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Recent resilience research has increasingly emphasized the importance of focusing on investigating the protective factors in mentally healthy populations, complementing the traditional focus on psychopathology. Social support has emerged as a crucial element within the complex interplay of individual and socio-environmental factors that shape resilience. However, the neural underpinnings of the relationship between social support and resilience, particularly in healthy subjects, remain largely unexplored. With advances in neuroimaging techniques, such as ultra-high field MRI at 7T and beyond, researchers can more effectively investigate the neural mechanisms underlying these factors. Thus, our study employed ultra-high field rs-fMRI to explore how social support moderates the relationship between psychological resilience and functional connectivity in a healthy cohort. We hypothesized that enhanced social support would amplify resilience-associated connectivity within neural circuits essential for emotional regulation, cognitive processing, and adaptive problem-solving, signifying a synergistic interaction where strong social networks bolster the neural underpinnings of resilience. (n = 30). Through seed-based functional connectivity analyses and interaction analysis, we aimed to uncover the neural correlates at the interplay of social support and resilience. Our findings indicate that perceived social support significantly (p<0.001) alters functional connectivity in the right and left FP, PCC, and left hippocampus, affirming the pivotal roles of these regions in the brain's resilience network. Moreover, we identified significant moderation effects of social support across various brain regions, each showing unique connectivity patterns. Specifically, the right FP demonstrated a significant interaction effect where high social support levels were linked to increased connectivity with regions involved in socio-cognitive processing, while low social support showed opposite effects. Similar patterns by social support levels were observed in the left FP, with connectivity changes in clusters associated with emotional regulation and cognitive functions. The PCC's connectivity was distinctly influenced by support levels, elucidating its role in emotional and social cognition. Interestingly, the connectivity of the left hippocampus was not significantly impacted by social support levels, indicating a unique pattern within this region. These insights highlight the importance of high social support levels in enhancing the neural foundations of resilience and fostering adaptive neurological responses to environmental challenges.
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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.
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Neoplasias Encefálicas , Glioma , Humanos , Imageamento por Ressonância Magnética/métodos , Glioma/diagnóstico por imagem , Glioma/metabolismo , Espectroscopia de Ressonância Magnética , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias Encefálicas/patologia , Tomografia por Emissão de Pósitrons/métodos , Tirosina , BiópsiaRESUMO
The cognitive impact of psychological trauma can manifest as a range of post-traumatic stress symptoms that are often attributed to impairments in learning from positive and negative outcomes, aka reinforcement learning. Research on the impact of trauma on reinforcement learning has mainly been inconclusive. This study aimed to circumscribe the impact of psychological trauma on reinforcement learning in the context of neural response in time and frequency domains. Two groups of participants were tested - those who had experienced psychological trauma and a control group who had not - while they performed a probabilistic classification task that dissociates learning from positive and negative feedback during a magnetoencephalography (MEG) examination. While the exposure to trauma did not exhibit any effects on learning accuracy or response time for positive or negative feedback, MEG cortical activity was modulated in response to positive feedback. In particular, the medial and lateral orbitofrontal cortices (mOFC and lOFC) exhibited increased activity, while the insular and supramarginal cortices showed decreased activity during positive feedback presentation. Furthermore, when receiving negative feedback, the trauma group displayed higher activity in the medial portion of the superior frontal cortex. The timing of these activity changes occurred between 160 and 600 ms post feedback presentation. Analysis of the time-frequency domain revealed heightened activity in theta and alpha frequency bands (4-10 Hz) in the lOFC in the trauma group. Moreover, dividing the two groups according to their learning performance, the activity for the non-learner subgroup was found to be lower in lOFC and higher in the supramarginal cortex. These differences were found in the trauma group only. The results highlight the localization and neural dynamics of feedback processing that could be affected by exposure to psychological trauma. This approach and associated findings provide a novel framework for understanding the cognitive correlates of psychological trauma in relation to neural dynamics in the space, time, and frequency domains. Subsequent work will focus on the stratification of cognitive and neural correlates as a function of various symptoms of psychological trauma. Clinically, the study findings and approach open the possibility for neuromodulation interventions that synchronize cognitive and psychological constructs for individualized treatment.
