Functional connectomics in depression: insights into therapies

Decreased resting-state functional connectivity and brain network abnormalities in the prefrontal cortex of elderly patients with Parkinson's disease accompanied by depressive symptoms

This study aimed to explore the brain network characteristics in elderly patients with Parkinson's disease (PD) with depressive symptoms. Thirty elderly PD patients with depressive symptoms (PD-D) and 26 matched PD patients without depressive symptoms (PD-NOD) were recruited based on HAMD-24 with a cut-off of 7. The resting-state functional connectivity (RSFC) was conducted by 53-channel functional near-infrared spectroscopy (fNIRS). There were no statistically significant differences in MMSE scores, disease duration, Hoehn-Yahr stage, daily levodopa equivalent dose, and MDS-UPDRS III between the two groups. However, compared to the PD-NOD group, the PD-D group showed significantly higher MDS-UPDRS II, HAMA-14, and HAMD-24. The interhemispheric FC strength and the FC strength between the left dorsolateral prefrontal cortex (DLPFC-L) and the left frontal polar area (FPA-L) was significantly lower in the PD-D group (FDR p < 0.05). As for graph theoretic metrics, the PD-D group had significantly lower degree centrality (aDc) and node efficiency (aNe) in the DLPFC-L and the FPA-L (FDR, p < 0.05), as well as decreased global efficiency (aEg). Pearson correlation analysis indicated moderate negative correlations between HAMD-24 scores and the interhemispheric FC strength, FC between DLPFC-L and FPA-L, aEg, aDc in FPA-L, aNe in DLPFC-L and FPA-L. In conclusion, PD-D patients show decreased integration and efficiency in their brain networks. Furthermore, RSFC between DLPFC-L and FPA-L regions is negatively correlated with depressive symptoms. These findings propose that targeting DLPFC-L and FPA-L regions via non-invasive brain stimulation may be a potential intervention for alleviating depressive symptoms in elderly PD patients.

Electroconvulsive therapy regulates brain connectome dynamics in patients with major depressive disorder

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.

METHODS

We collected longitudinal resting-state fMRI data from 80 MDD patients (50 with suicidal ideation and 30 without; SI and NSI, respectively) before and after ECT and 37 age- and sex-matched healthy controls. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.

RESULTS

At baseline, MDD patients had lower global modularity and higher modular variability in functional connectomes compared to controls. Network modularity increased and network variability decreased after ECT in MDD patients, predominantly located in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI, but not MDD-NSI patients, and pre-ECT modular variability could significantly predict symptom improvement in the MDD-SI group, but not in the MDD-NSI group.

CONCLUSIONS

We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with suicidal ideation. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Investigating the impact of rumination and adverse childhood experiences on resting-state neural activity and connectivity in depression

BACKGROUND

Both ruminative thought processes and adverse childhood experiences (ACEs) are well-established risk factors for the emergence and maintenance of depression. However, the neurobiological mechanisms underlying these associations remain poorly understood.

METHODS

We examined resting-state functional magnetic resonance imaging data (3 T Tim Trio MR scanner; Siemens, Erlangen) of 44 individuals diagnosed with an acute depressive episode. Specifically, we focused on investigating functional brain activity and connectivity within and between three large-scale neural networks associated with processes affected in depression: the default mode network (DMN), the salience network (SN), and the central executive network (CEN). Correlational and regression-based analyses were performed.

RESULTS

Our regions of interest analyses revealed that region-specific spontaneous neural activity in the anterior DMN was associated with self-reported trait rumination, specifically, the pregenual anterior cingulate cortex (pgACC). Furthermore, using a liberal statistical threshold, we found that spontaneous neural activity of the ventromedial prefrontal cortex and the pgACC were associated with depression symptom severity. Neither spontaneous neural activity in the SN and CEN nor functional connectivity within and across the investigated networks was associated with depression severity or rumination. Furthermore, there was no association between ACEs and brain activity and connectivity.

LIMITATIONS

Lack of a formal control group or low-risk group for comparison.

CONCLUSIONS

Overall, our results indicate network-specific changes in spontaneous brain activity, that are linked to both depression severity and rumination. Findings underscore the crucial role of the pgACC in depression and contribute to a dimensional and symptom-based understanding of depression-related network imbalances.

Volume of the Dentate Gyrus/CA4 Hippocampal subfield mediates the interplay between sleep quality and depressive symptoms

Background

Emerging evidence increasingly suggests that poor sleep quality is associated with depressive symptoms. The hippocampus might play a crucial role in the interplay between sleep disturbance and depressive symptomatology, e.g., hippocampal atrophy is typically seen in both insomnia disorder and depression. Thus, examining the role of hippocampal volume in the interplay between poor sleep quality and depressive symptoms in large healthy populations is vital.

