Markus Siegel

Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring

Goal-directed behavior requires the continuous monitoring and dynamic adjustment of ongoing actions. Here, we report a direct coupling between the event-related electroencephalogram (EEG), functional magnetic resonance imaging (fMRI), and behavioral measures of performance monitoring in humans. By applying independent component analysis to EEG signals recorded simultaneously with fMRI, we found the single-trial error-related negativity of the EEG to be systematically related to behavior in the subsequent trial, thereby reflecting immediate behavioral adjustments of a cognitive performance monitoring system. Moreover, this trial-by-trial EEG measure of performance monitoring predicted the fMRI activity in the rostral cingulate zone, a brain region thought to play a key role in processing of response errors. We conclude that investigations of the dynamic coupling between EEG and fMRI provide a powerful approach for the study of higher order brain functions.

Large-scale cortical correlation structure of spontaneous oscillatory activity

Little is known about the brain-wide correlation of electrophysiological signals. We found that spontaneous oscillatory neuronal activity exhibited frequency-specific spatial correlation structure in the human brain. We developed an analysis approach that discounts spurious correlation of signal power caused by the limited spatial resolution of electrophysiological measures. We applied this approach to source estimates of spontaneous neuronal activity reconstructed from magnetoencephalography. Overall, correlation of power across cortical regions was strongest in the alpha to beta frequency range (8–32 Hz) and correlation patterns depended on the underlying oscillation frequency. Global hubs resided in the medial temporal lobe in the theta frequency range (4–6 Hz), in lateral parietal areas in the alpha to beta frequency range (8–23 Hz) and in sensorimotor areas for higher frequencies (32–45 Hz). Our data suggest that interactions in various large-scale cortical networks may be reflected in frequency-specific power envelope correlations.

Neuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention

Oscillatory neuronal synchronization, within and between cortical areas, may mediate the selection of attended visual stimuli. However, it remains unclear at and between which processing stages visuospatial attention modulates oscillatory synchronization in the human brain. We thus combined magnetoencephalography (MEG) in a spatially cued motion discrimination task with source-reconstruction techniques and characterized attentional effects on neuronal synchronization across key stages of the human dorsal visual pathway. We found that visuospatial attention modulated oscillatory synchronization between visual, parietal, and prefrontal cortex in a spatially selective fashion. Furthermore, synchronized activity within these stages was selectively modulated by attention, but with markedly distinct spectral signatures and stimulus dependence between regions. Our data indicate that regionally specific oscillatory synchronization at most stages of the human dorsal visual pathway may enhance the processing of attended visual stimuli and suggest that attentional selection is mediated by frequency-specific synchronization between prefrontal, parietal, and early visual cortex.

Phase-dependent neuronal coding of objects in short-term memory

The ability to hold multiple objects in memory is fundamental to intelligent behavior, but its neural basis remains poorly understood. It has been suggested that multiple items may be held in memory by oscillatory activity across neuronal populations, but yet there is little direct evidence. Here, we show that neuronal information about two objects held in short-term memory is enhanced at specific phases of underlying oscillatory population activity. We recorded neuronal activity from the prefrontal cortices of monkeys remembering two visual objects over a brief interval. We found that during this memory interval prefrontal population activity was rhythmically synchronized at frequencies around 32 and 3 Hz and that spikes carried the most information about the memorized objects at specific phases. Further, according to their order of presentation, optimal encoding of the first presented object was significantly earlier in the 32 Hz cycle than that for the second object. Our results suggest that oscillatory neuronal synchronization mediates a phase-dependent coding of memorized objects in the prefrontal cortex. Encoding at distinct phases may play a role for disambiguating information about multiple objects in short-term memory.

Single-trial EEG-fMRI reveals the dynamics of cognitive function

Two major non-invasive techniques in cognitive neuroscience, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have complementary advantages with regard to their spatial and temporal resolution. Recent hardware and software developments have made it feasible to acquire EEG and fMRI data simultaneously. We emphasize the potential of simultaneous EEG and fMRI recordings to pursue new strategies in cognitive neuroimaging. Specifically, we propose that, by exploiting the combined spatiotemporal resolution of the methods, the integration of EEG and fMRI recordings on a single-trial level enables the rich temporal dynamics of information processing to be characterized within spatially well-defined neural networks.

A framework for local cortical oscillation patterns

Oscillations are a pervasive feature of neuronal activity in the cerebral cortex. Here, we propose a framework for understanding local cortical oscillation patterns in cognition: two classes of network interactions underlying two classes of cognitive functions produce different local oscillation patterns. Local excitatory-inhibitory interactions shape neuronal representations of sensory, motor and cognitive variables, and produce local gamma-band oscillations. By contrast, the linkage of such representations by integrative functions such as decision-making is mediated by long-range cortical interactions, which yield more diverse local oscillation patterns often involving the beta range. This framework reconciles different cortical oscillation patterns observed in recent studies and helps to understand the link between cortical oscillations and the fMRI signal. Our framework highlights the notion that cortical oscillations index the specific circuit-level mechanisms of cognition.

