Work Stress and Depressive Symptoms in Fishermen With a Smoking Habit: A Mediator Role of Nicotine Dependence and Possible Moderator Role of Expressive Suppression and Cognitive Reappraisal

Age, Disease, and Their Interaction Effects on Intrinsic Connectivity of Children and Adolescents in Autism Spectrum Disorder Using Functional Connectomics

Brain connectivity analysis has provided crucial insights to pinpoint the differences between autistic and typically developing (TD) children during development. The aim of this study is to investigate the functional connectomics of autism spectrum disorder (ASD) versus TD and underpin the effects of development, disease, and their interactions on the observed atypical brain connectivity patterns. Resting-state functional magnetic resonance imaging (rs-fMRI) from the Autism Brain Imaging Data Exchange (ABIDE) data set, which is stratified into two cohorts: children (9-12 years) and adolescents (13-16 years), is used for the analysis. Differences in various graph theoretical network measures are calculated between ASD and TD in each group. Furthermore, two-factor analysis of variance test is used to study the effect of age, disease, and their interaction on the network measures and the network edges. Furthermore, the differences in connection strength between TD and ASD subjects are assessed using network-based statistics. The results showed that ASD exhibits increased functional integration at the expense of decreased functional segregation. In ASD adolescents, there is a significant decrease in modularity suggesting a less robust modular organization, and an increase in participation coefficient suggesting more random integration and widely distributed connection edges. Furthermore, there is significant hypoconnectivity observed in the adolescent group especially in the default mode network, while the children group shows both hyper- and hypoconnectivity. This study lends support to a model of global atypical connections and further identifies functional networks and areas that are independently affected by age, disease, and their interaction.

Structural connectome differences in HIV infection: brain network segregation associated with nadir CD4 cell count

This study investigated structural brain organization using diffusion tensor imaging (DTI) in 35 HIV-positive and 35 HIV-negative individuals. We used global and nodal graph theory metrics to investigate whether HIV was associated with differences in brain network organization based on fractional anisotropy (FA) and mean diffusivity (MD). Participants also completed a comprehensive neuropsychological testing battery. For global network metrics, HIV-positive individuals displayed a lower FA clustering coefficient relative to HIV-negative individuals. For nodal network metrics, HIV-positive individuals had less MD nodal degree in the left thalamus. Within HIV-positive individuals, the FA global clustering coefficient was positively correlated with nadir CD4 cell count. Across the sample, cognitive performance was negatively correlated with characteristic path length and positively correlated with global efficiency for FA. These results suggest that, despite management with combination antiretroviral therapy, HIV infection is associated with altered structural brain network segregation and thalamic centrality and that low nadir CD4 cell count may be a risk factor. These graph theory metrics may serve as neural biomarkers to identify individuals at risk for HIV-related neurological complications.

Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients

Various important topological properties of healthy brain connectome have recently been identified. However, the manner in which brain lesion changes the functional network topology is unknown. We examined how critical specific brain areas are in the maintenance of network topology using multivariate support vector regression analysis on brain structural and resting-state functional imaging data in 96 patients with brain damages. Patients’ cortical lesion distribution patterns could significantly predict the functional network topology and a set of regions with significant weights in the prediction models were identified as “lesion hubs”. Intriguingly, we found two different types of lesion hubs, whose lesions associated with changes of network topology towards relatively different directions, being either more integrated (global) or more segregated (local), and correspond to hubs identified in healthy functional network in complex manners. Our results pose further important questions about the potential dynamics of the functional brain network after brain damage.

Subjective cognitive impairment and brain structural networks in Chinese gynaecological cancer survivors compared with age-matched controls: a cross-sectional study

Background

Subjective cognitive impairment can be a significant and prevalent problem for gynaecological cancer survivors. The aims of this study were to assess subjective cognitive functioning in gynaecological cancer survivors after primary cancer treatment, and to investigate the impact of cancer treatment on brain structural networks and its association with subjective cognitive impairment.

Methods

This was a cross-sectional survey using a self-reported questionnaire by the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) to assess subjective cognitive functioning, and applying DTI (diffusion tensor imaging) and graph theoretical analyses to investigate brain structural networks after primary cancer treatment.

