Functional Networks Research Articles

Disruptions in segregation mechanisms in fMRI-based brain functional network predict the major depressive disorder condition

This study investigates the functional brain network in major depressive disorder using network theory and a consensus network approach. At the macroscopic level, we found significant differences in connectivity measures such as node strength and clustering coefficient, with healthy controls exhibiting higher values. This is consistent with disruptions in functional brain network segregation in patients with major depressive disorder. Consensus network analysis revealed that the central executive and salience networks were predominant in healthy controls, whereas depressed patients showed greater overlap with the default mode network. No differences were found in network efficiency measures, indicating comparable brain network integration between healthy controls and major depressive disorder groups. Importantly, the clustering coefficient emerged as an effective diagnostic biomarker for depression, achieving high sensitivity (90%), specificity (92%), and overall precision (90%). Further analysis at the mesoscale level uncovered unique functional connections distinguishing healthy controls and major depressive disorder groups. Our findings underscore the utility of analyzing functional networks from the macroscale to the mesoscale, and provide insight into overcoming the challenges associated with intersubject variability and the multiple comparisons problem in network analysis.

Retinal Damage and Visual Network Reconfiguration Defines Visual Function Recovery in Optic Neuritis.

Recovery of vision after acute optic neuritis (AON) is critical to improving the quality of life of people with demyelinating diseases. The objective of the study was to prospectively assess the changes in visual acuity, retinal layer thickness, and cortical visual network in patients with AON to identify the predictors of permanent visual disability. We studied a prospective cohort of 88 consecutive patients with AON with 6-month follow-up using high and low-contrast (2.5%) visual acuity, color vision, retinal thickness from optical coherence tomography, latencies and amplitudes of multifocal visual evoked potentials, mean deviation of visual fields, and diffusion-based structural (n = 53) and functional (n = 19) brain MRI to analyze the cortical visual network. The primary outcome was 2.5% low-contrast vision, and data were analyzed with mixed-effects and multivariate regression models. We found that after 6 months, low-contrast vision and quality of vision remained moderately impaired. The thickness of the ganglion cell layer at baseline was a predictor of low-contrast vision 6 months later (ß = 0.49 [CI 0.11-0.88], p = 0.012). The structural cortical visual network at baseline predicted low-contrast vision, the best predictors being the betweenness of the right parahippocampal cortex (ß = -036 [CI -0.66 to 0.06], p = 0.021), the node strength of the right V3 (ß = 1.72 [CI 0.29-3.15], p = 0.02), and the clustering coefficient of the left intraparietal sulcus (ß = 57.8 [CI 12.3-103.4], p = 0.015). The functional cortical visual network at baseline also predicted low-contrast vision, the best predictors being the betweenness of the left ventral occipital cortex (ß = 8.6 [CI: 4.03-13.3], p = 0.009), the node strength of the right intraparietal sulcus (ß = -2.79 [CI: -5.1-0.4], p = 0.03), and the clustering coefficient of the left superior parietal lobule (ß = 501.5 [CI 50.8-952.2], p = 0.03). The assessment of the visual pathway at baseline predicts permanent vision disability after AON, indicating that damage is produced early after disease onset and that it can be used for defining vision impairment and guiding therapy.

MicroRNA-Targeted Gene Regulation in Salivary Gland Tissue of De Novo Parkinson's Disease Patients.

Although α-synucleinopathy has been confirmed in the submandibular gland (SMG) tissue of Parkinson's disease (PD) patients, in-depth disease-related molecular research, such as tissue-specific transcriptional signals, has not been performed. In the present study, disease-relevant tissue-specific transcriptional signals in SMG tissue from PD patients were investigated to identify potential diagnostic, prognostic, and pathophysiologic biomarkers. Here, seven de novo drug-naïve PD patients and six age- and sex-matched individuals without neurological or psychological diseases were enrolled. Total RNA sequencing (RNA-seq) and total small RNA-seq (smRNA-seq) were performed on SMG tissue and blood samples, with 26 RNA-seq and 26 smRNA-seq samples used for the final analysis. Differentially expressed genes (DEGs) and microRNAs in SMG tissue and blood from PD patients were obtained and their functional integration and interaction network were analyzed. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the DEGs interacted with cytokine-, inflammation-, and immune-related pathways. Synphilin-1 expression was significantly downregulated in SMG tissue of PD patients, and α-synuclein expression did not significantly differ between PD patients and controls in either SMG tissue or blood. Fifteen tissue-specific miRNA signals in SMG tissue were identified that showed better diagnostic ability compared with those in blood samples. The correlation between DEGs and environmental factors appeared altered in PD patients. The results indicated the DEGs and microRNA signatures identified in SMG tissue may be promising diagnostic and prognostic biomarkers. These molecular insights offer potential avenues for the development of novel therapeutic strategies targeting the underlying disease mechanisms in PD patients.

LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data.

We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing(stRNA-seq)data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including(i) 257 single-cell RNA sequencingandstRNA-seqdatasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1002988 cells and 367971 spatial spots, (iii) 15489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curationand (v) ceRNA expression profiles and follow-up clinical information of 20326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.

Mechanical equipment fault diagnosis method based on improved deep residual shrinkage network.

Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.

Factors Influencing Depression in People with Disabilities:Individual and Interpersonal Levels

Abstract Depression is an increasingly important social problem, and this also applies to the disabled, a health-vulnerable group. Although the distribution and influencing factors of depression may be different among the disabled population (young adults vs. the elderly, older adults with disabilities vs. disabilities due to old age), only the high prevalence of depression among the disabled is generally reported. This study seeks to determine the distribution and influencing factors of depression in disabled people according to sociodemographic standards. This study employed data from the 2021 Panel Survey on People with Disabilities (National Unit Survey) to investigate the influence of individual-level factors (health behaviors, health level, environment) and interpersonal-level factors (social network) on depression. Multivariate logistic regression analysis was performed while controlling for demographic sociological factors and disability characteristic factors. The experience of depression was among elderly disabled individuals (age 50+: 40.9%) than younger ones (age 19-49: 36.1%), and in disability with aging (49.3%) versus aging with disabilities (39.2%) (P < 0.05). Chronic diseases, daily living assistance, living environment, and emotional support from family and neighbors affected both young and elderly disabled individuals. Specifically, among the elderly disabled, exercise frequency (aOR=0.78; [CI]0.63-0.97), social network size (aOR=0.87; [CI]0.82-0.93), and social interaction frequency (aOR=0.68; [CI]0.51-0.91) were significant. Disability with aging was influenced by exercise frequency (aOR=0.29; [CI]0.09-0.95) and emotional support (aOR=0.37; [CI]0.17-0.77) from peers. older adults with disabilities were affected by not only individual-level factors (exercise frequency) but also both functional (emotional support) and structural (size of social network, frequency of meetings) aspects of their interpersonal social networks. Key messages • Depression experiences among older adults with disabilities are influenced by both functional and structural social networks. • Developing depression prevention strategies for people with disabilities requires a customized approach that takes into account age-specific characteristics.

Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24,539 Individuals.

Nicotine addiction, a multifaceted neuropsychiatric disorder, profoundly impacts brain functions through interactions with neural pathways. Despite its significance, the impact of tobacco smoking on the whole-brain functional connectome remains largely unexplored. We conducted a whole-brain analysis on 24,539 adults aged 40 and above from the UK Biobank cohort. Subjects were categorized into individuals who use nicotine and who do not use nicotine based on current and chronic tobacco smoking information. Functional connectivity was assessed using resting-state functional magnetic resonance imaging (rfMRI). We employed a network analysis method to assess the systematic effects of tobacco smoking on brain connectome by identifying subnetworks that show nicotine-use-related differences. Our analyses revealed two nicotine-use-related subnetworks with distinct network structure (permutation p-value < 0.001). In the first network, there is a significant decrease in resting-state functional connectivity (rsFC) between the basal ganglia regions (e.g., nucleus accumbens) and 73% of the remaining brain regions, emphasizing the central hub role of basal ganglia in addictive smoking behaviors. Additionally, a data-driven subnetwork mainly involving regions from frontal and occipital lobes, showed reduced rsFC among individuals who use nicotine. The results suggest significant alterations in the communication and coordination among the basal ganglia and the broader network of brain regions. The observed changes in rsFC indicate a widespread disruption in the connectivity patterns associated with nicotine use. This study identifies rsFC subnetworks related to chronic nicotine use through whole-brain connectome analysis. The findings confirm that widespread alterations in rsFC are centered around hub nodes within the basal ganglia, including bilateral nucleus accumbens, putamen, caudate, and globus pallidus. In addition, our analysis found a clique-forming subnetwork vulnerable to tobacco smoking consisting of regions from the visual, dorsal/ventral attention, and frontoparietal networks.

