Atypical characteristic changes of surface morphology and structural covariance network in developmental dyslexia

BACKGROUND

Developmental dyslexia (DD) is a neurodevelopmental disorder that is characterized by difficulties with all aspects of information acquisition in the written word, including slow and inaccurate word recognition. The neural basis behind DD has not been fully elucidated.

METHOD

The study included 22 typically developing (TD) children, 16 children with isolated spelling disorder (SpD), and 20 children with DD. The cortical thickness, folding index, and mean curvature of Broca's area, including the triangular part of the left inferior frontal gyrus (IFGtriang) and the opercular part of the left inferior frontal gyrus, were assessed to explore the differences of surface morphology among the TD, SpD, and DD groups. Furthermore, the structural covariance network (SCN) of the triangular part of the left inferior frontal gyrus was analyzed to explore the changes of structural connectivity in the SpD and DD groups.

RESULTS

The DD group showed higher curvature and cortical folding of the left IFGtriang than the TD group and SpD group. In addition, compared with the TD group and the SpD group, the structural connectivity between the left IFGtriang and the left middle-frontal gyrus and the right mid-orbital frontal gyrus was increased in the DD group, and the structural connectivity between the left IFGtriang and the right precuneus and anterior cingulate was decreased in the DD group.

CONCLUSION

DD had atypical structural connectivity in brain regions related to visual attention, memory and which might impact the information input and integration needed for reading and spelling.

The volume and structural covariance network of thalamic nuclei in patients with Wilson's disease: an investigation of the association with neurological impairment

OBJECTIVE

This study aimed to examine the volumes of thalamic nuclei and the intrinsic thalamic network in patients with Wilson's disease (WDs), and to explore the correlation between these volumes and the severity of neurological symptoms.

METHODS

A total of 61 WDs and 33 healthy controls (HCs) were included in the study. The volumes of 25 bilateral thalamic nuclei were measured using structural imaging analysis with Freesurfer, and the intrinsic thalamic network was evaluated through structural covariance network (SCN) analysis.

RESULTS

The results indicated that multiple thalamic nuclei were smaller in WDs compared to HCs, including mediodorsal medial magnocellular (MDm), anterior ventral (AV), central median (CeM), centromedian (CM), lateral geniculate (LGN), limitans-suprageniculate (L-Sg), reuniens-medial ventral (MV), paracentral (Pc), parafascicular (Pf), paratenial (Pt), pulvinar anterior (PuA), pulvinar inferior (PuI), pulvinar medial (PuM), ventral anterior (VA), ventral anterior magnocellular (VAmc), ventral lateral anterior (VLa), ventral lateral posterior (VLp), ventromedial (VM), ventral posterolateral (VPL), and right middle dorsal intralaminar (MDI). The study also found a negative correlation between the UWDRS scores and the volume of the right MDm. The intrinsic thalamic network analysis showed abnormal topological properties in WDs, including increased mean local efficiency, modularity, normalized clustering coefficient, small-world index, and characteristic path length, and a corresponding decrease in mean node betweenness centrality. WDs with cerebral involvement had a lower modularity compared to HCs.

CONCLUSIONS

The findings suggest that the majority of thalamic nuclei in WDs exhibit significant volume reduction, and the atrophy of the right MDm is closely related to the severity of neurological symptoms. The intrinsic thalamic network in WDs demonstrated abnormal topological properties, indicating a close relationship with neurological impairment.

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

BACKGROUND

Despite 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.

METHODS

A 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.

RESULTS

In 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.

CONCLUSIONS

From 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.

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.

Parahippocampus hypertrophy drives gray matter morphological alterations in migraine patients without aura

BACKGROUND

The aberrance of gray matter morphology in migraineurs has been widely investigated. However, it remains largely unknown whether there are illness duration-related hierarchical changes in the gray matter structure.

METHODS

A total of 86 migraine without aura (MwoA) patients and 73 healthy controls were included. The Voxel-Based Morphometry approach was utilized to compare the gray matter volume (GMV) differences between MwoA patients and healthy controls. The Structural Covariance Network analysis was conducted to quantify the cross-regional synchronous alterations of gray matter structure in MwoA patients. The Causal Structural Covariance Network analysis was performed to describe the progressive and hierarchical changes in the gray matter network of patients in the pathological progression of migraine.

