Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG

Mobile neuroimaging: What we have learned about the neural control of human walking, with an emphasis on EEG-based research

Our understanding of the neural control of human walking has changed significantly over the last twenty years and mobile brain imaging methods have contributed substantially to current knowledge. High-density electroencephalography (EEG) has the advantages of being lightweight and mobile while providing temporal resolution of brain changes within a gait cycle. Advances in EEG hardware and processing methods have led to a proliferation of research on the neural control of locomotion in neurologically intact adults. We provide a narrative review of the advantages and disadvantages of different mobile brain imaging methods, then summarize findings from mobile EEG studies quantifying electrocortical activity during human walking. Contrary to historical views on the neural control of locomotion, recent studies highlight the widespread involvement of many areas, such as the anterior cingulate, posterior parietal, prefrontal, premotor, sensorimotor, supplementary motor, and occipital cortices, that show active fluctuations in electrical power during walking. The electrocortical activity changes with speed, stability, perturbations, and gait adaptation. We end with a discussion on the next steps in mobile EEG research.

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Modulation of hippocampal theta oscillations via deep brain stimulation of the parietal cortex depends on cognitive state

The angular gyrus (AG) and posterior cingulate cortex (PCC) demonstrate extensive structural and functional connectivity with the hippocampus and other core recollection network regions. Consequently, recent studies have explored neuromodulation targeting these and other regions as a potential strategy for restoring function in memory disorders such as Alzheimer's Disease. However, determining the optimal approach for neuromodulatory devices requires understanding how parameters like selected stimulation site, cognitive state during modulation, and stimulation duration influence the effects of deep brain stimulation (DBS) on electrophysiological features relevant to episodic memory. We report experimental data examining the effects of high-frequency stimulation delivered to the AG or PCC on hippocampal theta oscillations during the memory encoding (study) or retrieval (test) phases of an episodic memory task. Results showed selective enhancement of anterior hippocampal slow theta oscillations with stimulation of the AG preferentially during memory retrieval. Conversely, stimulation of the PCC attenuated slow theta oscillations. We did not observe significant behavioral effects in this (open-loop) stimulation experiment, suggesting that neuromodulation strategies targeting episodic memory performance may require more temporally precise stimulation approaches.

Cleaning up the Brickyard: How Theory and Methodology Shape Experiments in Cognitive Neuroscience of Language

The capacity for language is a defining property of our species, yet despite decades of research, evidence on its neural basis is still mixed and a generalized consensus is difficult to achieve. We suggest that this is partly caused by researchers defining "language" in different ways, with focus on a wide range of phenomena, properties, and levels of investigation. Accordingly, there is very little agreement among cognitive neuroscientists of language on the operationalization of fundamental concepts to be investigated in neuroscientific experiments. Here, we review chains of derivation in the cognitive neuroscience of language, focusing on how the hypothesis under consideration is defined by a combination of theoretical and methodological assumptions. We first attempt to disentangle the complex relationship between linguistics, psychology, and neuroscience in the field. Next, we focus on how conclusions that can be drawn from any experiment are inherently constrained by auxiliary assumptions, both theoretical and methodological, on which the validity of conclusions drawn rests. These issues are discussed in the context of classical experimental manipulations as well as study designs that employ novel approaches such as naturalistic stimuli and computational modeling. We conclude by proposing that a highly interdisciplinary field such as the cognitive neuroscience of language requires researchers to form explicit statements concerning the theoretical definitions, methodological choices, and other constraining factors involved in their work.

A revisit of the amygdala theory of autism: Twenty years after

The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.

Neuro-nanotechnology: diagnostic and therapeutic nano-based strategies in applied neuroscience

Artificial, de-novo manufactured materials (with controlled nano-sized characteristics) have been progressively used by neuroscientists during the last several decades. The introduction of novel implantable bioelectronics interfaces that are better suited to their biological targets is one example of an innovation that has emerged as a result of advanced nanostructures and implantable bioelectronics interfaces, which has increased the potential of prostheses and neural interfaces. The unique physical-chemical properties of nanoparticles have also facilitated the development of novel imaging instruments for advanced laboratory systems, as well as intelligently manufactured scaffolds and microelectrodes and other technologies designed to increase our understanding of neural tissue processes. The incorporation of nanotechnology into physiology and cell biology enables the tailoring of molecular interactions. This involves unique interactions with neurons and glial cells in neuroscience. Technology solutions intended to effectively interact with neuronal cells, improved molecular-based diagnostic techniques, biomaterials and hybridized compounds utilized for neural regeneration, neuroprotection, and targeted delivery of medicines as well as small chemicals across the blood-brain barrier are all purposes of the present article.

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns, where techniques such as magnetoencephalography (MEG) are particularly useful. However, less attention has been given to fully utilizing the multidimensional nature of MEG data for extracting components that describe these patterns. Tensor factorizations of MEG yield components that encapsulate the data's multidimensional nature, providing parsimonious models identifying latent brain patterns for meaningful summarization of neural processes. To address the need for meaningful MEG signatures for studies of pediatric cohorts, we propose a tensor-based approach for extracting developmental signatures of multi-subject MEG data. We employ the canonical polyadic (CP) decomposition for estimating latent spatiotemporal components of the data, and use these components for group level statistical inference. Using CP decomposition along with hierarchical clustering, we were able to extract typical early and late latency event-related field (ERF) components that were discriminative of high and low performance groups ([Formula: see text]) and significantly correlated with major cognitive domains such as attention, episodic memory, executive function, and language comprehension. We demonstrate that tensor-based group level statistical inference of MEG can produce signatures descriptive of the multidimensional MEG data. Furthermore, these features can be used to study group differences in brain patterns and cognitive function of healthy children. We provide an effective tool that may be useful for assessing child developmental status and brain function directly from electrophysiological measurements and facilitate the prospective assessment of cognitive processes.

