Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

The Impact of Bispectral Index Monitoring on Outcomes in Spinal Cord Stimulation for Chronic Disorders of Consciousness

Objective

To observe whether maintaining the appropriate depth of anesthesia with Bispectral Index (BIS) can improve the prognosis of Spinal Cord stimulation (SCS) implantation in patients with chronic Disorders of consciousness (DoC).

Methods

103 patients with DoC undergoing SCS implantation were reviewed, and 83 patients with DoC were included according to the standard of inclusion and exclusion Criteria. Patients were divided into a BIS group (n =45) and a non-BIS group (n =38) according to whether BIS monitoring was used during the operation. The depth of anesthesia in the BIS group was maintained between 40-60. The anesthesiologist adjusted the depth of anesthesia in the non-BIS group according to clinical experience. Relevant information such as disease course, cause, anesthesia time, and operation time were collected. Preoperative CRS-R(preoperative) score, postoperative CRS-R(24h), and postoperative CRS-R(3m) changes were collected.

Results

The CRS-R(3m) score in the BIS group was higher than that in the non-BIS group (preoperative), and the difference was statistically significant (P < 0.05). In CRS-R (24h), the BIS group was higher than the non-BIS group, and the difference was statistically significant (X2=8.787, P =0.004). The improvement of consciousness was included in the multivariate Logistic regression analysis model, and it was found that the thalamus was an independent factor affecting the improvement of consciousness (P < 0.05). During follow-up, 1 patient in the BIS group had a decrease in consciousness from MCS- to VS/ UWS and 2 patients in the non-BIS group died during follow-up.

Conclusion

Patients can be benefit in hearing in CRS-R (24h). We recommend the use of BIS to monitor the depth of anesthesia in patients with DoC to improve patient outcomes.

Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease

This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs.

Contralesional macrostructural plasticity in patients with frontal low-grade glioma: a voxel-based morphometry study

PURPOSE

Neuroplasticity can partially compensate for the neurological deficits caused by brain tumors. However, the structural plasticity of the brain caused by brain tumors is not fully understood. This study aimed to assess the structural plasticity of the contralesional hemisphere in patients with frontal low-grade gliomas (LGGs).

METHODS

A total of 25 patients with left frontal LGGs (LFLGGs), 19 patients with right frontal LGGs (RFLGGs), and 25 healthy controls (HCs) were enrolled in this study. High-resolution structural T1-weighted imaging and fluid attenuation inversion recovery were performed on all participants. Voxel-based morphometry (VBM) analysis was used to detect differences in the brain structural plasticity between patients with unilateral LGGs and HCs.

RESULTS

VBM analysis revealed that compared with HCs, the gray matter volume (GMV) of the contralesional putamen and amygdala was significantly smaller and larger in the patients with RFLGGs and LFLGGs, respectively, while the GMVs of the contralesional cuneus and superior temporal gyrus (STG) were significantly larger in the patients with LFLGGs. The surviving clusters of the right hemisphere included 1357 voxels in the amygdala, 1680 voxels in the cuneus, 384 voxels in the STG, and 410 voxels in the putamen. The surviving clusters of the left hemisphere were 522 voxels in the amygdala and 320 voxels in the putamen.

CONCLUSION

The unilateral frontal LGGs are accompanied by structural plasticity in the contralesional cortex and vary with tumor laterality. Contralesional structural reorganization may be one of the physiological basis for functional reorganization or compensation in the frontal LGGs.

Altered topological properties of functional brain networks in patients with first episode, late-life depression before and after antidepressant treatment

Objectives

To preliminarily explore the functional activity and information integration of the brains under resting state based on graph theory in patients with first-episode, late-life depression (LLD) before and after antidepressant treatment.

Methods

A total of 50 patients with first-episode LLD and 40 non-depressed controls (NCs) were recruited for the present research. Participants underwent the RBANS test, the 17-item Hamilton depression rating scale (HAMD-17) test, and resting-state functional MRI scans (rs-fMRI). The RBANS test consists of 12 sub-tests that contribute to a total score and index scores across the five domains: immediate memory, visuospatial/constructional, language, attention, and delayed memory. Escitalopram or sertraline was adopted for treating depression, and the dosage of the drug was adjusted by the experienced psychiatrists. Of the 50 LLD patients, 27 cases who completed 6-month follow-ups and 27 NCs matched with age, sex, and education level were included for the final statistical analysis.

