Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Connectomic mapping of brain-spinal cord neural networks: future directions in assessing spinal cord injury at rest

Following spinal cord injury (SCI), the central nervous system undergoes significant reconstruction. The dynamic change in the interaction of the brain-spinal cord axis as well as in structure-function relations plays a vital role in the determination of neurological functions, which might have important clinical implications for the treatment and its efficacy evaluation of patients with SCI. Brain connectomes based on neuroimaging data is a relatively new field of research that maps the brain's large-scale structural and functional networks at rest. Importantly, increasing evidence shows that such resting-state signals can also be seen in the spinal cord. In the present review, we focus on the reconstruction of multi-level neural circuits after SCI. We also describe how the connectome concept could further our understanding of neuroplasticity after SCI. We propose that mapping the cortical-subcortical-spinal cord networks can provide novel insights into the pathologies of SCI.

Topologically convergent and divergent morphological gray matter networks in early-stage Parkinson's disease with and without mild cognitive impairment

Patients with Parkinson's disease with mild cognitive impairment (PD-M) progress to dementia more frequently than those with normal cognition (PD-N), but the underlying neurobiology remains unclear. This study aimed to define the specific morphological brain network alterations in PD-M, and explore their potential diagnostic value. Twenty-four PD-M patients, 17 PD-N patients, and 29 healthy controls (HC) underwent a structural MRI scan. Similarity between interregional gray matter volume distributions was used to construct individual morphological brain networks. These were analyzed using graph theory and network-based statistics (NBS), and their relationship to neuropsychological tests was assessed. Support vector machine (SVM) was used to perform individual classification. Globally, compared with HC, PD-M showed increased local efficiency (p = .001) in their morphological networks, while PD-N showed decreased normalized path length (p = .008). Locally, similar nodal deficits were found in the rectus and lingual gyrus, and cerebellum of both PD groups relative to HC; additionally in PD-M nodal deficits involved several frontal and parietal regions, correlated with cognitive scores. NBS found that similar connections were involved in the default mode and cerebellar networks of both PD groups (to a greater extent in PD-M), while PD-M, but not PD-N, showed altered connections involving the frontoparietal network. Using connections identified by NBS, SVM allowed discrimination with high accuracy between PD-N and HC (90%), PD-M and HC (85%), and between the two PD groups (65%). These results suggest that default mode and cerebellar disruption characterizes PD, more so in PD-M, whereas frontoparietal disruption has diagnostic potential.

Altered white matter structural networks in drug-naïve patients with obsessive-compulsive disorder

White matter (WM) alteration is considered to be a vital neurological mechanism of obsessive-compulsive disorder (OCD). However, little is known regarding the changes in topological organization of WM structural network in OCD. We acquired diffusion tensor imaging (DTI) datasets from 28 drug-naïve OCD patients and 28 well-matched healthy controls (HC). A deterministic fiber tracking approach was used to construct the whole-brain structural connectome. Group differences in global and nodal topological properties as well as rich-club organizations were compared by using graph theory analysis. The relationship between the altered network metrics and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was calculated. Compared with controls, OCD patients exhibited a significantly decreased small-worldness (σ), normalized clustering coefficient (γ) and shortest path length (L_p), as well as an increased global efficiency (E_glob). The nodal efficiency (E_nodal) was found to be reduced in the left middle frontal gyrus, and increased in the right parahippocampal gyrus and bilateral putamen in OCD patients. Besides, OCD patients showed increased rich-club, feeder and local connection strength, and the connection strength of the rich-club was positively correlated with the total Y-BOCS score. Our findings emphasized a central role for the complicatedly changed topological architecture of brain structural networks in the pathological mechanism underlying OCD.

Disrupted morphological grey matter networks in early-stage Parkinson's disease

While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.

Alterations of White Matter Network Properties in Patients With Functional Constipation

Background: The abnormalities in brain function and structure of patients with functional constipation (FC) have been identified using multiple neuroimaging studies and have confirmed the abnormal processing of visceral sensation at the level of the central nervous system (CNS) as an important reason for FC. As an important basis for central information transfer, the role of the white matter (WM) networks in the pathophysiology of FC has not been investigated. This study aimed to explore the topological organization of WM networks in patients with FC and its correlation with clinical variables. Methods and Analysis: In this study, 70 patients with FC and 45 age- and gender-matched healthy subjects (HS) were recruited. Diffusion tensor imaging (DTI) data and clinical variables were acquired from each participant. WM networks were constructed using the deterministic fiber tracking approach, and the global and nodal properties of the WM networks were compared using graph theory analysis between patients with FC and HS. The relationship between the representative nodal characteristics-nodal betweenness and clinical parameters was assessed using partial correlation analysis. Results: Patients with FC showed increased nodal characteristics in the left superior frontal gyrus (orbital part), right middle frontal gyrus (orbital part), and right anterior cingulate and paracingulate (P < 0.05, corrected for false discovery rate) and decreased nodal characteristics in the left caudate and left thalamus (P < 0.05, corrected for false discovery rate) compared with HS. The duration of FC was negatively correlated with the nodal betweenness of the left thalamus (r = -0.354, P = 0.04, corrected for false discovery rate). Conclusion: The results indicated the alternations in WM networks of patients with FC and suggested the abnormal visceral sensation processing in the CNS from the perspective of large-scale brain WM network.