RESULTS

MwoA patients had duration-stage related GMV hypertrophy in the left parahippocampus, as well as synergistic GMV aberrance in the parahippocampus and the medial inferior temporal gyrus and cerebellum. Moreover, the GMV alteration of the parahippocampus, and the surrounding hippocampus, amygdala, and bilateral anterior cerebellum, preceded and causally influenced the morphological changes of lateral parietal-temporal-occipital gyrus, as well as the motor cortex and prefrontal gyrus with the increasing illness duration in MwoA patients.

CONCLUSION

The current study indicated that gray matter structural alterations in the medial inferior temporal gyrus, especially the parahippocampus, is a critical pathological characteristic in MwoA patients, which drives the gray matter structure alteration of other regions. These findings provide further evidence for understanding the progressive gray matter morphological changes in migraine and may facilitate the development of neuromodulation therapies targeting this procession.

Multipronged investigation of morphometry and connectivity of hippocampal network in relation to risk for psychosis using ultrahigh field MRI

Hippocampal abnormalities are associated with psychosis-risk states. Given the complexity of hippocampal anatomy, we conducted a multipronged examination of morphometry of regions connected with hippocampus, and structural covariance network (SCN) and diffusion-weighted circuitry among 27 familial high-risk (FHR) individuals who were past the highest risk for conversion to psychoses and 41 healthy controls using ultrahigh-field high-resolution 7 Tesla (7T) structural and diffusion MRI data. We obtained fractional anisotropy and diffusion streams of white matter connections and examined correspondence of diffusion streams with SCN edges. Nearly 89 % of the FHR group had an axis-I disorder including 5 with schizophrenia. Therefore, we compared the entire FHR group regardless of the diagnosis (All_FHR = 27) and FHR-without-schizophrenia (n = 22) with 41 controls in this integrative multimodal analysis. We found striking volume loss in bilateral hippocampus, particularly the head, bilateral thalamus, caudate, and prefrontal regions. All_FHR and FHR-without-SZ SCNs showed significantly lower assortativity and transitivity but higher diameter compared to controls, but FHR-without-SZ SCN differed on every graph metric compared to All_FHR suggesting disarrayed network with no hippocampal hubs. Fractional anisotropy and diffusion streams were lower in FHR suggesting white matter network impairment. White matter edges showed significantly higher correspondence with SCN edges in FHR compared to controls. These differences correlated with psychopathology and cognitive measures. Our data suggest that hippocampus may be a "neural hub" contributing to psychosis risk. Higher correspondence of white matter tracts with SCN edges suggest that shared volume loss may be more coordinated among regions within the hippocampal white matter circuitry.

Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline

BACKGROUND

Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals.

METHODS

One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups.

RESULTS

Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group.

CONCLUSION

Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Introduction

Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD).

Method

We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144).

Result

The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD.

Conclusion

This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

Decoding transcriptional signatures of the association between free water and macroscale organizations in healthy adolescents

We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, p_spin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, p_spin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.

Structural covariance networks in schizophrenia: A systematic review Part I

BACKGROUND

Schizophrenia is proposed as a disorder of dysconnectivity. However, examination of complexities of dysconnectivity has been challenging. Structural covariance networks (SCN) provide important insights into the nature of dysconnectivity. This systematic review examines the SCN studies that employed statistical approaches to elucidate covariation of regional morphometric variations.

METHODS

A systematic search of literature was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia. Fifty-two studies met the criteria.

RESULTS

Early SCN studies began using correlational structure of selected regions. Over the last 3 decades, methodological approaches have grown increasingly sophisticated from examining selected brain regions using correlation tests on small sample sizes to recent approaches that use advanced statistical methods to examine covariance structure of whole-brain parcellations on larger samples. Although the results are not fully consistent across all studies, a pattern of fronto-temporal, fronto-parietal and fronto-thalamic covariation is reported. Attempts to associate SCN alterations with functional connectivity, to differentiate between disease-related and neurodevelopment-related morphometric changes, and to develop "causality-based" models are being reported. Clinical correlation with outcome, psychotic symptoms, neurocognitive and social cognitive performance are also reported.

