Disrupted structural brain connectome underlying the cognitive deficits in remitted late-onset depression

Structural Brain Connectivity and Treatment Improvement in Mood Disorder

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.

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.

Brain-cognition relationships in late-life depression: a systematic review of structural magnetic resonance imaging studies

BACKGROUND

Most patients with late-life depression (LLD) have cognitive impairment, and at least one-third meet diagnostic criteria for mild cognitive impairment (MCI), a prodrome to Alzheimer's dementia (AD) and other neurodegenerative diseases. However, the mechanisms linking LLD and MCI, and brain alterations underlying impaired cognition in LLD and LLD + MCI remain poorly understood.

METHODS

To address this knowledge gap, we conducted a systematic review of studies of brain-cognition relationships in LLD or LLD + MCI to identify circuits underlying impaired cognition in LLD or LLD + MCI. We searched MEDLINE, PsycINFO, EMBASE, and Web of Science databases from inception through February 13, 2023. We included studies that assessed cognition in patients with LLD or LLD + MCI and acquired: (1) T1-weighted imaging (T1) measuring gray matter volumes or thickness; or (2) diffusion-weighted imaging (DWI) assessing white matter integrity. Due to the heterogeneity in studies, we only conducted a descriptive synthesis.

RESULTS

Our search identified 51 articles, resulting in 33 T1 studies, 17 DWI studies, and 1 study analyzing both T1 and DWI. Despite limitations, reviewed studies suggest that lower thickness or volume in the frontal and temporal regions and widespread lower white matter integrity are associated with impaired cognition in LLD. Lower white matter integrity in the posterior cingulate region (precuneus and corpus callosum sub-regions) was more associated with impairment executive function and processing speed than with memory.

CONCLUSION

Future studies should analyze larger samples of participants with various degrees of cognitive impairment and go beyond univariate statistical models to assess reliable brain-cognition relationships in LLD.

Alpha peak activity in resting-state EEG is associated with depressive score

Introduction

Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders.

Methods

To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism.

Results

The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores.

Discussion

These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.

White matter tract-specific microstructural disruption is associated with depressive symptoms in isolated RBD

OBJECTIVE

White matter (WM) tract-specific changes may precede gray matter loss in isolated rapid eye movement sleep behavior disorder (iRBD). We aimed to evaluate tract-specific WM changes using tract-specific statistical analysis (TSSA) and their correlation with clinical variables in iRBD patients.

METHODS

This was a cross-sectional single-center study of 50 polysomnography-confirmed iRBD patients and 20 age- and sex-matched controls. We used TSSA to identify tract-specific fractional anisotropy (FA) and mean diffusivity (MD) in fourteen major fiber tracts and analyzed between-group differences in these values. Correlations between FA or MD values and clinical variables, including RBD symptom severity, depression and cognition, were evaluated.

RESULTS

Patients with iRBD showed lower FA in the right anterior thalamic radiation (ATR) and higher MD in the bilateral ATR and right inferior fronto-occipital fasciculus (IF-OF) than controls after adjusting for age, sex, and years of education. MD values in the IF-OF positively correlated with scores on the Korean version of the Rapid Eye Movement Sleep Behavior Disorder Questionnaire-Hong Kong (RBDQ-KR, p = 0.042) and the Korean version of the geriatric depression scale (GDS-K, p = 0.002) in iRBD patients. Only GDS-K scores independently correlated with IF-OF MD values after adjusting for RBDQ-KR scores (adjusted p = 0.026).

CONCLUSION

This study suggests WM microstructural disruption in the bilateral ATR and right IF-OF in patients with iRBD and that alterations in the IF-OF may contribute to depressive symptoms.

Altered intrinsic default mode network functional connectivity in patients with remitted geriatric depression and amnestic mild cognitive impairment

OBJECTIVES

Patients with geriatric depression exhibit a spectrum of symptoms ranging from mild to severe cognitive impairment which could potentially lead to the development of Alzheimer's disease (AD). The aim of the study is to assess the alterations of the default mode network (DMN) in remitted geriatric depression (RGD) patients and whether it could serve as an underlying neuropathological mechanism associated with the risk of progression of AD.