Results

There were significant differences in RBANS total score, immediate memory, visuospatial/constructional, language, attention, and delayed memory between LLD baseline group and NCs group (P < 0.05). Considering the global attribute indicators, the clustering coefficient of global indicators was lower in the LLD baseline group than in the NCs group, and the small-world attribute of functional brain networks existed in all three groups. The degree centrality and node efficiency of some brains were lower in the LLD baseline group than in the NCs group. After 6 months of antidepressant therapy, the scores of HAMD-17, immediate memory, language, and delayed memory in the LLD follow-up group were higher than those in the LLD baseline group. Compared with the LLD baseline group, the degree centrality and node efficiency of some brains in the cognitive control network were decreased in the LLD follow-up group.

Conclusions

The ability to integrate and divide labor of functional brain networks declines in LLD patients and linked with the depression severity. After the relief of depressive symptoms, the small-world attribute of functional brain networks in LLD patients persists. However, the information transmission efficiency and centrality of some brain regions continue to decline over time, perhaps related to their progressive cognitive impairment.

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

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

Altered 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.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Neural substrates for late-life depression: A selective review of structural neuroimaging studies

Recent neuroimaging studies have characterized the pathophysiology of late-life depression (LLD) as a dysfunction of the brain networks involved in the regulation of emotion, motivational behavior, cognitive control, executive function, and self-referential thinking. In this article, we reviewed LLD-associated structural neuroimaging markers such as white matter hyperintensity (WMH), white matter integrity measured by diffusion tensor imaging, cortical and subcortical volumes, and cortical thickness, which may provide a structural basis for brain network dysfunction in LLD. LLD was associated with greater severity or volumes of deep, periventricular, or overall WMH and with decreased white matter integrity in the brain regions belonging to the fronto-striatal-limbic circuits and reduced white matter tract integrity which connects these circuits, such as the cingulum, corpus callosum, or uncinate fasciculus. Decreased volumes or cortical thickness in the prefrontal cortex, orbitofrontal cortex, anterior and posterior cingulate cortex, several temporal and parietal regions, hippocampus, amygdala, striatum, thalamus, and the insula were associated with LLD. These structural neuroimaging findings were also associated with cognitive dysfunction, which is a prominent clinical feature in LLD. Several structural neuroimaging markers including the WMH burden, white matter integrity, and cortical and subcortical volumes predicted antidepressant response in LLD. These structural neuroimaging findings support the hypothesis that disruption of the brain networks involved in emotion regulation and cognitive processing by impaired structural connectivity is strongly associated with the pathophysiology of LLD.

Electroconvulsive therapy modulates functional interactions between submodules of the emotion regulation network in major depressive disorder

An increasing number of neuroimaging studies have consistently revealed that disrupted functional interactions within the cognitive emotion regulation network (ERN) contribute to the onset of major depressive disorders (MDD). To disentangle the functional reorganization of ERN after electroconvulsive therapy (ECT) in MDD is curial for understanding its neuropathology. Resting-state functional magnetic resonance imaging data was collected from 23 MDD patients before and after ECT, as well as 25 healthy controls. Network modularity analysis was used to identify the submodules and functional connectivity (FC) was used to investigate the functional reorganization of ERN in the MDD patients after ECT. Four submodules of ERN were identified, including emotion response module (ERM), emotion integration module (EIM), emotion generation module (EGM), and emotion execution module (EEM). The increased intra-modular FC of EEM and inter-modular FCs of EEM with EIM\ERM were found in MDD patients after ECT. Modular transition analysis revealed that left ventrolateral prefrontal cortex, supplementary motor area, posterior cingulate cortex, right angular gyrus, and right precentral gyrus were transferred across different submodules across the three groups. Further analyses showed correlations between changed FC and clinical symptoms in the MDD patients after ECT. Finally, we also identified 11 increased connections between nodes belonging to different submodules of ERN in MDD patients after ECT. These results showed that ECT could induce functional reorganization of intra- and inter-modules within the ERN, and the functional changes were related to therapeutic efficacy or memory impairments of ECT in MDD patients.

