Background/Objectives: Major Depressive Disorder (MDD) is a neuropsychiatric disorder affecting nearly 200 million people worldwide. While it has broad health effects, relatively little is known about how these vary across MDD ‘subtypes’, which reflect distinct symptom profiles. This systematic review examined the methods used to define several MDD subtypes and their associations with physical, cognitive, social, and functional health outcomes. Methods: A systematic search of PubMed was conducted in accordance with PRISMA 2020 guidelines to identify peer-reviewed studies published in English between 2014 and 2025. The final search was conducted on 21 January 2025. Studies were included if they examined adults with MDD subtypes and reported health-related outcomes. Risk of bias was assessed using the Newcastle–Ottawa Scale. A narrative synthesis was conducted due to heterogeneity in the subtype definitions and outcome measures. Results: Sixteen studies were included. Atypical and melancholic depression were most consistently associated with metabolic dysfunction, higher BMI, and a greater waist circumference. Melancholic depression was frequently associated with cognitive deficits, though results varied. Cognitive impairments were also observed in DSM-defined atypical depression, particularly in attention, vigilance, and social cognition. Anxious and melancholic depression may be associated with more severe social and functional impairment compared to other subtypes. However, the findings were limited by inconsistent definitions and outcome measures. Conclusions: Some subtypes, particularly atypical, melancholic, and anxious depression, are differentially associated with specific patterns of impairment, though inconsistencies limit firm conclusions. Registration: This review was retrospectively registered with the Open Science Framework (OSF): No specific funding was received.

Background/Objectives: Depression is a major disorder that has been described in terms of its underlying neurological characteristics, often measured via EEG. However, almost all previous research into the EEG correlates of depression has used a unitary model of Major Depressive Disorder (MDD), whereas there is strong evidence that MDD is heterogeneous in its symptomatology and neurological underpinnings. Methods: To investigate the EEG signatures of four subtypes of depression defined according to the previous literature, the Zung Self-rating Depression Scale was administered to 54 male and 46 female volunteers (M age = 32.53 yr). EEG data were collected during an Eyes Closed condition and examined for differences in connectivity across brain networks in the alpha- and beta-bands. Results: The results were examined in terms of the number and direction of connectivity differences between depressed and non-depressed participants within each depression subtype, the alpha- and beta-band connectivities, the regions of the brain that were connected, and the possible functional reasons why specific brain regions were differently connected for depressed and non-depressed participants within each MDD subtype. Conclusions: The results suggested some differences in the alpha- and beta-band connectivity between some of the MDD subtypes that are worth considering as representing different neurological signatures across the depression subtypes. These findings represent an initial challenge to defining depression as a unitary phenomenon, and suggest possible benefits for further research into the underlying neurological phenomena of depression subtypes.

Because of their potential to enhance pathways for diagnosis and treatment, a great deal of research has been conducted to identify brain biomarkers of mental illnesses such as depression and anxiety. Similarly, the investigation of the biomarkers of those protective factors that help individuals resist, or recover from, these mental illnesses is also directly relevant to clinical practice. One such protective factor is Psychological Resilience (PR) but relatively little is known about its neurobiological underpinnings. A literature search was conducted of electroencephalographic data and PR, revealing seven studies that reported brain electrical activity categorised into bands of specified frequencies. Several studies reported significant associations between PR and alpha band activity, and somewhat less on beta band activity, principally via their roles in emotion regulation and problem solving. However, despite having a major role in many aspects of cognition and brain function, and being implicated in depression, only two studies examined gamma band activity specifically, and their results were equivocal. Several possible reasons for these apparently null results of the association between gamma band activity and PR are discussed, leading to a brief description of gamma, and suggestions for future research into its association with PR.

Background/Objectives: Major Depression (MD) is a common disorder that has significant social and economic impacts. Approximately 30% of all MD patients are refractory to common treatments, representing a major obstacle to managing the impacts of depression. One potential explanation for the incomplete treatment efficacy in MD is a substantial divergence in the mechanisms and brain networks involved in different subtypes of the disorder. The aim of this study was to identify novel brain regional targets for MD clinical screening using a gene-informed approach. Methods: A new analysis pipeline, called “Analysis Tool for Local Association of Neuronal Transcript Expression” (ATLANTE), was generated and validated. The pipeline identifies brain regions based on the shared high expression of user-generated gene lists; in this study, the pipeline was applied to discover brain regions that may be significant to MD. Results: Nine discrete brain regions of interest to MD were identified, including the temporal pole, anterior transverse temporal gyrus (Heschl’s gyrus), olfactory tubercle, ventral tegmental area, postcentral gyrus, CA1 of the hippocampus, olfactory area, perirhinal gyrus, and posterior insular cortex. The application of network and clustering analyses identified genes of special importance, including, most notably, PRKN. Conclusions: This study provides two major insights. The first is that several brain regions have unique MD-associated genetic architectures, indicating a potential explanation for subtype-specific dysfunction. The second insight is that the PRKN gene, which is strongly associated with Parkinson’s disease, is a key player amongst the MD-associated genes. These findings reveal novel targets for the clinical screening of depression and reinforce a mechanistic connection between MD and Parkinson’s disease.

This review raises the importance of understanding the functions of gamma waves in the practice of clinical psychiatry. Measured by electroencephalograph (EEG), gamma waves are those electrical waves between 30 Hz and 200 Hz, and are relevant to psychiatry because of their ability to influence other brain electrical activity, and thence have associations with various mental disorders. To inform readers, gamma waves are defined and described, and some examples of their association with psychiatric disorders are briefly recounted. Four ways in which gamma waves might modulate brain activity and influence behaviour, potentially leading to a comprehensive model of gamma and its association with mental illness, are discussed. Inconsistencies in definition, measurement, and findings from gamma wave research are also described, and suggestions made for overcoming these limitations in future research.

Major depression (MD) is a condition characterised by persistent sadness and apathy, sometimes accompanied by changes in sleep, appetite, and energy levels. It is highly heterogeneous, and depressive subtypes exhibit differing symptom profiles and patterns of brain activity. Background/Objectives: Currently, there are no physiological diagnostic means to detect depression or depressive subtypes. An emerging biomarker may be the electroencephalogram (EEG) band, gamma, due to the role of this frequency in reward processing and cognition. The aim of this work was to complete an exploratory study to investigate the interaction between gamma band power, depression, and four depressive subtypes. Methods: A correlative study between resting-state gamma band power and individual scores on the Zung Self-rating Depression Scale (SDS) was completed using exact standardised low-resolution electromagnetic tomography (eLORETA) using EEG data from a community sample of 100 participants, including not depressed and depressed participants, and four depressive subtypes (anhedonia-, cognitive- and somatic-depression and depressed mood). Results: There was no significant positive correlation between gamma band power and overall depression score. However, there was a significant positive correlation between anhedonia and gamma band power, predominantly in the left anterior cingulate cortex, which may be consistent with dysfunctional reward processing, a characteristic of anhedonia. Additional areas of significance included the posterior cingulate cortex and left middle and superior frontal cortex. Conclusions: These results provide preliminary support for neurophysiological indicators of depressive subtypes and may help inform diagnosis and treatment guidance for depression and depressive subtypes in the future.