Distinct Longitudinal Changes in EEG Measures Reflecting Functional Network Disruption in ALS Cognitive Phenotypes.

Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in [Formula: see text]-band spectral power over time in the temporal region along with increased [Formula: see text]-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread ß-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.

A baseline for source localisation using the inverse modelling tool FREAR.

A global network of monitoring stations is set up that can measure tiny concentrations of airborne radioactivity as part of the verification regime of the Comprehensive Nuclear-Test-Ban Treaty. If Treaty-relevant detections are made, inverse atmospheric transport modelling is one of the methods that can be used to determine the source of the radioactivity. In order to facilitate the testing of novel developments in inverse modelling, two sets of test cases are constructed using real-world 133Xe detections associated with routine releases from a medical isotope production facility. One set consists of 24 cases with 5 days of observations in each case, and another set consists of 8 cases with 15 days of observations in each case. A series of inverse modelling techniques and several sensitivity experiments are applied to determine the (known) location of the medical isotope production facility. Metrics are proposed to quantify the quality of the source localisation. Finally, it is illustrated how the sets of test cases can be used to test novel developments in inverse modelling algorithms.

Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks.

Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals.Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods.Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature.Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.

Altered Pain Processing Associated with Administration of Dopamine Agonist and Antagonist in Healthy Volunteers.

Striatal dopamine dysfunction is associated with the altered top-down modulation of pain processing. The dopamine D2-like receptor family is a potential substrate for such effects due to its primary expression in the striatum, but evidence for this is currently lacking. Here, we investigated the effect of pharmacologically manipulating striatal dopamine D2 receptor activity on the anticipation and perception of acute pain stimuli in humans. Participants received visual cues that induced either certain or uncertain anticipation of two pain intensity levels delivered via a CO2 laser. Rating of the pain intensity and unpleasantness was recorded. Brain activity was recorded with EEG and analysed via source localisation to investigate neural activity during the anticipation and receipt of pain. Participants completed the experiment under three conditions, control (Sodium Chloride), D2 receptor agonist (Cabergoline), and D2 receptor antagonist (Amisulpride), in a repeated-measures, triple-crossover, double-blind study. The antagonist reduced an individuals' ability to distinguish between low and high pain following uncertain anticipation. The EEG source localisation showed that the agonist and antagonist reduced neural activations in specific brain regions associated with the sensory integration of salient stimuli during the anticipation and receipt of pain. During anticipation, the agonist reduced activity in the right mid-temporal region and the right angular gyrus, whilst the antagonist reduced activity within the right postcentral, right mid-temporal, and right inferior parietal regions. In comparison to control, the antagonist reduced activity within the insula during the receipt of pain, a key structure involved in the integration of the sensory and affective aspects of pain. Pain sensitivity and unpleasantness were not changed by D2R modulation. Our results support the notion that D2 receptor neurotransmission has a role in the top-down modulation of pain.

Predisposition to domain-wide maladaptive changes in predictive coding in auditory phantom perception.

Tinnitus is hypothesised to be a predictive coding problem. Previous research indicates lower sensitivity to prediction errors (PEs) in tinnitus patients while processing auditory deviants corresponding to tinnitus-specific stimuli. However, based on research with patients with hallucinations and no psychosis we hypothesise tinnitus patients may be more sensitive to PEs produced by auditory stimuli that are not related to tinnitus characteristics. Specifically in patients with minimal to no hearing loss, we hypothesise a more top-down subtype of tinnitus that may be driven by maladaptive changes in an auditory predictive coding network. To test this, we use an auditory oddball paradigm with omission of global deviants, a measure that is previously shown to empirically characterise hierarchical prediction errors (PEs). We observe: (1) increased predictions characterised by increased pre-stimulus response and increased alpha connectivity between the parahippocampus, dorsal anterior cingulate cortex and parahippocampus, pregenual anterior cingulate cortex and posterior cingulate cortex; (2) increased PEs characterised by increased P300 amplitude and gamma activity and increased theta connectivity between auditory cortices, parahippocampus and dorsal anterior cingulate cortex in the tinnitus group; (3) increased overall feed-forward connectivity in theta from the auditory cortex and parahippocampus to the dorsal anterior cingulate cortex; (4) correlations of pre-stimulus theta activity to tinnitus loudness and alpha activity to tinnitus distress. These results provide empirical evidence of maladaptive changes in a hierarchical predictive coding network in a subgroup of tinnitus patients with minimal to no hearing loss. The changes in pre-stimulus activity and connectivity to non-tinnitus specific stimuli suggest that tinnitus patients not only produce strong predictions about upcoming stimuli but also may be predisposed to stimulus a-specific PEs in the auditory domain. Correlations with tinnitus-related characteristics may be a biomarker for maladaptive changes in auditory predictive coding.