Frontal-executive and corticolimbic structural brain circuitry in older people with remitted depression, mild cognitive impairment, Alzheimer's dementia, and normal cognition

A history of depression is a risk factor for dementia. Despite strong epidemiologic evidence, the pathways linking depression and dementia remain unclear. We assessed structural brain alterations in white and gray matter of frontal-executive and corticolimbic circuitries in five groups of older adults putatively at-risk for developing dementia- remitted depression (MDD), non-amnestic MCI (naMCI), MDD+naMCI, amnestic MCI (aMCI), and MDD+aMCI. We also examined two other groups: non-psychiatric ("healthy") controls (HC) and individuals with Alzheimer's dementia (AD). Magnetic resonance imaging (MRI) data were acquired on the same 3T scanner. Following quality control in these seven groups, from diffusion-weighted imaging (n = 300), we compared white matter fractional anisotropy (FA), mean diffusivity (MD), and from T1-weighted imaging (n = 333), subcortical volumes and cortical thickness in frontal-executive and corticolimbic regions of interest (ROIs). We also used exploratory graph theory analysis to compare topological properties of structural covariance networks and hub regions. We found main effects for diagnostic group in FA, MD, subcortical volume, and cortical thickness. These differences were largely due to greater deficits in the AD group and to a lesser extent aMCI compared with other groups. Graph theory analysis revealed differences in several global measures among several groups. Older individuals with remitted MDD and naMCI did not have the same white or gray matter changes in the frontal-executive and corticolimbic circuitries as those with aMCI or AD, suggesting distinct neural mechanisms in these disorders. Structural covariance global metrics suggested a potential difference in brain reserve among groups.

Graph theoretical measures of the uncinate fasciculus subnetwork as predictors and correlates of treatment response in a transdiagnostic psychiatric cohort

The internalizing psychopathologies (IP) are a highly prevalent group of disorders for which little data exists to guide treatment selection. We examine whether graph theoretical metrics from white matter connectomes may serve as biomarkers of disease and predictors of treatment response. We focus on the uncinate fasciculus subnetwork, which has been previously implicated in these disorders. We compared baseline graph measures from a transdiagnostic IP cohort with controls. Patients were randomized to either SSRI or cognitive behavioral therapy and we determined if graph theory metrics change following treatment, and whether these changes correlated with treatment response. Lastly, we investigated whether baseline metrics correlated with treatment response. Several baseline nodal graph metrics differed at baseline. Of note, right amygdala betweenness centrality was increased in patients relative to controls. In addition, white matter integrity of the uncinate fasciculus was decreased at baseline in patients versus controls. The SSRI and CBT cohorts had increased left frontal superior orbital betweenness centrality and left frontal medial orbital clustering coefficient, respectively, suggesting the presence of treatment specific neural correlates of treatment response. This study provides insight on shared white matter network features of IPs and elucidates potential biomarkers of treatment response that may be modality-specific.

Neurological Soft Signs and Brain Network Abnormalities in Schizophrenia

Neurological soft signs (NSS) are often found in patients with schizophrenia. A wealth of neuroimaging studies have reported that NSS are related to disturbed cortical-subcortical-cerebellar circuitry in schizophrenia. However, the association between NSS and brain network abnormalities in patients with schizophrenia remains unclear. In this study, the graph theoretical approach was used to analyze brain network characteristics based on structural magnetic resonance imaging (MRI) data. NSS were assessed using the Heidelberg scale. We found that there was no significant difference in global network properties between individuals with high and low levels of NSS. Regional network analysis showed that NSS were associated with betweenness centrality involving the inferior orbital frontal cortex, the middle temporal cortex, the hippocampus, the supramarginal cortex, the amygdala, and the cerebellum. Global network analysis also demonstrated that NSS were associated with the distribution of network hubs involving the superior medial frontal cortex, the superior and middle temporal cortices, the postcentral cortex, the amygdala, and the cerebellum. Our findings suggest that NSS are associated with alterations in topological attributes of brain networks corresponding to the cortical-subcortical-cerebellum circuit in patients with schizophrenia, which may provide a new perspective for elucidating the neural basis of NSS in schizophrenia.

Structure related to function: prefrontal surface area has an indirect effect on the relationship between amygdala volume and trait neuroticism

