DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate

Disrupted Structural Brain Networks and Structural-Functional Decoupling in First-Episode Drug-Naïve Adolescent Major Depressive Disorder

PURPOSE

Major depressive disorder (MDD) tends to emerge during adolescence, but the neurobiology of adolescent MDD is still poorly understood. This study aimed to explore the topological organization of white matter structural networks and the relationship between structural and functional connectivity in adolescent MDD.

METHODS

Structural and functional magnetic resonance imaging data were acquired from 94 first-episode drug-naïve adolescent MDD patients and 78 healthy adolescents. Whole brain structural and functional brain networks were constructed for each subject. Then, the topological organization of structural brain networks and the coupling strength between structural and functional connectivity were analyzed.

RESULTS

Compared with controls, adolescent MDD patients showed disrupted small-world, rich-club, and modular organizations. Nodal centralities in the medial part of bilateral superior frontal gyrus, bilateral hippocampus, right superior occipital gyrus, right angular gyrus, bilateral precuneus, left caudate nucleus, bilateral putamen, right superior temporal gyrus, and right temporal pole part of superior temporal gyrus were significantly lower in adolescent MDD patients compared with controls. The coupling strength between structural and functional connectivity was significantly lower in adolescent MDD patients compared with controls.

DISCUSSION

Our findings suggest widespread disruption of structural brain networks and structural-functional decoupling in adolescent MDD, potentially leading to reduced network communication capacity.

Systematic Review: White Matter Microstructural Organization in Adolescents With Depression

Objective

A growing body of literature has focused on the neural mechanisms of depression. Our goal was to conduct a systematic review on the white matter microstructural differences in adolescents with depressive disorders vs adolescents without depressive disorders.

Method

We searched PubMed and PsycINFO for publications on August 3, 2022 (original search conducted in July 2021). The review was registered on PROSPERO (registration number: CRD42021268200), and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Eligible studies were original research papers comparing diffusion tensor/spectrum imaging findings in adolescents with vs without depression (originally ages 12-19 years, later expanded to 11-21 years). Studies were excluded if they focused on depression exclusively in the context of another condition, used only dimensional depressive symptom assessment(s), or used the same dataset as another included publication.

Results

The search yielded 575 unique records, of which 14 full-text papers were included (824 adolescents with depression and 686 without depression). The following white matter regions showed significant differences in fractional anisotropy in at least 3 studies: uncinate fasciculus, cingulum, anterior corona radiata, inferior fronto-occipital fasciculus, and corpus callosum (genu and body). Most studies reported decreased, rather than increased, fractional anisotropy in adolescents with depression. Limitations include the possibility for selective reporting bias and risk of imprecision, given the small sample sizes in some studies.

Conclusion

Our systematic review suggests aberrant white matter microstructure in limbic-cortical-striatal-thalamic circuits, and the corpus callosum, in adolescents with depression. Future research should focus on developmental trajectories in depression, identifying sources of heterogeneity and integrating findings across imaging modalities.

Age-related, multivariate associations between white matter microstructure and behavioral performance in three executive function domains

The executive function (EF) domains of working memory (WM), response inhibition (RI), and set shifting (SS) show maturational gains and are linked to neuroimaging-measured brain changes. This study explored ways in which maturation-linked differences in EF abilities are systematically associated with white matter microstructural differences from adolescence into young adulthood. Diffusion tensor imaging (DTI) and nine neurocognitive tests were collected from 120 healthy subjects ages 12-24. Analyses across the white matter skeleton were performed, focusing on fractional anisotropy (FA). Data were 'fused' using a multivariate technique (CCA+jICA), producing four independent components (ICs) depicting white matter FA values that covaried with test performance. Correlations between age and IC loading coefficients identified three EF-DTI profiles that may change developmentally. In one, SS performance was linked to greater reliance on the FA of ventral brain tracts, and less on dorsal tracts with age. In another, white matter microstructure was related to a pattern of strong WM and weak SS that became more pronounced with age. A final IC revealed that younger individuals with low RI and high WM/SS skills typically matured out of this cognitive imbalance, underscored by white matter changes with age. These novel multivariate results begin to emphasize the complexity of brain structure-cognition relationships in adolescents and young adults.

Altered single-subject gray matter structural networks in first-episode drug-naïve adolescent major depressive disorder

Although previous studies have demonstrated regional gray matter (GM) structural abnormalities in adolescents with major depressive disorder (MDD), how the topological organization of GM networks is affected in these patients is still unclear. Structural magnetic resonance imaging data were acquired from 100 first-episode drug-naïve adolescent MDD patients and 80 healthy controls (HCs). Whole-brain GM structural network was constructed for each subject, and a graph theory analysis was used to calculate the topological metrics of GM networks. Adolescent MDD patients showed significantly lower cluster coefficient and local efficiency compared to HCs. Compared to controls, adolescent MDD patients showed higher nodal centralities in the bilateral cuneus, left lingual gyrus, and right middle occipital gyrus and lower nodal centralities in the bilateral dorsolateral superior frontal gyrus, bilateral middle frontal gyrus, right anterior cingulate and paracingulate gyri, bilateral hippocampus, bilateral amygdala, bilateral caudate nucleus, and bilateral thalamus. Nodal centralities of the hippocampus were negatively associated with symptom severity and illness duration. Our findings suggest disrupted topological organization of GM structural networks in adolescent MDD patients. Impaired local segregation and abnormal nodal centralities in the prefrontal-subcortical-limbic areas and visual cortex regions may play important roles in the neurobiology of adolescent-onset MDD.

Impaired myelin ultrastructure is reversed by citalopram treatment in a mouse model for major depressive disorder

Major depressive disorder (MDD) is the most common and widespread mental disorder. Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for MDD. The relation between the inhibition of serotonin reuptake in the central nervous system and remission from MDD remains controversial, as reuptake inhibition occurs rapidly, but remission from MDD takes weeks to months. Myelination-related deficits and white matter abnormalities were shown to be involved in psychiatric disorders such as MDD. This may explain the delay in remission following SSRI administration. The raphe nuclei (RN), located in the brain stem, consist of clusters of serotonergic (5-HT) neurons that project to almost all regions of the brain. Thus, the RN are an intriguing area for research of the potential effect of SSRI on myelination, and their involvement in MDD. MicroRNAs (miRNAs) regulate many biological features that might be altered by antidepressants. Two cohorts of chronic unpredictable stress (CUS) mouse model for depression underwent behavioral tests for evaluating stress, anxiety, and depression levels. Following application of the CUS protocol and treatment with the SSRI, citalopram, 48 mice of the second cohort were tested via magnetic resonance imaging and diffusion tensor imaging for differences in brain white matter tracts. RN and superior colliculus were excised from both cohorts and measured for changes in miRNAs, mRNA, and protein levels of candidate genes. Using MRI-DTI scans we found lower fractional anisotropy and axial diffusivity in brains of stressed mice. Moreover, both miR-30b-5p and miR-101a-3p were found to be downregulated in the RN following CUS, and upregulated following CUS and citalopram treatment. The direct binding of these miRNAs to Qki, and the subsequent effects on mRNA and protein levels of myelin basic protein (Mbp), indicated involvement of these miRNAs in myelination ultrastructure processes in the RN, in response to CUS followed by SSRI treatment. We suggest that SSRIs are implicated in repairing myelin deficits resulting from chronic stress that leads to depression.

