Spatio-temporal Brain Dynamics Mediating Post-error Behavioral Adjustments.

Optimal behavior relies on flexible adaptation to environmental requirements, notably based on the detection of errors. The impact of error detection on subsequent behavior typically manifests as a slowing down of RTs following errors. Precisely how errors impact the processing of subsequent stimuli and in turn shape behavior remains unresolved. To address these questions, we used an auditory spatial go/no-go task where continual feedback informed participants of whether they were too slow. We contrasted auditory-evoked potentials to left-lateralized go and right no-go stimuli as a function of performance on the preceding go stimuli, generating a 2 × 2 design with "preceding performance" (fast hit [FH], slow hit [SH]) and stimulus type (go, no-go) as within-subject factors. SH trials yielded SH trials on the following trials more often than did FHs, supporting our assumption that SHs engaged effects similar to errors. Electrophysiologically, auditory-evoked potentials modulated topographically as a function of preceding performance 80-110 msec poststimulus onset and then as a function of stimulus type at 110-140 msec, indicative of changes in the underlying brain networks. Source estimations revealed a stronger activity of prefrontal regions to stimuli after successful than error trials, followed by a stronger response of parietal areas to the no-go than go stimuli. We interpret these results in terms of a shift from a fast automatic to a slow controlled form of inhibitory control induced by the detection of errors, manifesting during low-level integration of task-relevant features of subsequent stimuli, which in turn influences response speed.

Attention-related potentials allow for a highly accurate discrimination of mild cognitive impairment subtypes.

The three most frequent forms of mild cognitive impairment (MCI) are single-domain amnestic MCI (sd-aMCI), single-domain dysexecutive MCI (sd-dMCI) and multiple-domain amnestic MCI (md-aMCI). Brain imaging differences among single domain subgroups of MCI were recently reported supporting the idea that electroencephalography (EEG) functional hallmarks can be used to differentiate these subgroups. We performed event-related potential (ERP) measures and independent component analysis in 18 sd-aMCI, 13 sd-dMCI and 35 md-aMCI cases during the successful performance of the Attentional Network Test. Sensitivity and specificity analyses of ERP for the discrimination of MCI subgroups were also made. In center-cue and spatial-cue warning stimuli, contingent negative variation (CNV) was elicited in all MCI subgroups. Two independent components (ICA1 and 2) were superimposed in the time range on the CNV. The ICA2 was strongly reduced in sd-dMCI compared to sd-aMCI and md-aMCI (4.3 vs. 7.5% and 10.9% of the CNV component). The parietal P300 ERP latency increased significantly in sd-dMCI compared to md-aMCI and sd-aMCI for both congruent and incongruent conditions. This latency for incongruent targets allowed for a highly accurate separation of sd-dMCI from both sd-aMCI and md-aMCI with correct classification rates of 90 and 81%, respectively. This EEG parameter alone performed much better than neuropsychological testing in distinguishing sd-dMCI from md-aMCI. Our data reveal qualitative changes in the composition of the neural generators of CNV in sd-dMCI. In addition, they document an increased latency of the executive P300 component that may represent a highly accurate hallmark for the discrimination of this MCI subgroup in routine clinical settings.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Verbal labels selectively bias brain responses to high-energy foods.

The influence of external factors on food preferences and choices is poorly understood. Knowing which and how food-external cues impact the sensory processing and cognitive valuation of food would provide a strong benefit toward a more integrative understanding of food intake behavior and potential means of interfering with deviant eating patterns to avoid detrimental health consequences for individuals in the long run. We investigated whether written labels with positive and negative (as opposed to 'neutral') valence differentially modulate the spatio-temporal brain dynamics in response to the subsequent viewing of high- and low-energetic food images. Electrical neuroimaging analyses were applied to visual evoked potentials (VEPs) from 20 normal-weight participants. VEPs and source estimations in response to high- and low- energy foods were differentially affected by the valence of preceding word labels over the ~260-300 ms post-stimulus period. These effects were only observed when high-energy foods were preceded by labels with positive valence. Neural sources in occipital as well as posterior, frontal, insular and cingulate regions were down-regulated. These findings favor cognitive-affective influences especially on the visual responses to high-energetic food cues, potentially indicating decreases in cognitive control and goal-adaptive behavior. Inverse correlations between insular activity and effectiveness in food classification further indicate that this down-regulation directly impacts food-related behavior.

