixed spatial nodes or transition of the dominant activity/connectivity pattern over time. Here, we introduce an approach to capture spatial dynamics within functional domains (FDs), as well as temporal dynamics within and between FDs. The approach models the brain as a hierarchical functional architecture with different levels of granularity, where lower levels have higher functional homogeneity and less dynamic behavior and higher levels have less homogeneity and more dynamic behavior. First, a high-order spatial independent component analysis is used to approximate functional units. A functional unit is a pattern of regions with very similar functional activity over time. Next, functional units are used to construct FDs. Finally, functional modules (FMs) are calculated from FDs, providing an overall view of brain dynamics. Results highlight the spatial fluidity within FDs, including a broad spectrum of changes in regional associations, from strong coupling to complete decoupling. Moreover, FMs capture the dynamic interplay between FDs. Patients with schizophrenia show transient reductions in functional activity and state connectivity across several FDs, particularly the subcortical domain. Activity and connectivity differences convey unique information in many cases (e.g., the default mode) highlighting their complementarity information. The proposed hierarchical model to capture FD spatiotemporal variations provides new insight into the macroscale chronnectome and identifies changes hidden from existing approaches.

The present study deals with hiatus resolution through vowel reduction in Kurdish and Persian based on OT phonology. A collection of movies, TV series, and written texts formed the corpus of the study. In data description and analysis, the linguistic intuition of native Persian and Central Kurdish speakers were used. Based on the analysis, the authors concluded that Persian and Kurdish use glide formation through vowel reduction to resolve hiatus in certain phonetic environments. In Persian, to resolve hiatus, the two high vowels /i/ and /u/ are converted into a short vowel and the glides /j/ and /w/, respectively. In Central Kurdish, glide formation occurs in high vowels /i:, u:/ and mid-high vowels /ē:, ō:/. Glide formation in Persian is accompanied with a quantitative change in vowel /i:/ and a quantitative and qualitative change in /u:/. In contrast, glide formation in Central Kurdish is accompanied with only quantitative change in long vowels. The OT analysis revealed that the main factor driving hiatus resolution in both Persian and Kurdish is the need for an onset in syllable structure. In contexts in which glide formation occurs through vowel reduction, the active constraints in Persian and Kurdish are: ONSET, AGREE(place), MAX, DEP, IDENT(µ).

A series of novel phenylamino quinazolinone derivatives were designed and synthesized as potential tyrosinase inhibitors. Among these compounds, 9r emerged as the most potent derivative, exhibiting IC50 values of 17.02 ± 1.66 µM, compared to kojic acid as the positive control with an IC50 value of 27.56 ± 1.27 µM. Antioxidant assessment of 9r compounds showed 24.67% inhibition at 100 µM. Molecular docking studies of these derivatives were conducted, revealing their proper fitting within the enzyme's active site. Additionally, density functional theory analysis was performed on the potent derivatives, indicating their stability and reactivity. Notably, the highest values of the energy gap were observed in 9r and 9s derivatives, underscoring their potential efficacy. Further kinetic studies of compound 9r, identified as the most potent derivative, demonstrated a competitive mode of inhibition with a Ki value of 14.87 µM. Molecular dynamics simulations of the 9r-tyrosinase complex revealed stability over time, with a reduction in critical residual fluctuation during the simulation. Overall, these findings contribute to a deeper understanding of the potential therapeutic value of these derivatives as tyrosinase inhibitors. [ABSTRACT FROM AUTHOR]

Introduction: The increasing prevalence of antibiotic-resistant pathogens necessitates the urgent development of new antibacterial agents. Concurrently, synthetic chemistry is moving towards more sustainable practices that minimize environmental impact. This study aims to synthesize 3-aryl-2 H -benzo[b][1,4]oxazin-2-one derivatives, including the natural product cephalandole A, using a sustainable approach that avoids metal catalysts. Methods: We employed nucleophilic aromatic substitution (SNAr) under microwave-assisted conditions to facilitate the synthesis of the targeted compounds. This metal-free carbon–carbon coupling reaction was optimized for efficiency, yielding good results with reduced reaction times. The synthesized derivatives were then subjected to an in silico molecular docking study to predict their antibacterial potential against key bacterial targets, focusing on the binding affinity and interaction profiles. Results: The microwave-assisted SNAr method provided good yields of 55% to 82% and significantly reduced reaction times ranging from 7 to 12 minutes, simplifying the overall workup process. Among the synthesized compounds, 3-(1H -indol-3-yl)-6-methyl-2H-benzo[b][1,4]oxazin-2-one (6b) emerged as a promising candidate, demonstrating favorable binding interactions in the molecular docking studies. Discussion: The integration of sustainable synthetic methodologies with in silico screening offers a novel and effective strategy for drug discovery. Our findings highlight the potential of the synthesized compounds as antibacterial agents and emphasize the importance of adopting eco-friendly approaches in pharmaceutical chemistry. This research contributes to the global effort to combat antibiotic resistance by providing new compounds for further biological evaluation. [ABSTRACT FROM AUTHOR]