Subthalamic nucleus-language network connectivity predicts dopaminergic modulation of speech function in Parkinson's disease.

Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation.

White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.

Dopaminergic medication normalizes aberrant cognitive control circuit signalling in Parkinson's disease.

Dopaminergic medication is widely used to alleviate motor symptoms of Parkinson's disease, but these medications also impact cognition with significant variability across patients. It is hypothesized that dopaminergic medication impacts cognition and working memory in Parkinson's disease by modulating frontoparietal-basal ganglia cognitive control circuits, but little is known about the underlying causal signalling mechanisms and their relation to individual differences in response to dopaminergic medication. Here we use a novel state-space computational model with ultra-fast (490 ms resolution) functional MRI to investigate dynamic causal signalling in frontoparietal-basal ganglia circuits associated with working memory in 44 Parkinson's disease patients ON and OFF dopaminergic medication, as well as matched 36 healthy controls. Our analysis revealed aberrant causal signalling in frontoparietal-basal ganglia circuits in Parkinson's disease patients OFF medication. Importantly, aberrant signalling was normalized by dopaminergic medication and a novel quantitative distance measure predicted individual differences in cognitive change associated with medication in Parkinson's disease patients. These findings were specific to causal signalling measures, as no such effects were detected with conventional non-causal connectivity measures. Our analysis also identified a specific frontoparietal causal signalling pathway from right middle frontal gyrus to right posterior parietal cortex that is impaired in Parkinson's disease. Unlike in healthy controls, the strength of causal interactions in this pathway did not increase with working memory load and the strength of load-dependent causal weights was not related to individual differences in working memory task performance in Parkinson's disease patients OFF medication. However, dopaminergic medication in Parkinson's disease patients reinstated the relation with working memory performance. Our findings provide new insights into aberrant causal brain circuit dynamics during working memory and identify mechanisms by which dopaminergic medication normalizes cognitive control circuits.

Developmental maturation of causal signaling hubs in voluntary control of saccades and their functional controllability.

The ability to adaptively respond to behaviorally relevant cues in the environment, including voluntary control of automatic but inappropriate responses and deployment of a goal-relevant alternative response, undergoes significant maturation from childhood to adulthood. Importantly, the maturation of voluntary control processes influences the developmental trajectories of several key cognitive domains, including executive function and emotion regulation. Understanding the maturation of voluntary control is therefore of fundamental importance, but little is known about the underlying causal functional circuit mechanisms. Here, we use state-space and control-theoretic modeling to investigate the maturation of causal signaling mechanisms underlying voluntary control over saccades. We demonstrate that directed causal interactions in a canonical saccade network undergo significant maturation between childhood and adulthood. Crucially, we show that the frontal eye field (FEF) is an immature causal signaling hub in children during control over saccades. Using control-theoretic analysis, we then demonstrate that the saccade network is less controllable in children and that greater energy is required to drive FEF dynamics in children compared to adults. Our findings provide novel evidence that strengthening of causal signaling hubs and controllability of FEF are key mechanisms underlying age-related improvements in the ability to plan and execute voluntary control over saccades.

Can a Nomogram Predict Survival After Treatment for an Ankylosing Spondylitis Cervical Fracture in a Patient With Neurologic Impairment? A National, Multicenter Study.