Results

A total of 158 patients with gynaecological cancer (mean age, 45.86 years) and 130 age-matched non-cancer controls (mean age, 44.55 years) were assessed. Patients reported significantly greater subjective cognitive functioning on the FACT-Cog total score and two subscales of perceived cognitive impairment and perceived cognitive ability (all p values <0.001). Compared with patients who had received surgery only and non-cancer controls, patients treated with chemotherapy indicated the most altered global brain structural networks, especially in one of properties of small-worldness (p = 0.004). Reduced small-worldness was significantly associated with a lower FACT-Cog total score (r = 0.412, p = 0.024). Increased characteristic path length was also significantly associated with more subjective cognitive impairment (r = −0.388, p = 0.034).

Conclusion

When compared with non-cancer controls, a considerable proportion of gynaecological cancer survivors may exhibit subjective cognitive impairment. This study provides the first evidence of brain structural network alteration in gynaecological cancer patients at post-treatment, and offers novel insights regarding the possible neurobiological mechanism of cancer-related cognitive impairment (CRCI) in gynaecological cancer patients. As primary cancer treatment can result in a more random organisation of structural brain networks, this may reduce brain functional specificity and segregation, and have implications for cognitive impairment. Future prospective and longitudinal studies are needed to build upon the study findings in order to assess potentially relevant clinical and psychosocial variables and brain network measures, so as to more accurately understand the specific risk factors related to subjective cognitive impairment in the gynaecological cancer population. Such knowledge could inform the development of appropriate treatment and rehabilitation efforts to ameliorate cognitive impairment in gynaecological cancer survivors.

Multi-label Inductive Matrix Completion for Joint MGMT and IDH1 Status Prediction for Glioma Patients

MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses. Our MIMC model not only uses the training subjects with possibly missing MGMT/IDH1 labels, but also leverages the unlabeled testing subjects as a supplement to the limited training dataset. More importantly, we learn inductive labels, instead of directly using transductive labels, as the prediction results for the testing subjects, to alleviate the overfitting issue in small-sample-size studies. Furthermore, we design an optimization algorithm with guaranteed convergence based on the block coordinate descent method to solve the multivariate non-smooth MIMC model. Finally, by using a precious single-center multi-modality presurgical brain imaging and genetic dataset of primary HGG, we demonstrate that our method can produce accurate prediction results, outperforming the previous widely-used single- or multi-task machine learning methods. This study shows the promise of utilizing imaging-derived brain connectome phenotypes for prognosis of HGG in a non-invasive manner.

Disrupted Topologic Efficiency of White Matter Structural Connectome in Individuals with Subjective Cognitive Decline

Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. ^© RSNA, 2017 Online supplemental material is available for this article.

Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder

Bipolar disorder (BD), particularly BD II, is frequently misdiagnosed as unipolar depression (UD), leading to inappropriate treatment and poor clinical outcomes. Although depressive symptoms may be expressed similarly in UD and BD, the similarities and differences in the architecture of brain functional networks between the two disorders are still unknown. In this study, we hypothesized that UD and BD II patients would show convergent and divergent patterns of disrupted topological organization of the functional connectome, especially in the default mode network (DMN) and the limbic network. Brain resting-state functional magnetic resonance imaging (fMRI) data were acquired from 32 UD-unmedicated patients, 31 unmedicated BD II patients (current episode depressed) and 43 healthy subjects. Using graph theory, we systematically studied the topological organization of their whole-brain functional networks at the following three levels: whole brain, modularity and node. First, both the UD and BD II patients showed increased characteristic path length and decreased global efficiency compared with the controls. Second, both the UD and BD II patients showed disrupted intramodular connectivity within the DMN and limbic system network. Third, decreased nodal characteristics (nodal strength and nodal efficiency) were found predominantly in brain regions in the DMN, limbic network and cerebellum of both the UD and BD II patients, whereas differences between the UD and BD II patients in the nodal characteristics were also observed in the precuneus and temporal pole. Convergent deficits in the topological organization of the whole brain, DMN and limbic networks may reflect overlapping pathophysiological processes in unipolar and bipolar depression. Our discovery of divergent regional connectivity that supports emotion processing could help to identify biomarkers that will aid in differentiating these disorders.

