Hemisphere lateralization of graph theoretical network in end-stage knee osteoarthritis patients

Hemisphere functional lateralization is a prominent feature of the human brain. However, it is not known whether hemispheric lateralization features are altered in end-stage knee osteoarthritis (esKOA). In this study, we performed resting-state functional magnetic imaging on 46 esKOA patients and 31 healthy controls (HCs) and compared with the global and inter-hemisphere network to clarify the hemispheric functional network lateralization characteristics of patients. A correlation analysis was performed to explore the relationship between the inter-hemispheric network parameters and clinical features of patients. The node attributes were analyzed to explore the factors changing in the hemisphere network function lateralization in patients. We found that patients and HCs exhibited "small-world" brain network topology. Clustering coefficient increased in patients compared with that in HCs. The hemisphere difference in inter-hemispheric parameters including assortativity, global efficiency, local efficiency, clustering coefficients, small-worldness, and shortest path length. The pain course and intensity of esKOA were positively correlated with the right hemispheric lateralization in local efficiency, clustering coefficients, and the small-worldness, respectively. The significant alterations of several nodal properties were demonstrated within group in pain-cognition, pain-emotion, and pain regulation circuits. The abnormal lateralization inter-hemisphere network may be caused by the destruction of regional network properties.

Analysis of brain structural covariance network in Cushing disease

Background

Cushing disease (CD) is a rare clinical neuroendocrine disease. CD is characterized by abnormal hypercortisolism induced by a pituitary adenoma with the secretion of adrenocorticotropic hormone. Individuals with CD usually exhibit atrophy of gray matter volume. However, little is known about the alterations in topographical organization of individuals with CD. This study aimed to investigate the structural covariance networks of individuals with CD based on the gray matter volume using graph theory analysis.

Methods

High-resolution T1-weighted images of 61 individuals with CD and 53 healthy controls were obtained. Gray matter volume was estimated and the structural covariance network was analyzed using graph theory. Network properties such as hubs of all participants were calculated based on degree centrality.

Results

No significant differences were observed between individuals with CD and healthy controls in terms of age, gender, and education level. The small-world features were conserved in individuals with CD but were higher than those in healthy controls. The individuals with CD showed higher global efficiency and modularity, suggesting higher integration and segregation as compared to healthy controls. The hub nodes of the individuals with CD were Short insular gyri (G_insular_short_L), Anterior part of the cingulate gyrus and sulcus (G_and_S_cingul-Ant_R), and Superior frontal gyrus (G_front_sup_R).

Conclusions

Significant differences in the structural covariance network of patients with CD were found based on graph theory. These findings might help understanding the pathogenesis of individuals with CD and provide insight into the pathogenesis of this CD.

The effects of electroencephalography functional connectivity during emotional recognition among patients with major depressive disorder and healthy controls

BACKGROUND

The brain of major depressive disorder (MDD) is associated with altered functional connectivity (FC) compared to that of healthy individuals when processing positive and negative visual stimuli. Building upon alterations in brain connectivity, some researchers have employed electroencephalography (EEG) to study FC in MDD, aiming to enhance both diagnosis and treatment; however, the results have been inconsistent and the studies involving FC during emotional recognition are limited. This study aims to 1) investigate the effects of MDD on EEG patterns during visual emotional processing, 2) explore the therapeutic effects of antidepressant treatment on brain FC within the first week, and assess whether these effects can be predictive of treatment outcomes four weeks later, and 3) study baseline FC parameter biomarkers that can be used to predict treatment responsiveness in MDD patients.

METHODS

This clinical observational study recruited 38 healthy controls (HC) and 48 MDD patients. Patients underwent an EEG exam while looking at validated images of happy and sad faces at week 0 and 1. MDD patients were categorized into treatment responders and non-responders after 4 weeks of treatment. We conducted the FC analysis (node strength (NS), global efficiency (GE), and cluster coefficient (CC)) on HC and MDD patients using graph theoretical analysis. Multivariable linear regression was used to evaluate the influence of MDD on FC compared to HC, while controlling for confounding variables including age, gender, and academic degrees.

RESULTS

At week 0 and week 1, MDD patients revealed to have significant reductions in FC parameters (NS, GE and CC) compared to HC. When comparing MDD patients at week 1 post-antidepressant treatment and pre-treatment, no significant differences in FC changes were observed. Multivariable regression revealed a significant negative effect on FC of MDD. Compared to the treatment non-responsive group, the responsive group revealed a significantly higher FC in delta band frequency at baseline.

CONCLUSIONS

MDD patient group showed impaired FC during visual emotion-processing and we observed baseline FC parameters to be associated with treatment response at week 4. While signs of FC changes were observed in the brain after a week of treatment, it is possible that one week may still be insufficient to demonstrate significant alterations in the brain. Our results suggest the potential utilization of EEG-based FC as an indicative measure for predicting treatment response and monitoring treatment progress in MDD patients.

Altered functional brain networks in coronary heart disease: independent component analysis and graph theoretical analysis

Coronary heart disease (CHD) confers a high risk of cognitive and mental impairments in patients. This study aimed to explore the association of CHD with functional connectivity and topological properties of brain networks. A total of 27 patients with CHD and 44 healthy controls (HCs) participated in this study and underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Intra- and internetwork functional connectivity alterations were explored using independent component analysis in CHD patients. Furthermore, graph theoretical analysis was adopted to assess abnormalities in small-world properties and network efficiency metrics of brain networks. Compared to HCs, CHD patients exhibited increased functional connectivity between the posterior default mode network and posterior visual network, as well as decreased functional connectivity between the left frontoparietal network and auditory network. In terms of graph theoretical analysis, small-world network topology was identified in both CHD patients and HCs. Furthermore, the nodal local efficiency of the left putamen was significantly decreased in CHD patients compared to HCs. This study revealed alterations in brain functional connectivity and topological properties in CHD patients, shedding light on the potential neurological mechanism underlying cognitive and mental impairments in these patients and suggesting unexplored connections between CHD and higher order cognitive processing.

Disrupted white matter structural networks in individuals with alcohol dependence

Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.

Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment.

METHODS

Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD.

RESULTS

Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606.

CONCLUSIONS

This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.

