Monique from the Parkinson’s Association - The Patient Perspective

Before the actual conference started, we as students got a nice, spontaneous presentation from Monique, which was not actually scheduled for that day. Her presentation was about the image/stigma around Parkinson's disease. She explained that Parkinson's disease is mostly depicted as an old man who cannot move properly due to tremors. She stated that this is most of the time not the case. People only see the top of the iceberg of the symptoms of Parkinson's and not the “unseen” symptoms. Furthermore, she stated that Parkinson's patients are sensitive to the placebo effect. We can improve the lives of people with Parkinson’s if we can unlock the full potential of their minds

Sophia Gimple - Decision-making

An interesting presentation about decision-making was given by Sophia Gimple, who was at that moment a PhD student under the supervision of Christian Herff, Mark Janssen, and Yasin Temel. She let patients spin a wheel with different “prices,” and next the patients had to rate (from -5 to 5) how satisfied they were with their reward. She found a correlation between apathy and impulsivity. Impulsivity showed a strong relation with the maximum rating and with the number of times the button for “spinning the wheel” was pressed.

Keynote speaker Melvyn Roerdink - Augmented-Reality Treatment for

People with Parkinson’s Disease

Melyn Roerdink gave a very interesting presentation on the use of augmented reality in the treatment of Parkinson’s patients. One of the patients who tested and benefited from these augmented reality treatments was present at the conference. He demonstrated how cues (projected digital objects) can enhance therapy for Parkinson’s patients by helping them move more effectively and reducing freezing episodes. He went on to develop these cues into interactive games that patients can play. This innovative approach to Parkinson’s treatment offers an inspiring new perspective.

Meagan Hannemann - A Longitudinal Ultra-high field Imaging Study in Parkinson’s Disease

This was a PhD presentation on the early identification of MRI changes in Parkinson’s patients, conducted under the supervision of Mark Kuijf and David Linden. The purpose of the study was to correlate MRI characteristics with clinical phenotypes and the progression of symptoms. It was a longitudinal observational study with assessments conducted at baseline, after 2 years, and again after 4 years. It was interesting to see how a PhD pitch is presented and to learn more about the mechanisms behind such a longitudinal study.

Ricardo Loução - Cologne’s Status Quo

This was a presentation given by Ricardo Loução about neurofeedback and how this could improve the use of DBS for Parkinson Disease. Neurofeedback could give researchers information about the neural circuits that are involved in Parkison disease. They project analysed brain images of Parkison patients. Their finding was that more modulation of these neural circuits cause better outcomes for the patients. 

Alessandro Bongioanni - Dynamic Connectivity States during Resting State fMRI for Neurofeedback

Dynamic connectivity states during resting state fMRI for neurofeedback: This presentation explores how dynamic functional connectivity states during resting-state fMRI can be leveraged for neurofeedback in Parkinson’s disease. It examines how deep brain stimulation (DBS) broadly modulates cortical activity and how neurofeedback targets may be defined at different levels, from single brain regions to whole-brain connectivity and clinical symptoms. Data from three major sources are analyzed: the PPMI cohort of sporadic and genetic PD patients with controls, the Prague dataset of DBS patients scanned with stimulation ON and OFF, and the Toronto dataset with alternating DBS ON/OFF conditions. Results compare static and dynamic connectivity patterns across these datasets, highlighting multiple brain states and their modulation by DBS. The ongoing work also investigates correlations with genetic factors and aims to establish cross-dataset benchmarks for neurofeedback and treatment strategies.

Guillaume Carey - Advanced Structural MRI of Parkinson’s Disease related Anxiety at 7T

This presentation investigates advanced structural MRI methods at 7T to study anxiety in Parkinson’s disease beyond traditional volumetric measures. Standard volume analyses often miss early and subtle changes, so multimodal approaches are applied, including assessments of iron deposition, shape, neuromelanin signal, texture, and tractometry. The work highlights alterations in thalamic and midbrain nuclei linked to PD-related anxiety, suggesting that reduced thalamic volume and other microstructural changes contribute to symptoms. Data from the TRACK-PD study are used to examine early PD (<3 years from diagnosis), where these advanced metrics reveal abnormalities invisible to conventional volumetry. The conclusion emphasizes the value of integrative imaging approaches and longitudinal data to develop more reliable biomarkers for anxiety and broader non-motor symptoms in Parkinson’s disease.

Halim Ibrahim Baqapuri - A Randomized Controlled Trial of fMRI-based Neurofeedback for Motor Symptoms of Parkinson’s Disease

Halim Ibrahim Baqapuri presented the MOTOR-NF trial, which tests fMRI-based neurofeedback for improving motor symptoms in Parkinson’s patients. The study uses individual MRI brain mapping to let patients practice controlling motor regions like the putamen and supplementary motor area. Early results showed significant activation changes in these areas, and people who used “high-energy motion” strategies had the best neurofeedback outcomes. Patients shared that practicing at home felt very different from inside the scanner, and staying awake during sessions was sometimes a challenge. Recruitment is ongoing, with promising early results and the study under editorial review.

Keynote speaker Mark Janssen - Lessons learned from Intra-operative

Micro-electrode Recordings

Mark Janssen’s talk discussed how intra-operative micro-electrode recordings (MER) during DBS at Maastricht UMC help improve targeting and clinical outcomes for Parkinson’s patients. He shared a patient example where left and right STN signals differed, impacting therapy adjustments. MER data—like peak frequency and response thresholds—are now used with closed-loop DBS devices to fine-tune stimulation in real time, even as patient age or disease severity changes signal properties. The technique is safe, adds patient-specific insight, and continues to guide new technology for better treatment.​

Keynote speaker Robert Jech - Neuroimaging of DBS

Robert Jech presented about fMRI connectomics in Parkinson’s disease at the conference in Maastricht. He explained how advanced brain imaging shows how DBS (deep brain stimulation) changes large-scale brain network connectivity for patients. Highlights included new methods for mapping functional connections—like eigenvector centrality and seed-based analyses—which link network changes to clinical motor outcomes. His work found that increased connectivity in key motor and frontal regions correlated with better movement scores and DBS helped normalize activity patterns to resemble healthy controls. These techniques help guide future treatments and improve DBS programming for each patient.

Simone Nicola - Brain Connectomics Approach and Parkinson disease

The presentation outlined how brain connectomics and Network Control Theory can shed new light on the neural mechanisms underlying Parkinson’s disease (PD) and its treatments. Using data from Deep Brain Stimulation (DBS)patients and the 7-Tesla TRACK-PD study, it illustrated detailed preprocessing and analysis workflows for structural and functional MRI. Preliminary findings showed that DBS induces measurable changes in network connectivity, suggesting targeted modulation of brain circuits. The TRACK-PD results combined imaging and cognitive data, highlighting altered connectivity patterns associated with cognitive performance in PD. Looking ahead, the work aims to expand these analyses across larger datasets, explore regional coupling—especially in the thalamus—and apply machine learning to identify cognitive and network-based phenotypes that could guide more personalized treatment strategies.