Representation of Sound Objects within Early-Stage Auditory Areas: A Repetition Effect Study Using 7T fMRI.

Environmental sounds are highly complex stimuli whose recognition depends on the interaction of top-down and bottom-up processes in the brain. Their semantic representations were shown to yield repetition suppression effects, i. e. a decrease in activity during exposure to a sound that is perceived as belonging to the same source as a preceding sound. Making use of the high spatial resolution of 7T fMRI we have investigated the representations of sound objects within early-stage auditory areas on the supratemporal plane. The primary auditory cortex was identified by means of tonotopic mapping and the non-primary areas by comparison with previous histological studies. Repeated presentations of different exemplars of the same sound source, as compared to the presentation of different sound sources, yielded significant repetition suppression effects within a subset of early-stage areas. This effect was found within the right hemisphere in primary areas A1 and R as well as two non-primary areas on the antero-medial part of the planum temporale, and within the left hemisphere in A1 and a non-primary area on the medial part of Heschl's gyrus. Thus, several, but not all early-stage auditory areas encode the meaning of environmental sounds.

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction.

Clinical evolution after a non-reactive hypothermic EEG following cardiac arrest.

BACKGROUND: Lack of electroencephalography (EEG) background reactivity during therapeutic hypothermia (TH) has been associated with poor outcome in post-anoxic comatose patients. However, decision on intensive care withdrawal is based on normothermic (NT) evaluations. This study aims at exploring whether patients showing recovery of EEG reactivity in NT after a non-reactive EEG in TH differ from those remaining non-reactive. METHODS: Patients with non-reactive EEG during TH were identified from our prospective registry of consecutive comatose adults admitted after successful resuscitation from CA between April 2009 and June 2014. Variables including neurological examination, serum neuron-specific enolase (NSE), procalcitonin, and EEG features were compared regarding impact on functional outcome at 3 months. RESULTS: Seventy-two of 197 patients (37 %) had a non-reactive EEG background during TH with thirteen (18 %) evolving towards reactivity in NT. Compared to those remaining non-reactive (n = 59), they showed significantly better recovery of brainstem reflexes (p < 0.001), better motor responses (p < 0.001), transitory consciousness improvement (p = 0.008), and a tendency toward lower NSE (p = 0.067). One patient recovering EEG reactivity survived with good functional outcome at 3 months. CONCLUSIONS: Recovery of EEG reactivity from TH to NT seems to distinguish two patients' subgroups regarding early neurological assessment and transitory consciousness improvement, corroborating the role of EEG in providing information about cerebral functions. Understanding these dynamic changes encourages maintenance of intensive support in selected patients even after a non-reactive EEG background in TH, as a small subgroup may indeed recover with good functional outcome.

Quantifying the time for accurate EEG decoding of single value-based decisions.

BACKGROUND: Recent neuroimaging studies suggest that value-based decision-making may rely on mechanisms of evidence accumulation. However no studies have explicitly investigated the time when single decisions are taken based on such an accumulation process. NEW METHOD: Here, we outline a novel electroencephalography (EEG) decoding technique which is based on accumulating the probability of appearance of prototypical voltage topographies and can be used for predicting subjects' decisions. We use this approach for studying the time-course of single decisions, during a task where subjects were asked to compare reward vs. loss points for accepting or rejecting offers. RESULTS: We show that based on this new method, we can accurately decode decisions for the majority of the subjects. The typical time-period for accurate decoding was modulated by task difficulty on a trial-by-trial basis. Typical latencies of when decisions are made were detected at ∼500ms for 'easy' vs. ∼700ms for 'hard' decisions, well before subjects' response (∼340ms). Importantly, this decision time correlated with the drift rates of a diffusion model, evaluated independently at the behavioral level. COMPARISON WITH EXISTING METHOD(S): We compare the performance of our algorithm with logistic regression and support vector machine and show that we obtain significant results for a higher number of subjects than with these two approaches. We also carry out analyses at the average event-related potential level, for comparison with previous studies on decision-making. CONCLUSIONS: We present a novel approach for studying the timing of value-based decision-making, by accumulating patterns of topographic EEG activity at single-trial level.

Transcranial Magnetic Stimulation Combined with EEG Reveals Covert States of Elevated Excitability in the Human Epileptic Brain.

