Transcranial alternating current stimulation, commonly known as tACS, delivers weak electrical currents through electrodes placed on the scalp. Unlike direct-current stimulation, which maintains a relatively constant polarity, tACS oscillates between positive and negative phases at a selected frequency. Researchers use this rhythmic stimulation to influence patterns of neural activity that may be associated with attention, memory, perception, mood or other brain functions. The underlying idea is that external electrical rhythms may interact with the brain’s own oscillations, a phenomenon sometimes described as neural entrainment. Yet the effects of tACS can vary substantially from one person to another because brain anatomy, electrode placement, tissue conductivity and intrinsic neural dynamics differ across individuals.

The new closed-loop strategy attempts to address that variability. In a conventional stimulation experiment, investigators typically choose parameters such as current strength, frequency, electrode arrangement and stimulation duration before the session begins. Those settings may be based on previous research or an anatomical model, but they do not necessarily reflect the participant’s real-time brain state. A closed-loop system, by contrast, measures a biological signal, evaluates whether the desired response is emerging and then uses that information to adjust or optimize the intervention. In this trial, real-time fMRI serves as the measurement component, while tACS provides the controlled input to the brain.

Functional MRI does not record electrical impulses directly. Instead, it detects changes in blood oxygenation through the blood-oxygen-level-dependent, or BOLD, signal. When populations of neurons become more active, local changes in blood flow and oxygen use can alter the magnetic properties of surrounding tissue. These changes allow researchers to map activity across the brain with high spatial resolution. Although the BOLD signal is slower than the underlying neural events, it can reveal where stimulation-related changes are occurring. Using the imaging data during the experiment creates the possibility of identifying whether the brain is responding in the intended region or network and whether the selected stimulation settings are producing a measurable effect.

Combining fMRI and tACS, however, is technically demanding. Electrical stimulation inside an MRI scanner can generate artifacts in the imaging data, while the magnetic environment imposes strict safety and equipment requirements. The stimulation hardware must be designed to operate within the scanner without interfering with image acquisition or creating unacceptable risks. Researchers must also separate genuine physiological changes from signals caused by the stimulation equipment, electrode leads, movement or scanner noise. Real-time analysis adds another layer of complexity because images must be acquired, processed and interpreted quickly enough to inform the next stage of stimulation rather than merely being analyzed after the session is over.

The trial’s randomized design is important because it provides a structured way to compare conditions and assess whether changes are associated with the adaptive stimulation procedure rather than with expectation, repeated scanning or ordinary fluctuations in brain activity. Randomization can help reduce systematic differences between experimental conditions, while a proof-of-concept framework allows investigators to determine whether the full technical pipeline can function in practice. That pipeline includes participant preparation, electrode placement, MRI acquisition, artifact management, real-time signal processing, decision-making and stimulation control. Establishing that these components can work together is a necessary step before larger studies can evaluate clinical effectiveness.

The most significant promise of this approach is personalization. The brain is not a fixed electrical circuit with identical wiring from one person to the next. Even when two participants receive the same stimulation protocol, their brains may respond differently because of variations in skull thickness, cortical folding, network connectivity and baseline oscillatory activity. Real-time fMRI-guided optimization could eventually allow researchers to identify which stimulation parameters are most effective for a particular individual. Instead of assuming that a single frequency or electrode montage will work equally well for everyone, future systems might adjust the intervention according to each participant’s measured neural response.

Such technology could have implications for research into psychiatric and neurological conditions, although the present work should not be interpreted as proof that the system is ready to treat patients. Noninvasive stimulation is being investigated for conditions including depression, anxiety, chronic pain, addiction and cognitive disorders, but results across studies have often been mixed. One reason may be that stimulation protocols are not sufficiently sensitive to individual biology or to changes in brain state during an intervention. A responsive system could help researchers test whether adapting stimulation in real time improves consistency. It could also offer a way to study causal relationships between brain rhythms, distributed neural networks and behavior.

The approach may also change how scientists think about experimental control. Rather than treating the brain as a passive object that receives a predetermined dose of stimulation, closed-loop neuromodulation treats it as a dynamic system that continuously provides feedback. That perspective is already influential in other areas of neuroscience, including deep-brain stimulation and brain-computer interfaces. Applying it to tACS with real-time fMRI is especially ambitious because it combines a relatively accessible form of stimulation with one of the most information-rich tools for measuring human brain function. If refined, the method could help bridge the gap between broad population-level protocols and truly individualized interventions.

For now, the study is best understood as an engineering and methodological milestone rather than a finished therapy. The researchers’ central contribution is to demonstrate the feasibility of linking real-time functional imaging with adaptive transcranial electrical stimulation in a randomized experimental framework. Future investigations will need to determine how reliably the system identifies meaningful neural responses, how long those responses last, whether they translate into changes in behavior or symptoms and whether the approach can be scaled beyond specialized research scanners. The work nevertheless points toward a striking possibility: brain stimulation that does not merely send commands into the nervous system, but listens to the brain and adjusts its strategy in response.

Subject of Research: Closed-loop transcranial alternating current stimulation guided by real-time functional magnetic resonance imaging.

Article Title: Closed-loop transcranial electrical brain stimulation with fMRI: A proof-of-concept randomized trial of real-time fMRI-guided tACS optimization.

