2025Activity​​​‌ reportProject-TeamNECTARINE

3.1 Non-invasive neuromodulation​​

Neuromodulation 6 relies on​​ different techniques to modify​​​‌ the activity of the‌ brain, including Transcranial Magnetic‌​‌ Stimulation (TMS): it can​​ be used to modify​​​‌ the electroencephalography (EEG) power‌ spectrum, which disruption is‌​‌ a telltale sign of​​ some diseases (e.g. schizophrenia).​​​‌ In addition to electromagnetic‌ techniques, auditory stimulation could‌​‌ be used to modify​​ both recorded neural activity​​​‌ and behavior 5:‌ they could reduce attention‌​‌ loss during psychophysical tasks,​​ hence being beneficial to​​​‌ people suffering from Attention‌ deficit hyperactivity disorder (ADHD).‌​‌ These results are preliminary​​ and not patient-specific: our​​​‌ goal will be to‌ remove practical obstacles to‌​‌ implement efficient and personalized​​ neuromodulation.

Note that we​​​‌ focus here on non-invasive‌ methods, i.e. external electrical‌​‌ or magnetic stimulation methods,​​ or visual and auditory​​​‌ stimuli. This is in‌ contrast to invasive methods‌​‌ (e.g. intracranial stimulation with​​ electrodes surgically implanted in​​​‌ the brain), which are‌ harder to implement on‌​‌ large cohorts of patients​​ and do not provide​​​‌ them with a desirable‌ level of autonomy.

To‌​‌ empirically study the link​​ between brain activity, neuromodulation,​​​‌ and the behavior or‌ symptoms of the participants,‌​‌ we have leveraged different​​ datasets:

• Behavioral data from​​​‌ subjects performing a sustained‌ attention task with behavioral‌​‌ feedback;
• EEG recordings from​​ participants undergoing TMS stimulation,​​​‌ shared by our INSERM‌ colleagues;
• EEG recordings and‌​‌ behavioral performance from participants​​ performing a sustained attention​​​‌ task with auditory stimuli;‌
• EEG recordings from participants‌​‌ performing a sustained attention​​ task (and allowing to​​​‌ study brain activity during‌ epochs of attention loss),‌​‌ shared by our collaboration​​ partner from INSERM.

Then,​​​‌ we will move to‌ model-based approaches (see sections‌​‌ below) to statistically model​​ the links between behavior,​​​‌ brain activity, and the‌ effect of neuromodulation. Predicting‌​‌ the effect that a​​ given stimulation will have​​​‌ on brain activity or‌ on symptoms (either during‌​‌ in-silico simulations or during​​ actual experiments with patients)​​​‌ requires developing accurate models‌ of the brain. However,‌​‌ the brain is intrinsically​​ difficult to model, and​​​‌ no approach has proved‌ to be entirely satisfying.‌​‌ Simple models often lack​​ the expressivity to correctly​​​‌ fit data, while complex,‌ machine-learning-based models perform well‌​‌ in offline settings but​​ overfit in real online​​​‌ applications.

We use different‌ strategies to develop models‌​‌ that are usable for​​ non-invasive closed-loop neuromodulation, including​​​‌ mixed linear and non-linear‌ dynamics, neural ordinary differential‌​‌ equations, or the use​​ of regularized accuracy metrics​​​‌ such as the Bayesian‌ Information Criterion. Here again,‌​‌ this offline analysis will​​ be performed from human​​​‌ recordings already obtained from‌ our experimental collaborators.

3.2‌​‌ Mathematical models of the​​ brain

The brain is​​​‌ a complex system with‌ several spatial and temporal‌​‌ scales. The microscopic scales​​ are rather unstructured in​​​‌ space, and activity observations‌ show random fluctuations, whereas‌​‌ upper hierarchical levels at​​ the mesoscopic and macroscopic​​​‌ scales exhibit more regular‌ dynamics. In previous studies,‌​‌ we found that additive​​ random input on the​​​‌ microscopic scale to random‌ networks tunes the system's‌​‌ stability and may induce​​ stability changes. Such so-called​​​‌ bifurcations induce ordered structures,‌ being in space or‌​‌ time or in both.​​​‌ This additive noise-induced system​ evolution (ANISE) has been​‌ shown to describe successfully,​​ e.g., transitions between synchronization​​​‌ and desynchronization observed in​ electroencephalographic data.

