Familial Alzheimer's disease associated with heterozygous NPC1 mutation.

INTRODUCTION: NPC1 mutations are responsible for Niemann-Pick disease type C (NPC), a rare autosomal recessive neurodegenerative disease. Patients harbouring heterozygous NPC1 mutations may rarely show parkinsonism or dementia. Here, we describe for the first time a large family with an apparently autosomal dominant late-onset Alzheimer's disease (AD) harbouring a novel heterozygous NPC1 mutation. METHODS: All the five living siblings belonging to the family were evaluated. We performed clinical evaluation, neuropsychological tests, assessment of cerebrospinal fluid markers of amyloid deposition, tau pathology and neurodegeneration (ATN), structural neuroimaging and brain amyloid-positron emission tomography. Oxysterol serum levels were also tested. A wide next-generation sequencing panel of genes associated with neurodegenerative diseases and a whole exome sequencing analysis were performed. RESULTS: We detected the novel heterozygous c.3034G>T (p.Gly1012Cys) mutation in NPC1, shared by all the siblings. No other point mutations or deletions in NPC1 or NPC2 were found. In four siblings, a diagnosis of late-onset AD was defined according to clinical characterisation and ATN biomarkers (A+, T+, N+) and serum oxysterol analysis showed increased 7-ketocholesterol and cholestane-3ß,5α,6ß-triol. DISCUSSION: We describe a novel NPC1 heterozygous mutation harboured by different members of a family with autosomal dominant late-onset amnesic AD without NPC-associated features. A missense mutation in homozygous state in the same aminoacidic position has been previously reported in a patient with NPC with severe phenotype. The alteration of serum oxysterols in our family corroborates the pathogenic role of our NPC1 mutation. Our work, illustrating clinical and biochemical disease hallmarks associated with NPC1 heterozygosity in patients affected by AD, provides relevant insights into the pathogenetic mechanisms underlying this possible novel association.

Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.

BACKGROUND: Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases. RESULTS: In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback-Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD. CONCLUSION: Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson's disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

Clinical characteristics and outcomes of COVID-19-associated encephalopathy in children.

Apart from the typical respiratory symptoms, coronavirus disease 2019 (COVID-19) also affects the central nervous system, leading to central disorders such as encephalopathy and encephalitis. However, knowledge of pediatric COVID-19-associated encephalopathy is limited, particularly regarding specific subtypes of encephalopathy. This study aimed to assess the features of COVID-19-associated encephalopathy/encephalitis in children. We retrospectively analyzed a single cohort of 13 hospitalized children with COVID-19-associated encephalopathy. The primary outcome was the descriptive analysis of the clinical characteristics, magnetic resonance imaging and electroencephalography findings, treatment progression, and outcomes. Thirteen children among a total of 275 (5%) children with confirmed COVID-19 developed associated encephalopathy/encephalitis (median age, 35 months; range, 3-138 months). Autoimmune encephalitis was present in six patients, acute necrotizing encephalopathy in three, epilepsy in three, and central nervous system small-vessel vasculitis in one patient. Eight (62%) children presented with seizures. Six (46%) children exhibited elevated blood inflammatory indicators, cerebrospinal fluid inflammatory indicators, or both. Two (15%) critically ill children presented with multi-organ damage. The magnetic resonance imaging findings varied according to the type of encephalopathy/encephalitis. Electroencephalography revealed a slow background rhythm in all 13 children, often accompanied by epileptic discharges. Three (23%) children with acute necrotizing encephalopathy had poor prognoses despite immunotherapy and other treatments. Ten (77%) children demonstrated good functional recovery without relapse. This study highlights COVID-19 as a new trigger of encephalopathy/encephalitis in children. Autoimmune encephalitis is common, while acute necrotizing encephalopathy can induce poor outcomes. These findings provide valuable insights into the impact of COVID-19 on children's brains.

Research Progress on the Strategies for Crossing the Blood-Brain Barrier.