Abnormal Rich-Club Organization Associated with Compromised Cognitive Function in Patients with Schizophrenia and Their Unaffected Parents

Schizophrenia is considered to be a disorder of brain connectivity, which might result from a disproportionally impaired rich-club organization. The rich-club is composed of highly interconnected hub regions that play crucial roles in integrating information between different brain regions. Few studies have yet investigated whether the structural rich-club organization is impaired in patients and their first-degree relatives. In this study, we established a weighted network model of white matter connections using diffusion tensor imaging of 19 patients and 39 unaffected parents, 22 young healthy controls for the patients, and 25 old healthy controls for the parents. Feeder edges between rich-club nodes and non-rich-club nodes were significantly decreased in both schizophrenic patients and their unaffected parents compared with controls. Furthermore, the feeder edges showed significant positive correlations with the scores in Category Fluency Test-animal naming in the unaffected parents. Specific feeder edges exhibited discriminative power with accuracy of 84.4% in distinguishing unaffected parents from old healthy controls. Our findings suggest that impaired rich-club organization, especially impaired feeder edges, may be related to familial vulnerability to schizophrenia, possibly reflecting a genetic predisposition for schizophrenia.

APOE Genotype Effects on Intrinsic Brain Network Connectivity in Patients with Amnestic Mild Cognitive Impairment

Whether and how the apolipoprotein E (APOE) ε4 genotype specifically modulates brain network connectivity in patients with amnestic mild cognitive impairment (aMCI) remain largely unknown. Here, we employed resting-state (‘task-free’) functional MRI and network centrality approaches to investigate local (degree centrality, DC) and global (eigenvector centrality, EC) functional integrity in the whole-brain connectome in 156 older adults, including 66 aMCI patients (27 ε4-carriers and 39 non-carriers) and 90 healthy controls (45 ε4-carriers and 45 non-carriers). We observed diagnosis-by-genotype interactions on DC in the left superior/middle frontal gyrus, right middle temporal gyrus and cerebellum, with higher values in the ε4-carriers than non-carriers in the aMCI group. We further observed diagnosis-by-genotype interactions on EC, with higher values in the right middle temporal gyrus but lower values in the medial parts of default-mode network in the ε4-carriers than non-carriers in the aMCI group. Notably, these genotype differences in DC or EC were absent in the control group. Finally, the network connectivity DC values were negatively correlated with cognitive performance in the aMCI ε4-carriers. Our findings suggest that the APOE genotype selectively modulates the functional integration of brain networks in patients with aMCI, thus providing important insight into the gene-connectome interaction in this disease.

Altered Topological Properties of Brain Networks in Social Anxiety Disorder: A Resting-state Functional MRI Study

Recent studies involving connectome analysis including graph theory have yielded potential biomarkers for mental disorders. In this study, we aimed to investigate the differences of resting-state network between patients with social anxiety disorder (SAD) and healthy controls (HCs), as well as to distinguish between individual subjects using topological properties. In total, 42 SAD patients and the same number of HCs underwent resting functional MRI, and the topological organization of the whole-brain functional network was calculated using graph theory. Compared with the controls, the patients showed a decrease in 49 positive connections. In the topological analysis, the patients showed an increase in the area under the curve (AUC) of the global shortest path length of the network (L_p) and a decrease in the AUC of the global clustering coefficient of the network (C_p). Furthermore, the AUCs of L_p and C_p were used to effectively discriminate the individual SAD patients from the HCs with high accuracy. This study revealed that the neural networks of the SAD patients showed changes in topological characteristics, and these changes were prominent not only in both groups but also at the individual level. This study provides a new perspective for the identification of patients with SAD.

A connectivity-based test-retest dataset of multi-modal magnetic resonance imaging in young healthy adults

Recently, magnetic resonance imaging (MRI) has been widely used to investigate the structures and functions of the human brain in health and disease in vivo. However, there are growing concerns about the test-retest reliability of structural and functional measurements derived from MRI data. Here, we present a test-retest dataset of multi-modal MRI including structural MRI (S-MRI), diffusion MRI (D-MRI) and resting-state functional MRI (R-fMRI). Fifty-seven healthy young adults (age range: 19-30 years) were recruited and completed two multi-modal MRI scan sessions at an interval of approximately 6 weeks. Each scan session included R-fMRI, S-MRI and D-MRI data. Additionally, there were two separated R-fMRI scans at the beginning and at the end of the first session (approximately 20 min apart). This multi-modal MRI dataset not only provides excellent opportunities to investigate the short- and long-term test-retest reliability of the brain's structural and functional measurements at the regional, connectional and network levels, but also allows probing the test-retest reliability of structural-functional couplings in the human brain.