Stefan Dürschmid

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Significance To survive, organisms must constantly form predictions of the future based on past regularities. When predictions are violated, action may be needed. Different scales of environmental regularity need to encompass both subsecond repetitions and complex structures spanning longer timescales. How different parts of the brain monitor these temporal regularities and produce prediction error signals is unclear. Utilizing subdural electrocorticographic electrodes with an auditory paradigm involving local and global regularities, we show that frontal cortex is sensitive to the big picture, responding with high γ-band activity exclusively to globally unpredictable changes, whereas the temporal cortex equally responds to any change in the immediate history. These results reveal a hierarchy of predictive coding recorded directly from the human brain. Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80–150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

Phase-Amplitude Cross-Frequency Coupling in the Human Nucleus Accumbens Tracks Action Monitoring during Cognitive Control

The Nucleus Accumbens (NAcc) is an important structure for the transfer of information between cortical and subcortical structures, especially the prefrontal cortex and the hippocampus. However, the mechanism that allows the NAcc to achieve this integration is not well understood. Phase-amplitude cross-frequency coupling (PAC) of oscillations in different frequency bands has been proposed as an effective mechanism to form functional networks to optimize transfer and integration of information. Here we assess PAC between theta and high gamma oscillations as a potential mechanism that facilitates motor adaptation. To address this issue we recorded intracranial field potentials directly from the bilateral human NAcc in three patients while they performed a motor learning task that varied in the level of cognitive control needed to perform the task. As in rodents, PAC was observable in the human NAcc, transiently occurring contralateral to a movement following the motor response. Importantly, PAC correlated with the level of cognitive control needed to monitor the action performed. This functional relation indicates that the NAcc is engaged in action monitoring and supports the evaluation of motor programs during adaptive behavior by means of PAC.

Sensory Deviancy Detection Measured Directly Within the Human Nucleus Accumbens

Rapid changes in the environment evoke a comparison between expectancy and actual outcome to inform optimal subsequent behavior. The nucleus accumbens (NAcc), a key interface between the hippocampus and neocortical regions, is a candidate region for mediating this comparison. Here, we report event-related potentials obtained from the NAcc using direct intracranial recordings in 5 human participants while they listened to trains of auditory stimuli differing in their degree of deviation from repetitive background stimuli. NAcc recordings revealed an early mismatch signal (50-220 ms) in response to all deviants. NAcc activity in this time window was also sensitive to the statistics of stimulus deviancy, with larger amplitudes as a function of the level of deviancy. Importantly, this NAcc mismatch signal also predicted generation of longer latency scalp potentials (300-400 ms). The results provide direct human evidence that the NAcc is a key component of a network engaged in encoding statistics of the sensory environmental.

Deep Brain stimulation of the Nucleus Basalis of Meynert attenuates early EEG components associated with defective sensory gating in patients with Alzheimer disease – a two-case study

Alzheimer′s disease (AD) is associated with deterioration of memory and cognitive function and a degeneration of neurons of the nucleus basalis of Meynert (NBM). The NBM is the major input source of acetylcholine (ACh) to the cortex. The decreasing cholinergic innervation of the cortex due to degeneration of the NBM might be the cause of loss of memory function. NBM‐Deep brain stimulation (NBM‐DBS) is considered to serve as a potential therapeutic option for patients with AD by supporting residual cholinergic transmission to stabilize oscillatory activity in memory‐relevant circuits. However, whether DBS could improve sensory memory functions in patients with AD is not clear. Here, in a passive auditory oddball paradigm, patients with AD (N = 2) listened to repetitive background tones (standard tones) randomly interrupted by frequency deviants in two blocks with NBM‐DBS OFF and then NBM‐DBS ON, while age‐matched healthy controls (N = 6) repeated the experiment twice. The mismatch negativity in NBM‐DBS OFF significantly differed from controls in both blocks, but not under NBM‐DBS, which was likely due to a pronounced P50 increase overlapping with the N1 in NBM‐DBS OFF. This early complex of EEG components recovered under stimulation to a normal level as defined by responses in controls. In this temporal interval, we found in patients with NBM‐DBS ON (but not with NBM‐DBS OFF) and in controls a strong repetition suppression effect to standard tones – with more attenuated responses to frequently repeated standard tones. This highlights the role of NBM‐DBS for sensory gating of familiar auditory information into sensory memory.