BACKGROUND: Ankylosing spondylitis-related cervical spine fracture with neurologic impairment (ASCF-NI) is a rare but often lethal injury. Factors independently associated with survival after treatment remain poorly defined, and identifying patients who are likely to survive the injury remains challenging. QUESTIONS/PURPOSES: (1) What factors are independently associated with survival after treatment among patients with ASCF-NI? (2) Can a nomogram be developed that is sufficiently simple for clinicians to use that can identify patients who are the most likely to survive after injury? METHODS: This retrospective study was conducted based on a multi-institutional group of patients admitted and treated at one of 29 tertiary hospitals in China between March 1, 2003, and July 31, 2019. A total of 363 patients with a mean age of 53 ± 12 years were eventually included, 343 of whom were male. According to the National Household Registration Management System, 17% (61 of 363) died within 5 years of injury. Patients were treated using nonsurgical treatment or surgery, including procedures using the anterior approach, posterior approach, or combined anterior and posterior approaches. Indications for surgery included three-column injury, unstable fracture displacement, neurologic impairment or continuous progress, and intervertebral disc incarceration. By contrast, patients generally received nonsurgical treatment when they had a relatively stable fracture or medical conditions that did not tolerate surgery. Demographic, clinical, and treatment data were collected. The primary study goal was to identify which factors are independently associated with death within 5 years of injury, and the secondary goal was the development of a clinically applicable nomogram. We developed a multivariable Cox hazards regression model, and independent risk factors were defined by backward stepwise selection with the Akaike information criterion. We used these factors to create a nomogram using a multivariate Cox proportional hazards regression analysis. RESULTS: After controlling for potentially confounding variables, we found the following factors were independently associated with a lower likelihood of survival after injury: lower fracture site, more-severe peri-injury complications, poorer American Spinal Injury Association (ASIA) Impairment Scale, and treatment methods. We found that a C5 to C7 or T1 fracture (ref: C1 to C4 and 5; hazard ratio 1.7 [95% confidence interval 0.9 to 3.5]; p = 0.12), moderate peri-injury complications (ref: absence of or mild complications; HR 6.0 [95% CI 2.3 to 16.0]; p < 0.001), severe peri-injury complications (ref: absence of or mild complications; HR 30.0 [95% CI 11.5 to 78.3]; p < 0.001), ASIA Grade A (ref: ASIA Grade D; HR 2.8 [95% CI 1.1 to 7.0]; p = 0.03), anterior approach (ref: nonsurgical treatment; HR 0.5 [95% CI 0.2 to 1.0]; p = 0.04), posterior approach (ref: nonsurgical treatment; HR 0.4 [95% CI 0.2 to 0.8]; p = 0.006), and combined anterior and posterior approach (ref: nonsurgical treatment; HR 0.4 [95% CI 0.2 to 0.9]; p = 0.02) were associated with survival. Based on these factors, a nomogram was developed to predict the survival of patients with ASCF-NI after treatment. Tests revealed that the developed nomogram had good performance (C statistic of 0.91). CONCLUSION: The nomogram developed in this study will allow us to classify patients with different mortality risk levels into groups. This, coupled with the factors we identified, was independently associated with survival, and can be used to guide more appropriate treatment and care strategies for patients with ASCF-NI. LEVEL OF EVIDENCE: Level III, therapeutic study.

Transcriptomics analysis of cashmere fineness functional genes.

Liaoning cashmere goat (LCG) is a famous cashmere goat breed in China. Cashmere fineness, as an important index to evaluate cashmere quality, is also one of the problems to be improved for Liaoning cashmere goats. Transcriptome studies all mRNA transcribed by a specific tissue or cell in a certain period. It is a key link in the study of gene expression regulation. It plays an important role in the analysis of biological growth and disease. Transcriptome is spatio-temporal specific, that is, gene expression varies in different tissues or at different times. Three coarser and three fine LCG skin samples were sequenced by RNA-seq technology, and a total of 427 differentially expressed genes were obtained, including 291 up-regulated genes and 136 down-regulated genes. In the experiment, we screened out 16 genes that had significant differences in the expression of coarse and fine cashmere of Liaoning cashmere goats, so it was inferred that these 16 genes might have regulatory effects on cashmere fineness. Moreover, GO gene set enrichment analysis revealed that differential genes mainly consist of immune response, MHC protein complex, Heme binding and other pathways. KEGG analysis showed that transplant-versus-host disease and allograft rejection were the main pathways of differential genes.

High-throughput analysis of lncRNA in cows with naturally infected Staphylococcus aureus mammary gland.