Advances in P300 brain-computer interface spellers: toward paradigm design and performance evaluation

A brain-computer interface (BCI) is a non-muscular communication technology that provides an information exchange channel for our brains and external devices. During the decades, BCI has made noticeable progress and has been applied in many fields. One of the most traditional BCI applications is the BCI speller. This article primarily discusses the progress of research into P300 BCI spellers and reviews four types of P300 spellers: single-modal P300 spellers, P300 spellers based on multiple brain patterns, P300 spellers with multisensory stimuli, and P300 spellers with multiple intelligent techniques. For each type of P300 speller, we further review several representative P300 spellers, including their design principles, paradigms, algorithms, experimental performance, and corresponding advantages. We particularly emphasized the paradigm design ideas, including the overall layout, individual symbol shapes and stimulus forms. Furthermore, several important issues and research guidance for the P300 speller were identified. We hope that this review can assist researchers in learning the new ideas of these novel P300 spellers and enhance their practical application capability.

Evaluation and comparison of most prevalent artifact reduction methods for EEG acquired simultaneously with fMRI

Multimodal neuroimaging using EEG and fMRI provides deeper insights into brain function by improving the spatial and temporal resolution of the acquired data. However, simultaneous EEG-fMRI inevitably compromises the quality of the EEG and fMRI signals due to the high degree of interaction between the two systems. Fluctuations in the magnetic flux flowing through the participant and the EEG system, whether due to movement within the magnetic field of the scanner or to changes in magnetic field strength, induce electrical potentials in the EEG recordings that mask the much weaker electrical activity of the neuronal populations. A number of different methods have been proposed to reduce MR artifacts. We present an overview of the most commonly used methods and an evaluation of the methods using three sets of diverse EEG data. We limited the evaluation to open-access and easy-to-use methods and a reference signal regression method using a set of six carbon-wire loops (CWL), which allowed evaluation of their added value. The evaluation was performed by comparing EEG signals recorded outside the MRI scanner with artifact-corrected EEG signals recorded simultaneously with fMRI. To quantify and evaluate the quality of artifact reduction methods in terms of the spectral content of the signal, we analyzed changes in oscillatory activity during a resting-state and a finger tapping motor task. The quality of artifact reduction in the time domain was assessed using data collected during a visual stimulation task. In the study we utilized hierarchical Bayesian probabilistic modeling for statistical inference and observed significant differences between the evaluated methods in the success of artifact reduction and associated signal quality in both the frequency and time domains. In particular, the CWL system proved superior to the other methods evaluated in improving spectral contrast in the alpha and beta bands and in recovering visual evoked responses. Based on the results of the evaluation study, we proposed guidelines for selecting the optimal method for MR artifact reduction.

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics

Electrophysiological source imaging (ESI) is an indispensable tool for noninvasively studying brain function and dysfunction. The moderate to low spatial resolution of electroencephalography/magnetoencephalography recordings has been a major hindrance to their wide utility in the field. Although this challenge is mitigated significantly by ESI techniques, it is difficult for individuals without relevant training to select and optimize hyperparameters for these ESI solvers. We propose a deep learning–based source imaging methodology that incorporates current advances in biophysical computational models and neural networks into the ESI framework, and it requires minimal user intervention after the model is trained. Our work promises to enable precise and robust high-resolution spatiotemporal functional brain imaging for a variety of neuroscience research studies and clinical applications.

Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning–based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF’s capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.

Functional Localizers for Motor Areas of the Brain Using fMRI

Neuroimaging researchers increasingly take advantage of the known functional properties of brain regions to localize motor regions in the brain and investigate changes in their activity under various conditions. Using this noninvasive functional MRI (fMRI) method makes it possible to identify and localize brain activation. There are many localizers that can be used to identify brain areas, namely, motor areas such as functional localizer, anatomical localizer, or Atlas mask. Eighteen right-handed participants were recruited for this research to test the reliability of five localizers for primary motor cortex (M1), supplementary motor area (SMA), premotor cortex (PMC), motor cerebellum, and motor thalamus. Motor execution task, namely, hand clenching was used to activate M1, SMA, and motor cerebellum. A combined action observation and motor imagery (AOMI) task was used to functionally activate PMC. Finally, a mask based on Talairach coordinates Atlas was created and used to identify the motor thalamus. Our results show that all localizers were successfully activated in the desired regions of interest. Motor execution successfully activated M1, SMA, and motor cerebellum. A novel localizer based on AOMI was successfully activated in PMC, and the motor thalamus mask obtained from the thalamus mask was successfully implemented on each participant. In conclusion, all five localizers tested in this research were reliable and can be used for rt-fMRI neurofeedback research to define the regions of interest.

Gaining insight into the neural basis of resting-state fMRI signal

The blood oxygenation level-dependent (BOLD)-based resting-state functional magnetic resonance imaging (rsfMRI) has been widely used as a non-invasive tool to map brain-wide connectivity architecture. However, the neural basis underpinning the resting-state BOLD signal remains elusive. In this study, we combined simultaneous calcium-based fiber photometry with rsfMRI in awake animals to examine the relationship of the BOLD signal and spiking activity at the resting state. We observed robust couplings between calcium and BOLD signals in the dorsal hippocampus as well as other distributed areas in the default mode network (DMN), suggesting that the calcium measurement can reliably predict the rsfMRI signal. In addition, using the calcium signal recorded as the ground truth, we assessed the impacts of different rsfMRI data preprocessing pipelines on functional connectivity mapping. Overall, our results provide important evidence suggesting that spiking activity measured by the calcium signal plays a key role in the neural mechanism of resting-state BOLD signal.

