There are two major divisions of neuroscience: psychiatry and neurology. There is a new division however, human intelligence, given the ascent of artificial intelligence.
Neurology, though extremely complex has several anatomic nodes that makes showing and explaining what is wrong to be a problem with a direction, in some cases.
Neurology is unsolved. But at least, how to go after neurodegenerative diseases and others them have a less opaque map.
Psychiatry is different. While there are several anatomic references, even knowing what exactly is wrong is difficult. Labels name conditions. Description define or explain them like a dictionary and that’s it. Little about responsible components of the brain or even their mechanisms.
Descriptions are the basis of surveys, observations, assessments or questionnaires used in diagnosis in psychiatry. Descriptions and labels are obtained across The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision [DSM-5-TR].
There are no tests for psychiatric disorders. There are no biomarkers. The American Psychiatric Association is exploring some options that include “Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP), Genetic Markers, Immune Markers, Neurocircuitry.”
However, the jump is not even to biomarkers at least, the transition is to explain mental disorders by components of the brain. So, if any of the options can explain delusion, without mentioning false beliefs that would be an extreme leap. But it does not appear like any of the options would be able to do so.
The equivalent of what could be the millennium prize problem in neuroscience is to explain the brain by electrical and chemical signals.
Simply, given all the evidence from studies across neuroscience over decades, especially with advanced neuroimaging techniques, compute and facilities, electrical and chemical signals have been proven to be involved across all functions of mind.
Neurons are established to be responsible when the brain does [mind] functions for human life and experiences. But neurons do so with their electrical and chemical signals. This is empirically-supported. However, no lab has been able to explain mental health or disorders by neurons.
This is in part because neurons are cells. They are not that flexible to act in one way for a memory and differently for another memory, given the large number of memories, then feelings, emotions and regulations of internal senses.
So, modeling neurons is not just unlikely, it almost breaks the laws of physics, to do so to explain the mind. Now, the next option are electrical and chemical signals. They are flexible, abundant, and varying enough to mechanize functions.
It is what they are that can be used to explain mental disorders, conceptually. So, the opportunity is not even about getting to biomarkers for now, but about moving from the description of labels, to what can be displayed, as components and mechanisms of the brain to explain mental disorders. How?
It is postulated that electrical and chemical signals interact, in sets, to specify functions.
Neuroscience has established that neurons are in clusters. It is theorized that in those clusters electrical and chemical signals are in sets or in loops. It is where they make specific interactions, to define one function from the next.
But what makes their interactions distinct? It is postulated that they have states at the time of the interactions that determine how much they interact. These states are sometimes ubiquitous, while others are special to an area.
So, electrical and chemical signals interact, their interactions have attributes or states that grade, measure or qualify how much they interact.
It is the attributes that can be used to display and explain mental disorders, advancing neuroscience, for psychiatry. If this is done, it is also possible to use it to advance neurology especially against neurodegeneration.
Electrical and chemical signals can also be used to explain how human intelligence works in the brain. Stretching for new ways to improve problem-solving with human intelligence, as AI soars.
The postulate is detailed in Conceptual Biomarkers and Theoretical Biological Factors for Psychiatric and Intelligence Nosology.
The human mind is theorized to be the collection of all the electrical and chemical signals, in sets, with their interactions and features, in clusters of neurons across the central and peripheral nervous systems.
Interactions are principally the strikes of electrical signals on chemical signals, in sets. Features include prioritization, pre-prioritization, splits, old sequences, new sequences, principal spot, arrays, thick sets, thin sets, intensity and so on.
These attributes can be used to explain all mental disorders, conceptually.
This is the direction for the future of neuroscience, which would be something to pursue, far better than connectomics, obtaining a use case as early as October 31, 2026.
There is a recent [September 8, 2026] analysis on Quanta Magazine, AI Has Solved One of Math’s $1 Million Millennium Prize Problems, stating that, “On the morning of Tuesday, September 8, mathematicians at OpenAI announced that a group of 10,000 autonomous AI agents under their direction, running on an advanced model not available to the public, had found a “singularity” in the Navier-Stokes equations in three dimensions — thus resolving one of the six remaining(opens a new tab) Millennium Prize Problems posed in 2000 by the Clay Mathematics Institute, each of which carries a $1 million prize. Their result has been formally checked in the programming language Lean, giving mathematicians confidence that it is indeed correct.”
“If the result holds up to further scrutiny, it is, by a significant margin, the most important mathematical proof to have been arrived at by an artificial-intelligence model to date, possibly marking a fundamental turning point in how mathematicians tackle difficult problems.”
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