Distinct Neural-Functional Effects of Treatments With Selective Serotonin Reuptake Inhibitors, Electroconvulsive Therapy, and Transcranial Magnetic Stimulation and Their Relations to Regional Brain Function in Major Depression: A Meta-analysis

The association of posttraumatic stress disorder, depression, and head injury with mid-life cognitive function in civilian women

BACKGROUND

Despite evidence linking posttraumatic stress disorder (PTSD), depression, and head injury, separately, with worse cognitive performance, investigations of their combined effects on cognition are limited in civilian women.

METHODS

The Cogstate Brief Battery assessment was administered in 10,681 women from the Nurses' Health Study II cohort, mean age 64.9 years (SD = 4.6). Psychological trauma, PTSD, depression, and head injury were assessed using online questionnaires. In this cross-sectional analysis, we used linear regression models to estimate mean differences in cognition by PTSD/depression status and stratified by history of head injury.

RESULTS

History of head injury was prevalent (36%), and significantly more prevalent among women with PTSD and depression (57% of women with PTSD and depression, 21% of women with no psychological trauma or depression). Compared to having no psychological trauma or depression, having combined PTSD and depression was associated with worse performance on psychomotor speed/attention ( β  = -.15, p = .001) and learning/working memory ( β  = -.15, p < .001). The joint association of PTSD and depression on worse cognitive function was strongest among women with past head injury, particularly among those with multiple head injuries.

CONCLUSIONS

Head injury, like PTSD and depression, was highly prevalent in this sample of civilian women. In combination, these factors were associated with poorer performance on cognitive tasks, a possible marker of future cognitive health. Head injury should be further explored in future studies of PTSD, depression and cognition in women.

Toward a neurocircuit-based taxonomy to guide treatment of obsessive-compulsive disorder

An important challenge in mental health research is to translate findings from cognitive neuroscience and neuroimaging research into effective treatments that target the neurobiological alterations involved in psychiatric symptoms. To address this challenge, in this review we propose a heuristic neurocircuit-based taxonomy to guide the treatment of obsessive-compulsive disorder (OCD). We do this by integrating information from several sources. First, we provide case vignettes in which patients with OCD describe their symptoms and discuss different clinical profiles in the phenotypic expression of the condition. Second, we link variations in these clinical profiles to underlying neurocircuit dysfunctions, drawing on findings from neuropsychological and neuroimaging studies in OCD. Third, we consider behavioral, pharmacological, and neuromodulatory treatments that could target those specific neurocircuit dysfunctions. Finally, we suggest methods of testing this neurocircuit-based taxonomy as well as important limitations to this approach that should be considered in future research.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

Connectivity between the anterior insula and dorsolateral prefrontal cortex links early symptom improvement to treatment response

BACKGROUND

Early improvement (EI) following treatment with antidepressants is a widely reported predictor to the treatment response. This study aimed to identify the resting-state functional connectivity (rs-FC) and its related clinical features that link the treatment response at the time of EI.

METHODS

This study included 23 first-episode treatment-naive patients with MDD. After 2 weeks of antidepressant treatment, these patients received 3.0 Tesla resting-state functional magnetic resonance imaging scanning and were subgrouped into an EI group (N = 13) and a non-EI group (N = 10). Using the anterior insula (rAI) as a seed region, this study identified the rs-FC that were associated with both EI and the treatment response at week 12, and further tested the associations of the identified rs-FC with either the clinical features or the early symptom improvement.

RESULTS

Rs-FC between rAI and the left dorsolateral prefrontal cortex (dlPFC) was associated with EI (t₂₁ = -6.091, p = 0.022 after FDR correction for multiple comparisons). This rs-FC was also associated with an interaction between EI and the treatment response at the week 12 (t₂₁ = -5.361, p = 6.37e-5). Moreover, among the clinical features, this rs-FC was associated with the early symptom improvement in the insomnia, somatic symptoms, and anxiety symptoms, and these early symptom improvements were associated with the treatment response.

CONCLUSION

Rs-FC between the rAI and the left dlPFC played a crucial role in the early antidepressant effect, which linked the treatment response. The early treatment effect relating to rAI may represent an early symptom improvement in self-perceptual anxiety, somatic symptoms and insomnia.