Multimodal Autoencoder Predicts fNIRS Resting State From EEG Signals

In this work, we introduce a deep learning architecture for evaluation on multimodal electroencephalographic (EEG) and functional near-infrared spectroscopy (fNIRS) recordings from 40 epileptic patients. Long short-term memory units and convolutional neural networks are integrated within a multimodal sequence-to-sequence autoencoder. The trained neural network predicts fNIRS signals from EEG, sans a priori, by hierarchically extracting deep features from EEG full spectra and specific EEG frequency bands. Results show that higher frequency EEG ranges are predictive of fNIRS signals with the gamma band inputs dominating fNIRS prediction as compared to other frequency envelopes. Seed based functional connectivity validates similar patterns between experimental fNIRS and our model's fNIRS reconstructions. This is the first study that shows it is possible to predict brain hemodynamics (fNIRS) from encoded neural data (EEG) in the resting human epileptic brain based on power spectrum amplitude modulation of frequency oscillations in the context of specific hypotheses about how EEG frequency bands decode fNIRS signals.

Methodological issues of the central mechanism of two classic acupuncture manipulations based on fNIRS: suggestions for a pilot study

Background: Acupuncture reinforcing-reducing manipulation (ARRM) is a necessary procedure of traditional Chinese acupuncture and an essential factor affecting the therapeutic effect of acupuncture. Shaoshanhuo reinforcing method (SSH) and Toutianliang reducing method (TTL) are the most representative ARRMs. They integrate six single ARRMs and pose distinguished therapeutic effects of acupuncture. However, due to the complexity, diversity, and variation, investigating the mechanism of these two classic manipulations is insufficient. The neuroimaging technique is an important method to explore the central mechanism of SSH and TTL. This study attempted to design a randomized crossover trial based on functional near-infrared spectroscopy (fNIRS) to explore the mechanism of SSH and TTL, meanwhile, provide valuable methodological references for future studies. Methods: A total of 30 healthy subjects were finally included and analyzed in this study. fNIRS examination was performed to record the neural responses during the two most representative ARRMs. The cortical activation and the inter-network functional connectivity (FC) were explored. Results: The results found that SSH and TTL could elicit significant cerebral responses, respectively, but there was no difference between them. Conclusion: Neuroimaging techniques with a higher spatiotemporal resolution, combinations of therapeutic effects, and strict quality control are important to neuroimaging studies on SSH and TTL.

Exploring the extent of source imaging: Recent advances in noninvasive electromagnetic brain imaging

Electrophysiological source imaging (ESI) has been successfully employed in many brain imaging applications during the last 20 years. ESI estimates of underlying brain networks provide millisecond resolution of dynamic brain processes; yet, it remains to be a challenge to further improve the spatial resolution of ESI modality, in particular on its capability of imaging the extent of underlying brain sources. In this review, we discuss the recent developments in signal processing and machine learning that have made it possible to image the extent, i.e. size, of underlying brain sources noninvasively, using scalp electromagnetic measurements from electroencephalogram (EEG) and magnetoencephalogram (MEG) recordings.

Separating vascular and neuronal effects of age on fMRI BOLD signals

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Novel Technique for Noninvasive Detection of Localized Dynamic Brain Signals by Using Transcranial Static Magnetic Fields

The techniques for noninvasive measurement of brain function such as electroencephalography (EEG), magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), and functional near-infrared spectroscopy (fNIRS) have been used in diagnosing brain conditions. However, the conventional techniques have critical limitations of spatial or temporal resolution. Here, we developed a novel technique which enables the precise measurement of dynamic brain signals and localized identification of active brain regions. In this technique, termed as magnetically biased field (MBF), human brain signal is measured as the fluctuation of a transcranial static magnetic field emitted by a coil placed on the scalp. The validity of MBF was confirmed by the measurement of somatosensory evoked signals. Fast somatosensory evoked signals were successfully observed. Localized maximum positive and negative deflections appeared at the region which represents the right primary somatosensory area contralateral to the stimulated hand. The ability of MBF to detect dynamic brain activity precisely can have numerous applications such as diagnosing brain diseases and brain-machine interfaces.

ESB: A low-cost EEG Synchronization Box

Electroencephalography (EEG) is a neuroimaging technique with a temporal resolution in the millisecond scale. Popular ERPs and ERD/ERS functions, as well as EEG-fRMI data and hyperscanning methods requires a proper temporal alignment (namely, synchronization) with stimulus onsets and other devices. Hardware-based synchronization, based on a SYNC signal injected into the device, ensures a reliable timing. In this paper we describe the design, test and validation of an EEG Synchronization Box (ESB), able to condition and distribute a SYNC signal (analog and digital) to different devices simultaneously. ESB can be easily built by individuals with basic soldering skills and represents a cost-effective solution to the available commercial synchronization boxes, while preserving similar electrical and functional features.

The Heterogeneity of Anxious Phenotypes: Neural Responses to Errors in Treatment-Seeking Anxious and Behaviorally Inhibited Youths

OBJECTIVE

Although behaviorally inhibited (BI) temperament predicts risk for anxiety, anxiety in BI may involve distinct neural responses to errors. The current study examines the relations between anxiety and neural correlates of error processing both in youths identified as BI in early childhood and in youths seeking treatment for an anxiety disorder.

METHOD

All participants underwent functional magnetic resonance imaging using a flanker task to assess responses to errors. A study in healthy subjects assessed test-retest reliability to inform analyses in two other samples. For one sample, a cohort of BI youths (Low BI, n = 28; High BI, n = 27) was followed into adolescence. For the other, participants were recruited based on the presence or absence of an anxiety disorder. Using identical methods in medication-free subjects, analyses compared relations between anxiety and error processing across the two samples.