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Neural fingerprinting is the identification of individuals in a cohort based on neuroimaging recordings of brain activity. In magneto- and electroencephalography (M/EEG), it is common practice to use second-order statistical measures, such as correlation or connectivity matrices, when neural fingerprinting is performed. These measures or features typically require coupling between signal channels and often ignore the individual temporal dynamics. In this study, we show that, following recent advances in multivariate time series classification, such as the development of the RandOm Convolutional KErnel Transformation (ROCKET) classifier, it is possible to perform classification directly on short time segments from MEG resting-state recordings with remarkably high classification accuracies. In a cohort of 124 subjects, it was possible to assign windows of time series of 1 s in duration to the correct subject with above 99% accuracy. The achieved accuracies are vastly superior to those of previous methods while simultaneously requiring considerably shorter time segments.
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Tourette syndrome (TS) is a neuropsychiatric disorder characterized by motor and phonic tics, which several different theories, such as basal ganglia-thalamo-cortical loop dysfunction and amygdala hypersensitivity, have sought to explain. Previous research has shown dynamic changes in the brain prior to tic onset leading to tics, and this study aims to investigate the contribution of network dynamics to them. For this, we have employed three methods of functional connectivity to resting-state fMRI data - namely the static, the sliding window dynamic and the ICA based estimated dynamic; followed by an examination of the static and dynamic network topological properties. A leave-one-out (LOO-) validated regression model with LASSO regularization was used to identify the key predictors. The relevant predictors pointed to dysfunction of the primary motor cortex, the prefrontal-basal ganglia loop and amygdala-mediated visual social processing network. This is in line with a recently proposed social decision-making dysfunction hypothesis, opening new horizons in understanding tic pathophysiology.
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Tiques , Síndrome de Tourette , Humanos , Tiques/diagnóstico por imagem , Síndrome de Tourette/diagnóstico por imagem , Imageamento por Ressonância Magnética , Encéfalo/diagnóstico por imagem , Gânglios da BaseRESUMO
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.
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In our daily lives, we use eye movements to actively sample visual information from our environment ("active vision"). However, little is known about how the underlying mechanisms are affected by goal-directed behavior. In a study of 31 participants, magnetoencephalography was combined with eye-tracking technology to investigate how interregional interactions in the brain change when engaged in two distinct forms of active vision: freely viewing natural images or performing a guided visual search. Regions of interest with significant fixation-related evoked activity (FRA) were identified with spatiotemporal cluster permutation testing. Using generalized partial directed coherence, we show that, in response to fixation onset, a bilateral cluster consisting of four regions (posterior insula, transverse temporal gyri, superior temporal gyrus, and supramarginal gyrus) formed a highly connected network during free viewing. A comparable network also emerged in the right hemisphere during the search task, with the right supramarginal gyrus acting as a central node for information exchange. The results suggest that all four regions are vital to visual processing and guiding attention. Furthermore, the right supramarginal gyrus was the only region where activity during fixations on the search target was significantly negatively correlated with search response times. Based on our findings, we hypothesize that, following a fixation, the right supramarginal gyrus supplies the right supplementary eye field (SEF) with new information to update the priority map guiding the eye movements during the search task.
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The suppression of distracting information in order to focus on an actual cognitive goal is a key feature of executive functions. The use of brain imaging methods to investigate the underlying neurobiological brain activations that occur during conflict processing have demonstrated a strong involvement of the fronto-parietal attention network (FPAN). Surprisingly, the directional interconnections, their time courses and activations at different frequency bands remain to be elucidated, and thus, this constitutes the focus of this study. The shared information flow between brain areas of the FPAN is provided for frequency bands ranging from the theta to the lower gamma band (4-40 Hz). We employed an adaptation of the Simon task utilizing Magnetoencephalography (MEG). Granger causality was applied to investigate interconnections between the active brain regions, as well as their directionality. Following stimulus onset, the middle frontal precentral cortex and superior parietal cortex were significantly activated during conflict processing in a time window of between 300 to 600ms. Important differences in causality were found across frequency bands between processing of conflicting stimuli in the left as compared to the right visual hemifield. The exchange of information from and to the FPAN was most prominent in the beta band. Moreover, the anterior cingulate cortex and the anterior insula represented key areas for conflict monitoring, either by receiving input from other areas of the FPAN or by generating output themselves. This indicates that the salience network is at least partly involved in processing conflict information. The present study provides detailed insights into the underlying neural mechanisms of the FPAN, especially regarding its temporal characteristics and directional interconnections.
