Diagnostic Accuracy of MR Spectroscopic Imaging and 18F-FET PET for Identifying Glioma: A Biopsy-Controlled Hybrid PET/MRI Study.

Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, despite its low specificity for tumor tissue. This study compared the contribution of MR spectroscopic imaging (MRSI) and amino acid PET to improve the detection of tumor tissue. Methods: In 30 untreated patients with suspected glioma, O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) PET; 3-T MRSI with a short echo time; and fluid-attenuated inversion recovery, T2-weighted, and contrast-enhanced T1-weighted MRI were performed for stereotactic biopsy planning. Serial samples were taken along the needle trajectory, and their masks were projected to the preoperative imaging data. Each sample was individually evaluated neuropathologically. 18F-FET uptake and the MRSI signals choline (Cho), N-acetyl-aspartate (NAA), creatine, myoinositol, and derived ratios were evaluated for each sample and classified using logistic regression. The diagnostic accuracy was evaluated by receiver operating characteristic analysis. Results: On the basis of the neuropathologic evaluation of tissue from 88 stereotactic biopsies, supplemented with 18F-FET PET and MRSI metrics from 20 areas on the healthy-appearing contralateral hemisphere to balance the glioma/nonglioma groups, 18F-FET PET identified glioma with the highest accuracy (area under the receiver operating characteristic curve, 0.89; 95% CI, 0.81-0.93; threshold, 1.4 × background uptake). Among the MR spectroscopic metabolites, Cho/NAA normalized to normal brain tissue showed the highest diagnostic accuracy (area under the receiver operating characteristic curve, 0.81; 95% CI, 0.71-0.88; threshold, 2.2). The combination of 18F-FET PET and normalized Cho/NAA did not improve the diagnostic performance. Conclusion: MRI-based delineation of gliomas should preferably be supplemented by 18F-FET PET.

Perceptual Grouping in Autism Spectrum Disorder: An Exploratory Magnetoencephalography Study.

Visual information is organised according to visual grouping principles. In visual grouping tasks individuals with ASD have shown equivocal performance. We explored neural correlates of Gestalt grouping in individuals with and without ASD. Neuromagnetic activity of individuals with (15) and without (18) ASD was compared during a visual grouping task testing grouping by proximity versus similarity. Individuals without ASD showed stronger evoked responses with earlier peaks in response to both grouping types indicating an earlier neuronal differentiation between grouping principles in individuals without ASD. In contrast, individuals with ASD showed particularly prolonged processing of grouping by similarity suggesting a high demand of neural resources. The neuronal processing differences found could explain less efficient grouping performance observed behaviourally in ASD.

Ocular and cardiac artifact rejection for real-time analysis in MEG.

BACKGROUND: Recently, magnetoencephalography (MEG) based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods for neuroscience research. It is well known that artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming process. NEW METHOD: The method (referred to as ocular and cardiac artifact rejection for real-time analysis, OCARTA) is based on constrained independent component analysis (cICA), where a priori information of the underlying source signals is used to optimize and accelerate signal decomposition. Thereby, prior information is incorporated by using the subject's individual cardiac and ocular activity. The algorithm automatically uses different separation strategies depending on the underlying source activity. RESULTS: OCARTA was tested and applied to data from three different but most commonly used MEG systems (4D-Neuroimaging, VSM MedTech Inc. and Elekta Neuromag). Ocular and cardiac artifacts were effectively reduced within one iteration at a time delay of 1ms performed on a standard PC (Intel Core i5-2410M). COMPARISON WITH EXISTING METHODS: The artifact rejection results achieved with OCARTA are in line with the results reported for offline ICA-based artifact rejection methods. CONCLUSION: Due to the fast and subject-specific signal decomposition the new approach introduced here is capable of real-time ocular and cardiac artifact rejection.

A constrained ICA approach for real-time cardiac artifact rejection in magnetoencephalography.