BACKGROUND: Transcranial magnetic stimulation combined with electroencephalogram (TMS-EEG) can be used to explore the dynamical state of neuronal networks. In patients with epilepsy, TMS can induce epileptiform discharges (EDs) with a stochastic occurrence despite constant stimulation parameters. This observation raises the possibility that the pre-stimulation period contains multiple covert states of brain excitability some of which are associated with the generation of EDs. OBJECTIVE: To investigate whether the interictal period contains "high excitability" states that upon brain stimulation produce EDs and can be differentiated from "low excitability" states producing normal appearing TMS-EEG responses. METHODS: In a cohort of 25 patients with Genetic Generalized Epilepsies (GGE) we identified two subjects characterized by the intermittent development of TMS-induced EDs. The high-excitability in the pre-stimulation period was assessed using multiple measures of univariate time series analysis. Measures providing optimal discrimination were identified by feature selection techniques. The "high excitability" states emerged in multiple loci (indicating diffuse cortical hyperexcitability) and were clearly differentiated on the basis of 14 measures from "low excitability" states (accuracy = 0.7). CONCLUSION: In GGE, the interictal period contains multiple, quasi-stable covert states of excitability a class of which is associated with the generation of TMS-induced EDs. The relevance of these findings to theoretical models of ictogenesis is discussed.

Predicting the outcome of post-anoxic comatose patients based on single-trial EEG analysis

Les maladies cardio-vasculaires représentent la première cause de mortalité en Suisse. Après un arrêt cardio-respiratoire, une minorité des patients survit sans ou avec peu de séquelles fonctionnelles. L'évaluation du pronostic se fait classiquement selon des critères établis par l'Académie Américaine de Neurologie (AAN) en 2006, soit précédant l'introduction de l'hypothermie thérapeutique. Depuis, ces critères semblent insuffisants, et de nouveaux examens para-cliniques sont nécessaires afin d'identifier les patients ayant un pronostic favorable. La détection d'irrégularités auditives, et plus particulièrement l'évolution de cette détection sur plusieurs jours, pourrait être un indicateur du pronostic de patients comateux suite à une anoxie cérébrale. En effet, lors d'une violation de la régularité établie par des séries de sons identiques, deux signaux sont détectables à l'électro- encéphalographie (EEG). Le premier, dénommé «Mismatch negativity» (MMN), peut être enregistré après une violation locale d'une régularité établie au niveau de chaque son. Il reflète un processus inconscient et ne demandant pas de ressources attentionnelles. Le deuxième, dénommé « complexe P300 » survient par contre après une violation globale d'une régularité établie au niveau de groupes de sons. La littérature actuelle indique que ce deuxième phénomène requerrait la présence de capacités attentionnelles. Dans notre étude, nous avons testé l'existence de cette détection d'irrégularités auditives globales chez des patients dans une phase précoce de coma post-anoxique, sous hypothermie thérapeutique. Nous avons enregistré la réponse électro-encéphalographique lors de violations de régularités auditives globales, à l'aide d'un protocole expérimental qui intégrait en plus un paradigme de MMN classique, afin de tester la détection d'irrégularités auditives locales également. Notre analyse finale inclut 24 patients comateux ayant subi un arrêt cardio-respiratoire, et bénéficié du protocole hypothermie du Centre Hospitalier Universitaire Vaudois (CHUV) à Lausanne. Après une analyse multivariée des réponses électro-encéphalographiques de chaque tracé individuellement (« single-trial »), nous avons trouvé que 8 patients sur 24 pouvaient discriminer une irrégularité globale, alors qu'étant définis comateux selon l'échelle de Glasgow (GCS). De plus, l'amélioration de la détection d' irrégularités auditives entre deux EEG consécutifs (en hypo- puis normothermie), était un facteur de bon pronostic. Notre test pourrait ainsi être un complément para-clinique dans l'évaluation du pronostic de patients en coma post- anoxique.

Diagnosis of Alzheimer’s Disease from EEG by Means of Synchrony Measures in Optimized Frequency Bands

Several clinical studies have reported that EEG synchrony is affected by Alzheimer’s disease (AD). In this paper a frequency band analysis of AD EEG signals is presented, with the aim of improving the diagnosis of AD using EEG signals. In this paper, multiple synchrony measures are assessed through statistical tests (Mann–Whitney U test), including correlation, phase synchrony and Granger causality measures. Moreover, linear discriminant analysis (LDA) is conducted with those synchrony measures as features. For the data set at hand, the frequency range (5-6Hz) yields the best accuracy for diagnosing AD, which lies within the classical theta band (4-8Hz). The corresponding classification error is 4.88% for directed transfer function (DTF) Granger causality measure. Interestingly, results show that EEG of AD patients is more synchronous than in healthy subjects within the optimized range 5-6Hz, which is in sharp contrast with the loss of synchrony in AD EEG reported in many earlier studies. This new finding may provide new insights about the neurophysiology of AD. Additional testing on larger AD datasets is required to verify the effectiveness of the proposed approach.

EEG Windowed statitical wavelet deviation for estimation of muscular artifacts

Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp quantitative EEG (QEEG), based on the time-frequency shape analysis. The impact of the muscular activity on the EEG can be evaluated from this methodology. We present an application of this scoring as a preprocessing step for EEG signal analysis, in order to evaluate the amount of muscular activity for two set of EEG recordings for dementia patients with early stage of Alzheimer’s disease and control age-matched subjects.