CONCLUSIONS

Application of advanced statistical methods are beginning to provide insights into interesting patterns of regional covariance including correlations with clinical and cognitive data. Although these findings appear similar to morphometric studies, SCNs have the advantage of highlighting topology of these regions and their relationship to the disease and associated variables. Further studies are needed to investigate neurobiological underpinnings of shared covariance, and causal links to clinical domains.

Structural covariance networks in schizophrenia: A systematic review Part II

BACKGROUND

Examination of structural covariance network (SCN) is gaining prominence among the strategies to delineate dysconnectivity that case-control morphometric comparisons cannot address. Part II of this review extends on the part I of the review that included SCN studies using statistical approaches by examining SCN studies applying graph theoretic approaches to elucidate network properties in schizophrenia. This review also includes SCN studies using graph theoretic or statistical approaches on persons at-risk for schizophrenia.

METHODS

A systematic literature search was conducted for peer-reviewed publications using different keywords and keyword combinations for schizophrenia and risk for schizophrenia. Thirteen studies on schizophrenia and five on persons at risk for schizophrenia met the criteria.

RESULTS

A variety of findings from over the last 1½ decades showing qualitative and quantitative differences in the global and local structural connectome in schizophrenia are described. These observations include altered hub patterns, disrupted network topology and hierarchical organization of the brain, and impaired connections that may be localized to default mode, executive control, and dorsal attention networks. Some of these connectomic alterations were observed in persons at-risk for schizophrenia before the onset of the illness.

CONCLUSIONS

Observed disruptions may reduce network efficiency and capacity to integrate information. Further, global connectomic changes were not schizophrenia-specific but local network changes were. Existing studies have used different atlases for brain parcellation, examined different morphometric features, and patients at different stages of illness making it difficult to conduct meta-analysis. Future studies should harmonize such methodological differences to facilitate meta-analysis and also elucidate causal underpinnings of dysconnectivity.

Potential structural trait markers of depression in the form of alterations in the structures of subcortical nuclei and structural covariance network properties

It has been proposed recently that major depressive disorder (MDD) could represent an adaptation to conserve energy after the perceived loss of an investment in a vital source, such as group identity, personal assets, or relationships. Energy conserving behaviors associated with MDD may form a persistent marker in brain regions and networks involved in cognition and emotion regulation. In this study, we examined whether subcortical regions and volume-based structural covariance networks (SCNs) have state-independent alterations (trait markers). First-episode drug-naïve currently depressed (cMDD) patients (N = 131), remitted MDD (RD) patients (N = 67), and healthy controls (HCs, N = 235) underwent structural magnetic resonance imaging (MRI). Subcortical gray matter volumes (GMVs) were calculated in FreeSurfer software, and group differences in GMVs and SCN were analyzed. Compared to HCs, major findings were decreased GMVs of left pallidum and pulvinar anterior of thalamus in the cMDD and RD groups, indicative of a trait marker. Relative to HCs, subcortical SCNs of both cMDD and RD patients were found to have reduced small-world-ness and path length, which together may represent a trait-like topological feature of depression. In sum, the left pallidum, left pulvinar anterior of thalamus volumetric alterations may represent trait marker and reduced small-world-ness, path length may represent trait-like topological feature of MDD.

Altered structural covariance of hippocampal subregions in patients with Alzheimer's disease

BACKGROUND AND PURPOSE

Different atrophy of hippocampus subregions is a valuable indicator of patients with Alzheimer's disease (AD). To explore the relationship among the hippocampal subregions of patients with AD, altered gray matter structural covariance of hippocampal subregions in patients with AD was studied.

MATERIALS AND METHODS

Participants were selected from the Open Access Series of Imaging Studies Database. Pearson correlations among the volume of the hippocampal subregions were generated as structural covariance network. Topological metrics for all selected sparsity ranges were calculated in the healthy controls (HCs) and patients with AD by using the GRETNA software package. Spearman correlation analysis was performed to statistically analyze the volume and Mini-mental State Examination (MMSE) scores of the hippocampal subregions of the patients with AD, with age and gender as interference covariates and corrected for false discovery rate (FDR) (p < 0.05).