Neural substrates of cognitive capacity limitations

Cognition has a severely limited capacity: Adult humans can retain only about four items “in mind”. This limitation is fundamental to human brain function: Individual capacity is highly correlated with intelligence measures and capacity is reduced in neuropsychiatric diseases. Although human capacity limitations are well studied, their mechanisms have not been investigated at the single-neuron level. Simultaneous recordings from monkey parietal and frontal cortex revealed that visual capacity limitations occurred immediately upon stimulus encoding and in a bottom-up manner. Capacity limitations were found to reflect a dual model of working memory. The left and right halves of visual space had independent capacities and thus are discrete resources. However, within each hemifield, neural information about successfully remembered objects was reduced by adding further objects, indicating that resources are shared. Together, these results suggest visual capacity limitation is due to discrete, slot-like, resources, each containing limited pools of neural information that can be divided among objects.

Cortical Information Flow During Flexible Sensorimotor Decisions

Signal flow during sensorimotor choices Little is known about the flow of task signals across the brain. Siegel et al. simultaneously recorded from multiple units in the sensory, parietal, prefrontal, and motor cortex while monkeys were cued to perform one among two possible simple tasks. The proportion of neurons coding for stimuli, cues, tasks, and choices, and their response latency, varied across regions. Parietal and prefrontal brain regions encoded task information and choices with the same latency. Interestingly, all brain areas encoded all types of information. However, they differed functionally according to the proportions of neurons and their response latency. Science, this issue p. 1352 A dynamic network of cortical areas processing similar information but to different degrees is explored. During flexible behavior, multiple brain regions encode sensory inputs, the current task, and choices. It remains unclear how these signals evolve. We simultaneously recorded neuronal activity from six cortical regions [middle temporal area (MT), visual area four (V4), inferior temporal cortex (IT), lateral intraparietal area (LIP), prefrontal cortex (PFC), and frontal eye fields (FEF)] of monkeys reporting the color or motion of stimuli. After a transient bottom-up sweep, there was a top-down flow of sustained task information from frontoparietal to visual cortex. Sensory information flowed from visual to parietal and prefrontal cortex. Choice signals developed simultaneously in frontoparietal regions and travelled to FEF and sensory cortex. This suggests that flexible sensorimotor choices emerge in a frontoparietal network from the integration of opposite flows of sensory and task information.

Cortical hypersynchrony predicts breakdown of sensory processing during loss of consciousness.

Intrinsic cortical dynamics modulates the processing of sensory information and therefore may be critical for conscious perception. We tested this hypothesis by electroencephalographic recording of ongoing and stimulus-related brain activity during stepwise drug-induced loss of consciousness in healthy human volunteers. We found that progressive loss of consciousness was tightly linked to the emergence of a hypersynchronous cortical state in the alpha frequency range (8-14 Hz). This drug-induced ongoing alpha activity was widely distributed across the frontal cortex. Stimulus-related responses to median nerve stimulation consisted of early and midlatency response components in primary somatosensory cortex (S1) and a late component also involving temporal and parietal regions. During progressive sedation, the early response was maintained, whereas the midlatency and late responses were reduced and eventually vanished. The antagonistic relation between the late sensory response and ongoing alpha activity held for constant drug levels on the single-trial level. Specifically, the late response component was negatively correlated with the power and long-range coherence of ongoing frontal alpha activity. Our results suggest blocking of intracortical communication by hypersynchronous ongoing activity as a key mechanism for the loss of consciousness.

Integrating Top-Down and Bottom-Up Sensory Processing by Somato-Dendritic Interactions

The classical view of cortical information processing is that of a bottom-up process in a feedforward hierarchy. However, psychophysical, anatomical, and physiological evidence suggests that top-down effects play a crucial role in the processing of input stimuli. Not much is known about the neural mechanisms underlying these effects. Here we investigate a physiologically inspired model of two reciprocally connected cortical areas. Each area receives bottom-up as well as top-down information. This information is integrated by a mechanism that exploits recent findings on somato-dendritic interactions. (1) This results in a burst signal that is robust in the context of noise in bottom-up signals. (2) Investigating the influence of additional top-down information, priming-like effects on the processing of bottom-up input can be demonstrated. (3) In accordance with recent physiological findings, interareal coupling in low-frequency ranges is characteristically enhanced by top-down mechanisms. The proposed scheme combines a qualitative influence of top-down directed signals on the temporal dynamics of neuronal activity with a limited effect on the mean firing rate of the targeted neurons. As it gives an account of the system properties on the cellular level, it is possible to derive several experimentally testable predictions.