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Giro do Cíngulo/fisiologia , Rede Nervosa/fisiologia , Lobo Parietal/fisiologia , Desempenho Psicomotor/fisiologia , Adulto , Mapeamento Encefálico/métodos , Cognição/fisiologia , Conflito Psicológico , Humanos , Magnetoencefalografia/métodos , Masculino , Adulto JovemRESUMO
Simultaneous trimodal positron emission tomography/magnetic resonance imaging/electroencephalography (PET/MRI/EEG) resting state (rs) brain data were acquired from 10 healthy male volunteers. The rs-functional MRI (fMRI) metrics, such as regional homogeneity (ReHo), degree centrality (DC) and fractional amplitude of low-frequency fluctuations (fALFFs), as well as 2-[18F]fluoro-2-desoxy-d-glucose (FDG)-PET standardised uptake value (SUV), were calculated and the measures were extracted from the default mode network (DMN) regions of the brain. Similarly, four microstates for each subject, showing the diverse functional states of the whole brain via topographical variations due to global field power (GFP), were estimated from artefact-corrected EEG signals. In this exploratory analysis, the GFP of microstates was nonparametrically compared to rs-fMRI metrics and FDG-PET SUV measured in the DMN of the brain. The rs-fMRI metrics (ReHO, fALFF) and FDG-PET SUV did not show any significant correlations with any of the microstates. The DC metric showed a significant positive correlation with microstate C (rs = 0.73, p = .01). FDG-PET SUVs indicate a trend for a negative correlation with microstates A, B and C. The positive correlation of microstate C with DC metrics suggests a functional relationship between cortical hubs in the frontal and occipital lobes. The results of this study suggest further exploration of this method in a larger sample and in patients with neuropsychiatric disorders. The aim of this exploratory pilot study is to lay the foundation for the development of such multimodal measures to be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.
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Córtex Cerebral , Conectoma/métodos , Rede de Modo Padrão , Eletroencefalografia/métodos , Imageamento por Ressonância Magnética/métodos , Imagem Multimodal/métodos , Tomografia por Emissão de Pósitrons/métodos , Adulto , Biomarcadores , Córtex Cerebral/diagnóstico por imagem , Córtex Cerebral/fisiologia , Rede de Modo Padrão/diagnóstico por imagem , Rede de Modo Padrão/fisiologia , HumanosRESUMO
The use of hybrid PET/MR imaging facilitates the simultaneous investigation of challenge-related changes in ligand binding to neuroreceptors using PET, while concurrently measuring neuroactivation or blood flow with MRI. Having attained a steady state of the PET radiotracer using a bolus-infusion protocol, it is possible to observe alterations in ligand neuroreceptor binding through changes in distribution volumes. Here, we present an iterative procedure for establishing an administration scheme to obtain steady state [11C]flumazenil concentrations in grey matter in the human brain. In order to achieve a steady state in the shortest possible time, the bolus infusion ratio from a previous examination was adapted to fit the subsequent examination. 17 male volunteers were included in the study. Boli and infusions with different weightings were given to the subjects and were characterised by kbol values from 74 âmin down to 42 âmin. Metabolite analysis was used to ascertain the value of unmetabolised flumazenil in the plasma, and PET imaging was used to assess its binding in the grey matter. The flumazenil time-activity curves (TACs) in the brain were decomposed into activity contributions from pure grey and white matter and analysed for 12 âvol of interest (VOIs). The curves highlighted a large variability in metabolic rates between the subjects, with kbol â= â54.3 âmin being a reliable value to provide flumazenil equilibrium conditions in the majority of the VOIs and cases. The distribution volume of flumazenil in all 12 VOIs was determined.