We have​‌ extended recent corresponding studies​​ by considering delayed interactions​​​‌ along myelinated axonal fibers​ in the brain. These​‌ fibers connect single neurons​​ and the propagation time​​​‌ of traveling pulses along​ these fibers represents the​‌ interaction delay of neurons.​​ The transmission delay in​​​‌ random stochastic networks affects​ the system's stationary state​‌ and tunes its linear​​ response to external stimulation.​​​‌

Brain stimulation is a​ modern therapy in clinical​‌ practice. The various types​​ of stimulation affect the​​​‌ brain's internal structure and​ functioning, which results to​‌ learning processes and, in​​ case of mental disorders,​​​‌ to improving the patient's​ health condition. To better​‌ understand the conditions under​​ which neurostimulation may modulate​​​‌ the brain dynamics, we​ have studied the impact​‌ of heterogeneity in neural​​ systems on the stimulation.​​​‌ In fact, heterogenous neuron​ morphology in neural systems​‌ exhibiting spiking neural activity​​ strongly affects the system​​​‌ dynamics heavily. Since neural​ network heterogeneity may develop​‌ over time (on a​​ time scale of days​​​‌ or weeks) and may​ be different in each​‌ brain area, this neural​​ system diversity affects the​​​‌ impact of neurostimulation.

3.3​ Active Learning in cognitive​‌ experiments

Neuromodulation is classically​​ applied in an open-loop​​​‌ manner: the experimental parameters​ (i.e. the amplitudes and​‌ frequencies of the sounds​​ used in auditory stimulation,​​​‌ or the activation patterns​ of electromagnetic stimulation) are​‌ defined prior to the​​ experiment. This approach is​​​‌ suboptimal since inputs are​ neither adapted to the​‌ patient nor to changes​​ in time of their​​​‌ neural activity. In contrast,​ we propose to develop​‌ a closed-loop approach, in​​ which the stimulation parameters​​​‌ are continuously optimized as​ new recordings are being​‌ obtained, in order to​​ maximize the effect of​​​‌ neuromodulation. To do so,​ we will build upon​‌ a set of methods​​ called Optimal Experiment Design​​​‌ (OED, also referred to​ as Active Learning). OED​‌ has been used in​​ different settings, e.g. to​​​‌ improve neurophysiology experiments or​ to perform model estimation.​‌ Its use for neuromodulation​​ has been suggested but​​​‌ online optimization has not​ been implemented yet and​‌ several theoretical obstacles need​​ to be solved before​​​‌ it can be used​ for psychiatric disorders.

A​‌ first widely used solution​​ for OED is to​​​‌ implement a model-based approach​ (i.e. relying on the​‌ modelization approaches described above):​​ this implies to first​​​‌ build a model (either​ a mapping between observed​‌ inputs and outputs, or​​ a more complete digital​​​‌ twin) of the system​ of interest (e.g. the​‌ brain). This model is​​ usually trained using offline​​​‌ data to reproduce the​ output (e.g. EEG activity​‌ of the brain the​​ brain, or behavior of​​​‌ the participant) of the​ system as a function​‌ of its input, i.e.​​ the neuromodulation features. Then,​​​‌ this model can be​ used offline to predict​‌ the effect that a​​ given input will have​​​‌ on the system or​ can be updated on-the-fly​‌ to compute the next​​ best input given the​​ history of previous observations.​​​‌

Model-based approaches have shown‌ to be especially powerful‌​‌ when an accurate model​​ of the system can​​​‌ be obtained: for instance,‌ the transfer function of‌​‌ brain activity can be​​ precisely estimated and used​​​‌ for closed-loop neurostimulation. However,‌ a major drawback of‌​‌ model-based approaches is that​​ they are sensitive to​​​‌ model misspecifications: in settings‌ where the digital twin‌​‌ differs from the actual​​ system, the computed parameters​​​‌ may not be optimal.‌ For instance, the effect‌​‌ of experimental inputs on​​ the behavior of a​​​‌ subject are hard to‌ precisely model. In parallel,‌​‌ we will thus also​​ develop a complementary model-​​​‌ free and data-based approach,‌ i.e. without the need‌​‌ for a digital twin​​ of the system, which​​​‌ relies on minimal assumptions‌ about the ground-truth system.‌​‌

