Cortical Thickness Predicts Response Following 2 Weeks of SSRI Regimen in First-Episode, Drug-Naive Major Depressive Disorder: An MRI Study

Predicting treatment outcomes in major depressive disorder using brain magnetic resonance imaging: a meta-analysis

Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT outcomes. Future studies are needed to predict outcomes using multiple MRI features, and to clarify whether imaging features predict outcomes generally or differ depending on treatments.

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatment for major depressive disorder (MDD). We tested and compared machine learning-based methods that predict individual-level pharmacotherapeutic treatment response using cortical morphometry from multisite longitudinal cohorts. We conducted an international analysis of pooled data from six sites of the ENIGMA-MDD consortium (n = 262 MDD patients; age = 36.5 ± 15.3 years; 154 (59%) female; mean response rate = 57%). Treatment response was defined as a ≥ 50% reduction in symptom severity score after 4-12 weeks post-initiation of antidepressant treatment. Structural MRI was acquired before, or < 14 days after, treatment initiation. The cortex was parcellated using FreeSurfer, from which cortical thickness and surface area were measured. We tested several machine learning pipeline configurations, which varied in (i) the way we presented the cortical data (i.e., average values per region of interest, as a vector containing voxel-wise cortical thickness and surface area measures, and as cortical thickness and surface area projections), (ii) whether we included clinical data, and the (iii) machine learning model (i.e., gradient boosting, support vector machine, and neural network classifiers) and (iv) cross-validation methods (i.e., k-fold and leave-one-site-out) we used. First, we tested if the overall predictive performance of the pipelines was better than chance, with a corrected 10-fold cross-validation permutation test. Second, we compared if some machine learning pipeline configurations outperformed others. In an exploratory analysis, we repeated our first analysis in three subpopulations, namely patients (i) from a single site, (ii) with comparable response rates, and (iii) showing the least (first quartile) and the most (fourth quartile) treatment response, which we call the extreme (non-)responders subpopulation. Finally, we explored the effect of including subcortical volumetric data on model performance. Overall, performance predicting antidepressant treatment response was not significantly better than chance (balanced accuracy = 50.5%; p = 0.66) and did not vary with alternative pipeline configurations. Exploratory analyses revealed that performance across models was only significantly better than chance in the extreme (non-)responders subpopulation (balanced accuracy = 63.9%, p = 0.001). Including subcortical data did not alter the observed model performance. Cortical structural MRI alone could not reliably predict individual pharmacotherapeutic treatment response in MDD. None of the used machine learning pipeline configurations outperformed the others. In exploratory analyses, we found that predicting response in the extreme (non-)responders subpopulation was feasible on both cortical data alone and combined with subcortical data, which suggests that specific MDD subpopulations may exhibit response-related patterns in structural data. Future work may use multimodal data to predict treatment response in MDD.

Dysfunction of large-scale brain networks underlying cognitive impairments in shift work disorder

It has been demonstrated that shift work can affect cognitive functions. Several neuroimaging studies have revealed altered brain function and structure for patients with shift work disorder (SWD). However, knowledge on the dysfunction of large-scale brain networks underlying cognitive impairments in shift work disorder is limited. This study aims to identify altered functional networks associated with cognitive declines in shift work disorder, and to assess their potential diagnostic value. Thirty-four patients with shift work disorder and 36 healthy controls (HCs) were recruited to perform the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional scans. After surface-based preprocessing, we calculated within- and between-network functional connectivity (FC) using the Dosenbach atlas. Moreover, correlation analysis was done between altered functional connectivity of large-scale brain networks and scores of cognitive assessments in patients with shift work disorder. Finally, we established a classification model to provide features for patients with shift work disorder concerning the disrupted large-scale networks. Compared with healthy controls, increased functional connectivity within-networks across the seven brain networks, and between-networks involving ventral attention network (VAN)-subcortical network (SCN), SCN-frontoparietal network (FPN), and somatosensory network (SMN)-SCN were observed in shift work disorder. Decreased functional connectivity between brain networks was found in shift work disorder compared with healthy controls, including visual network (VN)-FPN, VN-default mode network (DMN), SMN-DMN, dorsal attention network (DAN)-DMN, VAN-DMN, and FPN-DMN. Furthermore, the altered functional connectivity of large-scale brain networks was significantly correlated with scores of immediate memory, visuospatial, and delayed memory in patients with shift work disorder, respectively. Abnormal functional connectivity of large-scale brain networks may play critical roles in cognitive dysfunction in shift work disorder. Our findings provide new evidence to interpret the underlying neural mechanisms of cognitive impairments in shift work disorder.

Effects of electroconvulsive therapy on cortical thickness in depression: a systematic review

OBJECTIVE

Electroconvulsive therapy (ECT) is one of the most studied and validated available treatments for severe or treatment-resistant depression. However, little is known about the neural mechanisms underlying ECT. This systematic review aims to critically review all structural magnetic resonance imaging studies investigating longitudinal cortical thickness (CT) changes after ECT in patients with unipolar or bipolar depression.

METHODS

We performed a search on PubMed, Medline, and Embase to identify all available studies published before April 20, 2023. A total of 10 studies were included.

RESULTS

The investigations showed widespread increases in CT after ECT in depressed patients, involving mainly the temporal, insular, and frontal regions. In five studies, CT increases in a non-overlapping set of brain areas correlated with the clinical efficacy of ECT. The small sample size, heterogeneity in terms of populations, comorbidities, and ECT protocols, and the lack of a control group in some investigations limit the generalisability of the results.