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Radioisótopos de Carbono/administração & dosagem , Flumazenil , Moduladores GABAérgicos , Substância Cinzenta , Imageamento por Ressonância Magnética , Tomografia por Emissão de Pósitrons , Células Receptoras Sensoriais , Substância Branca , Adulto , Flumazenil/administração & dosagem , Flumazenil/sangue , Flumazenil/farmacocinética , Moduladores GABAérgicos/administração & dosagem , Moduladores GABAérgicos/sangue , Moduladores GABAérgicos/farmacocinética , Substância Cinzenta/diagnóstico por imagem , Substância Cinzenta/efeitos dos fármacos , Substância Cinzenta/metabolismo , Humanos , Masculino , Imagem Multimodal , Células Receptoras Sensoriais/efeitos dos fármacos , Células Receptoras Sensoriais/metabolismo , Substância Branca/diagnóstico por imagem , Substância Branca/efeitos dos fármacos , Substância Branca/metabolismo , Adulto JovemRESUMO
In the past two decades, many studies have shown the paradoxical efficacy of zolpidem, a hypnotic used to induce sleep, in transiently alleviating various disorders of consciousness such as traumatic brain injury (TBI), dystonia, and Parkinson's disease. The mechanism of action of this effect of zolpidem is of great research interest. In this case study, we use magnetoencephalography (MEG) to investigate a fully conscious, ex-coma patient who suffered from neurological difficulties for a few years due to traumatic brain injury. For a few years after injury, the patient was under medication with zolpidem that drastically improved his symptoms. MEG recordings taken before and after zolpidem showed a reduction in power in the theta-alpha (4-12 Hz) and lower beta (15-20 Hz) frequency bands. An increase in power after zolpidem intake was found in the higher beta/lower gamma (20-43 Hz) frequency band. Source level functional connectivity measured using weighted-phase lag index showed changes after zolpidem intake. Stronger connectivity between left frontal and temporal brain regions was observed. We report that zolpidem induces a change in MEG resting power and functional connectivity in the patient. MEG is an informative and sensitive tool to detect changes in brain activity for TBI.
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Gamma-aminobutyric acid (GABA) and glutamate are believed to have inhibitory and exhibitory neuromodulatory effects that regulate the brain's response to sensory perception. Furthermore, frequency-specific synchronization of neuronal excitability within the gamma band (30-80 Hz) is attributable to a homeostatic balance between excitation and inhibition. However, our understanding of the physiological mechanism underlying gamma rhythms is based on animal models. Investigations of the relationship between GABA concentrations, glutamate concentrations, and gamma band activity in humans were mostly restricted to the visual cortex and are conflicting. Here, we performed a multimodal imaging study combining magnetic resonance spectroscopy (MRS) with electroencephalography (EEG) in the auditory cortex. In 14 healthy subjects, we investigated the impact of individual differences in GABA and glutamate concentration on gamma band response (GBR) following auditory stimulus presentation. We explored the effects of bulk GABA on the GBR across frequency (30-200 Hz) and time (-200 to 600 ms) and found no significant relationship. Furthermore, no correlations were found between gamma peak frequency or power measures and metabolite concentrations (GABA, glutamate, and GABA/glutamate ratio). These findings suggest that, according to MRS measurements, and given the auditory stimuli used in this study, GABA and glutamate concentrations are unlikely to play a significant role in the inhibitory and excitatory drive in the generation of gamma band activity in the auditory cortex. Hum Brain Mapp 38:3975-3987, 2017. © 2017 Wiley Periodicals, Inc.
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Córtex Auditivo/fisiologia , Percepção Auditiva/fisiologia , Ritmo Gama/fisiologia , Ácido gama-Aminobutírico/metabolismo , Estimulação Acústica , Adulto , Córtex Auditivo/diagnóstico por imagem , Eletroencefalografia , Ácido Glutâmico/metabolismo , Humanos , Imageamento por Ressonância Magnética , Masculino , Imagem Multimodal , Espectroscopia de Prótons por Ressonância Magnética , Adulto JovemRESUMO
BACKGROUND: Combining both high temporal and spatial resolution by means of simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is of relevance to neuroscientists. This combination, however, leads to a distortion of the EEG signal by the so-called cardio-ballistic artefacts. The aim of the present study was developing an approach to restore meaningful physiological EEG data from recordings at different magnetic fields. NEW METHODS: The distortions introduced by the magnetic field were corrected using a combination of concepts from independent component analysis (ICA) and mutual information (MI). Thus, the components were classified as either related to the cardio-ballistic artefacts or to the signals of interest. EEG data from two experimental paradigms recorded at different magnetic field strengths up to 9.4 T were analyzed: (i) spontaneous activity using an eyes-open/eyes-closed alternation, and (ii) responses to auditory stimuli, i.e. auditory evoked potentials. RESULTS: Even at ultra-high magnetic fields up to 9.4 T the proposed artefact rejection approach restored the physiological time-frequency information contained in the signal of interest and the data were suitable for subsequent analyses. COMPARISON WITH EXISTING METHODS: Blind source separation (BSS) has been used to retrieve information from EEG data recorded inside the MR scanner in previous studies. After applying the presented method on EEG data recorded at 4 T, 7 T, and 9.4 T, we could retrieve more information than from data cleaned with the BSS method. CONCLUSIONS: The present work demonstrates that EEG data recorded at ultra-high magnetic fields can be used for studying neuroscientific research question related to oscillatory activity.
