Structural Network Integrity Research Articles

BackgroundAnxiety is a common yet often underdiagnosed and undertreated comorbidity in multiple sclerosis (MS). While altered fear processing is a hallmark of anxiety in other populations, its neurobehavioral mechanisms in MS remain poorly understood. This study investigates the extent to which neurobehavioral mechanisms of fear generalization contribute to anxiety in MS.MethodsWe recruited 18 persons with MS (PwMS) and anxiety, 36 PwMS without anxiety, and 23 healthy persons (HPs). Participants completed a functional MRI (fMRI) fear generalization task to assess fear processing and diffusion-weighted MRI for graph-based structural connectome analyses.ResultsConsistent with findings in non-MS anxiety populations, PwMS with anxiety exhibit fear overgeneralization, perceiving non-threating stimuli as threatening. A machine learning model trained on HPs in a multivariate pattern analysis (MVPA) cross-decoding approach accurately predicts behavioral fear generalization in both MS groups using whole-brain fMRI fear response patterns. Regional fMRI prediction and graph-based structural connectivity analyses reveal that fear response activity and structural network integrity of partially overlapping areas, such as hippocampus (for fear stimulus comparison) and anterior insula (for fear excitation), are crucial for MS fear generalization. Reduced network integrity in such regions is a direct indicator of MS anxiety.ConclusionsOur findings demonstrate that MS anxiety is substantially characterized by fear overgeneralization. The fact that a machine learning model trained to associate fMRI fear response patterns with fear ratings in HPs predicts fear ratings from fMRI data across MS groups using an MVPA cross-decoding approach suggests that generic fear processing mechanisms substantially contribute to anxiety in MS.

Effect of superfine grinding chestnut powder on the structural and physicochemical properties of wheat dough

Relationship between brain structural network integrity and emotional symptoms in youth with perinatally-acquired HIV

Mechanical robust and highly conductive composite hydrogel reinforced by a combination of cellulose nanofibrils/polypyrrole toward high-performance strain sensor

New insights into the effects of starch-oleic acid-chlorogenic acid interactions on structural formation by hot-extrusion 3D printing: From perspective of nonlinear rheology

Maintaining good cognitive function is crucial for well-being across the lifespan. We proposed that the degree of cognitive maintenance is determined by the functional interactions within and between large-scale brain networks. Such connectivity can be represented by the white matter architecture of structural brain networks that shape intrinsic neuronal activity into integrated and distributed functional networks. We explored how the function-structure connectivity convergence, and the divergence of functional connectivity from structural connectivity, contribute to the maintenance of cognitive function across the adult lifespan. Multivariate analyses were used to investigate the relationship between function-structure connectivity convergence and divergence with multivariate cognitive profiles, respectively. Cognitive function was increasingly dependent on function-structure connectivity convergence as age increased. The dependency of cognitive function on connectivity was particularly strong for high-order cortical networks and subcortical networks. The results suggest that brain functional network integrity sustains cognitive functions in old age, as a function of the integrity of the brain’s structural connectivity.

The spatial difference of multi-layer climate change information flow and network construction: A comparison of “dual carbon” scenarios

In post-stroke aphasia, language improvements following speech therapy are variable and can only be partially explained by the lesion. Brain tissue integrity beyond the lesion (brain health) may influence language recovery and can be impacted by cardiovascular risk factors, notably diabetes. We examined the impact of diabetes on structural network integrity and language recovery. Seventy-eight participants with chronic post-stroke aphasia underwent six weeks of semantic and phonological language therapy. To quantify structural network integrity, we evaluated the ratio of long-to-short-range white matter fibers within each participant's whole brain connectome, as long-range fibers are more susceptible to vascular injury and have been linked to high level cognitive processing. We found that diabetes moderated the relationship between structural network integrity and naming improvement at 1month post treatment. For participants without diabetes (n = 59), there was a positive relationship between structural network integrity and naming improvement (t = 2.19, p = 0.032). Among individuals with diabetes (n = 19), there were fewer treatment gains and virtually no association between structural network integrity and naming improvement. Our results indicate that structural network integrity is associated with treatment gains in aphasia for those without diabetes. These results highlight the importance of post-stroke structural white matter architectural integrity in aphasia recovery.