Structural brain network connectivity in trichotillomania (hair-pulling disorder)

Neuroimaging studies suggest involvement of frontal, striatal, limbic and cerebellar regions in trichotillomania, an obsessive-compulsive related disorder. However, findings regarding the underlying neural circuitry remains limited and inconsistent. Graph theoretical analysis offers a way to identify structural brain networks in trichotillomania. T1-weighted MRI scans were acquired in adult females with trichotillomania (n = 23) and healthy controls (n = 16). Graph theoretical analysis was used to investigate structural networks as derived from cortical thickness and volumetric FreeSurfer output. Hubs, brain regions with highest connectivity in the global network, were identified, and group differences were determined. Regions with highest connectivity on a regional level were also determined. There were no differences in small-worldness or other network measures between groups. Hubs in the global network of trichotillomania patients included temporal, parietal, and occipital regions (at 2SD above mean network connectivity), as well as frontal and striatal regions (at 1SD above mean network connectivity). In contrast, in healthy controls hubs at 2SD represented different frontal, parietal and temporal regions, while at 1SD hubs were widespread. The inferior temporal gyrus, involved in object recognition as part of the ventral visual pathway, had significantly higher connectivity on a global and regional level in trichotillomania. The study included women only and sample size was limited. This study adds to the trichotillomania literature on structural brain network connectivity. Our study findings are consistent with previous studies that have implicated somatosensory, sensorimotor and frontal-striatal circuitry in trichotillomania, and partially overlap with structural connectivity findings in obsessive-compulsive disorder.

Epilepsy-related white matter network changes in patients with frontal lobe glioma

PURPOSE

Epilepsy is a common symptom in patients with frontal lobe glioma. Tumor-related epilepsy was recently considered a type of network disease. Glioma can severely influence the integrity of the white matter network. The association between white matter network changes and presurgical epilepsy remains unclear in glioma patients. This study aims to identify alterations to the subcortical brain networks caused by glioma and glioma-related epilepsy.

METHODS

Sixty-one patients with frontal lobe gliomas were enrolled and stratified into the epileptic and non-epileptic groups. Additionally, 14 healthy participants were enrolled after matching for age, sex, and education level. All participants underwent diffusion tensor imaging. Graph theoretical analysis was applied to reveal topological changes in their white matter networks. Regions affected by tumors were excluded from the analysis.

RESULTS

Global efficiency was significantly decreased (p = 0.008), while the shortest path length increased (p = 0.02) in the left and right non-epileptic groups compared to the controls. A total of five edges exhibited decreased fiber count in the non-epileptic group (p < 0.05, false discovery rate-corrected). The topological properties and connectional edges showed no significant differences when comparing the epileptic groups and the controls. Additionally, the degree centrality of several nodes connected to the alternated edges was also diminished.

CONCLUSIONS

Compared to the controls, the epilepsy groups showed raletively intact WM networks, while the non-epileptsy groups had damaged network with lower efficiency and longer path length. These findings indicated that the occurrence of glioma related epilepsy have association with white matter network intergrity.

Disrupted topological organization of functional brain networks is associated with cognitive impairment in hypertension patients: a resting-state fMRI study

PURPOSE

To investigate the alterations of topological organization of the whole brain functional networks in hypertension patients with cognitive impairment (HTN-CI) and characterize its relationship with cognitive scores.

METHODS

Fifty-seven hypertension patients with cognitive impairment and 59 hypertension patients with normal cognition (HTN-NC), and 49 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Graph theoretical analysis was used to investigate the altered topological organization of the functional brain networks. The global topological properties and nodal metrics were compared among the three groups. Network-based statistic (NBS) analysis was used to determine the connected subnetwork. The relationships between network metrics and cognitive scores were also characterized.

RESULTS

HTN-CI patients exhibited significantly decreased global efficiency, lambda, and increased shortest path length when compared with HCs. In addition, both HTN-CI and HTN-NC groups exhibited altered nodal degree centrality and nodal efficiency in the right precentral gyrus. The disruptions of global network metrics (lambda, Lp) and the nodal metrics (degree centrality and nodal efficiency) in the right precentral gyrus were positively correlated with the MoCA scores in HTN-CI. NBS analysis demonstrated that decreased subnetwork connectivity was present both in the HTN-CI and HTN-NC groups, which were mainly involved in the default mode network, frontoparietal network, and cingulo-opercular network.

CONCLUSION

This study demonstrated the alterations of topographical organization and subnetwork connectivity of functional brain networks in HTN-CI. In addition, the global and nodal network properties were correlated with cognitive scores, which may provide useful insights for the understanding of neuropsychological mechanisms underlying HTN-CI.

Short-term Medication Effects on Brain Functional Activity and Network Architecture in First-Episode psychosis: a longitudinal fMRI study

The effect of antipsychotic medications is critical for the long-term outcome of symptoms and functions during first-episode psychosis (FEP). However, how brain functions respond to the antipsychotic treatment in the early stage of psychosis and its underlying neural mechanisms remain unclear. In this study, we explored the cross-sectional and longitudinal changes of regional homogeneity (ReHo), whole-brain functional connectivity, and network topological properties via resting-state functional magnetic resonance images. Thirty-two drug-naïve FEP patients and 30 matched healthy volunteers (HV) were included, where 23 patients were re-visited with effective responses after two months of antipsychotic treatment. Compared to HV, drug-naive patients demonstrated significantly different patterns of functional connectivity involving the right thalamus. These functional alterations mainly involved decreased ReHo, increased nodal efficiency in the right thalamus, and increased thalamic-sensorimotor-frontoparietal connectivity. In the follow-up analysis, patients after treatment showed reduced ReHo and nodal clustering in visual networks, as well as disturbances of visual-somatomotor and hippocampus-superior frontal gyrus connectivity. The longitudinal changes of ReHo in the visual cortex were associated with an improvement in general psychotic symptoms. This study provides new evidence regarding alterations in brain function linked to schizophrenia onset and affected by antipsychotic medications. Moreover, our results demonstrated that the functional alterations at baseline were not fully modulated by antipsychotic medications, suggesting that antipsychotic medications may reduce psychotic symptoms but limit the effects in regions involved in disease pathophysiology.

Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma

BACKGROUND

To investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes.

METHODS

36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose-volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined.

RESULTS

Significant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a "decrease-increase/recovery" pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D_20cc and V₂₀ to right temporal lobe outperformed in this model.

CONCLUSION

Our findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.

Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease

Background

White matter hyperintensity (WMH) is one of the typical neuroimaging manifestations of cerebral small vessel disease (CSVD), and the WMH correlates closely to cognitive impairment (CI). CSVD patients with WMH own altered topological properties of brain functional network, which is a possible mechanism that leads to CI. This study aims to identify differences in the characteristics of some brain functional network among patients with different grades of WMH and estimates the correlations between these different brain functional network characteristics and cognitive assessment scores.