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Artefatos , Mapeamento Encefálico/métodos , Encéfalo/fisiologia , Eletroencefalografia/métodos , Imageamento por Ressonância Magnética/métodos , Imagem Multimodal/métodos , Estimulação Acústica , Adulto , Ritmo alfa , Percepção Auditiva/fisiologia , Mapeamento Encefálico/instrumentação , Potenciais Evocados Auditivos , Feminino , Humanos , Teoria da Informação , Campos Magnéticos , Imageamento por Ressonância Magnética/instrumentação , Masculino , Imagem Multimodal/instrumentação , Descanso , Processamento de Sinais Assistido por Computador , Percepção Visual/fisiologiaRESUMO
Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.
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Artefatos , Eletroencefalografia , Imageamento por Ressonância Magnética , Descanso , Processamento de Sinais Assistido por Computador , Adulto , Encéfalo/fisiologia , Feminino , Humanos , Masculino , Reprodutibilidade dos Testes , Fatores de TempoRESUMO
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.
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Artefatos , Movimentos Oculares/fisiologia , Coração/fisiologia , Magnetoencefalografia/métodos , Processamento de Sinais Assistido por Computador , Estimulação Acústica , Adolescente , Adulto , Algoritmos , Percepção Auditiva/fisiologia , Encéfalo/fisiologia , Criança , Eletrocardiografia/métodos , Eletroculografia/métodos , Humanos , Magnetoencefalografia/instrumentação , Pessoa de Meia-Idade , Reconhecimento Automatizado de Padrão/métodos , Fatores de Tempo , Adulto JovemRESUMO
BACKGROUND: The feasibility of recording electroencephalography (EEG) at ultra-high static magnetic fields up to 9.4 T was recently demonstrated and is expected to be incorporated into functional magnetic resonance imaging (fMRI) studies at 9.4 T. Correction of the pulse artefact (PA) is a significant challenge since its amplitude is proportional to the strength of the magnetic field in which EEG is recorded. NEW METHOD: We conducted a study in which different PA correction methods were applied to EEG data recorded inside a 9.4 T scanner in order to retrieve visual P100 and auditory P300 evoked potentials. We explored different PA reduction methods, including the optimal basis set (OBS) method as well as objective and subjective component rejection using independent component analysis (ICA). RESULTS: ICA followed by objective rejection of components is optimal for retrieving visual P100 and auditory P300 from EEG data recorded inside the scanner. COMPARISON WITH EXISTING METHODS: Previous studies suggest that OBS or OBS followed by ICA are optimal for retrieving evoked potentials at 3T. In our EEG data recorded at 9.4 T OBS performed alone was not fully optimal for the identification of evoked potentials. OBS followed by ICA was partially effective. CONCLUSIONS: In this study ICA has been shown to be an important tool for correcting the PA in EEG data recorded at 9.4 T, particularly when automated rejection of components is performed.
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Encéfalo/fisiologia , Encéfalo/efeitos da radiação , Potenciais Evocados Auditivos/efeitos da radiação , Potenciais Evocados Visuais/fisiologia , Potenciais Evocados Visuais/efeitos da radiação , Campos Magnéticos , Estimulação Acústica , Adulto , Encéfalo/irrigação sanguínea , Mapeamento Encefálico , Potenciais Evocados Auditivos/fisiologia , Feminino , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Oxigênio/sangue , Estimulação Luminosa , Análise de Componente Principal , Reprodutibilidade dos Testes , Adulto JovemRESUMO
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.