LncRNA (long non-coding RNA) is an RNA molecule with a length between 200 and 100,000 nt. It does not encode proteins and is involved in a variety of intracellular processes, becoming a research hotspot of genetics. To identify key lncRNAs associated with dairy mastitis, we collected mammary epithelial tissue samples of Normal disease-free Holstein cows (HCN) and unhealthy Holstein cows with Staphylococcus aureus (HCU) and performed RNA sequencing (RNA-seq) on the samples. A total of 270 differentially expressed lncRNAs and 500 differentially expressed mRNAs were identified by high-throughput sequencing and bioinformatics analysis. Furthermore, Hydrolase activity is the most enriched in GO, and ErbB signaling pathway is significantly enriched in KEGG. In addition, through qPCR validation of 5 candidate lncRNAs in HCN and HCU, four differentially expressed lncRNAs MSTRG.498, MSTRG57.1, MSTRG.41.1 and MSTRG 124.1 were confirmed to have significant differentially expressed in cow mastitis. Also, lncRNA MSTRG.498 and its target gene, SMC4, might directly or indirectly play a role in cow mastitis. The regulatory network of lncRNA-miRNA-mRNA has been inferred from a bioinformatics perspective, which may assist understand the underlying molecular mechanism of lncRNAs involved in regulating mastitis in cows. Our findings will provide meaningful resources for further research on the regulatory function of lncRNAs in cow mastitis.

CeRNA regulates network and expression and SNP effect on NFKBIA of cashmere fineness.

In this study, a total of 1140 Liaoning Cashmere Goats (LCG) were genotyped for single nucleotide polymorphism (SNP) of NFKBIA gene. There are 15 SNPs and 7 genotypes have been found, and G1547A (GG) genotype has been associated with cashmere fineness and cashmere yield. An integrated ceRNA regulatory network of NFKBIA gene was made. To prove NFKBIA and these non-coding RNAs (ncRNAs) may be related to cashmere fineness, we performed qPCR on these ncRNA in LCG coarse type skin (CT-LCG) and LCG fine type skin (FT-LCG). The result of qPCR showed lncRNA XLOC_011060 and ciRNA452 are at high expression level in CT-LCG, all miRNAs appear high expressed in FT-LCG, and mir-93 was the most significant difference between CT-LCG and FT-LCG. In addition, five miRNAs were selected for qPCR in different genotypes. The qPCR results showed that mir-93 might negatively regulate cashmere fineness and mir-17-5p may play a positive role in regulating cashmere fineness of individuals with G1355A (AG) genotype. These results demonstrated that NFKBIA gene is associated with cashmere fineness of LCG and G1547A (GG) genotype is the preferred marker genotype for cashmere fineness.

Analysis of m6A methylation in skin tissues of different sex Liaoning cashmere goats.

N6-methyladenosine (m6A) is the most frequent internal modification of mRNA and lncRNA in eukaryotes. We used two high-throughput sequencing method, m6A-seq and RNA-seq to identify pivotal m6A-modified genes in cashmere fineness and fiber growth. 8062 m6A peaks were detected by m6A-seq, including 2157 upregulated and 6445 downregulated. Furthermore, by comparing m6A-modified genes of the male Liaoning Cashmere Goat (M-LCG) and female Liaoning Cashmere Goat (F-LCG) skin tissues, we get 862 differentially expressed m6A-modified genes. To identify differently expressed m6A genes associated with cashmere fineness, 11 genes were selected for validation using real time fluorescent quantitative PCR in M-LCG and F-LCG. This study provides an acadamic basis on the molecular regulation mechanism of m6A modification in cashmere growth process.

Association analysis between reproduction genes INHA, PGR, RARG with lamb and other traits of Liaoning cashmere goats.

