• For more than a century, Alzheimer’s disease has been widely viewed as permanent and untreatable once it begins. As a result, most research has focused on preventing the disease or slowing its progression rather than attempting to reverse it.
• By studying multiple mouse models of Alzheimer’s alongside human Alzheimer’s brain tissue, researchers identified a critical biological problem at the center of the disease. They found that the brain’s inability to maintain healthy levels of a vital cellular energy molecule called NAD+ plays a major role in driving Alzheimer’s.
• In animal models, maintaining normal brain NAD+ levels prevented Alzheimer’s from developing. Even more striking, restoring NAD+ balance after the disease was already advanced allowed the brain to repair damage and fully restore cognitive function.
• These results suggest that treatments aimed at restoring the brain’s energy balance could potentially move Alzheimer’s therapy beyond slowing decline and toward meaningful recovery.
• The findings also open the door to further research, including the exploration of complementary strategies and carefully designed clinical trials to determine whether these results can translate to patients.

A Longstanding View of Alzheimer’s Is Being Questioned

For more than 100 years, Alzheimer’s disease (AD) has been widely viewed as a condition that cannot be undone. Because of this belief, most scientific efforts have focused on preventing the disease or slowing its progression, rather than attempting to restore lost brain function. Even after decades of research and billions of dollars in investment, no drug trial for Alzheimer’s has ever been designed with the goal of reversing the disease and recovering cognitive abilities.

That long-held assumption is now being challenged by researchers from University Hospitals, Case Western Reserve University, and the Louis Stokes Cleveland VA Medical Center. Their work set out to answer a bold question: can brains already damaged by advanced Alzheimer’s recover?

New Study Targets Brain Energy Failure

The research was led by Kalyani Chaubey, PhD, of the Pieper Laboratory and published on December 22 in Cell Reports Medicine. By examining both human Alzheimer’s brain tissue and multiple preclinical mouse models, the team identified a key biological failure at the center of the disease. They found that the brain’s inability to maintain normal levels of a critical cellular energy molecule called NAD+ plays a major role in driving Alzheimer’s. Importantly, maintaining proper NAD+ balance was shown to not only prevent the disease but also reverse it in experimental models.

NAD+ levels naturally decline throughout the body, including the brain, as people age. When NAD+ drops too low, cells lose the ability to carry out essential processes needed for normal function and survival. The researchers discovered that this decline is far more severe in the brains of people with Alzheimer’s. The same pattern was seen in mouse models of the disease.

How Alzheimer’s Was Modeled in the Lab

Although Alzheimer’s occurs only in humans, scientists study it using specially engineered mice that carry genetic mutations known to cause the disease in people. In this study, researchers used two such models. One group of mice carried multiple human mutations affecting amyloid processing, while the other carried a human mutation in the tau protein.

Amyloid and tau abnormalities are among the earliest and most significant features of Alzheimer’s. In both mouse models, these mutations led to widespread brain damage that closely mirrors the human disease. This included breakdown of the blood-brain barrier, damage to nerve fibers, chronic inflammation, reduced formation of new neurons in the hippocampus, weakened communication between brain cells, and extensive oxidative damage. The mice also developed severe memory and cognitive problems similar to those seen in people with Alzheimer’s.

Testing Whether Alzheimer’s Damage Could Be Reversed

After confirming that NAD+ levels dropped sharply in both human and mouse Alzheimer’s brains, the team explored two possibilities. They tested whether maintaining NAD+ balance before symptoms appeared could prevent Alzheimer’s, and whether restoring that balance after the disease had already progressed could reverse it.

This approach built on the group’s earlier work published in Proceeding of the National Academy of Sciences USA, which showed that restoring NAD+ balance led to both structural and functional recovery after severe, long-lasting traumatic brain injury. In the current study, the researchers used a well-characterized pharmacologic compound called P7C3-A20, developed in the Pieper laboratory, to restore NAD+ balance.