The brain tracks the energetic value in food images.

Do our brains implicitly track the energetic content of the foods we see? Using electrical neuroimaging of visual evoked potentials (VEPs) we show that the human brain can rapidly discern food's energetic value, vis à vis its fat content, solely from its visual presentation. Responses to images of high-energy and low-energy food differed over two distinct time periods. The first period, starting at approximately 165 ms post-stimulus onset, followed from modulations in VEP topography and by extension in the configuration of the underlying brain network. Statistical comparison of source estimations identified differences distributed across a wide network including both posterior occipital regions and temporo-parietal cortices typically associated with object processing, and also inferior frontal cortices typically associated with decision-making. During a successive processing stage (starting at approximately 300 ms), responses differed both topographically and in terms of strength, with source estimations differing predominantly within prefrontal cortical regions implicated in reward assessment and decision-making. These effects occur orthogonally to the task that is actually being performed and suggest that reward properties such as a food's energetic content are treated rapidly and in parallel by a distributed network of brain regions involved in object categorization, reward assessment, and decision-making.

How to monitor the brain in septic patients?

Brain injury is frequently observed after sepsis and may be primarily related to the direct effects of the septic insult on the brain (e.g., brain edema, ischemia, seizures) or to secondary/indirect injuries (e.g., hypotension, hypoxemia, hypocapnia, hyperglycemia). Management of brain injury in septic patients is first focused to exclude structural intracranial complications (e.g., ischemic/hemorrhagic stroke) and possible confounders (e.g., electrolyte alterations or metabolic disorders, such as dysglycemia). Sepsis-associated brain dysfunction is frequently a heterogeneous syndrome. Despite increasing understanding of main pathophysiologic determinants, therapy is essentially limited to protect the brain against further cerebral damage, by way of "simple" therapeutic manipulations of cerebral perfusion and oxygenation and by avoiding over-sedation. Non-invasive monitoring of cerebral perfusion and oxygenation with transcranial Doppler (TCD) and near-infrared spectroscopy (NIRS) is feasible in septic patients. Electroencephalography (EEG) allows detection of sepsis-related seizures and holds promise also as sedation monitoring. Brain CT-scan detects intra-cerebral structural lesions, while magnetic resonance imaging (MRI) provides important insights into primary mechanisms of sepsis-related direct brain injury, (e.g., cytotoxic vs. vasogenic edema) and the development of posterior reversible encephalopathy. Together with EEG and evoked potentials (EP), MRI is also important for coma prognostication. Emerging clinical evidence suggests monitoring of the brain in septic patients can be implemented in the ICU. The objective of this review was to summarize recent clinical data about the role of brain monitoring - including TCD, NIRS, EEG, EP, CT, and MRI - in patients with sepsis and to illustrate its potential utility for the diagnosis, management and prognostication.

Clinical neurophysiology in disorders of consciousness: brain function monitoring in the ICU and beyond.

Over the past two decades, electrophysiology has undergone unprecedented changes thanks to technical improvements, which simplify measurement and analysis and allow more compact data storage. This book covers in detail the spectrum of electrophysiology applications in patients with disorders of consciousness. Its content spans from clinical aspects of the management of subjects in the intensive care unit, including EEG, evoked potentials and related implications in terms of prognosis and patient management to research applications in subjects with ongoing consciousness impairment. While the first section provides up-to-date information for the interested clinician, the second part highlights the latest developments in this exciting field. The book comprehensively combines clinical and research information related to neurophysiology in disorder-of- consciousness patients, making it an easily accessible reference for neuro-ICU specialists, epileptologists and clinical neurophysiologists as well as researchers utilizing EEG and event-related potentials.