RESULTS

The structural covariance network properties of the hippocampal subregions of patients with AD changed. The clustering coefficient (Cp) and network efficiency (Ne) decreased, characteristic path length (Lp) increased, and the hub nodes changed. The volumes of left parasubiculum, right granule cell layer of dentate gyrus (GC-DG), right molecular layer of the hippocampus (molecular_layer_HP), right Cornu Ammonis (CA) regions CA1 of the hippocampus proper, right fimbria and right CA4 were significantly correlated with the MMSE scores.

CONCLUSIONS

The structural covariance network of the hippocampal subregions of patients with AD was reorganized, and the transmission efficiency was weakened. This study explored the changes in these subregions from the network level, which may provide a new perspective and theoretical basis for the neurobiological mechanisms of patients with AD.

Disrupted structural covariance network in first episode schizophrenia patients: Evidence from a large sample MRI-based morphometric study

BACKGROUND

Recent progress in neuroscience research has provided evidence that schizophrenia is a disease that involves dysconnectivity of brain networks. Widespread gray matter loss was commonly observed but how these gray matter abnormalities are characterized at the large-scale network-level in schizophrenia, especially patients with first-episode (FE-SCZ) remains unclear.

METHODS

In this study, gray matter structural network aberrations were investigated by applying structural covariance network analysis to 193 first episode schizophrenia patients and 178 age and gender-matched healthy controls (HCs). The mean gray matter volume in seed regions relating to eight specific networks (visual, auditory, sensorimotor, speech, semantic, default-mode, executive control, and salience) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain gray matter volume and each seed region for FE-SCZ and HCs.

RESULTS

The auditory network was less extended in FE-SCZ compared with HCs, with a significant decrease in the structural association between the Hesch's gyrus and the middle frontal gyrus and the superior frontal gyrus. Hyperconnectivity was observed in executive control network with a significant increase in the structural association between the dorsal lateral prefrontal cortex and the superior frontal gyrus and supplementary motor area.

CONCLUSION

Our research shows that seed based structural covariance analysis can well characterize multiple large-scale networks, the observed changes might underly the hallucinations and cognitive impairments observed in FE-SCZ. Given that these patients were experiencing their first episode of schizophrenia, our findings suggest that such structural network deficits are present at an early stage in this disorder.

The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains

Background

Although structural correlation network (SCN) analysis is an approach to evaluate brain networks, the neurobiological interpretation of SCNs is still problematic. Brain-derived neurotrophic factor (BDNF) is well-established as a representative protein related to neuronal differentiation, maturation, and survival. Since a valine-to-methionine substitution at codon 66 of the BDNF gene (BDNF Val66Met single nucleotide polymorphism (SNP)) is well-known to have effects on brain structure and function, we hypothesized that SCNs are affected by the BDNF Val66Met SNP. To gain insight into SCN analysis, we investigated potential differences between BDNF valine (Val) homozygotes and methionine (Met) carriers in the organization of their SCNs derived from inter-regional cortical thickness correlations.

Methods

Forty-nine healthy adult subjects (mean age = 41.1 years old) were divided into two groups according to their genotype (n: Val homozygotes = 16, Met carriers = 33). We obtained regional cortical thickness from their brain T1 weighted images. Based on the inter-regional cortical thickness correlations, we generated SCNs and used graph theoretical measures to assess differences between the two groups in terms of network integration, segregation, and modularity.

Results

The average local efficiency, a measure of network segregation, of BDNF Met carriers’ network was significantly higher than that of the Val homozygotes’ (permutation p-value = 0.002). Average shortest path lengths (a measure of integration), average local clustering coefficient (another measure of network segregation), small-worldness (a balance between integration and segregation), and modularity (a representative measure for modular architecture) were not significantly different between group (permutation p-values ≧ 0.01).

Discussion and Conclusion

Our results suggest that the BDNF Val66Met polymorphism may potentially influence the pattern of brain regional morphometric (cortical thickness) correlations. Comparing networks derived from inter-regional cortical thickness correlations, Met carrier SCNs have denser connections with neighbors and are more distant from random networks than Val homozygote networks. Thus, it may be necessary to consider potential effects of BDNF gene mutations in SCN analyses. This is the first study to demonstrate a difference between Val homozygotes and Met carriers in brain SCNs.

