
Scientists Develop New Method to Diagnose Parkinson’s Disease with 97% Accuracy
Researchers from Australia and Kuwait have developed an innovative, non-invasive method for diagnosing Parkinson’s disease with 97% accuracy. By analyzing brain responses to emotional stimuli using electroencephalography (EEG) and artificial intelligence (AI), the team uncovered specific emotional processing patterns in Parkinson’s patients. This method could transform how the disease is detected and treated, offering earlier intervention and improved outcomes.

What Was the Study About?
According to Neuroscience News, the study was conducted by researchers from the University of Canberra in Australia and the Kuwait College of Science and Technology. The results of the study were published in October 2024, in the journal Intelligent Computing in an article titled “Exploring Electroencephalography-Based Affective Analysis and Detection of Parkinson’s Disease.
The researchers wanted to see if they could diagnose Parkinson’s disease by studying how patients process emotions. Here’s how the study was set up, step by step:
The study involved 40 people —20 patients who were diagnosed with Parkinson’s disease and 20 healthy individuals as a comparison group. This balance helped the researchers identify clear differences in brain activity between the two groups.
Participants were shown video clips and images designed to evoke emotional reactions like happiness, fear, sadness, or surprise. For example, they might watch a scene from a dramatic movie or look at an image of something surprising.
While the participants were exposed to these emotional triggers, the researchers used EEG to record their brain activity. EEG is a non-invasive method that involves placing small sensors on the scalp to measure electrical signals in the brain.
After the brain activity was recorded, the data was processed using advanced computer algorithms. Two key features of brain activity were analyzed: spectral power vectors, which measure how the brain’s electrical signals vary across different frequencies, and common spatial patterns, which help identify differences in brain activity between Parkinson’s patients and healthy individuals, making it easier to distinguish the two groups.
The collected EEG data was fed into AI systems, specifically machine learning frameworks like convolutional neural networks. These systems analyzed the patterns in brain activity to find clear markers of Parkinson’s disease. The AI was able to achieve an impressive 97% accuracy in correctly identifying whether a participant had Parkinson’s disease or not.
Parkinson’s patients exhibited unique differences in how they processed emotional stimuli, setting them apart from healthy individuals. These differences provide important insights into the cognitive changes caused by the disease and its impact on emotional recognition.
Difficulty Recognizing Certain Emotions
Patients with Parkinson’s disease particularly struggled to recognize specific emotions such as fear, disgust, and surprise. This difficulty may stem from disruptions in brain areas responsible for emotion processing, such as the amygdala, insula, and prefrontal cortex. These regions play a critical role in identifying and interpreting facial expressions and emotional cues, functions that are impaired as Parkinson’s progresses. For instance, a patient may fail to detect fear in another person’s expression, which could affect their ability to respond appropriately in social or potentially dangerous situations.
Focus on Emotional Intensity Over Valence
Parkinson’s patients also tended to focus more on the arousal or intensity of emotions, rather than their valence—that is, whether the emotion is positive or negative. For example, they might react strongly to a loud or energetic scene, such as an explosion in a movie, without distinguishing whether it evokes joy or fear. This suggests that the disease may disrupt neural pathways that evaluate the overall emotional “tone” of a stimulus, leaving the brain more attuned to its level of excitement or energy instead.
Cognitive aspect
These emotional impairments reflect broader cognitive challenges experienced by Parkinson’s patients, particularly in executive functions like decision-making, attention, and social interaction. Recognizing and interpreting emotions is a key aspect of social cognition, which helps individuals navigate interpersonal relationships and understand social dynamics. For Parkinson’s patients, difficulties in processing emotions can lead to misunderstandings, reduced empathy, and a diminished ability to adapt to social cues.
For example:
- Decision-making: A patient who cannot recognize fear may struggle to assess risks in their environment, such as whether a situation is dangerous or safe.
- Social interactions: Misinterpreting emotional cues can lead to conflicts or strained relationships, as patients may respond inappropriately to others’ feelings.
