Structural Covariance Networks Research Articles

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy

ObjectiveMesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level.MethodsThis study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy.ResultsWe found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome.SignificanceOur research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy.

Altered individual gray matter structural covariance networks in early abstinence patients with alcohol dependence.

While alterations in cortical thickness have been widely observed in individuals with alcohol dependence, knowledge about cortical thickness-based structural covariance networks is limited. This study aimed to explore the topological disorganization of structural covariance networks based on cortical thickness at the single-subject level among patients with alcohol dependence. Structural imaging data were obtained from 61 patients with alcohol dependence during early abstinence and 59 healthy controls. The single-subject structural covariance networks were constructed based on cortical thickness data from 68 brain regions and were analyzed using graph theory. The relationships between network architecture and clinical characteristics were further investigated using partial correlation analysis. In the structural covariance networks, both patients with alcohol dependence and healthy controls displayed small-world topology. However, compared to controls, alcohol-dependent individuals exhibited significantly altered global network properties characterized by greater normalized shortest path length, greater shortest path length, and lower global efficiency. Patients exhibited lower degree centrality and nodal efficiency, primarily in the right precuneus. Additionally, scores on the Alcohol Use Disorder Identification Test were negatively correlated with the degree centrality and nodal efficiency of the left middle temporal gyrus. The results of this correlation analysis did not survive after multiple comparisons in the exploratory analysis. Our findings may reveal alterations in the topological organization of gray matter networks in alcoholism patients, which may contribute to understanding the mechanisms of alcohol addiction from a network perspective.

The thalamic covariance network is associated with cognitive deficits in patients with cerebral small vascular disease.

Abnormalities in the gray matter structure of cerebral small vessel disease (CSVD) have been observed throughout the brain. However, whether cortico-cortical connections exist between regions of gray matter atrophy in patients with CSVD has not been fully elucidated. This question was tested by comparing the gray matter covariance networks in CSVD patients with and without cognitive impairment (CI). We performed multivariate modeling of the gray matter volume measurements of 61 patients with CI (CSVD-CI), 85 patients without CI (CSVD-NC), and 108 healthy controls using source-based morphological analysis (SBM) to obtain gray matter structural covariance networks at the population level. Then, correlations between structural covariance networks and cognitive functions were analyzed in CSVD patients. Finally, a support vector machine (SVM) classifier was used with the gray matter covariance network as a classification feature to identify CI among the CSVD population. The results of the analysis of all the subjects showed that compared with healthy controls, the expression of the thalamic covariance network, cerebellum covariance network, and calcarine cortex covariance network was reduced in patients with CSVD. Moreover, CSVD-CI patients showed a significant reduction in the expression of the thalamic covariance network, encompassing the thalamus and the parahippocampal gyrus, relative to CSVD-NC patients, which persisted after excluding CSVD patients with thalamic lacunes. In patients with CSVD, cognitive functions were positively correlated with measures of the thalamic covariance network. More than 80% of CSVD patients with CI were correctly identified by the SVM classifier. Our findings provide new evidence to explain the distribution state of gray matter reduction in CSVD patients, and the thalamic covariance network is the core region for early gray matter reduction during the development of CSVD disease, which is related to cognitive deficits. Reduced expression of thalamic covariance networks may provide a neuroimaging biomarker for the early identification of cognitive impairment in CSVD patients.

Altered cortical thickness and structural covariance networks in chronic low back pain

BackgroundDespite regional brain structural changes having been reported in patients with chronic low back pain (CLBP), the topological properties of structural covariance networks (SCNs), which refer to the organization of the SCNs, remain unclear. This study applied graph theoretical analysis to explore the alterations of the topological properties of SCNs, aiming to comprehend the integration and separation of SCNs in patients with CLBP. MethodsA total of 38 patients with CLBP and 38 healthy controls (HCs), balanced for age and sex, were scanned using three-dimensional T1-weighted magnetic resonance imaging. The cortical thickness was extracted from 68 brain regions, according to the Desikan–Killiany atlas, and used to reconstruct the SCNs. Subsequently, graph theoretical analysis was employed to evaluate the alterations of the topological properties in the SCNs of patients with CLBP. ResultsIn comparison to HCs, patients with CLBP had less cortical thickness in the left superior frontal cortex. Additionally, the cortical thickness of the left superior frontal cortex was negatively correlated with the Visual Analogue Scale scores of patients with CLBP. Furthermore, patients with CLBP, relative to HCs, exhibited lower global efficiency and small-worldness, as well as a longer characteristic path length. This indicates a decline in the brain's capacity to transmit and process information, potentially impacting the processing of pain signals in patients with CLBP and contributing to the development of CLBP. In contrast, there were no significant differences in the clustering coefficient, local efficiency, nodal efficiency, nodal betweenness centrality, or nodal degree between the two groups. ConclusionsFrom the regional cortical thickness to the complex brain network level, our study demonstrated changes in the cortical thickness and topological properties of the SCNs in patients with CLBP, thus aiding in a better understanding of the pathophysiological mechanisms of CLBP.