Methods

110 CSVD patients underwent 3.0 T Magnetic resonance imaging scans and neuropsychological cognitive assessments. WMH of each participant was graded on the basis of Fazekas grade scale and was divided into two groups: (A) WMH score of 1–2 points (n = 64), (B) WMH score of 3–6 points (n = 46). Topological indexes of brain functional network were analyzed using graph-theoretical method. T-test and Mann–Whitney U test was used to compare the differences in topological properties of brain functional network between groups. Partial correlation analysis was applied to explore the relationship between different topological properties of brain functional networks and overall cognitive function.

Results

Patients with high WMH scores exhibited decreased clustering coefficient values, global and local network efficiency along with increased shortest path length on whole brain level as well as decreased nodal efficiency in some brain regions on nodal level (p < 0.05). Nodal efficiency in the left lingual gyrus was significantly positively correlated with patients' total Montreal Cognitive Assessment (MoCA) scores (p < 0.05). No significant difference was found between two groups on the aspect of total MoCA and Mini-mental State Examination (MMSE) scores (p > 0.05).

Conclusion

Therefore, we come to conclusions that patients with high WMH scores showed less optimized small-world networks compared to patients with low WMH scores. Global and local network efficiency on the whole-brain level, as well as nodal efficiency in certain brain regions on the nodal level, can be viewed as markers to reflect the course of WMH.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00769-7.

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

BACKGROUND

Postpartum 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.

METHODS

High-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.

RESULTS

The 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.

CONCLUSIONS

This 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.

The disrupted topological properties of structural networks showed recovery in ischemic stroke patients: a longitudinal design study

Introduction

Stroke is one of the leading causes of substantial disability worldwide. Previous studies have shown brain functional and structural alterations in adults with stroke. However, few studies have examined the longitudinal reorganization in whole-brain structural networks in stroke.

Methods

Here, we applied graph theoretical analysis to investigate the longitudinal topological organization of white matter networks in 20 ischemic stroke patients with a one-month interval between two timepoints. Two sets of clinical scores, Fugl-Meyer motor assessment (FMA) and neurological deficit scores (NDS), were assessed for all patients on the day the image data were collected.

Results

The stroke patients exhibited significant increases in FMA scores and significant reductions in DNS between the two timepoints. All groups exhibited small-world organization (σ  >  1) in the brain structural network, including a high clustering coefficient (γ  >  1) and a low normalized characteristic path length (λ ≈ 1). However, compared to healthy controls, stroke patients showed significant decrease in nodal characteristics at the first timepoint, primarily in the right supplementary motor area, right middle temporal gyrus, right inferior parietal lobe, right postcentral gyrus and left posterior cingulate gyrus. Longitudinal results demonstrated that altered nodal characteristics were partially restored one month later. Additionally, significant correlations between the nodal characteristics of the right supplementary motor area and the clinical scale scores (FMA and NDS) were observed in stroke patients. Similar behavioral-neuroimaging correlations were found in the right inferior parietal lobe.

Conclusion

Altered topological properties may be an effect of stroke, which can be modulated during recovery. The longitudinal results and the neuroimaging-behavioral relationship may provide information for understanding brain recovery from stroke. Future studies should detect whether observed changes in structural topological properties can predict the recovery of daily cognitive function in stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12868-021-00652-1.

Large-scale network topology reveals brain functional abnormality in Chinese dyslexic children

It has been revealed that dyslexic children learning alphabetic languages are characterized by aberrant topological organization of brain networks. However, little is known about the functional organization and the reconfiguration pattern of brain networks in Chinese dyslexic children. Using graph theoretical analysis and functional magnetic resonance images (fMRI), we examined this issue specifically from the perspective of functional integration and segregation. We first compared large-scale topological organizations between dyslexic children and typically developing children during a Chinese phonological rhyming task, and found that dyslexic children showed increased local efficiency and clustering coefficient compared with typically developing children, which were negatively correlated with task performance. Furthermore, dyslexic children and typically developing children could be accurately distinguished at the individual-subject level based on the nodal local efficiency or clustering coefficient. Second, we studied the group difference of network reconfiguration and found that dyslexic children showed more difficulty when shifting from the resting state to the phonological task. Our results suggest an over-segregated brain functional organization and deficits in brain network reconfiguration in Chinese dyslexic children, which helps to advance our knowledge on the neural mechanisms underlying dyslexia.

Dynamic reconfiguration of functional brain networks supporting response inhibition in a stop-signal task

Response inhibition is the ability to suppress automatic actions or behaviors that are not appropriate or are no longer adaptive to the situation. Although many studies have suggested regional brain activation, the nature of the reconfiguration of functional brain networks involved in response inhibition remains unknown. Here, we assessed brain changes associated with response inhibition using graph theoretical analysis applied to functional connectivity data acquired while subjects performed a simple stop-signal task. We identified several ways in which global network organization shifted to meet the demand for response inhibition. Increased demand for response inhibition was associated with a global network configuration with more efficient communication across the network (functional integration) and more specialized processing (functional segregation). Regions distributed in the frontoparietal network and attention networks were found to be highly efficient in the stop condition. Nodal efficiency was significantly associated with reaction time and showed a different pattern between the go and stop conditions. In addition, the conditional differences (stop vs. go) in nodal efficiency and regional task activation were common in the postcentral gyrus (PoCG) and superior temporal lobe gyrus (STG), and a negative correlation between these differences was found in the frontal and parietal lobes. These results provide compelling evidence that response inhibition is associated with truly global changes in brain functional connectivity and additional insights into how defects in response inhibition are associated with neurological or psychiatric difficulties.

Effects of TIP treatment on brain network topology of frontolimbic circuit in first-episode, treatment-naïve major depressive disorder

BACKGROUND

The Low Resistance Thought Induction Psychotherapy (TIP) is a comprehensive psychological treatment which could improve the clinical symptoms of major depressive disorder (MDD). However, the neural mechanisms for TIP treating MDD still remain unclear. This study aimed to investigate the topology of intrinsic connectivity network and the therapeutic effects of TIP in MDD on these topological properties.

METHODS

Longitudinal study was conducted in 20 first-episode, treatment-naive MDD patients at baseline and after 6 weeks (12 sessions) of TIP treatment based on resting-state functional magnetic resonance image (rsfMRI) in conjunction with graph theoretical analysis. We constructed functional connectivity matrices and extracted the attribute features of the small-world networks in both MDD and age-, education level-, and gender-matched healthy controls (HCs). The global and local small-world network properties were explored and compared between MDD at baseline and HCs. The therapeutic effect of TIP was examined by comparing alterations in global and local network properties between MDD at baseline and after treatment.

