Relation between structural and functional connectivity in major depressive disorder

Pilot study comparing effects of infrared neuromodulation and transcranial magnetic stimulation using magnetic resonance imaging

No prior work has directly compared the impacts of transcranial photobiomodulation (tPBM) and transcranial magnetic stimulation (TMS) on the human brain. This within-subjects pilot study compares the effects of tPBM and TMS of human somatomotor cortex on brain structural and functional connectivity. Eight healthy participants underwent four lab visits each, each visit consisting of a pre-stimulation MRI, stimulation or sham, and a post-stimulation MRI, respectively. Stimulation and sham sessions were counterbalanced across subjects. Collected measures included structural MRI data, functional MRI data from a finger-tapping task, resting state functional connectivity, and structural connectivity. Analyses indicated increased activation of the left somatomotor region during a right-hand finger-tapping task following both tPBM and TMS. Additionally, trending increases in left-lateralized functional and structural connectivity from M1 to thalamus were observed after tPBM, but not TMS. Thus, tPBM may be superior to TMS at inducing changes in connected nodes in the somatomotor cortex, although further research is warranted to explore the potential therapeutic benefits and clinical utility of tPBM.

Microstructure Abnormalities of Diffusion Tensor Imaging Measures in First-Episode, Treatment-Naïve Adolescents With Major Depressive Disorder: An Integrated AFQ and TBSS Study

PURPOSE

Structural changes during depressive episodes in adolescents with major depressive disorder (MDD) remains unclear due to participant heterogeneity, illness chronicity, and medication confounders. This study aimed to explore white matter (WM) microstructural changes in first-episode, treatment-naïve adolescents with MDD using an integrated diffusion tensor imaging (DTI) approach.

METHOD

We recruited 66 subjects, including 37 adolescents with MDD and 29 healthy controls. Two main DTI techniques, automated fiber quantification (AFQ) and tract-based spatial statistics (TBSS), were used to analyze fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) in WM tracts. DTI measures were then correlated with the depressive symptoms evaluated by Hamilton Depression Rating Scale scores (HAMD-17).

FINDINGS

In AFQ, MDD patients showed significant segmental differences in WM tracts compared to controls, including a negative correlation between SLF AD values and depression severity. TBSS revealed reduced FA in the cingulum, forceps minor, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, SLF, and uncinate fasciculus in MDD.

CONCLUSION

Our integrated DTI analysis in a unique first-episode, medication-naïve cohort revealed microstructural changes in adolescent MDD not previously reported. These findings may provide imaging markers for early detection and enhance our understanding of depression pathology in youth.

Distinguished multimodal imaging features affected by COVID-19 in major depressive disorder patients

OBJECTIVES

Growing attention has been directed toward the structural and functional alterations among individuals infected with COVID-19. However, data on its impact on patients with Major Depressive Disorder (MDD) remain limited.

METHODS

This study investigates the effects of COVID-19 on patients with MDD and healthy controls (HCs) using MRI scans. Participants were categorized into four groups: MDD patients before (n = 165) and after COVID-19 infection (n = 70), HCs before (n = 108) and after COVID-19 infection (n = 57). All participants underwent T1-weighted imaging, diffusion tensor imaging (DTI), and resting-state functional MRI from January 2022 to August 2023.

RESULTS

Structural alterations associated with COVID-19 were predominantly observed in the white matter (WM) rather than the gray matter (GM), with specific involvement noted in the superior longitudinal fasciculus tract, Forceps minor tract, and cingulum-cingulate gyrus tract among patients with MDD. Functional changes were spread from GM to WM. The bilateral supplementary motor area, the left angular gyrus, the left subcortical regions (amygdala and parahippocampal gyrus), and various WM tracts showed significant infection-related changes across groups.

CONCLUSION

COVID-19 infection induces significant microstructural damage of WM in healthy individuals and exacerbates white matter microstructural injury of MDD. These findings suggest that WM might be more susceptible to COVID-19 effects than GM in both MDD patients and HCs.

Regional Structural-Functional Connectivity Coupling in Major Depressive Disorder Is Associated With Neurotransmitter and Genetic Profiles

BACKGROUND

Abnormalities in structural-functional connectivity (SC-FC) coupling have been identified globally in patients with major depressive disorder (MDD). However, investigations have neglected the variability and hierarchical distribution of these abnormalities across different brain regions. Furthermore, the biological mechanisms that underlie regional SC-FC coupling patterns are not well understood.

METHODS

We enrolled 182 patients with MDD and 157 healthy control participants and quantified the intergroup differences in regional SC-FC coupling. Extreme gradient boosting (XGBoost), support vector machine, and random forest models were constructed to assess the potential of SC-FC coupling as biomarkers for MDD diagnosis and symptom prediction. Then, we examined the link between changes in regional SC-FC coupling in patients with MDD, neurotransmitter distributions, and gene expression.

RESULTS

We observed increased regional SC-FC coupling in the default mode network (t337 = 3.233) and decreased coupling in the frontoparietal network (t337 = -3.471) in patients with MDD compared with healthy control participants. XGBoost (area under the receiver operating characteristic curve = 0.853), support vector machine (area under the receiver operating characteristic curve = 0.832), and random forest (p < .05) models exhibited good prediction performance. The alterations in regional SC-FC coupling in patients with MDD were correlated with the distributions of 4 neurotransmitters (p < .05) and expression maps of specific genes. These enriched genes were implicated in excitatory neurons, inhibitory neurons, cellular metabolism, synapse function, and immune signaling. These findings were replicated on 2 brain atlases.

CONCLUSIONS

This work enhances our understanding of MDD and paves the way for the development of additional targeted therapeutic interventions.

Aesthetic processing in neurodiverse populations

Neurodiversity is a perspective on cognition which suggests a non-pathological view of individual cognitive differences. Aesthetics research on neurodivergent brains has generally been limited to neuropsychological cases. Although this research has been integral to establishing the neurological correlates of aesthetic experience, it is crucial to expand this paradigm to more psychologically complex disorders. We offer a review of research on aesthetic preference in neurodivergent brains beyond neuropsychological cases: across populations with psychotic disorder, anhedonia and depression, anxiety disorder, and autism. We identify stable patterns of aesthetic bias in these populations, relate these biases to symptoms at perceptual, emotional, and evaluative levels of cognition, review relevant neurological correlates, and connect this evidence to current neuroaesthetics theory. Critically, we synthesize the reviewed evidence and discuss its relevance for three brain networks regularly implicated in aesthetic processing: the mesocorticolimbic reward circuit, frontolimbic connections, and the default mode network. Finally, we propose that broadening the subject populations for neuroaesthetics research to include neurodiverse populations is instrumental for yielding new insights into aesthetic processing in the brain.

