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Bronchopulmonary dysplasia (BPD) affects the microstructure of white matter in preterm infants, but its influence on the changes of the brain structural network has not been elaborated. This study aims to investigate the connectivity characteristics of the brain structural network of BPD by using diffusion tensor imaging. Thirty-three infants with BPD and 26 infants without BPD were enrolled in this study. Brain structural networks were constructed utilizing automated anatomic labeling mapping by tracing the fibers between each pair of regions in individual space. We calculated network metrics such as global efficiency, local efficiency, clustering coefficients, characteristic path length, and small-worldness. Then we compared the network metrics of these infants with those of 57 healthy term infants of comparable postmenstrual age at magnetic resonance imaging scan. Finally, network-based statistics was used to analyze the differences in brain network connectivity between the groups with and without BPD. Preterm infants with BPD had higher local efficiency and clustering coefficient, lower global efficiency, and longer characteristic path length. Also, preterm infants with BPD had decreased strength of limbic connections mainly in four brain regions: the left lingual gyrus, the left calcarine fissure and surrounding cortex, the right parahippocampal gyrus, and the left precuneus. Our findings suggest that preterm infants with BPD have lower network integration and higher segregation at term-equivalent age, which may reflect a compensatory mechanism. In addition, BPD affects brain regions involved in visual as well as cognitive functions; these findings provide a new approach to diagnose potential brain damage in preterm infants with BPD. [ABSTRACT FROM AUTHOR]

Background: It remains unclear whether very preterm (VP) infants have the same level of brain structure and function as full-term (FT) infants. In addition, the relationship between potential differences in brain white matter microstructure and network connectivity and specific perinatal factors has not been well characterized. Objective: This study aimed to investigate the existence of potential differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA) and examine the potential association of these differences with perinatal factors. Methods: A total of 83 infants were prospectively selected for this study: 43 VP infants (gestational age, or GA: 27-32 weeks) and 40 FT infants (GA: 37-44 weeks). All infants at TEA underwent both conventional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Significant differences in white matter fractional anisotropy (FA) andmean diffusivity (MD) images between the VP and FT groups were observed using tract-based spatial statistics (TBSS). The fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then, a structural brain network was constructed, where the connection between each pair of nodes was defined by the number of fibers. Network-based statistics (NBS) were used to examine differences in brain network connectivity between the VP and FT groups. Additionally, multivariate linear regression was conducted to investigate potential correlations between fiber bundle numbers and network metrics (global efficiency, local efficiency, and small-worldness) and perinatal factors. Results: Significant differences in FA were observed between the VP and FT groups in several regions. These differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia (BPD), activity, pulse, grimace, appearance, respiratory (APGAR) score, gestational hypertension, and infection. Significant differences in network connectivity were observed between the VP and FT groups. Linear regression results showed significant correlations between maternal years of education, weight, the APGAR score, GA at birth, and network metrics in the VP group. Conclusions: The findings of this study shed light on the influence of perinatal factors on brain development in VP infants. These results may serve as a basis for clinical intervention and treatment to improve the outcome of preterm infants. [ABSTRACT FROM AUTHOR]

Inspired by rodents' ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents' behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system. [ABSTRACT FROM AUTHOR]

Marine ecosystems are highly dynamic, and their connectivity is affected by a complex range of biological, spatial, and oceanographic factors. Incorporating connectivity as a factor in the planning and management of marine protected areas (MPAs) is important yet challenging. Here, we implemented a novel integrative framework that uses intraspecific genetic and genomic data for multiple marine species to characterise connectivity across a recently established South Australian MPA network. We generated connectivity networks, estimated cross-species concordance of connectivity patterns, and tested the impact of key spatial and oceanographic factors on each species. Connectivity patterns varied markedly among species, but were most correlated among those with similar dispersal strategies. Ordination analyses revealed significant associations with both waterway distances and oceanographic advection models. Notably, waterway distances provided better predictive power in all-species combined analyses. We extended the practical relevance of our findings by employing spatial prioritisation with Marxan, using node values derived from both genetic and geographic connectivity networks. This allowed the identification of several priority areas for conservation, and substantiated the initial decision to employ spatial distance as a proxy for biological connectivity for the design of the South Australian marine park network. Our study establishes a baseline for connectivity monitoring in South Australian MPAs, and provides guidelines for adapting this framework to protected networks elsewhere in the world. • We incorporated connectivity as a factor in the planning and management of marine protected areas using intraspecific genetic and genomic data. • Connectivity networks, cross-species concordance, and key spatial and oceanographic factors were estimated for each species. • Ordination analyses revealed significant associations with both waterway distances and oceanographic advection models. • Spatial prioritisation with Marxan identified priority areas for conservation and spatial distance as a proxy for connectivity. • Our study provides guidelines for adapting this framework to other protected networks with intraspecies genetic data. [ABSTRACT FROM AUTHOR]

Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

Background: Dupont’s Lark is an endangered bird, whose fragmented distribution in Europe is entirely restricted to Spain. This lark, suffering pronounced population decline and range contraction, inhabits steppes that have long been used for grazing sheep and are now threatened by rural abandonment and land use changes. Thus, for conservation of the lark, increasing knowledge about the connectivity of the Spanish metapopulation and identifying the most important connectivity nodes are crucial. Methods: The study was carried out in Spain, using over 16,000 Dupont’s Lark georeferenced observations. We used distance buffers to define populations and subpopulations, based on the available scientific information. We identified potential stepping stones using a MaxEnt probability of presence model. Connectivity was assessed using Conefor software, using the centroid of each subpopulation and stepping stone as nodes. Each node was assigned a quantitative attribute based on total habitat area, within-node habitat quality and internal fragmentation. We evaluated different connectivity scenarios by potential movement thresholds (5–20–100 km) and presence or absence of stepping stones in the network. Results: Dupont’s Lark Iberian metapopulation comprises 24 populations and 100 subpopulations, plus 294 potential stepping stones. Movement thresholds and stepping stones had a strong influence in the potential network connectivity. The most important nodes are located in the core of the metapopulation, which shows connectivity among subpopulations in the different indices and scenarios evaluated. Peripheral populations are more isolated and require stepping stones or medium (20 km) or long (100 km) potential movement thresholds to join the network. Discussion: Metapopulation connectivity may be greater than expected, thanks to stepping stones and potential medium-distance movements. Connectivity is crucial for conservation and can be increased by preserving or improving adequate habitat in the most important nodes. Given the current species decline, steppe habitat should be urgently protected from land use changes and agriculture intensification, at least in the critical subpopulations and stepping stones. Long-term conservation of steppe lands and Dupont’s Lark in Spain requires the recovery of traditional grazing and more research on juvenile dispersion. Meanwhile, the conservation of potentially critical stepping stones should be incorporated to management plans. [ABSTRACT FROM AUTHOR]

Background Dupont’s Lark is an endangered bird, whose fragmented distribution in Europe is entirely restricted to Spain. This lark, suffering pronounced population decline and range contraction, inhabits steppes that have long been used for grazing sheep and are now threatened by rural abandonment and land use changes. Thus, for conservation of the lark, increasing knowledge about the connectivity of the Spanish metapopulation and identifying the most important connectivity nodes are crucial. Methods The study was carried out in Spain, using over 16,000 Dupont’s Lark georeferenced observations. We used distance buffers to define populations and subpopulations, based on the available scientific information. We identified potential stepping stones using a MaxEnt probability of presence model. Connectivity was assessed using Conefor software, using the centroid of each subpopulation and stepping stone as nodes. Each node was assigned a quantitative attribute based on total habitat area, within-node habitat quality and internal fragmentation. We evaluated different connectivity scenarios by potential movement thresholds (5–20–100 km) and presence or absence of stepping stones in the network. Results Dupont’s Lark Iberian metapopulation comprises 24 populations and 100 subpopulations, plus 294 potential stepping stones. Movement thresholds and stepping stones had a strong influence in the potential network connectivity. The most important nodes are located in the core of the metapopulation, which shows connectivity among subpopulations in the different indices and scenarios evaluated. Peripheral populations are more isolated and require stepping stones or medium (20 km) or long (100 km) potential movement thresholds to join the network. Discussion Metapopulation connectivity may be greater than expected, thanks to stepping stones and potential medium-distance movements. Connectivity is crucial for conservation and can be increased by preserving or improving adequate habitat in the most important nodes. Given the current species decline, steppe habitat should be urgently protected from land use changes and agriculture intensification, at least in the critical subpopulations and stepping stones. Long-term conservation of steppe lands and Dupont’s Lark in Spain requires the recovery of traditional grazing and more research on juvenile dispersion. Meanwhile, the conservation of potentially critical stepping stones should be incorporated to management plans.