RESULTS

At baseline, MDD showed altered small-worldness and aberrant nodal properties in the frontolimbic circuit particularly in the orbital frontal gyrus, insula, precuneus and middle cingulate gyrus as compared with HCs. Following 6 weeks treatment, the abnormalities in the small-worldness and the nodal metrics were modulated, which were accompanied by a significant improvement in the clinical symptoms.

CONCLUSIONS

Our findings contributed to the understanding of the abnormal topological patterns in the frontolimbic systems in MDD and implicated that these disruptions may be modified by TIP treatment.

Resting-State Functional Connectivity and Brain Network Abnormalities in Depressive Patients with Suicidal Ideation

Our study aimed to investigate whether changes in brain function measured with functional magnetic resonance imaging (fMRI) can be detected among individuals with depressive disorders and suicidal ideation. The association between depression severity and brain images is also discussed. Our study recruited 111 participants in three groups: 35 depressive patients with suicidal ideation (SI), 32 depressive patients without suicidal ideation (NS), and 44 healthy controls (HCs). All participants were scanned using 3T MRI to obtain resting-state functional images, and functional connectivity (FC), amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and graph theoretical analysis (GTA) were performed. We found functional activity differences, such as the hippocampus and thalamus, in the SI group compared with the NS group. We also concluded lower activity in the thalamus and cuneus regions were related to suicidal ideation. We also found several functional connectivity of the brain areas, such as hippocampus, cuneus, and frontal regions, in the SI group correlated with Hamilton Depression Rating Scale (HAM-D) and Hospital Anxiety and Depression Scale (HADS). A graph theoretical analysis (GTA) and network-based statistical (NBS) analysis revealed different topological organization and slightly better local segregation of the brain network in healthy participants compared with those in depressive patients with suicidal ideation. We suggest that brain functional connectivity may be affected in depressive patients with suicidal ideation.

Association of rs1059004 polymorphism in the OLIG2 locus with functional brain network in first-episode negative schizophrenia

Schizophrenia has often been viewed as a disorder of connectivity. The single nucleotide polymorphism rs1059004 in the oligodendrocyte lineage transcription factor 2 gene locus has been reported to be associated with schizophrenia. We measured the functional connectivity and functional brain network topology properties in 49 schizophrenic patients and 47 healthy controls. We compared the strength and diversity of the functional connectivity and topological properties of functional networks between different genotypes. The correlations among functional connectivity, topological properties and behavioral performances were also investigated in this study. We found that the connectivity strength of schizophrenic patients carrying the risk A allele was generally decreased whereas connectivity diversity was increased. Regarding topological properties, all groups showed small-world properties, the nodal efficiency showed significant differences in the right precuneus and left middle temporal pole between different genotypes in schizophrenic patients. Moreover, the nodal efficiency in the left middle temporal pole was positively correlated with the neuropsychological assessment battery results of the schizophrenic patients who were homozygous for the C allele. Our results elucidate the contribution of rs1059004 to the functional brain network, and may help enhance the present understanding of the role of risk gene in the functional dysconnectivity of schizophrenia.

A graph theory study of resting-state functional connectivity in children with Tourette syndrome

Little is known about the brain's functional organization during resting-state in children with Tourette syndrome (TS). We aimed to investigate this with a specific focus on the role of comorbid attention-deficit/hyperactivity disorder (ADHD). We applied graph theoretical analysis to resting-state functional magnetic resonance imaging data of 109 8-to-12-year-old children with TS (n = 46), ADHD without tics (n = 23), and healthy controls (n = 40). First, we compared these three groups, and in a second comparison four groups, distinguishing TS with (TS + ADHD, n = 19) and without comorbid ADHD (TS-ADHD, n = 27). Weighted brain graphs were constructed for both comparisons to investigate global efficiency, local efficiency, and clustering coefficient per acquired network. Local efficiency and clustering coefficient were significantly lower in children with TS-ADHD in the default mode network compared with healthy controls, and in the frontoparietal network compared with ADHD; we also found associations with higher tic severity. Our study supports a different functional brain network organization in children with TS-ADHD, compared with healthy controls and children with ADHD.

Topological reorganization of the default mode network in patients with poststroke depressive symptoms: A resting-state fMRI study

OBJECTIVE

This study mapped the topological configuration of the default mode network (DMN) in patients with depressive symptoms after acute ischemic stroke.

METHODS

The study sample comprised 63 patients: 36 with poststroke depressive symptoms (PSD) and 37 without PSD matched according to age, gender and the severity of stroke. PSD was defined by a cutoff of ≥ 7 on the 15-item Geriatric Depression Scale (GDS). Resting-state functional magnetic resonance imaging (fMRI) was used to examine functional connectivity (FC) to reconstruct the DMN. Network based statistics estimated the FC differences of the DMN between the PSD and non-PSD groups. Graph theoretical approaches were used to characterize the topological properties of this network.

RESULTS

The study sample mainly comprised patients with mild to moderate stroke. A widespread hyper-connected configuration of the functional DMN was characterized in PSD group. The orbital frontal, dorsolateral prefrontal, dorsal medial prefrontal and, ventromedial prefrontal corticis, the middle temporal gyrus and the inferior parietal lobule were the functional hubs related to PSD. The nodal topology in inferior parietal lobule and superior frontal gyrus, overlapping with dorsal medial prefrontal and, ventromedial prefrontal cortices, tended to be functionally integrated in patients with PSD. After False Discovery Rate correction, no significant difference between the PSD and non-PSD groups was found with respect to the global and nodal metrics of the DMN. However, the correlations between these altered network metrics and severity of PSD were lacking.

LIMITATIONS

The diagnosis of PSD was based on the GDS score rather than established with a structured clinical interview.

CONCLUSIONS

The DMN in PSD was functionally integrated and more specialized in some core hubs such as the inferior parietal lobule and dorsal prefrontal cortex. The configuration of the subnetwork like DMN may be more essential in the pathogenesis of PSD than single stroke lesions.

Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease

Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.