Reproductive traits have a high economic value in goat breeding, and increasing the number of lambs produced by ewes is of great importance to improve the production efficiency of goat farming. Lambing traits in goats are low heritability traits, but their genetic basis is ultimately determined by genes. This study aimed to investigate the relationship between INHA, RARG, and PGR gene polymorphisms and production performance, such as lambing, cashmere production, milk production, and body size in Liaoning cashmere goats. A total of six single nucleotide polymorphisms (SNPs) loci were identified in these three genes, G144A and T504C on the INHA gene, A56G, G144A, G490C on the RARG gene, and G109519T on the PGR gene. For lambing and cashmere production traits, the AA genotype of G144A on the INHA gene, TT on the T504C genotype, GG genotype of G144A on the INHA gene, A56G, G144A, and T504C on RARG and G109519T on PGR gene are dominant genotypes. AATT is a dominant haplotype combination. Allele G can be used as a molecular marker for lambing, cashmere, and milk production traits in Liaoning cashmere goats. Marker-assisted selection can be used for early selection to achieve improvement of genetic traits in Liaoning cashmere goats.

Association analysis for SNPs of BAAT and COL1A1 genes with cashmere production performance and other production traits in Liaoning cashmere goats.

This study aimed to investigate the relationship between the polymorphism of bile acid-CoA: amino acid N-acyltransferase (BAAT) and collagen type I alpha 1 chain (COL1A1) genes and the production performance of Liaoning Cashmere goat (LCG). The potential single nucleotide polymorphisms (SNPs) of LCG were detected by sequence comparison of BAAT and COL1A1 genes and PCR-Seq polymorphism, and the effect of SNPs on production performance was analyzed by SPSS software. The results showed that three SNPs loci were detected in BAAT gene: G7900A, T7967C, C7998T, and one SNP locus T6716C was detected in COL1AL gene. At G7900A locus, the dominant genotype for cashmere performance was GG, and the dominant genotype for body measurement traits and milk production traits was AG. At T7967C locus, the dominant genotype for cashmere performance was TT, and the dominant genotype for body measurement traits and milk production traits was CC. At C7998T locus, TT was the dominant genotype for cashmere performance, body measurement traits, and milk production traits. At the T6716C locus, TT was the dominant genotype for cashmere performance, body measurement traits, and milk production traits. H1H1: AACC is the dominant haplotype combination. Therefore, this study will provide a reliable reference for future research on cashmere production performance, body measurement traits, and milk production traits of LCG.

Association analysis for SNPs of KRT26 and TCHH genes with cashmere production performance, body measurement traits and milk production traits in Liaoning cashmere goats.

Cashmere fineness is getting thicker, which is one of the key problems in cashmere breeding, however, there have been no systematic studies on the molecular regulation of cashmere fineness. The aim of this study was to investigate the relationship between KRT26 and TCHH gene polymorphism and production performance in Liaoning cashmere goats (LCG). The potential single nucleotide polymorphisms (SNPs) of LCG were detected by sequence alignment and PCR-Seq polymorphism of KRT26 and TCHH genes and analyzed the effect of SNPs on production performance by SPSS software. Two SNPs sites (A559T and A6839G) of two genes were detected. The AA genotype of KRT26 A559T locus was the dominant genotype. AG and GG at TCHH A6839G locus were the dominant genotypes. AAAA was the dominant haplotype combination. The results showed that KRT26 and TCHH genes were associated with cashmere fineness of LCG, and A559T (AA) and A6839G (GG) genotypes were the preferred marker genotypes for cashmere fineness, which provided more theoretical basis for further research on cashmere fineness.

Association analysis for SNPs of MSTN and IGFBP-3 genes with body size and other production traits in Liaoning Cashmere Goats.

Liaoning cashmere goat (LCG) have tall bones, high cashmere production and outstanding meat production performance. In recent years, good breeding progress has not been made in terms of body size, meat yield, milk yield and other properties in terms of production. The study focused on the correlation between the SNPs of MSTN and IGFBP-3 genes with the body size performance, cashmere production and milk performance. The MSTN and IGFBP-3 gene sequence alignment and PCR-Seq polymorphism were used to detect the potential SNPs, and the correlation with production performance was analyzed by SPSS and SHEsis software. The results showed that the TT genotype at the T1662G locus of the MSTN gene is dominant and has significant advantages in body measurements such as sacrum height, chest width, and waist height. The C allele at the C4021T locus of IGFBP-3 gene shows an advantage in the body measurement performance. Among the haplotype combinations, H2H2:TGTC is preponderant combination for body size performance, H2H2:TGTC and H1H2:TGCC are preponderant combinations for cashmere production performance, H1H3:GGCC is preponderant combination for milk production performance. It may be a molecular marker for future selection and breeding.