4.1​​ Improvement of attention deficits​​​‌ by behavioral feedback

Attention‌ deficit hyperactivity disorder (ADHD)‌​‌ is a common psychiatric​​ disorder of neurodevelopmental origin,​​​‌ persisting into adulthood in‌ 50 to 70 percent‌​‌ of cases. Patients with​​ ADHD exhibit symptoms of​​​‌ hyperactivity, impulsivity and/or inattention‌ that have a significant‌​‌ impact on their family,​​ professional and social lives​​​‌ and, by extension, an‌ undeniable societal impact. In‌​‌ addition to these symptoms,​​ there are cognitive deficits,​​​‌ mainly attention deficits, which‌ may be the cause‌​‌ and/or may exacerbate them.​​

Our project proposes an​​​‌ alternative non-pharmacological treatment based‌ on performance feedback. By‌​‌ combining behavioural and neurophysiological​​ experiments with computational neural​​​‌ modelling, this project will‌ test the hypothesis that‌​‌ a feedback protocol that​​ is easy for patients​​​‌ to implement can improve‌ attention deficits in ADHD.‌​‌

4.2 Improvement of attention​​ deficits by auditory stimulation​​​‌

Neuromodulation not only encompasses‌ electrical (e.g. tDCS) and‌​‌ magnetic (e.g. TMS) methods​​ of brain stimulation, but​​​‌ also stimulation by auditory‌ or visual stimuli: such‌​‌ stimuli have indeed been​​ shown to significantly modify​​​‌ brain activity, and can‌ be used to actively‌​‌ control the activity of​​ specific neurons or brain​​​‌ areas. Here, we study‌ the possibility to reduce‌​‌ the symptoms of attention​​ deficits and to modify​​​‌ the EEG activity of‌ participants using auditory stimulation.‌​‌

Isochronic tones (IT) are​​ auditory stimuli which exhibit​​​‌ an oscillation at a‌ high trigger frequency, whose‌​‌ amplitude is modulated periodically​​ with a much lower​​​‌ beat frequency. The beat‌ frequency of such ITs‌​‌ is tuned to the​​ frequency of certain brainwaves​​​‌ to produce a desired‌ attention effect. Most previous‌​‌ studies implied binaural beats,​​ a different type of​​​‌ auditory stimuli whose impact‌ is controversial and still‌​‌ under discussion. Very first​​ own preliminary studies and​​​‌ current literature indicate that‌ ITs with beat frequencies‌​‌ in the $\beta$-​​ and $\gamma$- frequency​​​‌ bands also improve visual‌ attention. However, more detailed‌​‌ studies on the impact​​ of ITs are necessary​​​‌ to evaluate their impact.‌

4.3 Improvement of time‌​‌ prediction by neurostimulation

We​​ lack efficient treatment and​​​‌ prevention in psychiatry. Repetitive‌ Transcranial Magnetic Stimulation (rTMS)‌​‌ of the brain represents​​ a major therapeutic hope,​​​‌ but the lack of‌ knowledge on both the‌​‌ neurobiological and functional (cognitive)​​​‌ impact of the stimulation​ impedes clinical applications. In​‌ our project we target​​ time prediction due to​​​‌ its close relationship with​ clinical disorders in schizophrenia​‌ (SZ), i.e. disorders of​​ the sense of self.​​​‌ Targeting time prediction with​ rTMS should help restoring​‌ disorders of the sense​​ of self which are​​​‌ still difficult to treat.​ This clinical goal requires​‌ to address a fundamental​​ question, i.e. how the​​​‌ brain network’s connectivity alters​ its dynamics during time​‌ prediction and how it​​ is altered by rTMS​​​‌ on the cerebellum. To​ that aim we need​‌ to go beyond the​​ non-invasive methods available in​​​‌ humans, and build neural​ models to reveal major​‌ functional units and their​​ interactions in the brain.​​​‌ Here, the neural model​ will help to analyze​‌ the different factors impacting​​ the ability to predict​​​‌ events in time. Merging​ optimally the mathematical model​‌ with neural experimental data​​ will identify model parameters​​​‌ that account for the​ TMS effects in controls​‌ and patients, and help​​ tailor rTMS to the​​​‌ patients’ needs.

