Gray Matter Structural Covariance Networks Research Articles

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.

Gray matter structural covariance networks patterns associated with autopsy-confirmed LATE-NC compared to Alzheimer's disease pathology

BackgroundCases with the limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC), Alzheimer's disease (AD), and mixed AD+TDP-43 pathology (AD+LATE-NC) share similar symptoms, which makes it a challenge for accurate diagnosis. Exploring the patterns of gray matter structural covariance networks (SCNs) in these three types may help to clarify the underlying mechanism and provide a basis for clinical interventions. MethodsWe included ante-mortem MRI data of 10 LATE-NC, 39 CE, and 25 CE+LATE-NC from the ADNI autopsy sample. We used four regions of interest (left posterior cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral prefrontal cortex) to anchor the default mode network (DMN), salience network (SN), and executive control network (ECN). Finally, we assessed the SCN alternations using a multi-regression model-based linear-interaction analysis. ResultsCases with autopsy-confirmed LATE-NC and AD showed increased structural associations involving DMN, ECN, and SN. Cases with AD+LATE-NC showed increased structural association within DMN while decreased structural association between DMN and ECN. The volume of peak clusters showed significant associations with cognition and AD pathology. ConclusionsThis study showed different SCN patterns in the cases with LATE-NC, AD, and AD+LATE-NC, and indicated the network disconnection mechanism underlying these three neuropathological progressions. Further, SCN may serve as an effective biomarker to distinguish between different types of dementia.

Alteration in topological organization characteristics of gray matter covariance networks in patients with prediabetes.

Prediabetes is associated with an increased risk of cognitive impairment and neurodegenerative diseases. However, the exact mechanism of prediabetes-related brain diseases has not been fully elucidated. The brain structure of patients with prediabetes has been damaged to varying degrees, and these changes may affect the topological characteristics of large-scale brain networks. The structural covariance of connected gray matter has been demonstrated valuable in inferring large-scale structural brain networks. The alterations of gray matter structural covariance networks in prediabetes remain unclear. This study aims to examine the topological features and robustness of gray matter structural covariance networks in prediabetes. A total of 48 subjects were enrolled in this study, including 23 patients with prediabetes (the PD group) and 25 age-and sex-matched healthy controls (the Ctr group). All subjects' high-resolution 3D T1 images of the brain were collected by a 3.0 Tesla MR machine. Mini-mental state examination was used to evaluate the cognitive status of each subject. We calculated the gray matter volume of 116 brain regions with automated anatomical labeling (AAL) template, and constructed gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis. The area under the curve (AUC) in conjunction with permutation testing was employed for testing the differences in network measures, which included small world parameter (Sigma), normalized clustering coefficient (Gamma), normalized path length (Lambda), global efficiency, characteristic path length, local efficiency, mean clustering coefficient, and network robustness parameters. The network in both groups followed small-world characteristics, showing that Sigma was greater than 1, the Lambda was much higher than 1, and Gamma was close to 1. Compared with the Ctr group, the network of the PD group showed increased Sigma, Lambda, and Gamma across a range of network sparsity. The Gamma of the PD group was significantly higher than that in the Ctr group in the network sparsity range of 0.12-0.16, but there was no difference between the 2 groups (all P>0.05). The grey matter network showed an increased characteristic path length and a decreased global efficiency in the PD group, but AUC analysis showed that there was no significant difference between groups (all P>0.05). For the network separation measures, the local efficiency and mean clustering coefficient of the gray matter network in the PD group were significantly increased and AUC analysis also confirmed it (P=0.001 and P=0.004, respectively). In addition, network robustness analysis showed that the grey matter network of the PD group was more vulnerable to random damage (P=0.001). The prediabetic gray matter network shows an increased average clustering coefficient and local efficiency, and is more vulnerable to random damage than the healthy control, suggesting that the topological characteristics of the prediabetes grey matter covariant network have changed (network separation enhanced and network robustness reduced), which may provide new insights into the brain damage relevant to the disease.

Altered gray matter structural covariance networks in drug-naïve and treated early HIV-infected individuals.

