Shifts in structural connectome organization in the limbic and sensory systems of patients with episodic migraine.

Migraine is a complex neurological condition characterized by recurrent headaches, which is often accompanied by various neurological symptoms. Magnetic resonance imaging (MRI) is a powerful tool for investigating whole-brain connectivity patterns; however, systematic assessment of structural connectome organization has rarely been performed. In the present study, we aimed to examine the changes in structural connectivity in patients with episodic migraines using diffusion MRI. First, we computed structural connectivity using diffusion MRI tractography, after which we applied dimensionality reduction techniques to the structural connectivity and generated three low-dimensional eigenvectors. We subsequently calculated the manifold eccentricity, defined as the Euclidean distance between each data point and the center of the data in the manifold space. We then compared the manifold eccentricity between patients with migraines and healthy controls, revealing significant between-group differences in the orbitofrontal cortex, temporal pole, and sensory/motor regions. Between-group differences in subcortico-cortical connectivity further revealed significant changes in the amygdala, accumbens, and caudate nuclei. Finally, supervised machine learning effectively classified patients with migraines and healthy controls using cortical and subcortical structural connectivity features, highlighting the importance of the orbitofrontal and sensory cortices, in addition to the caudate, in distinguishing between the groups. Our findings confirmed that episodic migraine is related to the structural connectome changes in the limbic and sensory systems, suggesting its potential utility as a diagnostic marker for migraine.

Whole-brain structural connectome asymmetry in autism.

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning.

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

Reconsidering repurposing: long-term metformin treatment impairs cognition in Alzheimer's model mice.

Metformin, a primary anti-diabetic medication, has been anticipated to provide benefits for Alzheimer's disease (AD), also known as "type 3 diabetes". Nevertheless, some studies have demonstrated that metformin may trigger AD pathology and even elevate AD risk in humans. Despite this, limited research has elucidated the behavioral outcomes of metformin treatment, which would hold significant translational value. Thus, we aimed to perform thorough behavioral research on the prolonged administration of metformin to mice: We administered metformin (300 mg/kg/day) to transgenic 3xTg-AD and non-transgenic (NT) C57BL/6 mice over 1 and 2 years, respectively, and evaluated their behaviors across multiple domains via touchscreen operant chambers, including motivation, attention, memory, visual discrimination, and cognitive flexibility. We found metformin enhanced attention, inhibitory control, and associative learning in younger NT mice (≤16 months). However, chronic treatment led to impairments in memory retention and discrimination learning at older age. Furthermore, metformin caused learning and memory impairment and increased levels of AMPKα1-subunit, ß-amyloid oligomers, plaques, phosphorylated tau, and GSK3ß expression in AD mice. No changes in potential confounding factors on cognition, including levels of motivation, locomotion, appetite, body weight, blood glucose, and serum vitamin B12, were observed in metformin-treated AD mice. We also identified an enhanced amyloidogenic pathway in db/db mice, as well as in Neuro2a-APP695 cells and a decrease in synaptic markers, such as PSD-95 and synaptophysin in primary neurons, upon metformin treatment. Our findings collectively suggest that the repurposing of metformin should be carefully reconsidered when this drug is used for individuals with AD.

Atypical structural connectome asymmetry and associations with network communication in autism spectrum disorder.

Autism spectrum disorder is a common neurodevelopmental condition showing connectome disorganization in sensory and transmodal cortices. However, alterations in the inter-hemispheric asymmetry of structural connectome are remained to be investigated. Here, we studied structural connectome asymmetry in individuals with autism using dimensionality reduction techniques and assessed its topological underpinnings by associating with network communication measures. We found that the sensory and heteromodal association regions showed significant between-group differences in inter-hemispheric asymmetry between individuals with autism and neurotypical controls. In addition, the network communication ability was particularly altered between visual and limbic areas. Our findings provide insights for understanding structural connectome alteration in autism and its topological underpinnings.Clinical Relevance- This study provides insights into the understanding of atypical macroscale structural connectome organization in individuals with autism.

Comprehensive Evaluation System for Post-Metabolic Activity of Potential Thyroid-Disrupting Chemicals.

Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.

A systematic approach to metabolic characterization of thyroid-disrupting chemicals and their in vitro biotransformants based on prediction-assisted metabolomic analysis.

Thyroid-disrupting compounds (TDCs) are chemicals that modify thyroid gland function and disrupt hormonal homeostasis. Like other endocrine-disrupting chemicals (EDCs), TDCs often show altered activities following post-metabolic modification via endogenous enzymatic reaction. Hence, we developed evaluation system consisting of (1) in vitro metabolic reaction module, (2) high-resolution mass-spectrometry, and (3) human cell-based reporter gene assay. We developed the reaction module using rat S9 fraction where levothyroxine (T4) as a model compound, was subjected to phase-I or phase-I+II biotransformation. The metabolic profiles of the biotransformants were systematically configured based on in-silico prediction of potential products and experimental validation using liquid-chromatography Orbitrap mass-spectrometry. Thyroid agonistic activities of the biotransformants were evaluated by thyroid receptor-mediated stably transfected transcriptional activation assay using hTRE_HeLa cells. Indeed, we detected the increased activities following metabolic conversion of T4 in a dose-dependent manner. Note that the activity by phase-I+II reaction was much greater than by phase-I reaction (3.8-fold increase). Subsequently, we explored metabolic signatures, which potentially contributed to the hyperactivity by phase-I+II reaction. A total of 77 metabolic features were annotated based on the in-silico prediction, which included biotransformants with deiodination and conjugation. The glucuronide-conjugated form was found at the highest fold-increase (970-fold increase) whereas marginal increases were determined in the deiodinized forms (1.6-fold increase in T3 and 2.0-fold increase in rT3). Further, the systematic approach was evaluated and comparably analyzed by the metabolic profiles of bithionol, which is structurally related to T4. Our current result suggested the potential application of in vitro evaluation system to risk assessment of thyroid-disrupting activity.

Ostwald Ripening Rate of Orange Oil Emulsions: Effects of Molecular Structure of Emulsifiers and Their Oil Composition.

Ostwald ripening, one of the frequently observed instability of flavor oil emulsions, can be easily prevented by adding triacylglycerols to the oil phase. The effect of interfacial characteristics of the emulsion droplets (particularly thickness) on the effectiveness of triacylglycerol inhibition of Ostwald ripening was evaluated in this study. The prepared emulsions were stabilized with emulsifiers with different-size hydrophilic groups, which correlate to droplet interfacial thickness. Emulsions with an oil phase of pure orange oil were unstable due to Ostwald ripening. Modifying the oil phase by adding corn oil or medium-chain triacylglycerol (MCT) effectively inhibited droplet growth. Thicker and less dense droplet surfaces in the emulsions required more triacylglycerol, regardless of its type, to resist Ostwald ripening. When the oil phase contained the same amounts of triacylglycerols, MCT was more effective at inhibiting Ostwald ripening than corn oil. Compared with corn oil, MCT more effectively inhibited Ostwald ripening of the emulsions containing micelles. PRACTICAL APPLICATIONS: When food and beverage industries produce food products containing flavor or essential oils vulnerable to Ostwald ripening as emulsion forms, the findings in this work could provide useful information on the interfacial engineering of emulsions and on how to modify the oil compositions of emulsions using triacylglycerols to improve the stability of these emulsions against Ostwald ripening.