Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders

Effects of electroconvulsive therapy on functional connectome abnormalities in adolescents with depression and suicidal ideation

OBJECTIVES

Major depressive disorder (MDD) in adolescents is associated with an increased risk of suicide, and electroconvulsive therapy (ECT) is an effective treatment for MDD and suicidal ideation. To investigate underlying central mechanisms, this study examined functional connectome topological organization in adolescents with MDD and suicidal ideation prior to and following ECT.

METHODS

Resting-state fMRI images were collected from 28 adolescents with MDD and suicidal ideation and 31 demographically similar healthy adolescents. Whole-brain functional networks were constructed and topological metrics were analyzed using graph theory approaches.

RESULTS

Prior to ECT, depressed adolescents showed disrupted global and nodal properties, indicating altered functional connectivity. Following ECT, significant reductions in depression and suicidality symptoms were observed, with a 75 % response rate. ECT led to an increase in the small-worldness of the brain network, suggesting restoration of functional connectivity. Significant improvements were seen in nodal properties, particularly in the central executive network. Group-by-time interactions revealed differences between responders and non-responders in nodal degree and efficiency.

LIMITATIONS

Larger sample sizes and extended followed-up periods following ECT treatment are needed to further investigate the neural basis of clinical changes.

CONCLUSION

The results of this study reveal dynamic changes in brain network topology of adolescents with depression during the course of ECT, and have an advanced understanding of the neurobiological biomarkers associated with the efficacy of ECT treatment.

Brain structural changes underlying clinical symptom improvement following fast-acting treatments in treatment resistant depression

BACKGROUND

Electroconvulsive therapy (ECT), ketamine infusion (KI), and total sleep deprivation (TSD) are effective and fast in treating patients with treatment-resistant depression (TRD). However, it remains unclear whether the three treatments have the same effect on clinical symptom improvement and have common brain structural mechanisms.

METHODS

The current study included 127 TRD patients and 37 healthy controls, which were obtained from the Perturbation of the Treatment Resistant Depression Connectome Project. We aimed to investigate the shared and distinct brain structural changes underlying clinical symptom improvement among ECT, KI, and TSD treatments.

RESULTS

All of the three treatments significantly reduced the depressive symptoms in TRD patients, but they differently affected other clinical measurements. Neuroimaging results also revealed that all of ECT, KI, and TSD treatments significantly increased gray matter volume of left caudate after treatment in TRD patients. However, the gray matter volume of other brain regions including hippocampus, parahippocampus, amygdala, insula, fusiform gyrus, several occipital and temporal areas was increased only after ECT treatment. Still, the baseline or the change of gray matter volume did not correlate with the depressive symptom improvement for all of the three treatments.

LIMITATIONS

A higher sample size would be required to further validate our findings.

CONCLUSIONS

The results observed in the current study suggested that the ECT, KI, and TSD treatments differently affected clinical measurements and brain structures in TRD patients, though all of them were effective in depressive symptom improvement, which might facilitate the development of personalized treatment protocol for this disease.

Brain morphometry, stimulation charge, and seizure duration in electroconvulsive therapy

Background

Electroconvulsive therapy (ECT) is a well-established and effective treatment for severe depression and other conditions. Though ECT induces a generalized seizure, it is unclear why seizures are therapeutic. This study analyzed relationships between pre-treatment brain morphology, stimulation dose, and seizure duration to better understand ECT-induced seizures.

Methods

Pre-existing MRI data were analyzed from four cohorts with treatment refractory depression undergoing right unilateral (RUL) ECT (n=166). Regional brain morphometry and magnitude of electrical current (|E|) were analyzed, along with seizure duration and stimulation charge at seizure threshold (ECT1) and 6x seizure threshold (ECT2&3). Linear models controlled for age, sex, and cohort, corrected using false discovery rate q<0.05.

Results

Lower ECT1 stimulation charge correlated with less cortical surface area perpendicular to current flow, and greater |E| in nearby white matter. Lower ECT2&3 stimulation charge correlated with less cortical surface area and curvature near the temporal electrode, higher |E| in right amygdala and anterior hippocampus, and lower right thalamic and mid-hippocampal volume. Cortical surface area extending between electrodes (e.g., postcentral gyrus) positively correlated with ECT2&3 seizure duration. Successful antidepressant response associated with less cortical surface area near the temporal electrode and more |E| in anteromedial temporal lobe regions, the latter of which mediated the effects of the former on antidepressant response.

Conclusions

Pre-treatment brain morphology influences ECT-induced seizure, particularly in regions near ECT electrodes and those relevant to seizure initiation and modulation. Personalized dosing based on head morphology and other factors may improve antidepressant outcomes and reduce side effects.

A data integration method for new advances in development cognitive neuroscience

Combining existing datasets to investigate key questions in developmental cognitive neuroscience brings exciting opportunities and unique challenges. However, many data pooling methods require identical or harmonized methodologies that are often not feasible. We propose Integrative Data Analysis (IDA) as a promising framework to advance developmental cognitive neuroscience with secondary data analysis. IDA serves to test hypotheses by combining data of the same construct from commensurate (but not identical) measures. To overcome idiosyncrasies of neuroimaging data, IDA explicitly evaluates if measures across studies assess the same construct. Moreover, IDA allows investigators to examine meaningful individual variability by de-confounding source-specific differences. To demonstrate IDA's potential, we explain foundational concepts, outline necessary steps, and apply IDA to volumetric measures of hippocampal subfields from 443 4- to 17-year-olds across three independent studies. We identified commensurate measures of Cornu Ammonis (CA) 1, dentate gyrus (DG)/CA3, and Subiculum (Sub). Model testing supported use of IDA to create IDA factor scores. We found age-related differences in DG/CA3, not but CA1 and Sub volume in the integrated dataset. By successfully demonstrating IDA, our hope is that future innovations come from the combination of existing neuroimaging data to create representative integrated samples when testing critical developmental questions.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.