Covariance Networks Research Articles

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

ObjectiveExplore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).Materials and methodsUtilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.ResultsIn Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p < 0.05, FWE correction). The small-world network characteristics of the patient group’s brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p < 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.ConclusionThis study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Satellite remote sensing reveals the footprint of biodiversity on multiple ecosystem functions across the NEON eddy covariance network

Abstract Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao’s quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.

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.

Structural Covariance Networks in the Fetal Brain Reveal Altered Neurodevelopment for Specific Subtypes of Congenital Heart Disease.

Altered structural brain development has been identified in fetuses with congenital heart disease (CHD), suggesting that the neurodevelopmental impairment observed later in life might originate in utero. There are many interacting factors that may perturb neurodevelopment during the fetal period and manifest as structural brain alterations, such as altered cerebral substrate delivery and aberrant fetal hemodynamics. We extracted structural covariance networks from the log Jacobian determinants of 435 in utero T2 weighted image magnetic resonance imaging scans, (n=67 controls, 368 with CHD) acquired during the third trimester. We fit general linear models to test whether age, sex, expected cerebral substrate delivery, and CHD diagnosis were significant predictors of structural covariance. We identified significant effects of age, sex, cerebral substrate delivery, and specific CHD diagnosis across a variety of structural covariance networks, including primary motor and sensory cortices, cerebellar regions, frontal cortex, extra-axial cerebrospinal fluid, thalamus, brainstem, and insula, consistent with widespread coordinated aberrant maturation of specific brain regions over the third trimester. Structural covariance networks offer a sensitive, data-driven approach to explore whole-brain structural changes without anatomical priors. We used them to stratify a heterogenous patient cohort with CHD, highlighting similarities and differences between diagnoses during fetal neurodevelopment. Although there was a clear effect of abnormal fetal hemodynamics on structural brain maturation, our results suggest that this alone does not explain all the variation in brain development between individuals with CHD.

Large-scale structural covariance networks changes relate to executive function deficit in betel quid-dependent chewers.

Previous studies demonstrate deficits in executive function for betel quid-dependent (BQD) patients. Large-scale structural covariance network (SCN) based on gray matter (GM) morphometry may be able to explore the neural mechanism of executive dysfunction in BQD individuals. This study aims to identify spatial covariance patterns of GM volume and to investigate their association with executive dysfunction in BQD individuals. Sixty-four BQD individuals and 48 sex- and age-matched healthy controls (HCs) underwent T1-weighted structural MRI examination and executive function assessments, including the Backward Digit Span (BDS) test and Stroop Color and Word (SCW) test. Seventy SCNs based on GM volume covariance patterns were defined using independent component analysis. An SCN-based classifier was constructed to differentiate between BQD and HC individuals. Receiver operating characteristic (ROC) curves were applied to evaluate the performance of the SCN-based classifier. Linear regression analyses were performed to investigate the association between SCN network indices and executive function indices. Six SCNs had higher classifications for differentiating between BQD and HC individuals. The area under the ROC curve of the SCN-based classifier was 0.812 in the training set and 0.771 in the testing set. Furthermore, linear regression analyses demonstrated that the network indices in the thalamus were associated with BDS scores adjusted for age, sex, and education. Large-scale SCNs could provide potential imaging markers for differentiating BQD and HC groups. The loss of network index in the thalamus was associated with working memory, indicating that SCNs could reveal executive dysfunction in BQD individuals.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Intra-individual structural covariance network in schizophrenia patients with persistent auditory hallucinations

Neuroimaging studies have revealed that the mechanisms of auditory hallucinations are related to morphological changes in multiple cortical regions, but studies on brain network properties are lacking. This study aims to construct intra-individual structural covariance networks and reveal network changes related to auditory hallucinations. T1-weighted MRI images were acquired from 90 schizophrenia patients with persistent auditory hallucinations (pAH group), 55 schizophrenia patients without auditory hallucinations (non-pAH group), and 83 healthy controls (HC group). Networks were constructed using the voxel-based gray matter volume and the intra-individual structural covariance was based on the similarity between the morphological variations of any two regions. One-way ANCOVA was employed to compare global and local network metrics among the three groups, and edge analysis was conducted via network-based statistics. In the pAH group, Pearson correlation analysis between network metrics and clinical symptoms was conducted. Compared with the HC group, both the pAH group (p = 0.01) and the non-pAH group (p = 3.56 × 10−4) had lower nodal efficiency of the left medial superior frontal gyrus. Compared to the non-pAH group and HC group, the pAH group presented lower nodal efficiency of the temporal pole of the left superior temporal gyrus (p = 1.09 × 10−3; p = 7.67 × 10−4) and right insula (p = 0.02; p = 8.99 × 10−6), and lower degree centrality of the right insula (p = 0.04; p = 1.65 × 10−5). The pAH group had a subnetwork with reduced structural covariance centered by the left temporal pole of the superior temporal gyrus. In the pAH group, the normalized clustering coefficient (r = −0.36, p = 8.45 × 10−3) and small-worldness (r = −0.35, p = 9.89 × 10−3) were negatively correlated with the PANSS positive scale score. This study revealed network changes in schizophrenia patients with persistent auditory hallucinations, and provided new insights into the structural architecture related to auditory hallucinations at the network level.