BackgroundWhile regional brain structure and function alterations in HIV-infected individuals have been reported, knowledge about the topological organization in gray matter networks is limited. This research aims to investigate the effects of early HIV infection and combination antiretroviral therapy (cART) on gray matter structural covariance networks (SCNs) by employing graph theoretical analysis.MethodsSixty-five adult HIV+ individuals (25–50 years old), including 34 with cART (HIV+/cART+) and 31 medication-naïve (HIV+/cART–), and 35 demographically matched healthy controls (HCs) underwent high-resolution T1-weighted images. A sliding-window method was employed to create “age bins,” and SCNs (based on cortical thickness) were constructed for each bin by calculating Pearson's correlation coefficients. The group differences of network indices, including the mean nodal path length (Nlp), betweenness centrality (Bc), number of modules, modularity, global efficiency, local efficiency, and small-worldness, were evaluated by ANOVA and post-hoc tests employing the network-based statistics method.ResultsRelative to HCs, less efficiency in terms of information transfer in the parietal and occipital lobe (decreased Bc) and a compensated increase in the frontal lobe (decreased Nlp) were exhibited in both HIV+/cART+ and HIV+/cART– individuals (P < 0.05, FDR-corrected). Compared with HIV+/cART– and HCs, less specialized function segregation (decreased modularity and small-worldness property) and stronger integration in the network (increased Eglob and little changed path length) were found in HIV+/cART+ group (P < 0.05, FDR-corrected).ConclusionEarly HIV+ individuals exhibited a decrease in the efficiency of information transmission in sensory regions and a compensatory increase in the frontal lobe. HIV+/cART+ showed a less specialized regional segregation function, but a stronger global integration function in the network.

Structural Covariance Network as an Endophenotype in Alzheimer's Disease-Susceptible Single-Nucleotide Polymorphisms and the Correlations With Cognitive Outcomes.

The cognitive manifestations of Alzheimer’s disease (AD) are related to brain network degeneration, and genetic differences may mediate network degeneration. Several AD-susceptible loci have been reported to involve amyloid or tau cascades; however, their relationships with gray matter (GM) volume and cognitive outcomes have yet to be established. We hypothesized that single-nucleotide polymorphism genotype groups may interact with apolipoprotein E4 (ApoE4) status or independently exert an effect on cognitive outcomes. We also hypothesized that GM structural covariance networks (SCNs) may serve as an endophenotype of the genetic effect, which, in turn, may be related to neurobehavior test scores. Gray matter SCNs were constructed in 324 patients with AD using T1 magnetic resonance imaging with independent component analysis (ICA). We assessed the effects of 15 genetic loci (rs9349407, rs3865444, rs670139, rs744373, rs3851179, rs11136000, rs3764650, rs610932, rs6887649, rs7849530, rs4866650, rs3765728, rs34011, rs6656401, and rs597668) using additive, recessive, and dominant models on cognitive outcomes. Statistical analysis was performed to explore the independent role of each locus, interactions with ApoE4 status, and relationships to GM ICA network intensity score. For outcome measures, we used the Mini-Mental State Examination (MMSE), Cognitive Abilities Screening Instrument (CASI) total score, and short-term memory (STM) subscores, adjusted for the covariates of education, disease duration, and age. Clinically, the CD2AP G allele showed a protective role in MMSE, CASI total, and CASI-STM scores independently or via interactions with non-ApoE4 status, while the CR1 A genotype group was associated with lower STM subscores independent of ApoE4 status. Three loci showed synergic interactions with ApoE4: BIN 1, MS4A6A, and FTMT. Of the 15 meaningful ICA components, 5 SCNs (anterior and posterior hippocampus, right temporal, left thalamus, default mode network) showed relationships with general cognitive performance, in which only the ApoE4 and MS4A6A genotype groups were independently related to the hippocampus network. The genetic loci MS4A6A, BIN1, CLU, CR1, BIN1, PICALM, and FGF1 influenced the networks independently or in synergy. This study suggests that AD-susceptible loci may each exert clinical significance independently through interactions with ApoE4 status or through SCNs as an endophenotype and that this effect is associated with the cognitive outcomes.

Aberrant Topological Patterns of Structural Covariance Networks in Cognitively Normal Elderly Adults With Mild Behavioral Impairment.