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Altered sleep onset transition in depression: Evidence from EEG activity and EEG functional connectivity analyses

OBJECTIVE

Sleep disorders constitute a principal diagnostic criterion for depression, potentially reflecting the abnormal persistence of brain activity during the sleep onset (SO) transition. Here, we sought to explore the differences in the dynamic changes in the EEG activity and the EEG functional connectivity (FC) during the SO transition in depressed patients.

METHODS

Overnight polysomnography recordings were obtained from thirty-two depressed patients and thirty-three healthy controls. The multiscale permutation entropy (MSPE) and EEG relative power were extracted to characterize EEG activity, and weighted phase lag index (WPLI) was calculated to characterize EEG FC.

RESULTS

The intergroup differences in EEG activity of relative power and MSPE were reversed near SO, which attributed to slower rates of change among depressed patients. Regarding the characteristics of the EEG FC network, depressed patients exhibited significantly higher inter-hemispheric and interregional WPLI values in both delta and alpha bands throughout the SO transition, concomitant with different dynamic properties in the delta band FC. During the process after SO, patients exhibited increased inter-hemispheric long-range links, whereas controls showed more intra-hemispheric ones. Finally, we found significant correlations in the dynamic changes between different EEG measures.

CONCLUSIONS

Our research revealed that the abnormal changes during the SO transition in depressed patients were manifested in both homeostatic and dynamic aspects, which were reflected in EEG FC and EEG activity, respectively.

SIGNIFICANCE

These findings may elucidate the mechanism underlying sleep disorders in depression from the perspective of neural activity.

Structure-function coupling in macroscale human brain networks

Precisely how the anatomical structure of the brain gives rise to a repertoire of complex functions remains incompletely understood. A promising manifestation of this mapping from structure to function is the dependency of the functional activity of a brain region on the underlying white matter architecture. Here, we review the literature examining the macroscale coupling between structural and functional connectivity, and we establish how this structure-function coupling (SFC) can provide more information about the underlying workings of the brain than either feature alone. We begin by defining SFC and describing the computational methods used to quantify it. We then review empirical studies that examine the heterogeneous expression of SFC across different brain regions, among individuals, in the context of the cognitive task being performed, and over time, as well as its role in fostering flexible cognition. Last, we investigate how the coupling between structure and function is affected in neurological and psychiatric conditions, and we report how aberrant SFC is associated with disease duration and disease-specific cognitive impairment. By elucidating how the dynamic relationship between the structure and function of the brain is altered in the presence of neurological and psychiatric conditions, we aim to not only further our understanding of their aetiology but also establish SFC as a new and sensitive marker of disease symptomatology and cognitive performance. Overall, this Review collates the current knowledge regarding the regional interdependency between the macroscale structure and function of the human brain in both neurotypical and neuroatypical individuals.

The role of serotonin in depression-A historical roundup and future directions

Depression is one of the most common psychiatric disorders worldwide, affecting approximately 280 million people, with probably much higher unrecorded cases. Depression is associated with symptoms such as anhedonia, feelings of hopelessness, sleep disturbances, and even suicidal thoughts. Tragically, more than 700 000 people commit suicide each year. Although depression has been studied for many decades, the exact mechanisms that lead to depression are still unknown, and available treatments only help a fraction of patients. In the late 1960s, the serotonin hypothesis was published, suggesting that serotonin is the key player in depressive disorders. However, this hypothesis is being increasingly doubted as there is evidence for the influence of other neurotransmitters, such as noradrenaline, glutamate, and dopamine, as well as larger systemic causes such as altered activity in the limbic network or inflammatory processes. In this narrative review, we aim to contribute to the ongoing debate on the involvement of serotonin in depression. We will review the evolution of antidepressant treatments, systemic research on depression over the years, and future research applications that will help to bridge the gap between systemic research and neurotransmitter dynamics using biosensors. These new tools in combination with systemic applications, will in the future provide a deeper understanding of the serotonergic dynamics in depression.

The protective effect of vitamin D supplementation as adjunctive therapy to antidepressants on brain structural and functional connectivity of patients with major depressive disorder: a randomized controlled trial

BACKGROUND

Growing evidence points to the pivotal role of vitamin D in the pathophysiology and treatment of major depressive disorder (MDD). However, there is a paucity of longitudinal research investigating the effects of vitamin D supplementation on the brain of MDD patients.

METHODS

We conducted a double-blind randomized controlled trial in 46 MDD patients, who were randomly allocated into either VD (antidepressant medication + vitamin D supplementation) or NVD (antidepressant medication + placebos) groups. Data from diffusion tensor imaging, resting-state functional MRI, serum vitamin D concentration, and clinical symptoms were obtained at baseline and after an average of 7 months of intervention.

RESULTS

Both VD and NVD groups showed significant improvement in depression and anxiety symptoms but with no significant differences between the two groups. However, a greater increase in serum vitamin D concentration was found to be associated with greater improvement in depression and anxiety symptoms in VD group. More importantly, neuroimaging data demonstrated disrupted white matter integrity of right inferior fronto-occipital fasciculus along with decreased functional connectivity between right frontoparietal and medial visual networks after intervention in NVD group, but no changes in VD group.

CONCLUSIONS

These findings suggest that vitamin D supplementation as adjunctive therapy to antidepressants may not only contribute to improvement in clinical symptoms but also help preserve brain structural and functional connectivity in MDD patients.

Structure-function coupling and hierarchy-specific antidepressant response in major depressive disorder

BACKGROUND

Extensive research has explored altered structural and functional networks in major depressive disorder (MDD). However, studies examining the relationships between structure and function yielded heterogeneous and inconclusive results. Recent work has suggested that the structure-function relationship is not uniform throughout the brain but varies across different levels of functional hierarchy. This study aims to investigate changes in structure-function couplings (SFC) and their relevance to antidepressant response in MDD from a functional hierarchical perspective.