Structural and covariance network alterations of the hippocampus and amygdala in congenital hearing loss children

ObjectiveThe hippocampus and amygdala, as important components of the limbic system, play crucial roles in central remodeling in congenital hearing loss. This study aimed to investigate the morphological integrity and network properties of the subfields of hippocampus and amygdala in children with congenital hearing loss. MethodsA total of 24 children with congenital hearing loss and 17 age- and sex- matched healthy controls (HC) are included in the study. T1-weighted images are analyzed by segmenting the brain into cortical and subcortical regions. Intergroup difference of volumes were explored. Structural covariance networks for the whole brain and hippocampus-amygdala subregions were constructed. Between-group differences of network property are investigated by comparing area under a range of network sparsity. ResultsPatients with congenital hearing loss exhibited significantly larger volumes in the right dentate gyrus and CA3 of the hippocampus. However, there were no significant differences in total hippocampal or showed decreased global efficiency and increased characteristic path length, indicating reduced network integration. Lower betweenness centrality was observed in the left hippocampal fissure in the hearing loss group. The changes in volume and network topological properties are not affected by age and sex. ConclusionChildren with congenital hearing loss display specific volumetric increases in hippocampal subregions, suggesting compensatory adaptations to auditory deprivation. The hippocampus-amygdala network shows significant reorganization, potentially underpinning cognitive and behavioral development issues associated with congenital hearing loss. These findings highlight the importance of targeted neural substrates in understanding and addressing the developmental challenges faced by children with congenital hearing loss.

High‐Latitude Eddy Covariance Temporal Network Design and Optimization

AbstractEcosystems at high latitudes are changing rapidly in response to climate change. To understand changes in carbon fluxes across seasonal to multi‐decadal timescales, long‐term in situ measurements from eddy covariance networks are needed. However, there are large spatiotemporal gaps in the high‐latitude eddy covariance network. Here we used the relative extrapolation error index in machine learning‐based upscaled gross primary production as a measure of network representativeness and as the basis for a network optimization. We show that the relative extrapolation error index has steadily decreased from 2001 to 2020, suggesting diminishing upscaling errors. In experiments where we limit site activity by either setting a maximum duration or by ending measurements at a fixed time those errors increase significantly, in some cases setting the network status back more than a decade. Our experiments also show that with equal site activity across different theoretical network setups, a more spread out design with shorter‐term measurements functions better in terms of larger‐scale representativeness than a network with fewer long‐term towers. We developed a method to select optimized site additions for a network extension, which blends an objective modeling approach with expert knowledge. This method greatly outperforms an unguided network extension and can compensate for suboptimal human choices. For the Canadian Arctic we show several optimization scenarios and find that especially the Canadian high Arctic and north east tundra benefit greatly from addition sites. Overall, it is important to keep sites active and where possible make the extra investment to survey new strategic locations.

Comparative evaluation of interpretation methods in surface-based age prediction for neonates

Significant changes in brain morphology occur during the third trimester of gestation. The capability of deep learning in leveraging these morphological features has enhanced the accuracy of brain age predictions for this critical period. Yet, the opaque nature of deep learning techniques, often described as "black box" approaches, limits their interpretability, posing challenges in clinical applications. Traditional interpretable methods developed for computer vision and natural language processing may not directly translate to the distinct demands of neuroimaging. In response, our research evaluates the effectiveness and adaptability of two interpretative methods—regional age prediction and the perturbation-based saliency map approach—for predicting the brain age of neonates. Analyzing 664 T1 MRI scans with the NEOCIVET pipeline to extract brain surface and cortical features, we assess how these methods illuminate key brain regions for age prediction, focusing on technical analysis with clinical insight. Through a comparative analysis of the saliency index (SI) with relative brain age (RBA) and the examination of structural covariance networks, we uncover the saliency index's enhanced ability to pinpoint regions vital for accurate indication of clinical factors. Our results highlight the advantages of perturbation techniques in addressing the complexities of medical data, steering clinical interventions for premature neonates towards more personalized and interpretable approaches. This study not only reveals the promise of these methods in complex medical scenarios but also offers a blueprint for implementing more interpretable and clinically relevant deep learning models in healthcare settings.

