My Google Scholar profile: Thomas S. Binns
Research Article
Binns, T.S., Köhler, R.M., Vanhoecke, J., Chikermane, M., Gerster, M., Merk, T., Pellegrini, F., ..., Haufe, S., Kühn, A.A., Neumann, W.-J. (Pre-print). Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease. DOI: 10.1101/2024.04.14.586969.
Deep brain stimulation (DBS) is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinson's disease, subthalamic DBS clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex-basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully-invasive neural multisite recordings in patients undergoing DBS surgery with MRI-based whole-brain connectomics.
Our findings demonstrate that dopamine and DBS exert distinct mesoscale effects through modulation of local neural population synchrony. In contrast, at the macroscale, DBS mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex-basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and DBS, laying the foundation for advanced closed-loop neurostimulation therapies.
Software Poster
Binns, T.S., Pellegrini, F., Jurhar, T., Haufe, S. (2024). PyBispectra: an open-source toolbox for advanced electrophysiological signal processing based on the bispectrum. Neural Traces, Berlin, Germany.
Various forms of information can be extracted from neural timeseries data. Of this information, phase-amplitude coupling, time delays, and waveform characteristics are of great interest, providing crucial insights into neuronal function and dysfunction. However, the methods commonly used for analysing these features possess notable limitations. For example, quantifying phase-amplitude coupling with the popular modulation index requires the challenging definition of precise filter settings and is susceptible to source-mixing artefacts – as is time delay estimation with the traditional cross-correlation – and timeseries-based waveform analysis necessitates preprocessing steps that risk corrupting the shape of the underlying signal. In contrast, recent work has revealed the bispectrum – the Fourier transform of the third order moment – to be a powerful tool for the analysis of phase-amplitude coupling, time delay estimation, and non-sinusoidal waveform characteristics in neural signals. In fact, the bispectrum is immune to the above limitations, leaving a computationally efficient and robust tool for various signal analysis contexts. Despite these advantages, the bispectrum has seen relatively little use in the field of neuroscience, in part due to the lack of an accessible, easy-to-use toolbox.
Accordingly, we have created PyBispectra: an open-source Python-based toolbox providing the wider neuroscientific community with access to these advanced methods. PyBispectra includes tools for performing phase-amplitude coupling, time delay estimation, and waveform analysis – incorporating recent methodological improvements – as well as additional supporting tools based on recommendations from the literature for other cross-frequency coupling methods and spatio-spectral filtering. The toolbox is highly accessible with detailed API documentation and tutorials, and is written using object-oriented principles to support ease of understanding and adaptation of the source code. Finally, PyBispectra is a high-performance toolbox, incorporating parallel processing and low-level source code compilation to maximise compute speed. Altogether, bispectrum-based methods bring numerous advantages over traditional approaches, and PyBispectra offers neuroscience researchers access to these advanced signal processing methods in an open-source, easy-to-use toolbox.
Software Poster
Binns, T.S., Orabe, M., Nguyen, T.D., Köhler, R.M., Pellegrini, F., Haufe, S. (2024). Multivariate connectivity methods in the MNE-Python toolbox. Neural Traces, Berlin, Germany.
Effective connectivity analyses are a common approach for analysing electrophysiological data, characterising the interaction between timeseries signals in a frequency-resolved manner. Various methods exist for performing such analyses, with the traditional approach being generally bivariate, i.e. between two signals. However, when handling data from multi-channel setups – as is often the case in electrophysiology – examining the connectivity between each combination of signals results in high-dimensional results that are difficult to interpret. A common approach to handle this is to average the results across multiple connections, diminishing the signal-to-noise ratio if the interaction of interest is not present across all connections. In contrast, multivariate connectivity methods characterise interactions between two sets of signals, representing relationships between these groups in the component space. In this way, critical connectivity information is represented in a low-dimensional, more interpretable manner. Additionally, the enhanced signal-to-noise ratio offered by multivariate methods allows for interactions to be captured which may otherwise be lost with less sophisticated bivariate methods. A major factor in the limited uptake of multivariate methods is their lack of availability in popular signal processing packages.
Accordingly, we developed implementations of multivariate methods based on coherency and state-space Granger causality in MNE-Connectivity, a toolbox for estimating effective connectivity that extends the popular open-source MNE-Python signal processing toolbox .toolbox (Gramfort et al., 2013). To further promote their adoption, example use cases of these tools are presented as tutorials in the MNE-Connectivity documentation.
Software Poster
Binns, T.S., Pellegrini, F., Jurhar, T., Haufe, S. (2024). PyBispectra: an open-source toolbox for advanced electrophysiological signal processing based on the bispectrum. WIRED Conference, Paris, France.