A Novel Dictionary Learning Algorithm Based on Prior Knowledge for fMRI Data Analysis

ABSTRACTTask‐based functional magnetic resonance imaging (fMRI) has been widely utilized for brain activation detection and functional network analysis. In recent years, the K‐singular value decomposition (K‐SVD) algorithm has gained increasing attention in the research of fMRI data analysis methods. In this study, we propose a novel temporal feature region‐growing constrained K‐SVD algorithm that incorporates task‐based fMRI temporal prior knowledge and utilizes a region‐growing algorithm to infer potential activation locations. The algorithm incorporates temporal and spatial constraints to enhance the detection of brain activation. Specifically, this paper improves the three stages of the traditional K‐SVD algorithm. First, in the dictionary initialization stage, the automatic target generation process with an independent component analysis algorithm is utilized in conjunction with prior knowledge to enhance the accuracy of initialization. Second, in the sparse coding stage, the region‐growing algorithm is employed to infer potential activation locations based on temporal prior knowledge, thereby imposing spatial constraints to limit the extent of activation regions. Finally, in the dictionary learning stage, soft constraints and low correlation constraints are applied to reinforce the consistency with prior knowledge and enhance the robustness of learning for task‐related atoms. The proposed method was validated on simulated and real fMRI data, showing superior performance in detecting brain activation compared with traditional methods. The results indicate that the algorithm accurately identifies activated brain regions, providing an effective approach for studying brain function in clinical applications.

Dissecting neural correlates of theory of mind and executive functions in behavioral variant frontotemporal dementia

Behavioral variant frontotemporal dementia (bvFTD) is characterized by profound and early deficits in social cognition (SC) and executive functions (EF). To date it remains unclear whether deficits of the respective cognitive domains are based on the degeneration of distinct brain regions. In 103 patients with a diagnosis of bvFTD (possible/probable/definite: N = 40/58/5) from the frontotemporal lobar degeneration (FTLD) consortium Germany cohort (age 62.5±9.4 years, gender 38 female/65 male) we applied multimodal structural imaging, i.e. voxel-based morphometry, cortical thickness (CTH) and networks of structural covariance via source based morphometry. We cross-sectionally investigated associations with performance in a modified Reading the Mind in the Eyes Test (RMET; reflective of theory of mind - ToM) and five different tests reflective of EF (i.e. Hamasch-Five-Point Test, semantic and phonemic Fluency, Trail Making Test, Stroop interference). Finally, we investigated the conjunction of RMET correlates with functional networks commonly associated with SC respectively ToM and EF as extracted meta-analytically within the Neurosynth database. RMET performance was mainly associated with gray matter volume (GMV) and CTH within temporal and insular cortical regions and less within the prefrontal cortex (PFC), whereas EF performance was mainly associated with prefrontal regions (GMV and CTH). Overlap of RMET and EF associations was primarily located within the insula, adjacent subcortical structures (i.e. putamen) and the dorsolateral PFC (dlPFC). These patterns were more pronounced after adjustment for the respective other cognitive domain. Corroborative results were obtained in analyses of structural covariance networks. Overlap of RMET with meta-analytically extracted functional networks commonly associated with SC, ToM and EF was again primarily located within the temporal and insular region and the dlPFC. In addition, on a meta-analytical level, strong associations were found for temporal cortical RMET correlates with SC and ToM in particular. These data indicate a temporo-frontal dissociation of bvFTD related disturbances of ToM and EF, with atrophy of the anterior temporal lobe being critically involved in ToM deficits. The consistent overlap within the insular cortex may be attributable to the multimodal and integrative role of this region in socioemotional and cognitive processing.

Asynchronous Functional Brain Network Construction with Spatiotemporal Transformer for MCI Classification.