Automated interictal source localisation based on high-density EEG.

PURPOSE: To study the accuracy of automated interictal EEG source localisation based on high-density EEG, and to compare it to low-density EEG. METHODS: Thirty patients operated for pharmacoresistant focal epilepsy were retrospectively examined. Twelve months after resective brain surgery, 18 were seizure-free or had 'auras' only, while 12 had persistence of disabling seizures. Presurgical 257-channel EEG lasting 3-20 h was down-sampled to 25, 40, and 204 channels for separate analyses. For each electrode setup, interictal spikes were detected, clustered, and averaged automatically before validation by an expert reviewer. An individual 6-layer finite difference head model and the standardised low-resolution electromagnetic tomography were used to localise the maximum source activity of the most prevalent spike. Sublobar concordance with the resected brain area was visually assessed and related to favourable vs. unfavourable postsurgical outcome. RESULTS: Depending on the EEG setup, epileptic spikes were detected in 21-24 patients (70-80%). The median number of single spikes per average was 470 (range 17-15,066). Diagnostic sensitivity of EEG source localisation was 58-75%, specificity was 50-67%, and overall accuracy was 55-71%. There were no significant differences between low- and high-density EEG setups with 25 to 257 electrodes. CONCLUSION: Automated high-density EEG source localisation provides meaningful information in the majority of cases. With hundreds of single spikes averaged, diagnostic accuracy is similar in high- and low-density EEG. Therefore, low-density EEG may be sufficient for interictal EEG source localisation if high numbers of spikes are available.

Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation.

Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal-to-noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér-Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM-based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.

Neural component analysis: A spatial filter for electroencephalogram analysis.

BACKGROUND: Spatial filtering and source separation are valuable tools in the analysis of EEG data. However, despite the well-known spatial localisation of individual cognitive processes within the brain, the available methods for source separation, such as the widely used blind source separation technique, do not take into account the spatial distributions and locations of sources. This can result in sub-optimal source identification. NEW METHOD: We present a new method for deriving a spatial filter for EEG data that attempts to identify sources that are maximally spatially distinct from one another in terms of the spatial distributions of their projections. RESULTS: We first evaluate our method with simulated EEG and show that it is able to separate EEG signals into components with distinct spatial distributions that closely resemble the original simulated sources. We also evaluate our method with real EEG and show it is able to identify a spatial filter that can be used to significantly improve classification accuracy of the P300 event-related potential (ERP). COMPARISON WITH EXISTING METHODS: We compare our method to a state of the art blind source separation methods, fast independent component analysis (ICA) and common spatial patterns (CSP). We evaluate the methods suitability for a common source separation application, analysis of ERPs. CONCLUSIONS: Our results show that our method is well suited to identifying spatial filters for EEG analysis. This has potential applications in a wide range of EEG signal processing applications.

Towards Fast Plume Source Estimation with a Mobile Robot.

The estimation of the parameters of an odour source is of high relevance for multiple applications, but it can be a slow and error prone process. This work proposes a fast particle filter-based method for source term estimation with a mobile robot. Two strategies are implemented in order to reduce the computational cost of the filter and increase its accuracy: firstly, the sampling process is adapted by the mobile robot in order to optimise the quality of the data provided to the estimation process; secondly, the filter is initialised only after collecting preliminary data that allow limiting the solution space and use a shorter number of particles than it would be normally necessary. The method assumes a Gaussian plume model for odour dispersion. This models average odour concentrations, but the particle filter was proved adequate to fit instantaneous concentration measurements to that model, while the environment was being sampled. The method was validated in an obstacle free controlled wind tunnel and the validation results show its ability to quickly converge to accurate estimates of the plume's parameters after a reduced number of plume crossings.

