Performance and mechanism of co-culture of Monascus purpureus, Lacticaseibacillus casei, and Saccharomyces cerevisiae to enhance lovastatin production and lipid-lowering effects.

To facilitate lipid-lowering effects, a lovastatin-producing microbial co-culture system (LPMCS) was constituted with a novel strain Monascus purpureus R5 in combination with Lacticaseibacillus casei S5 and Saccharomyces cerevisiae J7, which increased lovastatin production by 54.21% compared with the single strain R5. Response Surface Methodology (RSM) optimization indicated lovastatin yield peaked at 7.43 mg/g with a fermentation time of 13.88 d, water content of 50.5%, and inoculum ratio of 10.27%. Meanwhile, lovastatin in LPMCS co-fermentation extracts (LFE) was qualitatively and quantitatively analyzed by Thin-Layer Chromatography (TLC) and High-Performance Liquid Chromatography (HPLC). Cellular experiments demonstrated that LFE exhibited no obvious cytotoxicity to L-02 cells and exhibited excellent biosafety. Most notably, high-dose LFE (100 mg/L) exhibited the highest reduction of lipid accumulation, total cholesterol, and triglycerides simultaneously in oleic acid-induced L-02 cells, which decreased by 71.59%, 38.64%, and 58.85% than untreated cells, respectively. Overall, LPMCS provides a potential approach to upgrade the lipid-lowering activity of Monascus-fermented products with higher health-beneficial effects.

Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI.

Studies of in mesial temporal lobe epilepsy (mTLE) patients with hippocampal sclerosis (HS) have reported reductions in both functional and structural connectivity between hippocampal structures and adjacent brain regions. However, little is known about the connectivity among the default mode network (DMN) in mTLE. Here, we hypothesized that both functional and structural connectivity within the DMN were disturbed in mTLE. To test this hypothesis, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were applied to examine the DMN connectivity of 20 mTLE patients, and 20 gender- and age-matched healthy controls. Combining these two techniques, we explored the changes in functional (temporal correlation coefficient derived from fMRI) and structural (path length and connection density derived from DTI tractography) connectivity of the DMN. Compared to the controls, we found that both functional and structural connectivity were significantly decreased between the posterior cingulate cortex (PCC)/precuneus (PCUN) and bilateral mesial temporal lobes (mTLs) in patients. No significant between-group difference was found between the PCC/PCUN and medial prefrontal cortex (mPFC). In addition, functional connectivity was found to be correlated with structural connectivity in two pairwise regions, namely between the PCC/PCUN and bilateral mTLs, respectively. Our results suggest that the decreased functional connectivity within the DMN in mTLE may be a consequence of the decreased connection density underpinning the degeneration of structural connectivity.

Selective aberrant functional connectivity of resting state networks in social anxiety disorder.

Several functional MRI (fMRI) activation studies have highlighted specific differences in brain response in social anxiety disorder (SAD) patients. Little is known, so far, about the changes in the functional architecture of resting state networks (RSNs) in SAD during resting state. We investigated statistical differences in RSNs on 20 SAD and 20 controls using independent component analysis. A diffuse impact on widely distributed RSNs and selective changes of RSN intrinsic functional connectivity were observed in SAD. Functional connectivity was decreased in the somato-motor (primary and motor cortices) and visual (primary visual cortex) networks, increased in a network including medial prefrontal cortex which is thought to be involved in self-referential processes, and increased or decreased in the default mode network (posterior cingulate cortex/precuneus, bilateral inferior parietal gyrus, angular gyrus, middle temporal gyrus, and superior and medial frontal gyrus) which has been suggested to be involved in episodic memory, and self-projection, the dorsal attention network (middle and superior occipital gyrus, inferior and superior parietal gyrus, and middle and superior frontal gyrus) which is thought to mediate goal-directed top-down processing, the core network (insula-cingulate cortices) which is associated with task control function, and the central-executive network (fronto-parietal cortices). A relationship between functional connectivity and disease severity was found in specific regions of RSNs, including medial and lateral prefrontal cortex, as well as parietal and occipital regions. Our results might supply a novel way to look into neuro-pathophysiological mechanisms in SAD patients.

Evaluating the effective connectivity of resting state networks using conditional Granger causality.

The human brain has been documented to be spatially organized in a finite set of specific coherent patterns, namely resting state networks (RSNs). The interactions among RSNs, being potentially dynamic and directional, may not be adequately captured by simple correlation or anticorrelation. In order to evaluate the possible effective connectivity within those RSNs, we applied a conditional Granger causality analysis (CGCA) to the RSNs retrieved by independent component analysis (ICA) from resting state functional magnetic resonance imaging (fMRI) data. Our analysis provided evidence for specific causal influences among the detected RSNs: default-mode, dorsal attention, core, central-executive, self-referential, somatosensory, visual, and auditory networks. In particular, we identified that self-referential and default-mode networks (DMNs) play distinct and crucial roles in the human brain functional architecture. Specifically, the former RSN exerted the strongest causal influence over the other RSNs, revealing a top-down modulation of self-referential mental activity (SRN) over sensory and cognitive processing. In quite contrast, the latter RSN was profoundly affected by the other RSNs, which may underlie an integration of information from primary function and higher level cognition networks, consistent with previous task-related studies. Overall, our results revealed the causal influences among these RSNs at different processing levels, and supplied information for a deeper understanding of the brain network dynamics.