Recently, magnetoencephalography (MEG)-based real-time brain computing interfaces (BCI) have been developed to enable novel and promising methods of neuroscience research and therapy. Artifact rejection prior to source localization largely enhances the localization accuracy. However, many BCI approaches neglect real-time artifact removal due to its time consuming processing. With cardiac artifact rejection for real-time analysis (CARTA), we introduce a novel algorithm capable of real-time cardiac artifact (CA) rejection. The method is based on constrained independent component analysis (ICA), where a priori information of the underlying source signal is used to optimize and accelerate signal decomposition. In CARTA, this is performed by estimating the subject's individual density distribution of the cardiac activity, which leads to a subject-specific signal decomposition algorithm. We show that the new method is capable of effectively reducing CAs within one iteration and a time delay of 1 ms. In contrast, Infomax and Extended Infomax ICA converged not until seven iterations, while FastICA needs at least ten iterations. CARTA was tested and applied to data from three different but most common MEG systems (4-D-Neuroimaging, VSM MedTech Inc., and Elekta Neuromag). Therefore, the new method contributes to reliable signal analysis utilizing BCI approaches.

Neuromagnetic oscillations to emotional faces and prosody.

Higher association cortices as well as unisensory areas can support multisensory integration [D. Senkowski et al. (2008) Trends Neurosci., 31, 401-409]. The present study investigated whether audiovisual integration of emotional information emerges early at unisensory or later at higher association cortices. Emotional stimuli were presented in three blocks: audiovisual (AV), auditory (A) and visual (V). Eighteen participants performed a delayed emotional recognition task (happy, angry or neutral prosody and/or facial expression) while whole-brain magnetoencephalography (MEG) data were obtained. Time-frequency evoked and total power analyses were performed on the sensor data, and source localization of the frequencies of interest performed via a synthetic aperture magnetometry beamformer. To examine crossmodal integration between bimodal and unimodal conditions, two contrasts were specified: AV > A and AV > V. In the AV > A contrast, early effects were observed on both the temporal and the occipital evoked responses. However, at the source level, early alpha suppression was limited to the occipital sources without changes in temporal cortices. In the AV > V contrast, sensor and source findings revealed increased alpha suppression only in temporal cortices, with no changes in visual cortex. Thus, no crossmodal effect in unisensory areas emerged. Instead, increased frontal alpha activity in both the AV > A and AV > V contrasts supports the view that affective information from face and prosody converges at higher association cortices.

Early sensory encoding of affective prosody: neuromagnetic tomography of emotional category changes.

In verbal communication, prosodic codes may be phylogenetically older than lexical ones. Little is known, however, about early, automatic encoding of emotional prosody. This study investigated the neuromagnetic analogue of mismatch negativity (MMN) as an index of early stimulus processing of emotional prosody using whole-head magnetoencephalography (MEG). We applied two different paradigms to study MMN; in addition to the traditional oddball paradigm, the so-called optimum design was adapted to emotion detection. In a sequence of randomly changing disyllabic pseudo-words produced by one male speaker in neutral intonation, a traditional oddball design with emotional deviants (10% happy and angry each) and an optimum design with emotional (17% happy and sad each) and nonemotional gender deviants (17% female) elicited the mismatch responses. The emotional category changes demonstrated early responses (<200 ms) at both auditory cortices with larger amplitudes at the right hemisphere. Responses to the nonemotional change from male to female voices emerged later ( approximately 300 ms). Source analysis pointed at bilateral auditory cortex sources without robust contribution from other such as frontal sources. Conceivably, both auditory cortices encode categorical representations of emotional prosodic. Processing of cognitive feature extraction and automatic emotion appraisal may overlap at this level enabling rapid attentional shifts to important social cues.

The temporal dynamics of insula activity to disgust and happy facial expressions: a magnetoencephalography study.