Full Cognitive Recovery Observed in Advanced Disease

The results were striking. Preserving NAD+ balance protected mice from developing Alzheimer’s, but even more surprising was what happened when treatment began after the disease was already advanced. In those cases, restoring NAD+ balance allowed the brain to repair the major pathological damage caused by the genetic mutations.

Both mouse models showed complete recovery of cognitive function. This recovery was also reflected in blood tests, which showed normalized levels of phosphorylated tau 217, a recently approved clinical biomarker used to diagnose Alzheimer’s in people. These findings provided strong evidence of disease reversal and highlighted a potential biomarker for future human trials.

Researchers Express Cautious Optimism

“We were very excited and encouraged by our results,” said Andrew A. Pieper, MD, PhD, senior author of the study and Director of the Brain Health Medicines Center, Harrington Discovery Institute at UH. “Restoring the brain’s energy balance achieved pathological and functional recovery in both lines of mice with advanced Alzheimer’s. Seeing this effect in two very different animal models, each driven by different genetic causes, strengthens the idea that restoring the brain’s NAD+ balance might help patients recover from Alzheimer’s.”

Dr. Pieper also holds the Morley-Mather Chair in Neuropsychiatry at UH and the CWRU Rebecca E. Barchas, MD, DLFAPA, University Professorship in Translational Psychiatry. He serves as Psychiatrist and Investigator in the Louis Stokes VA Geriatric Research Education and Clinical Center (GRECC).

A Shift in How Alzheimer’s Is Viewed

The findings suggest a fundamental change in how Alzheimer’s could be approached in the future. “The key takeaway is a message of hope — the effects of Alzheimer’s disease may not be inevitably permanent,” said Dr. Pieper. “The damaged brain can, under some conditions, repair itself and regain function.”

Dr. Chaubey added, “Through our study, we demonstrated one drug-based way to accomplish this in animal models, and also identified candidate proteins in the human AD brain that may relate to the ability to reverse AD.”

Why This Approach Differs From Supplements

Dr. Pieper cautioned against confusing this strategy with over the counter NAD+-precursors. He noted that such supplements have been shown in animal studies to raise NAD+ to dangerously high levels that promote cancer The method used in this research relies instead on P7C3-A20, a pharmacologic agent that helps cells maintain healthy NAD+ balance during extreme stress, without pushing levels beyond their normal range.

“This is important when considering patient care, and clinicians should consider the possibility that therapeutic strategies aimed at restoring brain energy balance might offer a path to disease recovery,” said Dr. Pieper.

Next Steps Toward Human Trials

The research also opens the door to additional studies and eventual testing in people. The technology is currently being commercialized by Glengary Brain Health, a Cleveland-based company co-founded by Dr. Pieper.

“This new therapeutic approach to recovery needs to be moved into carefully designed human clinical trials to determine whether the efficacy seen in animal models translates to human patients,” Dr. Pieper explained. “Additional next steps for the laboratory research include pinpointing which aspects of brain energy balance are most important for recovery, identifying and evaluating complementary approaches to Alzheimer’s reversal, and investigating whether this recovery approach is also effective in other forms of chronic, age-related neurodegenerative disease.”

In our new paper, we propose a different approach: biological computationalism. The label is meant to be provocative, but also to sharpen the conversation. Our main argument is that the standard computational framework is broken, or at least poorly suited to how brains actually work. For a long time, it has been tempting to picture the mind as software running on neural hardware, with the brain “computing” in roughly the way a conventional computer does. But real brains are not von Neumann machines, and forcing that comparison leads to shaky metaphors and fragile explanations. If we want a serious account of how brains compute, and what it would take to build minds in other substrates, we first need a broader definition of what “computation” can be.

Biological computation, as we describe it, has three core features.