Recently, the incidence of brain diseases, such as central nervous system degenerative diseases, brain tumors, and cerebrovascular diseases, has increased. However, the blood-brain barrier (BBB) limits the effective delivery of drugs to brain disease areas. Therefore, the mainstream direction of new drug development for these diseases is to engineer drugs that can better cross the BBB to exert their effects in the brain. This paper reviews the research progress and application of the main trans-BBB drug delivery strategies (receptor/transporter-mediated BBB crossing, focused ultrasound to open the BBB, adenosine agonist reversible opening of the BBB, aromatic resuscitation, transnasal administration, cell-mediated trans-BBB crossing, and viral vector system-mediated brain drug delivery). Meanwhile, the potential applications, advantages, and disadvantages of these strategies for crossing the BBB are analyzed. Finally, the future development prospects of strategies for crossing the BBB are also discussed. These strategies have potential value for treating brain diseases.

Small Scale, Big Impact: Nanotechnology-Enhanced Drug Delivery for Brain Diseases.

Central nervous system (CNS) diseases, ranging from brain cancers to neurodegenerative disorders like dementia and acute conditions such as strokes, have been heavily burdening healthcare and have a direct impact on patient quality of life. A significant hurdle in developing effective treatments is the presence of the blood-brain barrier (BBB), a highly selective barrier that prevents most drugs from reaching the brain. The tight junctions and adherens junctions between the endothelial cells and various receptors expressed on the cells make the BBB form a nonfenestrated and highly selective structure that is crucial for brain homeostasis but complicates drug delivery. Nanotechnology offers a novel pathway to circumvent this barrier, with nanoparticles engineered to ferry drugs across the BBB, protect drugs from degradation, and deliver medications to the designated area. After years of development, nanoparticle optimization, including sizes, shapes, surface modifications, and targeting ligands, can enable nanomaterials tailored to specific brain drug delivery settings. Moreover, smart nano drug delivery systems can respond to endogenous and exogenous stimuli that control subsequent drug release. Here, we address the importance of the BBB in brain disease treatment, summarize different delivery routes for brain drug delivery, discuss the cutting-edge nanotechnology-based strategies for brain drug delivery, and further offer valuable insights into how these innovations in nanoparticle technology could revolutionize the treatment of CNS diseases, presenting a promising avenue for noninvasive, targeted therapeutic interventions.

Neurofilament light chain but not glial fibrillary acidic protein is a potential biomarker of overt hepatic encephalopathy in patients with cirrhosis.

INTRODUCTION AND OBJECTIVES: Hepatic encephalopathy (HE) is a frequent complication of cirrhosis and may cause cerebral damage. Neurodegenerative diseases can induce the release of neuroproteins like neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in body fluids, including blood plasma. We investigated whether NfL and GFAP could serve as potential diagnostic plasma biomarkers for overt HE (oHE). MATERIALS AND METHODS: We included 85 patients from three prospective cohorts with different stages of liver disease and HE severity. The following patients were included: 1) 34 patients with primary sclerosing cholangitis (PSC) with compensated disease; 2) 17 patients with advanced liver disease without oHE before elective transjugular intrahepatic portosystemic shunt (TIPS) placement; 3) 17 intensive care unit (ICU) patients with oHE and 17 ICU patients without cirrhosis or oHE. Plasma NfL and GFAP were measured using single molecule assays. RESULTS: ICU oHE patients had higher NfL concentrations compared to pre-TIPS patients or ICU controls (p < 0.05, each). Median GFAP concentrations were equal in the ICU oHE and pre-TIPS patients or ICU controls. Plasma NfL and GFAP concentrations correlated with Model for End-Stage Liver Disease (MELD) scores (R = 0.58 and R = 0.40, p < 0.001, each). CONCLUSIONS: Plasma NfL deserves further evaluation as potential diagnostic biomarker for oHE and correlates with the MELD score.

Lactate and Lactylation in the Brain: Current Progress and Perspectives.

As the final product of glycolysis, lactate features not only as an energy substrate, a metabolite, and a signaling molecule in a variety of diseases-such as cancer, inflammation, and sepsis-but also as a regulator of protein lactylation; this is a newly proposed epigenetic modification that is considered to be crucial for energy metabolism and signaling in brain tissues under both physiological and pathological conditions. In this review, evidence on lactylation from studies on lactate metabolism and disease has been summarized, revealing the function of lactate and its receptors in the regulation of brain function and summarizing the levels of lactylation expression in various brain diseases. Finally, the function of lactate and lactylation in the brain and the potential mechanisms of intervention in brain diseases are presented and discussed, providing optimal perspectives for future research on the role of lactylation in the brain.

