DATE: August 12, 2025 at 04:00PM
SOURCE: PSYPOST.ORG

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TITLE: Machine learning helps tailor deep brain stimulation to improve gait in Parkinson’s disease

URL: https://www.psypost.org/machine-learning-helps-tailor-deep-brain-stimulation-to-improve-gait-in-parkinsons-disease/

A team of researchers at the University of California, San Francisco has developed a data-driven method for optimizing deep brain stimulation (DBS) settings that significantly improved walking performance in people with Parkinson’s disease. Published in the journal npj Parkinson’s Disease, the study used wearable sensors and implanted neural recording devices to analyze how different DBS settings affected walking, then applied machine learning to identify individualized stimulation parameters that enhanced gait. The results indicate that tailored DBS configurations can improve walking stability and speed, and suggest specific brain activity patterns linked to better mobility.

Parkinson’s disease is a progressive neurological condition that affects movement control. It arises from the loss of dopamine-producing neurons in the brain, leading to symptoms such as tremors, stiffness, slowness, and impaired balance. Gait disturbances—such as short shuffling steps, poor coordination, and freezing episodes—are among the most disabling symptoms, especially in later stages of the disease.

DBS is a surgical treatment in which electrodes are implanted in specific areas of the brain, typically the basal ganglia. These electrodes deliver electrical impulses to regulate abnormal brain activity. While DBS can be highly effective at reducing tremors and stiffness, its effects on walking tend to be inconsistent. This variability is partly due to the complexity of walking as a behavior, but also due to the lack of standardized methods for fine-tuning stimulation settings for gait.

DBS programming typically focuses on improving limb-related motor symptoms and is often conducted while the patient is seated. However, walking requires coordination across multiple brain regions, and existing programming practices do not consistently address gait. Complicating matters further, clinicians must choose from a wide range of stimulation parameters—amplitude, frequency, and pulse width—without clear guidance on how these settings affect walking behavior or neural dynamics.

The research team, led by Hamid Fekri Azgomi and Doris D. Wang, sought to overcome this challenge by designing a framework that integrates behavioral and neural data to guide DBS programming specifically for walking. Their goal was to uncover how different stimulation settings influenced both movement and underlying brain activity, and to create a predictive model that could identify optimal settings tailored to each individual.

“Gait disturbances are among the most disabling symptoms of Parkinson’s disease, severely affecting patients’ mobility, independence, and quality of life. While DBS has proven effective for alleviating other motor symptoms such as tremors and bradykinesia, its impact on gait remains unclear and inconsistent, making it challenging to determine optimal DBS settings for walking improvement,” said Wang, a functional neurosurgeon and an associate professor at UCSF and a faculty member in the UCSF–UC Berkeley Joint Graduate Program in Bioengineering.

“Recent advances in neurotechnology, including devices that can record brain activity while delivering stimulation in real time, have opened new opportunities to study the neural mechanisms underlying DBS. We launched this research to better understand how specific DBS parameters influence the brain circuits involved in walking. Our goal was to identify personalized stimulation settings that can help improve walking for individual patients with Parkinson’s disease.”

The study involved three people with Parkinson’s disease who had undergone DBS implantation targeting the globus pallidus (a part of the basal ganglia). Each participant also had electrodes placed over the motor cortex, a brain region involved in voluntary movement. These devices allowed simultaneous stimulation and recording of brain signals during walking tasks.

To capture the full range of motor behavior, participants were equipped with wearable sensors that tracked step length, stride speed, variability, and arm swing while walking in loops on a 6-meter track. The researchers developed a new measure called the Walking Performance Index (WPI), which combined these gait features into a single score that reflected overall walking ability.

The research team then systematically varied the DBS settings, adjusting amplitude, frequency, and pulse width within clinically safe ranges. For each configuration, participants completed walking trials while their brain activity and motion data were recorded. Subjective ratings from both patients and physical therapists were also collected.

A machine learning algorithm called a Gaussian Process Regressor was used to model the relationship between the stimulation parameters and WPI. This approach allowed the researchers to predict which combinations of settings would likely produce the best walking performance, even without testing every possible configuration. New predictions were tested in follow-up sessions and used to refine the model.

Each participant showed distinct patterns of gait improvement under different DBS configurations. The model successfully identified personalized settings that improved walking beyond what was achieved with standard clinical settings. One participant experienced an 18% improvement in walking performance with the new settings, while others showed smaller but meaningful gains.

