ReTune at DGKN 2024: An expert interview with Prof. Wolf-Julian Neumann, MD

Mar 07, 2024

ReTune at DGKN 2024: An expert interview with Prof. Wolf-Julian Neumann, MD

This year’s congress of the German Society for Clinical Neurophysiology and Functional Imaging (DGKN) took place in Frankfurt from 6 to 9 March. Members of the transregional collaborative research centre (TRR 295) ReTune were actively involved in this event. In our interview, Prof. Dr. Wolf-Julian Neumann provides insights into his work at ReTune and reports on current study results and his impressions of the DGKN congress.

  1. Prof. Neumann, you work as a project manager in the so-called “Area B” of ReTune. What are the main areas of research in this area?

In “Area B” of the transregional collaborative research centre ReTune, we focus on the investigation of disease- and symptom-specific neuronal networks in patients with movement disorders. One of the most common diseases that concerns us is idiopathic Parkinson’s syndrome. The number of people suffering from Parkinson’s worldwide has doubled since the 1990s to over 6 million and we must assume that by 2040 there will be 12 million people. ReTune is investigating how dynamic network therapies such as deep brain stimulation can further improve the quality of life of affected patients. For this to succeed, we need to better understand the disease mechanisms. To this end, the brain researchers, neurologists, and neurosurgeons in ReTune utilise a broad spectrum of state-of-the-art research techniques. We use brain network analyses to investigate how specific symptoms affect activity patterns in the brain and how these patterns can be altered by various factors, such as disease progression or therapeutic interventions. We use state-of-the-art technologies from the fields of invasive brain signalling, neurostimulation, machine learning and MRI connectomics. These give us insights into the complex interactions between brain function and neurological symptoms and provide new starting points for treatment.

  1. As part of the “Deep brain stimulation meets machine learning: New avenues for personalised neuromodulation” symposium at the DGKN congress, you presented current data from your work (Neural feature extraction from invasive neurophysiology for clinical brain computer interfaces and machine learning). Can you give us a brief summary of this?

I am very happy to do so, this topic is a focus of my work as Professor of Invasive Neurotechnology at the Charité. At the symposium, I presented a new software platform that integrates brain signal decoding with connectomics to increase the precision and efficacy of clinical brain-computer interfaces (BCI). This platform was validated by analysing 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. A key innovation factor here is the introduction of connectomics-informed movement decoders that work across cohorts of patients with Parkinson’s disease and epilepsy in the US, Europe and China in a plug-and-play fashion without further model training.  Furthermore, we identified network targets for emotion decoding in the left prefrontal and cingulate circuits in patients with major depression treated with deep brain stimulation. Finally, we demonstrated ways to improve seizure detection in reactive neurostimulation for epilepsy. Our platform enables fast and highly accurate decoding for precision medicine that can dynamically adapt neuromodulatory therapies to patients’ individual situations and needs in the future. This work represents a significant advance in the development of tailored therapies adapted to patients’ specific conditions and marks an important step towards more efficient and personalised treatment of brain diseases. The corresponding scientific article is available as a preprint here: We are currently processing the helpful comments of the blinded peer-reviewers of a high-ranking international journal in the field of biotechnology and hope that the article will be published in the same journal in spring 2024.

  1. In your second DGKN lecture, you presented results on the topic of “Invasive neurophysiology and MRI connectomics for movement decoding in Parkinson’s disease”. What were the most important findings of this study?

In my second presentation at the DGKN Congress, I presented two important studies that provide new insights into the neurophysiological characteristics of motor fluctuations in Parkinson’s disease and show ways in which machine learning-based methods can lead to new therapies.

The first study investigated the ability to initiate voluntary movements, a fundamental human behaviour that is impaired in Parkinson’s disease due to the loss of dopaminergic neurons. The restriction in the initiation of movement is known in the clinic as akinesia. Contrary to what is often portrayed in public, it is not trembling, but this inhibition of movement that is the leading symptom of Parkinson’s disease for many patients.  Both dopamine and deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms were previously unclear. Our study aimed to understand the common network effects of deep brain stimulation and dopamine to accelerate movement initiation. We analysed brain signals directly from implanted electrodes in patients with Parkinson’s syndrome who underwent deep brain stimulation. On these signals, we trained machine learning models as brain signal decoders to decode the will to initiate movement and investigated how long the latency between intention and execution is, first without therapy, then after the effect of dopamine and DBS. Our results showed that both dopamine and DBS significantly shortened the latency between motor intention and execution, suggesting a common therapeutic mechanism to alleviate akinesia. Both therapies modulated oscillatory brain network communication, which may provide mechanistic insights into symptom generation and inspire a new approach for the development of clinical brain-computer interfaces to support temporally precise movement initiation in brain disorders.

The second study is a collaboration with Prof Philip Starr from the Centre for Neurosurgery at the University of California San Francisco and focused on the balance between “antikinetic” and “prokinetic” patterns of neuronal oscillatory activity and their relationship to motor dysfunction. By using a sensor-enabled DBS system, motor symptoms in Parkinson’s patients could be continuously recorded and analysed over more than 900 hours of recording time in the home environment. The results showed that excessive activity in the gamma range (65-90 Hz) in the motor network correlates with dyskinesia. This supports the hypothesis that this brain rhythm could serve as a promising control signal for adaptive brain stimulation, marking a further step towards personalised and adaptive therapy strategies.

These studies illustrate the complexity of the neurophysiological processes in Parkinson’s disease and offer valuable approaches for the development of future therapies that are tailored to the individual needs of patients.

The first study has been published publicly as a preprint here and is currently under peer review in a high-ranking journal. The second study has already been published in one of the leading neurology journals “Brain”:

  1. Can you tell us about current research findings in the field of neuromodulation that will be of particular relevance to the public?

Current advances in the field of neuromodulation, in particular developments in adaptive deep brain stimulation (DBS), promise significant innovations for the treatment of brain diseases. One specific example that will soon be relevant for patients is the ADAPT-PD study, a multi-centre clinical study for adaptive DBS, which is the first to investigate the use of brain signals for therapy optimisation in a comprehensive clinical context. This promises to bring personalised DBS approaches to the market very soon. The first results were shown last year in the so-called DBS Think Tank and we are very excited about the full publication of the study results, which we expect this year.

In addition, the future use of machine learning in neuromodulation will enable unprecedented spatial and temporal precision of therapies. These technological advances will open up new horizons for the treatment of brain diseases by providing the basis for dynamically customisable therapies that can respond in real time to changes in the patient’s condition. The smart brain implants can then react precisely to the patient’s individual situation and support them in their everyday life with previously unrivalled precision. Specifically, the focus of this research is on optimising sleep-wake rhythms, treating gait disorders and developing neuroprostheses for speech and motor skills.

These three specific developments – the imminent introduction of personalised DBS approaches, the integration of machine learning for more precise neuromodulation and the focus on cell-specific interactions and mechanistic understandings – may soon represent a quantum leap in the treatment of brain diseases. They signal an era in which therapies can be made increasingly precise and personalised through innovative neurotechnology.

Thank you very much for the interview.

The Transregional Collaborative Research Centre (TRR 295) “ReTune”, funded by the German Research Foundation (DFG) with 10 million euros, aims to gain a better understanding of information processing in dynamic brain networks and to improve and expand therapy options for people with motor network disorders.

© Picture: DGKN