The network model to psychopathology states that mental disorders can be conceptualized and studied as causal systems of mutually reinforcing symptoms. This holds that symptoms are not passive indicators of a latent, common cause, of the disorder but agents in a causal system.

The causality hypothesis states that when causal relations among symptoms are strong, the onset of one symptom will lead to the onset of others. The connectivity hypothesis states that a strongly interconnected symptom network is vulnerable to a contagion effect of spreading activation through the network. Widespread symptom activation as a result of an external stressor can lead to symptoms persisting when the initiating stressor is removed. The centrality hypothesis states that highly central symptoms have greater potential to spread symptom activation throughout the network than do symptoms on the periphery. The comorbidity hypothesis states that symptoms can occur in multiple disorders and that some symptoms can thus bridge different disorders.

A mental disorder is characterized by both the state and the structure of the network (i.e. a mental disorder is characterized by a state of harmful equilibrium). The boundary between health and disorder will vary as a function of network structure. In weakly connected networks, activation varies dimensionally. However, in strongly connected networks, activation within the system rapidly leads to a state of psychopathology (i.e. more discrete rather than continuous).

The momentary perspective states that symptoms are aggregates of moment-to-moment experiences. According to this perspective, these experiences constitute the true building blocks of psychopathology. This highlights the importance of understanding the chronometry of experiences, symptoms and disorders.

The assumptions of the network model currently do not always align with how disorders are believed to operate.

There is a conditional positive manifold for most disorders. This states that symptoms of a positive disorder tend to be positively interconnected, even after controlling for shared variance among symptoms. This suggests meaningful clustering of symptoms in the syndromes we call mental disorders. Connectivity tends to be consistent across time and demographic groups. However, differences have been observed across countries.

Greater connectivity (i.e. network density) may confer risk for psychopathology. This is based on the fact that there appears to be greater connectivity between symptoms in people with more severe mental disorders. It is also possible that greater connectivity leads to disorder persistence. However, there is no consensus regarding these topics. There is some evidence that connectivity of negative mood state networks is associated with psychopathology but minimal evidence that broader networks of momentary experience exhibit such associations.

Node strength refers to the summed absolute strength of a node’s direct link. Non-DSM symptoms often exhibit elevated centrality (e.g. feeling disliked in depression) and some DSM-nodes are weakly connected to the network. It is not clear whether the symptoms which the DSM identifies as especially important are more central to less important DSM-symptoms. The DSM most likely has not captured all symptoms of a disorder and has not necessarily identified the most important symptoms.

Node centrality may indicate important symptoms. Central symptoms are more predictive of subsequent diagnosis than peripheral symptoms. A symptom’s centrality is positively associated with the strength of association between change in symptom and change in the remainder of the network.