Temporal prediction​ is essential in daily​‌ life and ensures the​​ adjustment to the environment.​​​‌ Previous experimental studies provided​ evidence, that neurostimulation of​‌ the cerebellum improves temporal​​ perception in animals and​​​‌ humans. The project aims​ to build neural models,​‌ which describe the network​​ dynamics within the cerebello-thalamo-cortical​​​‌ network subtending both the​ automatic and attention mechanisms​‌ involved in time prediction.​​

5.1 Footprint of​ research activities

According to​‌ the World Health Organization,​​ one in every eight​​​‌ people in the world​ live with a mental​‌ disorder, a number which​​ is likely to increase​​​‌ with an ageing population.​ This high prevalence of​‌ cognitive disorders significantly impacts​​ the quality of life,​​​‌ productivity, and exacerbates social​ and racial inequalities. Classical​‌ treatments, such as psychotherapy​​ and pharmacological medication, may​​​‌ fall short for some​ patients. Recently, a new​‌ line of treatments, called​​ neuromodulation, has shown promising​​​‌ results 6 in alleviating​ the symptoms of psychiatric​‌ disorders. Neuromodulation relies on​​ different non-invasive techniques to​​​‌ modify the activity of​ the brain, including Transcranial​‌ Magnetic Stimulation (TMS), Direct​​ Current Stimulation (tDCS) or​​​‌ Random Noise Stimulation (tRNS).​ The ease of use​‌ of these different techniques,​​ the first promising results​​​‌ they yield at alleviating​ cognitive impairments, and the​‌ great number of people​​ who will benefit from​​​‌ them, make neuromodulation a​ "low-hanging fruit" with exciting​‌ possible applications, and is​​ being investigated by members​​​‌ of the NECTARINE team.​

5.2 Impact of research​‌ results

Neuromodulation can be​​ used to modulate brain​​​‌ axctivity and may improve​ cognitive impairment, e.g. attention​‌ deficits, or can improve​​ time prediction in Schizophrenia.​​​‌ We aim to develop​ experimental protocols, which allow​‌ to improve deficits by​​ neuromodulation in a low-technology​​​‌ environment, such as the​ home of a patient.​‌

6.1​​ Real-time epoch classification and​​​‌ detection

Participants: Camille Gontier​.

To control brain​‌ activity, some settings do​​ not necessitate a continuous​​​‌ closed-loop model-based approach. For​ instance, ADHD is characterized​‌ by epochs of attention​​ loss: it is sufficient​​ to detect the neural​​​‌ or behavioral signatures of‌ these epochs, to prevent‌​‌ them and nudge the​​ participant. In a previous​​​‌ project we worked on‌ intracortical motor brain-computer interfaces‌​‌ (BCI) with human participants​​ and developed a classifier​​​‌ using neural data for‌ on-the-fly error detection and‌​‌ correction. We propose to​​ use a similar approach​​​‌ to detect epochs of‌ attention loss during a‌​‌ Sustained Attention to Response​​ Task (SART), for which​​​‌ human data have already‌ been obtained by the‌​‌ team of our experimental​​ collaborator Anne Bonnefond (UMR​​​‌ 1329): we are now‌ working on improving this‌​‌ classification accuracy using e.g.​​ recurrent neural networks.

6.2​​​‌ Model-free closed-loop parameters optimization‌

Participants: Camille Gontier,‌​‌ Axel Hutt.

In​​ some settings, a model-based​​​‌ approach to active learning‌ may not be suitable:‌​‌ the mapping between the​​ inputs and outputs of​​​‌ the system (i.e. the‌ link between neuromodulation and‌​‌ the behavior or brain​​ activity of the participant)​​​‌ may be too indirect‌ to be efficiently represented‌​‌ by parametric models (Bak​​ et al. 2016). We​​​‌ thus explored the possibility‌ to implement a data-driven,‌​‌ model-free optimization scheme, the​​ most basic one being​​​‌ gradient descent (GD):

where:

• ${\theta }_t$ is‌ the vector containing the‌​‌ input parameters values at​​ time $t$, i.e.​​​‌ the parameters describing neuromodulation,‌ for instance the electric‌​‌ current amplitude for electric​​ stimulation, or the sound​​​‌ frequency for auditory stimuli;‌
• $\mu$ is the learning‌​‌ rate;
• $ℒ$ is the​​ loss function that needs​​​‌ to be minimized (for‌ instance the performance metrics‌​‌ of the participant performing​​ a task, or the​​​‌ difference between the recorded‌ and desired brain activity).‌​‌

Preliminary simulation results we​​ recently obtained (yet unpublished)​​​‌ showed that GD can‌ be used to modify‌​‌ on the fly the​​ parameters to optimize the​​​‌ participant’s performance in a‌ dot motion detection task.‌​‌

6.3 Order in random​​ systems

Participants: Axel Hutt​​​‌.

The brain is‌ a complex system with‌​‌ several spatial and temporal​​ scales. The microscopic scales​​​‌ are rather unstructured in‌ space, and activity observations‌​‌ show random fluctuations, whereas​​ upper hierarchical levels at​​​‌ the mesoscopic and macroscopic‌ scales exhibit more regular‌​‌ dynamics. In previous studies,​​ we found that additive​​​‌ random input on the‌ microscopic scale to random‌​‌ networks tunes the system's​​ stability and may induce​​​‌ stability changes. Such so-called‌ bifurcations induce ordered structures,‌​‌ being in space or​​ time or in both.​​​‌ This additive noise-induced system‌ evolution (ANISE) has been‌​‌ shown to describe successfully​​ synchronization and desynchronization and​​​‌ 1/f power spectra 4‌ observed in electroencephalographic data.‌​‌

We have extended recent​​ corresponding studies by considering​​​‌ delayed interactions along myelinated‌ axonal fibers in the‌​‌ brain. These fibers connect​​ single neurons and the​​​‌ propagation time of traveling‌ pulses along these fibers‌​‌ represents the interaction delay​​ of neurons. The transmission​​​‌ delay in random stochastic‌ networks affects the system's‌​‌ stationary state and tunes​​ its linear response to​​​‌ external stimulation.

6.4 Influence‌ of neural network heterogeneity‌​‌ on neurostimulation impact

Participants:​​​‌ Axel Hutt.

Brain​ stimulation is a modern​‌ therapy in clinical practice.​​ The various types of​​​‌ stimulation affect the brain's​ internal structure and functioning,​‌ which results to learning​​ processes and, in case​​​‌ of mental disorders, to​ improving the patient's health​‌ condition. To better understand​​ the conditions under which​​​‌ neurostimulation may modulate the​ brain dynamics, we have​‌ studied the impact of​​ heterogeneity in neural systems​​​‌ on the stimulation, see​ e.g. 3, 2​‌. In fact, heterogenous​​ neuron morphology in neural​​​‌ systems exhibiting spiking neural​ activity strongly affects the​‌ system dynamics heavily. Since​​ neural network heterogeneity may​​​‌ develop over time (on​ a time scale of​‌ days or weeks) and​​ may be different in​​​‌ each brain area, this​ neural system diversity affects​‌ the impact of neurostimulation.​​

6.5 Auditory beat stimulation​​​‌ in humans affects subjects'​ sustained attention

Participants: Axel​‌ Hutt, Camille Gontier​​, Rosario Huaranca Quispe​​​‌.