DESIGN

Cross-sectional study.

PARTICIPANTS

A total of 154 participants, comprising 66 RGD subjects (which included 27 patients with comorbid amnestic mild cognitive impairment [aMCI] and 39 without aMCI [RGD]), 45 aMCI subjects without a history of depression (aMCI), and 43 matched healthy comparisons (HC), were recruited.

MEASUREMENTS

All participants completed neuropsychological tests and underwent resting-state functional magnetic resonance imaging (fMRI). Posterior cingulate cortex (PCC)-seeded DMN functional connectivity (FC) along with cognitive function were compared among the four groups, and correlation analyses were conducted.

RESULTS

In contrast to HC, RGD, aMCI, and RGD-aMCI subjects showed significant impairment across all domains of cognitive functions except for attention. Furthermore, compared with HC, there was a similar and significant decrease in PCC-seed FC in the bilateral medial superior frontal gyrus (M-SFG) in the RGD, aMCI, and RGD-aMCI groups.

CONCLUSIONS

The aberrations in rsFC of the DMN were associated with cognitive deficits in RGD patients and might potentially reflect an underlying neuropathological mechanism for the increased risk of developing AD. Therefore, altered connectivity in the DMN could serve as a potential neural marker for the conversion of geriatric depression to AD.

Elevated homocysteine levels, white matter abnormalities and cognitive impairment in patients with late-life depression

Background

Cognitive impairment in late−life depression (LLD) is considered to be caused by neurodegenerative changes. Elevated homocysteine (Hcy) levels may be linked to cognitive abnormalities associated with LLD. The important role of white matter (WM) damage in cognitive impairment and pathogenesis in patients with LLD has been widely reported. However, no research has explored the interrelationships of these features in patients with LLD.

Objective

The goal of the study was to examine the interrelationship between Hcy levels, cognition, and variations in WM microstructure detected by diffusion tensor imaging (DTI) in patients with LLD.

Methods

We recruited 89 healthy controls (HCs) and 113 patients with LLD; then, we measured the plasma Hcy levels of participants in both groups. All individuals performed a battery of neuropsychological tests to measure cognitive ability. Seventy-four patients with LLD and 68 HCs experienced a DTI magnetic resonance imaging (MRI) scan.

Results

Patients with LLD showed significantly lower fractional anisotropy (FA) values in the bilateral inferior longitudinal fasciculus than those of healthy participants. Only in LLD patients was Hcy concentration inversely associated to FA values in the forceps minor. Finally, multiple regression analyses showed that an interaction between Hcy levels and FA values in the right cingulum of the cingulate cortex and right inferior longitudinal fasciculus were independent contributors to the executive function of patients with LLD.

Conclusion

Our results highlight the complex interplay between elevated homocysteine levels and WM abnormalities in the pathophysiology of LLD-related cognitive impairment, consistent with the neurodegeneration hypothesis.

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.

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

Background

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

Methods

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

Results

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

Conclusion

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

Supplementary Information

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

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.

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 .

Disrupted Structural Brain Connectome Is Related to Cognitive Impairment in Patients With Ischemic Leukoaraiosis