Redefining the role of Magnetoencephalography in refractory epilepsy.

Magnetoencephalography (MEG) possesses a number of features, including excellent spatiotemporal resolution, that lend itself to the functional imaging of epileptic activity. However its current use is restricted to specific scenarios, namely in the diagnosis refractory focal epilepsies where electroencephalography (EEG) has been inconclusive. This review highlights the recent progress of MEG within epilepsy, including advances in the technique itself such as simultaneous EEG/MEG and intracranial EEG/MEG recording and room temperature MEG recording using optically pumped magnetometers, as well as improved post processing of the data during interictal and ictal activity for accurate source localisation of the epileptogenic focus. These advances should broaden the scope of MEG as an important part of epilepsy diagnostics in the future.

An iterative strategy for contaminant source localisation using GLMA optimization and Data Worth on two synthetic 2D Aquifers.

A contaminant source localisation strategy was developed considering unknown heterogeneous hydraulic conductivity field, unknown dispersivity and unknown location of a continuous contaminant source. The Gauss-Levenberg-Marquardt algorithm is combined with a data worth analysis to estimate the unknown parameters and identify the best locations of additional measurements. The data collection strategy is iterative, based on the ability of the additional dataset to decrease the uncertainties on the contaminant source location. Two 2D synthetic models are considered. The method is first illustrated with a simple model and a more complex model is then considered to evaluate the ability of the approach to locate the contaminant source from hydraulic heads and concentration data. This approach is parsimonious in terms of model runs and applicable to real cases. The results give a good estimate of the source location and the dispersivity, with acceptable NRMSE for each case. New observations introduced at each iteration decrease the standard deviation of the source location and improve the NRMSE. The estimated hydraulic conductivity field presents the same features as the original field.

Prediction-error signals to violated expectations about person identity and head orientation are doubly-dissociated across dorsal and ventral visual stream regions.

Predictive coding theories of perception highlight the importance of constantly updated internal models of the world to predict future sensory inputs. Importantly, such theories suggest that prediction-error signalling should be specific to the violation of predictions concerning distinct attributes of the same stimulus. To interrogate this as yet untested prediction, we focused on two different aspects of face perception (identity and orientation) and investigated whether cortical regions which process particular stimulus attributes also signal prediction violations with respect to those same stimulus attributes. We employed a paradigm using sequential trajectories of images to create perceptual expectations about face orientation and identity, and then parametrically violated each attribute. Using MEG data, we identified double dissociations of expectancy violations in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater prediction-error signals to identity violations than to orientation violations, whereas the left angular gyrus showed the converse pattern of results. Our results suggest that perceptual prediction-error signalling is directly linked to regions associated with the processing of different stimulus properties.

A neurophysiological investigation of anticipation to pain in Parkinson's disease.

Chronic pain is common in people with Parkinson's disease and is often considered to be caused by the motor impairments associated with the disease. Altered top-down processing of pain characterises several chronic pain conditions and occurs when the cortex modifies nociceptive processing in the brain and spinal cord. This contrasts with bottom-up modulation of pain whereby nociceptive processing is modified on its way up to the brain. Although several studies have demonstrated altered bottom-up pain processing in Parkinson's, the contribution of enhanced anticipation to pain and atypical top-down processing of pain has not been fully explored. During the anticipation to noxious stimuli, EEG source localisation reported an increased activation in the midcingulate cortex and supplementary motor area in the Parkinson's disease group compared to the healthy control group during mid [-1,500 -1,000]-and late anticipation [-500 0], indicating enhanced cortical activity before noxious stimulation. The Parkinson's disease group was also more sensitive to the laser and required a lower voltage level to induce pain. This study provides evidence supporting the hypothesis that enhanced top-down processing of pain may contribute to the development of chronic pain in Parkinson's. Additional research to establish whether the altered anticipatory response is unique to noxious stimuli is required as no control stimulus was used within the current study. With further research to confirm these findings, our results inform a scientific rationale for novel treatment strategies of pain in Parkinson's disease, including mindfulness, cognitive therapies and other approaches targeted at improving top-down processing of pain.

