Xiaohong Wan

The neural basis of intuitive best next-move generation in board game experts.

Compared with amateurs, expert shogi players show specific brain activation patterns. The superior capability of cognitive experts largely depends on quick automatic processes. To reveal their neural bases, we used functional magnetic resonance imaging to study brain activity of professional and amateur players in a board game named shogi. We found two activations specific to professionals: one in the precuneus of the parietal lobe during perception of board patterns, and the other in the caudate nucleus of the basal ganglia during quick generation of the best next move. Activities at these two sites covaried in relevant tasks. These results suggest that the precuneus-caudate circuit implements the automatic, yet complicated, processes of board-pattern perception and next-move generation in board game experts.

The neural basis of the hemodynamic response nonlinearity in human primary visual cortex: Implications for neurovascular coupling mechanism

It has been well recognized that the nonlinear hemodynamic responses of the blood oxygenation level-dependent (BOLD) functional MRI (fMRI) are important and ubiquitous in a series of experimental paradigms, especially for the event-related fMRI. Although this phenomenon has been intensively studied and it has been found that the post-capillary venous expansion is an intrinsically nonlinear mechanical process, the existence of an additional neural basis for the nonlinearity has not been clearly shown. In this paper, we assessed the correlation between the electric and vascular indices by performing simultaneous electroencephalography (EEG) and fMRI recordings in humans during a series of visual stimulation (i.e., radial checkerboard). With changes of the visual stimulation frequencies (from 0.5 to 16 Hz) and contrasts (from 1% to 100%), both the event related potentials (ERPs) and hemodynamic responses show nonlinear behaviors. In particular, the mean power of the brain electric sources and the neuronal efficacies (as originally defined in the hemodynamics model [Friston et al. Neuroimage, 12, 466-477, 2000], here represent the vascular inputs) in primary visual cortex consistently show a linear correlation for all subjects. This indicates that the hemodynamic response nonlinearity found in this paper primarily reflects the nonlinearity of underlying neural activity. Most importantly, this finding underpins a nonlinear neurovascular coupling. Specifically, it is shown that the transferring function of the neurovascular coupling is likely a power transducer, which integrates the fast dynamics of neural activity into the vascular input of slow hemodynamics.

Nonlinear local electrovascular coupling. I: A theoretical model

Here we present a detailed biophysical model of how brain electrical and vascular dynamics are generated within a basic cortical unit. The model was obtained from coupling a canonical neuronal mass and an expandable vasculature. In this proposal, we address several aspects related to electroencephalographic and functional magnetic resonance imaging data fusion: (1) the impact of the cerebral architecture (at different physical levels) on the observations; (2) the physiology involved in electrovascular coupling; and (3) energetic considerations to gain a better understanding of how the glucose budget is used during neuronal activity. The model has three components. The first is the canonical neural mass model of three subpopulations of neurons that respond to incoming excitatory synaptic inputs. The generation of the membrane potentials in the somas of these neurons and the electric currents flowing in the neuropil are modeled by this component. The second and third components model the electrovascular coupling and the dynamics of vascular states in an extended balloon approach, respectively. In the first part we describe, in some detail, the biophysical model and establish its face validity using simulations of visually evoked responses under different flickering frequencies and luminous contrasts. In a second part, a recursive optimization algorithm is developed and used to make statistical inferences about this forward/generative model from actual data. Hum. Brain Mapping 2006.

Fusing EEG and fMRI based on a bottom-up model : inferring activation and effective connectivity in neural masses

The elucidation of the complex machinery used by the human brain to segregate and integrate information while performing high cognitive functions is a subject of imminent future consequences. The most significant contributions to date in this field, known as cognitive neuroscience, have been achieved by using innovative neuroimaging techniques, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), which measure variations in both the time and the space of some interpretable physical magnitudes. Extraordinary maps of cerebral activation involving function-restricted brain areas, as well as graphs of the functional connectivity between them, have been obtained from EEG and fMRI data by solving some spatio-temporal inverse problems, which constitutes a top-down approach. However, in many cases, a natural bridge between these maps/graphs and the causal physiological processes is lacking, leading to some misunderstandings in their interpretation. Recent advances in the comprehension of the underlying physiological mechanisms associated with different cerebral scales have provided researchers with an excellent scenario to develop sophisticated biophysical models that permit an integration of these neuroimage modalities, which must share a common aetiology. This paper proposes a bottom-up approach, involving physiological parameters in a specific mesoscopic dynamic equations system. Further observation equations encapsulating the relationship between the mesostates and the EEG/fMRI data are obtained on the basis of the physical foundations of these techniques. A methodology for the estimation of parameters from fused EEG/fMRI data is also presented. In this context, the concepts of activation and effective connectivity are carefully revised. This new approach permits us to examine and discuss some future prospects for the integration of multimodal neuroimages.

Nonlinear local electrovascular coupling. II: From data to neuronal masses.