Differences in White Matter Structural Networks in Family Risk of Major Depressive Disorder and Suicidality: A Connectome Analysis

Background

Depression and suicide are leading global causes of disability and death and are highly familial. Family and individual history of depression are associated with neurobiological differences including decreased white matter connectivity; however, this has only been shown for individual regions. We use graph theory models to account for the network structure of the brain with high levels of specialization and integration and examine whether they differ by family history of depression or of suicidality within a three-generation longitudinal family study with well-characterized clinical histories.

Methods

Clinician interviews across three generations were used to classify family risk of depression and suicidality. Then, we created weighted network models using 108 cortical and subcortical regions of interest for 96 individuals using diffusion tensor imaging derived fiber tracts. Global and local summary measures (clustering coefficient, characteristic path length, and global and local efficiencies) and network-based statistics were utilized for group comparison of family history of depression and, separately, of suicidality, adjusted for personal psychopathology.

Results

Clustering coefficient (connectivity between neighboring regions) was lower in individuals at high family risk of depression and was associated with concurrent clinical symptoms. Network-based statistics showed hypoconnected subnetworks in individuals with high family risk of depression and of suicidality, after controlling for personal psychopathology. These subnetworks highlighted cortical-subcortical connections including between the superior frontal cortex, thalamus, precuneus, and putamen.

Conclusions

Family history of depression and of suicidality are associated with hypoconnectivity between subcortical and cortical regions, suggesting brain-wide impaired information processing, even in those personally unaffected.

Edge-centric functional network analyses reveal disrupted network configuration in autism spectrum disorder

BACKGROUND

Neuroscientific evidence suggests that the pathological symptoms associated with autism spectrum disorders (ASD) are not confined to a single brain region but involve networks of the brain on a larger spatial scale. Analyzing diagrams of edge-edge interactions could provide important perspectives on the organization and function of complex systems.

METHODS

Resting-state fMRI data from 238 ASD patients and 311 healthy controls (HCs) were included in the current study. We used the thalamus as the mediating node to calculate the edge functional connectivity (eFC) of the brain network and compared the ASD subjects and HCs.

RESULTS

Compared with the HCs, the ASD subjects exhibited abnormalities in the central node thalamus and four brain regions (amygdala, nucleus accumbens, pallidum and hippocampus), as well as in the eFC formed by the inferior frontal gyrus (IFG) (or middle temporal gyrus (MTG)). In addition, ASD subjects showed variable characteristics of the eFC between nodes in different networks.

CONCLUSIONS

The changes in these brain regions may be due to the disturbance in the reward system, which leads to coherence in the instantaneous comovement of the functional connections formed by these brain regions in ASD. This notion also reveals a functional network feature between the cortical and subcortical regions in ASD.

Multi-level predictors of depression symptoms in the Adolescent Brain Cognitive Development (ABCD) study

BACKGROUND

While identifying risk factors for adolescent depression is critical for early prevention and intervention, most studies have sought to understand the role of isolated factors rather than across a broad set of factors. Here, we sought to examine multi-level factors that maximize the prediction of depression symptoms in US children participating in the Adolescent Brain and Cognitive Development (ABCD) study.

METHODS

A total of 7,995 participants from ABCD (version 3.0 release) provided complete data at baseline and 1-year follow-up data. Depression symptoms were measured with the Child Behavior Checklist. Predictive features included child demographic, environmental, and structural and resting-state fMRI variables, parental depression history and demographic characteristics. We used linear (elastic net regression, EN) and non-linear (gradient-boosted trees, GBT) predictive models to identify which set of features maximized prediction of depression symptoms at baseline and, separately, at 1-year follow-up.

RESULTS

Both linear and non-linear models achieved comparable results for predicting baseline (EN: MAE = 3.757; R2  = 0.156; GBT: MAE = 3.761; R2  = 0.147) and 1-year follow-up (EN: MAE = 4.255; R2  = 0.103; GBT: MAE = 4.262; R2  = 0.089) depression. Parental history of depression, greater family conflict, and shorter child sleep duration were among the top predictors of concurrent and future child depression symptoms across both models. Although resting-state fMRI features were relatively weaker predictors, functional connectivity of the caudate was consistently the strongest neural feature associated with depression symptoms at both timepoints.

CONCLUSIONS

Consistent with prior research, parental mental health, family environment, and child sleep quality are important risk factors for youth depression. Functional connectivity of the caudate is a relatively weaker predictor of depression symptoms but may represent a biomarker for depression risk.

Electroconvulsive therapy changes temporal dynamics of intrinsic brain activity in depressed patients

Electroconvulsive therapy (ECT) has been demonstrated to be effective in treating depressed patients. Previous neuroimaging studies have focused mainly on alterations in static brain activity and connectivity to study the effects of ECT in depressed patients. However, it remains unclear whether the temporal dynamics of brain activity are associated with mechanisms of ECT in depressed patients. We measured the dynamics of spontaneous brain activity using dynamic amplitude of low-frequency fluctuation (dALFF) in healthy controls (n = 40) and patients diagnosed with unipolar depression (UD, n = 36) or bipolar disorder (BD, n = 9) before and after ECT. Furthermore, the temporal variability of intrinsic brain activity (iBA) was quantified as the variance of dALFF across sliding window. In addition, correlation analysis was performed to investigate the relationships among dALFF, depressive symptoms, and cognitive function in depressed patients. We lack second resting-state functional magnetic resonance imaging (rs-fMRI) data for healthy controls. After ECT, patients showed decreased brain dynamics (less temporal variability) in the right dorsal anterior cingulate cortex (dACC) and the right precuneus, whereas they showed increased brain dynamics in the bilateral superior medial frontal cortex (mSFC). No significant correlation was found between the dALFF and clinical variables in depressed patients. Our findings suggest that right dACC, right precuneus, and bilateral mSFC play an important role in response to ECT depressed patients from the perspective of dynamic local brain activity, indicating that the dALFF variability may be useful in further understanding the mechanisms of ECT's antidepressant effects.

White Matter Tracts Associated With Deep Brain Stimulation Targets in Major Depressive Disorder: A Systematic Review

Background

Deep brain stimulation (DBS) has been proposed as a last-resort treatment for major depressive disorder (MDD) and has shown potential antidepressant effects in multiple clinical trials. However, the clinical effects of DBS for MDD are inconsistent and suboptimal, with 30-70% responder rates. The currently used DBS targets for MDD are not individualized, which may account for suboptimal effect.

Objective

We aim to review and summarize currently used DBS targets for MDD and relevant diffusion tensor imaging (DTI) studies.

Methods

A literature search of the currently used DBS targets for MDD, including clinical trials, case reports and anatomy, was performed. We also performed a literature search on DTI studies in MDD.

Results

A total of 95 studies are eligible for our review, including 51 DBS studies, and 44 DTI studies. There are 7 brain structures targeted for MDD DBS, and 9 white matter tracts with microstructural abnormalities reported in MDD. These DBS targets modulate different brain regions implicated in distinguished dysfunctional brain circuits, consistent with DTI findings in MDD.

Conclusions

In this review, we propose a taxonomy of DBS targets for MDD. These results imply that clinical characteristics and white matter tracts abnormalities may serve as valuable supplements in future personalized DBS for MDD.

Altered White Matter Connectivity in Young Acutely Underweight Patients With Anorexia Nervosa

OBJECTIVE

Reductions of gray matter volume and cortical thickness in anorexia nervosa (AN) are well documented. However, findings regarding the integrity of white matter (WM) as studied via diffusion weighted imaging (DWI) are remarkably heterogeneous, and WM connectivity has been examined only in small samples using a limited number of regions of interest. The present study investigated whole-brain WM connectivity for the first time in a large sample of acutely underweight patients with AN.