Altered topological structure of the brain white matter in maltreated children through topological data analysis.

Childhood maltreatment may adversely affect brain development and consequently influence behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in the global topology of the brain white matter structural covariance network among children. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging and diffusion tensor imaging. We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children with a typically developing control group, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly suggest that TDA can be a valuable framework to model altered topological structures of the brain. The MATLAB codes and processed data used in this study can be found at https://github.com/laplcebeltrami/maltreated.

Abnormal longitudinal changes of structural covariance networks of cortical thickness in mild traumatic brain injury with posttraumatic headache

BackgroundIt is widely acknowledged that mild traumatic brain injury (MTBI) leads to either functionally or anatomically abnormal brain regions. Structural covariance networks (SCNs) that depict coordinated regional maturation patterns are commonly employed for investigating brain structural abnormalities. However, the dynamic nature of SCNs in individuals with MTBI who suffer from posttraumatic headache (PTH) and their potential as biomarkers have hitherto not been investigated. MethodsThis study included 36 MTBI patients with PTH and 34 well-matched healthy controls (HCs). All participants underwent magnetic resonance imaging scans and were assessed with clinical measures during the acute and subacute phases. Structural covariance matrices of cortical thickness were generated for each group, and global as well as nodal network measures of SCNs were computed. ResultsMTBI patients with PTH demonstrated reduced headache impact and improved cognitive function from the acute to subacute phase. In terms of global network metrics, MTBI patients exhibited an abnormal normalized clustering coefficient compared to HCs during the acute phase, although no significant difference in the normalized clustering coefficient was observed between the groups during the subacute phase. Regarding nodal network metrics, MTBI patients displayed alterations in various brain regions from the acute to subacute phase, primarily concentrated in the prefrontal cortex (PFC). ConclusionsThese findings indicate that the cortical thickness topography in the PFC determines the typical structural-covariance topology of the brain and may serve as an important biomarker for MTBI patients with PTH.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set.

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD. Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes. A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings. Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms. Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Mapping structural covariance networks of emotional withdrawal symptoms in males with methamphetamine use disorder during abstinence.

Individuals with methamphetamine use disorder (MUD) often experience anxiety and depressive symptoms during abstinence, which can worsen the likelihood of relapse. Thus, it is essential to understand the neuro-mechanism behind methamphetamine use and its associated emotional withdrawal symptoms in order to develop effective clinical strategies. This study aimed to evaluate associations between emotional withdrawal symptoms and structural covariance networks (SCNs) based on cortical thickness (CTh) across the brain. The CTh measures were obtained from Tl-weighted MRI data from a sample of 48 males with MUD during abstinence and 48 male healthy controls. The severity of anxiety and depressive symptoms was assessed by the Hamilton Anxiety Scale (HAMA) and depression (HAMD) scales. Two important nodes belonging to the brain reward system, the right rostral anterior cingulate cortex (rACC) and medial prefrontal cortex (medPFC), were selected as seeds to conduct SCNs and modulation analysis by emotional symptoms. MUDs showed higher structural covariance between the right rACC and regions in the dorsal attention, right frontoparietal, auditory, visual and limbic networks. They also displayed higher structural covariance between the right medPFC and regions in the limbic network. Moreover, the modulation analysis showed that higher scores on HAMA were associated with increased covariance between the right rACC and the left parahippocampal and isthmus cingulate cortex in the default mode network. These outcomes shed light on the complex neurobiological mechanisms underlying methamphetamine use and its associated emotional withdrawal symptoms and may provide new insights into the development of effective treatments for MUD.

Structural Network Topologies Are Associated with Deep Brain Stimulation Outcomes in Meige Syndrome

Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.

Progressive increase of brain gray matter volume in individuals with regular soccer training

The study aimed to investigate alterations in gray matter volume in individuals undergoing regular soccer training, using high-resolution structural data, while also examining the temporal precedence of such structural alterations. Both voxel-based morphometry and source-based morphometry (SBM) methods were employed to analyze volumetric changes in gray matter between the soccer and control groups. Additionally, a causal network of structural covariance (CaSCN) was built using granger causality analysis on brain structural data ordering by training duration. Significant increases in gray matter volume were observed in the cerebellum in the soccer group. Additionally, the results of the SBM analysis revealed significant increases in gray matter volume in the calcarine and thalamus of the soccer group. The analysis of CaSCN demonstrated that the thalamus had a prominent influence on other brain regions in the soccer group, while the calcarine served as a transitional node, and the cerebellum acted as a prominent node that could be easily influenced by other brain regions. In conclusion, our study identified widely affected regions with increased gray matter volume in individuals with regular soccer training. Furthermore, a temporal precedence relationship among these regions was observed.

