Differences In Brain Networks Research Articles

Kernel based statistic: identifying topological differences in brain networks

BackgroundBrain network describing interconnections between brain regions contains abundant topological information. It is a challenge for the existing statistical methods (e.g., t test) to investigate the topological differences of brain networks. MethodsWe proposed a kernel based statistic framework for identifying topological differences in brain networks. In our framework, the topological similarities between paired brain networks were measured by graph kernels. Then, graph kernels are embedded into maximum mean discrepancy for calculating kernel based test statistic. Based on this test statistic, we adopted conditional Monte Carlo simulation to compute the statistical significance (i.e., P value) and statistical power. We recruited 33 patients with Alzheimer's disease (AD), 33 patients with early mild cognitive impairment (EMCI), 33 patients with late mild cognitive impairment (LMCI) and 33 normal controls (NC) in our experiment. There are no statistical differences in demographic information between patients and NC. The compared state-of-the-art statistical methods include t test, t squared test, two-sample permutation test and non-normal test. ResultsWe applied the proposed shortest path matched kernel to our framework for investigating the statistical differences of shortest path topological structures in brain networks of AD and NC. We compared our method with the existing state-of-the-art statistical methods in brain network characteristic including clustering coefficient and functional connection among EMCI, LMCI, AD, and NC. The results indicate that our framework can capture the statistically discriminative shortest path topological structures, such as shortest path from right rolandic operculum to right supplementary motor area (P = 0.00314, statistical power = 0.803). In clustering coefficient and functional connection, our framework outperforms the state-of-the-art statistical methods, such as P = 0.0013 and statistical power = 0.83 in the analysis of AD and NC. ConclusionOur proposed kernel based statistic framework not only can be used to investigate the topological differences of brain network, but also can be used to investigate the static characteristics (e.g., clustering coefficient and functional connection) of brain network.

Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study.

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain. We constructed a set of brain networks based on consecutive short EEG segments recorded during face and non-face (ketch) processing respectively, and analyzed the topological characteristic of these brain networks by graph theory. We found that the topological differences of the backbone of original brain networks (the minimum spanning tree, MST) between face and ketch processing changed dynamically. Specifically, during face processing, the MST was more line-like over alpha band in 0–100 ms time window after stimuli onset, and more star-like over theta and alpha bands in 100–200 and 200–300 ms time windows. The results indicated that the brain network was more efficient for information transfer and exchange during face processing compared with non-face processing. In the MST, the nodes with significant differences of betweenness centrality and degree were mainly located in the left frontal area and ventral visual pathway, which were involved in the face-related regions. In addition, the special MST patterns can discriminate between face and ketch processing by an accuracy of 93.39%. Our results suggested that special MST structures of dynamic brain networks reflected the potential mechanism of face processing in human brain.

EEG Cortical Activities and Networks Altered by Watching 2D/3D Virtual Reality Videos

Abstract. Virtual reality (VR), which can represent real-life events and situations, is being increasingly applied to many fields, such as education, entertainment, and medical rehabilitation. Correspondingly, the neural information processing of VR has attracted attention. However, the underlying neural mechanisms of VR environments have not yet been fully revealed. The purpose of this study was to examine the possible differences in brain activities and networks between the less immersive 2D and the fully immersive 3D VR environments. 3D VR videos and the same 2D scenes were presented to the participants and the scalp electroencephalogram (EEG) was recorded, respectively. Power spectral density (PSD) and the functional connectivity of these EEG signals were analyzed. The results showed that 3D VR videos significantly enhanced the PSD of θ rhythm (4–7 Hz) in the frontal lobe; decreased the PSD of α rhythm (8–13 Hz) in the parietal and the occipital lobes; increased the PSD of β rhythm (14–30 Hz) in the frontal, the parietal, the temporal, and the occipital lobes, relative to 2D VR watching. Furthermore, 3D versus 2D VR-induced alterations in the patterns of brain networks were similar to the patterns of PSD. Specifically, for the θ rhythm, 3D VR significantly enhanced the frontal and the temporal brain functional connectivity; for the α rhythm, 3D VR increased the parietal and the occipital networks; for the β rhythm, 3D VR remarkably increased the frontal, the occipital, the frontal-temporal and the frontal-occipital brain functional connectivity, relative to 2D VR. These significant differences between 3D and 2D VR video-watching suggest that the neural information processing of cortical activities and networks is correlated to the degree of immersion. The present results, collected with previous researches, implicate that some visual-related information processes, such as visual attention, visual perception, and visual immersion are more robust in 3D VR environments.