METHOD

DWI data from predominantly adolescent patients with acute AN (n = 96, mean age = 16.3 years) and age-matched healthy control participants (n = 96, mean age = 17.2 years) were analyzed. WM connectivity networks were generated from fiber-tractography-derived streamlines connecting 233 cortical/subcortical regions. To identify group differences, network-based statistic was used while taking head motion, WM, and ventricular volume into account.

RESULTS

Patients with AN were characterized by 6 WM subnetworks with abnormal architecture, as indicated by increased fractional anisotropy located primarily in parietal-occipital regions and accompanied by reduced radial diffusivity. Group differences based on number of streamlines reached only nominal significance.

CONCLUSION

Our study reveals pronounced alterations in the WM connectome in young patients with AN. In contrast to known reductions in gray matter in the acutely underweight state of AN, this pattern does not necessarily indicate a deterioration of the WM network. Future studies using advanced MRI sequences will have to clarify interrelations with axonal packing or myelination, and whether the changes should be considered a consequence of undernutrition or a vulnerability for developing or maintaining AN.

Graph theory approach for the structural-functional brain connectome of depression

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015-2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

The integrated understanding of structural and functional connectomes in depression: A multimodal meta-analysis of graph metrics

BACKGROUND

From the perspective of information processing, an integrated understanding of the structural and functional connectomes in depression patients is important, a multimodal meta-analysis is required to detect the robust alterations in graph metrics across studies.

METHODS

Following a systematic search, 952 depression patients and 1447 controls in nine diffusion magnetic resonance imaging (dMRI) and twelve rest state functional MRI (rs-fMRI) studies with high methodological quality met the inclusion criteria and were included in the meta-analysis.

RESULTS

Regarding the dMRI results, no significant differences of meta-analytic metrics were found; regarding the rs-fMRI results, the modularity and local efficiency were found to be significantly lower in the depression group than in the controls (Hedge's g = -0.330 and -0.349, respectively).

CONCLUSION

Our findings suggested a lower modularity and network efficiency in the rs-fMRI network in depression patients, indicating that the pathological imbalances in brain connectomes needs further exploration.

LIMITATIONS

Included number of trials was low and heterogeneity should be noted.

In(s) and out(s) of adolescent depression - Trajectories of development and recovery

While the role of biological markers in understanding major depressive disorder (MDD) in adults have been studied extensively, less has been done to identify the biomarkers of MDD development and recovery in adolescence. With the majority of mental health disorders starting in adolescence, identifying biomarkers of transition and recovery from MDD early in life is critical for developing effective prevention strategies. Considering most of the child and adolescent populations come from low-and-middle-income countries (LMICs), it is vital to focus on adolescent populations in these settings. With most studies coming from high-income countries (HICs), evidence suggests that elevated morning cortisol levels including cortisol awakening response (CAR), increased peripheral inflammation and brain abnormalities such as cortico-limbic dysregulation or blunted activity in reward related regions in response to positive information are associated with MDD and being at-risk for MDD development in adolescence. We also find that some of the biological mechanisms of recovery from MDD, mainly normalisation in the cortico-limbic dysregulation, are reported following psychological therapy, suggesting shared pathways leading to MDD vulnerability and recovery. Although, only a few studies include adolescent populations. Understanding molecular mechanisms through which psychological interventions are effective, as well as molecular markers of transition to depression in individuals at-risk, are important to inform effective prevention and intervention strategies.

Diffusion tensor imaging brain structural clustering patterns in major depressive disorder

Using magnetic resonance diffusion tensor imaging data from 45 patients with major depressive disorder (MDD) and 41 healthy controls (HCs), network indices based on a 246-region Brainnetcome Atlas were investigated in the two groups, and in the MDD subgroups that were subgrouped based on their duration of the disease. Correlation between the network indices and the duration of illness was also examined. Differences were observed between the MDDS subgroup (short disease duration) and the HC group, but not between the MDD and HC groups. Compared with the HCs, the clustering coefficient (CC) values of MDDS were higher in precentral gyrus, and caudal lingual gyrus; the CC of MDDL subgroup (long disease duration) was higher in postcentral gyrus and dorsal granular insula in the right hemisphere. Network resilience analyses showed that the MDDS group was higher than the HC group, representing relatively more randomized networks in the diseased brains. The correlation analyses showed that the caudal lingual gyrus in the right hemisphere and the rostral lingual gyrus in the left hemisphere were particularly correlated with disease duration. The analyses showed that duration of the illness appears to have an impact on the networking patterns. Networking abnormalities in MDD patients could be blurred or hidden by the heterogeneity of the MDD clinical subgroups. Brain plasticity may introduce a recovery effect to the abnormal network patterns seen in patients with a relative short term of the illness, as the abnormalities may disappear in MDDL .

White Matter Changes Following Chronic Restraint Stress and Neuromodulation: A Diffusion Magnetic Resonance Imaging Study in Young Male Rats

Background

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuromodulation technique, is an effective treatment for depression. However, few studies have used diffusion magnetic resonance imaging to investigate the longitudinal effects of rTMS on the abnormal brain white matter (WM) described in depression.

Methods

In this study, we acquired diffusion magnetic resonance imaging from young adult male Sprague Dawley rats to investigate 1) the longitudinal effects of 10- and 1-Hz low-intensity rTMS (LI-rTMS) in healthy animals; 2) the effect of chronic restraint stress (CRS), an animal model of depression; and 3) the effect of 10 Hz LI-rTMS in CRS animals. Diffusion magnetic resonance imaging data were analyzed using tract-based spatial statistics and fixel-based analysis.

Results

Similar changes in diffusion and kurtosis fractional anisotropy were induced by 10- and 1-Hz stimulation in healthy animals, although changes induced by 10-Hz stimulation were detected earlier than those following 1-Hz stimulation. Additionally, 10-Hz stimulation increased axial and mean kurtosis within the external capsule, suggesting that the two protocols may act via different underlying mechanisms. Brain maturation–related changes in WM, such as increased corpus callosum, fimbria, and external and internal capsule fiber cross-section, were compromised in CRS animals compared with healthy control animals and were rescued by 10-Hz LI-rTMS. Immunohistochemistry revealed increased myelination within the corpus callosum in LI-rTMS–treated CRS animals compared with those that received sham or no stimulation.

Conclusions

Overall, decreased WM connectivity and integrity in the CRS model corroborate findings in patients experiencing depression with high anxiety, and the observed LI-rTMS–induced effects on WM structure suggest that LI-rTMS might rescue abnormal WM by increasing myelination.

Disrupted rich-club network organization and individualized identification of patients with major depressive disorder

BACKGROUND

Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear.

METHODS

We acquired resting-state functional magnetic resonance imaging (R-fMRI) and diffusion tensor imaging (DTI) from 31 unmedicated MDD patients and 32 cognitively normal (CN) subjects and completed a series of neuropsychological tests. Rich-club organization, network properties, and coupling between structural and functional connectivity (SC-FC) were explored. Furthermore, whether these indices could potentially deliver effective clinical predictive value for MDD patients were examined.