Construction and analysis of functional brain networks (FBNs) with resting-state functional magnetic resonance imaging (rs-fMRI) is a promising method to diagnose functional brain diseases. Nevertheless, the existing methods suffer from several limitations. First, the functional connectivities (FCs) of the FBN are usually measured by the temporal co-activation level between rs-fMRI time series from regions of interest (ROIs). While enjoying simplicity, the existing approach implicitly assumes simultaneous co-activation of all the ROIs, and models only their synchronous dependencies. However, the FCs are not necessarily always synchronous due to the time lag of information flow and cross-time interactions between ROIs. Therefore, it is desirable to model asynchronous FCs. Second, the traditional methods usually construct FBNs at individual level, leading to large variability and degraded diagnosis accuracy when modeling asynchronous FBN. Third, the FBN construction and analysis are conducted in two independent steps without joint alignment for the target diagnosis task. To address the first limitation, this paper proposes an effective sliding-window-based method to model spatiotemporal FCs in Transformer. Regarding the second limitation, we propose to learn common and individual FBNs adaptively with the common FBN as prior knowledge, thus alleviating the variability and enabling the network to focus on the individual disease-specific asynchronous FCs. To address the third limitation, the common and individual asynchronous FBNs are built and analyzed by an integrated network, enabling end-to-end training and improving the flexibility and discriminativity. The effectiveness of the proposed method is consistently demonstrated on three data sets for mild cognitive impairment (MCI) diagnosis.

White matter functional networks in the developing brain

BackgroundFunctional magnetic resonance imaging (fMRI) is widely used to depict neural activity and understand human brain function. Studies show that functional networks in gray matter undergo complex transformations from neonatal age to childhood, supporting rapid cognitive development. However, white matter functional networks, given the much weaker fMRI signal, have not been characterized until recently, and changes in white matter functional networks in the developing brain remain unclear.PurposeAims to examine and compare white matter functional networks in neonates and 8-year-old children.MethodsWe acquired resting-state fMRI data on 69 full-term healthy neonates and 38 healthy 8-year-old children using a same imaging protocol and studied their brain white matter functional networks using a similar pipeline. First, we utilized the ICA method to extract white matter functional networks. Next, we analyzed the characteristics of the white matter functional networks from both time-domain and frequency-domain perspectives, specifically, intra-network functional connectivity (intra-network FC), inter-network functional connectivity (inter-network FC), and fractional amplitude of low-frequency fluctuation (fALFF). Finally, the differences in the above functional networks’ characteristics between the two groups were evaluated. As a supplemental measure and to confirm with literature findings on gray matter functional network changes in the developing brain, we also studied and reported functional networks in gray matter.ResultsWhite matter functional networks in the developing brain can be depicted for both the neonates and the 8-year-old children. White matter intra-network FC within the optic radiations, corticospinal tract, and anterior corona radiata was lower in 8-year-old children compared to neonates (p &amp;lt; 0.05). Inter-network FC between cerebral peduncle (CP) and anterior corona radiation (ACR) was higher in 8-year-olds (p &amp;lt; 0.05). Additionally, 8-year-olds showed a greater distribution of brain activity energy in the high-frequency range of 0.01–0.15 Hz. Significant developmental differences in brain white matter functional networks exist between the two group, characterized by increased inter-network FC, decreased intra-network FC, and higher high-frequency energy distribution. Similar findings were also observed in gray matter functional networks.ConclusionWhite matter functional networks can be reliably measured in the developing brain, and the differences in these networks reflect functional differentiation and integration in brain development.

Disentangling the impact of motion artifact correction algorithms on functional near-infrared spectroscopy-based brain network analysis.

Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results. We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses. We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms. Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern. The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

Integrative Metabolome and Proteome Analysis of Cerebrospinal Fluid in Parkinson’s Disease

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra. Recent studies have highlighted the significant role of cerebrospinal fluid (CSF) in reflecting pathophysiological PD brain conditions by analyzing the components of CSF. Based on the published literature, we created a single network with altered metabolites in the CSF of patients with PD. We analyzed biological functions related to the transmembrane of mitochondria, respiration of mitochondria, neurodegeneration, and PD using a bioinformatics tool. As the proteome reflects phenotypes, we collected proteome data based on published papers, and the biological function of the single network showed similarities with that of the metabolomic network. Then, we analyzed the single network of integrated metabolome and proteome. In silico predictions based on the single network with integrated metabolomics and proteomics showed that neurodegeneration and PD were predicted to be activated. In contrast, mitochondrial transmembrane activity and respiration were predicted to be suppressed in the CSF of patients with PD. This review underscores the importance of integrated omics analyses in deciphering PD’s complex biochemical networks underlying neurodegeneration.

Reductions in the white-gray functional connectome in preclinical Alzheimer's disease and their associations with amyloid and cognition.

The magnitudes and patterns of alterations of the white-gray matter (WM-GM) functional connectome in preclinical Alzheimer's disease (AD), and their associations with amyloid and cognition, remain unclear. We compared regional WM-GM functional connectivity (FC) and network properties in subjects with preclinical AD (or AD dementia) and controls (total n=344). Their associations with positron emission tomography AV45-measured amyloid beta (Aβ) load and modified Preclinical Alzheimer Cognitive Composite (mPACC) scores were examined. Preclinical AD subjects showed lower FC in specific WM-GM pairs and reduced segregation of control, dorsal attention, and somatomotor networks. A major portion of the reduced FC and network segregations were linked to elevated Aβ. Reduced FC of one WM-GM pair correlated with impaired mPACC. AD dementia exhibited broader reductions and stronger associations. The WM-GM functional connectome undergoes regional and systemic dysfunctions as early as in the preclinical stage, correlating with amyloid deposition and predicting cognitive impairment. Preclinical Alzheimer's disease (AD) subjects showed lower functional connectivity in specific white-gray matter (WM-GM) pairs and reduced segregations of control, dorsal attention, and somatomotor networks. A major portion of the reduced connectivity and network segregations were linked to elevated amyloid beta load. Only one WM-GM pair's reduced connectivity was linearly correlated with impaired cognitive composite scores. AD dementia showed more extensive reductions in connectivity, network integration, and segregation, with stronger associations with amyloid elevation and cognitive impairment. The WM-GM functional connectome offers a distinct perspective for understanding changes in brain functional architecture throughout the AD continuum.

High-order brain network feature extraction and classification method of first-episode schizophrenia: an EEG study

IntroductionA multimodal persistent topological feature extraction and classification method is proposed to enhance the recognition accuracy of first-episode schizophrenia patients. This approach addresses the limitations of traditional higher-order brain network analyses that rely on single persistent features (e.g., persistent images).MethodsThe study utilized resting-state EEG data from 198 subjects recruited at Huilongguan Hospital in Beijing, comprising 102 males and 96 females, with a mean age of 30 years and mean education of 14 years. Persistent topological features were extracted using adaptive thresholding during persistent homology (PH) filtrations. The distribution of these features was visualized through heatmaps and persistence entropies, while the generation process was elucidated using Betti curves and persistence landscapes.ResultsThe classification performance of the multimodal persistent topological features was assessed using various machine learning classifiers. The classifier yielding the highest performance was selected for comparison with traditional brain network features derived from graph theory and single persistent topological features. The results revealed significant topological changes in first-episode schizophrenia patients throughout the persistent homology filtering compared to healthy subjects. The univariate feature selection algorithm achieved a classification accuracy of 94.6% with a combination of attributes meeting the criterion of AC ≥ 0.6.DiscussionThe proposed method demonstrates clinical significance for the early identification and diagnosis of first-episode schizophrenia patients, offering a new research perspective for constructing higher-order functional connectivity networks and extracting topological structure features.

