Background: The link between depression and neurodegeneration is complex and unclear. It is debated whether depression is a risk factor, a prodrome, a consequence, or unrelated. Objectives: This review examines these possibilities to clarify their connection, focusing primarily on Alzheimer’s disease, vascular dementia, Parkinson’s disease, and other highly comorbid neurodegenerative diseases. Methods: Eligibility criteria: The studies included in this review focused on neurodegenerative diseases with high comorbidity with depression, published in peer-reviewed English-language journals, providing empirical evidence on the link between the two conditions or theoretical frameworks that point to other studies. Non-human studies and those irrelevant to this connection were excluded. Source of evidence: AI-supported tools identified relevant articles. Results: Most studies suggest depression may contribute to neurodegeneration, but clinical, neuroimaging, and longitudinal evidence also support its role as a prodrome or consequence, indicating a bidirectional relationship. Conclusions: Despite extensive research, the connection remains unclear, highlighting the need for further investigation into underlying mechanisms and interdependencies, focusing on longitudinal studies by examining causality.

Action representation and the sharing of feature coding within the Action Observation Network (AON) remain debated, and our understanding of how the brain consistently encodes action features across sensory modalities under variable, naturalistic conditions is still limited. Here, we introduce a theoretically-based taxonomic model of action representation that categorizes action-related features into six conceptual domains: Space, Effector, Agent & Object, Social, Emotion, and Linguistic. We assessed the predictive power of this model on human brain activity by acquiring functional MRI (fMRI) data from participants exposed to audiovisual, visual-only, or auditory-only versions of the same naturalistic movie. Using a multi-voxel encoding analysis and variance partitioning, we demonstrated that our model significantly predicts cortical activity within the AON, with a comparable effect size across modalities. The Effector and Social domains contributed most to the model predictions and domain-specific representations were largely stable across sensory modalities. This study elucidates how the human brain robustly encodes action-related information across different sensory modalities, revealing that certain action domains have a stronger influence on neural representation in a modality-general manner. Overall, this research enhances our understanding of how the brain integrates complex action information from multiple sensory inputs, offering insights into the generalized nature of action representation in human cognition and paving the way for further exploration into multisensory integration.