METHODS

We compared regional SFC between individuals with MDD (n = 258) and healthy controls (HC, n = 99) using resting-state functional magnetic resonance imaging and diffusion tensor imaging. We also compared antidepressant non-responders (n = 55) and responders (n = 68, defined by a reduction in depressive severity of >50%). To evaluate variations in altered and response-associated SFC across the functional hierarchy, we ranked significantly different regions by their principal gradient values and assessed patterns of increase or decrease along the gradient axis. The principal gradient value, calculated from 219 healthy individuals in the Human Connectome Project, represents a region's position along the principal gradient axis.

RESULTS

Compared to HC, MDD patients exhibited increased SFC in unimodal regions (lower principal gradient) and decreased SFC in transmodal regions (higher principal gradient) (p < 0.001). Responders primarily had higher SFC in unimodal regions and lower SFC in attentional networks (median principal gradient) (p < 0.001).

CONCLUSIONS

Our findings reveal opposing SFC alterations in low-level unimodal and high-level transmodal networks, underscoring spatial variability in MDD pathology. Moreover, hierarchy-specific antidepressant effects provide valuable insights into predicting treatment outcomes.

Immediate and long-term electrophysiological biomarkers of antidepressant-like behavioral effects after subanesthetic ketamine and medial prefrontal cortex deep brain stimulation treatment

Introduction

Both ketamine (KET) and medial prefrontal cortex (mPFC) deep brain stimulation (DBS) are emerging therapies for treatment-resistant depression, yet our understanding of their electrophysiological mechanisms and biomarkers is incomplete. This study investigates aperiodic and periodic spectral parameters, and the signal complexity measure sample entropy, within mPFC local field potentials (LFP) in a chronic corticosterone (CORT) depression model after ketamine and/or mPFC DBS.

Methods

Male rats were intraperitoneally administered CORT or vehicle for 21 days. Over the last 7 days, animals receiving CORT were treated with mPFC DBS, KET, both, or neither; then tested across an array of behavioral tasks for 9 days.

Results

We found that the depression-like behavioral and weight effects of CORT correlated with a decrease in aperiodic-adjusted theta power (5-10 Hz) and an increase in sample entropy during the administration phase, and an increase in theta peak frequency and a decrease in the aperiodic exponent once the depression-like phenotype had been induced. The remission-like behavioral effects of ketamine alone correlated with a post-treatment increase in the offset and exponent, and decrease in sample entropy, both immediately and up to eight days post-treatment. The remission-like behavioral effects of mPFC DBS alone correlated with an immediate decrease in sample entropy, an immediate and sustained increase in low gamma (20-50 Hz) peak width and aperiodic offset, and sustained improvements in cognitive function. Failure to fully induce remission-like behavior in the combinatorial treatment group correlated with a failure to suppress an increase in sample entropy immediately after treatment.

Conclusion

Our findings therefore support the potential of periodic theta parameters as biomarkers of depression-severity; and periodic low gamma parameters and cognitive measures as biomarkers of mPFC DBS treatment efficacy. They also support sample entropy and the aperiodic spectral parameters as potential cross-modal biomarkers of depression severity and the therapeutic efficacy of mPFC DBS and/or ketamine. Study of these biomarkers is important as objective measures of disease severity and predictive measures of therapeutic efficacy can be used to personalize care and promote the translatability of research across studies, modalities, and species.

Cortical excitation/inhibition ratios in patients with major depression treated with electroconvulsive therapy: an EEG analysis

Electroconvulsive therapy (ECT) is an effective treatment for major depression, but its working mechanisms are poorly understood. Modulation of excitation/inhibition (E/I) ratios may be a driving factor. Here, we estimate cortical E/I ratios in depressed patients and study whether these ratios change over the course of ECT in relation to clinical effectiveness. Five-minute resting-state electroencephalography (EEG) recordings of 28 depressed patients were recorded before and after their ECT course. Using a novel method based on critical dynamics, functional E/I (fE/I) ratios in the frequency range of 0.5-30 Hz were estimated in frequency bins of 1 Hz for the whole brain and for pre-defined brain regions. Change in Hamilton Depression Rating Scale (HDRS) score was used to estimate clinical effectiveness. To account for test-retest variability, repeated EEG recordings from an independent sample of 31 healthy controls (HC) were included. At baseline, no differences in whole brain and regional fE/I ratios were found between patients and HC. At group level, whole brain and regional fE/I ratios did not change over the ECT course. However, in responders, frontal fE/I ratios in the frequencies 12-28 Hz increased significantly (pFDR < 0.05 [FDR = false discovery rate]) over the ECT course. In non-responders and HC, no changes occurred over time. In this sample, frontal fE/I ratios increased over the ECT course in relation to treatment response. Modulation of frontal fE/I ratios may be an important mechanism of action of ECT.

The Role of Subgenual Resting-State Connectivity Networks in Predicting Prognosis in Major Depressive Disorder

Background

A seminal study found higher subgenual frontal cortex resting-state connectivity with 2 left ventral frontal regions and the dorsal midbrain to predict better response to psychotherapy versus medication in individuals with treatment-naïve major depressive disorder (MDD). Here, we examined whether these subgenual networks also play a role in the pathophysiology of clinical outcomes in MDD with early treatment resistance in primary care.

Methods

Forty-five people with current MDD who had not responded to ≥2 serotonergic antidepressants (n = 43, meeting predefined functional magnetic resonance imaging minimum quality thresholds) were enrolled and followed over 4 months of standard care. Functional magnetic resonance imaging resting-state connectivity between the preregistered subgenual frontal cortex seed and 3 previously identified left ventromedial, ventrolateral prefrontal/insula, and dorsal midbrain regions was extracted. The clinical outcome was the percentage change on the self-reported 16-item Quick Inventory of Depressive Symptomatology.

Results

We observed a reversal of our preregistered hypothesis in that higher resting-state connectivity between the subgenual cortex and the a priori ventrolateral prefrontal/insula region predicted favorable rather than unfavorable clinical outcomes (rs39 = -0.43, p = .006). This generalized to the sample including participants with suboptimal functional magnetic resonance imaging quality (rs43 = -0.35, p = .02). In contrast, no effects (rs39 = 0.12, rs39 = -0.01) were found for connectivity with the other 2 preregistered regions or in a whole-brain analysis (voxel-based familywise error-corrected p < .05).

Conclusions

Subgenual connectivity with the ventrolateral prefrontal cortex/insula is relevant for subsequent clinical outcomes in current MDD with early treatment resistance. Its positive association with favorable outcomes could be explained primarily by psychosocial rather than the expected pharmacological changes during the follow-up period.