Methylphenidate remediates aberrant brain network dynamics in children with attention-deficit/hyperactivity disorder: A randomized controlled trial.

Methylphenidate is a widely used first-line treatment for attention deficit/hyperactivity disorder (ADHD), but the underlying circuit mechanisms are poorly understood. Here we investigate whether a single dose of osmotic release oral system methylphenidate can remediate attention deficits and aberrancies in functional circuit dynamics in cognitive control networks, which have been implicated in ADHD. In a randomized placebo-controlled double-blind crossover design, 27 children with ADHD were scanned twice with resting-state functional MRI and sustained attention was examined using a continuous performance task under methylphenidate and placebo conditions; 49 matched typically-developing (TD) children were scanned once for comparison. Dynamic time-varying cross-network interactions between the salience (SN), frontoparietal (FPN), and default mode (DMN) networks were examined in children with ADHD under both administration conditions and compared with TD children. Methylphenidate improved sustained attention on a continuous performance task in children with ADHD, when compared to the placebo condition. Children with ADHD under placebo showed aberrancies in dynamic time-varying cross-network interactions between the SN, FPN and DMN, which were remediated by methylphenidate. Multivariate classification analysis confirmed that methylphenidate remediates aberrant dynamic brain network interactions. Furthermore, dynamic time-varying network interactions under placebo conditions predicted individual differences in methylphenidate-induced improvements in sustained attention in children with ADHD. These findings suggest that a single dose of methylphenidate can remediate deficits in sustained attention and aberrant brain circuit dynamics in cognitive control circuits in children with ADHD. Findings identify a novel brain circuit mechanism underlying a first-line pharmacological treatment for ADHD, and may inform clinically useful biomarkers for evaluating treatment outcomes.

Insights from an autism imaging biomarker challenge: Promises and threats to biomarker discovery.

MRI has been extensively used to identify anatomical and functional differences in Autism Spectrum Disorder (ASD). Yet, many of these findings have proven difficult to replicate because studies rely on small cohorts and are built on many complex, undisclosed, analytic choices. We conducted an international challenge to predict ASD diagnosis from MRI data, where we provided preprocessed anatomical and functional MRI data from > 2,000 individuals. Evaluation of the predictions was rigorously blinded. 146 challengers submitted prediction algorithms, which were evaluated at the end of the challenge using unseen data and an additional acquisition site. On the best algorithms, we studied the importance of MRI modalities, brain regions, and sample size. We found evidence that MRI could predict ASD diagnosis: the 10 best algorithms reliably predicted diagnosis with AUC∼0.80 - far superior to what can be currently obtained using genotyping data in cohorts 20-times larger. We observed that functional MRI was more important for prediction than anatomical MRI, and that increasing sample size steadily increased prediction accuracy, providing an efficient strategy to improve biomarkers. We also observed that despite a strong incentive to generalise to unseen data, model development on a given dataset faces the risk of overfitting: performing well in cross-validation on the data at hand, but not generalising. Finally, we were able to predict ASD diagnosis on an external sample added after the end of the challenge (EU-AIMS), although with a lower prediction accuracy (AUC=0.72). This indicates that despite being based on a large multisite cohort, our challenge still produced biomarkers fragile in the face of dataset shifts.

Proteomic analysis of coarse and fine skin tissues of Liaoning cashmere goat.