RESULTS

Error-processing exhibited acceptable reliability. Within a ventromedial-prefrontal-cortex (vmPFC) cluster, anxiety related to error processing only in youths whose early-life BI status was known. In the high BI group, anxiety related to reduced neural response to errors. No such associations manifested in treatment-seeking youths. Other analyses mapped relations between error-processing and anxiety in each sample on its own. However, only the vmPFC cluster statistically differentiated the neural correlates of anxiety in BI.

CONCLUSION

BI temperament may define a unique pathway into anxiety involving perturbed neural responding to errors. Although BI is a risk factor for later anxiety, the neural and associated features of anxiety in BI youths may differ from those in treatment-seeking youths.

Altered Causal Coupling Pathways within the Central-Autonomic-Network in Patients Suffering from Schizophrenia

The multivariate analysis of coupling pathways within physiological (sub)systems focusing on identifying healthy and diseased conditions. In this study, we investigated a part of the central-autonomic-network (CAN) in 17 patients suffering from schizophrenia (SZO) compared to 17 age–gender matched healthy controls (CON) applying linear and nonlinear causal coupling approaches (normalized short time partial directed coherence, multivariate transfer entropy). Therefore, from all subjects continuous heart rate (successive beat-to-beat intervals, BBI), synchronized maximum successive systolic blood pressure amplitudes (SYS), synchronized calibrated respiratory inductive plethysmography signal (respiratory frequency, RESP), and the power P_EEG of frontal EEG activity were investigated for 15 min under resting conditions. The CAN revealed a bidirectional coupling structure, with central driving towards blood pressure (SYS), and respiratory driving towards P_EEG. The central-cardiac, central-vascular, and central-respiratory couplings are more dominated by linear regulatory mechanisms than nonlinear ones. The CAN showed significantly weaker nonlinear central-cardiovascular and central-cardiorespiratory coupling pathways, and significantly stronger linear central influence on the vascular system, and on the other hand significantly stronger linear respiratory and cardiac influences on central activity in SZO compared to CON, and thus, providing better understanding of the interrelationship of central and autonomic regulatory mechanisms in schizophrenia might be useful as a biomarker of this disease.

Characterizing the role of the structural connectome in seizure dynamics

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.

Spatial and Temporal Characteristics of Visual Field Progression in Glaucoma Assessed by Parallel Factor Analysis

Purpose

To explore spatial and temporal characteristics of glaucomatous visual field (VF) progression through multi-way decomposition of data.

Methods

Six serial VF exams with intervals of 6.0 ± 1.0 months in 121 pre-perimetric glaucoma eyes and 80 perimetric glaucoma eyes were arranged into a three-dimensional cube. The data were decomposed using parallel factor analysis.

Results

Three tri-linear components (i.e., spatial scores, temporal loadings, and subject-specific loadings) were identified. Component 1 clearly showed differences between superior and inferior hemispheres, linear trends over time, and wide variability in perimetric glaucoma. Findings were compatible with well-known characteristics of glaucomatous VF defects. Component 2 showed nasal and central areas in contrast with superior, inferior, and temporal peripheral locations, whereas component 3 showed a contrast between nasal and temporal hemispheres. Both components 2 and 3 failed to show clear temporal trends.

Conclusions

Identification of spatio-temporal patterns shows new possibilities for a multi-way decomposition methodology for earlier diagnosis and prediction of glaucomatous VF progression.

Towards understanding neural network signatures of motor skill learning in Parkinson's disease and healthy aging

In the past decade, neurorehabilitation has been shown to be an effective therapeutic supplement for patients with Parkinson's disease (PD). However, patients still experience severe problems with the consolidation of learned motor skills. Knowledge on the neural correlates underlying this process is thus essential to optimize rehabilitation for PD. This review investigates the existing studies on neural network connectivity changes in relation to motor learning in healthy aging and PD and critically evaluates the imaging methods used from a methodological point of view. The results indicate that despite neurodegeneration there is still potential to modify connectivity within and between motor and cognitive networks in response to motor training, although these alterations largely bypass the most affected regions in PD. However, so far training-related changes are inferred and possible relationships are not substantiated by brain-behavior correlations. Furthermore, the studies included suffer from many methodological drawbacks. This review also highlights the potential for using neural network measures as predictors for the response to rehabilitation, mainly based on work in young healthy adults. We speculate that future approaches, including graph theory and multimodal neuroimaging, may be more sensitive than brain activation patterns and model-based connectivity maps to capture the effects of motor learning. Overall, this review suggests that methodological developments in neuroimaging will eventually provide more detailed knowledge on how neural networks are modified by training, thereby paving the way for optimized neurorehabilitation for patients.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

Graph theory analysis reveals how sickle cell disease impacts neural networks of patients with more severe disease

Sickle cell disease (SCD) is a hereditary blood disorder associated with many life-threatening comorbidities including cerebral stroke and chronic pain. The long-term effects of this disease may therefore affect the global brain network which is not clearly understood. We performed graph theory analysis of functional networks using non-invasive fMRI and high resolution EEG on thirty-one SCD patients and sixteen healthy controls. Resting state data were analyzed to determine differences between controls and patients with less severe and more severe sickle cell related pain. fMRI results showed that patients with higher pain severity had lower clustering coefficients and local efficiency. The neural network of the more severe patient group behaved like a random network when performing a targeted attack network analysis. EEG results showed the beta1 band had similar results to fMRI resting state data. Our data show that SCD affects the brain on a global level and that graph theory analysis can differentiate between patients with different levels of pain severity.

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Graph theory used to study global impact of long term sickle cell disease on brain.

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EEG and fMRI results compared to study spatial and temporal impact of disease.

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More severe patients have less clustering and efficiency.

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Small world values correlated with past hospitalizations, linking results to pain.