Individual Structural Covariance Network Predicts Long-Term Motor Improvement in Parkinson's Disease with STN-DBS.

The efficacy of long-term chronic subthalamic nucleus deep brain stimulation (STN-DBS) in treating Parkinson's Disease (PD) exhibits substantial variability among individuals. The preoperative identification of suitable DBS candidates through predictive means becomes crucial. Our study aims to investigate the predictive value of characterizing individualized structural covariance networks for long-term efficacy of DBS, offering patients a precise and cost-effective preoperative screening tool. We included 138 PD patients and 40 healthy controls. We developed individualized structural covariance networks from T1-weighted images utilizing Network Template Perturbation, and computed the networks' topological characteristics. Patients were categorized according to their long-term motor improvement following STN-DBS. Intergroup analyses were conducted on individual network edges and topological indices, alongside correlation analyses with long-term outcomes for the entire patient cohort. Finally, machine learning (ML) algorithms were employed for regression and classification to predict post-DBS motor improvement. Among the PD patients, six edges (left Middle Frontal and left Caudate Nucleus, right Olfactory and right Insula, left Superior Medial Frontal Gyrus and right Insula, right Middle Frontal and left Paracentral Lobule, right Middle Frontal and Cerebellum, left Lobule VIIb of the Cerebellum and the vermis of the Cerebellum) exhibited significant results in intergroup comparisons and correlation analyses. Increased degree centrality and local efficiency of the cerebellum, parahippocampal gyrus, and postcentral gyrus were associated with DBS improvement. A regression model constructed from these six edges revealed a significant correlation between predicted and observed changes in the unified Parkinson's disease rating scale (R=0.671, P<0.001), and receiver operating characteristic analysis demonstrated an area under the curve of 0.802, effectively distinguishing between patients with good and moderate improvement post-DBS. Our findings reveal the link between individual structural covariance network fingerprints in PD patients and long-term motor outcome following STN-DBS. Additionally, binary and continuous cerebellum-basal ganglia-frontal structural covariance network edges have emerged as potential predictive biomarkers for DBS motor outcome. subthalamic nucleus deep brain stimulation = STN-DBS; Parkinson's Disease = PD; machine learning = ML); Network Template Perturbation = NTP.

Trauma-related intrusive memories and anterior hippocampus structural covariance: an ecological momentary assessment study in posttraumatic stress disorder

Trauma-related intrusive memories (TR-IMs) are hallmark symptoms of posttraumatic stress disorder (PTSD), but their neural correlates remain partly unknown. Given its role in autobiographical memory, the hippocampus may play a critical role in TR-IM neurophysiology. The anterior and posterior hippocampi are known to have partially distinct functions, including during retrieval of autobiographical memories. This study aimed to investigate the relationship between TR-IM frequency and the anterior and posterior hippocampi morphology in PTSD. Ninety-three trauma-exposed adults completed daily ecological momentary assessments for fourteen days to capture their TR-IM frequency. Participants then underwent anatomical magnetic resonance imaging to obtain measures of anterior and posterior hippocampal volumes. Partial least squares analysis was applied to identify a structural covariance network that differentiated the anterior and posterior hippocampi. Poisson regression models examined the relationship of TR-IM frequency with anterior and posterior hippocampal volumes and the resulting structural covariance network. Results revealed no significant relationship of TR-IM frequency with hippocampal volumes. However, TR-IM frequency was significantly negatively correlated with the expression of a structural covariance pattern specifically associated with the anterior hippocampus volume. This association remained significant after accounting for the severity of PTSD symptoms other than intrusion symptoms. The network included the bilateral inferior temporal gyri, superior frontal gyri, precuneus, and fusiform gyri. These novel findings indicate that higher TR-IM frequency in individuals with PTSD is associated with lower structural covariance between the anterior hippocampus and other brain regions involved in autobiographical memory, shedding light on the neural correlates underlying this core symptom of PTSD.

Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people.