Pathophysiology in cortico-amygdala circuits and excessive aversion processing: the role of oligodendrocytes and myelination

Stress-related psychiatric illnesses, such as major depressive disorder, anxiety and post-traumatic stress disorder, present with alterations in emotional processing, including excessive processing of negative/aversive stimuli and events. The bidirectional human/primate brain circuit comprising anterior cingulate cortex and amygdala is of fundamental importance in processing emotional stimuli, and in rodents the medial prefrontal cortex-amygdala circuit is to some extent analogous in structure and function. Here, we assess the comparative evidence for: (i) Anterior cingulate/medial prefrontal cortex<->amygdala bidirectional neural circuits as major contributors to aversive stimulus processing; (ii) Structural and functional changes in anterior cingulate cortex<->amygdala circuit associated with excessive aversion processing in stress-related neuropsychiatric disorders, and in medial prefrontal cortex<->amygdala circuit in rodent models of chronic stress-induced increased aversion reactivity; and (iii) Altered status of oligodendrocytes and their oligodendrocyte lineage cells and myelination in anterior cingulate/medial prefrontal cortex<->amygdala circuits in stress-related neuropsychiatric disorders and stress models. The comparative evidence from humans and rodents is that their respective anterior cingulate/medial prefrontal cortex<->amygdala circuits are integral to adaptive aversion processing. However, at the sub-regional level, the anterior cingulate/medial prefrontal cortex structure-function analogy is incomplete, and differences as well as similarities need to be taken into account. Structure-function imaging studies demonstrate that these neural circuits are altered in both human stress-related neuropsychiatric disorders and rodent models of stress-induced increased aversion processing. In both cases, the changes include altered white matter integrity, albeit the current evidence indicates that this is decreased in humans and increased in rodent models. At the cellular-molecular level, in both humans and rodents, the current evidence is that stress disorders do present with changes in oligodendrocyte lineage, oligodendrocytes and/or myelin in these neural circuits, but these changes are often discordant between and even within species. Nonetheless, by integrating the current comparative evidence, this review provides a timely insight into this field and should function to inform future studies-human, monkey and rodent-to ascertain whether or not the oligodendrocyte lineage and myelination are causally involved in the pathophysiology of stress-related neuropsychiatric disorders.

DiffMDD: A Diffusion-Based Deep Learning Framework for MDD Diagnosis Using EEG

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model's robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module's effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

Impact of brain parcellation on prediction performance in models of cognition and demographics

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?

Depression is currently one of the most complicated public health problems with the rising number of patients, increasing partly due to pandemics, but also due to increased existential insecurities and complicated aetiology of disease. Besides the tsunami of mental health issues, there are limitations imposed by ambiguous clinical rules of assessment of the symptoms and obsolete and inefficient standard therapy approaches. Here we are summarizing the neuroimaging results pointing out the actual complexity of the disease and novel attempts to detect depression that are evidence-based, mostly related to electrophysiology. It is repeatedly shown that the complexity of resting-state EEG recorded in patients suffering from depression is increased compared to healthy controls. We are discussing here how that can be interpreted and what we can learn about future effective therapies. Also, there is evidence that novel options of treatment, like different modalities of electromagnetic stimulation, are successful just because they are capable of decreasing that aberrated complexity. And complexity measures extracted from electrophysiological signals of depression patients can serve as excellent features for further machine learning models in order to automatize detection. In addition, after initial detection and even selection of responders for further therapy route, it is possible to monitor the therapeutic flow for one person, which leads us to possible tailored treatment for patients suffering from depression.

Corticolimbic structural connectivity encapsulates real-world emotional reactivity and happiness

Emotional reactivity to everyday events predicts happiness, but the neural circuits underlying this relationship remain incompletely understood. Here, we combined experience sampling methods and diffusion magnetic resonance imaging to examine the association among corticolimbic structural connectivity, real-world emotional reactivity and daily experiences of happiness from 79 young adults (35 females). Participants recorded momentary assessments of emotional and happiness experiences five times a day for a week, approximately 2 weeks after brain scanning. Model-based emotional reactivity scores, which index the degree to which moment-to-moment affective state varies with the occurrence of positive or negative events, were computed. Results showed that stronger microstructural integrity of the uncinate fasciculus and the external capsule was associated with both greater positive and negative emotional reactivity scores. The relationship between these fiber tracts and experienced happiness was explained by emotional reactivity. Importantly, this indirect effect was observed for emotional reactivity to positive but not negative real-world events. Our findings suggest that the corticolimbic circuits supporting socioemotional functions are associated with emotional reactivity and happiness in the real world.

Measures of Connectivity and Dorsolateral Prefrontal Cortex Volumes and Depressive Symptoms Following Treatment With Selective Serotonin Reuptake Inhibitors in Adolescents

Importance

Selective serotonin reuptake inhibitors (SSRIs) are considered a first-line pharmacological treatment for adolescent depression with moderate or higher levels of symptom severity. Thus, it is important to understand neurobiological changes related to SSRIs during the course of treatment for adolescents with depression.

Objective

To examine neurobiological changes associated with SSRI treatment in adolescents with major depressive disorder (MDD) by measuring longitudinal changes in volume and resting-state functional connectivity (rsFC) in the dorsolateral prefrontal cortex (DLPFC), a core region of cognitive control.

Design, Setting, and Participants

This cohort study was conducted with an open-label design. Adolescents with MDD and healthy controls were recruited at the Seoul National University Hospital (Seoul, South Korea). Adolescents with MDD were treated with escitalopram for 8 weeks. Data analysis was conducted between April 2021 and February 2022.

Main Outcomes and Measures

Depressive symptoms were assessed using the Children's Depression Rating Scale-Revised. The outcome measure was defined as the change in Children's Depression Rating Scale-Revised scores from week 0 (before treatment) to week 8 (after treatment) or upon termination. Participants completed structural and resting-state functional magnetic resonance imaging (rsfMRI) assessments before (week 0) and after (week 8) SSRI treatment. Repeated measures analysis of variance and liner mixed model analyses were used to examine the longitudinal associations of SSRI treatment with DLPFC volume and rsFC between responders who showed at least a 40% decrease in depressive symptoms and nonresponders who did not.