The insula has consistently been shown to be involved in processing stimuli that evoke the emotional response of disgust. Recently, its specificity for processing disgust has been challenged and a broader role of the insula in the representation of interoceptive information has been suggested. Studying the temporal dynamics of insula activation during emotional processing can contribute valuable information pertaining to this issue. Few studies have addressed the insula's putative specificity to disgust and the dynamics of its underlying neural processes. In the present study, neuromagnetic responses of 13 subjects performing an emotional continuous performance task (CPT) to faces with disgust, happy, and neutral expressions were obtained. Magnetic field tomography extracted the time course of bilateral insula activities. Right insula activation was stronger to disgust and happy than neutral facial expressions at about 200 ms after stimulus onset. Later only at about 350 ms after stimulus onset the right insula was activated stronger to disgust than happy facial expressions. Thus, the early right insula response reflects activation to emotionally arousing stimuli regardless of valence, and the later right insula response differentiates disgust from happy facial expressions. Behavioral performance but not the insula activity differed between 100 ms and 1000 ms presentation conditions. Present findings support the notion that the insula is involved in the representation of interoceptive information.

Integration of amplitude and phase statistics for complete artifact removal in independent components of neuromagnetic recordings.

In magnetoencephalography (MEG) and electroencephalography (EEG), independent component analysis is widely applied to separate brain signals from artifact components. A number of different methods have been proposed for the automatic or semiautomatic identification of artifact components. Most of the proposed methods are based on amplitude statistics of the decomposed MEG/EEG signal. We present a fully automated approach based on amplitude and phase statistics of decomposed MEG signals for the isolation of biological artifacts such as ocular, muscle, and cardiac artifacts (CAs). The performance of different artifact identification measures was investigated. In particular, we show that phase statistics is a robust and highly sensitive measure to identify strong and weak components that can be attributed to cardiac activity, whereas a combination of different measures is needed for the identification of artifacts caused by ocular and muscle activity. With the introduction of a rejection performance parameter, we are able to quantify the rejection quality for eye blinks and CAs. We demonstrate in a set of MEG data the good performance of the fully automated procedure for the removal of cardiac, ocular, and muscle artifacts. The new approach allows routine application to clinical measurements with small effect on the brain signal.

A new toolbox for combining magnetoencephalographic source analysis and cytoarchitectonic probabilistic data for anatomical classification of dynamic brain activity.

Size and location of activated cortical areas are often identified in relation to their surrounding macro-anatomical landmarks such as gyri and sulci. The sulcal pattern, however, is highly variable. In addition, many cortical areas are not linked to well defined landmarks, which in turn do not have a fixed relationship to functional and cytoarchitectonic boundaries. Therefore, it is difficult to unambiguously attribute localized neuronal activity to the corresponding cortical areas in the living human brain. Here we present new methods that are implemented in a toolbox for the objective anatomical identification of neuromagnetic activity with respect to cortical areas. The toolbox enables the platform independent integration of many types of source analysis obtained from magnetoencephalography (MEG) together with probabilistic cytoarchitectonic maps obtained in postmortem brains. The probability maps provide information about the relative frequency of a given cortical area being located at a given position in the brain. In the new software, the neuromagnetic data are analyzed with respect to cytoarchitectonic maps that have been transformed to the individual subject brain space. A number of measures define the degree of overlap between and distance from the activated areas and the corresponding cytoarchitectonic maps. The implemented algorithms enable the investigator to quantify how much of the reconstructed current density can be attributed to distinct cortical areas. Dynamic correspondence patterns between the millisecond-resolved MEG data and the static cytoarchitectonic maps are obtained. We show examples for auditory and visual activation patterns. However, size and location of the postmortem brain areas as well as the inverse method applied to the neuromagnetic data bias the anatomical classification. Therefore, the adaptation to the respective application and a combination of the objective quantities are discussed.

Time course of regional brain activations during facial emotion recognition in humans.

Recognition of facial expressions of emotions is very important for communication and social cognition. Neuroimaging studies showed that numerous brain regions participate in this complex function. To study spatiotemporal aspects of the neural representation of facial emotion recognition we recorded neuromagnetic activity in 12 healthy individuals by means of a whole head magnetoencephalography system. Source reconstructions revealed that several cortical and subcortical brain regions produced strong neural activity in response to emotional faces at latencies between 100 and 360 ms that were much stronger than those to neutral as well as to blurred faces. Orbitofrontal cortex and amygdala showed affect-related activity at short latencies already within 180 ms after stimulus onset. Some of the emotion-responsive regions were repeatedly activated during the stimulus presentation period pointing to the assumption that these reactivations represent indicators of a distributed interacting circuitry.