Recent research on​ binaural beat stimulation has​‌ raised the question whether​​ it can improve sustained​​​‌ attention. Neurotypicals and subjects​ with attention deficits of​‌ single gender performed a​​ visual attention task under​​​‌ auditory noise, monoaural and​ binaural beat stimulation, while​‌ recording electroencephalographic activity (EEG).​​ We found that attention​​​‌ deficits perform with longer​ reaction times than neurotypical​‌ subjects. To explore EEG​​ activity, two periods of​​​‌ interest were distinguished: before​ a correct detection and​‌ before a miss, supposed​​ to reflect respectively moments​​​‌ of engagement versus disengagement​ of attention. Under noise​‌ stimulation, neurotypicals have larger​​ frontal ERP-components P300 and​​​‌ $\alpha$-spectral power and​ lower parietal $\theta /‌\beta$ spectral power ratio​​ in correct trials than​​​‌ in missed trials, whereas​ subjects with attention deficits​‌ show the inverse relation.​​ Moreover, neurotypicals exhibit a​​​‌ negative relation of frontal​ $\delta$-power and $\mathrm{\theta ‌}/\beta$ ratio in​​ a time window of​​​‌ 6s before targets, whereas​ subjects with attention deficits​‌ show positively related $\mathrm{\delta }$- and $\alpha$-power​​​‌ in this time window.​ Binaural beats diversify these​‌ results. Neurotypical subjects respond​​ with a longer reaction​​​‌ time compared to noise​ stimulation, while attention-deficit subjects​‌ respond equally. Moreover, frontal​​ P300 and $\alpha$-power​​​‌ and parietal $\theta /\beta$ ratio resemble corresponding​‌ results under noise stimulation,​​ whereas brain activity in​​​‌ subjects with attention deficits​ is rather heterogeneous. In​‌ addition, in attention-deficit subjects​​ frontal and parietal $\mathrm{\delta ‌}$- and $\alpha$-power​ are positively related in​‌ a 6s time window​​ before targets. In sum,​​​‌ under noise stimulation we​ found behavioral and electrophysiological​‌ biomarkers, which were inverse​​ in neurotypicals and subjects​​​‌ with attention deficits. Binaural​ beats break up these​‌ relations in both subject​​ groups and they have​​​‌ not been found to​ be beneficial, neither in​‌ behavior nor in electrophysiological​​ biomarkers.

6.6 Behavioral feedback​​​‌ improves sustained attention in​ human subjects

Participants: Axel​‌ Hutt, Negin Majzoubi​​.

Cognitive studies have​​​‌ shown that providing feedback​ has a positive effect​‌ on performance levels. Our​​ hypotheses are that performance​​​‌ feedback can also improve​ attention deficits in adults​‌ with ADHD. Behaviourally, feedback​​ could improve performance, particularly​​ by increasing the number​​​‌ of correct responses and‌ reducing response time. The‌​‌ objective of project, which​​ is part of a​​​‌ PhD thesis, is to‌ create a new digital‌​‌ method: a non-pharmacological treatment​​ based on performance feedback.​​​‌ It is complemented by‌ EEG observations and statistical‌​‌ analysis utilizing machine learning​​ techniques. First analysis steps​​​‌ on behavioral data indicate‌ that performance feedback improves‌​‌ sustained attention. The subsequent​​ analysis aims to correlate​​​‌ these results in behavioral‌ data to EEG signal‌​‌ features.

6.7 Modulation of​​ temporal prediction by transcranial​​​‌ magnetic stimulation in the‌ context of Schizophrenia

Participants:‌​‌ Axel Hutt, Telma​​ Nette, Salma Lakhal​​​‌.

To know when‌ an event will occur,‌​‌ be ready to perceive​​ it and react to​​​‌ it, we use what‌ is called temporal prediction.‌​‌ It allows us to​​ anticipate the arrival of​​​‌ an event and optimise‌ our behaviour. However, the‌​‌ ability to predict the​​ arrival of an event​​​‌ is impaired in people‌ with schizophrenia. This deterioration‌​‌ is visible on a​​ scale of seconds but​​​‌ also on a scale‌ of milliseconds. This scale‌​‌ is suggestive of a​​ role for the cerebellum.​​​‌

There is currently no‌ treatment for such a‌​‌ highly debilitating disorder. One​​ clinical approach involves the​​​‌ use of transcranial magnetic‌ stimulation (TMS) applied to‌​‌ the cerebellum. This is​​ targeted because of its​​​‌ role in millisecond-scale prediction,‌ its involvement in a‌​‌ cerebellar-thalamo-cortical network that allows​​ for the adjustment of​​​‌ predictions, and because of‌ what is known about‌​‌ cerebellar dysfunction in schizophrenia.​​

The impact and mechanisms​​​‌ of action of TMS‌ are poorly understood, so‌​‌ it is necessary to​​ verify that the chosen​​​‌ stimulation does not worsen‌ the condition of people‌​‌ with schizophrenia before applying​​ these methods to patients.​​​‌ This study is therefore‌ being conducted first on‌​‌ healthy volunteers to verify​​ whether a TMS session​​​‌ targeting the cerebellum can‌ effectively modify the temporal‌​‌ processes likely to play​​ a role in the​​​‌ pathophysiology of schizophrenia. To‌ answer this question, we‌​‌ recruited human subjects who​​ underwent TMS sessions and​​​‌ observed EEG activity during‌ prediction tasks. First analysis‌​‌ of the EEG revealed​​ an impact of TMS​​​‌ on the evoked potential‌ component CNV. In a‌​‌ subsequent step, we have​​ started to analyze the​​​‌ functional connectivity between brain‌ areas based on EEG‌​‌ activity.