RESULTS

The MDD patients showed disrupted structural rich-club organization and modularity, as well as a distinct correlation pattern between global efficiency and rich-club organization. Importantly, reduced SC-FC coupling, reflecting a decreased agreement in the integrity of the networks, was significantly associated with the strength of structural rich-club connections in the MDD patients. Furthermore, the disrupted structural rich-club organization, which was primarily located in the default mode network (DMN) and executive control network (ECN), emerged as a valuable indicator to distinguish between MDD and CN.

CONCLUSIONS

Findings of this study identified that the disrupted rich-club structural organization significantly influenced brain structural network modularity and integrity and could serve as a promising biological marker for the identification of MDD patients.

A systematic review of the association between biological markers and environmental stress risk factors for adolescent depression

INTRODUCTION

Although the aetiology and pathophysiology of depression are multifactorial, to date most studies have examined either biological or environmental mechanisms without looking at the integration of both; with most studies conducted in high-income countries (HICs). Therefore, we conducted a systematic review of worldwide studies investigating the relationship between biological and environmental stress risk factors for major depressive disorder (MDD) in adolescence.

METHODS

We searched MEDLINE (via Ovid), PsycINFO, Cochrane Database of Systematic Reviews, Web of Science (Core Collection), Lilacs, African Journals Online and Global Health for prospective and cross-sectional studies that examined the association between biological markers and environmental stress risk factors in MDD during adolescence.

FINDINGS

Of 11,089 articles identified, 21 were included, with only two from middle-income countries. Increased inflammation, telomere length and brain abnormalities, including blunted reward-related activity, white matter disruptions, and altered volume of limbic brain regions, were associated with increased risk for MDD mainly in the context of early life adversity. There is little evidence suggesting that the neurobiological changes investigated were associated with MDD in the context of recent life stress.

INTERPRETATION

The developmental trajectory of depression appears to start with early life adversities and occurs in the context of immune and brain abnormalities. Understanding these biopsychosocial processes will help to improve our ability to detect individuals at risk of developing depression in adolescence. However, generalizability is limited by few studies examining both biological and environmental stress risk factors and a lack of studies on adolescents and young adults in low-and-middle-income countries (LMICs).

Altered structural connectome in non-lesional newly diagnosed focal epilepsy: Relation to pharmacoresistance

Despite an expanding literature on brain alterations in patients with longstanding epilepsy, few neuroimaging studies investigate patients with newly diagnosed focal epilepsy (NDfE). Understanding brain network impairments at diagnosis is necessary to elucidate whether or not brain abnormalities are principally due to the chronicity of the disorder and to develop prognostic markers of treatment outcome. Most adults with NDfE do not have MRI-identifiable lesions and the reasons for seizure onset and refractoriness are unknown. We applied structural connectomics to T1-weighted and multi-shell diffusion MRI data with generalized q-sampling image reconstruction using Network Based Statistics (NBS). We scanned 27 patients within an average of 3.7 (SD = 2.9) months of diagnosis and anti-epileptic drug treatment outcomes were collected 24 months after diagnosis. Seven patients were excluded due to lesional NDfE and outcome data was available in 17 patients. Compared to 29 healthy controls, patients with non-lesional NDfE had connectomes with significantly decreased quantitative anisotropy in edges connecting right temporal, frontal and thalamic nodes and increased diffusivity in edges between bilateral temporal, frontal, occipital and parietal nodes. Compared to controls, patients with persistent seizures showed the largest effect size (|d|>=1) for decreased anisotropy in right parietal edges and increased diffusivity in edges between left thalamus and left parietal nodes. Compared to controls, patients who were rendered seizure-free showed the largest effect size for decreased anisotropy in the edge connecting the left thalamus and right temporal nodes and increased diffusivity in edges connecting right frontal nodes. As demonstrated by large effect sizes, connectomes with decreased anisotropy (edge between right frontal and left insular nodes) and increased diffusivity (edge between right thalamus and left parietal nodes) were found in patients with persistent seizures compared to patients who became seizure-free. Patients who had persistent seizures showed larger effect sizes in all network metrics than patients who became seizure-free when compared to each other and compared to controls. Furthermore, patients with focal-to-bilateral tonic-clonic seizures (FBTCS, N = 11) had decreased quantitative anisotropy in a bilateral network involving edges between temporal, parietal and frontal nodes with greater effect sizes than those of patients without FBTCS (N = 9). NBS findings between patients and controls indicated that structural network changes are not necessarily a consequence of longstanding refractory epilepsy and instead are present at the time of diagnosis. Computed effect sizes suggest that there may be structural network MRI-markers of future pharmacoresistance and seizure severity in patients with a new diagnosis of focal epilepsy.

White Matter-Based Structural Brain Network of Major Depression

Major depressive disorder (MDD) is frequently characterized as a disorder of the disconnection syndrome. Diffusion tensor imaging (DTI) has played a critical role in supporting this view, with much investigation providing a large amount of evidence of structural connectivity abnormalities in the disorder. Recent research on the human connectome combined neuroimaging techniques with graph theoretic methods to highlight the disrupted topological properties of large-scale structural brain networks under depression, involving global metrics (e.g., global and local efficiencies), and local nodal properties (e.g., degree and betweenness), as well as other related metrics, including a modular structure, assortativity, and (rich) hubs. Here, we review the studies of white matter networks in the case of MDD with the application of these techniques, focusing principally on the consistent findings and the clinical significance of DTI-based network research, while discussing the key methodological issues that frequently arise in the field. The already published literature shows that MDD is associated with a widespread structural connectivity deficit. Topological alteration of structural brain networks in the case of MDD points to decreased overall connectivity strength and reduced global efficiency as well as decreased small-worldness and network resilience. These structural connectivity disturbances entail potential functional consequences, although the relationship between the two is very sophisticated and requires further investigation. In summary, the present study comprehensively maps the structural connectomic disturbances in patients with MDD across the entire brain, which adds important weight to the view suggesting connectivity abnormalities of this disorder and highlights the potential of network properties as diagnostic biomarkers in the psychoradiology field. Several common methodological issues of the study of DTI-based networks are discussed, involving sample heterogeneity and fiber crossing problems and the tractography algorithms. Finally, suggestions for future perspectives, including imaging multimodality, a longitudinal study and computational connectomics, in the further study of white matter networks under depression are given. Surmounting these challenges and advancing the research methods will be required to surpass the simple mapping of connectivity changes to illuminate the underlying psychiatric pathological mechanism.

Distinct Features of Cerebral Blood Flow and Spontaneous Neural Activity as Integrated Predictors of Early Response to Antidepressants

AIMS

The purpose of this study is to explore whether pre-treatment features of brain function can discriminate non-responders to antidepressant medication in the early phase.

METHODS

Forty-four treatment-responsive depressed (RD) patients, 36 non-responsive depressed (NRD) patients, and 42 healthy controls (HCs) were recruited. Regional cerebral blood flow (CBF) and amplitude of low-frequency fluctuation (ALFF) values were calculated for all subjects. Correlation analyses were used to explore the relationship between symptom improvement and CBF/ALFF. Receiver operating characteristics (ROC) and the 10-fold cross-validation support vector machine (SVM) classifier were applied for the discrimination of treatment response.

RESULTS

Compared with the HCs, the RD and NRD groups exhibited lower CBF and ALFF in the right posterior lobe of the cerebellum. Compared with the NRD group, the RD group showed distinct CBF patterns in the left frontal striatal regions and right frontal cerebellar regions, as well as distinct ALFF features in the left frontoparietal striatum and right frontotemporal striatal cerebellar regions. The ROC and SVM classifier revealed the optimal power to distinguish the RD and NRD groups based on the combined measures (i.e., CBF and ALFF).

