There is a new [February 11, 2026] paper in JAMA Psychiatry, One-Year Actigraphy Study of Sleep and Rest-Activity Rhythms as Markers of Relapse in Depression, stating that, “Are actigraphy-derived sleep and rest-activity rhythms associated with relapse in depression?”
n “In this cohort study of 93 deeply phenotyped adults with remitted depression, lower baseline sleep regularity, relative amplitude (RA), sleep efficiency, and higher wake after sleep onset were associated with an approximately 2-fold higher relapse risk. Lower RA remained predictive after adjusting for concurrent Montgomery-Åsberg Depression Rating Scale scores. Results suggest that actigraphy metrics may serve as scalable biomarkers to identify individuals at higher risk of relapse, supporting the use of digital technology for relapse monitoring.” n n In simpler terms, “Individuals with a more irregular sleep profile had nearly double the risk of relapse. The strongest predictor of relapse was whether a person’s body detected less difference between daytime activity and nighttime rest. How much time spent awake during the night after already falling asleep also predicted increased risk of depression relapse. Participants’ sleep schedules became more erratic before a relapse took place.” n n What exactly is depression in the brain? This question is not about the dictionary definition or simply about what is defined in The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision [DSM-5-TR]. The question is tied to descriptions of depression for processes in the brain, by components, and their mechanisms. n n Simply, when there is a major depression and when there isn’t, what is the difference? How do the same components that can be used to explain this be used to explain other mental disorders? n n How does psychiatry move from labels to component-based descriptions, then digital displays, to have a better sense of what is ongoing within? How does this set the stage to develop biomarkers for psychiatric disorders within a decade? n n The American Psychiatric Association is exploring adjustments to the next iteration of the DSM. What would indicate progress is not just more labels, but to use responsible components to describe and map mental disorders. n n This will also be useful for developing the first major nosology for human intelligence, amid the rise of artificial intelligence. For example, there is no equivalent of a DSM for human intelligence. There is no definition in neuroscience for what human intelligence is, its types, or its mechanisms in the brain. Artificial intelligence is already thriving too fast, so human intelligence would have to evolve into new directions. n n Conceptual gaps in psychiatry and now, the new aspect of neuroscience, intelligence, mean that the possibility for progress entails seeking out processes to match them to labels. n n This is the postulation in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology. n n It looks beyond neurons to electrical and chemical signals. It explores that in clusters of neurons, electrical and chemical signals are in sets, and it is those sets that they mechanize and specify functions. n n Electrical and chemical signals are the most likely options, given empirically supported evidence in neuroscience. Genes are not the mind, nor are glia. n n Human intelligence is another major case, for priority this year, as AI encroaches on more human tasks. It is possible to have this new nosology done for psychiatry and human intelligence before August 31, 2026. n n There is a recent [February 17, 2026. 3:40 PM ET] analysis in The Atlantic, AI Agents Are Taking America by Storm, stating that, “Some academics are testing Claude Code’s ability to autonomously generate papers; others are using agents for biology research. Journalists have been experimenting with Claude Code to write data-driven articles from scratch, and earlier this month, a pair used the bot to create a mock competitor to Monday.com, a public software company worth billions. In under an hour, they had a working prototype. Although the actual quality of all of these AI-generated papers and analyses remains unclear, the progress is both stunning and alarming.”
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