Brain dynamics sustaining rapid rule extraction from speech

Language acquisition is a complex process that requires the synergic involvement of different cognitive functions, which include extracting and storing the words of the language and their embedded rules for progressive acquisition of grammatical information. As has been shown in other fields that study learning processes, synchronization mechanisms between neuronal assemblies might have a key role during language learning. In particular, studying these dynamics may help uncover whether different oscillatory patterns sustain more item-based learning of words and rule-based learning from speech input. Therefore, we tracked the modulation of oscillatory neural activity during the initial exposure to an artificial language, which contained embedded rules. We analyzed both spectral power variations, as a measure of local neuronal ensemble synchronization, as well as phase coherence patterns, as an index of the long-range coordination of these local groups of neurons. Synchronized activity in the gamma band (2040 Hz), previously reported to be related to the engagement of selective attention, showed a clear dissociation of local power and phase coherence between distant regions. In this frequency range, local synchrony characterized the subjects who were focused on word identification and was accompanied by increased coherence in the theta band (48 Hz). Only those subjects who were able to learn the embedded rules showed increased gamma band phase coherence between frontal, temporal, and parietal regions.

Counteracting incentive sensitization in severe alcohol dependence using deep brain stimulation of the nucleus accumbens: clinical and basic science aspects

The ventral striatum / nucleus accumbens has been implicated in the craving for drugs and alcohol which is a major reason for relapse of addicted people. Craving might be induced by drug-related cues. This suggests that disruption of craving-related neural activity in the nucleus accumbens may significantly reduce craving in alcohol-dependent patients. Here we report on preliminary clinical and neurophysiological evidence in three male patients who were treated with high frequency deep brain stimulation of the nucleus accumbens bilaterally. All three had been alcohol dependent for many years, unable to abstain from drinking, and had experienced repeated relapses prior to the stimulation. After the operation, craving was greatly reduced and all three patients were able to abstain from drinking for extended periods of time. Immediately after the operation but prior to connection of the stimulation electrodes to the stimulator, local field potentials were obtained from the externalized cables in two patients while they performed cognitive tasks addressing action monitoring and incentive salience of drug related cues. LFPs in the action monitoring task provided further evidence for a role of the nucleus accumbens in goal-directed behaviors. Importantly, alcohol related cue stimuli in the incentive salience task modulated LFPs even though these cues were presented outside of the attentional focus. This implies that cue-related craving involves the nucleus accumbens and is highly automatic.

Feedback-related Brain Potential Activity Complies with Basic Assumptions of Associative Learning Theory

Feedback-related negativity (FRN) is an ERP component that distinguishes positive from negative feedback. FRN has been hypothesized to be the product of an error signal that may be used to adjust future behavior. In addition, associative learning models assume that the trial-to-trial learning of cueoutcome mappings involves the minimization of an error term. This study evaluated whether FRN is a possible electrophysiological correlate of this error term in a predictive learning task where human subjects were asked to learn different cueoutcome relationships. Specifically, we evaluated the sensitivity of the FRN to the course of learning when different stimuli interact or compete to become a predictor of certain outcomes. Importantly, some of these cues were blocked by more informative or predictive cues (i.e., the blocking effect). Interestingly, the present results show that both learning and blocking affect the amplitude of the FRN component. Furthermore, independent analyses of positive and negative feedback event-related signals showed that the learning effect was restricted to the ERP component elicited by positive feedback. The blocking test showed differences in the FRN magnitude between a predictive and a blocked cue. Overall, the present results show that ERPs that are related to feedback processing correspond to the main predictions of associative learning models. ■