Determining the effects of LLD and MCI on brain decline according to machine learning and a structural covariance network analysis

BACKGROUND

Late-life depression (LLD) and mild cognitive impairment (MCI) are risk factors for Alzheimer disease (AD). However, the interactive effect between LLD and MCI in the progression to AD remains unknown. The purpose of this research is to clarify whether this interaction exists and determined the characteristics of the structural change patterns in LLD and MCI.

METHOD

To address this question, a total 225 participants (91 with intact cognitive function (IC), 34 with MCI, 35 with LLD-IC, 47 with LLD-MCI and 18 with AD) were recruited for the current study and their T1 scanning were acquired. Machine learning was applied to estimate the brain's age gap according to grey matter information (thickness and volume was calculated based on the Human Connectome Project Multi-Modal Parcellation version 1.0 and the Desikan atlas). A structural covariance network (SCN) was constructed based on grey matter volume. Rich-club analysis, global network properties and the Jaccard distance were utilized to describe the topological features in each cohort. Their cognitive functions (executive function, processing speed and memory) were evaluated by a full-scale battery of neuropsychological tests.

RESULT

The interactive effect between LLD and MCI was detected through the brain age gap. The estimated age was positively correlated with processing speed and memory in LLD and non-LLD subjects. In the SCN analysis, the rich-club coefficient and global network properties were disrupted in the MCI group, but remained normal in the LLD-IC, LLD-MCI and AD groups. There was a significant discrepancy in brain structural change patterns between the AD and other cohorts by the Jaccard distance.

CONCLUSION

The application of machine learning reflects that synergies between LLD and MCI could increase the risk of developing AD. According to the SCN, the structural coordination was disrupted in MCI and was kept normal in the other cohorts, while the discrepancies in brain structural change patterns appeared in AD. Overall, the brain age gap could be a potential predictor of AD, and the Jaccard distance has the potential to be a new type of SCN analysis indicator.

The anhedonia is differently modulated by structural covariance network of NAc in bipolar disorder and major depressive disorder

During depressive episode, bipolar disorder (BD) patients share indistinguishable depression symptoms with major depressive disorder (MDD).However, whether neural correlates underlying the anhedonia, a core feature of depression, is different between BD and MDD remains unknown. To explore neural correlates underlying the anhedonia in BD and MDD, structural T1-weighted images from 36 depressed BD patients, 40 depressed MDD patients matched for depression severity and 34 health controls (HCs) were scanned. Considering the vital role of nucleus accumbens (NAc) in the anhedonia, we constructed the structural covariance network of NAc for each subject. Then, we explored altered structural covariance network of NAc and its interaction with the anhedonia severity in BD and MDD patients. As a result, BD and MDD patients shared decreased structural covariance of NAc connected to prefrontal gyrus, bilateral striatum extending to bilateral anterior insula. Apart from these regions, BD patients presented specifically increased structural covariance of NAc connected to left hippocampus extending to thalamus. The interaction between structural covariance network of NAc and the anhedonia severity in MDD was mainly associated anterior insula (AIC), amygdala, anterior cingulate cortex (ACC)and caudate while that in BD was mainly located in striatum and prefrontal cortex. Our results found that BD and MDD patients presented commonly and distinctly altered structural covariance network of NAc. What is more, the neural correlates underlying the anhedonia in BD and MDD might be different.

Phenotype Network and Brain Structural Covariance Network of Anxiety

Network-based approach for psychological phenotypes assumes the dynamical interactions among the psychiatric symptoms, psychological characteristics, and neurocognitive performances arise, as they coexist, propagate, and inhibit other components within the network of mental phenomena. For differential types of dataset from which the phenotype network is to be estimated, a Gaussian graphical model, an Ising model, a directed acyclic graph, or an intraindividual covariance network could be used. Accordingly, these network-based approaches for anxiety-related psychological phenomena have been helpful in quantitative and pictorial understanding of qualitative dynamics among the diverse psychological phenomena as well as mind-environment interactions. Brain structural covariance refers to the correlative patterns of diverse brain morphological features among differential brain regions comprising the brain, as calculated per participant or across the participants. These covarying patterns of brain morphology partly overlap with longitudinal patterns of brain cortical maturation and also with propagating pattern of brain morphological changes such as cortical thinning and brain volume reduction in patients diagnosed with neurologic or psychiatric disorders along the trajectory of disease progression. Previous studies that used the brain structural covariance network could show neural correlates of specific anxiety disorder such as panic disorder and also elucidate the neural underpinning of anxiety symptom severity in diverse psychiatric and neurologic disorder patients.