Mild behavioral impairment (MBI), characterized by the late-life onset of sustained and meaningful neuropsychiatric symptoms, is increasingly recognized as a prodromal stage of dementia. However, the underlying neural mechanisms of MBI remain unclear. Here, we examined alterations in the topological organization of the structural covariance networks of patients with MBI (N = 32) compared with normal controls (N = 38). We found that the gray matter structural covariance networks of both the patients with MBI and controls exhibited a small-world topology evidenced by sigma value larger than one. The patients with MBI had significantly decreased clustering coefficients at several network densities and local efficiency at densities ranging from 0.05 to 0.26, indicating decreased local segregation. No significant differences in the characteristic path length, gamma value, sigma value, or global efficiency were detected. Locally, the patients with MBI showed significantly decreased nodal betweenness centrality in the left middle frontal gyrus, right inferior frontal gyrus (opercular part), and left Heschl gyrus and increased betweenness centrality in the left gyrus rectus, right insula, bilateral precuneus, and left thalamus. Moreover, the difference in the bilateral precuneus survived after correcting for multiple comparisons. In addition, a different number and distribution of hubs was identified in patients with MBI, showing more paralimbic hubs than observed in the normal controls. In conclusion, we revealed abnormal topological patterns of the structural covariance networks in patients with MBI and offer new insights into the network dysfunctional mechanisms of MBI.

Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease.

Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

Altered gray matter structural covariance networks in postpartum depression: a graph theoretical analysis

BackgroundPostpartum depression (PPD) is a serious postpartum mental health problem worldwide. To date, minimal is known about the alteration of topographical organization in the brain structural covariance network of patients with PPD. This study investigates the brain structural covariance networks of patients with PPD by using graph theoretical analysis. MethodsHigh-resolution 3D T1 structural images were acquired from 21 drug-naive patients with PPD and 18 healthy postpartum women. Cortical thickness was extracted from 64 brain regions to construct the whole-brain structural covariance networks by calculating the Pearson correlation coefficients, and their topological properties (e.g., small-worldness, efficiency, and nodal centrality) were analyzed by using graph theory. Nonparametric permutation tests were further used for group comparisons of topological metrics. A node was set as a hub if its betweenness centrality (BC) was at least two standard deviations higher than the mean nodal centrality. Network-based statistic (NBS) was used to determine the connected subnetwork. ResultsThe PPD and control groups showed small-worldness of group networks, but the small-world network was more evidently in the PPD group. Moreover, the PPD group showed increased network local efficiency and almost similar network global efficiency. However, the difference of the network metrics was not significant across the range of network densities. The hub nodes of the patients with PPD were right inferior parietal lobule (BC = 13.69) and right supramarginal gyrus (BC = 13.15), whereas those for the HCs were left cuneus (BC = 14.96), right caudal anterior-cingulate cortex (BC = 15.51), and right precuneus gyrus (BC = 15.74). NBS demonstrated two disrupted subnetworks that are present in PPD: the first subnetwork with decreased internodal connections is mainly involved in the cognitive-control network and visual network, and the second subnetwork with increased internodal connections is mainly involved in the default mode network, cognitive-control network and visual network. ConclusionsThis study demonstrates the alteration of topographical organization in the brain structural covariance network of patients with PPD, providing in sight on the notion that PPD could be characterized as a systems-level disorder.

Altered gray matter structural covariance networks at both acute and chronic stages of mild traumatic brain injury.

Cognitive and emotional impairments observed in mild traumatic brain injury (mTBI) patients may reflect variances of brain connectivity within specific networks. Although previous studies found altered functional connectivity (FC) in mTBI patients, the alterations of brain structural properties remain unclear. In the present study, we analyzed structural covariance (SC) for the acute stages of mTBI (amTBI) patients, the chronic stages of mTBI (cmTBI) patients, and healthy controls. We first extracted the mean gray matter volume (GMV) of seed regions that are located in the default-mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), and the visual network (VN). Then we determined and compared the SC for each seed region among the amTBI, the cmTBI and the healthy controls. Compared with healthy controls, the amTBI patients showed lower SC for the ECN, and the cmTBI patients showed higher SC for the both DMN and SN but lower SC for the SMN. The results revealed disrupted ECN in the amTBI patients and disrupted DMN, SN and SMN in the cmTBI patients. These alterations suggest that early disruptions in SC between bilateral insula and the bilateral prefrontal cortices may appear in amTBI and persist into cmTBI, which might be potentially related to the cognitive and emotional impairments.

Abnormal Gray Matter Structural Covariance Networks in Children With Bilateral Cerebral Palsy.