Results

Ninety-five adolescents with MDD and 57 healthy controls were initially recruited. The final analyses of volume included 36 responders (mean [SD] age, 15.0 [1.6] years; 25 girls [69.4%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). Analyses of rsFC included 33 responders (mean [SD] age, 15.2 [1.5] years; 21 girls [63.6%]) and 26 nonresponders (mean [SD] age, 15.3 [1.5] years; 19 girls [73.1%]). The longitudinal associations of SSRI treatment were more evident in responders than in nonresponders. Responders showed significantly increased right DLPFC volume, decreased bilateral DLPFC rsFC with the superior frontal gyri, and decreased left DLPFC rsFC with the ventromedial PFC after treatment compared with before treatment. Furthermore, increased right DLPFC volume was correlated with decreased rsFC between the right DLPFC and superior frontal gyri after SSRI treatment.

Conclusions and Relevance

The preliminary results of this cohort study suggest that the DLPFC volumetric and rsFC changes may serve as potential neurobiological treatment markers that are associated with symptom improvement in adolescents with MDD.

Functional connectivity gradients of the cingulate cortex

Heterogeneity of the cingulate cortex is evident in multiple dimensions including anatomy, function, connectivity, and involvement in networks and diseases. Using the recently developed functional connectivity gradient approach and resting-state functional MRI data, we found three functional connectivity gradients that captured distinct dimensions of cingulate hierarchical organization. The principal gradient exhibited a radiating organization with transitions from the middle toward both anterior and posterior parts of the cingulate cortex and was related to canonical functional networks and corresponding behavioral domains. The second gradient showed an anterior-posterior axis across the cingulate cortex and had prominent geometric distance dependence. The third gradient displayed a marked differentiation of subgenual and caudal middle with other parts of the cingulate cortex and was associated with cortical morphology. Aside from providing an updated framework for understanding the multifaceted nature of cingulate heterogeneity, the observed hierarchical organization of the cingulate cortex may constitute a novel research agenda with potential applications in basic and clinical neuroscience.

Transcriptomic and Macroscopic Architectures of Multimodal Covariance Network Reveal Molecular-Structural-Functional Co-alterations

Human cognition is usually underpinned by intrinsic structure and functional neural co-activation in spatially distributed brain regions. Owing to lacking an effective approach to quantifying the covarying of structure and functional responses, how the structural-functional circuits interact and how genes encode the relationships, to deepen our knowledge of human cognition and disease, are still unclear. Here, we propose a multimodal covariance network (MCN) construction approach to capture interregional covarying of the structural skeleton and transient functional activities for a single individual. We further explored the potential association between brain-wide gene expression patterns and structural-functional covarying in individuals involved in a gambling task and individuals with major depression disorder (MDD), adopting multimodal data from a publicly available human brain transcriptomic atlas and 2 independent cohorts. MCN analysis showed a replicable cortical structural-functional fine map in healthy individuals, and the expression of cognition- and disease phenotype-related genes was found to be spatially correlated with the corresponding MCN differences. Further analysis of cell type-specific signature genes suggests that the excitatory and inhibitory neuron transcriptomic changes could account for most of the observed correlation with task-evoked MCN differences. In contrast, changes in MCN of MDD patients were enriched for biological processes related to synapse function and neuroinflammation in astrocytes, microglia, and neurons, suggesting its promising application in developing targeted therapies for MDD patients. Collectively, these findings confirmed the correlations of MCN-related differences with brain-wide gene expression patterns, which captured genetically validated structural-functional differences at the cellular level in specific cognitive processes and psychiatric patients.

A meta-analysis on the uncinate fasciculus in depression

Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological mechanism of depression. However, studies testing this hypothesis have yielded inconsistent results. The present meta-analysis consolidates evidence from 44 studies comparing fractional anisotropy (FA) and radial diffusivity (RD), two metrics characterizing WM microstructure, of the UNC in individuals with depression (n = 5016) to healthy individuals (n = 18 425). We conduct meta-regressions to identify demographic and clinical characteristics that contribute to cross-study heterogeneity in UNC findings. UNC FA was reduced in individuals with depression compared to healthy individuals. UNC RD was comparable between individuals with depression and healthy individuals. Comorbid anxiety explained inter-study heterogeneity in UNC findings. Depression is associated with perturbations in UNC microstructure, specifically with respect to UNC FA and not UNC RD. The association between depression and UNC microstructure appears to be moderated by anxiety. Future work should unravel the cellular mechanisms contributing to aberrant UNC microstructure in depression; clarify the relationship between UNC microstructure, depression, and anxiety; and link UNC microstructure to psychological processes, such as emotion regulation.

Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods

Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

Alterations in Human Hippocampus Subregions across the Lifespan: Reflections on White Matter Structure and Functional Connectivity

During growth and aging, the role of the hippocampus in memory depends on its interactions with related brain regions. Particularly, two subregions, anterior hippocampus (aHipp) and posterior hippocampus (pHipp), play different and critical roles in memory processing. However, age-related changes of hippocampus subregions on structure and function are still unclear. Here, we investigated age-related structural and functional characteristics of 106 participants (7-85 years old) in resting state based on fractional anisotropy (FA) and functional connectivity (FC) in aHipp and pHipp in the lifespan. The correlation between FA and FC was also explored to identify the coupling. Furthermore, the Wechsler Abbreviated Scale of Intelligence (WASI) was used to explore the relationship between cognitive ability and hippocampal changes. Results showed that there was functional separation and integration in aHipp and pHipp, and the number of functional connections in pHipp was more than that in aHipp across the lifespan. The age-related FC changes showed four different trends (U-shaped/inverted U-shaped/linear upward/linear downward). And around the age of 40 was a critical period for transformation. Then, FA analyses indicated that all effects of age on the hippocampal structures were nonlinear, and the white matter integrity of pHipp was higher than that of aHipp. In the functional-structural coupling, we found that the age-related FA of the right aHipp (aHipp.R) was negatively related to the FC. Finally, through the WASI, we found that the age-related FA of the left aHipp (aHipp.L) was positively correlated with verbal IQ (VERB) and vocabulary comprehension (VOCAB.T), the FA of aHipp.R was only positively correlated with VERB, and the FA of the left pHipp (pHipp.L) was only positively correlated with VOCAB.T. These FC and FA results supported that age-related normal memory changes were closely related to the hippocampus subregions. We also provided empirical evidence that memory ability was altered with the hippocampus, and its efficiency tended to decline after age 40.

Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies

Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.