Topographic abnormality of slow cortical potentials in schizophrenia

A recent study from our laboratory has provided evidence for the generation of slow potentials occurring in anticipation to task-performance feedback stimuli, in multiple association cortical areas, consistently including two prefrontal areas. In the present study, we intended to determine whether these slow potentials would indicate some abnormality (topographic) in schizophrenic patients, and thus serve as an indication of abnormal association cortex activity. We recorded slow potentials while subjects performed a paired-associates memory task. A 123-channel EEG montage and common average reference were used for 20 unmedicated schizophrenic (mean duration of illness: 11.3 ± 9.2 years; mean number of previous hospitalizations: 1.2 ± 1.9) and 22 healthy control subjects during a visual paired-associates matching task. For the topographic analysis, we used a simple index of individual topographic deviation from normality, corrected for absolute potential intensities. Slow potentials were observed in all subjects. Control subjects showed a simple spatial pattern of voltage extrema (left central positive and right prefrontal negative), whereas schizophrenic patients presented a more complex, fragmented pattern. Topographic deviation was significantly different between groups (P < 0.001). The increased topographic complexity in schizophrenics could be visualized in grand averages computed across subjects. Increased topographic complexity could also be seen when grand averages were computed for subgroups of patients assembled either according to task-performance (high versus low) or by their scores on psychopathological scales. There was no significant correlation between topographic deviation and psychopathology scores. We conclude that the slow potential topographic abnormalities of schizophrenia indicate an abnormality in the configuration of large-scale electrical activity in association cortices.

Watching the infant brain learn words: Effects of vocabulary size and experience

Previous investigations comparing auditory event-related potentials (ERPs) to words whose meanings infants did or did not comprehend, found bilateral differences in brain activity to known versus unknown words in 13-month-old infants, in contrast with unilateral, left hemisphere, differences in activity in 20-month-old infants. We explore two alternative explanations for these findings. Changes in hemispheric specialization may result from a qualitative shift in the way infants process known words between 13 and 20 months. Alternatively, hemispheric specialization may arise from increased familiarity with the individual words tested. We contrasted these two explanations by measuring ERPs from 20-month-old infants with high and low production scores, for novel words they had just learned. A bilateral distribution of ERP differences was observed in both groups of infants, though the difference was larger in the left hemisphere for the high producers. These findings suggest that word familiarity is an important factor in determining the distribution of brain regions involved in word learning. An emerging left hemispheric specialization may reflect increased efficiency in the manner in which infants process familiar and novel words. (c) 2004 Elsevier Inc. All rights reserved.

Toward construction of an inexpensive brain computer interface for goal oriented applications

The paper describes the implementation of an offline, low-cost Brain Computer Interface (BCI) alternative to more expensive commercial models. Using inexpensive general purpose clinical EEG acquisition hardware (Truscan32, Deymed Diagnostic) as the base unit, a synchronisation module was constructed to allow the EEG hardware to be operated precisely in time to allow for recording of automatically time stamped EEG signals. The synchronising module allows the EEG recordings to be aligned in stimulus time locked fashion for further processing by the classifier to establish the class of the stimulus, sample by sample. This allows for the acquisition of signals from the subject’s brain for the goal oriented BCI application based on the oddball paradigm. An appropriate graphical user interface (GUI) was constructed and implemented as the method to elicit the required responses (in this case Event Related Potentials or ERPs) from the subject.

Single-trial event-related potential analysis for brain-computer interfaces

Abstract. Different types of mental activity are utilised as an input in Brain-Computer Interface (BCI) systems. One such activity type is based on Event-Related Potentials (ERPs). The characteristics of ERPs are not visible in single-trials, thus averaging over a number of trials is necessary before the signals become usable. An improvement in ERP-based BCI operation and system usability could be obtained if the use of single-trial ERP data was possible. The method of Independent Component Analysis (ICA) can be utilised to separate single-trial recordings of ERP data into components that correspond to ERP characteristics, background electroencephalogram (EEG) activity and other components with non- cerebral origin. Choice of specific components and their use to reconstruct “denoised” single-trial data could improve the signal quality, thus allowing the successful use of single-trial data without the need for averaging. This paper assesses single-trial ERP signals reconstructed using a selection of estimated components from the application of ICA on the raw ERP data. Signal improvement is measured using Contrast-To-Noise measures. It was found that such analysis improves the signal quality in all single-trials.