CONCLUSION

Distinct features of CBF and ALFF in the frontal striatal network may serve as promising neuroimaging predictors for identifying patients with blunted responsiveness, which may facilitate personalized antidepressant treatment.

Reduced anxiety and changes in amygdala network properties in adolescents with training for awareness, resilience, and action (TARA)

Mindfulness-based approaches show promise to improve emotional health in youth and may help treat and prevent adolescent depression and anxiety. However, there is a fundamental gap in understanding the neural reorganization that takes place as a result of such interventions. The Training for Awareness, Resilience, and Action (TARA) program, initially developed for depressed adolescents, uses a framework drawn from neuroscience, mindfulness, yoga, and modern psychotherapeutic techniques to promote emotional health. The goal of this study was to assess the effects of the TARA training on emotional health and structural white matter brain networks in healthy youth. We analyzed data from 23 adolescents who underwent the 12-week TARA training in a controlled within-subject study design and whose brain networks were assessed using diffusion MRI connectomics. Compared to the control time period, adolescents showed a significant decrease in anxiety symptoms with TARA (Cohen's d = -0.961, p = 0.006); moreover, the node strength of the Right Amygdala decreased significantly after TARA (Cohen's d = -1.026, p = 0.004). Post-hoc analyses indicated that anxiety at baseline before TARA was positively correlated with Right Amygdala node strength (r = 0.672, p = 0.001). While change in Right Amygdala node strength with TARA was not correlated with change in anxiety (r = 0.146, p = 0.51), it was associated with change in depression subscale of Anhedonia / Negative Affect (r = 0.575, p = 0.004, exploratory analysis), possibly due to overlapping constructs captured in our anxiety and depression scales. Our results suggest that increased structural connectivity of Right Amygdala may underlie increased anxiety in adolescents and be lowered through anxiety-reducing training such as TARA. The results of this study contribute to our understanding of the neural mechanisms of TARA and may facilitate neuroscience-based prevention and treatment of adolescent anxiety and depression.

Girls' brain structural connectivity in late adolescence relates to history of depression symptoms

BACKGROUND

Girls' depressive symptoms typically increase in adolescence, with individual differences in course and severity being key risk factors for impaired emotional functioning in young adulthood. Given the continued brain white matter (WM) maturation that occurs in adolescence, the present study tested whether structural connectivity patterns in late adolescence are associated with variation in the course of depression symptom severity throughout adolescence.

METHOD

Participants were girls (N = 115) enrolled in a multiyear prospective cohort study of risk for depression. Initial depression severity (intercept) at age 10 and change in severity (linear slope) across ages 10-19 were examined in relation to WM tractography collected at age 19. Network-based statistic analyses were used to identify clusters showing variation in structural connectivity in association with depressive symptom intercept, slope, and their interaction.

RESULTS

Higher initial depressive severity and steeper positive slope (separately) were associated with greater structural connectivity between temporal, subcortical socioaffective, and occipital regions. Intercept showed more connectivity associations than slope. The interaction effect indicated that higher initial symptom severity and a steeper negative slope (i.e., alleviating symptoms) were related to greater connectivity between cognitive control regions. Moderately severe symptoms that worsened over time were followed by greater connectivity between self-referential and cognitive regions (e.g., posterior cingulate and frontal gyrus).

CONCLUSIONS

Higher depressive symptom severity in early adolescence and increasing symptom severity over time may forecast structural connectivity differences in late adolescence, particularly in pathways involving cognitive and emotion-processing regions. Understanding how clinical course relates to neurobiological correlates may inform new treatment approaches to adolescent depression.

Neural Correlates of Smartphone Dependence in Adolescents

Increases in depressive and suicide-related symptoms among United States adolescents have been recently linked to increased use of smartphones. Understanding of the brain mechanisms that underlie the potential smartphone dependence may help develop interventions to address this important problem. In this exploratory study, we investigated the neural mechanisms underlying potential smartphone dependence in a sample of 19 adolescent volunteers who completed self-assessments of their smartphone dependence, depressive symptoms, and sleep problems. All 19 adolescents underwent diffusion MRI that allowed for assessment of white matter structural connectivity within the framework of connectomics. Based on previous literature on the neurobiology of addiction, we hypothesized a disruption of network centrality of three nodes in the mesolimbic network: Nucleus Accumbens, anterior cingulate cortex, and amygdala. Our results showed positive correlations between the node centrality of the right amygdala and self-reported smartphone dependence, between smartphone dependence and sleep problems, and between sleep problems and depressive symptoms. A higher phone dependence was observed in females compared to males. Supported by these results, we propose a model of how smartphone dependence can be linked to aberrations in brain networks, sex, sleep disturbances, and depression in adolescents.

The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway

BACKGROUND

Robust evidence has shown that abnormal function networks, particularly the salience network (SN), are observed in depressed patients. Although white matter structural connectivity may predict time-varying functional connectivity, including symptom phenotype, in psychiatric disorders, there is still a gap in elucidating the concurrent dynamic functional and structural connectivity profiles of the SN in depressed patients.

METHODS

We measured static and dynamic functional connectivity (FC) of the SN using resting-state fMRI BOLD time series in 76 subjects (21 with major depressive disorder (MDD), 27 with bipolar depression (BD), and 28 healthy controls (HC)). Hamilton Depression Scale total score was used to measure depression severity. Furthermore, we investigated the concurrent structural connectivity using diffusion kurtosis imaging (DKI)-based tractography.

RESULTS

Our findings suggested that in the presence of MDD, both structural and dynamic (but not static) FC were reduced in the SN, particularly affecting the left prefronto-insular pathways (L.aPFC-L.insula). MDD patients showed decreased connectivity variability within the SN compared with HC. The aberrant dynamic FC in the prefronto-insular pathways of the SN related to severity of depressive symptoms in MDD. Furthermore, compared with BD patients, those with MDD showed significantly decreased dynamic FC in the left prefronto-parietal system (L.aPFC-lateral parietal cortex).

LIMITATIONS

The generalizability of our findings is, to some extent, constrained by the small sample size.

CONCLUSIONS

The integrity of SN connectivity, particularly the prefronto-insular pathway, appears to be a crucial signature of MDD. The perturbed dynamic interaction of SN with prefrontal regions may underlie the clinical severity in depressed patients.

MRI Insights Into Adolescent Neurocircuitry-A Vision for the Future

Adolescence is the time of onset of many psychiatric disorders. Half of pediatric patients present with comorbid psychiatric disorders that complicate both their medical and psychiatric care. Currently, diagnosis and treatment decisions are based on symptoms. The field urgently needs brain-based diagnosis and personalized care. Neuroimaging can shed light on how aberrations in brain circuits might underlie psychiatric disorders and their development in adolescents. In this perspective article, we summarize recent MRI literature that provides insights into development of psychiatric disorders in adolescents. We specifically focus on studies of brain structural and functional connectivity. Ninety-six included studies demonstrate the potential of MRI to assess psychiatrically relevant constructs, diagnose psychiatric disorders, predict their development or predict response to treatment. Limitations of the included studies are discussed, and recommendations for future research are offered. We also present a vision for the role that neuroimaging may play in pediatrics and primary care in the future: a routine neuropsychological and neuropsychiatric imaging (NPPI) protocol for adolescent patients, which would include a 30-min brain scan, a quality control and safety read of the scan, followed by computer-based calculation of the structural and functional brain network metrics that can be compared to the normative data by the pediatrician. We also perform a cost-benefit analysis to support this vision and provide a roadmap of the steps required for this vision to be implemented.