Altered single-subject gray matter structural networks in social anxiety disorder

Previous fMRI studies have reported more random brain functional graph configurations in social anxiety disorder (SAD). However, it is still unclear whether the same configurations would occur in gray matter (GM) graphs. Structural MRI was performed on 49 patients with SAD and on 51 age- and gender-matched healthy controls (HC). Single-subject GM networks were obtained based on the areal similarities of GM, and network topological properties were analyzed using graph theory. Group differences in each topological metric were compared, and the structure-function coupling was examined. These network measures were further correlated with the clinical characteristics in the SAD group. Compared with controls, the SAD patients demonstrated globally decreased clustering coefficient and characteristic path length. Altered topological properties were found in the fronto-limbic and sensory processing systems. Altered metrics were associated with the illness duration of SAD. Compared with the HC group, the SAD group exhibited significantly decreased structural-functional decoupling. Furthermore, structural-functional decoupling was negatively correlated with the symptom severity in SAD. These findings highlight less-optimized topological configuration of the brain structural networks in SAD, which may provide insights into the neural mechanisms underlying the excessive fear and avoidance of social interactions in SAD.

Effects of a probiotic add-on treatment on fronto-limbic brain structure, function, and perfusion in depression: Secondary neuroimaging findings of a randomized controlled trial

BACKGROUND

Probiotics are suggested to improve depressive symptoms via the microbiota-gut-brain axis. We have recently shown a beneficial clinical effect of probiotic supplementation in patients with depression. Their underlying neural mechanisms remain unknown.

METHODS

A multimodal neuroimaging approach including diffusion tensor imaging, resting-state functional MRI, and arterial spin labeling was used to investigate the effects of a four-weeks probiotic supplementation on fronto-limbic brain structure, function, and perfusion and whether these effects were related to symptom changes.

RESULTS

Thirty-two patients completed both imaging assessments (18 placebo and 14 probiotics group). Probiotics maintained mean diffusivity in the left uncinate fasciculus, stabilized it in the right uncinate fasciculus, and altered resting-state functional connectivity (rsFC) between limbic structures and the temporal pole to a cluster in the precuneus. Moreover, a cluster in the left superior parietal lobule showed altered rsFC to the subcallosal cortex, the left orbitofrontal cortex, and limbic structures after probiotics. In the probiotics group, structural and functional changes were partly related to decreases in depressive symptoms.

LIMITATIONS

This study has a rather small sample size. An additional follow-up MRI session would be interesting for seeing clearer changes in the relevant brain regions as clinical effects were strongest in the follow-up.

CONCLUSION

Probiotic supplementation is suggested to prevent neuronal degeneration along the uncinate fasciculus and alter fronto-limbic rsFC, effects that are partly related to the improvement of depressive symptoms. Elucidating the neural mechanisms underlying probiotics' clinical effects on depression provide potential targets for the development of more precise probiotic treatments.

Astrocytes as Context for the Involvement of Myelin and Nodes of Ranvier in the Pathophysiology of Depression and Stress-Related Disorders

Astrocytes, despite some shared features as glial cells supporting neuronal function in gray and white matter, participate and adapt their morphology and neurochemistry in a plethora of distinct regulatory tasks in specific neural environments. In the white matter, a large proportion of the processes branching from the astrocytes' cell bodies establish contacts with oligodendrocytes and the myelin they form, while the tips of many astrocyte branches closely associate with nodes of Ranvier. Stability of myelin has been shown to greatly depend on astrocyte-to-oligodendrocyte communication, while the integrity of action potentials that regenerate at nodes of Ranvier has been shown to depend on extracellular matrix components heavily contributed by astrocytes. Several lines of evidence are starting to show that in human subjects with affective disorders and in animal models of chronic stress there are significant changes in myelin components, white matter astrocytes and nodes of Ranvier that have direct relevance to connectivity alterations in those disorders. Some of these changes involve the expression of connexins supporting astrocyte-to-oligodendrocyte gap junctions, extracellular matrix components produced by astrocytes around nodes of Ranvier, specific types of astrocyte glutamate transporters, and neurotrophic factors secreted by astrocytes that are involved in the development and plasticity of myelin. Future studies should further examine the mechanisms responsible for those changes in white matter astrocytes, their putative contribution to pathological connectivity in affective disorders, and the possibility of leveraging that knowledge to design new therapies for psychiatric disorders.

The implication of a diversity of non-neuronal cells in disorders affecting brain networks

In the central nervous system (CNS) neurons are classically considered the functional unit of the brain. Analysis of the physical connections and co-activation of neurons, referred to as structural and functional connectivity, respectively, is a metric used to understand their interplay at a higher level. A myriad of glial cell types throughout the brain composed of microglia, astrocytes and oligodendrocytes are key players in the maintenance and regulation of neuronal network dynamics. Microglia are the central immune cells of the CNS, able to affect neuronal populations in number and connectivity, allowing for maturation and plasticity of the CNS. Microglia and astrocytes are part of the neurovascular unit, and together they are essential to protect and supply nutrients to the CNS. Oligodendrocytes are known for their canonical role in axonal myelination, but also contribute, with microglia and astrocytes, to CNS energy metabolism. Glial cells can achieve this variety of roles because of their heterogeneous populations comprised of different states. The neuroglial relationship can be compromised in various manners in case of pathologies affecting development and plasticity of the CNS, but also consciousness and mood. This review covers structural and functional connectivity alterations in schizophrenia, major depressive disorder, and disorder of consciousness, as well as their correlation with vascular connectivity. These networks are further explored at the cellular scale by integrating the role of glial cell diversity across the CNS to explain how these networks are affected in pathology.

Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders

In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

Dysregulation of adult hippocampal neuroplasticity in major depression: pathogenesis and therapeutic implications

Major depressive disorder (MDD) was previously hypothesized to be a disease of monoamine deficiency in which low levels of monoamines in the synaptic cleft were believed to underlie depressive symptoms. More recently, however, there has been a paradigm shift toward a neuroplasticity hypothesis of depression in which downstream effects of antidepressants, such as increased neurogenesis, contribute to improvements in cognition and mood. This review takes a top-down approach to assess how changes in behavior and hippocampal-dependent circuits may be attributed to abnormalities at the molecular, structural, and synaptic level. We conclude with a discussion of how antidepressant treatments share a common effect in modulating neuroplasticity and consider outstanding questions and future perspectives.

Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression

Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.