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

ICA Cleaning procedure for EEG signals analysis: application to Alzheimer's disease detection

To develop systems in order to detect Alzheimer’s disease we want to use EEG signals. Available database is raw, so the first step must be to clean signals properly. We propose a new way of ICA cleaning on a database recorded from patients with Alzheimer's disease (mildAD, early stage). Two researchers visually inspected all the signals (EEG channels), and each recording's least corrupted (artefact-clean) continuous 20 sec interval were chosen for the analysis. Each trial was then decomposed using ICA. Sources were ordered using a kurtosis measure, and the researchers cleared up to seven sources per trial corresponding to artefacts (eye movements, EMG corruption, EKG, etc), using three criteria: (i) Isolated source on the scalp (only a few electrodes contribute to the source), (ii) Abnormal wave shape (drifts, eye blinks, sharp waves, etc.), (iii) Source of abnormally high amplitude ( �100 �V). We then evaluated the outcome of this cleaning by means of the classification of patients using multilayer perceptron neural networks. Results are very satisfactory and performance is increased from 50.9% to 73.1% correctly classified data using ICA cleaning procedure.

Stroking or Buzzing? A Comparison of Somatosensory Touch Stimuli Using 7 Tesla fMRI.

Studying body representations in the brain helps us to understand how we humans relate to our own bodies. The in vivo mapping of the somatosensory cortex, where these representations are found, is greatly facilitated by the high spatial resolution and high sensitivity to brain activation available at ultra-high field. In this study, the use of different stimulus types for somatotopic mapping of the digits at ultra-high field, specifically manual stroking and mechanical stimulation, was compared in terms of sensitivity and specificity of the brain responses. Larger positive responses in digit regions of interest were found for manual stroking than for mechanical stimulation, both in terms of average and maximum t-value and in terms of number of voxels with significant responses to the tactile stimulation. Responses to manual stroking were higher throughout the entire post-central sulcus, but the difference was especially large on its posterior wall, i.e. in Brodmann area 2. During mechanical stimulation, cross-digit responses were more negative than during manual stroking, possibly caused by a faster habituation to the stimulus. These differences indicate that manual stroking is a highly suitable stimulus for fast somatotopic mapping procedures, especially if Brodmann area 2 is of interest.

Physiological noise in human cerebellar fMRI.

OBJECTIVES: To compare physiological noise contributions in cerebellar and cerebral regions of interest in high-resolution functional magnetic resonance imaging (fMRI) data acquired at 7T, to estimate the need for physiological noise removal in cerebellar fMRI. MATERIALS AND METHODS: Signal fluctuations in high resolution (1 mm isotropic) 7T fMRI data were attributed to one of the following categories: task-induced BOLD changes, slow drift, signal changes correlated with the cardiac and respiratory cycles, signal changes related to the cardiac rate and respiratory volume per unit of time or other. [Formula: see text] values for all categories were compared across regions of interest. RESULTS: In this high-resolution data, signal fluctuations related to the phase of the cardiac cycle and cardiac rate were shown to be significant, but comparable between cerebellar and cerebral regions of interest. However, respiratory related signal fluctuations were increased in the cerebellar regions, with explained variances that were up to 80 % higher than for the primary motor cortex region. CONCLUSION: Even at a millimetre spatial resolution, significant correlations with both cardiac and respiratory RETROICOR components were found in all healthy volunteer data. Therefore, physiological noise correction is highly likely to improve the temporal signal-to-noise ratio (SNR) for cerebellar fMRI at 7T, even at high spatial resolution.

EEG reactivity to pain in comatose patients: Importance of the stimulus type.

INTRODUCTION: Electroencephalogram (EEG) background reactivity is a potentially interesting outcome predictor in comatose patients, especially after cardiac arrest, but recent studies report only fair interrater reliability. Furthermore, there are no definite guidelines for its testing. We therefore investigated the EEG effect of standardized noxious stimuli in comatose patients not reactive to auditory stimuli. METHODS: In this prospective study we applied a protocol using three different painful stimuli (bilateral nipple pinching, pinprick at the nose base, finger-nail compression on each side), grouped in three distinct clusters with an alternated sequence, during EEG recordings in comatose patients. We only analyzed recordings showing any reactivity to pain. Fisher and χ2 tests were used as needed to assess contingency tables. RESULTS: Of 42 studies, 12 did not show any background reactivity, 2 presented SIRPIDs, and 2 had massive artefacts; we thus analyzed 26 EEGs recorded in 17 patients (4 women, 24%). Nipple pinching more frequently induced a change in EEG background activity (p<0.001), with a sensitivity of 97.4% for reactivity. Neither the order of the stimuli in the cluster (p=0.723), nor the cluster order (p=0.901) influenced the results. CONCLUSION: In this pilot study, bilateral, synchronous nipple pinching seems to be the most efficient method to test nociceptive EEG reactivity in comatose patients. This approach may enhance interrater reliability, but deserves confirmation in larger cohorts.