Structural brain characteristics of epilepsy patients with comorbid migraine without aura

Migraine is a common bi-directional comorbidity of epilepsy, indicating potential complex interactions between the two conditions. However, no previous studies have used brain morphology analysis to assess possible interactions between epilepsy and migraine. Voxel-based morphometry (VBM), surface-based morphometry (SBM), and structural covariance networks (SCNs) can be used to detect morphological changes with high accuracy. We recruited 30 individuals with epilepsy and comorbid migraine without aura (EM), along with 20 healthy controls (HC) and 30 epilepsy controls (EC) without migraine. We used VBM, SBM, and SCN analysis to compare differences in gray matter volume, cortical thickness, and global level and local level graph theory indexes between the EM, EC, and HC groups to investigate structural brain changes in the EM patients. VBM analysis showed that the EM group had gray matter atrophy in the right temporal pole compared with the HC group (p < 0.001, false discovery rate correction [FDR]). Furthermore, the headache duration in the EM group was negatively correlated with the gray matter volume of the right temporal pole (p < 0.05). SBM analysis showed cortical atrophy in the left insula, left posterior cingulate gyrus, left postcentral gyrus, left middle temporal gyrus, and left fusiform gyrus in the EM compared with the HC group (p < 0.001, family wise error correction). We found a positive correlation between headache frequency and the cortical thickness of the left middle temporal gyrus (p < 0.05). SCN analysis revealed no differences in global parameters between the three groups. The area under the curve (AUC) of the nodal betweenness centrality in the right postcentral gyrus was lower in the EM group compared with the HC group (p < 0.001, FDR correction), and the AUC of the nodal degree in the right fusiform gyrus was lower in the EM group compared with the EC group (p < 0.001, FDR correction). We found clear differences in brain structure in the EM patients compared with the HC group. Accordingly, migraine episodes may influence brain structure in epilepsy patients. Conversely, abnormal brain structure may be an important factor in the development of epilepsy with comorbid migraine without aura. Further studies are needed to investigate the role of brain structure in individuals with epilepsy and comorbid migraine without aura.

Effect of Dialysis on Structural Brain Connectivity in Patients with End-Stage Renal Disease

Introduction: Patients with end-stage renal disease (ESRD) are known to have reduced structural and functional brain connectivity in the brain regions associated with cognitive function. However, the effect of dialysis on brain connectivity remains unclear. This study aimed to evaluate the effects of dialysis on structural brain connectivity in patients with ESRD. Methods: This prospective study included 20 patients with ESRD in the pre-dialysis stage and 35 healthy controls. The patients underwent T2-weighted and three-dimensional T1-weighted magnetic resonance imaging before and 3 months after dialysis initiation. Moreover, the cortical thickness was calculated. We applied graph theoretical analysis to calculate the structural covariance network based on cortical thickness. We compared the cortical thickness and structural covariance network of patients with ESRD in the pre-dialysis stage with those of healthy controls and with those of patients with ESRD in the post-dialysis stage. Results: The mean cortical thickness in both hemispheres was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (2.296 vs. 2.354, p = 0.030; 2.282 vs. 2.362, p = 0.004, respectively) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (2.333 vs. 2.296, p = 0.001; 2.322 vs. 2.282, p = 0.002, respectively). Analysis of the structural covariance network revealed that the assortative coefficient was lower in patients with ESRD in the pre-dialysis stage than in healthy controls (−0.062 vs. −0.031, p = 0.029) and was higher in patients with ESRD in the post-dialysis stage than in those in the pre-dialysis stage (−0.002 vs. −0.062, p = 0.042). Conclusion: We observed differences in the cortical thickness and structural covariance networks before and after dialysis in patients with ESRD. This indicates that dialysis affects structural brain connectivity, contributing to the understanding of the pathophysiological mechanism of cognitive function alterations resulting from dialysis in patients with ESRD.

Progressive gray matter atrophy in parkinsonian variant of multiple system atrophy assessed by using causal structural covariance network.