Altered Variability and Concordance of Dynamic Resting-State Functional Magnetic Resonance Imaging Indices in Patients With Major Depressive Disorder and Childhood Trauma

Childhood trauma is a non-specific risk factor for major depressive disorder (MDD). resting-state functional magnetic resonance imaging (R-fMRI) studies have demonstrated changes in regional brain activity in patients with MDD who experienced childhood trauma. However, previous studies have mainly focused on static characteristics of regional brain activity. This study aimed to determine the specific brain regions associated with MDD with childhood trauma by performing temporal dynamic analysis of R-fMRI data in three groups of patients: patients with childhood trauma-associated MDD (n = 48), patients without childhood trauma-associated MDD (n = 30), and healthy controls (n = 103). Dynamics and concordance of R-fMRI indices were calculated and analyzed. In patients with childhood trauma-associated MDD, a lower dynamic amplitude of low-frequency fluctuations was found in the left lingual gyrus, whereas a lower dynamic degree of centrality was observed in the right lingual gyrus and right calcarine cortex. Patients with childhood trauma-associated MDD showed a lower voxel-wise concordance in the left middle temporal and bilateral calcarine cortices. Moreover, group differences (depressed or not) significantly moderated the relationship between voxel-wise concordance in the right calcarine cortex and childhood trauma history. Overall, patients with childhood trauma-associated MDD demonstrated aberrant variability and concordance in intrinsic brain activity. These aberrances may be an underlying neurobiological mechanism that explains MDD from the perspective of temporal dynamics.

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.

A two-center radiomic analysis for differentiating major depressive disorder using multi-modality MRI data under different parcellation methods

BACKGROUND

The present study aimed to explore the difference in the brain function and structure between patients with major depressive disorder (MDD) and healthy controls (HCs) using two-center and multi-modal MRI data, which would be helpful to investigate the pathogenesis of MDD.

METHODS

The subjects were collected from two hospitals. One including 140 patients with MDD and 138 HCs was used as primary cohort. Another one including 29 patients with MDD and 52 HCs was used as validation cohort. Functional and structural magnetic resonance images (MRI) were acquired to extract four types of features: functional connectivity (FC), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and gray matter volume (GMV). Then classifiers using different combinations among the four types of selected features were respectively built to discriminate patients from HCs. Different templates were applied and the results under different templates were compared.

RESULTS

The classifier built with the combination of FC, ALFF, and GMV under the AAL template discriminated patients from HCs with the best performance (AUC=0.916, ACC=84.8%). The regions selected in all the different templates were mainly located in the default mode network, affective network, prefrontal cortex.

LIMITATIONS

First, the sample size of the validation cohort was limited. Second, diffusion tensor imaging data were not collected.

CONCLUSION

The performance of classifier was improved by using multi-modal MRI imaging. Different templates would be suitable for different types of analysis. The regions selected in all the different templates are possibly the core regions to investigate the pathophysiology of MDD.

Gene-gene interaction and new onset of major depressive disorder: Findings from a Chinese freshmen nested case-control study

BACKGROUND

The gene-gene interaction is known to be the genetic cause of major depressive disorder (MDD). Several genes have been found to be related to MDD. The objectives of this study were to verify the susceptibility genes of MDD in a sample of university students in China, and to investigate possible gene-gene interactions in relation to the risk of MDD.

METHODS

7,627 Chinese Han freshmen were enrolled at baseline survey in 2018. After a 2-year follow-up, 170 new onset MDD cases and 680 controls with DNA samples reserved were sequenced and genotyped for 4 selected Single Nucleotide Polymorphisms (SNPs) in a nested case-control study (ratio of 1:4). Chi-square test was used to identify the relationships between SNPs and risk of MDD. Generalized multifactor dimensionality reduction (GMDR) was used to analyze the gene-gene interactions.

RESULTS

The 2-year incidence of MDD in Chinese college students was 3.75% (95% CI: 3.24%, 4.34%). There was no statistical difference in MDD incidences between males (3.74%, 95% CI: 3.12%, 4.49%) and females (3.77%, 95% CI: 2.97%, 4.78%) (p>0.05). TMEM161B (rs768705) was positively associated with new onset MDD (χ² = 0.75, p = 0.023). The AG genotype of rs768705 was significant (OR=1.640, 95%CI:1.414-2.358). The gene-gene interaction between TMEM161B (rs768705) and LHPP (rs35936514) was statistically significant in this nested case-control study (p = 0.011). The CV consistency was 9/10 and the testing accuracy was 0.5274.

LIMITATIONS

The results could not be inferred to other ethnics.

CONCLUSIONS

This study provided evidence that combined rs768705 (TMEM161B) and rs35936514 (LHPP) may modulate the risk of MDD.

Sleep-Dependent Anomalous Cortical Information Interaction in Patients With Depression

Depression is a prevalent mental illness with high morbidity and is considered the main cause of disability worldwide. Brain activity while sleeping is reported to be affected by such mental illness. To explore the change of cortical information flow during sleep in depressed patients, a delay symbolic phase transfer entropy of scalp electroencephalography signals was used to measure effective connectivity between cortical regions in various frequency bands and sleep stages. The patient group and the control group shared similar patterns of information flow between channels during sleep. Obvious information flows to the left hemisphere and to the anterior cortex were found. Moreover, the occiput tended to be the information driver, whereas the frontal regions played the role of the receiver, and the right hemispheric regions showed a stronger information drive than the left ones. Compared with healthy controls, such directional tendencies in information flow and the definiteness of role division in cortical regions were both weakened in patients in most frequency bands and sleep stages, but the beta band during the N1 stage was an exception. The computable sleep-dependent cortical interaction may provide clues to characterize cortical abnormalities in depressed patients and should be helpful for the diagnosis of depression.

Cognitive Appraisal of Sleep and Brain Activation in Response to Sleep-Related Sounds in Healthy Adults

PURPOSE

Sounds play important roles in promoting and disrupting sleep. How our brain processes sleep-related sounds and individual differences in processing sleep-related sounds must be determined to understand the role of sound in sleep. We investigated neural responses to sleep-related sounds and their associations with cognitive appraisals of sleep.

PARTICIPANTS AND METHODS

Forty-four healthy adults heard sleep-related and neutral sounds during functional magnetic resonance imaging using a 3T scanner. They also completed the Dysfunctional Beliefs and Attitudes about Sleep (DBAS) questionnaire, which was used to assess cognitive appraisals of sleep. We conducted a voxel-wise whole-brain analysis to compare brain activation in response to sleep-related and neutral sounds. We also examined the association between the DBAS score and brain activity in response to sleep-related sounds (vs neutral sounds) using region of interest (ROI) and whole-brain correlation analyses. The ROIs included the anterior cingulate cortex (ACC), anterior insula (AI), and amygdala.