Hybrid Brain Computation in Real Time

First, biological computation is hybrid. It mixes discrete events with continuous dynamics. Neurons fire spikes, synapses release neurotransmitters, and networks shift through event-like states. At the same time, these events unfold within constantly changing physical conditions such as voltage fields, chemical gradients, ionic diffusion, and time-varying conductances. The brain is not purely digital, and it is not simply an analog machine either. Instead, it works as a multi-layered system where continuous processes influence discrete events, and discrete events reshape the continuous background, over and over, in an ongoing feedback loop.

Why Brain Computation Cannot Be Separated by Scale

Second, biological computation is scale-inseparable. In conventional computing, it is often possible to cleanly separate software from hardware, or a “functional level” from an “implementation level.” In the brain, that kind of separation breaks down. There is no neat dividing line where you can point to the algorithm on one side and the physical mechanism on the other. Cause and effect run across many scales at once, from ion channels to dendrites to circuits to whole-brain dynamics, and these levels do not behave like independent modules stacked in layers. In biological systems, changing the “implementation” changes the “computation,” because the two are tightly intertwined.

Metabolism and Energy Constraints Shape Intelligence

Third, biological computation is metabolically grounded. The brain operates under strict energy limits, and those limits shape its structure and function everywhere. This is not just an engineering detail. Energy constraints influence what the brain can represent, how it learns, which patterns remain stable, and how information is coordinated and routed. From this perspective, the tight coupling across levels is not accidental complexity. It is an energy optimization strategy that supports robust, flexible intelligence under severe metabolic limits.

The Algorithm Is the Substrate

Taken together, these three features point to a conclusion that can feel strange if you are used to classical computing ideas. Computation in the brain is not abstract symbol manipulation. It is not simply about moving representations around according to formal rules while the physical medium is treated as “mere implementation.” In biological computation, the algorithm is the substrate. The physical organization does not just enable the computation, it is what the computation consists of. Brains do not merely run a program. They are a specific kind of physical process that computes by unfolding through time.

What This Means for AI and Synthetic Minds

This view also exposes a limitation in how people often describe modern AI. Even powerful systems mostly simulate functions. They learn mappings from inputs to outputs, sometimes with impressive generalization, but the computation is still a digital procedure running on hardware built for a very different style of computing. Brains, by contrast, carry out computation in physical time. Continuous fields, ion flows, dendritic integration, local oscillatory coupling, and emergent electromagnetic interactions are not just biological “details” that can be ignored while extracting an abstract algorithm. In our view, these are the computational primitives of the system. They are the mechanisms that enable real-time integration, resilience, and adaptive control.

Not Biology Only, But Biology Like Computation

This does not mean we think consciousness is somehow restricted to carbon-based life. We are not arguing “biology or nothing.” Our claim is narrower and more practical. If consciousness (or mind-like cognition) depends on this kind of computation, then it may require biological-style computational organization, even if it is built in new substrates. The key issue is not whether the substrate is literally biological, but whether the system instantiates the right kind of hybrid, scale-inseparable, metabolically (or more generally energetically) grounded computation.

A Different Target for Building Conscious Machines

That reframes the goal for anyone trying to build synthetic minds. If brain computation cannot be separated from how it is physically realized, then scaling digital AI alone may not be enough. This is not because digital systems cannot become more capable, but because capability is only part of the puzzle. The deeper risk is that we may be optimizing the wrong thing by improving algorithms while leaving the underlying computational ontology unchanged. Biological computationalism suggests that building truly mind-like systems may require new kinds of physical machines whose computation is not organized as software on hardware, but spread across levels, dynamically linked, and shaped by the constraints of real-time physics and energy.

So if we want something like synthetic consciousness, the central question may not be, “What algorithm should we run?” It may be, “What kind of physical system must exist for that algorithm to be inseparable from its own dynamics?” What features are required, including hybrid event-field interactions, multi-scale coupling without clean interfaces, and energetic constraints that shape inference and learning, so that computation is not an abstract description layered on top but an intrinsic property of the system itself?

That is the shift biological computationalism calls for. It moves the challenge from finding the right program to finding the right kind of computing matter.