- Emotional regulation: Difficulty identifying emotions can make it harder for patients to regulate their own emotional responses, leading to frustration or anxiety.
Neural Basis for These Changes
Parkinson’s disease is associated with a loss of dopamine-producing neurons in areas of the brain that are critical for both movement and cognitive functions, such as the basal ganglia and the prefrontal cortex. These regions are closely connected to emotional processing centers like the amygdala, which helps explain why emotional recognition and regulation are affected alongside motor symptoms.
In summary, Parkinson’s disease doesn’t just affect the body—it significantly impacts how the brain processes emotions and handles cognitive tasks related to social interaction and decision-making. Understanding these changes can lead to better support systems for patients and more comprehensive approaches to treatment that address both motor and non-motor symptoms.
Why Was This Method Chosen?
EEG is a safe and widely available tool for measuring brain activity. It doesn’t involve any invasive procedures, like surgery or injections, making it comfortable for patients. By combining EEG with AI, the researchers were able to analyze complex patterns in brain data quickly and accurately, which would be impossible to do manually.
Why Is This Study Important?
This research stands out for several reasons. First, it uses objective brain data rather than relying on subjective observations from doctors or patients. Second, it achieves a much higher accuracy than traditional diagnostic methods. Third, it offers a potential way to detect Parkinson’s disease early, even before noticeable physical symptoms appear. Early detection is crucial for managing the disease effectively.
Key Findings of the Study
High Accuracy in Diagnosis: The method achieved a diagnostic accuracy of 97%, making it one of the most reliable tools for identifying Parkinson’s disease.
Difficulty Recognizing Specific Emotions: Parkinson’s patients struggled to identify emotions like fear, disgust, and surprise. For example, they might watch a suspenseful movie scene and fail to recognize the fear in a character’s expression.
Focus on Emotional Intensity: Patients paid more attention to how “strong” an emotion was rather than whether it was positive or negative. For instance, a loud and energetic scene might feel just as impactful whether it’s happy or scary.
AI Integration for Data Analysis: The use of machine learning allowed researchers to analyze complex brain data quickly and accurately, identifying patterns unique to Parkinson’s patients.
Potential for Early Detection: Emotional processing changes might occur in the early stages of Parkinson’s, even before physical symptoms like tremors appear. This means the method could help diagnose the disease earlier, leading to better outcomes.
How This Relates to Everyday Life
This research has real-world implications for both patients and healthcare providers. For patients, it offers a simpler, less stressful way to get diagnosed. Instead of waiting for symptoms to become severe, people could be diagnosed earlier, potentially slowing disease progression. For doctors, it provides an objective tool that complements their clinical expertise.
Imagine someone in their 50s who begins experiencing subtle changes, like difficulty understanding emotional cues in conversations. Under the traditional approach, they might wait years before receiving a Parkinson’s diagnosis. With this new method, an EEG test could provide clear answers much sooner.
Why This Matters for Science and Society
Advancing Science and Medicine: This study demonstrates the power of combining neuroscience and AI to solve complex medical challenges. It provides a new way to study how Parkinson’s affects the brain, beyond just physical symptoms. The findings also highlight the importance of understanding emotional and cognitive changes in neurological disorders.
Benefits for Patients: The new method could improve patients’ quality of life by enabling earlier diagnosis and treatment. It could also reduce the stress and uncertainty associated with traditional diagnostic methods, which often rely on subjective observations and self-reported symptoms.
Broader Societal Impact: Raising awareness about how Parkinson’s affects emotional and cognitive processes can foster greater understanding and empathy toward those living with the disease. The research also highlights the importance of creating accessible and non-invasive diagnostic tools for all neurological conditions.
Conclusion
This study offers a revolutionary approach to diagnosing Parkinson’s disease by analyzing brain responses to emotional stimuli. Using EEG and AI, researchers achieved near-perfect accuracy, providing a fast, reliable, and non-invasive diagnostic method. As this technology continues to evolve, it holds the potential to transform how Parkinson’s is detected and managed, improving outcomes for millions of patients worldwide.