Functional connectome and neuropsychiatric symptom clusters of Alzheimer's disease

BACKGROUND

Neuropsychiatric symptoms (NPSs) are important aspects of Alzheimer's disease (AD). Investigation of the effect of functional network abnormalities on clustered NPSs may uncover loci of altered connectivity for more targeted pharmacological and behavioral interventions in AD. The study aimed to investigate functional connectivity in AD and the clustered NPSs, as assessed by the Neuropsychiatric Inventory (NPI).

METHODS

In one hundred and fifty-nine patients with mild dementia stage of AD, graph metrics measuring functional connectivity at global network- and local network-level were assessed by closeness-centrality, betweenness-centrality, average-path-length, local-efficiency, and clustering-coefficient, respectively. The relationship between the NPI composite score and functional connectivity was assessed.

RESULTS

In AD, an increase in behavioral composite score was associated with changes in functional connectivity at local network-level, and regions displayed the changes was left lingual gyrus, left sub-genual ACC nodes, and left supra-genual ACC nodes (P < 0.05). An increase in affective composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was right caudate (P = 0.014). An increase in psychotic composite score was associated with changes in functional connectivity at global network-level, and regions displayed the change was left precuneus and right dorsolateral superior frontal gyrus (P < 0.05).

LIMITATIONS

Cognitively normal elderly subjects and longitudinal follow-up will be needed to see the evolution of NPS clusters and pathological changes in the functional connectivity at global or local network-level.

CONCLUSIONS

Different NPS clusters corresponded to distinct changes in functional connectivity at global and local network-level.

Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network ("connectome") analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

State-Independent and -Dependent Structural Connectivity Alterations in Depression

Some brain abnormalities persist at the remission phase, that is, the state-independent abnormalities, which may be one of the reasons for the high recurrence of major depressive disorder (MDD). Hence, it is of great significance to identify state-independent abnormalities of MDD through longitudinal investigation. Ninety-nine MDD patients and 118 healthy controls (HCs) received diffusion tensor imaging scanning at baseline. After 6-month antidepressant treatment, 68 patients received a second scan, among which 59 patients achieved full clinical remission. Differences in whole-brain structural connectivity (SC) between patients with MDD at baseline and HCs were estimated by two-sample t-tests. Masked with significantly changed SCs in MDD, two-sample t-tests were conducted between the remitted MDD subgroup at follow-up and HCs, and paired t-tests were implemented to compare the differences of SC in the remitted MDD subgroup before and after treatment. Significantly decreased SC between the right insula and the anterior temporal cortex (ATC), between the right ATC and the posterior temporal cortex (PTC), between the left ATC and the auditory cortex as well as increased connectivity between the right posterior cingulate cortex (PCC) and the left medial parietal cortex (MPC) were observed in the MDD group compared with the HC group at baseline (p < 0.05, FDR corrected). The decreased connectivity between the right insula and the ATC and increased connectivity between the right PCC and the left MPC persisted in the remitted MDD subgroup at follow-up (p < 0.05, FDR corrected). The decreased SC between the right insula and the ATC and increased SC between the right PCC and left MPC showed state-independent characters, which may be implicated in the sustained negative attention bias and motor retardation in MDD. In contrast, the decreased SC between the right ATC and the PTC and between the left ATC and the auditory cortex seemed to be state-dependent.

Application of machine learning to structural connectome to predict symptom reduction in depressed adolescents with cognitive behavioral therapy (CBT)

Purpose

Adolescent major depressive disorder (MDD) is a highly prevalent, incapacitating and costly illness. Many depressed teens do not improve with cognitive behavioral therapy (CBT), a first-line treatment for adolescent MDD, and face devastating consequences of increased risk of suicide and many negative health outcomes. “Who will improve with CBT?” is a crucial question that remains unanswered, and treatment planning for adolescent depression remains biologically unguided. The purpose of this study was to utilize machine learning applied to patients' brain imaging data in order to help predict depressive symptom reduction with CBT.

Methods

We applied supervised machine learning to diffusion MRI-based structural connectome data in order to predict symptom reduction in 30 depressed adolescents after three months of CBT. A set of 21 attributes was chosen, including the baseline depression score, age, gender, two global network properties, and node strengths of brain regions previously implicated in depression. The practical and robust J48 pruned tree classifier was utilized with a 10-fold cross-validation.

Results

The classification resulted in an 83% accuracy of predicting depressive symptom reduction. The resulting tree of size seven with only three attributes highlights the role of the right thalamus in predicting depressive symptom reduction with CBT. Additional analysis showed a significant negative correlation between the change in the depressive symptoms and the node strength of the right thalamus.

Conclusions

Our results demonstrate that a machine learning algorithm that exclusively uses structural connectome data and the baseline depression score can predict with a high accuracy depressive symptom reduction in adolescent MDD with CBT. This knowledge can help improve treatment planning for adolescent depression.

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Machine learning predicted symptom reduction in depressed teens with 83% accuracy.

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Resulting prunned classification tree size was 7, with only 3 attributes.

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Change in depression symptoms correlated with node strength of the right thalamus.

Connectome analysis of functional and structural hemispheric brain networks in major depressive disorder

Neuroimaging studies have shown topological disruptions of both functional and structural whole-brain networks in major depressive disorder (MDD). This study examined common and specific alterations between these two types of networks and whether the alterations were differentially involved in the two hemispheres. Multimodal MRI data were collected from 35 MDD patients and 35 healthy controls, whose functional and structural hemispheric networks were constructed, characterized, and compared. We found that functional brain networks were profoundly altered at multiple levels, while structural brain networks were largely intact in patients with MDD. Specifically, the functional alterations included decreases in intra-hemispheric (left and right) and inter-hemispheric (heterotopic) functional connectivity; decreases in local, global and normalized global efficiency for both hemispheric networks; increases in normalized local efficiency for the left hemispheric networks; and decreases in intra-hemispheric integration and inter-hemispheric communication in the dorsolateral superior frontal gyrus, anterior cingulate gyrus and hippocampus. Regarding hemispheric asymmetry, largely similar patterns were observed between the functional and structural networks: the right hemisphere was over-connected and more efficient than the left hemisphere globally; the occipital and partial regions exhibited leftward asymmetry, and the frontal and temporal sites showed rightward lateralization with regard to regional connectivity profiles locally. Finally, the functional-structural coupling of intra-hemispheric connections was significantly decreased and correlated with the disease severity in the patients. Overall, this study demonstrates modality- and hemisphere-dependent and invariant network alterations in MDD, which are helpful for understanding elaborate and characteristic patterns of integrative dysfunction in this disease.

Test-Retest Reliability of Graph Theoretic Metrics in Adolescent Brains

Graph theory analysis of structural brain networks derived from diffusion tensor imaging (DTI) has become a popular analytical method in neuroscience, enabling advanced investigations of neurological and psychiatric disorders. The purpose of this study was to investigate (1) the effects of edge weighting schemes and (2) the effects of varying interscan periods on graph metrics within the adolescent brain. We compared a binary (B) network definition with three weighting schemes: fractional anisotropy (FA), streamline count, and streamline count with density and length correction (SDL). Two commonly used global and two local graph metrics were examined. The analysis was conducted with two groups of adolescent volunteers who received DTI scans either 12 weeks apart (16.62 ± 1.10 years) or within the same scanning session (30 min apart) (16.65 ± 1.14 years). The intraclass correlation coefficient was used to assess test-retest reliability and the coefficient of variation (CV) was used to assess precision. On average, each edge scheme produced reliable results at both time intervals. Weighted measures outperformed binary measures, with SDL weights producing the most reliable metrics. All edge schemes except FA displayed high CV values, leaving FA as the only edge scheme that consistently showed high precision while also producing reliable results. Overall findings suggest that FA weights are more suited for DTI connectome studies in adolescents.