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability.

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.

Individual Differences of Functional Brain Networks from Resting-state fMRI and Delay Discount Rate

Individual Differences of Functional Brain Networks from Resting-state fMRI and Delay Discount Rate

Functional and structural differences in brain networks involved in language processing and control in highly proficient early and late bilinguals

Age of acquisition and proficiency have been identified as possible determinants in the neural localization of second language representation and in the nature of control mechanisms. However, the relationship is not yet clear between age of acquisition and proficiency on the one hand, and language switching and executive functions on the other. In the present study, we used anatomical and functional Magnetic Resonance (MR) Imaging to investigate the impact of age of acquisition. Two groups of highly proficient early and late bilinguals, carefully matched in other measures of proficiency, performed an overt picture-naming task in blocked and switched conditions. We found that switching between languages in highly proficient bilinguals involves an increase of activity in areas reported as components associated with domain-general inhibitory processes. There was no effect of task language, only of whether the task involved language switching. Comparing the two groups indicated that highly proficient bilinguals use the same neural network for language production and control regardless of age of acquisition. Structural differences were apparent between the two groups in regions associated with language control. We propose that language switching recruits a neural network that is engaged for domain-general executive control functions and that proficiency rather than age of acquisition exerts influence on language representations. • Proficient bilinguals switched between their two languages symmetrically. • This was true for the late learners as well as for early learners. • Switching languages activates more brain regions than using one language. • Switching activates brain regions involved with general inhibition.

Mechanisms Underlying the Cognitive and Behavioural Effects of Maternal Obesity.

The widespread consumption of ‘western’-style diets along with sedentary lifestyles has led to a global epidemic of obesity. Epidemiological, clinical and preclinical evidence suggests that maternal obesity, overnutrition and unhealthy dietary patterns programs have lasting adverse effects on the physical and mental health of offspring. We review currently available preclinical and clinical evidence and summarise possible underlying neurobiological mechanisms by which maternal overnutrition may perturb offspring cognitive function, affective state and psychosocial behaviour, with a focus on (1) neuroinflammation; (2) disrupted neuronal circuities and connectivity; and (3) dysregulated brain hormones. We briefly summarise research implicating the gut microbiota in maternal obesity-induced changes to offspring behaviour. In animal models, maternal obesogenic diet consumption disrupts CNS homeostasis in offspring, which is critical for healthy neurodevelopment, by altering hypothalamic and hippocampal development and recruitment of glial cells, which subsequently dysregulates dopaminergic and serotonergic systems. The adverse effects of maternal obesogenic diets are also conferred through changes to hormones including leptin, insulin and oxytocin which interact with these brain regions and neuronal circuits. Furthermore, accumulating evidence suggests that the gut microbiome may directly and indirectly contribute to these maternal diet effects in both human and animal studies. As the specific pathways shaping abnormal behaviour in offspring in the context of maternal obesogenic diet exposure remain unknown, further investigations are needed to address this knowledge gap. Use of animal models permits investigation of changes in neuroinflammation, neurotransmitter activity and hormones across global brain network and sex differences, which could be directly and indirectly modulated by the gut microbiome.

Structural brain network differences in bipolar disorder using with similarity-based approach.