Biosynthesized iron sulfide nanoparticles by mixed consortia for enhanced extracellular electron transfer in a microbial fuel cell.

The bioanode of mixed consortia was for the first time used to in-situ synthesize iron sulfide nanoparticles in a microbial fuel cell (MFC) over a long-term period (46 days). These poorly crystalline nanoparticles with an average size of 29.97 ± 7.1 nm, comprising of FeS and FeS2, significantly promoted extracellular electron transfer and thus the electricity generation of the MFC. A maximum power density of 519.00 mW/m2 was obtained from the MFC, which was 1.92 times as high as that of the control. The cell viability was promoted by a small amount of iron sulfide nanoparticles but inhibited by the thick nanoparticle "shell" covered on the bacterial cells. Some electroactive and sulfur reducing bacteria (eg. Enterobacteriaceae, Desulfovibrio, and Geobacter) were specifically enriched on the anode. This study provides a novel insight for improving the performance of bioelectrochemical systems through in-situ sustainable nanomaterials biofabrication by mixed consortia.

Hilbert phase entropy imaging of fMRI time series.

Functional magnetic resonance imaging (fMRI) data-processing methods in the time domain include correlation analysis and the general linear model, among others. Virtually, many fMRI processing strategies utilise temporal information and ignore or pay little attention to phase information, resulting in an unnecessary loss of efficiency. We proposed a novel method named Hilbert phase entropy imaging (HPEI) that used the discrete Hilbert transform of the magnitude time series to detect brain functional activation. The data from two simulation studies and two in vivo fMRI studies that both contained block-design and event-related experiments revealed that the HPEI method enabled the effective detection of brain functional activation and the distinction of different response patterns. Our results demonstrate that this method is useful as a complementary analysis, but hypothesis-constrained, in revealing additional information regarding the complex nature of fMRI time series.

Altered effective connectivity network of the amygdala in social anxiety disorder: a resting-state FMRI study.

The amygdala is often found to be abnormally recruited in social anxiety disorder (SAD) patients. The question whether amygdala activation is primarily abnormal and affects other brain systems or whether it responds "normally" to an abnormal pattern of information conveyed by other brain structures remained unanswered. To address this question, we investigated a network of effective connectivity associated with the amygdala using Granger causality analysis on resting-state functional MRI data of 22 SAD patients and 21 healthy controls (HC). Implications of abnormal effective connectivity and clinical severity were investigated using the Liebowitz Social Anxiety Scale (LSAS). Decreased influence from inferior temporal gyrus (ITG) to amygdala was found in SAD, while bidirectional influences between amygdala and visual cortices were increased compared to HCs. Clinical relevance of decreased effective connectivity from ITG to amygdala was suggested by a negative correlation of LSAS avoidance scores and the value of Granger causality. Our study is the first to reveal a network of abnormal effective connectivity of core structures in SAD. This is in support of a disregulation in predescribed modules involved in affect control. The amygdala is placed in a central position of dysfunction characterized both by decreased regulatory influence of orbitofrontal cortex and increased crosstalk with visual cortex. The model which is proposed based on our results lends neurobiological support towards cognitive models considering disinhibition and an attentional bias towards negative stimuli as a core feature of the disorder.

Altered functional connectivity and small-world in mesial temporal lobe epilepsy.

BACKGROUND: The functional architecture of the human brain has been extensively described in terms of functional connectivity networks, detected from the low-frequency coherent neuronal fluctuations that can be observed in a resting state condition. Little is known, so far, about the changes in functional connectivity and in the topological properties of functional networks, associated with different brain diseases. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we investigated alterations related to mesial temporal lobe epilepsy (mTLE), using resting state functional magnetic resonance imaging on 18 mTLE patients and 27 healthy controls. Functional connectivity among 90 cortical and subcortical regions was measured by temporal correlation. The related values were analyzed to construct a set of undirected graphs. Compared to controls, mTLE patients showed significantly increased connectivity within the medial temporal lobes, but also significantly decreased connectivity within the frontal and parietal lobes, and between frontal and parietal lobes. Our findings demonstrated that a large number of areas in the default-mode network of mTLE patients showed a significantly decreased number of connections to other regions. Furthermore, we observed altered small-world properties in patients, along with smaller degree of connectivity, increased n-to-1 connectivity, smaller absolute clustering coefficients and shorter absolute path length. CONCLUSIONS/SIGNIFICANCE: We suggest that the mTLE alterations observed in functional connectivity and topological properties may be used to define tentative disease markers.

Kernel Granger causality mapping effective connectivity on FMRI data.

Although it is accepted that linear Granger causality can reveal effective connectivity in functional magnetic resonance imaging (fMRI), the issue of detecting nonlinear connectivity has hitherto not been considered. In this paper, we address kernel Granger causality (KGC) to describe effective connectivity in simulation studies and real fMRI data of a motor imagery task. Based on the theory of reproducing kernel Hilbert spaces, KGC performs linear Granger causality in the feature space of suitable kernel functions, assuming an arbitrary degree of nonlinearity. Our results demonstrate that KGC captures effective couplings not revealed by the linear case. In addition, effective connectivity networks between the supplementary motor area (SMA) as the seed and other brain areas are obtained from KGC.