Structural networks analysis for depression combined with graph theory and the properties of fiber tracts via diffusion tensor imaging

Previous studies have suggested that major depressive disorder was associated with topological properties of impaired white matter. However, most related studies only use one property of nerve fibers to construct whole-brain structural brain network. Considering white matter changes variously, We hypothesized whether the alternations of white matter topological properties could reflect different impairment of white matter integrity. In addition, it is still unknown whether impaired integrity of the white matter fiber tracts has relationship with abnormal topological properties in MDD. This study investigated the impaired white matter by using graph theoretic analyses in a cohort of 37 MDD patients and 38 matched control subjects. In addition, we further investigated fiber tracts differences in three interregional connectivity matrixes of significant different topological regions in MDD. Our graph theoretic analyses demonstrated that 7 different regions were observed for the local measures in patients with MDD compared with control groups. These regions were the central nodes of cortical-limbic network, frontal-cingulate network, default mode network (DMN), cognitive control network(CCN)and affective network (AN). In addition, two impaired white matter pathways which included inferior longitudinal fasciculus (ILF) and cingulum were observed in MDD using fiber tracts analysis. We speculate impaired integrity of ILF is due to the alternations in the number of axons or myelination. The results further demonstrated that the number of fiber tracts of anterior cingulum was associated with the depression scores in MDD.

Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder

This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.

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.

Aberrant Structural Brain Connectivity in Adolescents with Attentional Problems Who Were Born Prematurely

BACKGROUND AND PURPOSE

Differences in structural brain connectivity that underlie inattention have been previously investigated in adolescents with attention deficit/hyperactivity disorder, but not in the context of premature birth, which is often associated with attentional problems. The purpose of this study was to identify the neural correlates of attentional problems in adolescents born prematurely and determine neonatal predictors of those neural correlates and attention problems.

MATERIALS AND METHODS

The study included 24 adolescents (12.5 ± 1.8 years of age; 12 girls, 12 boys) who were born prematurely and underwent MR imaging of the brain and cognitive assessment, both shortly after birth and as adolescents. Structural connectivity was assessed at adolescence using diffusion tensor imaging and tractography.

RESULTS

Of the 24 subjects, 12 had attention deficits. A set of axonal pathways connecting the frontal, parietal, temporal, and occipital lobes had significantly lower fractional anisotropy in subjects with attentional problems. The temporoparietal connection between the left precuneus and left middle temporal gyrus was the most significantly underconnected interlobar axonal pathway. Low birth weight and ventriculomegaly, but not white matter injury or intraventricular hemorrhage on neonatal MR imaging, predicted temporoparietal hypoconnectivity in adolescence. However, neither birth weight nor other neonatal characteristics were associated with attention deficits directly.

CONCLUSIONS

We identified an aberrant structural brain connectivity pattern, involving temporoparietal hypoconnectivity, in prematurely born adolescents with attentional problems. We also identified birth weight as a potential neonatal predictor of the temporoparietal hypoconnectivity. These findings add to our understanding of the neural basis and etiology of inattention in adolescents after premature birth.

Microstructural changes in patients with Parkinson disease and REM sleep behavior disorder: depressive symptoms versus non-depressed

Search for Parkinson's disease (PD) progression biomarkers has led to the identification of both motor and non-motor symptoms relevant of prodromal PD that could be eye-opening to the spreading underlying Lewy body pathogenesis. One most robust predictor of PD is the REM sleep behavior disorder (RBD), and one most common early non-motor symptom of PD is depression. With RBD, frequently coexisting with clinical depression and both predicting dopamine transmission dysfunction, we aimed to survey structural associates of depressive symptoms in early PD patients with comorbid RBD. Through diffusion MRI connectometry, we tracked fiber differences comparing DWI images obtained from 14 patients with depressive symptoms and 18 without depression from a group with comorbid RBD and PD. DWI images and patients were recruited from the Parkinson's Progression Markers Initiative database. PD-RBD patients with depressive symptoms showed pathways with significantly reduced connectivity in the right cingulum, left and right fornix, left inferior longitudinal fasciculus, right corticospinal tract, left middle cerebellar peduncle and genu of corpus callosum (FDR = 0.0228). Diffusivity alteration of the mentioned fibers in depressed, early PD patients with RBD might reflect underlying PD pathology and serve as common structural DWI signatures for early PD diagnosis.

High levels of mitochondrial DNA are associated with adolescent brain structural hypoconnectivity and increased anxiety but not depression

BACKGROUND

Adolescent anxiety and depression are highly prevalent psychiatric disorders that are associated with altered molecular and neurocircuit profiles. Recently, increased mitochondrial DNA copy number (mtDNA-cn) has been found to be associated with several psychopathologies in adults, especially anxiety and depression. The associations between mtDNA-cn and anxiety and depression have not, however, been investigated in adolescents. Moreover, to date there have been no studies examining associations between mtDNA-cn and brain network alterations in mood disorders in any age group.

METHODS

The first aim of this study was to compare salivary mtDNA-cn between 49 depressed and/or anxious adolescents and 35 well-matched healthy controls. The second aim of this study was to identify neural correlates of mtDNA-cn derived from diffusion tensor imaging (DTI) and tractography, in the full sample of adolescents.

RESULTS

There were no diagnosis-specific alterations in mtDNA-cn. However, there was a positive correlation between mtDNA-cn and levels of anxiety, but not depression, in the full sample of adolescents. A subnetwork of connections largely corresponding to the left fronto-occipital fasciculus had significantly lower fractional anisotropy (FA) values in adolescents with higher than median mtDNA-cn.

LIMITATIONS

Undifferentiated analysis of free and intracellular mtDNA and use of DTI-based tractography represent this study's limitations.

CONCLUSIONS

The results of this study help elucidate the relationships between clinical symptoms, molecular changes, and neurocircuitry alterations in adolescents with and without anxiety and depression, and they suggest that increased mtDNA-cn is associated both with increased anxiety symptoms and with decreased fronto-occipital structural connectivity in this population.

Network-based approaches to examining stress in the adolescent brain

Exposure to stress, particularly in periods of rapid brain maturation such as adolescence, can profoundly influence developmental processes that undergird the organization of structural and functional brain networks and that may mediate the association between stressful experiences and maladaptive outcomes. While studies in translational developmental neuroscience often focus on how specific brain regions or targeted connections are altered by stress and psychiatric disease, the emerging field of network science may be especially valuable for elucidating the impact of stress on the intricate connectomics of the adolescent brain. Here we review recent studies that use graph theory and other network science approaches to understand normative adolescent brain development, effects of childhood maltreatment on the brain, and disorders characterized by pathological responses to stress in adolescents. Overall, these studies demonstrate that graph theory can be useful in identifying and quantifying developmental processes related to segregation, integration, and localized hub influence that are affected by stress exposure and that may lead to psychopathology. Finally, we discuss limitations in the current application of graph theory in this area and suggest what we believe are important directions for future work.