Investigation of chemotherapy-induced brain structural alterations in breast cancer patients with generalized q-sampling MRI and graph theoretical analysis

BACKGROUND

Breast neoplasms are the most common cancer among women in Taiwan. Cognitive deficits are common complications of breast cancer survivors treated with chemotherapy. The most frequently observed disorders involve executive function and memory impairment. With improvements in tumor intervention and the consequent increase in the number of cancer survivors, the quality of life of patients has become an important issue. We are interested in the early effects of chemotherapy on the brain structures of patients. In addition, generalized q-sampling imaging (GQI), a wide range of q-space datasets for a more accurate and sophisticated diffusion MR approach, was first used in this topic.

METHODS

As diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing fibers, we attempted to use GQI, which can overcome these difficulties and is advantageous over DTI for tractography of the crossing fibers. This cross-sectional study included two groups: breast cancer survivors who had completed their chemotherapy (n = 19) and healthy controls (n = 20). All participants underwent diffusion MRI exams and neuropsychological assessments. We included four parts in our image analysis, i.e., voxel-based statistical analysis, multiple regression analysis, graph theoretical analysis and network-based statistical analysis.

RESULTS

The results from the voxel-based statistical analysis showed significantly lower GFA and NQA values in the breast cancer group than those in the control group. We found significant positive correlations between the FACT-Cog and GQI indices. In the graph theoretical analysis, the breast cancer group demonstrated significantly longer characteristic path length. Adjuvant chemotherapy affected the integrity of white matter and resulted in poor cognitive performance, as indicated by the correlations between the neuropsychological assessment scales and the GQI indices. In addition, it was found that the characteristic path lengths in the breast cancer group increased, indicating that the brain network integration became worse.

CONCLUSIONS

Our study demonstrated alterations in structural brain networks and associated neuropsychological deficits among breast cancer survivors.

Aberrant functional connectivity network in subjective memory complaint individuals relates to pathological biomarkers

Background

Individuals with subjective memory complaints (SMC) feature a higher risk of cognitive decline and clinical progression of Alzheimer's disease (AD). However, the pathological mechanism underlying SMC remains unclear. We aimed to assess the intrinsic connectivity network and its relationship with AD-related pathologies in SMC individuals.

Methods

We included 44 SMC individuals and 40 normal controls who underwent both resting-state functional MRI and positron emission tomography (PET). Based on graph theory approaches, we detected local and global functional connectivity across the whole brain by using degree centrality (DC) and eigenvector centrality (EC) respectively. Additionally, we analyzed amyloid deposition and tauopathy via florbetapir-PET imaging and cerebrospinal fluid (CSF) data. The voxel-wise two-sample T-test analysis was used to examine between-group differences in the intrinsic functional network and cerebral amyloid deposition. Then, we correlated these network metrics with pathological results.

Results

The SMC individuals showed higher DC in the bilateral hippocampus (HP) and left fusiform gyrus and lower DC in the inferior parietal region than controls. Across all subjects, the DC of the bilateral HP and left fusiform gyrus was positively associated with total tau and phosphorylated tau₁₈₁. However, no significant between-group difference existed in EC and cerebral amyloid deposition.

Conclusion

We found impaired local, but not global, intrinsic connectivity networks in SMC individuals. Given the relationships between DC value and tau level, we hypothesized that functional changes in SMC individuals might relate to pathological biomarkers.

Whole mouse brain structural connectomics using magnetic resonance histology

Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.

Topological Alterations and Symptom-Relevant Modules in the Whole-Brain Structural Network in Semantic Dementia

BACKGROUND

Semantic dementia (SD) is characterized by a selective decline in semantic processing. Although the neuropsychological pattern of this disease has been identified, its topological global alterations and symptom-relevant modules in the whole-brain anatomical network have not been fully elucidated.

OBJECTIVE

This study aims to explore the topological alteration of anatomical network in SD and reveal the modules associated with semantic deficits in this disease.

METHODS

We first constructed the whole-brain white-matter networks of 20 healthy controls and 19 patients with SD. Then, the network metrics of graph theory were compared between these two groups. Finally, we separated the network of SD patients into different modules and correlated the structural integrity of each module with the severity of the semantic deficits across patients.

RESULTS

The network of the SD patients presented a significantly reduced global efficiency, indicating that the long-distance connections were damaged. The network was divided into the following four distinctive modules: the left temporal/occipital/parietal, frontal, right temporal/occipital, and frontal/parietal modules. The first two modules were associated with the semantic deficits of SD.

CONCLUSION

These findings illustrate the skeleton of the neuroanatomical network of SD patients and highlight the key role of the left temporal/occipital/parietal module and the left frontal module in semantic processing.

Assessment of brain functional connectome alternations and correlation with depression and anxiety in major depressive disorders

Major depressive disorder (MDD) is highly prevalent, recurrent, and associated with functional impairment, morbidity, and mortality. Herein, we aimed to identify disruptions in functional connectomics among subjects with MDD by using resting-state functional magnetic resonance imaging (rs-fMRI). Sixteen subjects with MDD and thirty health controls completed resting-state fMRI scans and clinical assessments (e.g., Hamilton Depression Rating Scale (HAMD) and Hospital Anxiety and Depression Scale (HADS)). We found higher amplitude of low frequency fluctuations (ALFF) bilaterally in the hippocampus and amygdala among MDD subjects when compared to healthy controls. Using graph theoretical analysis, we found decreased clustering coefficient, local efficiency, and transitivity in the MDD patients. Our findings suggest a potential biomarker for differentiating individuals with MDD from individuals without MDD.

Abnormal small-world brain functional networks in obsessive-compulsive disorder patients with poor insight

BACKGROUND

There are limited data on neurobiological correlates of poor insight in obsessive-compulsive disorder (OCD). This study explored whether specific changes occur in small-world network (SWN) properties in the brain functional network of OCD patients with poor insight.

METHOD

Resting-state electroencephalograms (EEGs) were recorded for 12 medication-free OCD patients with poor insight, 50 medication-free OCD patients with good insight, and 36 healthy controls.

RESULTS

Both of the OCD groups exhibited topological alterations in the brain functional network characterized by abnormal small-world parameters at the beta band. However, the alterations at the theta band only existed in the OCD patients with poor insight.

LIMITATIONS

A relatively small sample size. Subjects were naïve to medications and those with Axis I comorbidity were excluded, perhaps limiting generalizability.

CONCLUSIONS

Disrupted functional integrity at the beta bands of the brain functional network may be related to OCD, while disrupted functional integrity at the theta band may be associated with poor insight in OCD patients, thus this study might provide novel insight into our understanding of the pathophysiology of OCD.

