David M. Groppe

Mass univariate analysis of event-related brain potentials/fields I: A critical tutorial review

Event-related potentials (ERPs) and magnetic fields (ERFs) are typically analyzed via ANOVAs on mean activity in a priori windows. Advances in computing power and statistics have produced an alternative, mass univariate analyses consisting of thousands of statistical tests and powerful corrections for multiple comparisons. Such analyses are most useful when one has little a priori knowledge of effect locations or latencies, and for delineating effect boundaries. Mass univariate analyses complement and, at times, obviate traditional analyses. Here we review this approach as applied to ERP/ERF data and four methods for multiple comparison correction: strong control of the familywise error rate (FWER) via permutation tests, weak control of FWER via cluster-based permutation tests, false discovery rate control, and control of the generalized FWER. We end with recommendations for their use and introduce free MATLAB software for their implementation.

Neurophysiological investigation of spontaneous correlated and anticorrelated fluctuations of the BOLD signal.

Analyses of intrinsic fMRI BOLD signal fluctuations reliably reveal correlated and anticorrelated functional networks in the brain. Because the BOLD signal is an indirect measure of neuronal activity and anticorrelations can be introduced by preprocessing steps, such as global signal regression, the neurophysiological significance of correlated and anticorrelated BOLD fluctuations is a source of debate. Here, we address this question by examining the correspondence between the spatial organization of correlated BOLD fluctuations and correlated fluctuations in electrophysiological high γ power signals recorded directly from the cortical surface of 5 patients. We demonstrate that both positive and negative BOLD correlations have neurophysiological correlates reflected in fluctuations of spontaneous neuronal activity. Although applying global signal regression to BOLD signals results in some BOLD anticorrelations that are not apparent in the ECoG data, it enhances the neuronal-hemodynamic correspondence overall. Together, these findings provide support for the neurophysiological fidelity of BOLD correlations and anticorrelations.

Mass univariate analysis of event-related brain potentials/fields II: Simulation studies

Mass univariate analysis is a relatively new approach for the study of ERPs/ERFs. It consists of many statistical tests and one of several powerful corrections for multiple comparisons. Multiple comparison corrections differ in their power and permissiveness. Moreover, some methods are not guaranteed to work or may be overly sensitive to uninteresting deviations from the null hypothesis. Here we report the results of simulations assessing the accuracy, permissiveness, and power of six popular multiple comparison corrections (permutation-based control of the familywise error rate [FWER], weak control of FWER via cluster-based permutation tests, permutation-based control of the generalized FWER, and three false discovery rate control procedures) using realistic ERP data. In addition, we look at the sensitivity of permutation tests to differences in population variance. These results will help researchers apply and interpret these procedures.

Identifying reliable independent components via split-half comparisons

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the methods validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.

Thinking ahead or not? Natural aging and anticipation during reading.

Despite growing evidence of young adults neurally pre-activating word features during sentence comprehension, less clear is the degree to which this generalizes to older adults. Using ERPs, we tested for linguistic prediction in younger and older readers by means of indefinite articles (as and ans) preceding more and less probable noun continuations. Although both groups exhibited cloze probability-graded noun N400s, only the young showed significant article effects, indicating probabilistic sensitivity to the phonology of anticipated upcoming nouns. Additionally, both age groups exhibited prolonged increased frontal positivities to less probable nouns, although in older adults this effect was prominent only in a subset with high verbal fluency (VF). This ERP positivity to contextual constraint violations offers additional support for prediction in the young. For high VF older adults, the positivity may indicate they, too, engage in some form of linguistic pre-processing when implicitly cued, as may have occurred via the articles.

Physiology of functional and effective networks in epilepsy.

Epilepsy is a network phenomenon characterized by atypical activity during seizure both at the level of single neurons and neural populations. The etiology of epilepsy is not completely understood but a common theme among proposed mechanisms is abnormal synchronization between neuronal populations. Recent advances in novel imaging and recording technologies have enabled the inference of comprehensive maps of both the anatomical and physiological inter-relationships between brain regions. Clinical protocols established for diagnosis and treatment of epilepsy utilize both advanced neuroimaging techniques and neurophysiological data. These growing clinical datasets can be further exploited to better understand the complex connectivity patterns in the epileptic brain. In this article, we review results and insights gained from the growing body of research focused on epilepsy from a network perspective. In particular, we put an emphasis on two different notions of network connectivity: functional and effective; and studies investigating these notions in epilepsy are highlighted. We also discuss limitations and opportunities in data collection and analyses that will further our understanding of epileptic networks and the mechanisms of seizures.

