Altered task modulation of global signal topography in the default-mode network of unmedicated major depressive disorder.

BACKGROUND: Altered global signal (GS) topography features in the resting-state fMRI of major depressive disorder (MDD), showing abnormally strong global signal representation in the default-mode network (DMN). Whether the abnormal local to global change also shapes activity during task states, and how it relates to psychopathological symptoms, e.g., abnormally slow time speed of motor, cognitive, and affective symptoms, remains unknown. METHODS: We investigated fMRI-based GS with its topographical representation during task states in unmedicated 51 MDD subjects and 28 healthy subjects. Task-related global signal correlation (GSCORR) was probed by a novel paradigm testing the processing of negative/neutral emotions during different time speeds, i.e., slow and fast. RESULTS: We observed a significant interaction between time speed and emotion of GSCORR in various DMN regions in healthy subjects. Next, we showed that MDD exhibits reduced task-related GSCORR in various DMN regions during specifically the fast processing of negative emotions. Finally, we demonstrated that GSCORR in DMN and other brain regions (motor-related regions, inferior frontal cortex) correlated with the degree of psychomotor retardation especially during the fast emotional stimuli. LIMITATIONS: The measurement of interoceptive variables like respiration rate or heart rate were not included in our fMRI acquisition. CONCLUSION: Together, we demonstrated the functional relevance of GS topography by showing reduced GSCORR in DMN during specifically the fast processing of negative emotions in MDD, suggesting the abnormal slowness, i.e., reduced time speed, to be a key feature of both brain and symptoms in MDD.

Mechanisms of repetitive transcranial magnetic stimulation for anti-depression: Evidence from preclinical studies.

This review summarizes the anti-depressant mechanisms of repetitive transcranial magnetic stimulation in preclinical studies, including anti-inflammatory effects mediated by activation of nuclear factor-E2-related factor 2 signaling pathway, anti-oxidative stress effects, enhancement of synaptic plasticity and neurogenesis via activation of the endocannabinoid system and brain derived neurotrophic factor signaling pathway, increasing the content of monoamine neurotransmitters via inhibition of Sirtuin 1/monoamine oxidase A signaling pathway, and reducing the activity of the hypothalamic-pituitary-adrenocortical axis. We also discuss the shortcomings of transcranial magnetic stimulation in preclinical studies such as inaccurate positioning, shallow depth of stimulation, and difficulty in elucidating the neural circuit mechanism up- and down-stream of the stimulation target brain region.

Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model.

AIMS: Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population. METHODS: The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period. RESULTS: AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2  = .17, P < .001) at baseline in the combined high-risk population (N = 72). CONCLUSIONS: The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.