Objective: Previous studies have shown differences in the regional brain structure and function between patients with bipolar disorder (BD) and healthy subjects, but little is known about the structural connectivity between BD patients and healthy subjects. In this study, we evaluated the disease-related changes in regional structural connectivity derived from gray matter magnetic resonance imaging (MRI) scans. Methods: The subjects were 73 patients with BD and 80 healthy volunteers who underwent 3-Tesla MRI. Network metrics, such as the small world properties, were computed. We also performed rendering of the network metric images such as the degree, betweenness centrality, and clustering coefficient, on individual brain image. Then, we estimated the differences between them, and evaluate the relationships between the clinical symptoms and the network metrics in the patients with BD. Results: BD patients showed a lower clustering coefficient in the right parietal region and left occipital region, compared with healthy subjects. A weak negative correlation between Young mania rating scale and clustering coefficient was found in left anterior cingulate cortex. Conclusions: We found differences in gray matter structural connectivity between BD patients and healthy subjects by a similarity-based approach. These points may provide objective biological information as an adjunct to the clinical diagnosis of BD.

Learning Clique Subgraphs in Structural Brain Network Classification with Application to Crystallized Cognition

Structural brain networks constructed from diffusion MRI are important biomarkers for understanding human brain structure and its relation to cognitive functioning. There is increasing interest in learning differences in structural brain networks between groups of subjects in neuroimaging studies, leading to a variable selection problem in network classification. Traditional methods often use independent edgewise tests or unstructured generalized linear model (GLM) with regularization on vectorized networks to select edges distinguishing the groups, which ignore the network structure and make the results hard to interpret. In this paper, we develop a symmetric bilinear logistic regression (SBLR) with elastic-net penalty to identify a set of small clique subgraphs in network classification. Clique subgraphs, consisting of all the interconnections among a subset of brain regions, have appealing neurological interpretations as they may correspond to some anatomical circuits in the brain related to the outcome. We apply this method to study differences in the structural connectome between adolescents with high and low crystallized cognitive ability, using the crystallized cognition composite score, picture vocabulary and oral reading recognition tests from NIH Toolbox. A few clique subgraphs containing several small sets of brain regions are identified between different levels of functioning, indicating their importance in crystallized cognition.

Individual Differences in Intrinsic Brain Networks Predict Symptom Severity in Autism Spectrum Disorders.

The neurobiology of heterogeneous neurodevelopmental disorders such as Autism Spectrum Disorders (ASD) is still unknown. We hypothesized that differences in subject-level properties of intrinsic brain networks were important features that could predict individual variation in ASD symptom severity. We matched cases and controls from a large multicohort ASD dataset (ABIDE-II) on age, sex, IQ, and image acquisition site. Subjects were matched at the individual level (rather than at group level) to improve homogeneity within matched case-control pairs (ASD: n = 100, mean age = 11.43years, IQ = 110.58; controls: n = 100, mean age = 11.43years, IQ = 110.70). Using task-free functional magnetic resonance imaging, we extracted intrinsic functional brain networks using projective non-negative matrix factorization. Intrapair differences in strength in subnetworks related to the salience network (SN) and the occipital-temporal face perception network were robustly associated with individual differences in social impairment severity (T = 2.206, P = 0.0301). Findings were further replicated and validated in an independent validation cohort of monozygotic twins (n = 12; 3 pairs concordant and 3 pairs discordant for ASD). Individual differences in the SN and face-perception network are centrally implicated in the neural mechanisms of social deficits related to ASD.

Use of multilayer network modularity and spatiotemporal network switching rate to explore changes of functional brain networks in Alzheimer's disease.

Alzheimer's disease (AD) is progressive neurodegenerative disease. It is important to identify effective biomarkers to explore changes of complex functional brain networks in AD patients based on functional magnetic resonance imaging (fMRI). Recently, four fMRI brain network parameters were frequently used, including regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (f/ALFF) and degree centrality (DC). However, these parameters only present the changes of brain networks in a full time quantum, but ignore changes over a short period of time and lack space information. In this study we propose a new brain network parameter for fMRI, called multilayer network modularity and spatiotemporal network switching rate (stNSR). This parameter is calculated combing Pearson correlation sliding Hamming window and the Louvain algorithm. To verify the efficiency of stNSR, we selected 61 AD patients and 110 healthy controls (HC) from Xuanwu Hospital, Beijing, China. First, we used two-sample t test to identify regions of interest (ROI) between AD patients and HCs. Second, we calculated the stNSR values in these ROIs, and compared them with ReHo, ALFF, f/ALFF, and DC values between AD and HC groups. The results showed that, stNSR values in left calcimine fissure and surrounding cortex, left Lingual gyrus and left cerebellum inferior significantly increased, while stNSR values significantly decreased in left Para hippocampal gyrus, left temporal and superior temporal gyrus. As a comparison, changes in these ROIs could not be observed using ReHo, ALFF, f/ALFF, and DC. The results indicated that stNSR may reflect differences of brain networks between AD patients and HCs.