This study used functional magnetic resonance imaging to explore the neural networks of pain during labor and its relief. It was hypothesized that epidural analgesia would affect the neural activities and the underlying network connectivity. Analysis using dynamic causal modelling and functional connectivity was performed to investigate the spatial activity and network connection of labor pain with and without epidural analgesia. This Institutional Review Board approved study acquired Magnetic Resonance Imaging from 15 healthy women of spontaneous normal delivery (with/without epidural analgesia = 7/8, aged 29.6 ± 2.3 and 29.3 ± 4.8 years old respectively) using a 1.5 Tesla scanner. Numerical rating score of pain was evaluated by a research nurse in the beginning of the first stage of labor and approximately 30 min after imaging examination. Six regions of interested from the activated clusters and literature were selected for dynamic causal modelling, which included primary and secondary somatosensory cortex, middle frontal gyrus, anterior cingulate cortex, insula and lentiform. Functional connectivity was calculated from selected sensory and affective regions. All analyses were performed by using software of statistical parametric mapping version 8 and CONN functional connectivity toolbox. The result showed that the experience of labor pain can lead to activations within a distributed brain network. The pain relief from epidural analgesia can be accompanied with altered functional connectivity, which was most evident in the cingulo-frontal system. The present study, therefore, provides an overview of a pain-related neural network that occur during labor and upon epidural analgesia. [ABSTRACT FROM AUTHOR]

Background: The prediction of neurodevelopmental outcomes in preterm infants is one of the clinical challenges of pediatrics. Despite the recent interest in brain development and white matter connectivity using a network-based analysis, very little is known about the brain network of at term-equivalent age in preterm infants. Objective: We aimed to investigate the structural brain network using diffusion MRI following preterm delivery at term-equivalent age compared with term infants and explored the influence of gestational age (GA) and clinical factors. Method: Diffusion tensor imaging data were acquired prospectively from 55 preterm neonates without apparent brain abnormalities (mean gestational age: 29.43 weeks) and 21 full-term infants at term-equivalent age. The global structural brain networks were produced by probabilistic white matter tractography in combination with the Johns Hopkins University neonate atlas to quantify connectivity between different cortical regions. Results: Compared with full-term infants, preterm infants had significantly lower global efficiency (p = 0.048) and increased small worldness (p = 0.012) after correcting for sex and age at MRI scan. The increased small worldness in the brain network at term-equivalent age was significantly linearly correlated with lower GA after adjusting for sex and the effects of postmenstrual age at MRI scan on the data in preterm infants (β = –0.020, p = 0.037). In multivariate analysis, infants with chronic lung disease had significantly decreased changes in clustering (p = 0.014) and local efficiency (p = 0.027). Conclusion: The accelerated small worldness in preterm infants suggests that the structural brain network after preterm birth is reorganized in maximizing integrated and segregated processing, implying resilience against prematurity-associated pathology. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) research on the human brainstem (BS) and spinal cord (SC) has identified extensive BS/SC resting-state networks (RSNs) by showing spontaneous coordinated blood oxygenation-level dependent (BOLD) signal fluctuations in the absence of a stimulus. Studies have shown that these networks can be influenced by participants’ level of arousal or attention (e.g., watching a video), and linked network function to autonomic homeostatic regulation. Here we explore how the cognitive state of expecting pain can influence connectivity in these networks. Data from two studies (a predictable pain stimulus study, and a resting-state study) were compared to show the effects of expecting pain on BS/SC networks, and how networks differed from networks associated with the resting-state. In each study, BOLD fMRI data were obtained from the cervical SC and brainstem in healthy participants at 3 tesla using a T2-weighted single-shot fast spin-echo imaging method. Functional connectivity was investigated within the entire 3D volume by means of structural equation modeling (SEM) and analyses of covariance (ANCOVA). Results showed extensive connectivity within/across BS and SC regions during the expectation of pain, and ANCOVA analyses showed that connectivity in specific components of these networks varied with individual pain sensitivity. Comparing these results to RSN fluctuations revealed commonalities in coordination between BS and SC regions, and specific BS–BS connectivity fluctuations unique to the expectation of pain. Based on the regions involved, these results provide evidence of brainstem regulation specific to the expectation of pain.

