LRP4-related signalling pathways and their regulatory role in neurological diseases.

The mechanism of action of low-density lipoprotein receptor related protein 4 (LRP4) is mediated largely via the Agrin-LRP4-MuSK signalling pathway in the nervous system. LRP4 contributes to the development of synapses in the peripheral nervous system (PNS). It interacts with signalling molecules such as the amyloid beta-protein precursor (APP) and the wingless type protein (Wnt). Its mechanisms of action are complex and mediated via interaction between the pre-synaptic motor neuron and post-synaptic muscle cell in the PNS, which enhances the development of the neuromuscular junction (NMJ). LRP4 may function differently in the central nervous system (CNS) than in the PNS, where it regulates ATP and glutamate release via astrocytes. It mayaffect the growth and development of the CNS by controlling the energy metabolism. LRP4 interacts with Agrin to maintain dendrite growth and density in the CNS. The goal of this article is to review the current studies involving relevant LRP4 signaling pathways in the nervous system. The review also discusses the clinical and etiological roles of LRP4 in neurological illnesses, such as myasthenia gravis, Alzheimer's disease and epilepsy. In this review, we provide a theoretical foundation for the pathogenesis and therapeutic application of LRP4 in neurologic diseases.

Oral pathogens exacerbate Parkinson's disease by promoting Th1 cell infiltration in mice.

BACKGROUND: Parkinson's disease (PD) is a common chronic neurological disorder with a high risk of disability and no cure. Periodontitis is an infectious bacterial disease occurring in periodontal supporting tissues. Studies have shown that periodontitis is closely related to PD. However, direct evidence of the effect of periodontitis on PD is lacking. Here, we demonstrated that ligature-induced periodontitis with application of subgingival plaque (LIP-SP) exacerbated motor dysfunction, microglial activation, and dopaminergic neuron loss in 1-Methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mice. RESULTS: The 16S rRNA gene sequencing revealed that LIP-SP induced oral and gut dysbiosis. Particularly, Veillonella parvula (V. parvula) and Streptococcus mutans (S. mutans) from oral ligatures were increased in the fecal samples of MPTP + LIP-SP treated mice. We further demonstrated that V. parvula and S. mutans played crucial roles in LIP-SP mediated exacerbation of motor dysfunction and neurodegeneration in PD mice. V. parvula and S. mutans caused microglial activation in the brain, as well as T helper 1 (Th1) cells infiltration in the brain, cervical lymph nodes, ileum and colon in PD mice. Moreover, we observed a protective effect of IFNγ neutralization on dopaminergic neurons in V. parvula- and S. mutans-treated PD mice. CONCLUSIONS: Our study demonstrates that oral pathogens V. parvula and S. mutans necessitate the existence of periodontitis to exacerbate motor dysfunction and neurodegeneration in MPTP-induced PD mice. The underlying mechanisms include alterations of oral and gut microbiota, along with immune activation in both brain and peripheral regions. Video Abstract.

Transcranial brain atlas-based optimization for functional near-infrared spectroscopy optode arrangement: Theory, algorithm, and application.

The quality of optode arrangement is crucial for group imaging studies when using functional near-infrared spectroscopy (fNIRS). Previous studies have demonstrated the promising effectiveness of using transcranial brain atlases (TBAs), in a manual and intuition-based way, to guide optode arrangement when individual structural MRI data are unavailable. However, the theoretical basis of using TBA to optimize optode arrangement remains unclear, which leads to manual and subjective application. In this study, we first describe the theoretical basis of TBA-based optimization of optode arrangement using a mathematical framework. Second, based on the theoretical basis, an algorithm is proposed for automatically arranging optodes on a virtual scalp. The resultant montage is placed onto the head of each participant guided by a low-cost and portable navigation system. We compared our method with the widely used 10/20-system-assisted optode arrangement procedure, using finger-tapping and working memory tasks as examples of both low- and high-level cognitive systems. Performance, including optode montage designs, locations on each participant's scalp, brain activation, as well as ground truth indices derived from individual MRI data were evaluated. The results give convergent support for our method's ability to provide more accurate, consistent and efficient optode arrangements for fNIRS group imaging than the 10/20 method.

Corrigendum: Feasibility of Functional Near-Infrared Spectroscopy (fNIRS) to Investigate the Mirror Neuron System: An Experimental Study in a Real-Life Situation.

[This corrects the article DOI: 10.3389/fnhum.2018.00086.].

Feasibility of Functional Near-Infrared Spectroscopy (fNIRS) to Investigate the Mirror Neuron System: An Experimental Study in a Real-Life Situation.

The mirror neuron system (MNS), mainly including the premotor cortex (PMC), inferior frontal gyrus (IFG), superior parietal lobule (SPL), and rostral inferior parietal lobule (IPL), has attracted extensive attention as a possible neural mechanism of social interaction. Owing to high ecological validity, functional near-infrared spectroscopy (fNIRS) has become an ideal approach for exploring the MNS. Unfortunately, for the feasibility of fNIRS to detect the MNS, none of the four dominant regions were found in previous studies, implying a very limited capacity of fNIRS to investigate the MNS. Here, we adopted an experimental paradigm in a real-life situation to evaluate whether the MNS activity, including four dominant regions, can be detected by using fNIRS. Specifically, 30 right-handed subjects were asked to complete a table-setting task that included action execution and action observation. A double density probe configuration covered the four regions of the MNS in the left hemisphere. We used a traditional channel-based group analysis and also a ROI-based group analysis to find which regions are activated during both action execution and action observation. The results showed that the IFG, adjacent PMC, SPL, and IPL were involved in both conditions, indicating the feasibility of fNIRS to detect the MNS. Our findings provide a foundation for future research to explore the functional role of the MNS in social interaction and various disorders using fNIRS.

A Gaussian mixture model based adaptive classifier for fNIRS brain-computer interfaces and its testing via simulation.

OBJECTIVE: Functional near infra-red spectroscopy (fNIRS) is a promising brain imaging technology for brain-computer interfaces (BCI). Future clinical uses of fNIRS will likely require operation over long time spans, during which neural activation patterns may change. However, current decoders for fNIRS signals are not designed to handle changing activation patterns. The objective of this study is to test via simulations a new adaptive decoder for fNIRS signals, the Gaussian mixture model adaptive classifier (GMMAC). APPROACH: GMMAC can simultaneously classify and track activation pattern changes without the need for ground-truth labels. This adaptive classifier uses computationally efficient variational Bayesian inference to label new data points and update mixture model parameters, using the previous model parameters as priors. We test GMMAC in simulations in which neural activation patterns change over time and compare to static decoders and unsupervised adaptive linear discriminant analysis classifiers. MAIN RESULTS: Our simulation experiments show GMMAC can accurately decode under time-varying activation patterns: shifts of activation region, expansions of activation region, and combined contractions and shifts of activation region. Furthermore, the experiments show the proposed method can track the changing shape of the activation region. Compared to prior work, GMMAC performed significantly better than the other unsupervised adaptive classifiers on a difficult activation pattern change simulation: 99% versus <54% in two-choice classification accuracy. SIGNIFICANCE: We believe GMMAC will be useful for clinical fNIRS-based brain-computer interfaces, including neurofeedback training systems, where operation over long time spans is required.