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Cognitive performance in Major Depressive Disorder (MDD) is frequently impaired and related to functional outcomes. Repetitive Transcranial Magnetic Stimulation (rTMS) may exert its effects on MDD acting both on depressive symptoms and neurocognition. Furthermore, cognitive status could predict the therapeutic response of depressive symptoms to rTMS. However, cognitive performances as a predictor of rTMS response in MDD has not been thoroughly investigated. This review aims to evaluate the role of pre-treatment cognitive performance as a predictor of clinical response to rTMS, and the effects of rTMS on neurocognition in MDD.A systematic review of studies evaluating neurocognition in MDD as an outcome and/or predictor of response to rTMS was conducted using PubMed/Medline and Embase.Fifty-eight articles were identified: 25 studies included neurocognition as a predictor of response to rTMS; 56 used cognitive evaluation as an outcome of rTMS. Baseline cognitive performance and cognitive improvements after rTMS predicted clinical response to rTMS. Moreover, rTMS improved cognition in MDD.Cognitive assessment could predict improvement of depression in MDD patients undergoing rTMS and help selecting patients that could have beneficial effects from rTMS. A routine cognitive assessment might stratify MDD patients and track rTMS related cognitive improvement.

Introduction Depression and demoralization are highly prevalent among individuals with physical illnesses but their relationship is still unclear. Objective To examine the relationship between clinical features of depression and demoralization with the network approach to psychopathology. Methods Participants were recruited from the medical wards of a University Hospital in Italy. The Demoralization Scale (DS) was used to assess demoralization, while the Patient Health Questionnaire-9 (PHQ-9) to assess depressive symptoms. The structure of the depression-demoralization symptom network was examined and complemented by the analysis of topological overlap and Exploratory Graph Analysis (EGA) to identify the most relevant groupings (communities) of symptoms and their connections. The stability of network models was estimated with bootstrap procedures and results were compared with factor analysis. Results Life feeling pointless, low mood/discouragement, hopelessness and feeling trapped were among the most central features of the network. EGA identified four communities: (1) Neurovegetative Depression, (2) Loss of purpose, (3) Frustrated Isolation and (4) Low mood and morale. Loss of purpose and low mood/morale were largely connected with other communities through anhedonia, hopelessness and items related to isolation and lack of emotional control. Results from EGA displayed good stability and were comparable to those from factor analysis. Limitations Cross-sectional design; sample heterogeneity Conclusions Among general hospital inpatients, features of depression and demoralization are independent, with the exception of low mood and self-reproach. The identification of symptom groupings around entrapment and helplessness may provide a basis for a dimensional characterization of depressed/demoralized patients, with possible implications for treatment.