Article References: Soleimani, G., Kuplicki, R., Mulyana, B. et al. “Closed-loop transcranial electrical brain stimulation with fMRI: A proof-of-concept randomized trial of real-time fMRI-guided tACS optimization.” Translational Psychiatry (2026). https://doi.org/10.1038/s41398-026-04319-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41398-026-04319-5

Keywords: tACS, transcranial electrical stimulation, functional MRI, fMRI, closed-loop neuromodulation, brain stimulation, neurotechnology, personalized medicine, neuroscience, neural oscillations

Levodopa remains the most effective medication for controlling many of the movement symptoms associated with Parkinson’s disease. After entering the brain, it is converted into dopamine, the chemical messenger that becomes deficient as dopamine-producing neurons degenerate. The medication can improve slowness, rigidity and, in some patients, tremor, but its effects are not always stable. As the disease progresses, the therapeutic window may narrow, meaning that the dose needed to improve movement can approach the dose that causes involuntary movements known as dyskinesias. This fluctuation makes treatment highly individual and creates a need for biological measurements that reveal how the brain responds, rather than relying only on outward symptoms.

Deep brain stimulation, or DBS, offers a unique opportunity to search for those measurements. In DBS, surgeons implant electrodes into carefully selected structures deep within the brain, most commonly the subthalamic nucleus for Parkinson’s disease. A pulse generator then delivers patterned electrical stimulation intended to normalize abnormal neural signaling. The therapy can reduce motor symptoms and lessen dependence on medication, but programming it remains a complex process. Clinicians must choose stimulation contacts, electrical amplitude, pulse width and frequency, often through repeated adjustments over weeks or months. Physiological biomarkers—measurable signals linked to brain or body function—could make this process more precise by showing when stimulation is engaging the intended circuits and how levodopa changes the same signals.

The term “physiomarker” encompasses a broad range of measurable biological features. In Parkinson’s research, these may include neural oscillations recorded from implanted electrodes, muscle activity measured with electromyography, motion captured by wearable sensors, or patterns in a patient’s walking, tremor and muscle tone. One widely studied signal is beta-band activity, a rhythm in the approximate 13-to-30-hertz range that is often associated with motor control and becomes unusually prominent in Parkinson’s disease. Dopamine replacement and DBS can both influence abnormal beta activity, although the relationship is not simple or identical in every patient. By examining levodopa-related changes during stimulation, researchers hope to identify signals that reflect therapeutic benefit, medication state or the risk of unwanted movements.

The importance of studying the two treatments together lies in their overlapping but distinct mechanisms. Levodopa changes the chemical environment of motor circuits by restoring dopamine-related signaling, while DBS changes the electrical dynamics of those circuits through externally delivered pulses. A biomarker that responds to levodopa may not respond in the same way to stimulation, and a signal that reflects improvement under medication may behave differently when DBS is active. Separating these effects could help clinicians determine whether a symptom is best addressed by adjusting a drug dose, changing stimulation settings or combining both approaches. It could also clarify why patients with apparently similar symptoms can require very different treatment strategies.

The study’s focus is particularly relevant to the development of adaptive DBS, sometimes called closed-loop stimulation. Conventional DBS delivers stimulation according to fixed settings, even though a patient’s symptoms and brain state can vary across the day with medication cycles, fatigue, stress, sleep and movement demands. Adaptive systems aim to detect a physiological signal and automatically adjust stimulation in response. For such systems to work safely, researchers must know which biomarkers are reliable, how quickly they change, and whether they represent improvement, medication fluctuations or the emergence of dyskinesia. Levodopa-related physiomarkers could become part of the biological language that future stimulators use to tailor therapy moment by moment.

The research also speaks to a broader shift in neurology: treatment is increasingly being evaluated through objective, continuously collected data. A patient’s report remains essential, but a clinic visit offers only a brief snapshot of a condition that may change substantially throughout the day. Wearable sensors and implanted recording technologies can capture movement and neural activity over longer periods, potentially revealing patterns that are invisible during a conventional examination. If a physiological signal can be consistently linked to levodopa response during DBS, it may eventually support more individualized dosing, improve the interpretation of stimulation effects and reduce the trial-and-error process that currently accompanies advanced Parkinson’s care.

However, biomarkers are not automatically ready for clinical use simply because they are measurable. A useful marker must be reproducible across patients, stable enough to guide decisions and closely connected to outcomes that matter, such as walking, hand function, speech, balance or involuntary movement. Parkinson’s disease is biologically diverse, and the same neural rhythm may carry different information in different people or brain regions. Medication timing also matters: levodopa absorption, metabolism and delayed effects can alter the signals being recorded. Stimulation itself may interfere with sensing, creating technical challenges for devices that must deliver electrical pulses while simultaneously detecting subtle neural activity. These limitations make careful validation essential before a physiomarker can control therapy automatically.

By placing levodopa-related signals at the center of DBS research, de Neeling, Oehrn, Stam and their colleagues contribute to a field seeking a more detailed map of Parkinson’s treatment response. The significance of the work lies not only in any single biomarker, but in the possibility of connecting medication, electrical stimulation and measurable physiology within one framework. Such an approach could help transform DBS from a largely manually programmed therapy into a responsive treatment that adapts to the patient’s changing state. The findings will need to be interpreted alongside larger clinical studies and long-term testing, but the direction is clear: the future of Parkinson’s care may depend on listening to the brain’s signals as carefully as clinicians observe the patient’s movements.

Subject of Research: Levodopa-related physiological biomarkers during deep brain stimulation in Parkinson’s disease

Article Title: Levodopa-related physiomarkers during deep brain stimulation in Parkinson’s disease

Article References: de Neeling, M.G.J., Oehrn, C.R., Stam, M.J. et al. “Levodopa-related physiomarkers during deep brain stimulation in Parkinson’s disease.” npj Parkinson’s Disease (2026). https://doi.org/10.1038/s41531-026-01521-6

Image Credits: AI Generated

DOI: 10.1038/s41531-026-01521-6

Keywords: Parkinson’s disease, levodopa, deep brain stimulation, physiomarkers, biomarkers, adaptive DBS, dopamine, neuromodulation