Attention-deficit-hyperactivity disorder (ADHD) is a neurodevelopmental disorder neurobiologically conceptualized as a network disorder in white and gray matter. A relatively new branch in ADHD research is sensory processing. Here, altered sensory processing i.e., sensory hypersensitivity, is reported, especially in the auditory domain. However, our perception is driven by a complex interplay across different sensory modalities. Our brain is specialized in binding those different sensory modalities to a unified percept-a process called multisensory integration (MI) that is mediated through fronto-temporal and fronto-parietal networks. MI has been recently described to be impaired for complex stimuli in adult patients with ADHD. The current study relates MI in adult ADHD with diffusion-weighted imaging. Connectome-based and graph-theoretic analysis was applied to investigate a possible relationship between the ability to integrate multimodal input and network-based ADHD pathophysiology. Multishell, high-angular resolution diffusion-weighted imaging was performed on twenty-five patients with ADHD (six females, age: 30.08 (SD: 9.3) years) and twenty-four healthy controls (nine females; age: 26.88 (SD: 6.3) years). Structural connectome was created and graph theory was applied to investigate ADHD pathophysiology. Additionally, MI scores, i.e., the percentage of successful multisensory integration derived from the McGurk paradigm, were groupwise correlated with the structural connectome. Structural connectivity was elevated in patients with ADHD in network hubs mirroring altered default-mode network activity typically reported for patients with ADHD. Compared to controls, MI was associated with higher connectivity in ADHD between Heschl's gyrus and auditory parabelt regions along with altered fronto-temporal network integrity. Alterations in structural network integrity in adult ADHD can be extended to multisensory behavior. MI and the respective network integration in ADHD might represent the maturational cortical delay that extends to adulthood with respect to sensory processing.

Brain network dysfunction is increasingly recognised in Alzheimer’s disease (AD). However, the causes of brain connectivity disruption are still poorly understood. Recently, neuroinflammation has been identified as an important factor in AD pathogenesis. Microglia participate in the construction and maintenance of healthy neuronal networks, but pro-inflammatory microglia can also damage these circuits. We hypothesised that microglial activation is independently associated with brain connectivity disruption in AD. We performed a cross-sectional multimodal imaging study and interrogated the relationship between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 42 participants (12 Aβ-positive MCI, 14 Aβ-positive AD and 16 Aβ-negative healthy controls) were recruited. Participants had 11C-PBR28 and 18F-flutemetamol PET to quantify Aβ deposition and microglial activation, T1-weighted, diffusion tensor and resting-state functional MRI to assess structural network and functional network. 11C-PBR28 uptake, structural network integrity and functional network orgnisation were compared across diagnostic groups and the relationship between neuroinflammation and brain network was tested in 26 Aβ-positive patients. Increased 11C-PBR28 uptake, decreased FA, network small-worldness and local efficiency were observed in AD patients. Cortical 11C-PBR28 uptake correlated negatively with structural integrity (standardised β = −0.375, p = 0.037) and network local efficiency (standardised β = −0.468, p < 0.001), independent of cortical thickness and Aβ deposition, while Aβ was not. Network structural integrity, small-worldness and local efficiency, and cortical thickness were positively associated with cognition. Our findings suggest cortical neuroinflammation coincide with structural and functional network disruption independent of Aβ and cortical atrophy. These findings link the brain connectivity change and pathological process in Alzheimer’s disease, and suggest a pathway from neuroinflammation to systemic brain dysfunction.

On controlling dynamic complex networks

Injuries to the knee anterior cruciate ligament (ACL) are common, with a known but poorly understood association with intrinsic and extrinsic risk factors. Some of these factors are enzymatically or mechanically mediated, creating acute focal injuries that may cause significant ligament damage. Understanding the relationship between the basic molecular structure and external loading of the ACL requires a hierarchical connection between the two levels. In the present study, a multi-domain frame was developed connecting the molecular dynamics of the collagen networks to the continuum mechanics of the ACL. The model was used to elucidate the effect of the two possible collagen degradation mechanisms on the aggregate ACL behaviour. Results indicated that collagen content and ACL stiffness were reduced significantly, regardless of the degradation mechanism. Furthermore, the volumetric degradation at the molecular level had a devastating effect on the mechanical behaviour of the ACL when it was compared with the superficial degradation. ACL damage initiation and propagation were clearly influenced by collagen degradation. To summarise, the new insights provided by the predicted results revealed the significance of the collagen network structural integrity to the aggregate mechanical response of the ACL and, hence, underlined the biomechanical factors that may help develop an engineering-based approach towards improving the therapeutic intervention for ACL pathologies.