Adaptive and Wireless Recordings of Electrophysiological Signals During Concurrent Magnetic Resonance Imaging

Strong electromagnetic fields that occur during functional magnetic resonance imaging (fMRI) presents a challenging environment for concurrent electrophysiological recordings. Here, we present a miniaturized, wireless platform-"MR-Link" (Multimodal Recording Link) that provides a hardware solution for simultaneous electrophysiological and fMRI signal acquisition. The device detects the changes in the electromagnetic field during fMRI to synchronize amplification and sampling of electrophysiological signals with minimal artifacts. It wirelessly transmits the recorded data at a frequency detectable by the MR-receiver coil. The transmitted data is readily separable from MRI in the frequency domain. To demonstrate its efficacy, we used this device to record electrocardiograms and somatosensory evoked potential during concurrent fMRI scans. The device minimized the fMRI-induced artifacts in electrophysiological data and wirelessly transmitted the data back to the receiver coil without compromising the fMRI signal quality. The device is compact (22 mm dia., 2 gms) and can be placed within the MRI bore to precisely synchronize with fMRI. Therefore, MR-Link offers an inexpensive system by eliminating the need for amplifiers with a high dynamic range, high-speed sampling, additional storage, or synchronization hardware for electrophysiological signal acquisition. It is expected to enable a broader range of applications of simultaneous fMRI and electrophysiology in animals and humans.

Multivariate assessment of the central-cardiorespiratory network structure in neuropathological disease

OBJECTIVE

The new interdisciplinary field of network physiology is increasingly becoming a focus of interest in medicine. Autonomic nervous system (ANS) dysfunction is well described in schizophrenia (SZO). However, the linear and nonlinear coupling between the ANS and central nervous system (CNS) has only been partly addressed until now. This coupling can be assumed to be a feedback-feedforward network, reacting with flexible and adaptive responses to internal and external factors.

APPROACH

For the first time, in this study we investigated linear and nonlinear short-term central-cardiorespiratory coupling of 17 patients suffering from paranoid SZO in comparison to 17 age-gender matched healthy subjects analyzing heart rate (HR), respiration (RESP), and the power of frontal electroencephalogram (EEG) activity (P _EEG). The objective is to determine how the different regulatory aspects of the CNS-ANS affect the central-cardiorespiratory network (CCRN). To quantify these couplings within the CCRN normalized short time partial directed coherence and the new multivariate high-resolution joint symbolic dynamics were applied.

MAIN RESULTS

We found that the CCRN in SZO can be characterized as a bidirectional one, with stronger central driving mechanisms (P _EEG  →  HR) towards HR regulation than vice versa, and with stronger respiratory influence (RESP  →  P _EEG) on central activity than vice versa. This suggests that the central-cardiorespiratory process (closed-loop) is mainly focused on adapting the HR via the sinoatrial node than focusing on respiratory regulation. On the other hand, the feedback-loop from ANS to CNS is strongly dominated via respiratory activity.

SIGNIFICANCE

We demonstrated a considerably significantly different CCRN structure in SZO with a strong central influence on the cardiac system and a strong respiratory influence on the CNS. Moreover, this study provides a more in-depth understanding of the interplay of the central and autonomic regulatory network in healthy subjects and SZO patients.

Electrophysiological Source Imaging: A Noninvasive Window to Brain Dynamics

Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.

Noninvasive measurement of dynamic brain signals using light penetrating the brain

Conventional techniques for the noninvasive measurement of brain activity involve critical limitations in spatial or temporal resolution. Here, we propose the method for noninvasive brain function measurement with high spatiotemporal resolution using optical signals. We verified that diffused near-infrared light penetrating through the upper jaw and into the skull, which we term as optoencephalography (OEG), leads to the detection of dynamic brain signals that vary concurrently with the electrophysiological neural activity. We measured the OEG signals following the stimulation of the median nerve in common marmosets. The OEG signal response was tightly coupled with the electrophysiological response represented by the somatosensory evoked potential (SSEP). The OEG measurement is also shown to offer rather clear discrimination of brain signals.

Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2-20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

Altered Functional Connectivity Following an Inflammatory White Matter Injury in the Newborn Rat: A High Spatial and Temporal Resolution Intrinsic Optical Imaging Study

Very preterm newborns have an increased risk of developing an inflammatory cerebral white matter injury that may lead to severe neuro-cognitive impairment. In this study we performed functional connectivity (fc) analysis using resting-state optical imaging of intrinsic signals (rs-OIS) to assess the impact of inflammation on resting-state networks (RSN) in a pre-clinical model of perinatal inflammatory brain injury. Lipopolysaccharide (LPS) or saline injections were administered in postnatal day (P3) rat pups and optical imaging of intrinsic signals were obtained 3 weeks later. (rs-OIS) fc seed-based analysis including spatial extent were performed. A support vector machine (SVM) was then used to classify rat pups in two categories using fc measures and an artificial neural network (ANN) was implemented to predict lesion size from those same fc measures. A significant decrease in the spatial extent of fc statistical maps was observed in the injured group, across contrasts and seeds (^*p = 0.0452 for HbO₂ and ^**p = 0.0036 for HbR). Both machine learning techniques were applied successfully, yielding 92% accuracy in group classification and a significant correlation r = 0.9431 in fractional lesion volume prediction (^**p = 0.0020). Our results suggest that fc is altered in the injured newborn brain, showing the long-standing effect of inflammation.