7.1 International initiatives

7.2 National initiatives

At‌ the national level, the‌​‌ NECTARINE team collaborates with​​ the team Psychiatrie at​​​‌ INSERM 1329 in Strasbourg.‌ In this collaboration with‌​‌ the INSERM-colleagues Anne Bonnefond​​ and Anne Giersch, Axel​​​‌ Hutt and his collaboration‌ partners attempt to relate‌​‌ behavioral and electrophysiological observation​​ data in humans to​​​‌ the subjects’ state of‌ attention and perception of‌​‌ time. These objectives are​​​‌ part of two corresponding​ co-supervised PhD-projects.

These collaborators​‌ have already shared datasets​​ for offline analysis with​​​‌ us, including EEG recordings​ with TMS stimulation from​‌ human participants. Overall, having​​ a network of experimental​​​‌ collaborators with existing datasets​ and access to human​‌ recording equipment will significantly​​ reduce the project’s risks​​​‌ and accelerate its evolution.​

The team and its​‌ scientific partner at the​​ University Clinic in Freiburg,​​​‌ Germany, has received financial​ support for an interdisciplinary​‌ project by ITI HealthTech​​ in Strasbourg. It permits​​​‌ us to purchase neurophysiological​ recording devices and recruit​‌ Master students. The title​​ of the 18-month project​​​‌ is Digital Medication by​ auditory beat stimulation to​‌ improve attention in Major​​ Depressive Disorder.

7.3​​​‌ Regional initiatives

Axel Hutt​ has been working with​‌ the University Clinic in​​ Freiburg / Germany motivated​​​‌ by the former Regional​ Network NEUREX. The​‌ project partners at the​​ University Clinic in Freiburg​​​‌ are Dr. Stefan Vestring​ and Viktoria Galuba working​‌ on neuroplasticity in humans​​ by visual neuromodulation.

8.1 Promoting scientific activities​​

8.2 Teaching -​​​‌ Supervision - Juries -​ Educational and pedagogical outreach​‌

9.1 Major​​ publications

• 1 articleG.​​​‌Gabriel Alves Castro,‌ A.Anne Bonnefond,‌​‌ B.-T.Bich-Thuy Pham and​​ A.Axel Hutt.​​​‌ Sustained attention in attention-deficit‌ subjects and the impact‌​‌ of binaural beat stimulation​​ evaluated by behavior and​​​‌ EEG.Experimental Brain‌ ResearchSeptember 2025,‌​‌ 211HALDOIback​​ to text
• 2 article​​​‌D.David Dahmen,‌ A.Axel Hutt,‌​‌ G.Giacomo Indiveri,​​ A.Ann Kennedy,​​​‌ J.Jeremie Lefebvre,‌ L.Luca Mazzucato,‌​‌ A.Adilson E. Motter​​, R.Rishikesh Narayanan​​​‌, M.Melika Payvand‌, H.Henrike Planert‌​‌ and R.Richard Gast​​. How Heterogeneity Shapes​​​‌ Dynamics and Computation in‌ the Brain.Neuron‌​‌2025HALDOIback​​ to text

9.2 Publications​​​‌ of the year

9.3 Cited​​​‌ publications

• 5 articleL.‌L Chaieb, E.‌​‌EC Wilpert, T.​​TP Reber and J.​​​‌J Fell. Auditory‌ beat stimulation and its‌​‌ effects on cognition and​​ mood States.Front​​​‌ Psychiatry62015,‌ 70DOIback to‌​‌ textback to text​​
• 6 articleA.A.​​​‌ Gupta, N.N.‌ Vardalakis and F.F.B.‌​‌ Wagner. Neuroprosthetics: from​​ sensorimotor to cognitive disorders​​​‌.Commun Biol.6‌2023, 14DOI‌​‌back to textback​​ to textback to​​​‌ text