Imaging the human hippocampus with optically-pumped magnetoencephalography.

Optically-pumped (OP) magnetometers allow magnetoencephalography (MEG) to be performed while a participant's head is unconstrained. To fully leverage this new technology, and in particular its capacity for mobility, the activity of deep brain structures which facilitate explorative behaviours such as navigation, must be detectable using OP-MEG. One such crucial brain region is the hippocampus. Here we had three healthy adult participants perform a hippocampal-dependent task - the imagination of novel scene imagery - while being scanned using OP-MEG. A conjunction analysis across these three participants revealed a significant change in theta power in the medial temporal lobe. The peak of this activated cluster was located in the anterior hippocampus. We repeated the experiment with the same participants in a conventional SQUID-MEG scanner and found similar engagement of the medial temporal lobe, also with a peak in the anterior hippocampus. These OP-MEG findings indicate exciting new opportunities for investigating the neural correlates of a range of crucial cognitive functions in naturalistic contexts including spatial navigation, episodic memory and social interactions.

Patterned functional network disruption in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease primarily affecting motor function, with additional evidence of extensive nonmotor involvement. Despite increasing recognition of the disease as a multisystem network disorder characterised by impaired connectivity, the precise neuroelectric characteristics of impaired cortical communication remain to be fully elucidated. Here, we characterise changes in functional connectivity using beamformer source analysis on resting-state electroencephalography recordings from 74 ALS patients and 47 age-matched healthy controls. Spatiospectral characteristics of network changes in the ALS patient group were quantified by spectral power, amplitude envelope correlation (co-modulation) and imaginary coherence (synchrony). We show patterns of decreased spectral power in the occipital and temporal (δ- to ß-band), lateral/orbitofrontal (δ- to θ-band) and sensorimotor (ß-band) regions of the brain in patients with ALS. Furthermore, we show increased co-modulation of neural oscillations in the central and posterior (δ-, θ- and γl -band) and frontal (δ- and γl -band) regions, as well as decreased synchrony in the temporal and frontal (δ- to ß-band) and sensorimotor (ß-band) regions. Factorisation of these complex connectivity patterns reveals a distinct disruption of both motor and nonmotor networks. The observed changes in connectivity correlated with structural MRI changes, functional motor scores and cognitive scores. Characteristic patterned changes of cortical function in ALS signify widespread disease-associated network disruption, pointing to extensive dysfunction of both motor and cognitive networks. These statistically robust findings, that correlate with clinical scores, provide a strong rationale for further development as biomarkers of network disruption for future clinical trials.

Variation in Reported Human Head Tissue Electrical Conductivity Values.

Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the whole-skull, the spongiform layer of the skull, isotropic, perpendicularly- and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. This large variation should be acknowledged, and care should be taken when creating volume conductor models, ideally constructing them on an individual basis, rather than assuming them from the literature. When personalised models are unavailable, it is suggested weighted average means from the current meta-analysis are used. Assigning conductivity as: 0.41 S/m for the scalp, 0.02 S/m for the whole skull, or when better modelled as a three-layer skull 0.048 S/m for the spongiform layer, 0.007 S/m for the inner compact and 0.005 S/m for the outer compact, as well as 1.71 S/m for the CSF, 0.47 S/m for the grey matter, 0.22 S/m for WM and 50.4 for the BSCR.

A Comparative Study of Bio-Inspired Odour Source Localisation Strategies from the State-Action Perspective.

Locating odour sources with robots is an interesting problem with many important real-world applications. In the past years, the robotics community has adapted several bio-inspired strategies to search for odour sources in a variety of environments. This work studies and compares some of the most common strategies from a behavioural perspective with the aim of knowing: (1) how different are the behaviours exhibited by the strategies for the same perceptual state; and (2) which are the most consensual actions for each perceptual state in each environment. The first step of this analysis consists of clustering the perceptual states, and building histograms of the actions taken for each cluster. In case of (1), a histogram is made for each strategy separately, whereas for (2), a single histogram containing the actions of all strategies is produced for each cluster of states. Finally, statistical hypotheses tests are used to find the statistically significant differences between the behaviours of the strategies in each state. The data used for performing this study was gathered from a purpose-built simulator which accurately simulates the real-world phenomena of odour dispersion and air flow, whilst being sufficiently fast to be employed in learning and evolutionary robotics experiments. This paper also proposes an xml-inspired structure for the generated datasets that are used to store the perceptual information of the robots over the course of the simulations. These datasets may be used in learning experiments to estimate the quality of a candidate solution or for measuring its novelty.