Disrupted Brain Network Topology in Drug-naïve Essential Tremor Patients with and Without Depression : A Resting State Functional Magnetic Resonance Imaging Study

PURPOSE

This study was carried out to investigate brain functional connectome and its potential relationships with the disease severity and emotion function in patients with essential tremor with and without depressive symptoms by using resting-state functional magnetic resonance imaging and graph theory approaches.

METHODS

In this study 33 essential tremor patients with depression, 45 essential tremor patients without depression and 79 age and gender-matched healthy controls were recruited to undergo a 3.0‑T imaging scan. The whole brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions, and the topologic properties were analyzed by using graph theory approaches and network-based statistic approaches. Nonparametric permutation test was also used for group comparisons of topological metrics. Correlation analyses between topographic features and the clinical characteristics were performed.

RESULTS

The functional connectome in both essential tremor patients with and without depression showed abnormalities at the global level (decrease in clustering coefficient, global efficiency, and local efficiency but increase in characteristic path length) and at the nodal level (decrease nodal centralities in the cerebellum, motor cortex, prefrontal-limbic regions, default mode network) (p < 0.05, false discovery rate corrected). Moreover, essential tremor patients with depression showed higher node efficiency in superior frontal gyrus and posterior cingulate gyrus compared to essential tremor without depression.

CONCLUSION

Our results may provide insights into the underlying pathophysiology of essential tremor patients with and without depression and aid the development of some potential biomarkers of the depressive symptoms in patients with essential tremor.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

The effects of cognitive behavioral therapy on the whole brain structural connectome in unmedicated patients with obsessive-compulsive disorder

Cognitive behavioral therapy (CBT) is considered a first-line treatment for patients with obsessive-compulsive disorder (OCD), and it possesses advantages over pharmacological treatments in stronger tolerance to distress, lower rates of drop out and relapse, and no physical "side-effects". Previous studies have reported CBT-related alterations in focal brain regions and connections. However, the effects of CBT on whole-brain structural networks have not yet been elucidated. Here, we collected diffusion MRI data from 34 unmedicated OCD patients before and after 12 weeks of CBT. Fifty healthy controls (HCs) were also scanned twice at matched intervals. We constructed individual brain white matter connectome and performed a graph-theoretical network analysis to investigate the effects of CBT on whole-brain structural topology. We observed significant group-by-time interactions on the global network clustering coefficient and the nodal clustering of the left lingual gyrus, the left middle temporal gyrus, the left precuneus, and the left fusiform gyrus of 26 CBT responders in OCD patients. Further analysis revealed that these CBT responders showed prominently higher global and nodal clustering compared to HCs at baseline and reduced to normal levels after CBT. Such significant changes in the nodal clustering of the left lingual gyrus were also found in 8 CBT non-responders. The pre-to-post decreases in nodal clustering of the left lingual gyrus and the left fusiform gyrus positively correlated with the improvements in obsessive-compulsive symptoms in the CBT-responding patients. These findings indicated that the network segregation of the whole-brain white matter network in OCD patients was abnormally higher and might recover to normal after CBT, which provides mechanistic insights into the CBT response in OCD and potential imaging biomarkers for clinical practice.

Alteration of single-subject gray matter networks in major depressed patients with suicidality