Bilateral cerebral palsy (BCP) is a common movement disorder in children, which often results in lifelong motor disability. One main symptom of BCP is the limitation of hand function in everyday activities. However, the neuroanatomical basis of this prominent hand impairment is yet to discover. Recent advances mainly focus on the lesions of BCP, but the views on the atypical development of cortical parcellations are extremely lacking. Here, in our study, neuroimaging with network analysis was employed to evaluate the changes of structural covariance networks (SCNs) in BCP children. We aimed to elucidate the alteration of SCNs based on cortical thickness (CT), and to reveal the relationship of CT and hand function in the participants with BCP. SCNs were constructed using covariance between regional CT, which was acquired from T1-weighted images of 19 children with BCP and 19 demographically matched healthy controls (HCs). Compared with HCs, BCP children showed increased CT in several regions involving the bilateral areas (lateral occipital, lingual, and fusiform) and right areas (cuneus, pericalcarine, inferior temporal, middle temporal, superior temporal, and insula). Decreased CT was found in the left superior temporal and right superior parietal cortices. Global network analyses revealed significantly decreased normalized clustering and small-worldness in the BCP network. The area under the curve (AUC) of global network measures varied slightly between the BCP and HC networks. The resistance of the both SCNs to the target and random attack showed no significant difference. Also, the BCP foci (right superior temporal and subtemporal cortex) showed a significantly negative correlation between the CT and manual ability. In this work, we identified the CT-based SCNs changes in children with BCP. The abnormal topological organization of SCNs was revealed, indicating abnormal CT, incongruous development of structural wiring, destructive nodal profiles of betweenness, and moved hub distribution in BCP children. This may provide a neuroanatomical hallmark of BCP in the developing brain. Therefore, our results may not only reflect neurodevelopmental aberrations but also compensatory mechanisms.

Gray matter structural covariance networks changes along the Alzheimer's disease continuum.

Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1–42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T−), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker.

Altered Topological Properties of Gray Matter Structural Covariance Networks in Minimal Hepatic Encephalopathy.

Background and Aims: Liver cirrhosis commonly induces brain structural impairments that are associated with neurological complications (e.g., minimal hepatic encephalopathy (MHE)), but the topological characteristics of the brain structural network are still less well understood in cirrhotic patients with MHE. This study aimed to conduct the first investigation on the topological alterations of brain structural covariance networks in MHE.Methods: This study included 22 healthy controls (HCs) and 22 cirrhotic patients with MHE. We calculated the gray matter volume of 90 brain regions using an automated anatomical labeling (AAL) template, followed by construction of gray matter structural covariance networks by thresholding interregional structural correlation matrices as well as graph theoretical analysis.Results: MHE patients showed abnormal small-world properties of the brain structural covariance network, i.e., decreased clustering coefficient and characteristic path length and lower small-worldness parameters, which indicated a tendency toward more random architecture. In addition, MHE patients lost hubs in the prefrontal and parietal regions, although they had new hubs in the temporal and occipital regions. Compared to HC, MHE patients had decreased regional degree/betweenness involving several regions, primarily the prefrontal and parietal lobes, motor region, insula and thalamus. In addition, the MHE group also showed increased degree/betweenness in the occipital lobe and hippocampus.Conclusion: These results suggest that MHE leads to altered coordination patterns of gray matter morphology and provide structural evidence supporting the idea that MHE is a neurological complication related to disrupted neural networks.

Salience Network and Depressive Severities in Parkinson's Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis.

Purpose: In Parkinson’s disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance.Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores.Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046).Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.

Genetic effect of MTHFR C677T polymorphism on the structural covariance network and white-matter integrity in Alzheimer's disease.

The 677 C to T transition in the MTHFR gene is a genetic determinant for hyperhomocysteinemia. We investigated whether this polymorphism modulates gray matter (GM) structural covariance networks independently of white-matter integrity in patients with Alzheimer's disease (AD). GM structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed-based analysis. The patients were divided into two genotype groups: C homozygotes (n = 73) and T carriers (n = 62). Using diffusion tensor imaging and white-matter parcellation, 11 fiber bundle integrities were compared between the two genotype groups. Cognitive test scores were the major outcome factors. The T carriers had higher homocysteine levels, lower posterior cingulate cortex GM volume, and more clusters in the dorsal medial lobe subsystem showing stronger covariance strength. Both posterior cingulate cortex seed and interconnected peak cluster volumes predicted cognitive test scores, especially in the T carriers. There were no between-group differences in fiber tract diffusion parameters. The MTHFR 677T polymorphism modulates posterior cingulate cortex-anchored structural covariance strength independently of white matter integrities. Hum Brain Mapp 38:3039-3051, 2017. © 2017 The Authors Human Brain Mapping Published Wiley by Periodicals, Inc.