Response to repetitive transcranial magnetic stimulation (rTMS) among individuals with major depressive disorder (MDD) varies widely. The neural mechanisms underlying rTMS are thought to involve changes in large-scale networks. Whether structural network integrity and plasticity are associated with response to rTMS therapy is unclear. Structural MRIs were acquired from a series of 70 adult healthy controls and 268 persons with MDD who participated in two arms of a large randomized, non-inferiority trial, THREE-D, comparing intermittent theta-burst stimulation to high-frequency rTMS of the left dorsolateral prefrontal cortex (DLPFC). Patients were grouped according to percentage improvement on the 17-item Hamilton Depression Rating Score at treatment completion. For the entire sample and then for each treatment arm, multivariate analyses were used to characterize structural covariance networks (SCN) from cortical gray matter thickness, volume, and surface area maps from T1-weighted MRI. The association between SCNs and clinical improvement was assessed. For both study arms, cortical thickness and volume SCNs distinguished healthy controls from MDD (p = 0.005); however, post-hoc analyses did not reveal a significant association between pre-treatment SCN expression and clinical improvement. We also isolated an anticorrelated SCN between the left DLPFC rTMS target site and the subgenual anterior cingulate cortex across cortical measures (p = 0.0004). Post-treatment change in cortical thickness SCN architecture was associated with clinical improvement in treatment responders (p = 0.001), but not in non-responders. Structural network changes may underpin clinical response to rTMS, and SCNs are useful for understanding the pathophysiology of depression and neural mechanisms of plasticity and response to circuit-based treatments.

Developmental dyscalculia (DD) is a developmental learning disability associated with deficits in processing numerical and mathematical information. Several studies demonstrated functional network alterations in DD. Yet, there are no studies, which examined the structural network integrity in DD. We compared whole‐brain maps of volume based structural covariance between 19 (4 males) children with DD and 18 (4 males) typically developing children. We found elevated structural covariance in the DD group between the anterior intraparietal sulcus to the middle temporal and frontal gyrus (p < 0.05, corrected). A hippocampus subfield analysis showed higher structural covariance in the DD group for area CA3 to the parahippocampal and calcarine sulcus, angular gyrus and anterior part of the intraparietal sulcus as well as to the lingual gyrus. Lower structural covariance in this group was seen for the subiculum to orbitofrontal gyrus, anterior insula and middle frontal gyrus. In contrast, the primary motor cortex (control region) revealed no difference in structural covariance between groups. Our results extend functional magnetic resonance studies by revealing abnormal gray matter integrity in children with DD. These findings thus indicate that the pathophysiology of DD is mediated by both structural and functional abnormalities in a network involved in number processing and memory function.

To investigate whether neuroinflammation and β-amyloid (Aβ) deposition influence brain structural and functional connectivity in Alzheimer's spectrum, we conducted a cross-sectional multimodal imaging study and interrogated the associations between imaging biomarkers of neuroinflammation, Aβ deposition, brain connectivity and cognition. 58 participants (25 MCI, 16 AD dementia and 17 healthy controls) were recruited and scanned with 11 C-PBR28 and 18 F-flutemetamol PET, T1-weighted, diffusion tensor and resting-state functional MRI. Brain structural and functional connectivity were assessed by global white matter integrity and functional topology metrics, while neuroinflammation and Aβ deposition were evaluated by 11 C-PBR28 and 18 F-flutemetamol uptake, respectively. Changes of the biomarkers were compared between diagnostic groups and robust regression analyses at both voxel and regional level were performed on Aβ positive patients, who were considered to be representative of Alzheimer's continuum. Increased 11 C-PBR28 and 18 F-flutemetamol uptake, decreased FA values, impaired small-worldness and local efficiency of functional network were observed in the AD cohort. In Aβ-positive patients, cortical 11 C-PBR28 uptake correlated with decreased structural integrity and network local efficiency independent of 18 F-flutemetamol uptake and cortical thickness. Network structural integrity and cortical thickness correlated with functional metrics, including small-worldness and local efficiency, which were all associated with cognition. Our findings suggest that cortical neuroinflammation may lead to disruption of structural and functional brain network independent of amyloid deposition and cortical atrophy, which in turn can lead to cognitive impairment in AD.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

Deep neural networks (DNNs) have strong fitting ability on a variety of computer vision tasks, but they also require intensive computing power and large storage space, which are not always available in portable smart devices. Although a lot of studies have contributed to the compression of image classification networks, there are few model compression algorithms for object detection models. In this paper, we propose a general compression pipeline for one-stage object detection networks to meet the real-time requirements. Firstly, we propose a softer pruning strategy on the backbone to reduce the number of filters. Compared with original direct pruning, our method can maintain the integrity of network structure and reduce the drop of accuracy. Secondly, we transfer the knowledge of the original model to the small model by knowledge distillation to reduce the accuracy drop caused by pruning. Finally, as edge devices are more suitable for integer operations, we further transform the 32-bit floating point model into the 8-bit integer model through quantization. With this pipeline, the model size and inference time are compressed to 10% or less of the original, while the mAP is only reduced by 2.5% or less. We verified that performance of the compression pipeline on the Pascal VOC dataset.