While regional brain alterations and functional connectivity in depressed suicidal patients have previously been reported, knowledge about gray matter (GM) structural networks is limited. The aim of this study was to explore the GM of depressed suicidal brains from the single-subject structural network level. This was a cross-sectional study, in which 50 healthy controls (HC, 31 ± 9 years), 50 major depressed patients without suicidality (NSD, 29 ± 10 years), and 50 major depressed patients with suicidality (SU, 29 ± 12 years) were enrolled. T₁ -weighted images (T₁ WI) were acquired with three-dimensional-magnetization prepared rapid gradient echo sequence in 3.0 T magnetic resonance. The analysis was performed using the automated Computational Anatomy Toolbox (CAT12) within Statistical Parametric Mapping while running MATLAB. The T₁ images were segmented into GM, white matter, and cerebrospinal fluid. Then single-subject structural networks were constructed based on the morphological similarity of GM regions. Global network topological properties, including clustering coefficient (C_p ), characterpath length (L_p ), normalized clustering coefficient (γ), normalized characteristic path length (λ), small-worldness (σ), global efficiency (E_glob ), local efficiency (E_loc ), and nodal network topological properties, including nodal efficiency, degree, and betweenness centrality, were measured using graph theory analysis. Statistical tests performed were analysis of variance, Pearson correlation analysis, and multiple linear regression analysis. Decreased E_glob and increased shortest L_p were observed in SU group compared to HC and NSD groups (p < 0.05). The NSD and SU groups had an increased λ and decreased E_loc compared to the HC group (p < 0.05). Altered nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in the SU group compared with the HC and NSD groups (all p < 0.05). The GM network in the SU group showed decreased segregation and weaker integration, that is weaker small-worldness, compared to the NSD and HC groups. Abnormal nodal efficiency was found in the fronto-striatum-limbic-thalamic circuit in suicidal brains. This study provides new evidence for therapeutic targets for patients with depression and suicidality. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Topological network mechanisms of clinical response to antidepressant treatment in drug-naive major depressive disorder

AIM

There is rapidly increasing evidence that remission of MDD is associated with substantial changes in functional brain connectivity. These New data have provided a holistic view on the mechanism of antidepressants on multiple levels that goes beyond their conventional effects on neurotransmitters.

METHOD

The study was approved by the Local Ethics Committee of Istanbul Medipol University (10840098-604.01.01-E.65129) and followed the Helsinki Declaration principles. In our study, we have evaluated the effect of six weeks of treatment with antidepressants (escitalopram and duloxetine), and tested the underlying brain functional connectivity through a Graph analysis approach in a well-defined first-episode, drug-naive, and non-comorbid population with MDD.

RESULTS

Beyond indicating that there was a significant correlation between the antidepressant response and topological characteristics of the brain, our results suggested that global rather than regional network alterations may be implicated in the antidepressant effect.

CONCLUSION

Despite the small-sample size and non-controlled study design, our study provides important and relevant clinical data regarding the underlying mechanisms of the antidepressants on topological dynamics in the human brain.

White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression

Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective-WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 anatomical regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (Hamilton Depression Rating Scale) in discovery sample (r = 0.34, p = 0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a potential neuromarker for the classification and prediction of MDD patients.

Brain structural connectome in relation to PRRT2 mutations in paroxysmal kinesigenic dyskinesia

This study explored the topological characteristics of brain white matter structural networks in patients with Paroxysmal Kinesigenic Dyskinesia (PKD), and the potential influence of the brain network stability gene PRRT2 on the structural connectome in PKD. Thirty-five PKD patients with PRRT2 mutations (PKD-M), 43 PKD patients without PRRT2 mutations (PKD-N), and 40 demographically-matched healthy control (HC) subjects underwent diffusion tensor imaging. Graph theory and network-based statistic (NBS) approaches were performed; the topological properties of the white matter structural connectome were compared across the groups, and their relationships with the clinical variables were assessed. Both disease groups PKD-M and PKD-N showed lower local efficiency (implying decreased segregation ability) compared to the HC group; PKD-M had longer characteristic path length and lower global efficiency (implying decreased integration ability) compared to PKD-N and HC, independently of the potential effects of medication. Both PKD-M and PKD-N had decreased nodal characteristics in the left thalamus and left inferior frontal gyrus, the alterations being more pronounced in PKD-M patients, who also showed abnormalities in the left fusiform and bilateral middle temporal gyrus. In the connectivity characteristics assessed by NBS, the alterations were more pronounced in the PKD-M group versus HC than in PKD-N versus HC. As well as the white matter alterations in the basal ganglia-thalamo-cortical circuit related to PKD with or without PRRT2 mutations, findings in the PKD-M group of weaker small-worldness and more pronounced regional disturbance show the adverse effects of PRRT2 gene mutations on brain structural connectome.

Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics

BACKGROUND

Previous studies using resting-state functional neuroimaging have revealed alterations in whole-brain images, connectome-wide functional connectivity and graph-based metrics in groups of patients with schizophrenia relative to groups of healthy controls. However, it is unclear which of these measures best captures the neural correlates of this disorder at the level of the individual patient.

METHODS

Here we investigated the relative diagnostic value of these measures. A total of 295 patients with schizophrenia and 452 healthy controls were investigated using resting-state functional Magnetic Resonance Imaging at five research centres. Connectome-wide functional networks were constructed by thresholding correlation matrices of 90 brain regions, and their topological properties were analyzed using graph theory-based methods. Single-subject classification was performed using three machine learning (ML) approaches associated with varying degrees of complexity and abstraction, namely logistic regression, support vector machine and deep learning technology.