Multiple system atrophy (MSA), a rare neurodegenerative disease, is usually accompanied by brain morphological alterations. However, the causal relationships between progressive gray matter atrophy in MSA parkinsonian (MSA-P) subtype remain unknown. In total, thirty-five MSA-P patients and thirty-five healthy controls (HC) underwent three-dimensional high-resolution T1-weighted structural imaging and voxel-based morphometry analysis. The causal structural covariance network (CaSCN) of gray matter was assessed to explore the causal relationships in MSA-P. With greater illness duration, the reduction of gray matter was originated from right cerebellum and progressed to bilateral cerebellum, fusiform gyrus, insula, putamen, caudate nucleus, frontal lobe, right angular gyrus, right precuneus, left middle occipital lobe and left inferior temporal lobe, then expanded to midbrain, bilateral para-hippocampus, thalamus, temporal lobe, inferior parietal lobule (IPL), precentral gyrus, postcentral gyrus and middle cingulate cortex. The right cerebellum was revealed to be the core node of the directional network and projected positive causal effects to bilateral cerebellum, caudate nucleus and left IPL. MSA-P patients showed progression of gray matter atrophy over time, with the right cerebellum probably as a primary hub. Furthermore, the early structural vulnerability of cerebellum in MSA-P may play a pivotal role in the modulation of motor and non-motor circuits at the structural level.

Structural covariance network activity in the medial prefrontal cortex is modulated by childhood abuse in adolescents with depression

Aberrant structural covariance (SC) in the medial prefrontal cortex (mPFC) is believed to play a crucial role in adolescent-onset major depressive disorder (AO-MDD). However, the effect of childhood abuse (CA) on SC in AO-MDD patients is still unknown. Here, we measured anomalous SC in the mPFC of AO-MDD patients and assessed the potential modulation of this feature by CA. We acquired T1-weighted structural images of AO-MDD patients (n = 93) and healthy controls (HCs, n = 81). Using voxel-based morphometry analysis, we calculated gray matter volumes for each subject. Subsequently, we classified abnormal SC in the mPFC into three subtypes according to overall CA. Compared with HCs, AO-MDD patients showed alterations in the structural covariance network of the mPFC, which is a central region in the default mode network (DMN). We also found an anterior-posterior dissociation in the structural covariance connectivity of the DMN. A history of CA modulated bilateral mPFC SC. These changes were primarily focused on the SC between the mPFC and the limbic system, indicating a gap in the rate of neural maturation between these regions. In summary, the DMN and frontal-limbic system, which are involved in emotional processing, appear to play a significant role in the development of AO-MDD. These findings highlight the crucial effects of CA on neurophysiological alterations in individuals with AO-MDD.

Neural correlates of anhedonia in young adults with subthreshold depression: A graph theory approach for cortical-subcortical structural covariance

BackgroundAnhedonia is an enduring symptom of subthreshold depression (StD) and predict later onset of major depressive disorder (MDD). Brain structural covariance describes the inter-regional distribution of morphological changes compared to healthy controls (HC) and reflects brain maturation and disease progression. We investigated neural correlates of anhedonia from the structural covariance. MethodsT1-weighted brain magnetic resonance images were acquired from 79 young adults (26 StD, 30 MDD, and 23 HC). Intra-individual structural covariance networks of 68 cortical surface area (CSAs), 68 cortical thicknesses (CTs), and 14 subcortical volumes were constructed. Group-level hubs and principal edges were defined using the global and regional graph metrics, compared between groups, and examined for the association with anhedonia severity. ResultsGlobal network metrics were comparable among the StD, MDD, and HC. StD exhibited lower centralities of left pallidal volume than HC. StD showed higher centralities than HC in the CSAs of right rostral anterior cingulate cortex (ACC) and pars triangularis, and in the CT of left pars orbitalis. Less anhedonia was associated with higher centralities of left pallidum and right amygdala, higher edge betweenness centralities in the structural covariance (EBSC) of left postcentral gyrus-parahippocampal gyrus and LIPL-right amygdala. More anhedonia was associated with higher centralities of left inferior parietal lobule (LIPL), left postcentral gyrus, left caudal ACC, and higher EBSC of LIPL-left postcentral gyrus, LIPL-right lateral occipital gyrus, and left caudal ACC-parahippocampal gyrus. LimitationsThis study has a cross-sectional design. ConclusionsStructural covariance of brain morphologies within the salience and limbic networks, and among the salience-limbic-default mode-somatomotor-visual networks, are possible neural correlates of anhedonia in depression.