Varsayılan Mod ve Fronto Parietal Ağlarında Fonksiyonel Bağlanabilirlik ile Cinsiyet Farklılıklarının İncelenmesi

Gender is known to play a critical role in the brain's anatomy, function, and also in human behavior. Identifying differences in functional connectivity between male and female brains will help explain the prevalence and symptomatology of many neurological and psychiatric conditions. Imaging of the brain during rest reveals spontaneous low-frequency fluctuations (<0.1 Hz) that have a temporal correlation between functionally related areas in the fMRI signal. These correlations, called functional connectivity (FC), were obtained using seed to voxel analysis. FC analyzes were compared between male and female brains, including 50 healthy individuals (25 females, 25 males) using the right hand between aged 19 to 41 years old, using resting state functional magnetic resonance imaging (rs-fMRI). Data in this study was obtained from the Newyork_a data set under the 1000 Functional Connectomes Project shared publicly. In our study, gender differences in the default mode network, which is shown as having a central role among the rest state networks, and in the fronto parietal network that best reflects the cognitive network, were examined. In some seed regions of our study, regions showing higher FC were found in both female and male. While the seed regions showing positive correlation in DMN are MPFC and LP (L), the seed region showing negative correlation is LP (L). The seed regions that show positive correlation in FPN are LPFC (L), LPFC (R) and PPC (L), while the seed regions that show negative correlation are LPFC (R), PPC (L) and PPC (R). As the result of our study, the most important finding is that all of our regions with positive correlation were associated with the cerebellum. Our regions associated with regions that show negative correlation showed scattered settlement in the brain. Our study revealed that gender differences in brain networks should be considered when examining neurological and neuropsychiatric disorders. In addition, our results have shown that gender differences in brain networks require more research.

HINT: A hierarchical independent component analysis toolbox for investigating brain functional networks using neuroimaging data

BackgroundIndependent component analysis (ICA) is a popular tool for investigating brain organization in neuroscience research. In fMRI studies, an important goal is to study how brain networks are modulated by subjects’ clinical and demographic variables. Existing ICA methods and toolboxes don’t incorporate subjects’ covariates effects in ICA estimation of brain networks, which potentially leads to loss in accuracy and statistical power in detecting brain network differences between subjects’ groups. New methodWe introduce a Matlab toolbox, HINT (Hierarchical INdependent component analysis Toolbox), that provides a hierarchical covariate-adjusted ICA (hc-ICA) for modeling and testing covariate effects and generates model-based estimates of brain networks on both the population- and individual-level. HINT provides a user-friendly Matlab GUI that allows users to easily load images, specify covariate effects, monitor model estimation via an EM algorithm, specify hypothesis tests, and visualize results. HINT also has a command line interface which allows users to conveniently run and reproduce the analysis with a script. Comparison to existing methodsHINT implements a new multi-level probabilistic ICA model for group ICA. It provides a statistically principled ICA modeling framework for investigating covariate effects on brain networks. HINT can also generate and visualize model-based network estimates for user-specified subject groups, which greatly facilitates group comparisons. ResultsWe demonstrate the steps and functionality of HINT with an fMRI example data to estimate treatment effects on brain networks while controlling for other covariates. Results demonstrate estimated brain networks and model-based comparisons between the treatment and control groups. In comparisons using synthetic fMRI data, HINT shows desirable statistical power in detecting group differences in networks especially in small sample sizes, while maintaining a low false positive rate. HINT also demonstrates similar or increased accuracy in reconstructing both population- and individual-level source signal maps as compared to some state-of-the-art group ICA methods. ConclusionHINT can provide a useful tool for both statistical and neuroscience researchers to evaluate and test differences in brain networks between subject groups.