Deactivation in the posterior mid-cingulate cortex reflects perceptual transitions during binocular rivalry: Evidence from simultaneous EEG-fMRI

Binocular rivalry is a phenomenon in which perception spontaneously shifts between two different images that are dichoptically presented to the viewer. By elucidating the cortical networks responsible for these stochastic fluctuations in perception, we can potentially learn much about the neural correlates of visual awareness. We obtained concurrent EEG-fMRI data for a group of 20 healthy human subjects during the continuous presentation of dichoptic visual stimuli. The two eyes’ images were tagged with different temporal frequencies so that eye specific steady-state visual evoked potential (SSVEP) signals could be extracted from the EEG data for direct comparison with changes in fMRI BOLD activity associated with binocular rivalry. We additionally included a smooth replay condition that emulated the perceptual transitions experienced during binocular rivalry as a control stimulus. We evaluated a novel SSVEP-informed fMRI analysis in this study in order to delineate the temporal dynamics of rivalry-related BOLD activity from both an electrophysiological and behavioral perspective. In this manner, we assessed BOLD activity during rivalry that was directly correlated with peaks and crosses of the two rivaling, frequency-tagged SSVEP signals, for comparison with BOLD activity associated with subject reported perceptual transitions. Our findings point to a critical role of a right lateralized fronto-parietal network in the processing of bistable stimuli, given that BOLD activity in the right superior/inferior parietal lobules was significantly elevated throughout binocular rivalry and in particular during perceptual transitions, compared with the replay condition. Based on the SSVEP-informed analysis, rivalry was further associated with significantly enhanced BOLD suppression in the posterior mid-cingulate cortex during perceptual transitions, compared with SSVEP crosses. Overall, this work points to a careful interplay between early visual areas, the right posterior parietal cortex and the mid-cingulate cortex in mediating the spontaneous perceptual changes associated with binocular rivalry and has significant implications for future multimodal imaging studies of perception and awareness.

A unified canonical correlation analysis-based framework for removing gradient artifact in concurrent EEG/fMRI recording and motion artifact in walking recording from EEG signal

Artifacts cause distortion and fuzziness in electroencephalographic (EEG) signal and hamper EEG analysis, so it is necessary to remove them prior to the analysis. Particularly, artifact removal becomes a critical issue in experimental protocols with significant inherent recording noise, such as mobile EEG recordings and concurrent EEG-fMRI acquisitions. In this paper, we proposed a unified framework based on canonical correlation analysis for artifact removal. Raw signals were reorganized to construct a pair of matrices, based on which sources were sought through maximizing autocorrelation. Those sources related to artifacts were then removed by setting them as zeros, and the remaining sources were used to reconstruct artifact-free EEG. Both simulated and real recorded data were utilized to assess the proposed framework. Qualitative and quantitative results showed that the proposed framework was effective to remove artifacts from EEG signal. Specifically, the proposed method outperformed independent component analysis method for mitigating motion-related artifacts and had advantages for removing gradient artifact compared to the classical method (average artifacts subtraction) and the state-of-the-art method (optimal basis set) in terms of the combination of performance and computational complexity.

Characterization of functional brain activity and connectivity using EEG and fMRI in patients with sickle cell disease

Sickle cell disease (SCD) is a red blood cell disorder that causes many complications including life-long pain. Treatment of pain remains challenging due to a poor understanding of the mechanisms and limitations to characterize and quantify pain. In the present study, we examined simultaneously recording functional MRI (fMRI) and electroencephalogram (EEG) to better understand neural connectivity as a consequence of chronic pain in SCD patients. We performed independent component analysis and seed-based connectivity on fMRI data. Spontaneous power and microstate analysis was performed on EEG-fMRI data. ICA analysis showed that patients lacked activity in the default mode network (DMN) and executive control network compared to controls. EEG-fMRI data revealed that the insula cortex's role in salience increases with age in patients. EEG microstate analysis showed patients had increased activity in pain processing regions. The cerebellum in patients showed a stronger connection to the periaqueductal gray matter (involved in pain inhibition), and negative connections to pain processing areas. These results suggest that patients have reduced activity of DMN and increased activity in pain processing regions during rest. The present findings suggest resting state connectivity differences between patients and controls can be used as novel biomarkers of SCD pain.

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Simultaneous EEG-fMRI recordings revealed altered connectivity in sickle cell patients.

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Reduced activity observed in default mode network and executive control network.

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Patients' salience network strength increases with age; opposite seen in controls.

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EEG-fMRI parameters reflect disease severity in sickle cell patients.

Overview of MEG

Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.

Decoding Brain States Based on Magnetoencephalography From Prespecified Cortical Regions

Brain state decoding based on whole-head MEG has been extensively studied over the past decade. Recent MEG applications pose an emerging need of decoding brain states based on MEG signals originating from prespecified cortical regions. Toward this goal, we propose a novel region-of-interest-constrained discriminant analysis algorithm (RDA) in this paper. RDA integrates linear classification and beamspace transformation into a unified framework by formulating a constrained optimization problem. Our experimental results based on human subjects demonstrate that RDA can efficiently extract the discriminant pattern from prespecified cortical regions to accurately distinguish different brain states.

Simultaneous EEG-fMRI for working memory of the human brain

Memory plays an important role in human life. Memory can be divided into two categories, i.e., long term memory and short term memory (STM). STM or working memory (WM) stores information for a short span of time and it is used for information manipulations and fast response activities. WM is generally involved in the higher cognitive functions of the brain. Different studies have been carried out by researchers to understand the WM process. Most of these studies were based on neuroimaging modalities like fMRI, EEG, MEG etc., which use standalone processes. Each neuroimaging modality has some pros and cons. For example, EEG gives high temporal resolution but poor spatial resolution. On the other hand, the fMRI results have a high spatial resolution but poor temporal resolution. For a more in depth understanding and insight of what is happening inside the human brain during the WM process or during cognitive tasks, high spatial as well as high temporal resolution is desirable. Over the past decade, researchers have been working to combine different modalities to achieve a high spatial and temporal resolution at the same time. Developments of MRI compatible EEG equipment in recent times have enabled researchers to combine EEG-fMRI successfully. The research publications in simultaneous EEG-fMRI have been increasing tremendously. This review is focused on the WM research involving simultaneous EEG-fMRI data acquisition and analysis. We have covered the simultaneous EEG-fMRI application in WM and data processing. Also, it adds to potential fusion methods which can be used for simultaneous EEG-fMRI for WM and cognitive tasks.