Task modulates functional connectivity networks in free viewing behavior

In free visual exploration, eye-movement is immediately followed by dynamic reconfiguration of brain functional connectivity. We studied the task-dependency of this process in a combined visual search-change detection experiment. Participants viewed two (nearly) same displays in succession. First time they had to find and remember multiple targets among distractors, so the ongoing task involved memory encoding. Second time they had to determine if a target had changed in orientation, so the ongoing task involved memory retrieval. From multichannel EEG recorded during 200 ms intervals time-locked to fixation onsets, we estimated the functional connectivity using a weighted phase lag index at the frequencies of theta, alpha, and beta bands, and derived global and local measures of the functional connectivity graphs. We found differences between both memory task conditions for several network measures, such as mean path length, radius, diameter, closeness and eccentricity, mainly in the alpha band. Both the local and the global measures indicated that encoding involved a more segregated mode of operation than retrieval. These differences arose immediately after fixation onset and persisted for the entire duration of the lambda complex, an evoked potential commonly associated with early visual perception. We concluded that encoding and retrieval differentially shape network configurations involved in early visual perception, affecting the way the visual input is processed at each fixation. These findings demonstrate that task requirements dynamically control the functional connectivity networks involved in early visual perception.

Graph theoretical analysis of functional network for comprehension of sign language

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t₍₂₄₎=2.379, p=0.026), small-worldness (t₍₂₄₎=2.604, p=0.016) and modularity (t₍₂₄₎=3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action.

Maximal clique method for the automated analysis of NMR TOCSY spectra of complex mixtures

Characterization of the chemical components of complex mixtures in solution is important in many areas of biochemistry and chemical biology, including metabolomics. The use of 2D NMR total correlation spectroscopy (TOCSY) experiments has proven very useful for the identification of known metabolites as well as for the characterization of metabolites that are unknown by taking advantage of the good resolution and high sensitivity of this homonuclear experiment. Due to the complexity of the resulting spectra, automation is critical to facilitate and speed-up their analysis and enable high-throughput applications. To better meet these emerging needs, an automated spin-system identification algorithm of TOCSY spectra is introduced that represents the cross-peaks and their connectivities as a mathematical graph, for which all subgraphs are determined that are maximal cliques. Each maximal clique can be assigned to an individual spin system thereby providing a robust deconvolution of the original spectrum for the easy extraction of critical spin system information. The approach is demonstrated for a complex metabolite mixture consisting of 20 compounds and for E. coli cell lysate.

Gender differences in the structural connectome of the teenage brain revealed by generalized q-sampling MRI

The question of whether there are biological differences between male and female brains is a fraught one, and political positions and prior expectations seem to have a strong influence on the interpretation of scientific data in this field. This question is relevant to issues of gender differences in the prevalence of psychiatric conditions, including autism, attention deficit hyperactivity disorder (ADHD), Tourette's syndrome, schizophrenia, dyslexia, depression, and eating disorders. Understanding how gender influences vulnerability to these conditions is significant. Diffusion magnetic resonance imaging (dMRI) provides a non-invasive method to investigate brain microstructure and the integrity of anatomical connectivity. Generalized q-sampling imaging (GQI) has been proposed to characterize complicated fiber patterns and distinguish fiber orientations, providing an opportunity for more accurate, higher-order descriptions through the water diffusion process. Therefore, we aimed to investigate differences in the brain's structural network between teenage males and females using GQI. This study included 59 (i.e., 33 males and 26 females) age- and education-matched subjects (age range: 13 to 14 years). The structural connectome was obtained by graph theoretical and network-based statistical (NBS) analyses. Our findings show that teenage male brains exhibit better intrahemispheric communication, and teenage female brains exhibit better interhemispheric communication. Our results also suggest that the network organization of teenage male brains is more local, more segregated, and more similar to small-world networks than teenage female brains. We conclude that the use of an MRI study with a GQI-based structural connectomic approach like ours presents novel insights into network-based systems of the brain and provides a new piece of the puzzle regarding gender differences.

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The GQI-based structural connectomic study provides a new piece of the puzzle regarding gender differences.

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Male brains exhibit better intrahemispheric communication, and female exhibit better interhemispheric communication.

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The network organization of teenage male brains is more local and more segregated than teenage female brains.

Left hemisphere structural connectivity abnormality in pediatric hydrocephalus patients following surgery

Neuroimaging research in surgically treated pediatric hydrocephalus patients remains challenging due to the artifact caused by programmable shunt. Our previous study has demonstrated significant alterations in the whole brain white matter structural connectivity based on diffusion tensor imaging (DTI) and graph theoretical analysis in children with hydrocephalus prior to surgery or in surgically treated children without programmable shunts. This study seeks to investigate the impact of brain injury on the topological features in the left hemisphere, contratelateral to the shunt placement, which will avoid the influence of shunt artifacts and makes further group comparisons feasible for children with programmable shunt valves. Three groups of children (34 in the control group, 12 in the 3-month post-surgery group, and 24 in the 12-month post-surgery group, age between 1 and 18 years) were included in the study. The structural connectivity data processing and analysis were performed based on DTI and graph theoretical analysis. Specific procedures were revised to include only left brain imaging data in normalization, parcellation, and fiber counting from DTI tractography. Our results showed that, when compared to controls, children with hydrocephalus in both the 3-month and 12-month post-surgery groups had significantly lower normalized clustering coefficient, lower small-worldness, and higher global efficiency (all p < 0.05, corrected). At a regional level, both patient groups showed significant alteration in one or more regional connectivity measures in a series of brain regions in the left hemisphere (8 and 10 regions in the 3-month post-surgery and the 12-month post-surgery group, respectively, all p < 0.05, corrected). No significant correlation was found between any of the global or regional measures and the contemporaneous neuropsychological outcomes [the General Adaptive Composite (GAC) from the Adaptive Behavior Assessment System, Second Edition (ABAS-II)]. However, one global network measure (global efficiency) and two regional network measures in the insula (local efficiency and between centrality) tested at 3-month post-surgery were found to correlate with GAC score tested at 12-month post-surgery with statistical significance (all p < 0.05, corrected). Our data showed that the structural connectivity analysis based on DTI and graph theory was sensitive in detecting both global and regional network abnormality when the analysis was conducted in the left hemisphere only. This approach provides a new avenue enabling the application of advanced neuroimaging analysis methods in quantifying brain damage in children with hydrocephalus surgically treated with programmable shunts.