Gender-based functional connectivity differences in brain networks in childhood

Background and objectiveUnderstanding the effect of gender differences on the brain can provide important information to characterize normal changes throughout life and to increase the likelihood of sex-specific approaches for neurological and psychiatric diseases. In this study, Functional Connectivity (FC), Amplitude of Low-Frequency Fluctuations (ALFF) and fractional ALFF (fALFF) analyzes will be compared between female and male brains between the ages of 7 and 18 years using resting state-functional magnetic resonance imaging (rs-fMRI). MethodsThe rs-fMRI data in this study has been provided by The New York University (NYU) Child Study Center of the publicly shared ADHD200 database. From the NYU dataset, 68 (34 females, 34 males) healthy subjects in the age range of 7–18 years were selected. The female group (mean age: 12.3271±3.1380) and male group (mean age: 11.8766±2.9697) consisted of right-handed, small head motion and similar IQ values. FC was obtained by seed voxel analysis and the effect of low-frequency fluctuations on gender was examined by ALFF and fALFF analyses. Two-sample t-test was used to compare female and male groups with the significance thresholds set to FDR-corrected p<0.05. ResultsIn the results of our study, both in the ALFF, fALFF analyses and the seed regions belonging to many network regions, higher FC rates were found in girls than boys. Our results show that the females’ language functions, visual functions such as object detection and recognition, working memory, executive functions, and episodic memory are more developed than males in this age range. In addition, as another result of our study, the seed regions are statistically stronger where the higher activation of female participants than male participants has concentrated in the left hemisphere. ConclusionsGender differences in brain networks should be taken into consideration when examining childhood cognitive and neuropsychiatric disorders and the results should also be evaluated according to gender. Evaluation of gender differences in childhood can increase the likelihood of early and definitive diagnosis and correct treatment for neurological diseases and can help doctors and scientists find new diagnostic tools to discover brain differences.

Alterations of connectivity patterns in functional brain networks in patients with mild traumatic brain injury: A longitudinal resting-state functional magnetic resonance imaging study.

In the present study, we aimed to characterise changes in functional brain networks in individuals who had sustained uncomplicated mild traumatic brain injury (mTBI). We assessed the progression of these changes into the chronic phase. We also attempted to explore how these changes influenced the severity of post-concussion symptoms as well as the cognitive profile of the patients. A total of 65 patients were prospectively recruited for an advanced magnetic resonance imaging (MRI) scan within 7 days of sustaining mTBI. Of these, 25 were reassessed at 6 months post injury. Differences in functional brain networks were analysed between cases and age- and sex-matched healthy controls using independent component analysis of resting-state functional MRI. Our study revealed reduced functional connectivity in multiple networks, including the anterior default mode network, central executive network, somato-motor and auditory network in patients who had sustained mTBI. A negative correlation between network connectivity and severity of post-concussive symptoms was observed. Follow-up studies performed 6 months after injury revealed an increase in network connectivity, along with an improvement in the severity of post-concussion symptoms. Neurocognitive tests performed at this time point revealed a positive correlation between the functional connectivity and the test scores, along with a persistence of negative correlation between network connectivity and post-concussive symptom severity. Our results suggest that uncomplicated mTBI is associated with specific abnormalities in functional brain networks that evolve over time and may contribute to the severity of post-concussive symptoms and cognitive deficits.

Sex, physics, and anxiety

Education Anxiety negatively affects academic performance, but little is known about the neural mechanisms underlying anxiety and STEM learning. To develop a more complete model of anxiety-related mechanisms and learning strategies, Gonzalez et al. evaluated anxiety and large-scale brain connectivity in 101 undergraduate physics students, collecting both self-reporting questionnaire and neuroimaging data. Results identified sex-specific relationships between STEM anxiety and brain connectivity, with male students exhibiting distinct internetwork connectivity for STEM and clinical anxiety and female students demonstrating no significant within-sex correlations. Using these data together with additional study results, the authors show that sex differences in brain networks are not fixed and that STEM anxiety is related to changes in both female and male students' brains during the physics-learning process. NPJ Sci. Learn. 4 , 18 (2019)