Ischemic leukoaraiosis (ILA) is related to cognitive impairment and vascular dementia in the elderly. One possible mechanism could be the disruption of white matter (WM) tracts and network function that connect distributed brain regions involved in cognition. The purpose of this study was to investigate the relationship between structural connectome and cognitive functions in ILA patients. A total of 89 patients with ILA (Fazekas score ≥ 3) and 90 healthy controls (HCs) underwent comprehensive neuropsychological examinations and diffusion tensor imaging scans. The tract-based spatial statistics approach was employed to investigate the WM integrity. Graph theoretical analysis was further applied to construct the topological architecture of the structural connectome in ILA patients. Partial correlation analysis was used to investigate the relationships between network measures and cognitive performances in the ILA group. Compared with HCs, the ILA patients showed widespread WM integrity disruptions. The ILA group displayed increased characteristic path length (L _p) and decreased global network efficiency at the level of the whole brain relative to HCs, and reduced nodal efficiencies, predominantly in the frontal-subcortical and limbic system regions. Furthermore, these structural connectomic alterations were associated with cognitive impairment in ILA patients. The association between WM changes (i.e., fractional anisotropy and mean diffusivity measures) and cognitive function was mediated by the structural connectivity measures (i.e., local network efficiency and L _p). In conclusion, cognitive impairment in ILA patients is related to microstructural disruption of multiple WM fibers and topological disorganization of structural networks, which have implications in understanding the relationship between ILA and the possible attendant cognitive impairment.

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.

Concurrent alterations of white matter microstructure and functional activities in medication-free major depressive disorder

Although numerous studies have revealed the structural and functional alterations in major depressive disorder (MDD) using unimodal diffusion magnetic resonance imaging (MRI) or functional MRI, however, the potential associations between changed microstructure and corresponding functional activities in the MDD has been largely uninvestigated. Herein, 27 medication-free MDD patients and 54 gender-, age-, and educational level-matched healthy controls (HC) were used to investigate the concurrent alterations of white matter microstructure and functional activities using tract-based spatial statistics (TBSS) analyses, fractional amplitude of low-frequency fluctuation (fALFF), and degree centrality (DC). The TBSS analyses revealed significantly decreased fractional anisotropy (FA) in the superior longitudinal fasciculus (SLF I) in the MDD patients compared to HC. Correlation analyses showed that decreased FA in the SLF I was significantly correlated with fALFF in left pre/postcentral gyrus and binary, weighted DC in right posterior cerebellum. Moreover, the fALFF in left pre/postcentral gyrus significantly reduced in MDD patients while binary and weighted DC in right posterior cerebellum significantly increased in MDD patients. Our results revealed concurrent structural and functional changes in MDD patients suggesting that the underlying structural disruptions are an important indicator of functional abnormalities.

Functional Disorganization of Small-World Brain Networks in Patients With Ischemic Leukoaraiosis

Cognitive impairment is a key clinical feature of ischemic leukoaraiosis (ILA); however, the underlying neurobiological mechanism is still unclear. ILA has been associated with widespread gray and white matter (WM) damage mainly located in cortical-cortical and cortico-subcortical pathways. A total of 36 patients with ILA (Fazekas rating score ≥2) and 31 healthy controls (HCs) underwent comprehensive neuropsychological assessments (covering four cognitive domains, i.e., information processing speed, episodic memory, executive and visuospatial function) and resting-state functional MRI scans. Graph theory-based analyses were employed to explore the topological organization of the brain connectome in ILA patients, and we further sought to explore the associations of connectome-based metrics and neuropsychological performances. An efficient small-world architecture in the functional brain connectome was observed in the ILA and control groups. Moreover, compared with the HCs, the ILA patients showed increased path length and decreased network efficiency (i.e., global and local efficiency) in their functional brain networks. Further network-based statistic (NBS) analysis revealed a functional-disconnected network in ILA, which is comprised of functional connections linking different brain modules (i.e., default mode, frontoparietal, ventral attention and limbic systems) and connections within single modules (i.e., ventral attention and limbic systems). Intriguingly, the abnormal network metrics correlated with cognitive deficits in ILA patients. Therefore, our findings provide further evidence to support the concept that ILA pathologies could disrupt brain connections, impairing network functioning, and cognition via a “disconnection syndrome.”

Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model

AIMS

Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population.

METHODS

The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.

RESULTS

AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2  = .17, P < .001) at baseline in the combined high-risk population (N = 72).

CONCLUSIONS

The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.