RESULTS

Connectome-wide functional connectivity allowed single-subject classification of patients and controls with higher accuracy (average: 81%) than both whole-brain images (average: 53%) and graph-based metrics (average: 69%). Classification based on connectome-wide functional connectivity was driven by a distributed bilateral network including the thalamus and temporal regions.

CONCLUSION

These results were replicated across the three employed ML approaches. Connectome-wide functional connectivity permits differentiation of patients with schizophrenia from healthy controls at single-subject level with greater accuracy; this pattern of results is consistent with the 'dysconnectivity hypothesis' of schizophrenia, which states that the neural basis of the disorder is best understood in terms of system-level functional connectivity alterations.

Reduced spatiotemporal brain dynamics are associated with increased depressive symptoms after a relationship breakup

Depressive symptoms following a stressful life event, such as a relationship breakup, are common, and constitute a potent risk factor for the onset of a major depressive episode. Resting-state neuroimaging studies have increasingly identified abnormal whole-brain communication in patients with depression, but it is currently unclear whether depressive symptoms in individuals without a clinical diagnosis have reliable neural underpinnings. We investigated to what extent the severity of depressive symptoms in a non-clinical sample was associated with imbalances in the complex dynamics of the brain during rest. To this end, a novel intrinsic ignition approach was applied to resting-state neuroimaging data from sixty-nine participants with varying degrees of depressive symptoms following a relationship breakup. Ignition-based measures of integration, hierarchy, and metastability were calculated for each participant, revealing a negative correlation between these measures and depressive ratings. We found that the severity of depressive symptoms was associated with deficits in the brain's capacity to globally integrate and process information over time. Furthermore, we found that increased depressive symptoms were associated with reduced spatial diversity (i.e., hierarchy) and reduced temporal variability (i.e., metastability) in the functional organization of the brain. These findings suggest the merit of investigating constrained dynamical complexity as it is sensitive to the level of depressive symptoms even in a non-clinical sample.

Progress in psychoradiology, the clinical application of psychiatric neuroimaging

Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.

Large-scale white matter network reorganization in posttraumatic stress disorder

Recently, graph theoretical approaches applied to neuroimaging data have advanced understanding of the human brain connectome and its abnormalities in psychiatric disorders. However, little is known about the topological organization of brain white matter networks in posttraumatic stress disorder (PTSD). Seventy-six patients with PTSD and 76 age, gender, and years of education-matched trauma-exposed controls were studied after the 2008 Sichuan earthquake using diffusion tensor imaging and graph theoretical approaches. Topological properties of brain networks including global and nodal measurements and modularity were analyzed. At the global level, patients showed lower clustering coefficient (p = .016) and normalized characteristic path length (p = .035) compared with controls. At the nodal level, increased nodal centralities in left middle frontal gyrus, superior and inferior temporal gyrus and right inferior occipital gyrus were observed (p < .05, corrected for false-discovery rate). Modularity analysis revealed that PTSD patients had significantly increased inter-modular connections in the fronto-parietal module, fronto-striato-temporal module, and visual and default mode modules. These findings indicate a PTSD-related shift of white matter network topology toward randomization. This pattern was characterized by an increased global network integration, reflected by increased inter-modular connections with increased nodal centralities involving fronto-temporo-occipital regions. This study suggests that extremely stressful life experiences, when they lead to PTSD, are associated with large-scale brain white matter network topological reconfiguration at global, nodal, and modular levels.

Disrupted brain functional networks in drug-naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis

Neuroimaging studies have revealed functional brain network abnormalities in attention deficit hyperactivity disorder (ADHD), but the results have been inconsistent, potentially related to confounding medication effects. Furthermore, specific topological alterations in functional networks and their role in behavioral inhibition dysfunction remain to be established. Resting-state functional magnetic resonance imaging was performed on 51 drug-naïve children with ADHD and 55 age-matched healthy controls. Brain functional networks were constructed by thresholding the partial correlation matrices of 90 brain regions, and graph theory was used to analyze network topological properties. The Stroop test was used to assess cognitive inhibitory abilities. Nonparametric permutation tests were used to compare the topological architectures in the two groups. Compared with healthy subjects, brain networks in ADHD patients demonstrated altered topological characteristics, including lower global (FDR q = 0.01) and local efficiency (p = 0.032, uncorrected) and a longer path length (FDR q = 0.01). Lower nodal efficiencies were found in the left inferior frontal gyrus and anterior cingulate cortex in the ADHD group (FDR both q < 0.05). Altered global and nodal topological efficiencies were associated with the severity of inhibitory cognitive control deficits and hyperactivity symptoms in ADHD (p <0 .05). Alterations in network topologies in drug-naïve ADHD patients indicate weaker small-worldization with decreased segregation and integration of functional brain networks. Deficits in the cingulo-fronto-parietal attention network were associated with inhibitory control deficits.