Combining EEG Microstates with fMRI Structural Features for Modeling Brain Activity

Combining information from Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) has been a topic of increased interest recently. The main advantage of the EEG is its high temporal resolution, in the scale of milliseconds, while the main advantage of fMRI is the detection of functional activity with good spatial resolution. The advantages of each modality seem to complement each other, providing better insight in the neuronal activity of the brain. The main goal of combining information from both modalities is to increase the spatial and the temporal localization of the underlying neuronal activity captured by each modality. This paper presents a novel technique based on the combination of these two modalities (EEG, fMRI) that allow a better representation and understanding of brain activities in time. EEG is modeled as a sequence of topographies, based on the notion of microstates. Hidden Markov Models (HMMs) were used to model the temporal evolution of the topography of the average Event Related Potential (ERP). For each model the Fisher score of the sequence is calculated by taking the gradient of the trained model parameters. The Fisher score describes how this sequence deviates from the learned HMM. Canonical Partial Least Squares (CPLS) were used to decompose the two datasets and fuse the EEG and fMRI features. In order to test the effectiveness of this method, the results of this methodology were compared with the results of CPLS using the average ERP signal of a single channel. The presented methodology was able to derive components that co-vary between EEG and fMRI and present significant differences between the two tasks.

Multimodal neuroimaging computing: the workflows, methods, and platforms

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders

Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.

Thalamocortical relationship in epileptic patients with generalized spike and wave discharges--A multimodal neuroimaging study

Unlike focal or partial epilepsy, which has a confined range of influence, idiopathic generalized epilepsy (IGE) often affects the whole or a larger portion of the brain without obvious, known cause. It is important to understand the underlying network which generates epileptic activity and through which epileptic activity propagates. The aim of the present study was to investigate the thalamocortical relationship using non-invasive imaging modalities in a group of IGE patients. We specifically investigated the roles of the mediodorsal nuclei in the thalami and the medial frontal cortex in generating and spreading IGE activities. We hypothesized that the connectivity between these two structures is key in understanding the generation and propagation of epileptic activity in brains affected by IGE. Using three imaging techniques of EEG, fMRI and EEG-informed fMRI, we identified important players in generation and propagation of generalized spike-and-wave discharges (GSWDs). EEG-informed fMRI suggested multiple regions including the medial frontal area near to the anterior cingulate cortex, mediodorsal nuclei of the thalamus, caudate nucleus among others that related to the GSWDs. The subsequent seed-based fMRI analysis revealed a reciprocal cortical and bi-thalamic functional connection. Through EEG-based Granger Causality analysis using (DTF) and adaptive DTF, within the reciprocal thalamocortical circuitry, thalamus seems to serve as a stronger source in driving cortical activity from initiation to the propagation of a GSWD. Such connectivity change starts before the GSWDs and continues till the end of the slow wave discharge. Thalamus, especially the mediodorsal nuclei, may serve as potential targets for deep brain stimulation to provide more effective treatment options for patients with drug-resistant generalized epilepsy.

High-definition transcranial direct current stimulation induces both acute and persistent changes in broadband cortical synchronization: a simultaneous tDCS-EEG study

The goal of this study was to develop methods for simultaneously acquiring electrophysiological data during high-definition transcranial direct current stimulation (tDCS) using high-resolution electroencephalography (EEG). Previous studies have pointed to the after-effects of tDCS on both motor and cognitive performance, and there appears to be potential for using tDCS in a variety of clinical applications. However, little is known about the real-time effects of tDCS on rhythmic cortical activity in humans due to the technical challenges of simultaneously obtaining electrophysiological data during ongoing stimulation. Furthermore, the mechanisms of action of tDCS in humans are not well understood. We have conducted a simultaneous tDCS-EEG study in a group of healthy human subjects. Significant acute and persistent changes in spontaneous neural activity and event-related synchronization (ERS) were observed during and after the application of high-definition tDCS over the left sensorimotor cortex. Both anodal and cathodal stimulation resulted in acute global changes in broadband cortical activity which were significantly different than the changes observed in response to sham stimulation. For the group of eight subjects studied, broadband individual changes in spontaneous activity during stimulation were apparent both locally and globally. In addition, we found that high-definition tDCS of the left sensorimotor cortex can induce significant ipsilateral and contralateral changes in event-related desynchronization and ERS during motor imagination following the end of the stimulation period. Overall, our results demonstrate the feasibility of acquiring high-resolution EEG during high-definition tDCS and provide evidence that tDCS in humans directly modulates rhythmic cortical synchronization during and after its administration.

SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI

BACKGROUND

Binocular rivalry is a perceptual phenomenon that arises when two incompatible images are presented separately, one to each eye, and the observer experiences involuntary perceptual alternations between the two images. If the two images are flickering at two distinct frequencies, electroencephalography (EEG) can be used to track the frequency-tagged steady-state visually evoked potential (SSVEP) driven by each image as they compete for awareness, providing an objective measure of the subjective perceptual state. This spontaneous alternation in perceptual dominance is believed to be driven by neural processes across widespread regions in the brain, but the real-time mechanisms of these processes remain unclear.

NEW METHOD

The goal of this study was to determine the feasibility of investigating binocular rivalry using a simultaneous EEG-fMRI approach in order to leverage the high temporal resolution of EEG with the high spatial resolution of fMRI.