Astaxanthin impact on brain: health potential and market perspective.

Nowadays, there is an emergent interest in new trend-driven biomolecules to improve health and wellbeing, which has become an interesting and promising field, considering their high value and biological potential. Astaxanthin is one of these promising biomolecules, with impressive high market growth, especially in the pharmaceutical and food industries. This biomolecule, obtained from natural sources (i.e., microalgae), has been reported in the literature to have several beneficial health effects due to its biological properties. These benefits seem to be mainly associated with Astaxanthin's high antioxidant and anti-inflammatory properties, which may act on several brain issues, thus attenuating symptoms. In this sense, several studies have demonstrated the impact of astaxanthin on a wide range of diseases, namely on brain disorders (such as Alzheimer's disease, Parkinson, depression, brain stroke and autism). Therefore, this review highlights its application in mental health and illness. Furthermore, a S.W.O.T. analysis was performed to display an approach from the market/commercial perspective. However, to bring the molecule to the market, there is still a need for more studies to increase deep knowledge regarding the real impact and mechanisms in the human brain.HIGHLIGHTSAstaxanthin has been mainly extracted from the algae Haematococcus pluvialisAstaxanthin, bioactive molecule with high antioxidant and anti-inflammatory propertiesAstaxanthin has an important protective effect on brain disordersAstaxanthin is highly marketable, mainly for food and pharmaceutical industries.

Deep learning referral suggestion and tumour discrimination using explainable artificial intelligence applied to multiparametric MRI.

OBJECTIVES: An appropriate and fast clinical referral suggestion is important for intra-axial mass-like lesions (IMLLs) in the emergency setting. We aimed to apply an interpretable deep learning (DL) system to multiparametric MRI to obtain clinical referral suggestion for IMLLs, and to validate it in the setting of nontraumatic emergency neuroradiology. METHODS: A DL system was developed in 747 patients with IMLLs ranging 30 diseases who underwent pre- and post-contrast T1-weighted (T1CE), FLAIR, and diffusion-weighted imaging (DWI). A DL system that segments IMLLs, classifies tumourous conditions, and suggests clinical referral among surgery, systematic work-up, medical treatment, and conservative treatment, was developed. The system was validated in an independent cohort of 130 emergency patients, and performance in referral suggestion and tumour discrimination was compared with that of radiologists using receiver operating characteristics curve, precision-recall curve analysis, and confusion matrices. Multiparametric interpretable visualisation of high-relevance regions from layer-wise relevance propagation overlaid on contrast-enhanced T1WI and DWI was analysed. RESULTS: The DL system provided correct referral suggestions in 94 of 130 patients (72.3%) and performed comparably to radiologists (accuracy 72.6%, McNemar test; p = .942). For distinguishing tumours from non-tumourous conditions, the DL system (AUC, 0.90 and AUPRC, 0.94) performed similarly to human readers (AUC, 0.81~0.92, and AUPRC, 0.88~0.95). Solid portions of tumours showed a high overlap of relevance, but non-tumours did not (Dice coefficient 0.77 vs. 0.33, p < .001), demonstrating the DL's decision. CONCLUSIONS: Our DL system could appropriately triage patients using multiparametric MRI and provide interpretability through multiparametric heatmaps, and may thereby aid neuroradiologic diagnoses in emergency settings. CLINICAL RELEVANCE STATEMENT: Our AI triages patients with raw MRI images to clinical referral pathways in brain intra-axial mass-like lesions. We demonstrate that the decision is based on the relative relevance between contrast-enhanced T1-weighted and diffusion-weighted images, providing explainability across multiparametric MRI data. KEY POINTS: • A deep learning (DL) system using multiparametric MRI suggested clinical referral to patients with intra-axial mass-like lesions (IMLLs) similar to radiologists (accuracy 72.3% vs. 72.6%). • In the differentiation of tumourous and non-tumourous conditions, the DL system (AUC, 0.90) performed similar with radiologists (AUC, 0.81-0.92). • The DL's decision basis for differentiating tumours from non-tumours can be quantified using multiparametric heatmaps obtained via the layer-wise relevance propagation method.

MicroRNA­124: an emerging therapeutic target in central nervous system disorders.