Seeing scenes: topographic visual hallucinations evoked by direct electrical stimulation of the parahippocampal place area.

In recent years, functional neuroimaging has disclosed a network of cortical areas in the basal temporal lobe that selectively respond to visual scenes, including the parahippocampal place area (PPA). Beyond the observation that lesions involving the PPA cause topographic disorientation, there is little causal evidence linking neural activity in that area to the perception of places. Here, we combined functional magnetic resonance imaging (fMRI) and intracranial EEG (iEEG) recordings to delineate place-selective cortex in a patient implanted with stereo-EEG electrodes for presurgical evaluation of drug-resistant epilepsy. Bipolar direct electrical stimulation of a cortical area in the collateral sulcus and medial fusiform gyrus, which was place-selective according to both fMRI and iEEG, induced a topographic visual hallucination: the patient described seeing indoor and outdoor scenes that included views of the neighborhood he lives in. By contrast, stimulating the more lateral aspect of the basal temporal lobe caused distortion of the patients perception of faces, as recently reported (Parvizi et al., 2012). Our results support the causal role of the PPA in the perception of visual scenes, demonstrate that electrical stimulation of higher order visual areas can induce complex hallucinations, and also reaffirm direct electrical brain stimulation as a tool to assess the function of the human cerebral cortex.

Exemplar Selectivity Reflects Perceptual Similarities in the Human Fusiform Cortex

While brain imaging studies emphasized the category selectivity of face-related areas, the underlying mechanisms of our remarkable ability to discriminate between different faces are less understood. Here, we recorded intracranial local field potentials from face-related areas in patients presented with images of faces and objects. A highly significant exemplar tuning within the category of faces was observed in high-Gamma (80-150 Hz) responses. The robustness of this effect was supported by single-trial decoding of face exemplars using a minimal (n = 5) training set. Importantly, exemplar tuning reflected the psychophysical distance between faces but not their low-level features. Our results reveal a neuronal substrate for the establishment of perceptual distance among faces in the human brain. They further imply that face neurons are anatomically grouped according to well-defined functional principles, such as perceptual similarity.

Dominant frequencies of resting human brain activity as measured by the electrocorticogram

The brains spontaneous, intrinsic activity is increasingly being shown to reveal brain function, delineate large scale brain networks, and diagnose brain disorders. One of the most studied and clinically utilized types of intrinsic brain activity are oscillations in the electrocorticogram (ECoG), a relatively localized measure of cortical synaptic activity. Here we objectively characterize the types of ECoG oscillations commonly observed over particular cortical areas when an individual is awake and immobile with eyes closed, using a surface-based cortical atlas and cluster analysis. Both methods show that [1] there is generally substantial variability in the dominant frequencies of cortical regions and substantial overlap in dominant frequencies across the areas sampled (primarily lateral central, temporal, and frontal areas), [2] theta (4-8 Hz) is the most dominant type of oscillation in the areas sampled with a mode around 7 Hz, [3] alpha (8-13 Hz) is largely limited to parietal and occipital regions, and [4] beta (13-30 Hz) is prominent peri-Rolandically, over the middle frontal gyrus, and the pars opercularis. In addition, the cluster analysis revealed seven types of ECoG spectral power densities (SPDs). Six of these have peaks at 3, 5, 7 (narrow), 7 (broad), 10, and 17 Hz, while the remaining cluster is broadly distributed with less pronounced peaks at 8, 19, and 42 Hz. These categories largely corroborate conventional sub-gamma frequency band distinctions (delta, theta, alpha, and beta) and suggest multiple sub-types of theta. Finally, we note that gamma/high gamma activity (30+ Hz) was at times prominently observed, but was too infrequent and variable across individuals to be reliably characterized. These results should help identify abnormal patterns of ECoG oscillations, inform the interpretation of EEG/MEG intrinsic activity, and provide insight into the functions of these different oscillations and the networks that produce them. Specifically, our results support theories of the importance of theta oscillations in general cortical function, suggest that alpha activity is primarily related to sensory processing/attention, and demonstrate that beta networks extend far beyond primary sensorimotor regions.

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 Brocas 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. Hum Brain Mapp 35:5736–5753, 2014.