The neural substrate of high intelligence performances remains not well understood. Based on diffusion tensor imaging (DTI) which provides microstructural information of white matter fibers, we proposed in this work to investigate the relationship between structural brain connectivity and intelligence quotient (IQ) scores. Fifty-seven children (8–12 y.o.) underwent a MRI examination, including conventional T1-weighted and DTI sequences, and neuropsychological testing using the fourth edition of Wechsler Intelligence Scale for Children (WISC-IV), providing an estimation of the Full-Scale Intelligence Quotient (FSIQ) based on four subscales: verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). Correlations between the IQ scores and both graphs and diffusivity metrics were explored. First, we found significant correlations between the increased integrity of WM fiber-bundles and high intelligence scores. Second, the graph theory analysis showed that integration and segregation graph metrics were positively and negatively correlated with WISC-IV scores, respectively. These results were mainly driven by significant correlations between FSIQ, VCI, and PRI and graph metrics in the temporal and parietal lobes. In conclusion, these findings demonstrated that intelligence performances are related to the integrity of WM fiber-bundles as well as the density and homogeneity of WM brain networks.

Executive dysfunction affects 40% of stroke patients, but is poorly predicted by characteristics of the stroke itself. Stroke typically occurs on a background of cerebrovascular burden, which impacts cognition and brain network structural integrity. We used structural equation modelling to investigate whether measures of white matter microstructural integrity (fractional anisotropy and mean diffusivity) and cerebrovascular risk factors better explain executive dysfunction than markers of stroke severity.126 stroke patients (mean age 68.4 years) were scanned three months post-stroke and compared to 40 age- and sex-matched control participants on neuropsychological measures of executive function. Executive function was below what would be expected for age and education level in stroke patients as measured by the organizational components of the Rey Complex Figure Test, F(3,155) = 17, R2 = 0.25, p < 0.001 (group significant predictor at p < 0.001) and the Trail-Making Test (B), F(3,157) = 3.70, R2 = 0.07, p < 0.01 (group significant predictor at p < 0.001). A multivariate structural equation model illustrated the complex relationship between executive function, white matter integrity, stroke characteristics and cerebrovascular risk (root mean square error of approximation = 0.02).Pearson’s correlations confirmed a stronger relationship between executive dysfunction and white matter integrity (r = − 0.74, p < 0.001), than executive dysfunction and stroke severity (r = 0.22, p < 0.01). The relationship between executive function and white matter integrity is mediated by cerebrovascular burden. White matter microstructural degeneration of the superior longitudinal fasciculus in the executive control network better explains executive dysfunction than markers of stroke severity. Executive dysfunction and incident stroke can be both considered manifestations of cerebrovascular risk factors.

BackgroundPolycyclic aromatic hydrocarbons (PAHs) are of environmental and public health concerns and contribute to adverse skin attributes such as premature skin aging and pigmentary disorder. However, little information is available on the potential roles of chronic urban PAH pollutant exposure on the cutaneous microbiota. Given the roles of the skin microbiota have on healthy and undesirable skin phenotypes and the relationships between PAHs and skin properties, we hypothesize that exposure of PAHs may be associated with changes in the cutaneous microbiota. In this study, the skin microbiota of over two hundred Chinese individuals from two cities in China with varying exposure levels of PAHs were characterized by bacterial and fungal amplicon and shotgun metagenomics sequencing.ResultsSkin site and city were strong parameters in changing microbial communities and their assembly processes. Reductions of bacterial-fungal microbial network structural integrity and stability were associated with skin conditions (acne and dandruff). Multivariate analysis revealed associations between abundances of Propionibacterium and Malassezia with host properties and pollutant exposure levels. Shannon diversity increase was correlated to exposure levels of PAHs in a dose-dependent manner. Shotgun metagenomics analysis of samples (n = 32) from individuals of the lowest and highest exposure levels of PAHs further highlighted associations between the PAHs quantified and decrease in abundances of skin commensals and increase in oral bacteria. Functional analysis identified associations between levels of PAHs and abundance of microbial genes of metabolic and other pathways with potential importance in host-microbe interactions as well as degradation of aromatic compounds.ConclusionsThe results in this study demonstrated the changes in composition and functional capacities of the cutaneous microbiota associated with chronic exposure levels of PAHs. Findings from this study will aid the development of strategies to harness the microbiota in protecting the skin against pollutants.EdPNeYfwVemgFQnh9BH8shVideo