Trait neuroticism refers to individual differences in negative emotional response to threat, frustration, or loss, operationally defined by elevated levels of irritability, anger, sadness, anxiety, worry, hostility, self-consciousness, and vulnerability to mental and physical difficulties. While functional studies have been fairly consistent when identifying regions associated with neuroticism during emotional stimuli, structural imagining studies do not tend to find a relationship between amygdala volume and trait neuroticism. There is a great deal of functional evidence that frontoparietal areas are related to the amygdala, and to emotional reactivity more generally, as a function of their involvement in emotion regulation. Specifically, top-down emotion appraisal and expression appear to involve parts of the dorsolateral and dorsomedial prefrontal cortices, which operate at least in part via the indirect modulation of the amygdala. It was hypothesized that cortical surface area and cortical thickness in regions associated with emotion appraisal/expression and emotional attention (i.e., superior frontal and rostral middle frontal gyri, respectively) would have an indirect effect on the relationship between amygdala volume and self-reported neuroticism (respectively), potentially explaining the inconsistency in the structural literature. In sample of 1106 adults, superior frontal and rostral middle frontal gyri, as parcellated by Freesurfer, were examined as potentially restricting variance in a model of indirect effects, which may elucidate the overall relationship between cortical and subcortical gray matter volume and trait neuroticism. Results indicated that, despite no association between bilateral amygdala volume and trait neuroticism, when right superior frontal surface area was entered into the model of indirect effects, a significant relationship between amygdala volume and trait neuroticism emerged. Two of the three remaining models indicated that cortical surface area had an indirect effect on the relationship between amygdala volume and trait neuroticism. These findings highlight the relationship between structural and functional neuroimaging studies. Specifically, the results indicate that when volume is related to behavior, individual differences in higher-order cortical regions, particularly surface area, may help to better understand the relationship between emotion and subcortical gray matter volume.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

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

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Focal application of accelerated iTBS results in global changes in graph measures

Graph analysis was used to study the effects of accelerated intermittent theta burst stimulation (aiTBS) on the brain's network topology in medication‐resistant depressed patients. Anatomical and resting‐state functional MRI (rs‐fMRI) was recorded at baseline and after sham and verum stimulation. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS). Using various graph measures, the different effects of sham and verum aiTBS were calculated. It was also investigated whether changes in graph measures were correlated to clinical responses. Furthermore, by correlating baseline graph measures with the changes in HDRS in terms of percentage, the potential of graph measures as biomarker was studied. Although no differences were observed between the effects of verum and sham stimulation on whole‐brain graph measures and changes in graph measures did not correlate with clinical response, the baseline values of clustering coefficient and global efficiency showed to be predictive of the clinical response to verum aiTBS. Nodal effects were found throughout the whole brain. The distribution of these effects could not be linked to the strength of the functional connectivity between the stimulation site and the node. This study showed that the effects of aiTBS on graph measures distribute beyond the actual stimulation site. However, additional research into the complex interactions between different areas in the brain is necessary to understand the effects of aiTBS in more detail.

Multimodal Investigation of Network Level Effects Using Intrinsic Functional Connectivity, Anatomical Covariance, and Structure-to-Function Correlations in Unmedicated Major Depressive Disorder

Converging evidence suggests that major depressive disorder (MDD) affects multiple large-scale brain networks. Analyses of the correlation or covariance of regional brain structure and function applied to structural and functional MRI data may provide insights into systems-level organization and structure-to-function correlations in the brain in MDD. This study applied tensor-based morphometry and intrinsic connectivity distribution to identify regions of altered volume and intrinsic functional connectivity in data from unmedicated individuals with MDD (n=17) and healthy comparison participants (HC, n=20). These regions were then used as seeds for exploratory anatomical covariance and connectivity analyses. Reduction in volume in the anterior cingulate cortex (ACC) and lower structural covariance between the ACC and the cerebellum were observed in the MDD group. Additionally, individuals with MDD had significantly lower whole-brain intrinsic functional connectivity in the medial prefrontal cortex (mPFC). This mPFC region showed altered connectivity to the ventral lateral PFC (vlPFC) and local circuitry in MDD. Global connectivity in the ACC was negatively correlated with reported depressive symptomatology. The mPFC-vlPFC connectivity was positively correlated with depressive symptoms. Finally, we observed increased structure-to-function correlation in the PFC/ACC in the MDD group. Although across all analysis methods and modalities alterations in the PFC/ACC were a common finding, each modality and method detected alterations in subregions belonging to distinct large-scale brain networks. These exploratory results support the hypothesis that MDD is a systems level disorder affecting multiple brain networks located in the PFC and provide new insights into the pathophysiology of this disorder.

Multiple cortical thickness sub-networks and cognitive impairments in first episode, drug naïve patients with late life depression: A graph theory analysis

BACKGROUND

Coordinated and pattern-wise changes in large scale gray matter structural networks reflect neural circuitry dysfunction in late life depression (LLD), which in turn is associated with emotional dysregulation and cognitive impairments. However, due to methodological limitations, there have been few attempts made to identify individual-level structural network properties or sub-networks that are involved in important brain functions in LLD.

METHODS

In this study, we sought to construct individual-level gray matter structural networks using average cortical thicknesses of several brain areas to investigate the characteristics of the gray matter structural networks in normal controls and LLD patients. Additionally, we investigated the structural sub-networks correlated with several clinical measurements including cognitive impairment and depression severity.