The central nervous system (CNS) consists of neuron and non-neuron cells including neural stem/precursor cells (NSPCs), neuroblasts, glia cells (mainly astrocyte, oligodendroglia and microglia), which thereby form a precise and complicated network and exert diverse functions through interactions of numerous bioactive ingredients. MicroRNAs (miRNAs), with small size approximately ~ 21nt and as well-documented post-transcriptional key regulators of gene expression, are a cluster of evolutionarily conserved endogenous non-coding RNAs. More than 2000 different miRNAs has been discovered till now. MicroRNA-124(miR-124), the most brain-rich microRNA, has been validated to possess important functions in the central nervous system, including neural stem cell proliferation and differentiation, cell fate determination, neuron migration, synapse plasticity and cognition, cell apoptosis etc. According to recent studies, herein, we provide a review of this conversant miR-124 to further understand the potential functions and therapeutic and clinical value in brain diseases.

Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study.

BACKGROUND: This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. METHODS: Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. RESULTS: At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88-0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918-0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78-459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854-0.973, I2 = 93%). CONCLUSION: This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN).

Heterogeneity of PNPT1 neuroimaging: mitochondriopathy, interferonopathy or both?

BACKGROUND: Biallelic variants in PNPT1 cause a mitochondrial disease of variable severity. PNPT1 (polynucleotide phosphorylase) is a mitochondrial protein involved in RNA processing where it has a dual role in the import of small RNAs into mitochondria and in preventing the formation and release of mitochondrial double-stranded RNA into the cytoplasm. This, in turn, prevents the activation of type I interferon response. Detailed neuroimaging findings in PNPT1-related disease are lacking with only a few patients reported with basal ganglia lesions (Leigh syndrome) or non-specific signs. OBJECTIVE AND METHODS: To document neuroimaging data in six patients with PNPT1 highlighting novel findings. RESULTS: Two patients exhibited striatal lesions compatible with Leigh syndrome; one patient exhibited leukoencephalopathy and one patient had a normal brain MRI. Interestingly, two unrelated patients exhibited cystic leukoencephalopathy resembling RNASET2-deficient patients, patients with Aicardi-Goutières syndrome (AGS) or congenital CMV infection. CONCLUSION: We suggest that similar to RNASET2, PNPT1 be searched for in the setting of cystic leukoencephalopathy. These findings are in line with activation of type I interferon response observed in AGS, PNPT1 and RNASET2 deficiencies, suggesting a common pathophysiological pathway and linking mitochondrial diseases, interferonopathies and immune dysregulations.

Disentangling molecular and clinical stratification patterns in beta-galactosidase deficiency.

INTRODUCTION: This study aims to define the phenotypic and molecular spectrum of the two clinical forms of ß-galactosidase (ß-GAL) deficiency, GM1-gangliosidosis and mucopolysaccharidosis IVB (Morquio disease type B, MPSIVB). METHODS: Clinical and genetic data of 52 probands, 47 patients with GM1-gangliosidosis and 5 patients with MPSIVB were analysed. RESULTS: The clinical presentations in patients with GM1-gangliosidosis are consistent with a phenotypic continuum ranging from a severe antenatal form with hydrops fetalis to an adult form with an extrapyramidal syndrome. Molecular studies evidenced 47 variants located throughout the sequence of the GLB1 gene, in all exons except 7, 11 and 12. Eighteen novel variants (15 substitutions and 3 deletions) were identified. Several variants were linked specifically to early-onset GM1-gangliosidosis, late-onset GM1-gangliosidosis or MPSIVB phenotypes. This integrative molecular and clinical stratification suggests a variant-driven patient assignment to a given clinical and severity group. CONCLUSION: This study reports one of the largest series of b-GAL deficiency with an integrative patient stratification combining molecular and clinical features. This work contributes to expand the community knowledge regarding the molecular and clinical landscapes of b-GAL deficiency for a better patient management.

Applications of Induced Pluripotent Stem Cell-Derived Glia in Brain Disease Research and Treatment.

Glia are integral components of neural networks and are crucial in both physiological functions and pathological processes of the brain. Many brain diseases involve glial abnormalities, including inflammatory changes, mitochondrial damage, calcium signaling disturbance, hemichannel opening, and loss of glutamate transporters. Induced pluripotent stem cell (iPSC)-derived glia provide opportunities to study the contributions of glia in human brain diseases. These cells have been used for human disease modeling as well as generating new therapies. This chapter introduces glial involvement in brain diseases, then summarizes different methods of generating iPSC-derived glia disease models of these cells. Finally, strategies for treating disease using iPSC-derived glia are discussed. The goal of this chapter is to provide an overview and shed light on the applications of iPSC-derived glia in brain disease research and treatment.