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We studied the structural connectivity of left hemisphere brain network in children with hydrocephalus post-surgery

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Children with hydrocephalus post-surgery had significantly abnormal structural connectivity in the left hemisphere based on graph analysis

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Significant correlation was found between graph measures at 3-months post-surgery and developmental outcome at 12-month post-surgery

Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder

Background

Recent brain imaging findings suggest that there are widely distributed abnormalities affecting the brain connectivity in individuals with autism spectrum disorder (ASD). Using graph theoretical analysis, it is possible to investigate both global and local properties of brain’s wiring diagram, i.e., the connectome.

Methods

We acquired diffusion-weighted magnetic resonance imaging data from 14 adult males with high-functioning ASD and 19 age-, gender-, and IQ-matched controls. As with diffusion tensor imaging-based tractography, it is not possible to detect complex (e.g., crossing) fiber configurations, present in 60–90 % of white matter voxels; we performed constrained spherical deconvolution-based whole brain tractography. Unweighted and weighted structural brain networks were then reconstructed from these tractography data and analyzed with graph theoretical measures.

Results

In subjects with ASD, global efficiency was significantly decreased both in the unweighted and the weighted networks, normalized characteristic path length was significantly increased in the unweighted networks, and strength was significantly decreased in the weighted networks. In the local analyses, betweenness centrality of the right caudate was significantly increased in the weighted networks, and the strength of the right superior temporal pole was significantly decreased in the unweighted networks in subjects with ASD.

Conclusions

Our findings provide new insights into understanding ASD by showing that the integration of structural brain networks is decreased and that there are abnormalities in the connectivity of the right caudate and right superior temporal pole in subjects with ASD.

Electronic supplementary material

The online version of this article (doi:10.1186/s13229-015-0058-4) contains supplementary material, which is available to authorized users.

Dynamics of the connectome in Huntington's disease: A longitudinal diffusion MRI study

Objectives

To longitudinally investigate the connectome in different stages of Huntington's disease (HD) by applying graph theoretical analysis to diffusion MRI data.

Experimental design

We constructed weighted structural networks and calculated their topological properties. Twenty-two premanifest (preHD), 10 early manifest HD and 24 healthy controls completed baseline and 2 year follow-up scans. We stratified the preHD group based on their predicted years to disease onset into a far (preHD-A) and near (preHD-B) to disease onset group. We collected clinical and behavioural measures per assessment time point.

Principle observations

We found a significant reduction over time in nodal betweenness centrality both in the early manifest HD and preHD-B groups as compared to the preHD-A and control groups, suggesting a decrease of importance of specific nodes to overall network organization in these groups (FDR adjusted ps < 0.05). Additionally, we found a significant longitudinal decrease of the clustering coefficient in preHD when compared to healthy controls (FDR adjusted p < 0.05), which can be interpreted as a reduced capacity for internodal information processing at the local level. Furthermore, we demonstrated dynamic changes to hub-status loss and gain both in preHD and early manifest HD. Finally, we found significant cross-sectional as well as longitudinal relationships between graph metrics and clinical and neurocognitive measures.

Conclusions

This study demonstrates divergent longitudinal changes to the connectome in (pre) HD compared to healthy controls. This provides novel insights into structural correlates associated with clinical and cognitive functions in HD and possible compensatory mechanisms at play in preHD.

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Investigates characteristics of the connectome in Huntington's disease (HD).

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HD patients showed longitudinal changes in their structural connectome.

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Connectome dynamics correlated with changes in clinical and cognitive measures.

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Connectomics provides novel insights into compensatory strategies of the diseased brain.

Abnormal structural connectivity in the brain networks of children with hydrocephalus

Increased intracranial pressure and ventriculomegaly in children with hydrocephalus are known to have adverse effects on white matter structure. This study seeks to investigate the impact of hydrocephalus on topological features of brain networks in children. The goal was to investigate structural network connectivity, at both global and regional levels, in the brains in children with hydrocephalus using graph theory analysis and diffusion tensor tractography. Three groups of children were included in the study (29 normally developing controls, 9 preoperative hydrocephalus patients, and 17 postoperative hydrocephalus patients). Graph theory analysis was applied to calculate the global network measures including small-worldness, normalized clustering coefficients, normalized characteristic path length, global efficiency, and modularity. Abnormalities in regional network parameters, including nodal degree, local efficiency, clustering coefficient, and betweenness centrality, were also compared between the two patients groups (separately) and the controls using two tailed t-test at significance level of p < 0.05 (corrected for multiple comparison). Children with hydrocephalus in both the preoperative and postoperative groups were found to have significantly lower small-worldness and lower normalized clustering coefficient than controls. Children with hydrocephalus in the postoperative group were also found to have significantly lower normalized characteristic path length and lower modularity. At regional level, significant group differences (or differences at trend level) in regional network measures were found between hydrocephalus patients and the controls in a series of brain regions including the medial occipital gyrus, medial frontal gyrus, thalamus, cingulate gyrus, lingual gyrus, rectal gyrus, caudate, cuneus, and insular. Our data showed that structural connectivity analysis using graph theory and diffusion tensor tractography is sensitive to detect abnormalities of brain network connectivity associated with hydrocephalus at both global and regional levels, thus providing a new avenue for potential diagnosis and prognosis tool for children with hydrocephalus.

The graph theoretical analysis of the SSVEP harmonic response networks

Steady-state visually evoked potentials (SSVEP) have been widely used in the neural engineering and cognitive neuroscience researches. Previous studies have indicated that the SSVEP fundamental frequency responses are correlated with the topological properties of the functional networks entrained by the periodic stimuli. Given the different spatial and functional roles of the fundamental frequency and harmonic responses, in this study we further investigated the relation between the harmonic responses and the corresponding functional networks, using the graph theoretical analysis. We found that the second harmonic responses were positively correlated to the mean functional connectivity, clustering coefficient, and global and local efficiencies, while negatively correlated with the characteristic path lengths of the corresponding networks. In addition, similar pattern occurred with the lowest stimulus frequency (6.25 Hz) at the third harmonic responses. These findings demonstrate that more efficient brain networks are related to larger SSVEP responses. Furthermore, we showed that the main connection pattern of the SSVEP harmonic response networks originates from the interactions between the frontal and parietal-occipital regions. Overall, this study may bring new insights into the understanding of the brain mechanisms underlying SSVEP.

Disrupted structural connectivity network in treatment-naive depression

BACKGROUND

Neuroimaging studies suggest that treatment-naive depression (TD) is characterized by abnormal functional connectivity between specific brain regions. However, the question surrounding the structural basis of functional aberrations in TD patients still remains.