Background: It remains unclear whether very preterm (VP) infants have the same level of brain structure and function as full-term (FT) infants. In addition, the relationship between potential differences in brain white matter microstructure and network connectivity and specific perinatal factors has not been well characterized., Objective: This study aimed to investigate the existence of potential differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA) and examine the potential association of these differences with perinatal factors., Methods: A total of 83 infants were prospectively selected for this study: 43 VP infants (gestational age, or GA: 27-32 weeks) and 40 FT infants (GA: 37-44 weeks). All infants at TEA underwent both conventional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Significant differences in white matter fractional anisotropy (FA) and mean diffusivity (MD) images between the VP and FT groups were observed using tract-based spatial statistics (TBSS). The fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then, a structural brain network was constructed, where the connection between each pair of nodes was defined by the number of fibers. Network-based statistics (NBS) were used to examine differences in brain network connectivity between the VP and FT groups. Additionally, multivariate linear regression was conducted to investigate potential correlations between fiber bundle numbers and network metrics (global efficiency, local efficiency, and small-worldness) and perinatal factors., Results: Significant differences in FA were observed between the VP and FT groups in several regions. These differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia (BPD), activity, pulse, grimace, appearance, respiratory (APGAR) score, gestational hypertension, and infection. Significant differences in network connectivity were observed between the VP and FT groups. Linear regression results showed significant correlations between maternal years of education, weight, the APGAR score, GA at birth, and network metrics in the VP group., Conclusions: The findings of this study shed light on the influence of perinatal factors on brain development in VP infants. These results may serve as a basis for clinical intervention and treatment to improve the outcome of preterm infants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Liu, Nie, Wang, He, Ma, Han, Mao, Liu, Zu, Mu and Wu.)

Introduction Potential alterations of intrinsic functional connectivity in idiopathic restless legs syndrome (RLS) are to be assumed since RLS is considered a network disorder. Whole-brain-based investigation of intrinsic functional connectivity networks including the sensorimotor systems in patients with RLS was compared with matched healthy controls. Methods ‘Resting-state’ functional MRI (1.5 T) from 26 patients with RLS and 26 matched controls were analyzed using standardized seed-based analysis procedures. The motor/sensorimotor, sensory thalamic, ventral and dorsal attention, basal ganglia-thalamic, cingulate, and brainstem networks were used for voxel-based group comparisons between RLS patients and controls. Results Significantly increased connectivities were observed in the sensory thalamic, ventral and dorsal attention, basal ganglia-thalamic, and cingulate networks in RLS patients, whereas no differences could be demonstrated for the motor/sensorimotor and the brainstem system. The pattern of functional connectivity alterations was positively correlated with increasing symptom severity. Conclusions Abnormally increased regional BOLD synchronization appears to be a key feature of intrinsic brain architecture in RLS. Alterations in cortical and sub-cortical functional networks support the notion that the underlying pathophysiology of RLS is beyond the sensorimotor and the brainstem system and may be also associated with altered attentional control of sensory inputs. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best single-parameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis. [ABSTRACT FROM AUTHOR]