The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging.

BACKGROUND: Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment. PURPOSE: To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR). MATERIAL AND METHODS: A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared. RESULTS: Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence (P = 0.001). CONCLUSION: The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR.

Brain Waste Removal System and Sleep: Photobiomodulation as an Innovative Strategy for Night Therapy of Brain Diseases.

Emerging evidence suggests that an important function of the sleeping brain is the removal of wastes and toxins from the central nervous system (CNS) due to the activation of the brain waste removal system (BWRS). The meningeal lymphatic vessels (MLVs) are an important part of the BWRS. A decrease in MLV function is associated with Alzheimer's and Parkinson's diseases, intracranial hemorrhages, brain tumors and trauma. Since the BWRS is activated during sleep, a new idea is now being actively discussed in the scientific community: night stimulation of the BWRS might be an innovative and promising strategy for neurorehabilitation medicine. This review highlights new trends in photobiomodulation of the BWRS/MLVs during deep sleep as a breakthrough technology for the effective removal of wastes and unnecessary compounds from the brain in order to increase the neuroprotection of the CNS as well as to prevent or delay various brain diseases.

Tonate Virus and Fetal Abnormalities, French Guiana, 2019.

We report a case of vertical transmission of Tonate virus in a pregnant woman from French Guiana. The fetus showed severe necrotic and hemorrhagic lesions of the brain and spinal cord. Clinicians should be made aware of possible adverse fetal outcomes in pregnant women infected with Tonate virus.

In Vivo 13C Magnetic Resonance Spectroscopy for Assessing Brain Biochemistry in Health and Disease.

Magnetic resonance spectroscopy (MRS) is a non-invasive technique that contributes to the elucidation of brain biochemistry. 13C MRS enables the detection of specific neurochemicals and their neuroenergetic correlation with neuronal function. The synergistic outcome of 13C MRS and the infusion of 13C-labeled substrates provide an understanding of neurometabolism and the role of glutamate/gamma-aminobutyric acid (GABA) neurotransmission in diseases, such as Alzheimer's disease, schizophrenia, and bipolar disorder. Moreover, 13C MRS provides a window into the altered flux rate of different pathways, including the tricarboxylic acid cycle (TCA) and the glutamate/glutamine/GABA cycle, in health and in various diseases. Notably, the metabolic flux rate of the TCA cycle often decreases in neurodegenerative diseases. Additionally, 13C MRS can be used to investigate several psychiatric and neurological disorders as it directly reflects the real-time production and alterations of key brain metabolites. This review aims to highlight the chronology, the technological advancements, and the applications of 13C MRS in various brain diseases.

Effect of Particle Size and Surface Charge on Nanoparticles Diffusion in the Brain White Matter.

PURPOSE: Brain disorders have become a serious problem for healthcare worldwide. Nanoparticle-based drugs are one of the emerging therapies and have shown great promise to treat brain diseases. Modifications on particle size and surface charge are two efficient ways to increase the transport efficiency of nanoparticles through brain-blood barrier; however, partly due to the high complexity of brain microstructure and limited visibility of Nanoparticles (NPs), our understanding of how these two modifications can affect the transport of NPs in the brain is insufficient. METHODS: In this study, a framework, which contains a stochastic geometric model of brain white matter (WM) and a mathematical particle tracing model, was developed to investigate the relationship between particle size/surface charge of the NPs and their effective diffusion coefficients (D) in WM. RESULTS: The predictive capabilities of this method have been validated using published experimental tests. For negatively charged NPs, both particle size and surface charge are positively correlated with D before reaching a size threshold. When Zeta potential (Zp) is less negative than -10 mV, the difference between NPs' D in WM and pure interstitial fluid (IF) is limited. CONCLUSION: A deeper understanding on the relationships between particle size/surface charge of NPs and their D in WM has been obtained. The results from this study and the developed modelling framework provide important tools for the development of nano-drugs and nano-carriers to cure brain diseases.