Oscillatory Dynamics Track Motor Performance Improvement in Human Cortex

Improving performance in motor skill acquisition is proposed to be supported by tuning of neural networks. To address this issue we investigated changes of phase-amplitude cross-frequency coupling (paCFC) in neuronal networks during motor performance improvement. We recorded intracranially from subdural electrodes (electrocorticogram; ECoG) from 6 patients who learned 3 distinct motor tasks requiring coordination of finger movements with an external cue (serial response task, auditory motor coordination task, go/no-go). Performance improved in all subjects and all tasks during the first block and plateaued in subsequent blocks. Performance improvement was paralled by increasing neural changes in the trial-to-trial paCFC between theta (; 4–8 Hz) phase and high gamma (HG; 80–180 Hz) amplitude. Electrodes showing this covariation pattern (Pearsons r ranging up to .45) were located contralateral to the limb performing the task and were observed predominantly in motor brain regions. We observed stable paCFC when task performance asymptoted. Our results indicate that motor performance improvement is accompanied by adjustments in the dynamics and topology of neuronal network interactions in the and HG range. The location of the involved electrodes suggests that oscillatory dynamics in motor cortices support performance improvement with practice.

A Comparative Study on the Detection of Covert Attention in Event-Related EEG and MEG Signals to Control a BCI

In brain-computer interface (BCI) applications the detection of neural processing as revealed by event-related potentials (ERPs) is a frequently used approach to regain communication for people unable to interact through any peripheral muscle control. However, the commonly used electroencephalography (EEG) provides signals of low signal-to-noise ratio, making the systems slow and inaccurate. As an alternative noninvasive recording technique, the magnetoencephalography (MEG) could provide more advantageous electrophysiological signals due to a higher number of sensors and the magnetic fields not being influenced by volume conduction. We investigated whether MEG provides higher accuracy in detecting event-related fields (ERFs) compared to detecting ERPs in simultaneously recorded EEG, both evoked by a covert attention task, and whether a combination of the modalities is advantageous. In our approach, a detection algorithm based on spatial filtering is used to identify ERP/ERF components in a data-driven manner. We found that MEG achieves higher decoding accuracy (DA) compared to EEG and that the combination of both further improves the performance significantly. However, MEG data showed poor performance in cross-subject classification, indicating that the algorithms ability for transfer learning across subjects is better in EEG. Here we show that BCI control by covert attention is feasible with EEG and MEG using a data-driven spatial filter approach with a clear advantage of the MEG regarding DA but with a better transfer learning in EEG.

Efficient recognition of event-related potentials in high-density MEG recordings

In brain-computer interfacing (BCI), the recognition of task-specific event-related potentials such as P300 responses is an established approach to regaining communication in severely paralyzed people. However, a reliable detection of single trial potentials is challenging, because they are strongly affected by noise. Furthermore, potentials with their subcomponents are often distributed over several channels. With high density sensor arrays, a hypothesis-driven selection of channels, as often performed in BCIs based on electroencephalography (EEG), is challenging. We present a new data-driven approach that constructs spatio-temporal filters, considerably reducing the number of channels, reducing noise, and simultaneously determining the underlying brain dynamics. The extracted signals can be easily used to recognize the event sequence on which users focus their attention, without applying multivariate classification. We evaluated the approach using high density magnetoencephalography (MEG) data, recorded during a BCI experiment based on P300 responses. Compared to the subjects performance achieved with the initial decoding approach, the recognition rate increased significantly from 74.1% (std: 14.8%) to 95.1% (std: 4.9%) correct detections, which implies an information transfer rate improvement from 6.9 bit/min to 13.1 bit/min on average over 17 subjects.