RESULTS

We observed that small worldness, clustering coefficients, global and local efficiency, and hub structures in the brains of LLD patients were significantly different from healthy controls. We further found that a sub-network including the anterior cingulate, dorsolateral prefrontal cortex and superior prefrontal cortex is significantly associated with attention control and executive function. The severity of depression was associated with the sub-networks comprising the salience network, including the anterior cingulate and insula.

LIMITATIONS

We investigated cortico-cortical connectivity, but omitted the subcortical structures such as the striatum and thalamus.

CONCLUSION

We report differences in patterns between several clinical measurements and sub-networks from large-scale and individual-level cortical thickness networks in LLD.

Modulatory interactions of resting-state brain functional connectivity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is mediated by chronic dysregulation of complex neural circuits, particularly the specific neurotransmitters or other neural substrates. Recently, both increases and decreases in resting-state functional connectivity have been observed in patients with MDD. However, previous research has only assessed the functional connectivity within a specific network or some regions of interests, without considering the modulatory effects of the entire brain regions. To fill in the research gap, this study employed PPI (physiophysiological interaction) to investigate the functional connectivity in the entire brain regions. Apart from the traditional PPI used for cognitive research, current PPI analysis is more suitable for exploring the neural mechanism in MDD patients. Besides, this PPI method does not require a new cognitive estimation task and can assess the modulatory effects on different part of brain without prior setting of regions of interest.

METHODS

First, we recruited 76 outpatients with major depressive disorder, and conducted MRI scan to acquire structural and functional images. As referred to the previous study of resting-state networks, we identified eight well-defined intrinsic resting-state networks by using independent component analysis. Subsequently, we explored the regions that exhibited synchronous modulatory interactions within the network by executing PPI analysis.

RESULTS

Our findings indicated that the modulatory effects between healthy crowed and patient are different. By using PPI analysis in neuroimaging can help us to understand the mechanisms of neural disruptions in MDD patients. In addition, this study provides new insight into the complicated relationships between three or more regions of brain, as well as different brain networks functions in external and internal.

CONCLUSION

Furthermore, the functional connectivity may deepen our knowledge regarding the complex brain functions in MDD patients and suggest a new multimodality treatment for MDD including targeted therapy and transcranial magnetic stimulation.

Structural network connectivity impairment and depressive symptoms in cerebral small vessel disease

BACKGROUND

Cerebral small vessel disease (SVD) can disrupt mood regulation circuits and cause depressive symptoms which may occur prior to onset of other symptoms. However, the topological network alterations in SVD with depressive symptoms remained unclear. We aim to investigate how these changes in structural network were related to depressive symptoms in SVD.

METHODS

We recruited 20 SVD with depressive symptoms (SVD+D), 20 SVD without depressive symptoms (SVD-D) and 16 healthy control (HC) individuals. Graph theory and diffusion tensor imaging (DTI) were applied to construct a structural network. We compared networks between groups, and examined the relationships between network properties, conventional measures of MRI, and depressive symptoms.

RESULTS

The structural network was significantly disrupted in global and regional levels in both SVD groups. SVD+D group showed more severe impairment of global network efficiency, and lower nodal efficiency and less connections within multiple regions like hippocampus, amygdala and several cortical structures. The disruption of network connectivity was associated with depressive symptoms and MRI measures of SVD, however, no mediation effect of network efficiency was detected between MRI measures and depressive symptoms.

LIMITATION

The relatively small sample size and lower spatial resolution of DTI-based network limited our power of investigation.

CONCLUSIONS

The brain structural network is significantly disrupted in SVD+D and the impairment is related to severity of vascular damages and depressive symptoms. The study provides evidence for the role of structural network damage in SVD-related depressive symptoms and might be a potential novel disease marker for SVD and comorbid depression.

Cortical and Subcortical Structural Plasticity Associated with the Glioma Volumes in Patients with Cerebral Gliomas Revealed by Surface-Based Morphometry