RESULTS

We have developed novel techniques for artifact removal and signal optimization for the rivalry-related SSVEP data collected simultaneously during fMRI.

COMPARISON WITH EXISTING METHODS

Our methods address several significant technical challenges of recording SSVEP data in the noisy fMRI environment, and enabled us to successfully reconstruct SSVEP signatures of rivalry in a group of healthy human subjects.

CONCLUSION

Further development and application of these techniques will enable more comprehensive integration of EEG and fMRI data collected simultaneously and could have significant implications for EEG-fMRI studies of brain activity in general.

Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives

Many studies over the past two decades have shown that people can use brain signals to convey their intent to a computer using brain-computer interfaces (BCIs). BCI systems extract specific features of brain activity and translate them into control signals that drive an output. Recently, a category of BCIs that are built on the rhythmic activity recorded over the sensorimotor cortex, i.e., the sensorimotor rhythm (SMR), has attracted considerable attention among the BCIs that use noninvasive neural recordings, e.g., electroencephalography (EEG), and have demonstrated the capability of multidimensional prosthesis control. This paper reviews the current state and future perspectives of SMR-based BCI and its clinical applications, in particular focusing on the EEG SMR. The characteristic features of SMR from the human brain are described and their underlying neural sources are discussed. The functional components of SMR-based BCI, together with its current clinical applications, are reviewed. Finally, limitations of SMR-BCIs and future outlooks are also discussed.

Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA)

Many important problems in the analysis of neuroimages can be formulated as discovering the relationship between two sets of variables, a task for which linear techniques such as canonical correlation analysis (CCA) have been commonly used. However, to further explore potential nonlinear processes that might co-exist with linear ones in brain function, a more flexible method is required. Here, we propose a new unsupervised and data-driven method, termed the eigenspace maximal information canonical correlation analysis (emiCCA), which is capable of automatically capturing the linear and/or nonlinear relationships between various data sets. A simulation confirmed the superior performance of emiCCA in comparison with linear CCA and kernel CCA (a nonlinear version of CCA). An emiCCA framework for functional magnetic resonance imaging (fMRI) data processing was designed and applied to data from a real motor execution fMRI experiment. This analysis uncovered one linear (in primary motor cortex) and a few nonlinear networks (e.g., in the supplementary motor area, bilateral insula, and cerebellum). This suggests that these various task-related brain areas are part of networks that also contribute to the execution of movements of the hand. These results suggest that emiCCA is a promising technique for exploring various data.

Using functional MRI alone for localization in focal epilepsy

In the present study, we developed a method for the purpose of localizing epilepsy related hemodynamic foci for patients suffering intractable focal epilepsy using resting state fMRI alone. We studied two groups of subjects: five patients with intractable focal epilepsy, and ten healthy volunteers performing motor tasks. Spatial independent component analysis (ICA) was performed on the fMRI alone data and a set of independent component (IC) selection criteria was developed to identify epilepsy related ICs. The method was then evaluated in the healthy group with motor tasks. In all five surgery patients, there was at least one identified IC concordant with surgical resection. In the motor task study of healthy subjects, our method revealed components with concordant spatial and temporal features as expected from the unilateral motor tasks. These results suggest the lateralization and localization value of fMRI alone in presurgical evaluation for patients with intractable unilateral focal epilepsy. The proposed method is noninvasive in nature and easy to implement. It has the potential to be incorporated in current presurgical workup for the diagnosis of intractable focal epilepsy patients.

Advances in Electrophysiological Research

Electrophysiological measures of brain function are effective tools to understand neurocognitive phenomena and sensitive indicators of pathophysiological processes associated with various clinical conditions, including alcoholism. Individuals with alcohol use disorder (AUD) and their high-risk offspring have consistently shown dysfunction in several electrophysiological measures in resting state (i.e., electroencephalogram) and during cognitive tasks (i.e., event-related potentials and event-related oscillations). Researchers have recently developed sophisticated signal-processing techniques to characterize different aspects of brain dynamics, which can aid in identifying the neural mechanisms underlying alcoholism and other related complex disorders.These quantitative measures of brain function also have been successfully used as endophenotypes to identify and help understand genes associated with AUD and related disorders. Translational research also is examining how brain electrophysiological measures potentially can be applied to diagnosis, prevention, and treatment.

Evoked effective connectivity of the human neocortex

The role of cortical connectivity in brain function and pathology is increasingly being recognized. While in vivo magnetic resonance imaging studies have provided important insights into anatomical and functional connectivity, these methodologies are limited in their ability to detect electrophysiological activity and the causal relationships that underlie effective connectivity. Here, we describe results of cortico-cortical evoked potential (CCEP) mapping using single pulse electrical stimulation in 25 patients undergoing seizure monitoring with subdural electrode arrays. Mapping was performed by stimulating adjacent electrode pairs and recording CCEPs from the remainder of the electrode array. CCEPs reliably revealed functional networks and showed an inverse relationship to distance between sites. Coregistration to Brodmann areas (BA) permitted group analysis. Connections were frequently directional with 43% of early responses and 50% of late responses of connections reflecting relative dominance of incoming or outgoing connections. The most consistent connections were seen as outgoing from motor cortex, BA6-BA9, somatosensory (SS) cortex, anterior cingulate cortex, and Broca's area. Network topology revealed motor, SS, and premotor cortices along with BA9 and BA10 and language areas to serve as hubs for cortical connections. BA20 and BA39 demonstrated the most consistent dominance of outdegree connections, while BA5, BA7, auditory cortex, and anterior cingulum demonstrated relatively greater indegree. This multicenter, large-scale, directional study of local and long-range cortical connectivity using direct recordings from awake, humans will aid the interpretation of noninvasive functional connectome studies.