Structural network integrity of the central executive network is associated with the therapeutic effect of rTMS in treatment resistant depression

Repetitive transcranial magnetic stimulation (rTMS) is a first-line option for treatment-resistant depression (TRD), but prediction of treatment outcome remains a clinical challenge. The present study aimed to compare structural and functional covariance networks (SCNs and FCNs) between remitters and nonremitters. We determined the predictive capacities of SCNs and FCNs to discriminate the two groups. Fifty TRD patients underwent a course of rTMS to the left dorsolateral prefrontal cortex. They were categorized into remitters (n = 22) and nonremitters (n = 28) based on HDRS≤7 at the end of treatment. Baseline structural and functional magnetic imaging (sMRI and fMRI) of the patients and 42 healthy controls were collected. SCNs and FCNs were defined based on structural and functional covariance of gray mater volume (GMV) and fractional amplitude of low-frequency fluctuations (fALFF) from sMRI and fMRI, respectively. Structural/functional network integrity of these networks (default mode network [DMN], central executive network [CEN] and salience network [SN]) were compared between the three groups. In patients, associations between SCNs and FCNs with clinical improvements were studied using linear correlation analysis. Receiver-operating characteristic (ROC) analysis was conducted to confirm the utility of the SCNs and FCNs in classifying clinical sub-groups. Nonremitters exhibited lower structural integrity in CEN than remitters and controls. Higher structural integrity of CEN was related to clinical improvement (r = 0.423, p = .002), and structural integrity distinguished remitters and nonremitters with a fairly high accuracy (AUC = 0.71, p = .008). No group differences or correlation with clinical changes were found in FCNs. Results suggest the CEN may play a role mediating clinical improvement in rTMS for depression. Structural covariance networks may be features to consider in prediction of clinical improvement.

Abnormal topological organization of structural covariance networks in amyotrophic lateral sclerosis

Neuroimaging studies of patients with amyotrophic lateral sclerosis (ALS) have shown widespread alterations in structure, function, and connectivity in both motor and non-motor brain regions, suggesting multi-systemic neurobiological abnormalities that might impact large-scale brain networks. Here, we examined the alterations in the topological organization of structural covariance networks of ALS patients (N = 60) compared with normal controls (N = 60). We found that structural covariance networks of ALS patients showed a consistent rearrangement towards a regularized architecture evidenced by increased path length, clustering coefficient, small-world index, and modularity, as well as decreased global efficiency, suggesting inefficient global integration and increased local segregation. Locally, ALS patients showed decreased nodal degree and betweenness in the gyrus rectus and/or Heschl's gyrus, and increased betweenness in the supplementary motor area, triangular part of the inferior frontal gyrus, supramarginal gyrus and posterior cingulate cortex. In addition, we identified a different number and distribution of hubs in ALS patients, showing more frontal and subcortical hubs than in normal controls. In conclusion, we reveal abnormal topological organization of structural covariance networks in ALS patients, and provide network-level evidence for the concept that ALS is a multisystem disorder with a cerebral involvement extending beyond the motor areas.

Structural covariance network centrality in maltreated youth with posttraumatic stress disorder