Depressive disorders: Processes leading to neurogeneration and potential novel treatments

Mood disorders are wide spread with estimates that one in seven of the population are affected at some time in their life (Kessler et al., 2012). Many of those affected with severe depressive disorders have cognitive deficits which may progress to frank neurodegeneration. There are several peripheral markers shown by patients who have cognitive deficits that could represent causative factors and could potentially serve as guides to the prevention or even treatment of neurodegeneration. Circadian rhythm misalignment, immune dysfunction and oxidative stress are key pathologic processes implicated in neurodegeneration and cognitive dysfunction in depressive disorders. Novel treatments targeting these pathways may therefore potentially improve patient outcomes whereby the primary mechanism of action is outside of the monoaminergic system. Moreover, targeting immune dysfunction, oxidative stress and circadian rhythm misalignment (rather than primarily the monoaminergic system) may hold promise for truly disease modifying treatments that may prevent neurodegeneration rather than simply alleviating symptoms with no curative intent. Further research is required to more comprehensively understand the contributions of these pathways to the pathophysiology of depressive disorders to allow for disease modifying treatments to be discovered.

Structural connectomics of anxious arousal in early adolescence: Translating clinical and ethological findings

Etiological explanations of clinical anxiety can be advanced through understanding the neural mechanisms associated with anxiety in youth prior to the emergence of psychopathology. In this vein, the present study sought to investigate how trait anxiety is related to features of the structural connectome in early adolescence. 40 adolescents (21 female, mean age = 13.49 years) underwent a diffusion-weighted imaging scan. We hypothesized that the strength of several a priori defined structural connections would vary with anxious arousal based on previous work in human clinical neuroscience and adult rodent optogenetics. First, connection strength of caudate to rostral middle frontal gyrus was predicted to be anticorrelated with anxious arousal, predicated on extant work in clinically-diagnosed adolescents. Second, connection strength of amygdala to rostral anterior cingulate and to medial orbital frontal cortex would be positively and negatively correlated with anxious arousal, respectively, predicated on rodent optogenetics showing the former pathway is anxiogenic and the latter is anxiolytic. We also predicted that levels of anxiety would not vary with measures of global network topology, based on reported null findings. Results support that anxiety in early adolescence is associated with (1) the clinical biomarker connecting caudate to frontal cortex, and (2) the anxiogenic pathway connecting amygdala to rostral anterior cingulate, both in left but not right hemisphere. Findings support that in early adolescence, anxious arousal may be related to mechanisms that increase anxiogenesis, and not in a deficit in regulatory mechanisms that support anxiolysis.

Anhedonia correlates with abnormal functional connectivity of the superior temporal gyrus and the caudate nucleus in patients with first-episode drug-naive major depressive disorder

BACKGROUND

Recent empirical findings have suggested that imbalanced neural networks may underlie the pathophysiology of major depressive disorder (MDD). However, the contribution of the superior temporal gyrus (STG) and the caudate nucleus to its pathophysiology remains unclear.

METHODS

Functional magnetic resonance imaging (MRI) date were acquired from 40 patients with first-episode drug-naive MDD and 36 matched healthy controls during wakeful rest. We used whole-brain voxel-wise statistical maps to quantify within-group resting state functional connectivity (RSFC) and between-group differences of bilateral caudate and STG seeds.

RESULTS

Compared with healthy controls, first-episode MDD patients were found to have reduced connectivity between the ventral caudate and several brain regions including the superior frontal gyrus (SFG), the superior parietal lobule (SPL) and the middle temporal gyrus (MTG), as well as increased connectivity with the cuneus. We also found increased connectivity between the left STG and the precuneus, the angular gyrus and the cuneus. Moreover, we found that increased anhedonia severity was correlated with the magnitude of ventral caudate functional connectivity with the cuneus and the MTG in MDD patients.

LIMITATIONS

Due to our small sample size, we did not correct the statistical threshold in the correlation analyses between clinical variables and connectivity abnormalities.

CONCLUSIONS

The present study suggests that anhedonia is mainly associated with altered ventral caudate-cortical connectivity and highlights the importance of the ventral caudate in the neurobiology of MDD.

Alternations of White Matter Structural Networks in First Episode Untreated Major Depressive Disorder with Short Duration

It is crucial to explore the pathogenesis of major depressive disorder (MDD) at the early stage for the better diagnostic and treatment strategies. It was suggested that MDD might be involving in functional or structural alternations at the brain network level. However, at the onset of MDD, whether the whole brain white matter (WM) alterations at network level are already evident still remains unclear. In the present study, diffusion MRI scanning was adopt to depict the unique WM structural network topology across the entire brain at the early stage of MDD. Twenty-one first episode, short duration (<1 year) and drug-naïve depression patients, and 25 healthy control (HC) subjects were recruited. To construct the WM structural network, atlas-based brain regions were used for nodes, and the value of multiplying fiber number by the mean fractional anisotropy along the fiber bundles connected a pair of brain regions were used for edges. The structural network was analyzed by graph theoretic and network-based statistic methods. Pearson partial correlation analysis was also performed to evaluate their correlation with the clinical variables. Compared with HCs, the MDD patients had a significant decrease in the small-worldness (σ). Meanwhile, the MDD patients presented a significantly decreased subnetwork, which mainly involved in the frontal-subcortical and limbic regions. Our results suggested that the abnormal structural network of the orbitofrontal cortex and thalamus, involving the imbalance with the limbic system, might be a key pathology in early stage drug-naive depression. And the structural network analysis might be potential in early detection and diagnosis of MDD.

Feasibility and Preliminary Efficacy of a Novel RDoC-Based Treatment Program for Adolescent Depression: "Training for Awareness Resilience and Action" (TARA)-A Pilot Study

BACKGROUND

The novel group treatment program Training for Awareness, Resilience, and Action (TARA) was developed to target specific mechanisms based on neuroscience findings in adolescent depression and framed within the National Institute of Mental Health Research Domain Criteria. TARA contains training of autonomic and emotional self-regulation, interoceptive awareness, relational skills, and value-based committed action.

METHODS

We performed a single-arm trial to test the feasibility and preliminary efficacy of TARA in reducing depression and anxiety levels and assessed whether the specific targeted domains of function reflected the hypothesized symptom change. Twenty-six adolescents (14-18 years old, 7 males and 19 females) participated in the 12-week group program. Assessment was performed before (T0), immediately after (T1), and 3 months after the end of TARA (T2).

RESULTS

Significant improvement was seen in depression symptoms (Reynolds Adolescent Depression Scale Second Edition) between T0-T1 (t-value = -3.56, p = 0.002, CI = -6.64, -1.77) and T0-T2 (t-value = -4.17, p < 0.001, CI = -11.20, -3.75) and anxiety symptoms (Multidimensional Anxiety Scale for Children) between T0-T1 (t-value = -2.26, p = 0.033, CI = -4.61, -0.21) and T0-T2 (t-value = -3.06, p = 0.006, 95% confidence interval = -9.02, -1.73). Significant improvements in psychological flexibility, sleep, and mindfulness skills were also found between T0 and T2.

LIMITATIONS

The sample size was small without a control condition. The pilot design did not allow for testing the hypothesized brain changes and effect of TARA on relevant systemic biomarkers.

CONCLUSION

TARA is feasible in a sample of clinically depressed and/or anxious adolescents and preliminary efficacy was demonstrated by reduced depression and anxiety symptoms. The specific symptom and behavioral outcomes corresponded well with the hypothesized mechanisms of change.