Postlesional plasticity has been identified in patients with cerebral gliomas by inducing a large functional reshaping of brain networks. Although numerous non-invasive functional neuroimaging methods have extensively investigated the mechanisms of this functional redistribution in patients with cerebral gliomas, little effort has been made to investigate the structural plasticity of cortical and subcortical structures associated with the glioma volume. In this study, we aimed to investigate whether the contralateral cortical and subcortical structures are able to actively reorganize by themselves in these patients. The compensation mechanism following contralateral cortical and subcortical structural plasticity is considered. We adopted the surface-based morphometry to investigate the difference of cortical and subcortical gray matter (GM) volumes in a cohort of 14 healthy controls and 13 patients with left-hemisphere cerebral gliomas [including 1 patients with World Health Organization (WHO I), 8 WHO II, and 4 WHO III]. The glioma volume ranges from 5.1633 to 208.165 cm². Compared to healthy controls, we found significantly increased GM volume of the right cuneus and the left thalamus, as well as a trend toward enlargement in the right globus pallidus in patients with cerebral gliomas. Moreover, the GM volumes of these regions were positively correlated with the glioma volumes of the patients. These results provide evidence of cortical and subcortical enlargement, suggesting the usefulness of surface-based morphometry to investigate the structural plasticity. Moreover, the structural plasticity might be acted as the compensation mechanism to better fulfill its functions in patients with cerebral gliomas as the gliomas get larger.

Childhood maltreatment is associated with alteration in global network fiber-tract architecture independent of history of depression and anxiety

Childhood maltreatment is a major risk factor for psychopathology. It is also associated with alterations in the network architecture of the brain, which we hypothesized may play a significant role in the development of psychopathology. In this study, we analyzed the global network architecture of physically healthy unmedicated 18-25 year old subjects (n=262) using diffusion tensor imaging (DTI) MRI and tractography. Anatomical networks were constructed from fiber streams interconnecting 90 cortical or subcortical regions for subjects with no-to-low (n=122) versus moderate-to-high (n=140) exposure to maltreatment. Graph theory analysis revealed lower degree, strength, global efficiency, and maximum Laplacian spectra, higher pathlength, small-worldness and Laplacian skewness, and less deviation from artificial networks in subjects with moderate-to-high exposure to maltreatment. On balance, local clustering was similar in both groups, but the different clusters were more strongly interconnected in the no-to-low exposure group. History of major depression, anxiety and attention deficit hyperactivity disorder did not have a significant impact on global network measures over and above the effect of maltreatment. Maltreatment is an important factor that needs to be taken into account in studies examining the relationship between network differences and psychopathology.

Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder

Major depressive disorder (MDD) has been associated with disruptions in the topological organization of brain morphological networks in group-level data. Such disruptions have not yet been identified in single-patients, which is needed to show relations with symptom severity and to evaluate their potential as biomarkers for illness. To address this issue, we conducted a cross-sectional structural brain network study of 33 treatment-naive, first-episode MDD patients and 33 age-, gender-, and education-matched healthy controls (HCs). Weighted graph-theory based network models were used to characterize the topological organization of brain networks between the two groups. Compared with HCs, MDD patients exhibited lower normalized global efficiency and higher modularity in their whole-brain morphological networks, suggesting impaired integration and increased segregation of morphological brain networks in the patients. Locally, MDD patients exhibited lower efficiency in anatomic organization for transferring information predominantly in default-mode regions including the hippocampus, parahippocampal gyrus, precuneus and superior parietal lobule, and higher efficiency in the insula, calcarine and posterior cingulate cortex, and in the cerebellum. Morphological connectivity comparisons revealed two subnetworks that exhibited higher connectivity strength in MDD mainly involving neocortex-striatum-thalamus-cerebellum and thalamo-hippocampal circuitry. MDD-related alterations correlated with symptom severity and differentiated individuals with MDD from HCs with a sensitivity of 87.9% and specificity of 81.8%. Our findings indicate that single subject grey matter morphological networks are often disrupted in clinically relevant ways in treatment-naive, first episode MDD patients. Circuit-specific changes in brain anatomic network organization suggest alterations in the efficiency of information transfer within particular brain networks in MDD. Hum Brain Mapp 38:2482-2494, 2017. © 2017 Wiley Periodicals, Inc.

Gamma-knife subcaudate tractotomy for treatment-resistant depression and target characteristics: a case report and review

Stereotactic subcaudate tractotomy has previously been suggested to be an effective treatment for depression. This is the first study to report the use of gamma-knife subcaudate tractotomy for treatment-resistant depression. A 49-year-old woman with major depressive disorder had been treated for 30 years, with nine suicide attempts during that time. The right and left target maximum diameter was 11 mm within 50 % isodose lines. The target was located more posteriorly and inferiorly than the subgenual cingulate target typically used for deep-brain stimulation. The maximum radiation dose was 130 Gy. During the 4 months after surgery, the patient improved gradually from 23 to 4 according to the Hamilton Rating Scale for Depression and antidepressant medication was discontinued. Target-sized focal lesions were identified and no edema was seen postoperatively. No aggravation or neurologic deficit occurred during the 2.5 years of follow-up. Gamma-knife subcaudate tractotomy for depression is a minimally invasive technique. Investigations of the effectiveness and safety profile in a larger group are warranted.