Genetic effect of interleukin-1 beta (C-511T) polymorphism on the structural covariance network and white matter integrity in Alzheimer\u2019s disease

BackgroundInflammatory processes play a pivotal role in the degenerative process of Alzheimer’s disease. In humans, a biallelic (C/T) polymorphism in the promoter region (position-511) (rs16944) of the interleukin-1 beta gene has been significantly associated with differences in the secretory capacity of interleukin-1 beta. In this study, we investigated whether this functional polymorphism mediates the brain networks in patients with Alzheimer’s disease.MethodsWe enrolled a total of 135 patients with Alzheimer’s disease (65 males, 70 females), and investigated their gray matter structural covariance networks using 3D T1 magnetic resonance imaging and their white matter macro-structural integrities using fractional anisotropy. The patients were classified into two genotype groups: C-carriers (n = 108) and TT-carriers (n = 27), and the structural covariance networks were constructed using seed-based analysis focusing on the default mode network medial temporal or dorsal medial subsystem, salience network and executive control network. Neurobehavioral scores were used as the major outcome factors for clinical correlations.ResultsThere were no differences between the two genotype groups in the cognitive test scores, seed, or peak cluster volumes and white matter fractional anisotropy. The covariance strength showing C-carriers > TT-carriers was the entorhinal-cingulum axis. There were two peak clusters (Brodmann 6 and 10) in the salience network and four peak clusters (superior prefrontal, precentral, fusiform, and temporal) in the executive control network that showed C-carriers < TT-carriers in covariance strength. The salience network and executive control network peak clusters in the TT group and the default mode network peak clusters in the C-carriers strongly predicted the cognitive test scores.ConclusionsInterleukin-1 beta C-511 T polymorphism modulates the structural covariance strength on the anterior brain network and entorhinal-interconnected network which were independent of the white matter tract integrity. Depending on the specific C-511 T genotype, different network clusters could predict the cognitive tests.

Altered Gray Matter Structural Covariance Networks in Early Stages of Alzheimer's Disease.

Clinical symptoms observed in Alzheimer's disease (AD) patients may reflect variations within specific large-scale brain networks, modeling AD as a disconnection syndrome. The present magnetic resonance imaging study aims to compare the organization of gray matter structural covariance networks between 109 cognitively unimpaired controls (CTRL) and 109 AD patients positive to beta-amyloid at the early stages of the disease, using voxel-based morphometry. The default-mode network (DMN; medial temporal lobe subsystem) was less extended in AD patients in comparison with CTRL, with a significant decrease in the structural association between the entorhinal cortex and the medial prefrontal and the dorsolateral prefrontal cortices. The DMN (midline core subsystem) was also less extended in AD patients. Trends toward increased structural association were observed in the salience and executive control networks. The observed changes suggest that early disruptions in structural association between heteromodal association cortices and the entorhinal cortex could contribute to an isolation of the hippocampal formation, potentially giving rise to the clinical hallmark of AD, progressive memory impairment. It also provides critical support to the hypothesis that the reduced connectivity within the DMN in early AD is accompanied by an enhancement of connectivity in the salience and executive control networks.

The impact of aging on gray matter structural covariance networks

Previous anatomical volumetric studies have shown that healthy aging is associated with gray matter tissue loss in specific cerebral regions. However, these studies may have potentially missed critical elements of age-related brain changes, which largely exist within interrelationships among brain regions. This magnetic resonance imaging research aims to assess the effects of aging on the organization of gray matter structural covariance networks. Here, we used voxel-based morphometry on high-definition brain scans to compare the patterns of gray matter structural covariance networks that sustain different sensorimotor and high-order cognitive functions among young (n=88, mean age=23.5±3.1 years, female/male=55/33) and older (n=88, mean age=67.3±5.9 years, female/male=55/33) participants. This approach relies on the assumption that functionally correlated brain regions show correlations in gray matter volume as a result of mutually trophic influences or common experience-related plasticity. We found reduced structural association in older adults compared with younger adults, specifically in high-order cognitive networks. Major differences were observed in the structural covariance networks that subserve the following: a) the language-related semantic network, b) the executive control network, and c) the default-mode network. Moreover, these cognitive functions are typically altered in the older population. Our results indicate that healthy aging alters the structural organization of cognitive networks, shifting from a more distributed (in young adulthood) to a more localized topological organization in older individuals.