Various forms of information can be extracted from neural timeseries data. Of this information, phase-amplitude coupling, time delays, and waveform characteristics are of great interest, providing crucial insights into neuronal function and dysfunction. However, the methods commonly used for analysing these features possess notable limitations. For example, quantifying phase-amplitude coupling with the popular modulation index requires the challenging definition of precise filter settings and is susceptible to source-mixing artefacts – as is time delay estimation with the traditional cross-correlation – and timeseries-based waveform analysis necessitates preprocessing steps that risk corrupting the shape of the underlying signal. In contrast, recent work has revealed the bispectrum – the Fourier transform of the third order moment – to be a powerful tool for the analysis of phase-amplitude coupling, time delay estimation, and non-sinusoidal waveform characteristics in neural signals. In fact, the bispectrum is immune to the above limitations, leaving a computationally efficient and robust tool for various signal analysis contexts. Despite these advantages, the bispectrum has seen relatively little use in the field of neuroscience, in part due to the lack of an accessible, easy-to-use toolbox.
Accordingly, we have created PyBispectra: an open-source Python-based toolbox providing the wider neuroscientific community with access to these advanced methods. PyBispectra includes tools for performing phase-amplitude coupling, time delay estimation, and waveform analysis – incorporating recent methodological improvements – as well as additional supporting tools based on recommendations from the literature for other cross-frequency coupling methods and spatio-spectral filtering. The toolbox is highly accessible with detailed API documentation and tutorials, and is written using object-oriented principles to support ease of understanding and adaptation of the source code. Finally, PyBispectra is a high-performance toolbox, incorporating parallel processing and low-level source code compilation to maximise compute speed. Altogether, bispectrum-based methods bring numerous advantages over traditional approaches, and PyBispectra offers neuroscience researchers access to these advanced signal processing methods in an open-source, easy-to-use toolbox.
Research Article
Köhler, R.M., Binns, T.S., Merk, T., Zhu, G., Yin, Z., Zhao, B., Chikermane, M., ..., Kühn, A.A., Haynes, J.-D., Neumann, W.-J. (Pre-print). Dopamine and neuromodulation accelerate the neural dynamics of volitional action in Parkinson’s disease. DOI: 10.1101/2023.10.30.564700.
The ability to initiate volitional action is fundamental to human behavior. Loss of dopaminergic neurons in Parkinson’s disease (PD) is associated with impaired action initiation, also termed akinesia. Dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown.
We recorded invasive neural activity from both sensorimotor cortex and subthalamic nucleus (STN) in 25 PD patients performing self-initiated movements. Readiness potentials and brain signal decoding revealed long latencies between the neural representation of motor intention and execution. Dopamine and STN-DBS shortened these latencies, while shifting directional cortico-subthalamic oscillatory coupling from antikinetic beta (13-35 Hz) to prokinetic theta (4-10 Hz) rhythms. Our study highlights a key role for dopamine and basal ganglia in the evolution of preparatory brain signals and orchestration of neural dynamics in encoding of volitional action.
Research Poster Winner of best poster prize
Binns, T.S., Köhler, R.M., Vanhoecke, J., Chikermane, M., Gerster, M., Merk, T., Pellegrini, F., Faust, K., Schneider, G.-H., Kühn, A.A., Haufe, S., Neumann, W.-J. (2023). Dopamine and stimulation distinctly modulate cortico-subthalamic communication in Parkinson’s disease. ReTune Fall School, Apolda, Germany.
Building on earlier work, this poster shows results of the first-ever within-subject examination of cortico-subthalamic interaction in Parkinson's disease across medication and deep brain stimulation states using invasive recordings of subthalamic nucleus local field potentials and the cortex with electrocorticography. The results highlight that dopamine and stimulation alter distinct aspects of communication with unique spectral profiles, and further supports a role for cortico-subthalamic interactions in Parkinson's disease pathology. Presented at the Early-Career Fall School of the German Research Foundation (DFG)-funded ReTune research centre.
Research Article
Merk, T., Köhler, R.M., Peterson, V., Lyra, L., Vanhoecke, J., Chikermane, M., Binns, T.S., ..., Kühn, A.A., Richardson, R.M., Neumann, W.-J. (Pre-print). Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants. DOI: 10.21203/rs.3.rs-3212709/v1.
Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy.
First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson’s disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.