Loneliness in late-life depression: structural and functional connectivity during affective processing

BACKGROUND

Late-life depression (LLD) in the elderly was reported to present with emotion dysregulation accompanied by high perceived loneliness. Previous research has suggested that LLD is a disorder of connectivity and is associated with aberrant network properties. On the other hand, perceived loneliness is found to adversely affect the brain, but little is known about its neurobiological basis in LLD. The current study investigated the relationships between the structural connectivity, functional connectivity during affective processing, and perceived loneliness in LLD.

METHOD

The current study included 54 participants aged >60 years of whom 31 were diagnosed with LLD. Diffusion tensor imaging (DTI) data and task-based functional magnetic resonance imaging (fMRI) data of an affective processing task were collected. Network-based statistics and graph theory techniques were applied, and the participants' perceived loneliness and depression level were measured. The affective processing task included viewing affective stimuli.

RESULTS

Structurally, a loneliness-related sub-network was identified across all subjects. Functionally, perceived loneliness was related to connectivity differently in LLD than that in controls when they were processing negative stimuli, with aberrant networking in subcortical area.

CONCLUSIONS

Perceived loneliness was identified to have a unique role in relation to the negative affective processing in LLD at the functional brain connectional and network levels. The findings increas our understanding of LLD and provide initial evidence of the neurobiological mechanisms of loneliness in LLD. Loneliness might be a potential intervention target in depressive patients.

The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis

Late-life depression (LLD) has been associated with both generalized and focal neuroanatomical changes including gray matter atrophy and white matter abnormalities. However, previous literature has not been consistent and, in particular, its impact on the topology organization of brain networks remains to be established. In this multimodal study, we first examined cortical thickness, and applied graph theory to investigate structural covariance networks in LLD. Thirty-three subjects with LLD and 25 controls underwent T1-weighted, fluid-attenuated inversion recovery and clinical assessments. Freesurfer was used to perform vertex-wise comparisons of cortical thickness, whereas the Graph Analysis Toolbox (GAT) was implemented to construct and analyze the structural covariance networks. LLD showed a trend of lower thickness in the left insular region (p < 0.001 uncorrected). In addition, the structural network of LLD was characterized by greater segregation, particularly showing higher transitivity (i.e., measure of clustering) and modularity (i.e., tendency for a network to be organized into subnetworks). It was also less robust against random failure and targeted attacks. Despite relative cortical preservation, the topology of the LLD network showed significant changes particularly in segregation. These findings demonstrate the potential for graph theoretical approaches to complement conventional structural imaging analyses and provide novel insights into the heterogeneous etiology and pathogenesis of LLD.

Brain structural connectivity in late-life major depressive disorder

Disrupted brain connectivity might explain both the pathogenesis and consequences of late-life major depressive disorder (LLD). However, it remains difficult to ascertain whether and how specific circuits are affected. We reviewed literature regarding brain connectivity in LLD, and we specifically focused on the role of structural pathology. LLD is associated with greater levels of cerebrovascular disease, and greater levels of cerebrovascular disease are associated with both depression development and treatment responsiveness. Cerebrovascular disease is most often measured as white matter hyperintensity (WMH) burden, and histopathology studies suggest WMH reflect myelin damage and fluid accumulation (among other underlying pathology). WMHs appear as confluent caps around the ventricles (periventricular), as well as isolated lesions in the deep white matter. The underlying tissue damage and implications for brain connectivity may differ by WMH location or severity. WMHs are associated with lower white matter microstructural integrity (measured with diffusion tensor imaging) and altered brain function (measured with functional MRI). LLD is also associated with lower white matter microstructural integrity and grey matter loss which may also alter the network properties and function of the brain. Damage to brain structure reflected by WMH, reduced white matter microstructural integrity, and atrophy may affect brain function, and are therefore likely pathophysiological mechanisms of LLD. Additional research is needed to fully characterize the developmental course and pathology underlying these imaging markers, and to understand how structural damage explains LLD's various clinical manifestations.

Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery

Neuroanatomy has entered a new era, culminating in the search for the connectome, otherwise known as the brain's wiring diagram. While this approach has led to landmark discoveries in neuroscience, potential neurosurgical applications and collaborations have been lagging. In this article, the authors describe the ideas and concepts behind the connectome and its analysis with graph theory. Following this they then describe how to form a connectome using resting state functional MRI data as an example. Next they highlight selected insights into healthy brain function that have been derived from connectome analysis and illustrate how studies into normal development, cognitive function, and the effects of synthetic lesioning can be relevant to neurosurgery. Finally, they provide a précis of early applications of the connectome and related techniques to traumatic brain injury, functional neurosurgery, and neurooncology.

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment

BACKGROUND

The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown.

AIMS

To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy non-depressed controls.

METHODS

79 elderly participants (LLD (n=23), aMCI (n=18), comorbid LLD and aMCI (n=13), and controls (n=25)) completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity MRI data.

RESULTS

LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organisation was most variable in the comorbid group, followed by patients with LLD-only. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance.

CONCLUSIONS

Considering the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment.

A Window into the Brain: Advances in Psychiatric fMRI

Functional magnetic resonance imaging (fMRI) plays a key role in modern psychiatric research. It provides a means to assay differences in brain systems that underlie psychiatric illness, treatment response, and properties of brain structure and function that convey risk factor for mental diseases. Here we review recent advances in fMRI methods in general use and progress made in understanding the neural basis of mental illness. Drawing on concepts and findings from psychiatric fMRI, we propose that mental illness may not be associated with abnormalities in specific local regions but rather corresponds to variation in the overall organization of functional communication throughout the brain network. Future research may need to integrate neuroimaging information drawn from different analysis methods and delineate spatial and temporal patterns of brain responses that are specific to certain types of psychiatric disorders.

Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease

Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.

Frontal-insula gray matter deficits in first-episode medication-naïve patients with major depressive disorder

OBJECTIVE

This study is designed to investigate the gray matter volume (GMV) deficits in patients with first-episode medication-naïve major depressive disorder (MDD).

METHODS

We enrolled 38 patients with first-episode medication-naïve MDD and 27 controls in this project. Voxel-based morphometry was used to compare GMV differences between two groups. Besides, the relationship between GMV of patients and the severity of clinical symptoms was estimated to confirm the role of GMV deficits in clinical symptoms. The correlation between total GMV and illness duration was also performed to elucidate the impacts of untreated duration on the GMV.

RESULTS

We found that first-episode medication-naïve MDD patients had significant GMV deficits in bilateral superior frontal gyri, left middle frontal gyrus, left medial frontal gyrus and left insula. The GMV of patient group was negatively correlated with the severity of clinical symptoms and the illness duration.

CONCLUSION

A pattern of GMV deficits in fronto-insula might represent the biomarker for first-episode medication-naïve MDD.

The involvement of secondary neuronal damage in the development of neuropsychiatric disorders following brain insults

Neuropsychiatric disorders are one of the leading causes of disability worldwide and affect the health of billions of people. Previous publications have demonstrated that neuropsychiatric disorders can cause histomorphological damage in particular regions of the brain. By using a clinical symptom-comparing approach, 55 neuropsychiatric signs or symptoms related usually to 14 types of acute and chronic brain insults were identified and categorized in the present study. Forty percent of the 55 neuropsychiatric signs and symptoms have been found to be commonly shared by the 14 brain insults. A meta-analysis supports existence of the same neuropsychiatric signs or symptoms in all brain insults. The results suggest that neuronal damage might be occurring in the same or similar regions or structures of the brain. Neuronal cell death, neural loss, and axonal degeneration in some parts of the brain (the limbic system, basal ganglia system, brainstem, cerebellum, and cerebral cortex) might be the histomorphological basis that is responsible for the neuropsychiatric symptom clusters. These morphological alterations may be the result of secondary neuronal damage (a cascade of progressive neural injury and neuronal cell death that is triggered by the initial insult). Secondary neuronal damage causes neuronal cell death and neural injury in not only the initial injured site but also remote brain regions. It may be a major contributor to subsequent neuropsychiatric disorders following brain insults.

The 2013 International Psychogeriatric Association Junior Research Awards in Psychogeriatrics

Judging for the 2013 International Psychogeriatric Association (IPA) Junior Research Awards in Psychogeriatrics was undertaken by a panel of six experts, selected by the IPA executive, which I had the honor to chair. All three award-winning papers appear in this issue of International Psychogeriatrics immediately following this guest editorial. I am confident that, like their many predecessors awarded over more than two decades, they will be highly cited (Pachana, 2012) and will be seen in due course as crucial to the development of the young and very promising researchers who have received this prestigious acknowledgment of their excellent work.