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Magnetoencefalografia/métodos , Análise de Componente Principal/métodos , Processamento de Sinais Assistido por Computador , Adolescente , Adulto , Algoritmos , Artefatos , Interfaces Cérebro-Computador , Criança , Coração/fisiologia , Humanos , Pessoa de Meia-Idade , Adulto JovemRESUMO
BACKGROUND: Polarized light imaging (PLI) has evolved into a powerful neuroimaging tool to analyze fiber tracts with submillimeter resolution in microtome sections of postmortem human brain tissue. In PLI polarized light changes its polarization state while passing through birefringent tissue, i.e., myelinated axons, which results in sinusoidal signals that characterize different fiber orientations. Noise, light scatter and filter inhomogeneities of the polarimeter interfere with the original sinusoidal PLI signals, which have direct effects on the accuracy of subsequent fiber modeling. New method: In our recent publications we have shown that the sinusoidal signal at each pixel location in PLI images can be restored utilizing independent component analysis (ICA). We now have further improved the signal separation quality by introducing a new constrained ICA algorithm (cICAP) where the component selection is directly included. In cICAP an analytical expression of the expected signal of interest is implemented as a priori information. RESULTS: The algorithm precisely decomposes the deteriorated PLI signals into its underlying source signals. As such, the approach enhances sinusoidal basis functions and is therefore optimal for the extraction of independent spatial maps from PLI images. Comparison with existing methods: The new algorithm performs better and is faster compared to other well-known ICA algorithms. CONCLUSION: The decomposition in cICAP is optimal with respect to separation and identification of the sinusoidal nature of the PLI signal. In this way the identification of the relevant components is automatically included and does not require any further component selection tool.
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Algoritmos , Encéfalo , Processamento de Imagem Assistida por Computador/métodos , Microscopia de Polarização/métodos , Neuroimagem/métodos , Cadáver , HumanosRESUMO
Polarized light imaging (PLI) enables the visualization of fiber tracts with high spatial resolution in microtome sections of postmortem brains. Vectors of the fiber orientation defined by inclination and direction angles can directly be derived from the optical signals employed by PLI analysis. The polarization state of light propagating through a rotating polarimeter is varied in such a way that the detected signal of each spatial unit describes a sinusoidal signal. Noise, light scatter and filter inhomogeneities, however, interfere with the original sinusoidal PLI signals, which in turn have direct impact on the accuracy of subsequent fiber tracking. Recently we showed that the primary sinusoidal signals can effectively be restored after noise and artifact rejection utilizing independent component analysis (ICA). In particular, regions with weak intensities are greatly enhanced after ICA based artifact rejection and signal restoration. Here, we propose a user independent way of identifying the components of interest after decomposition; i.e., components that are related to gray and white matter. Depending on the size of the postmortem brain and the section thickness, the number of independent component maps can easily be in the range of a few ten thousand components for one brain. Therefore, we developed an automatic and, more importantly, user independent way of extracting the signal of interest. The automatic identification of gray and white matter components is based on the evaluation of the statistical properties of the so-called feature vectors of each individual component map, which, in the ideal case, shows a sinusoidal waveform. Our method enables large-scale analysis (i.e., the analysis of thousands of whole brain sections) of nerve fiber orientations in the human brain using polarized light imaging.
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Algoritmos , Encéfalo/citologia , Interpretação de Imagem Assistida por Computador/métodos , Iluminação/métodos , Microscopia de Polarização/métodos , Fibras Nervosas Mielinizadas/ultraestrutura , Neurônios/citologia , Reconhecimento Automatizado de Padrão/métodos , Inteligência Artificial , Humanos , Aumento da Imagem/métodos , Reprodutibilidade dos Testes , Sensibilidade e EspecificidadeRESUMO
Signal transmission between different brain regions requires connecting fiber tracts, the structural basis of the human connectome. In contrast to animal brains, where a multitude of tract tracing methods can be used, magnetic resonance (MR)-based diffusion imaging is presently the only promising approach to study fiber tracts between specific human brain regions. However, this procedure has various inherent restrictions caused by its relatively low spatial resolution. Here, we introduce 3D-polarized light imaging (3D-PLI) to map the three-dimensional course of fiber tracts in the human brain with a resolution at a submillimeter scale based on a voxel size of 100 µm isotropic or less. 3D-PLI demonstrates nerve fibers by utilizing their intrinsic birefringence of myelin sheaths surrounding axons. This optical method enables the demonstration of 3D fiber orientations in serial microtome sections of entire human brains. Examples for the feasibility of this novel approach are given here. 3D-PLI enables the study of brain regions of intense fiber crossing in unprecedented detail, and provides an independent evaluation of fiber tracts derived from diffusion imaging data.