Software Poster
Binns, T.S., Pellegrini, F., Jurhar, T., Haufe, S. (2023). PyBispectra: an open-source toolbox for advanced electrophysiological signal processing based on the bispectrum. Bernstein Conference, Berlin, Germany. DOI: 10.12751/nncn.bc2023.149
Various forms of information can be extracted from neural timeseries data. Of this information, phase-amplitude coupling, time delays, and waveform characteristics are of great interest, providing crucial insights into neuronal function and dysfunction. However, the methods commonly used for analysing these features possess notable limitations. For example, quantifying phase-amplitude coupling with the popular modulation index requires the challenging definition of precise filter settings and is susceptible to source-mixing artefacts – as is time delay estimation with the traditional cross-correlation – and timeseries-based waveform analysis necessitates preprocessing steps that risk corrupting the shape of the underlying signal. In contrast, recent work has revealed the bispectrum – the Fourier transform of the third order moment – to be a powerful tool for the analysis of phase-amplitude coupling, time delay estimation, and non-sinusoidal waveform characteristics in neural signals. In fact, the bispectrum is immune to the above limitations, leaving a computationally efficient and robust tool for various signal analysis contexts. Despite these advantages, the bispectrum has seen relatively little use in the field of neuroscience, in part due to the lack of an accessible, easy-to-use toolbox.
Accordingly, we have created PyBispectra: an open-source Python-based toolbox providing the wider neuroscientific community with access to these advanced methods. PyBispectra includes tools for performing phase-amplitude coupling, time delay estimation, and waveform analysis – incorporating recent methodological improvements – as well as additional supporting tools based on recommendations from the literature for other cross-frequency coupling methods and spatio-spectral filtering. The toolbox is highly accessible with detailed API documentation and tutorials, and is written using object-oriented principles to support ease of understanding and adaptation of the source code. Finally, PyBispectra is a high-performance toolbox, incorporating parallel processing and low-level source code compilation to maximise compute speed. Altogether, bispectrum-based methods bring numerous advantages over traditional approaches, and PyBispectra offers neuroscience researchers access to these advanced signal processing methods in an open-source, easy-to-use toolbox.
Research Poster
Binns, T.S., Köhler, R.M., Vanhoecke, J., Chikermane, M., Merk, T., Pellegrini, F., Faust, K., Schneider, G.-H., Kühn, A.A., Haufe, S., Neumann, W.-J. (2023). Parkinson's disease: Invasive mapping of cortico-subthalamic communications in humans. International Basal Ganglia Society Conference XIV, Stockholm, Sweden.
Building on our earlier work in the Interventional and Cognitive Neuromodulation group of Wolf-Julian Neumann, this poster shows results of the first-ever examination of cortico-subthalamic interaction in Parkinson's disease in different dopaminergic medication and stimulation conditions using invasive recordings of subthalamic nucleus local field potentials and the cortex with electrocorticography. These results highlight pathological changes in cortico-subthalamic connectivity that are differentially altered by medication and stimulation.
Research Poster
Binns, T.S., Merk, T., Köhler, R., Chikermane, M., Dzaye, A., Vanhoecke, J., Faust, K., Schneider, G.-H., Kühn, A.A., Neumann, W.-J. (2022). Invasive mapping of cortico-subthalamic connectivity in Parkinson's disease. Second Expert Summit on the Future of Deep Brain Stimulation, Würzburg, Germany.
This poster shows preliminary results of the first-ever examination of cortico-subthalamic interaction in Parkinson's disease in different dopaminergic medication conditions using invasive recordings of subthalamic nucleus local field potentials and the cortex with electrocorticography. These early results indicate that further analyses may address fundamental issues regarding pathological frequency band networks in Parkinson's disease, as well as the mechanisms of dopaminergic medication on these networks.
Research Article
Schultze-Kraft, M., Jonany, V., Binns, T.S., Soch, J., Blankertz, B. and Haynes, J.-D. (2021). Suppress me if you can: neurofeedback of the readiness potential. eNeuro, 8(2). doi.org/10.1523/eneuro.0425-20.2020.
Voluntary movements are usually preceded by a slow, negative-going brain signal over motor areas, the so-called readiness potential (RP). To date, the exact nature and causal role of the RP in movement preparation have remained heavily debated. Although the RP is influenced by several motorical and cognitive factors, it has remained unclear whether people can learn to exert mental control over their RP, for example, by deliberately suppressing it. If people were able to initiate spontaneous movements without eliciting an RP, this would challenge the idea that the RP is a necessary stage of the causal chain leading up to a voluntary movement.
We tested the ability of participants to control the magnitude of their RP in a neurofeedback experiment. Participants performed self-initiated movements, and after every movement, they were provided with immediate feedback about the magnitude of their RP. They were asked to find a strategy to perform voluntary movements such that the RPs were as small as possible. We found no evidence that participants were able to to willfully modulate or suppress their RPs while still eliciting voluntary movements. This suggests that the RP might be an involuntary component of voluntary action over which people cannot exert conscious control.
Review Article
Binns, T.S. (2020). Has neuroscience disproven free will?. BNA Bulletin, 1 August, p. 20.
A short review article examining whether research into the readiness potential, a neural marker of upcoming movement, has disproven free will. Published in the British Neuroscience Association's summer 2020 Bulletin.