METHODS

In the present study, diffusion tensor imaging tractography was employed to construct structural connectivity networks in 22 early adult-onset, first-episode TD patients and 19 healthy controls (HC). Graph theory and network-based statistic (NBS) were then employed to investigate systematically the alteration of whole brain structural topological organization and structural connectivity in TD patients.

RESULTS

Graph theoretical analysis revealed that, compared with HC, TD patients exhibited altered structural topological measures, including decreased shortest path length, normalized clustering coefficient, normalized shortest path length, and small-worldness, as well as increased global and local efficiency. NBS results further revealed that TD patients showed two altered structural sub-networks. One sub-network mainly involved connections between the right orbitofrontal cortex (OFC) and the right insula, putamen, caudate, hippocampus, fusiform gyrus, inferior temporal gyrus and lingual gyrus. The other sub-network mainly included connections between the left OFC and the left gyrus rectus, insula, putamen, caudate, thalamus, pallidum and middle occipital gyrus.

CONCLUSIONS

The findings suggest that TD patients exhibit a disruption in the topological organization of structural brain networks. The altered orbitofrontal connectivity may particularly contribute to the manifestation of symptoms in TD patients. The abnormalities may facilitate understanding of the functional disturbances of mood and cognition in the disease.

Investigation of global and local network properties of music perception with culturally different styles of music

BACKGROUND

Graph theoretical analysis has recently become a popular research tool in neuroscience, however, there have been very few studies on brain responses to music perception, especially when culturally different styles of music are involved.

METHODS

Electroencephalograms were recorded from ten subjects listening to Chinese traditional music, light music and western classical music. For event-related potentials, phase coherence was calculated in the alpha band and then constructed into correlation matrices. Clustering coefficients and characteristic path lengths were evaluated for global properties, while clustering coefficients and efficiency were assessed for local network properties.

RESULTS

Perception of light music and western classical music manifested small-world network properties, especially with a relatively low proportion of weights of correlation matrices. For local analysis, efficiency was more discernible than clustering coefficient. Nevertheless, there was no significant discrimination between Chinese traditional and western classical music perception.

CONCLUSIONS

Perception of different styles of music introduces different network properties, both globally and locally. Research into both global and local network properties has been carried out in other areas; however, this is a preliminary investigation aimed at suggesting a possible new approach to brain network properties in music perception.

Characteristics of the default mode functional connectivity in normal ageing and Alzheimer's disease using resting state fMRI with a combined approach of entropy-based and graph theoretical measurements

Cognitive decline in normal ageing and Alzheimer's disease (AD) emerges from functional disruption in the coordination of large-scale brain systems sustaining cognition. Integrity of these systems can be examined by correlation methods based on analysis of resting state functional magnetic resonance imaging (fMRI). Here we investigate functional connectivity within the default mode network (DMN) in normal ageing and AD using resting state fMRI. Images from young and elderly controls, and patients with AD were processed using spatial independent component analysis to identify the DMN. Functional connectivity was quantified using integration and indices derived from graph theory. Four DMN sub-systems were identified: Frontal (medial and superior), parietal (precuneus-posterior cingulate, lateral parietal), temporal (medial temporal), and hippocampal (bilateral). There was a decrease in antero-posterior interactions (lower global efficiency), but increased interactions within the frontal and parietal sub-systems (higher local clustering) in elderly compared to young controls. This decreased antero-posterior integration was more pronounced in AD patients compared to elderly controls, particularly in the precuneus-posterior cingulate region. Conjoint knowledge of integration measures and graph indices in the same data helps in the interpretation of functional connectivity results, as comprehension of one measure improves with understanding of the other. The approach allows for complete characterisation of connectivity changes and could be applied to other resting state networks and different pathologies.

Sex differences in the relationship between white matter connectivity and creativity

Creative cognition emerges from a complex network of interacting brain regions. This study investigated the relationship between the structural organization of the human brain and aspects of creative cognition tapped by divergent thinking tasks. Diffusion weighted imaging (DWI) was used to obtain fiber tracts from 83 segmented cortical regions. This information was represented as a network and metrics of connectivity organization, including connectivity strength, clustering and communication efficiency were computed, and their relationship to individual levels of creativity was examined. Permutation testing identified significant sex differences in the relationship between global connectivity and creativity as measured by divergent thinking tests. Females demonstrated significant inverse relationships between global connectivity and creative cognition, whereas there were no significant relationships observed in males. Node specific analyses revealed inverse relationships across measures of connectivity, efficiency, clustering and creative cognition in widespread regions in females. Our findings suggest that females involve more regions of the brain in processing to produce novel ideas to solutions, perhaps at the expense of efficiency (greater path lengths). Males, in contrast, exhibited few, relatively weak positive relationships across these measures. Extending recent observations of sex differences in connectome structure, our findings of sexually dimorphic relationships suggest a unique topological organization of connectivity underlying the generation of novel ideas in males and females.

Graph theoretical analysis indicates cognitive impairment in MS stems from neural disconnection

BACKGROUND

The mechanisms underlying cognitive impairment in MS are still poorly understood. However, due to the specific pathology of MS, one can expect alterations in connectivity leading to physical and cognitive impairment.

AIM

In this study we aimed at assessing connectivity differences in EEG between cognitively impaired (CI) and cognitively preserved (CP) MS patients. We also investigated the influence of the measures used to construct networks.

METHODS

We included 308 MS patients and divided them into two groups based on their cognitive score. Graph theoretical network analyses were conducted based on networks constructed using different connectivity measures, i.e. correlation, correlation in the frequency domain, coherence, partial correlation, the phase lag index and the imaginary part of coherency. The most commonly encountered network parameters were calculated and compared between the two groups using Wilcoxon's rank test. Clustering coefficients and path lengths were normalized to a randomized mean clustering coefficient and path length for each patient. False discovery rate was used to correct for the multiple comparisons and Cohen's d effect sizes are reported.

RESULTS

Coherence analysis suggests that theta and delta connectivity is significantly smaller in cognitively impaired patients. Small-worldness differences are found in networks based on correlation, theta and delta coherence and correlation in the frequency domain. Modularity was related to age but not to cognition.

CONCLUSION

Cognitive deterioration in MS is a symptom that seems to be caused by neural disconnections, probably the white matter tracts connecting both hemispheres, and leads to a wide range in network differences which can be assessed by applying GTA to EEG data. In the future, these results may lead to cheaper and more objective assessments of cognitive impairment in MS.