“One surprising finding was the level of variability in how different DBS settings influenced gait. We expected some variation, but the extent to which each individual responded differently to specific stimulation patterns highlighted just how complex and patient-specific gait control is in Parkinson’s disease.”

In addition to behavioral data, the researchers analyzed neural signals from the brain during walking. They found that reductions in beta-band activity (a type of brain rhythm between 12–30 Hz) in the globus pallidus were consistently associated with better walking. These reductions were especially pronounced during specific phases of the gait cycle, such as when the opposite leg was bearing weight.

“The discovery that certain neural features were consistently associated with improved walking performance helped validate the potential of using brain signals to guide DBS therapy. These findings challenged the traditional one-size-fits-all approach and reinforced the importance of letting the brain guide therapy, highlighting the need for personalized, adaptive stimulation strategies informed by each patient’s neural activity.”

Importantly, while the precise patterns of improvement varied across individuals, the machine learning model was able to adapt to each person’s unique neural and motor responses. In follow-up periods, one participant voluntarily used the model-recommended settings for several hours each day, demonstrating the practicality of implementing these changes outside the lab.

The study highlights the importance of personalized DBS programming for addressing gait disturbances in Parkinson’s disease. Standard programming practices may not account for the complex and individual-specific brain dynamics involved in walking. By using real-time brain recordings and data-driven modeling, this approach offers a path toward more effective and targeted treatments.

“The most important takeaway is that brain stimulation for Parkinson’s disease can, and should, be personalized. Our study shows that gait improvement is not just about turning DBS on or off, but about finding the right settings designed to everyone’s brain activity and walking patterns.”

“By modeling the relationship between stimulation parameters, neural activity, and gait performance, we demonstrate the potential for data-driven, individualized DBS therapies that go beyond standard approaches. This opens the door to more precise and effective treatment strategies, particularly for challenging symptoms like gait dysfunction that have not responded consistently to conventional DBS. This work lays the foundation for adaptive DBS systems that adjust therapy in real time, based on how the patient’s brain and body respond, bringing us one step closer to intelligent neuromodulation in everyday care.”

The study was limited by its small sample size—only three participants were included. While the results are promising, larger studies are needed to confirm the findings and assess how well this approach generalizes to more diverse patient populations. In addition, the study focused on straight walking; future work should explore how turning, freezing, and obstacle navigation respond to tailored stimulation.

“While our results are promising and highlight the potential of personalized DBS to improve gait in Parkinson’s disease, it is important to recognize that this study was conducted in a small cohort of patients. These findings do not imply that a single DBS setting will universally restore gait. Instead, the takeaway is that data-driven, individualized approaches, grounded in both neural signals and behavioral metrics, can offer a more systematic and responsive way to optimize therapy.”

The researchers hope to expand their work by incorporating longer walking trials, larger datasets, and more advanced algorithms. They envision future DBS systems that use neural biomarkers and machine learning to continuously adapt stimulation in real-time, improving mobility throughout the day. This could dramatically reduce the burden on patients and clinicians during programming sessions and improve quality of life for people with Parkinson’s disease.

“We hope this work lays the foundation for intelligent, adaptive DBS systems that continuously adjust therapy based on a patient’s real-time brain and movement signals. The identified gait-optimized DBS settings have the potential to inform future adaptive DBS designs and move clinical practice beyond static programming toward closed-loop systems that respond to each individual’s dynamic needs throughout the day. Additionally, the neural features associated with improved walking performance could support clinicians during programming visits, making the process more efficient and objective. Similar data-driven approaches could be extended to optimize treatment for a broader range of motor and non-motor symptoms.”

“This study represents a collaborative effort between clinicians, engineers, and neuroscientists, highlighting the value of interdisciplinary work in advancing personalized neuromodulation therapies. Our findings offer a step toward more adaptive DBS systems for gait dysfunction and illustrate the power of integrating neural signals, behavioral metrics, and machine learning to tailor treatments to individual needs.”

The study, “Modeling and optimizing deep brain stimulation to enhance gait in Parkinson’s disease: personalized treatment with neurophysiological insights,” was authored by Hamid Fekri Azgomi, Kenneth H. Louie, Jessica E. Bath, Kara N. Presbrey, Jannine P. Balakid, Jacob H. Marks, Thomas A. Wozny, Nicholas B. Galifianakis, Marta San Luciano, Simon Little, Philip A. Starr, and Doris D. Wang.