Abstract: Time-variant Granger Causality Index (tvGCI) was applied to simulated and measured BOLD signals to investigate the reliability of time-variant analysis approaches for the identification of directed interrelations between brain areas on the basis of fMRI data. Single-shot fMRI data of a single image slice with short repetition times (200 ms, 16000 frames/subject, 64×64 voxels) were acquired from 5 healthy subjects during an externally-driven, self-paced finger-tapping paradigm (57–59 single taps for each subject). BOLD signals were derived from the pre-supplementary motor area (preSMA), the supplementary motor area (SMA), and the primary motor cortex (M1). The simulations were carried out by means of a Dynamic Causal Modelling (DCM) approach. The tvGCI as well as time-variant Partial Directed Coherence (tvPDC) were used to identify the modelled connectivity network (connectivity structure – CS – of the DCM). Different CSs were applied by using dynamic systems (Generalized Dynamic Neural Network — GDNN) and trivariate autoregressive (AR) processes. The influence of the low-pass characteristics of the simulated hemodynamic response (Balloon model) and of the measuring noise was tested. Additionally, our modelling strategy considered “spontaneous” BOLD fluctuations before, during, and after the appearance of the event-related BOLD component. Couplings which were extracted from the simulated signals were statistically evaluated (tvGCI for shuffled data, confidence tubes for tvGCI courses). We demonstrate that connections of our CS models can be correctly identified during the event-related BOLD component and with signal-to-noise-ratios corresponding to those of the measured data. The results based on simulations can be used to examine the reliability of connectivity identification based on BOLD signals by means of time-variant as well as time-invariant connectivity measures and enable a better interpretation of the analysis results using fMRI data. A readiness-BOLD response was only detected in one subject. However, in two subjects a strong time-variant connection (tvGCI) from preSMA to SMA was observed 3 s before the tapping was executed. This connection was accompanied by a weaker rise of the tvGCI from preSMA to M1. These preceding interrelations were confirmed in the other subjects by the dynamics of tvGCI courses. Based on the results of tvGCI analysis, the time-evolution of an individual connectivity network is shown for each subject. [Copyright &y& Elsevier]

Functional magnetic resonance imaging (fMRI) research on the human brainstem (BS) and spinal cord (SC) has identified extensive BS/SC resting-state networks (RSNs) by showing spontaneous coordinated blood oxygenation-level dependent (BOLD) signal fluctuations in the absence of a stimulus. Studies have shown that these networks can be influenced by participants' level of arousal or attention (e.g., watching a video), and linked network function to autonomic homeostatic regulation. Here we explore how the cognitive state of expecting pain can influence connectivity in these networks. Data from two studies (a predictable pain stimulus study, and a resting-state study) were compared to show the effects of expecting pain on BS/SC networks, and how networks differed from networks associated with the resting-state. In each study, BOLD fMRI data were obtained from the cervical SC and brainstem in healthy participants at 3 tesla using a T2-weighted single-shot fast spin-echo imaging method. Functional connectivity was investigated within the entire 3D volume by means of structural equation modeling (SEM) and analyses of covariance (ANCOVA). Results showed extensive connectivity within/across BS and SC regions during the expectation of pain, and ANCOVA analyses showed that connectivity in specific components of these networks varied with individual pain sensitivity. Comparing these results to RSN fluctuations revealed commonalities in coordination between BS and SC regions, and specific BS–BS connectivity fluctuations unique to the expectation of pain. Based on the regions involved, these results provide evidence of brainstem regulation specific to the expectation of pain. [ABSTRACT FROM AUTHOR]

There is a growing interest in functional magnetic resonance imaging (fMRI) studies on connectivity networks in the brain when subjects are under exposure to natural sensory stimulation. Because of a complicated coupling between spontaneous and evoked brain activity under real-world stimulation, there is no critical mapping between the experimental inputs and corresponding brain responses. The dataset contains auditory fMRI scans and T1-weighted anatomical scans acquired under eyes-closed and eyes-open conditions. Within each scanning condition, the subject was presented 12 different sound clips, including human voices followed by animal vocalizations. The dataset is meant to be used to assess brain dynamics and connectivity networks under natural sound stimulation; it also allows for empirical investigation of changes in fMRI responses between eyes-closed and eyes-open conditions, between animal vocalizations and human voices, as well as between the 12 different sound clips during auditory stimulation. The dataset is a supplement to the research findings in the paper "Brain dynamics and connectivity networks under natural auditory stimulation" published in NeuroImage., (© 2019 The Authors.)