Center-based childcare during infancy: The relations with functional brain networks and self-regulation in young children

Given the substantial increase in children attending center-based childcare over the past decades, the consequences of center-based childcare for children’s development have gained more attention in developmental research. However, the relation between center-based childcare and children’s neurocognitive development remains relatively underexplored. The aim of this study was therefore to examine the relations between quantity of center-based childcare during infancy and the neurocognitive development (both functional brain networks and self-regulation) of 584 Dutch children. Small-world brain networks and children’s self-regulation were assessed during infancy (around 10 months of age) and the preschool period (2–6 years of age). The findings revealed that the quantity of center-based childcare during infancy was unrelated to individual differences in children’s functional brain networks. However, spending more hours per week in center-based childcare was positively related to the development of self-regulation in preschool age children, regardless of children’s sex or the levels of exposure to risk and maternal support in the home environment. More insight into the positive effects of center-based childcare on children’s development from infancy to toddlerhood can help to increase our insight into a better work–life balance and labor force participation of parents with young children. Moreover, this study highlights that Dutch center-based childcare offers opportunities to invest in positive child outcomes in children, including self-regulation.

Functional reorganization of brain regions supporting artificial grammar learning across the first half year of life.

Pre-babbling infants can track nonadjacent dependencies (NADs) in the auditory domain. While this forms a crucial prerequisite for language acquisition, the neurodevelopmental origins of this ability remain unknown. We applied functional near-infrared spectroscopy in neonates and 6- to 7-month-old infants to investigate the neural substrate supporting NAD learning and detection using tone sequences in an artificial grammar learning paradigm. Detection of NADs was indicated by left prefrontal activation in neonates while by left supramarginal gyrus (SMG), superior temporal gyrus (STG), and inferior frontal gyrus activation in 6- to 7-month-olds. Functional connectivity analyses further indicated that the neonate activation pattern during the test phase benefited from a brain network consisting of prefrontal regions, left SMG and STG during the rest and learning phases. These findings suggest a left-hemispheric learning-related functional brain network may emerge at birth and serve as the foundation for the later engagement of these regions for NAD detection, thus, providing a neural basis for language acquisition.

Metabolite profiling and transcriptome analyses reveal defense regulatory network against pink tea mite invasion in tea plant

BackgroundThe tea plant Camellia sinensis (L.) O. Kuntze is a perennial crop, invaded by diversity of insect pest species, and pink tea mite is one of the most devastating pests for sustainable tea production. However, molecular mechanism of defense responses against pink tea mites in tea is still unknown. In this study, metabolomics and transcriptome profiles of susceptible and resistant tea varieties were compared before and after pink tea mite infestation.ResultsMetabolomics analysis revealed that abundance levels of polyphenol-related compounds changed significantly before and after infestation. At the transcript level, nearly 8 GB of clean reads were obtained from each sequenced library, and a comparison of infested plants of resistant and susceptible tea varieties revealed 9402 genes with significant differential expression. An array of genes enriched in plant pathogen interaction and biosynthetic pathways of phenylpropanoids showed significant differential regulation in response to pink tea mite invasion. In particular, the functional network linkage of disease resistant proteins, phenylalanine ammonia lyase, flavanone -3-hydroxylase, hydroxycinnamoyl-CoA shikimate transferase, brassinosteroid-6-oxidase 1, and gibberellin 2 beta-dioxygenase induced dynamic defense signals to suppress prolonged pink tea mite attacks. Further integrated analyses identified a complex network of transcripts and metabolites interlinked with precursors of various flavonoids that are likely modulate resistance against to pink tea mite.ConclusionsOur results characterized the profiles of insect induced metabolic and transcript reprogramming and identified a defense regulatory network that can potentially be used to fend off pink tea mites damage.

Whole-brain modular dynamics at rest predict sensorimotor learning performance

Abstract Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising, due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatio-temporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants’ learning performance was predicted by the degree of coordination of modular reconfiguration, and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.

Stationary correlation pattern in highly non-stationary MEG recordings of healthy subjects and its relation to former EEG studies.

In this study, we analyze magnetoencephalographic (MEG) recordings from 48 clinically healthy subjects obtained from the Human Connectome Project (HCP) while they performed a working memory task and a motor task. Our results reveal a well-developed, stable interrelation pattern that spans the entire scalp and is nearly universal, being almost task- and subject-independent. Additionally, we demonstrate that this pattern closely resembles a stationary correlation pattern (SCP) observed in EEG signals under various physiological and pathological conditions (the distribution of Pearson correlations are centered at about 0.75). Furthermore, we identify the most effective EEG reference for studying the brain's functional network derived from lag-zero cross-correlations. We contextualize these findings within the theory of complex dynamical systems operating near a critical point of a phase transition.