RESULTS

The whole-brain analysis revealed increased activation in the temporal regions and decreased activation in the ACC in response to sleep-related sounds compared to neutral sounds. The ROI and whole-brain correlation analyses showed that higher DBAS scores, indicating a negative appraisal of sleep, were significantly correlated with increased activation of the ACC, right medial prefrontal cortex, and brainstem in response to sleep-related sounds.

CONCLUSION

These results indicate that the temporal cortex and ACC, which are implicated in affective sound processing, may play important roles in the processing of sleep-related sounds. The positive association between the neural responses to sleep-related sounds and DBAS scores suggest that negative and dysfunctional appraisals of sleep may be an important factor in individual differences in the processing of sleep-related sounds.

Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10-80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2-20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

Brain Structural and Functional Connectivity: A Review of Combined Works of Diffusion Magnetic Resonance Imaging and Electro-Encephalography

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.

Dysfunction of the anterior and intermediate hippocampal functional network in major depressive disorders across the adult lifespan

Accumulating evidence indicates that structural and functional abnormalities in hippocampal formation are linked to major depressive disorder (MDD). However, the resting-state functional connectivity (RSFC) of hippocampal subfields in MDD remains unclear. This cross-sectional study aimed to investigate the RSFC of hippocampal subfields in a large sample of MDD patients. The results revealed that patients with MDD showed lower RSFC between the right anterior hippocampus and the insula, and the RSFC was inversely correlated with anxiety symptoms of depression. Depressed patients also showed decreased RSFC between the bilateral intermediate hippocampus and left nucleus accumbens (NAcc), and the hippocampus-NAcc circuit was negatively correlated with core symptoms of depression. The functional connectivity between the right anterior hippocampus and left postcentral gyrus increased with ageing in MDD patients compared with healthy controls. These findings suggest that the functional network of hippocampal subfields may underlie anxiety and core depression symptoms.

Exploring brain connectivity changes in major depressive disorder using functional-structural data fusion: A CAN-BIND-1 study

There is a growing interest in examining the wealth of data generated by fusing functional and structural imaging information sources. These approaches may have clinical utility in identifying disruptions in the brain networks that underlie major depressive disorder (MDD). We combined an existing software toolbox with a mathematically dense statistical method to produce a novel processing pipeline for the fast and easy implementation of data fusion analysis (FATCAT‐awFC). The novel FATCAT‐awFC pipeline was then utilized to identify connectivity (conventional functional, conventional structural and anatomically weighted functional connectivy) changes in MDD patients compared to healthy comparison participants (HC). Data were acquired from the Canadian Biomarker Integration Network for Depression (CAN‐BIND‐1) study. Large‐scale resting‐state networks were assessed. We found statistically significant anatomically‐weighted functional connectivity (awFC) group differences in the default mode network and the ventral attention network, with a modest effect size (d < 0.4). Functional and structural connectivity seemed to overlap in significance between one region‐pair within the default mode network. By combining structural and functional data, awFC served to heighten or reduce the magnitude of connectivity differences in various regions distinguishing MDD from HC. This method can help us more fully understand the interconnected nature of structural and functional connectivity as it relates to depression.

Structural Connectivity Between Rostral Anterior Cingulate Cortex and Amygdala Predicts First Onset of Depressive Disorders in Adolescence

BACKGROUND

Adolescent-onset depressive disorders (DDs) are associated with deficits in the regulation of negative affect across modalities (self-report, behavioral paradigms, and neuroimaging), which may manifest prior to first-onset DDs. Whether the neurocircuitry governing emotional regulation predates DDs is unclear. This study tested whether a critical pathway for emotion regulation (rostral anterior cingulate cortex-amygdala structural connectivity) predicts first-onset DDs in adolescent females.

METHODS

Diffusion tensor imaging data were acquired on adolescent females (n = 212) without a history of DDs and the cohort was reassessed for first-onset DDs over the next 27 months.

RESULTS

A total of 26 girls developed first onsets of DDs in the 27 months after imaging. Multivariate logistic regression showed that lower weighted average fractional anisotropy of uncinate fasciculus tracts between the rostral anterior cingulate cortex and amygdala prospectively predicted first onset of DDs (adjusted odds ratio = 0.44, p = .005), above and beyond established risk factors including baseline depression symptom severity, history of anxiety disorders, parental history of depression, parental education, and age.

CONCLUSIONS

This study provides evidence for the first time showing that aberrant structural connectivity between the rostral anterior cingulate cortex and amygdala prospectively predates first onset of DDs in adolescent females. These results highlight the importance of a well-established neural circuit implicated in the regulation of negative affect as a likely etiological factor and a promising target for intervention and prevention of DDs.

Face Recognition Brain Functional Connectivity in Patients With Major Depression: A Brain Source Localization Study by ERP

Objective: Patients with major depressive disorder (MDD) presents with face recognition defects. These defects negatively affect their social interactions. However, the cause of these defects is not clear. This study sought to explore whether MDD patients develop facial perceptual processing disorders with characteristics of brain functional connectivity (FC). Methods: Event-related potential (ERP) was used to explore differences between 20 MDD patients and 20 healthy participants with face and non-face recognition tasks based on 64 EEG parameters. After pre-processing of EEG data and source reconstruction using the minimum-norm estimate (MNE), data were converted to AAL90 template to obtain a time series of 90 brain regions. EEG power spectra were determined using Fieldtrip incorporating a Fast Fourier transform. FC was determined for all pairs of brain signals for theta band using debiased estimate of weighted phase-lag index (wPLI) in Fieldtrip. To explore group differences in wPLI, independent t-tests were performed with p < 0.05 to indicate statistical significance. False discovery rate (FDR) correction was used to adjust p-values. Results: The findings showed that amplitude induction by face pictures was higher compared with that of non-face pictures both in MDD and healthy control (HC) groups. Face recognition amplitude in MDD group was lower compared with that in the HC group. Two time periods with significant differences were then selected for further analysis. Analysis showed that FC was stronger in the MDD group compared with that in the HC group in most brain regions in both periods. However, only one FC between two brain regions in HC group was stronger compared with that in the MDD group. Conclusion: Dysfunction in brain FC among MDD patients is a relatively complex phenomenon, exhibiting stronger and multiple connectivity with several brain regions of emotions. The findings of the current study indicate that the brain FC of MDD patients is more complex and less efficient in the initial stage of face recognition.