Aging relates to a disproportionately weaker functional architecture of brain networks during rest and task states

Functional connectivity – the co-activation of brain regions – forms the basis of the brain’s functional architecture. Often measured during resting-state (i.e., in a task-free setting), patterns of functional connectivity within and between brain networks change with age. These patterns are of interest to aging researchers because age differences in resting-state connectivity relate to older adults’ relative cognitive declines. Less is known about age differences in large-scale brain networks during directed tasks. Recent work in younger adults has shown that patterns of functional connectivity are highly correlated between rest and task states. Whether this finding extends to older adults remains largely unexplored. To this end, we assessed younger and older adults’ functional connectivity across the whole brain using fMRI while participants underwent resting-state or completed directed tasks (e.g., a reasoning judgement task). Resting-state and task functional connectivity were less strongly correlated in older as compared to younger adults. This age-dependent difference could be attributed to significantly lower consistency in network organization between rest and task states among older adults. Older adults had less distinct or segregated networks during resting-state. This more diffuse pattern of organization was exacerbated during directed tasks. Finally, the default mode network, often implicated in neurocognitive aging, contributed strongly to this pattern. These findings establish that age differences in functional connectivity are state-dependent, providing greater insight into the mechanisms by which aging may lead to cognitive declines.

Effects of spatial smoothing on group-level differences in functional brain networks.

Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.

Resting State Brain Network Function in Elderly: The Formation of Social Ties.

.Background: The purpose of this study is to determine the relevance of the relationship between brain network and the social ties management.Methods: Participants are based on 52 Korean seniors aged 65 and older who live in Ganghwa-gun, Incheon. We used a closed-triad index (CTI), which is the most basic unit of analysis in the study of group phenomena. This index is a social networking variable that has been shown to have a different implication depending on the subject’s condition and role. After two questionnaire surveys were conducted at three years intervals, participants were classified into an increased group and a decreased group according to the change of CTI. Resting-state fMRI analysis were followed to investigate the difference of brain networks between groups. Results: According to the analysis of the study, the whole participants who had increased in number of CTI has higher local efficiency than the group of the participants who had no effect or decreased in CTI. Conclusions: Our study suggests that social relationship, which is substantially related to brain network, is a major factor in successful aging. Lastly, since there is a restriction that the study cannot explain the causal aspect of the brain network and the triad-relationship, there is a need for further investigation.

P.108 Effects of intermittent fasting on body weight, antioxidant defence system, autonomic nervous system balance and activity in female Wistar rats

The aim of the present study was to investigate the differences in the topological organization of functional brain networks between children with growth hormone deficiency (GHD) and those with idiopathic short stature (ISS).Thirty-one children with GHD and fifty-three children with ISS were recruited based on the results of GH stimulation tests. Resting-state fMRI data were acquired from all children. Whole brain functional connectivity density (FCD) analysis and subsequent seed-based functional connectivity analysis were used to explore the differences in functional brain networks between the children with ISS and GHD. Correlation analyses among the results of clinical laboratory examinations, neuropsychological scales and FCD values of different brain regions were applied.Compared with the ISS group, the GHD group exhibited significantly decreased FCDs in the left postcentral gyrus, right precentral gyrus and left cerebellar lobules 7b and 6. The subsequent functional connectivity analysis found decreased functional connectivity between lobules 7b and 6 of the left cerebellum as well as the left postcentral gyrus and right precentral gyrus in the GHD group compared to that in the ISS group. In addition, the FCD values of region 6 of the left cerebellum in the GHD group were negatively correlated with the scores on the Symptom Checklist-90 and Eysenck Personality Questionnaire. The FCD value of the left postcentral gyrus in children with ISS positively correlated with IGFBP-3 levels and was approximately correlated with IGF-1 levels.These findings highlight the impact of growth hormone deficiency on the brain network that mainly involves the somatosensory, somatic motor and cerebellum networks, which may contribute to the behavioural problems observed in these children.