The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray-matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community-based older people preceding the conversion to physio-cognitive decline syndrome (PCDS). Voxel-wise brain GMV and established SCNs were compared between PCDS and non-PCDS converters. The study included 343 individuals (60.2±6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6-year follow-up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum-based (to right frontal pole and left middle frontal gyrus) and caudate-based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. Neuroanatomic precursors of dual cognitive and mobility impairments are identified. Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS. Altered cerebellum- and caudate-based SCNs drive PCDS transformation. This research establishes a foundation for understanding PCDS as a specific dementia syndrome.

Transcriptional Patterns of Brain Structural Covariance Network Abnormalities Associated With Suicidal Thoughts and Behaviors in Major Depressive Disorder

BackgroundAlthough brain structural covariance network (SCN) abnormalities have been associated with suicidal thoughts and behaviors (STBs) in individuals with major depressive disorder (MDD), previous studies have reported inconsistent findings based on small sample sizes, and underlying transcriptional patterns remain poorly understood. MethodsUsing a multicenter magnetic resonance imaging dataset including 218 MDD patients with STBs, 230 MDD patients without STBs, and 263 healthy control participants, we established individualized SCNs based on regional morphometric measures and assessed network topological metrics using graph theoretical analysis. Machine learning methods were applied to explore and compare the diagnostic value of morphometric and topological features in identifying MDD and STBs at the individual level. Brainwide relationships between STBs-related connectomic alterations and gene expression were examined using partial least squares regression. ResultsGroup comparisons revealed that SCN topological deficits associated with STBs were identified in the prefrontal, anterior cingulate, and lateral temporal cortices. Combining morphometric and topological features allowed for individual-level characterization of MDD and STBs. Topological features made a greater contribution to distinguishing between patients with and without STBs. STBs-related connectomic alterations were spatially correlated with the expression of genes enriched for cellular metabolism and synaptic signaling. ConclusionsThese findings revealed robust brain structural deficits at the network level, highlighting the importance of SCN topological measures in characterizing individual suicidality and demonstrating its linkage to molecular function and cell types, providing novel insights into the neurobiological underpinnings and potential markers for prediction and prevention of suicide.

Prediction of clinical progression of subjective cognitive decline through alterations in morphology and structural covariance networks

AbstractBackgroundSubjective cognitive decline (SCD) is a preclinical, asymptomatic stage of Alzheimer's disease (AD). Early identification and assessment of progressive SCD is crucial for preventing the onset of AD.MethodsThe study recruited 60 individuals diagnosed with SCD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Participants were divided into two groups: progressive SCD (pSCD, 23 individuals) and stable SCD (sSCD, 37 individuals) based on their progression to mild cognitive impairment (MCI) within 5 years. Cortical thickness, volumes of the hippocampus subfield, and subcortical regions were analyzed using T1‐weighted images and the FreeSurfer software. Network‐based statistics (NBS) were performed to compare structural covariance networks (SCNs) between the two groups.ResultsResults showed that the pSCD group showed significant atrophy of the hippocampal‐fimbria (p = .018) and cortical thinning in the left transverse temporal (cluster size 71.84 mm2, cluster‐wise corrected p value = .0004) and left middle temporal gyrus (cluster size 45.05 mm2, cluster‐wise corrected p value = .00639). The combination of these MRI features demonstrated high accuracy (AUC of 0.86, sensitivity of 78.3%, and specificity of 89.3%). NBS analysis revealed that pSCD individuals showed an increase in structural networks within the default mode network (DMN) and a decrease in structural connections between the somatomotor network (Motor) and DMN networks.ConclusionOur findings demonstrate that atrophy of the hippocampus and thinning of the cortex may serve as effective biomarkers for early identification of individuals at high risk of cognitive decline. Changes in connectivity within and outside of the DMN may play a crucial role in the pathophysiology of pSCD.

Mapping individual structural covariance network in development brain with dynamic time warping.

A conspicuous property of brain development or maturity is coupled with coordinated or synchronized brain structural co-variation. However, there is still a lack of effective approach to map individual structural covariance network. Here, we developed a novel individual structural covariance network method using dynamic time warping algorithm and applied it to delineate developmental trajectories of topological organizations of structural covariance network from childhood to early adulthood with a large sample of 655 individuals from Human Connectome Project-Development dataset. We found that the individual structural covariance network exhibited small-worldness property and the network global topological characteristics including small-worldness, global efficiency, local efficiency, and modularity linearly increase with age while the shortest path length linearly decreases with age. The nodal topological properties including betweenness and degree increased with age in language and emotion regulation related brain areas, while it decreased with age mainly in visual cortex, sensorimotor area, and hippocampus. Moreover, the topological attributes of structural covariance network as features could predict the age of each individual. Taken together, our results demonstrate that dynamic time warping can effectively map individual structural covariance network to uncover the developmental trajectories of network topology, which may facilitate future investigations to establish the links of structural co-variations with respect to cognition and disease vulnerability.

Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

Abnormal developmental of structural covariance networks in young adults with heavy cannabis use: a 3-year follow-up study

Heavy cannabis use (HCU) exerts adverse effects on the brain. Structural covariance networks (SCNs) that illustrate coordinated regional maturation patterns are extensively employed to examine abnormalities in brain structure. Nevertheless, the unexplored aspect remains the developmental alterations of SCNs in young adults with HCU for three years, from the baseline (BL) to the 3-year follow-up (FU). These changes demonstrate dynamic development and hold potential as biomarkers. A total of 20 young adults with HCU and 22 matched controls were recruited. All participants underwent magnetic resonance imaging (MRI) scans at both the BL and FU and were evaluated using clinical measures. Both groups used cortical thickness (CT) and cortical surface area (CSA) to construct structural covariance matrices. Subsequently, global and nodal network measures of SCNs were computed based on these matrices. Regarding global network measures, the BL assessment revealed significant deviations in small-worldness and local efficiency of CT and CSA in young adults with HCU compared to controls. However, no significant differences between the two groups were observed at the FU evaluation. Young adults with HCU displayed changes in nodal network measures across various brain regions during the transition from BL to FU. These alterations included abnormal nodal degree, nodal efficiency, and nodal betweenness in widespread areas such as the entorhinal cortex, superior frontal gyrus, and parahippocampal cortex. These findings suggest that the topography of CT and CSA plays a role in the typical structural covariance topology of the brain. Furthermore, these results indicate the effect of HCU on the developmental changes of SCNs in young adults.

Altered grey matter structural covariance in chronic moderate–severe traumatic brain injury

Traumatic brain injury (TBI) alters brain network connectivity. Structural covariance networks (SCNs) reflect morphological covariation between brain regions. SCNs may elucidate how altered brain network topology in TBI influences long-term outcomes. Here, we assessed whether SCN organisation is altered in individuals with chronic moderate–severe TBI (≥ 10 years post-injury) and associations with cognitive performance. This case–control study included fifty individuals with chronic moderate–severe TBI compared to 75 healthy controls recruited from an ongoing longitudinal head injury outcome study. SCNs were constructed using grey matter volume measurements from T1-weighted MRI images. Global and regional SCN organisation in relation to group membership and cognitive ability was examined using regression analyses. Globally, TBI participants had reduced small-worldness, longer characteristic path length, higher clustering, and higher modularity globally (p < 0.05). Regionally, TBI participants had greater betweenness centrality (p < 0.05) in frontal and central areas of the cortex. No significant associations were observed between global network measures and cognitive ability in participants with TBI (p > 0.05). Chronic moderate–severe TBI was associated with a shift towards a more segregated global network topology and altered organisation in frontal and central brain regions. There was no evidence that SCNs are associated with cognition.

Mapping progressive damage epicenters in epilepsy with generalized tonic-clonic seizures by causal structural covariance network density (CaSCNd)

AimsMapping progressive patterns of structural damage in epilepsies with idiopathic and secondarily generalized tonic-clonic seizures with causal structural covariance networks and multiple analysis strategies. MethodsPatients with idiopathic generalized tonic-clonic seizures (IGTCS) (n = 114) and secondarily generalized tonic-clonic seizures (SGTCS) (n = 125) were recruited. Morphometric parameter of gray matter volume was analyzed on structural MRI. Structural covariance network based on granger causality analysis (CaSCN) was performed on the cross-sectional morphometric data sorted by disease durations of patients. Seed-based CaSCN analysis was firstly carried out to map the progressive and influential patterns of damage to thalamus-related structures. A novel technique for voxel-based CaSCN density (CaSCNd) analysis was further proposed, enabling for identifying the epicenter of structural brain damage during the disease process. ResultsThe thalamus-associated CaSCNs demonstrated different patterns of progressive damage in two types of generalized tonic-clonic seizures. In IGTCS, the structural damage was predominantly driven from the thalamus, and expanded to the cortex, while in SGTCS, the damage was predominantly driven from the cortex, and expanded to the thalamus through the basal ganglia. CaSCNd analysis revealed that the IGTCS had an out-effect epicenter in the thalamus, whereas the SGTCS had equipotent in- and out-effects in the thalamus, cortex, and basal ganglia. ConclusionCaSCN revealed distinct damage patterns in the two types of GTCS, featuring with measurement of structural brain damage from the accumulating effect over a relatively long time period. Our work provided evidence for understanding network impairment mechanism underlying different GTCSs.