URL: https://www.psypost.org/machine-learning-helps-tailor-deep-brain-stimulation-to-improve-gait-in-parkinsons-disease/

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DATE: August 12, 2025 at 02:00PM
SOURCE: PSYPOST.ORG

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TITLE: Larger social networks associated with reduced dementia risk

URL: https://www.psypost.org/larger-social-networks-associated-with-reduced-dementia-risk/

A systematic review of studies on dementia and social networking found that individuals in better cognitive health tend to have larger and more integrated networks of social contacts. Those with more extensive social networks were more likely to demonstrate cognitive resilience and less likely to develop dementia. The paper was published in Neuroscience.

Dementia is a condition marked by a significant decline in cognitive abilities that interferes with daily life. There are different types of dementia, but the most common is Alzheimer’s disease. People with dementia typically experience memory loss, particularly for recent events, while older memories often remain clearer for longer. They also tend to have difficulty with language, problem-solving, and reasoning. Changes in mood, personality, and behavior are common as the condition progresses.

Dementia results from damage to brain cells, which disrupts communication between them. Although it occurs more frequently in older adults, it is not considered a normal part of aging. Some types of dementia are caused by treatable conditions such as vitamin deficiencies or thyroid problems and can be reversed if detected early. However, most types are progressive and incurable, so treatment focuses on symptom management, slowing cognitive decline, and supporting both patients and caregivers.

Study author Faheem Arshad and his colleagues aimed to integrate findings from studies examining the relationship between social networking and dementia. Previous research has suggested that social networking—defined as the recognition and maintenance of meaningful social connections—may offer protection against cognitive decline. In other words, individuals with richer social networks were less likely to develop dementia. Components of these networks include marital status, the number of people one is in contact with, the frequency of those interactions, satisfaction with those relationships, and perceived support.

The authors analyzed the results of 17 studies published between 2000 and 2024. The mean age of participants ranged from 40 to 90 years. Six studies were conducted in the United States, three in Germany, two in the United Kingdom, and one each in China, France, Sweden, Ireland, Iceland, and India. Thirteen of the studies included follow-up periods ranging from one to fifteen years. In total, the review synthesized data from 20,678 participants.

The studies consistently reported that individuals with poorer social networks were more likely to develop dementia. In contrast, those with larger and more integrated social networks were less likely to be diagnosed with dementia during the follow-up periods.

Four longitudinal studies examined how qualities of social networks at a baseline time point were associated with cognitive decline or dementia onset over time. These studies found higher rates of cognitive decline among participants with smaller or less integrated networks. Some studies also reported that individuals with better social networks tended to show healthier brain structures over time. This was especially evident in the amygdala, a brain region involved in emotion and social behavior, which appeared to be better preserved in those with more robust social networks.

“Our systematic review suggests a strong association between poor SN [social networking] and increased risk of dementia and cognitive decline, especially in AD [Alzheimer’s disease] patients. Hence, larger, more integrated social networks contribute to cognitive resilience and reduced disease severity,” the study authors concluded.

The study contributes to the scientific knowledge about dementia. However, it should be noted that the design of the studies included in this analysis does not allow any definitive causal inferences to be derived. While it is possible that good social networks protect from dementia, it is also possible that people in better cognitive health are better able to maintain social contacts producing the observed associations.

The paper, “Association between social networking and dementia: A systematic review of observational studies,” was authored by Faheem Arshad, Deenadayalan Boopalan, Sonali Arora, Howard J. Rosen, and Suvarna Alladi.

URL: https://www.psypost.org/larger-social-networks-associated-with-reduced-dementia-risk/

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DATE: August 12, 2025 at 12:00PM
SOURCE: PSYPOST.ORG

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TITLE: New research reveals what makes self-forgiveness possible or out of reach

URL: https://www.psypost.org/new-research-reveals-what-makes-self-forgiveness-possible-or-out-of-reach/

Some people struggle to forgive themselves because they remain stuck in cycles of guilt, shame, and self-condemnation that threaten their sense of identity and agency, according to a study published in Self & Identity.