CONCLUSIONS

Our findings support the idea that ECT can increase CT in patients with unipolar and bipolar depression. It remains unclear whether these changes are related to the clinical response. Future larger studies with longer follow-up are warranted to thoroughly address the potential role of CT as a biomarker of clinical response after ECT.

Altered Functional Connectivity of Large-Scale Brain Networks in Psychogenic Erectile Dysfunction Associated with Cognitive Impairments

Purpose

Several studies have demonstrated that psychogenic erectile dysfunction (pED) patients potentially suffer from cognitive dysfunction. Despite that previous neuroimaging studies have reported abnormal functional connections of brain areas associated with cognitive function in pED, the underlying mechanisms of cognitive dysfunction in pED remain elusive. This study aims to investigate the underlying mechanisms of cognitive dysfunction by analyzing large-scale brain networks.

Patients and Methods

A total of 30 patients with pED and 30 matched healthy controls (HCs) were recruited in this study and scanned by resting-state functional magnetic resonance imaging. The Dosenbach Atlas was used to define large-scale networks across the brain. The resting-state functional connectivity (FC) within and between large-scale brain networks was calculated to compare pED patients with HCs. The relationship among cognitive performances and altered FC of large-scale brain networks was further explored in pED patients.

Results

Our results showed that the decreased FC within visual network, and between visual network and default mode network, visual network and frontoparietal network, and ventral attention and default mode network were found in pED patients. Furthermore, there was a positive correlation between immediate memory score and FC within visual network. The visuospatial score was negatively correlated with decreased FC between ventral attention network and default mode network.

Conclusion

Taken together, our findings revealed the relationship between cognitive impairments and altered FC between large-scale brain networks in pED patients, providing the new evidence about the neural mechanisms of cognitive dysfunction in pED patients.

The heterogeneity of late-life depression and its pathobiology: a brain network dysfunction disorder

Depression is frequent in older individuals and is often associated with cognitive impairment and increasing risk of subsequent dementia. Late-life depression (LLD) has a negative impact on quality of life, yet the underlying pathobiology is still poorly understood. It is characterized by considerable heterogeneity in clinical manifestation, genetics, brain morphology, and function. Although its diagnosis is based on standard criteria, due to overlap with other age-related pathologies, the relationship between depression and dementia and the relevant structural and functional cerebral lesions are still controversial. LLD has been related to a variety of pathogenic mechanisms associated with the underlying age-related neurodegenerative and cerebrovascular processes. In addition to biochemical abnormalities, involving serotonergic and GABAergic systems, widespread disturbances of cortico-limbic, cortico-subcortical, and other essential brain networks, with disruption in the topological organization of mood- and cognition-related or other global connections are involved. Most recent lesion mapping has identified an altered network architecture with "depressive circuits" and "resilience tracts", thus confirming that depression is a brain network dysfunction disorder. Further pathogenic mechanisms including neuroinflammation, neuroimmune dysregulation, oxidative stress, neurotrophic and other pathogenic factors, such as β-amyloid (and tau) deposition are in discussion. Antidepressant therapies induce various changes in brain structure and function. Better insights into the complex pathobiology of LLD and new biomarkers will allow earlier and better diagnosis of this frequent and disabling psychopathological disorder, and further elucidation of its complex pathobiological basis is warranted in order to provide better prevention and treatment of depression in older individuals.

Sequelae of COVID-19 among previously hospitalized patients up to 1 year after discharge: a systematic review and meta-analysis

OBJECTIVE

Although complications and clinical symptoms of COVID-19 have been elucidated, the prevalence of long-term sequelae of COVID-19 is less clear in previously hospitalized COVID-19 patients. This review and meta-analysis present the occurrence of different symptoms up to 1 year of follow-up for previously hospitalized patients.

METHODS

We performed a systematic review from PubMed and Web of Science using keywords such as "COVID-19", "SARS-CoV-2", "sequelae", "long-term effect" and included studies with at least 3-month of follow-up. Meta-analyses using random-effects models were performed to estimate the pooled prevalence for different sequelae. Subgroup analyses were conducted by different follow-up time, regions, age and ICU admission.

RESULTS

72 articles were included in the meta-analyses after screening 11,620 articles, identifying a total of 167 sequelae related to COVID-19 from 88,769 patients. Commonly reported sequelae included fatigue (27.5%, 95% CI 22.4-33.3%, range 1.5-84.9%), somnipathy (20.1%, 95% CI 14.7-26.9%, range 1.2-64.8%), anxiety (18.0%, 95% CI 13.8-23.1%, range 0.6-47.8%), dyspnea (15.5%, 95% CI 11.3-20.9%, range 0.8-58.4%), PTSD (14.6%, 95% CI 11.3-18.7%, range 1.2-32.0%), hypomnesia (13.4%, 95% CI 8.4-20.7%, range 0.6-53.8%), arthralgia (12.9%, 95% CI 8.4-19.2%, range 0.0-47.8%), depression (12.7%, 95% CI 9.3-17.2%, range 0.6-37.5%), alopecia (11.2%, 95% CI 6.9-17.6%, range 0.0-47.0%) over 3-13.2 months of follow-up. The prevalence of most symptoms reduced after > 9 months of follow-up, but fatigue and somnipathy persisted in 26.2% and 15.1%, respectively, of the patients over a year. COVID-19 patients from Asia reported a lower prevalence than those from other regions.

CONCLUSIONS

This review identified a wide spectrum of COVID-19 sequelae in previously hospitalized COVID-19 patients, with some symptoms persisting up to 1 year. Management and rehabilitation strategies targeting these symptoms may improve quality of life of recovered patients.