P63. Detection of error potentials from EEG and MEG recordings and its value for BMI control

Objective Brain-Machine Interfaces (BMIs) can help to regain communication and mobility in severely disabled persons. Especially spelling devices, rehabilitation of stroke patients and prosthesis control are fields of application. However, noninvasive BMIs, commonly using electroencephalography (EEG), suffer from poor signal quality, resulting in erroneous commands. In order to detect such erroneous commands, error potentials (ErrPs) generated in the brain after a user perceived a negative feedback can be decoded. The aim of this study was to investigate how accurate the presence of ErrPs can be detected from simultaneously recorded EEG and magnetoencephalography (MEG). Methods In a BMI experiment involving 19 participants, the selection of a covertly attended object was decoded from EEG/MEG and presented as feedback ( Reichert et al., 2017 ). To facilitate investigation of ErrPs, we artificially presented negative feedback to achieve at least 40% incorrect feedback. Using spatial filtering and SVM classification, we determined the probability of successfully detecting an ErrP. While an accurate error detection permits a reduction of errors made by the covert attention detector (i.e. rejection of potentially erroneous commands), the error rate of the ErrP classification inevitably also introduces accidental rejection of correct commands. In order to evaluate the potential benefit of ErrP detection in a BMI, we define a probability measure that takes into account errors of both the covert attention detector and the error detector. Results The components extracted by the data-driven spatial filter showed a positive deflection between 200 and 500 ms after feedback presentation, mainly driving the ErrP decoding. The correctness of perceived feedback could be decoded reliably (EEG: 71.9% SE: 1.5%; MEG: 72.7%, SE: 1.2%). However, the actual BMI revealed higher accuracies (EEG: 87.9%, SE: 2.2%; MEG: 95.8%, SE: 1.0%) compared to the ErrP detector. Thus, when applying ErrP detection, the number of erroneous selections was reduced but concurrently an even higher number of correct selections was rejected, which significantly reduced the information transfer rate. Probability theory suggests that ErrP detection only is advantageous if error detection rates exceed the accuracy of the feedback generating BMI itself. Conclusions Our results indicate that EEG and MEG are comparably suitable to detect the perception of erroneous feedback from brain activity recordings. The achieved prediction rate is in accordance with other approaches reported in the literature using EEG. However, those prediction rates only are advantageous, if the performance of the BMI is lower than that of the ErrP detector. Thus, highly accurate detection of errors would be required to efficiently correct errors made by a BMI.

An Efficient Decoder for the Recognition of Event-Related Potentials in High-Density MEG Recordings

Brain–computer interfacing (BCI) is a promising technique for regaining communication and control in severely paralyzed people. Many BCI implementations are based on the recognition of task-specific event-related potentials (ERP) such as P300 responses. However, because of the high signal-to-noise ratio in noninvasive brain recordings, reliable detection of single trial ERPs is challenging. Furthermore, the relevant signal is often heterogeneously distributed over several channels. In this paper, we introduce a new approach for recognizing a sequence of attended events from multi-channel brain recordings. The framework utilizes spatial filtering to reduce both noise and signal space considerably. We introduce different models that can be used to construct the spatial filter and evaluate the approach using magnetoencephalography (MEG) data involving P300 responses, recorded during a BCI experiment. Compared to the accuracy achieved in the BCI experiment performed without spatial filtering, the recognition rate increased significantly to up to 95.3% on average (SD: 5.3%). In combination with the data-driven spatial filter construction we introduce here, our framework represents a powerful method to reliably recognize a sequence of brain potentials from high-density electrophysiological data, which could greatly improve the control of BCIs.

Perimovement decrease of alpha/beta oscillations in the human nucleus accumbens

The present work clarifies how the nucleus accumbens contributes to action. This region is often assumed to influence behavior “off-line” by evaluating outcomes. Studying rare recordings of local field potentials from the human nucleus accumbens, we observe a perimovement decrease of alpha and beta oscillations in seven of eight individuals, a signal that, in the motor system, is directly related to action preparation. Our results support the idea of an online role of this region for imminent action.