Proteomics is the study of all proteins expressed by a cell or even an organism. However, knowledge of proteins that regulate the fineness of cashmere is limited. Liaoning cashmere goat (LCG) is a valuable genetic resource of China. The skin samples of Liaoning cashmere goats during the growing period were collected, performed tandem mass tag (TMT) method, and identified 117 differentially expressed proteins in CT_LCG (course type) and FT_LCG (fine type). To verify proteins differentially expressed in LCG, we performed PRM validation on three candidate proteins (ALB, SDC1, and ITGB4) in CT-LCG and FT-LCG. Furthermore, primary metabolic process and lysosome are most enriched in the GO and KEGG pathways, respectively. In addition, we also derived a protein-protein interaction (PPI) regulatory network from the perspective of bioinformatics. This study sought to elucidate the molecular mechanism of differential proteins regulating cashmere fineness of Liaoning cashmere goats by using TMT quantitative proteomics analysis. Differentially expressed proteins ALB and SDC1 may regulate cashmere fineness; ITGB4 can become a promising protein for further study. They can be used as key proteins to lay a foundation for studying cashmere fineness of Liaoning cashmere goats.

Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD.

Attention-deficit hyperactivity disorder (ADHD) is associated with pervasive impairments in attention and cognitive control. Although brain circuits underlying these impairments have been extensively investigated with resting-state fMRI, little is known about task-evoked functional brain circuits and their relation to cognitive control deficits and inattention symptoms in children with ADHD. Children with ADHD and age, gender and head motion matched typically developing (TD) children completed a Go/NoGo fMRI task. We used multivariate and dimensional analyses to investigate impairments in two core cognitive control systems: (i) cingulo-opercular "salience" network (SN) anchored in the right anterior insula, dorsal anterior cingulate cortex (rdACC), and ventrolateral prefrontal cortex (rVLPFC) and (ii) dorsal frontoparietal "central executive" (FPN) network anchored in right dorsolateral prefrontal cortex (rDLPFC) and posterior parietal cortex (rPPC). We found that multivariate patterns of task-evoked effective connectivity between brain regions in SN and FPN distinguished the ADHD and TD groups, with rDLPFC-rPPC connectivity emerging as the most distinguishing link. Task-evoked rdACC-rVLPFC connectivity was positively correlated with NoGo accuracy, and negatively correlated with severity of inattention symptoms. Brain-behavior relationships were robust against potential age, gender, and head motion confounds. Our findings highlight aberrancies in task-evoked modulation of SN and FPN connectivity in children with ADHD. Crucially, cingulo-frontal connectivity was a common locus of deficits in cognitive control and clinical measures of inattention symptoms. Our study provides insights into a parsimonious systems neuroscience model of cognitive control deficits in ADHD, and suggests specific circuit biomarkers for predicting treatment outcomes in childhood ADHD.

Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention.

Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.

High-throughput analysis of CircRNA in cows with naturally infected Staphylococcus aureus mammary gland.

Circular RNAs (CircRNA) are a special type of non-coding RNA molecule with a closed ring structure and are not affected by RNA exonucases. It has stable expression, is not easy to degrade, and exists in most eukaryotes. However, circRNA regulation of cow mastitis has not been widely recognized. Mammary epithelial tissues were collected from healthy Holstein cows (HCN) and mastitis Holstein cows (HCU). RNA sequencing (RNA SEQ) was performed for the differentially expressed circRNAs, and analysis results showed that 19 differentially expressed circRNAs were identified in HCN and HCU, among which 6 circRNAs were up-regulated and 13 circRNAs were down-regulated. We randomly selected nine circRNAs for Q-PCR verification, and the results showed consistent expression. Three circRNAs: circRNA2860, circRNA5323 and circRNA4027 were confirmed to be significantly differentially expressed circRNAs in cow mastitis. Also, their host genes TRPS1, SLC12A2 and MYH11 might be directly or indirectly play a role in cow mastitis. Furthermore, RNA polymerase transcription factor binding and tight junction are most enriched in GO and KEGG pathways, respectively. In addition, the regulatory network of circRNA-miRNA has been inferred from a bioinformatics perspective, which may help to understand the underlying molecular mechanism of circRNAs involved in regulating mastitis in cows.