Childhood maltreatment is associated with posttraumatic stress disorder (PTSD) and elevated rates of adolescent and adult psychopathology including major depression, bipolar disorder, substance use disorders, and other medical comorbidities. Gray matter volume changes have been found in maltreated youth with (versus without) PTSD. However, little is known about the alterations of brain structural covariance network topology derived from cortical thickness in maltreated youth with PTSD. High-resolution T1-weighted magnetic resonance imaging scans were from demographically matched maltreated youth with PTSD (N = 24), without PTSD (N = 64), and non-maltreated healthy controls (n = 67). Cortical thickness data from 148 cortical regions was entered into interregional partial correlation analyses across participants. The supra-threshold correlations constituted connections in a structural brain network derived from four types of centrality measures (degree, betweenness, closeness, and eigenvector) estimated network topology and the importance of nodes. Between-group differences were determined by permutation testing. Maltreated youth with PTSD exhibited larger centrality in left anterior cingulate cortex than the other two groups, suggesting cortical network topology specific to maltreated youth with PTSD. Moreover, maltreated youth with versus without PTSD showed smaller centrality in right orbitofrontal cortex, suggesting that this may represent a vulnerability factor to PTSD following maltreatment. Longitudinal follow-up of the present results will help characterize the role that altered centrality plays in vulnerability and resilience to PTSD following childhood maltreatment.

Loss of integrity and atrophy in cingulate structural covariance networks in Parkinson's disease

BACKGROUND

In Parkinson's disease (PD), the relation between cortical brain atrophy on MRI and clinical progression is not straightforward. Determination of changes in structural covariance networks - patterns of covariance in grey matter density - has shown to be a valuable technique to detect subtle grey matter variations. We evaluated how structural network integrity in PD is related to clinical data.

METHODS

3 Tesla MRI was performed in 159 PD patients. We used nine standardized structural covariance networks identified in 370 healthy subjects as a template in the analysis of the PD data. Clinical assessment comprised motor features (Movement Disorder Society-Unified Parkinson's Disease Rating Scale; MDS-UPDRS motor scale) and predominantly non-dopaminergic features (SEverity of Non-dopaminergic Symptoms in Parkinson's Disease; SENS-PD scale: postural instability and gait difficulty, psychotic symptoms, excessive daytime sleepiness, autonomic dysfunction, cognitive impairment and depressive symptoms). Voxel-based analyses were performed within networks significantly associated with PD.

RESULTS

The anterior and posterior cingulate network showed decreased integrity, associated with the SENS-PD score, p = 0.001 (β = - 0.265, ηp2 = 0.070) and p = 0.001 (β = - 0.264, ηp2 = 0.074), respectively. Of the components of the SENS-PD score, cognitive impairment and excessive daytime sleepiness were associated with atrophy within both networks.

CONCLUSIONS

We identified loss of integrity and atrophy in the anterior and posterior cingulate networks in PD patients. Abnormalities of both networks were associated with predominantly non-dopaminergic features, specifically cognition and excessive daytime sleepiness. Our findings suggest that (components of) the cingulate networks display a specific vulnerability to the pathobiology of PD and may operate as interfaces between networks involved in cognition and alertness.

Imaging structural covariance in the development of intelligence

Verbal and non-verbal intelligence in children is highly correlated, and thus, it has been difficult to differentiate their neural substrates. Nevertheless, recent studies have shown that verbal and non-verbal intelligence can be dissociated and focal cortical regions corresponding to each have been demonstrated. However, the pattern of structural covariance corresponding to verbal and non-verbal intelligence remains unexplored. In this study, we used 586 longitudinal anatomical MRI scans of subjects aged 6-18 years, who had concurrent intelligence quotient (IQ) testing on the Wechsler Abbreviated Scale of Intelligence. Structural covariance networks (SCNs) were constructed using interregional correlations in cortical thickness for low-IQ (Performance IQ=100±8, Verbal IQ=100±7) and high-IQ (PIQ=121±8, VIQ=120±9) groups. From low- to high-VIQ group, we observed constrained patterns of anatomical coupling among cortical regions, complemented by observations of higher global efficiency and modularity, and lower local efficiency in high-VIQ group, suggesting a shift towards a more optimal topological organization. Analysis of nodal topological properties (regional efficiency and participation coefficient) revealed greater involvement of left-hemispheric language related regions including inferior frontal and superior temporal gyri for high-VIQ group. From low- to high-PIQ group, we did not observe significant differences in anatomical coupling patterns, global and nodal topological properties. Our findings indicate that people with higher verbal intelligence have structural brain differences from people with lower verbal intelligence - not only in localized cortical regions, but also in the patterns of anatomical coupling among widely distributed cortical regions, possibly resulting to a system-level reorganization that might lead to a more efficient organization in high-VIQ group.