Electroconvulsive Therapy Modulates Resting-State EEG Oscillatory Pattern and Phase Synchronization in Nodes of the Default Mode Network in Patients With Depressive Disorder

Introduction: Electroconvulsive therapy (ECT) has antidepressant effects, but it also has possible cognitive side effects. The effects of ECT on neuronal oscillatory pattern and phase synchronization, and the relationship between clinical response or cognitive change and electroencephalogram (EEG) measurements remain elusive. Methods: Individuals with unipolar depressive disorder receiving bilateral ECT were recruited. Five minutes of resting, eyes-closed, 19-lead EEG recordings were obtained before and after a course of ECT. Non-overlapping 60 artifact-free epocs of 2-s duration were used for the analyses. We used exact low resolution electromagnetic tomography (eLORETA) to compute the whole-brain three-dimensional intracortical distribution of current source density (CSD) and phase synchronization among 28 regions-of-interest (ROIs). Paired t-tests were used to identify cortical voxels and connectivities showing changes after ECT. Montgomery Asberg Depression Rating Scale (MADRS) and Mini-Mental State Examination (MMSE) were used to evaluate the severity of depression and the global cognitive function. Correlation analyses were conducted to identify the relationship between changes in the EEG measurements and changes in MADRS or MMSE. Results: Thirteen depressed patients (five females, mean age: 58.4 years old) were included. ECT increased theta CSD in the anterior cingulate cortex (ACC), and decreased beta CSD in the frontal pole (FP), and gamma CSD in the inferior parietal lobule (IPL). ECT increased theta phase synchronization between the posterior cingulate cortex (PCC) and the anterior frontal cortex, and decreased beta phase synchronization between the PCC and temporal regions. A decline in beta synchronization in the left hemisphere was associated with cognitive changes after ECT. Conclusion: ECT modulated resting-state EEG oscillatory patterns and phase synchronization in central nodes of the default mode network (DMN). Changes in beta synchronization in the left hemisphere might explain the ECT-related cognitive side effects.

Striatal dopamine deficits predict reductions in striatal functional connectivity in major depression: a concurrent 11C-raclopride positron emission tomography and functional magnetic resonance imaging investigation

Major depressive disorder (MDD) is characterized by the altered integration of reward histories and reduced responding of the striatum. We have posited that this reduced striatal activation in MDD is due to tonically decreased stimulation of striatal dopamine synapses which results in decremented propagation of information along the cortico-striatal-pallido-thalamic (CSPT) spiral. In the present investigation, we tested predictions of this formulation by conducting concurrent functional magnetic resonance imaging (fMRI) and ¹¹C-raclopride positron emission tomography (PET) in depressed and control (CTL) participants. We scanned 16 depressed and 14 CTL participants with simultaneous fMRI and ¹¹C-raclopride PET. We estimated raclopride binding potential (BP_ND), voxel-wise, and compared MDD and CTL samples with respect to BP_ND in the striatum. Using striatal regions that showed significant between-group BP_ND differences as seeds, we conducted whole-brain functional connectivity analysis using the fMRI data and identified brain regions in each group in which connectivity with striatal seed regions scaled linearly with BP_ND from these regions. We observed increased BP_ND in the ventral striatum, bilaterally, and in the right dorsal striatum in the depressed participants. Further, we found that as BP_ND increased in both the left ventral striatum and right dorsal striatum in MDD, connectivity with the cortical targets of these regions (default-mode network and salience network, respectively) decreased. Deficits in stimulation of striatal dopamine receptors in MDD could account in part for the failure of transfer of information up the CSPT circuit in the pathophysiology of this disorder.

Treatment Associated Changes of Functional Connectivity of Midbrain/Brainstem Nuclei in Major Depressive Disorder

Previous functional magnetic resonance imaging (fMRI) studies demonstrated an abnormally coordinated network functioning in Major Depression Disorder (MDD) during rest. The main monoamine-producing nuclei within midbrain/brainstem are functionally integrated within these specific networks. Therefore, we aimed to investigate the resting-state functional connectivity (RSFC) of these nuclei in 45 MDD patients and differences between patients receiving two different classes of antidepressant drugs. Patients showed reduced RSFC from the ventral tegmental area (VTA) to dorsal anterior cingulate cortex (dACC) and stronger RSFC to the left amygdala and dorsolateral prefrontal cortex (DLPFC). Patients treated with antidepressants influencing noradrenergic and serotonergic neurotransmission showed different RSFC from locus coeruleus to DLPFC compared to patients treated with antidepressants influencing serotonergic neurotransmission only. In the opposite contrast patients showed stronger RSFC from dorsal raphe to posterior brain regions. Enhanced VTA-RSFC to amygdala as a central region of the salience network may indicate an over‐attribution of the affective salience to internally-oriented processes. Significant correlation between decreased VTA-dACC functional connectivity and the BDI-II somatic symptoms indicates an association with diminished volition and behavioral activation in MDD. The observed differences in the FC of the midbrain/brainstem nuclei between two classes of antidepressants suggest differential neural effects of SSRIs and SNRIs.