Methods

We investigated the association between self-reported sleep quality, depressive symptoms, and hippocampal total and subfields' volumes in 1603 healthy young adults from the Behavioral Brain Research Project. Mediation analysis explored the mediating role of hippocampal volumes between sleep quality and depressive symptoms.

Results

Self-reported sleep quality and depressive symptoms were positively correlated. In addition, it negatively related to three hippocampal subfields but not total hippocampal volume. In particular, hippocampal subfield DG and CA4 volumes mediated the interrelationship between poor sleep quality and depressive symptoms.

Conclusions

Our findings improved the current understanding of the relationship between sleep disturbance, depressive symptomatology, and hippocampal subfields in healthy populations. Considering the crucial role of DG in hippocampal neurogenesis, our results suggest that poor sleep quality may contribute to depression through a reduction of DG volume leading to impaired neurogenesis which is crucial for the regulation of mood.

Neuroproteomics: Unveiling the Molecular Insights of Psychiatric Disorders with a Focus on Anxiety Disorder and Depression

Anxiety and depression are two of the most common mental disorders worldwide, with a lifetime prevalence of up to 30%. These disorders are complex and have a variety of overlapping factors, including genetic, environmental, and behavioral factors. Current pharmacological treatments for anxiety and depression are not perfect. Many patients do not respond to treatment, and those who do often experience side effects. Animal models are crucial for understanding the complex pathophysiology of both disorders. These models have been used to identify potential targets for new treatments, and they have also been used to study the effects of environmental factors on these disorders. Recent proteomic methods and technologies are providing new insights into the molecular mechanisms of anxiety disorder and depression. These methods have been used to identify proteins that are altered in these disorders, and they have also been used to study the effects of pharmacological treatments on protein expression. Together, behavioral and proteomic research will help elucidate the factors involved in anxiety disorder and depression. This knowledge will improve preventive strategies and lead to the development of novel treatments.

Functional reorganization of intranetwork and internetwork connectivity in patients with Ménière's disease

Ménière's disease (MD) is associated with functional reorganization not only in the auditory or sensory cortex but also in other control and cognitive areas. In this study, we examined intranetwork and internetwork connectivity differences between 55 MD patients and 70 healthy controls (HC) in 9 well-defined resting-state networks. Functional connectivity degree was lower in MD compared to HC in 19 brain areas involved in the somatomotor, auditory, ventral attention, default mode, limbic, and deep gray matter networks. In addition, we observed lower intranetwork connectivity in the auditory, ventral attention, and limbic networks, as well as lower internetwork connectivity between the somatomotor and limbic networks, and between the auditory and somatomotor, deep gray matter, and ventral attention networks, and between the deep gray matter and default mode network. Furthermore, we identified 81 pairs of brain areas with significant differences in functional connectivity between MD patients and HC at the edge level. Notably, the left amygdala's functional connectivity degree was positively correlated with MD's disease stage, and the ventral attention network's intranetwork connectivity was positively correlated with the healthy side vestibular ratio. Our findings suggest that these functional network reorganization alterations may serve as potential biomarkers for predicting clinical progression, evaluating disease severity, and gaining a better understanding of MD's pathophysiology. Large-scale network studies using neuroimaging techniques can provide additional insights into the underlying mechanisms of MD.

Functional connectome through the human life span

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

The neural basis of resting-state fMRI functional connectivity in fronto-limbic circuits revealed by chemogenetic manipulation

Measures of fMRI resting-state functional connectivity (rs-FC) are an essential tool for basic and clinical investigations of fronto-limbic circuits. Understanding the relationship between rs-FC and neural activity in these circuits is therefore vital. Here we introduced inhibitory designer receptors exclusively activated by designer drugs (DREADDs) into the macaque amygdala and activated them with a highly selective and potent DREADD agonist, deschloroclozapine. We evaluated the causal effect of activating the DREADD receptors on rs-FC and neural activity within circuits connecting amygdala and frontal cortex. Interestingly, activating the inhibitory DREADD increased rs-FC between amygdala and ventrolateral prefrontal cortex. Neurophysiological recordings revealed that the DREADD-induced increase in fMRI rs-FC was associated with increased local field potential coherency in the alpha band (6.5-14.5Hz) between amygdala and ventrolateral prefrontal cortex. Thus, our multi-disciplinary approach reveals the specific signature of neuronal activity that underlies rs-FC in fronto-limbic circuits.