Dysfunction of attention switching networks in amyotrophic lateral sclerosis.

OBJECTIVE: To localise and characterise changes in cognitive networks in Amyotrophic Lateral Sclerosis (ALS) using source analysis of mismatch negativity (MMN) waveforms. RATIONALE: The MMN waveform has an increased average delay in ALS. MMN has been attributed to change detection and involuntary attention switching. This therefore indicates pathological impairment of the neural network components which generate these functions. Source localisation can mitigate the poor spatial resolution of sensor-level EEG analysis by associating the sensor-level signals to the contributing brain sources. The functional activity in each generating source can therefore be individually measured and investigated as a quantitative biomarker of impairment in ALS or its sub-phenotypes. METHODS: MMN responses from 128-channel electroencephalography (EEG) recordings in 58 ALS patients and 39 healthy controls were localised to source by three separate localisation methods, including beamforming, dipole fitting and exact low resolution brain electromagnetic tomography. RESULTS: Compared with controls, ALS patients showed significant increase in power of the left posterior parietal, central and dorsolateral prefrontal cortices (false discovery rate = 0.1). This change correlated with impaired cognitive flexibility (rho = 0.45, 0.45, 0.47, p = .042, .055, .031 respectively). ALS patients also exhibited a decrease in the power of dipoles representing activity in the inferior frontal (left: p = 5.16 × 10-6, right: p = 1.07 × 10-5) and left superior temporal gyri (p = 9.30 × 10-6). These patterns were detected across three source localisation methods. Decrease in right inferior frontal gyrus activity was a good discriminator of ALS patients from controls (AUROC = 0.77) and an excellent discriminator of C9ORF72 expansion-positive patients from controls (AUROC = 0.95). INTERPRETATION: Source localization of evoked potentials can reliably discriminate patterns of functional network impairment in ALS and ALS subgroups during involuntary attention switching. The discriminative ability of the detected cognitive changes in specific brain regions are comparable to those of functional magnetic resonance imaging (fMRI). Source analysis of high-density EEG patterns has excellent potential to provide non-invasive, data-driven quantitative biomarkers of network disruption that could be harnessed as novel neurophysiology-based outcome measures in clinical trials.

Autonomous Multi-Robot Search for a Hazardous Source in a Turbulent Environment.

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao-Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics.

Conflict processing in juvenile patients with neurofibromatosis type 1 (NF1) and healthy controls - Two pathways to success.

Neurofibromatosis Type 1 (NF1) is a monogenetic autosomal-dominant disorder with a broad spectrum of clinical symptoms and is commonly associated with cognitive deficits. Patients with NF1 frequently exhibit cognitive impairments like attention problems, working memory deficits and dysfunctional inhibitory control. The latter is also relevant for the resolution of cognitive conflicts. However, it is unclear how conflict monitoring processes are modulated in NF1. To examine this question in more detail, we used a system neurophysiological approach combining high-density ERP recordings with source localisation analyses in juvenile patients with NF1 and controls during a flanker task. Behaviourally, patients with NF1 perform significantly slower than controls. Specifically on trials with incompatible flanker-target pairings, however, the patients with NF1 made significantly fewer errors than healthy controls. Yet, importantly, this overall successful conflict resolution was reached via two different routes in the two groups. The healthy controls seem to arrive at a successful conflict monitoring performance through a developing conflict recognition via the N2 accompanied by a selectively enhanced N450 activation in the case of perceived flanker-target conflicts. The presumed dopamine deficiency in the patients with NF1 seems to result in a reduced ability to process conflicts via the N2. However, NF1 patients show an increased N450 irrespective of cognitive conflict. Activation differences in the orbitofrontal cortex (BA11) and anterior cingulate cortex (BA24) underlie these modulations. Taken together, juvenile patients with NF1 and juvenile healthy controls seem to accomplish conflict monitoring via two different cognitive neurophysiological pathways.