Gray matter structural alterations of cortico-striato-thalamo-cortical loop in familial Paroxysmal Kinesigenic Dyskinesia

BackgroundPrevious studies have indicated that patients with Paroxysmal Kinesigenic Dyskinesia (PKD) exhibit reduced gray matter volume in certain brain regions within the cortico-striato-thalamo-cortical (CSTC) loop. However, a comprehensive investigation specifically targeting the CSTC loop in PKD has never been conducted. ObjectivesTo provide evidence for the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, this study carried out a surface-based morphometry (SBM), voxel-based morphometry (VBM), and structural covariance networks (SCN) combined analysis in familial PKD patients. MethodsA total of 8 familial PKD patients and 10 healthy family members were included in the study and underwent Brain MRI examinations. Based on 3D T1 MPRAGE data, neuroimaging metrics of cortical thickness from SBM, subcortical nuclei volume from VBM, and covariance coefficient from SCN were used to systematically investigate the brain structural alterations along the CSTC loop of PKD patients. ResultsA significant decrease in the average cortical thickness of the left S1 region in the PKD group was observed. The volumes of subcortical nuclei, including the thalamus, putamen, and globus pallidus were reduced, with a pronounced effect observed in the bilateral putamen. And the structural covariance connection between the left putamen and the left globus pallidus was significantly strengthened. ConclusionsThe study confirms the involvement of the CSTC loop in the pathogenesis of PKD from the perspective of structural alterations, and the findings may provide potential targets for objective diagnosis and therapeutic monitoring of PKD.

Learning to Allocate Time-Bound and Dynamic Tasks to Multiple Robots Using Covariant Attention Neural Networks

Abstract In various applications of multi-robotics in disaster response, warehouse management, and manufacturing, tasks that are known a priori and tasks added during run time need to be assigned efficiently and without conflicts to robots in the team. This multi-robot task allocation (MRTA) process presents itself as a combinatorial optimization (CO) problem that is usually challenging to be solved in meaningful timescales using typical (mixed)integer (non)linear programming tools. Building on a growing body of work in using graph reinforcement learning to learn search heuristics for such complex CO problems, this paper presents a new graph neural network architecture called the covariant attention mechanism (CAM). CAM can not only generalize but also scale to larger problems than that encountered in training, and handle dynamic tasks. This architecture combines the concept of covariant compositional networks used here to embed the local structures in task graphs, with a context module that encodes the robots’ states. The encoded information is passed onto a decoder designed using multi-head attention mechanism. When applied to a class of MRTA problems with time deadlines, robot ferry range constraints, and multi-trip settings, CAM surpasses a state-of-the-art graph learning approach based on the attention mechanism, as well as a feasible random-walk baseline across various generalizability and scalability tests. Performance of CAM is also found to be at par with a high-performing non-learning baseline called BiG-MRTA, while noting up to a 70-fold improvement in decision-making efficiency over this baseline.

A causal effect study of cortical morphology and related covariate networks in classical trigeminal neuralgia patients.

Structural covariance networks and causal effects within can provide critical information on gray matter reorganization and disease-related hierarchical changes. Based on the T1WI data of 43 classical trigeminal neuralgia patients and 45 controls, we constructed morphological similarity networks of cortical thickness, sulcal depth, fractal dimension, and gyrification index. Moreover, causal structural covariance network analyses were conducted in regions with morphological abnormalities or altered nodal properties, respectively. We found that patients showed reduced sulcal depth, gyrification index, and fractal dimension, especially in the salience network and the default mode network. Additionally, the integration of the fractal dimension and sulcal depth networks was significantly reduced, accompanied by decreased nodal efficiency of the bilateral temporal poles, and right pericalcarine cortex within the sulcal depth network. Negative causal effects existed from the left insula to the right caudal anterior cingulate cortex in the gyrification index map, also from bilateral temporal poles to right pericalcarine cortex within the sulcal depth network. Collectively, patients exhibited impaired integrity of the covariance networks in addition to the abnormal gray matter morphology in the salience network and default mode network. Furthermore, the patients may experience progressive impairment in the salience network and from the limbic system to the sensory system in network topology, respectively.

No sex difference in maturation of brain morphology during the perinatal period.

Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.

Changes in functional connectivity and structural covariance between the fronto-parietal network and medial orbitofrontal cortex are associated with disinhibition in restrained eaters.

Disinhibition, characterized by a loss of dietary control, is a significant risk factor for diet failure and the onset of eating disorders in restrained eaters. This study employs resting-state functional connectivity and structural covariance network analyses to explore the neural associations underlying this behavior. By analyzing functional MRI data from 63 female college students, we found that increased disinhibition correlates with enhanced functional connectivity between the medial orbitofrontal cortex and key components of the inhibition system, particularly within the fronto-parietal network. Moreover, we observed a relationship between the structural covariance of the medial orbitofrontal cortex and the inferior parietal lobule and the severity of disinhibition. Importantly, the functional connectivity between the medial orbitofrontal cortex and the inferior parietal lobule predicts the severity of binge eating symptoms in these individuals. These findings indicate that imbalances in the interaction between the brain's reward and inhibition systems can lead to dietary failures and eating disorders, emphasizing the need for targeted interventions.