Why do some people find it nearly impossible to forgive themselves, even years after a mistake? While self-forgiveness is considered a key step in emotional healing, the path to forgiving oneself can be deeply complex. Researchers have found that excessive guilt and shame are linked to many forms of psychological distress, including depression and anxiety, but the actual experience of being “stuck” in self-blame has been understudied. In the current research, Lydia Woodyatt and colleagues fill this gap by exploring the lived experiences of people who either had or had not been able to forgive themselves for a perceived wrongdoing.

The authors were particularly interested in a theoretical tension between two basic psychological needs: agency (the ability to control and influence one’s life) and social-moral identity (the need to see oneself as a good person). Previous studies suggest that failing to meet either of these needs can block self-forgiveness. For instance, people may avoid taking responsibility in order to protect their moral identity, but doing so can leave them feeling powerless. Conversely, accepting full responsibility may preserve a sense of agency but lead to overwhelming shame. To understand how these conflicting needs play out in real life, the authors adopted a qualitative, narrative-based approach.

The researchers recruited a diverse community sample of 80 adults across the United States using Amazon Mechanical Turk. Participants were prompted to recall and describe either a time they were able to forgive themselves for wrongdoing, or a time they were unable to do so. Of the final group, 41 participants described being unable to forgive themselves, while 39 described being able to. The events spanned a range of contexts, including interpersonal betrayals, personal failures, harm to others, and more. The authors also collected demographic information such as age (21-79 years), gender (53.8% female), and ethnicity (68.8% White).

Participants answered a series of open-ended questions, including prompts such as why they felt the need to forgive themselves, what strategies they used to try to do so, and how they felt about the event now. The average response time was longer for those unable to self-forgive (10 minutes) compared to those who had self-forgiven (7 minutes), suggesting greater cognitive and emotional complexity. Researchers conducted a reflexive thematic analysis, supplemented by inter-rater reliability checks, to identify key psychological patterns in the responses.

Four central themes emerged. First, participants who could not forgive themselves often described the event as if it were still happening. The past felt vividly present and replayed in their minds with intense emotional weight. These individuals remained “stuck,” feeling as if they had not moved forward in life. In contrast, those who had self-forgiven emphasized a shift in focus toward the future. They still acknowledged regret, but no longer felt consumed by it, and described an active decision to release the emotional hold of the past.

Second, issues of personal agency emerged as a defining feature. Those unable to self-forgive frequently alternated between feeling responsible and trying to deny or downplay their role. This tension was especially acute when the wrongdoing involved caring for others, such as in cases of parental regret, accidental harm, or even after being victimized themselves. In contrast, those who were able to forgive themselves accepted both responsibility and their human limitations. For them, self-forgiveness did not mean letting themselves off the hook but recognizing what they could and could not control.

A third theme involved social-moral identity. Participants who remained stuck in self-condemnation often described feeling incompatible with their own moral self-image. They questioned whether they were “good” people and sometimes resorted to self-punishment to reinforce this internal conflict. Meanwhile, participants who had forgiven themselves tended to accept that being a good person could coexist with having made mistakes. They reframed the experience as a lesson and sometimes even used it to recommit to important values, such as being a better parent or friend.

Finally, coping strategies differed between the two groups. Those who had not self-forgiven typically used avoidance, trying to distract themselves or suppress painful thoughts. While this offered short-term relief, it often prolonged emotional distress. On the other hand, those who had forgiven themselves described a painful but productive process of “working through” their guilt. This included allowing themselves to feel the full emotional impact of their actions, talking with others, and making meaning from the event. For them, the goal wasn’t just to feel better, but to understand themselves better.

The authors note that their analysis was shaped by their own theoretical lenses and that participant responses may have been influenced by how questions were framed. The sample was also limited to English-speaking U.S. adults, which may not capture cultural variations in guilt, shame, and forgiveness.

This study highlights that self-forgiveness often requires navigating complex psychological tensions and engaging deeply with one’s values, emotions, and identity. By understanding these conflicts, clinicians and researchers can better support individuals who are stuck in self-blame.

The research, “What makes self-forgiveness so difficult (for some)? Understanding the lived experience of those stuck in self-condemnation,” was authored by Lydia Woodyatt, Melissa de Vel-Palumbo, Anna Barron, Christiana Harous, Michael Wenzel, and Shannon de Silva.

URL: https://www.psypost.org/new-research-reveals-what-makes-self-forgiveness-possible-or-out-of-reach/

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