In the companion article a local electrovascular coupling (LEVC) model was proposed to explain the continuous dynamics of electrical and vascular states within a cortical unit. These states produce certain mesoscopic reflections whose discrete time series can be reconstructed from electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In this article we develop a recursive optimization algorithm based on the local linearization (LL) filter and an innovation method to make statistical inferences about the LEVC model from both EEG and fMRI data, i.e., to estimate the unobserved states and the unknown parameters of the model. For a better understanding, the LL filter is described from a Bayesian point of view, providing the particulars for the case of hybrid data (e.g., EEG and fMRI), which could be sampled at different rates. The dynamics of the exogenous synaptic inputs going into the cortical unit are also estimated by introducing a set of Gaussian radial basis functions. In order to study the dynamics of the electrical and vascular states in the striate cortex of humans as well as their local interrelationships, we applied this algorithm to EEG and fMRI recordings obtained concurrently from two subjects while passively observing a radial checkerboard with a white/black pattern reversal. The EEG and fMRI data from the first subject was used to estimate the electrical/vascular states and parameters of the LEVC model in V1 for a 4.0 Hz reversion frequency. We used the EEG data from the second subject to investigate the changes in the dynamics of the electrical states when the frequency of reversion is varied from 0.5–4.0 Hz. Then we made use of the estimated electrical states to predict the effects on the vasculature that such variations produce. Hum Brain Mapp, 2007.

Artifact reduction for simultaneous EEG/fMRI recording: Adaptive FIR reduction of imaging artifacts

OBJECTIVE We present a new method of effectively removing imaging artifacts of electroencephalography (EEG) and extensively conserving the time-frequency features of EEG signals during simultaneous functional magnetic resonance imaging (fMRI) scanning under conventional conditions. METHODS Under the conventional conditions of a 5000 Hz EEG sampling rate, but in the absence of the MRI slice-timing signals, the imaging artifact during each slice scanning is theoretically inferred to be a linear combination of the average artifact waveform and its derivatives, deduced by band-limited Taylors expansion. Technically, the imaging artifact reduction algorithm is equivalent to an adaptive finite impulse response (FIR) filter. RESULTS The capability of this novel method removing the imaging artifacts of EEG recording during fMRI scanning has been demonstrated by a phantom experiment. Moreover, the effectiveness of this method in conserving the time-frequency features of EEG activity has been evaluated by both visually evoked experiments and alpha waves. CONCLUSIONS The adaptive FIR method is an effective method of removing the imaging artifacts under conventional conditions, and also conserving the time-frequency EEG signals. SIGNIFICANCE The proposed adaptive FIR method, removing the imaging artifacts, combined with the wavelet-based non-linear noise reduction (WNNR) method [Wan X, Iwata K, Riera J, Ozaki T, Kitamura M, Kawashima R. Artifact reduction for EEG/fMRI recording: Nonlinear reduction of ballistocardiogram artifacts. Clin Neurophysiol 2006;117:668-80], reducing the ballistocardiogram artifacts (BAs), makes it feasible to obtain accurate EEG signals from the simultaneous EEG recordings during fMRI scanning.

Developing intuition: neural correlates of cognitive-skill learning in caudate nucleus.

The superior capability of cognitive experts largely depends on automatic, quick information processing, which is often referred to as intuition. Intuition develops following extensive long-term training. There are many cognitive models on intuition development, but its neural basis is not known. Here we trained novices for 15 weeks to learn a simple board game and measured their brain activities in early and end phases of the training while they quickly generated the best next-move to a given board pattern. We found that the activation in the head of caudate nucleus developed over the course of training, in parallel to the development of the capability to quickly generate the best next-move, and the magnitude of the caudate activity was correlated with the subjects performance. In contrast, cortical activations, which already appeared in the early phase of training, did not further change. Thus, neural activation in the caudate head, but not those in cortical areas, tracked the development of capability to quickly generate the best next-move, indicating that circuitries including the caudate head may automate cognitive computations.

Functional Signalers of Changes in Visual Stimuli: Cortical Responses to Increments and Decrements in Motion Coherence

How does our brain detect changes in a natural scene? While changes by increments of specific visual attributes, such as contrast or motion coherence, can be signaled by an increase in neuronal activity in early visual areas, like the primary visual cortex (V1) or the human middle temporal complex (hMT+), respectively, the mechanisms for signaling changes resulting from decrements in a stimulus attribute are largely unknown. We have discovered opposing patterns of cortical responses to changes in motion coherence: unlike areas hMT+, V3A and parieto-occipital complex (V6+) that respond to changes in the level of motion coherence monotonically, human areas V4 (hV4), V3B, and ventral occipital always respond positively to both transient increments and decrements. This pattern of responding always positively to stimulus changes can emerge in the presence of either coherence-selective neuron populations, or neurons that are not tuned to particular coherences but adapt to a particular coherence level in a stimulus-selective manner. Our findings provide evidence that these areas possess physiological properties suited for signaling increments and decrements in a stimulus and may form a part of cortical vigilance system for detecting salient changes in the environment.

Neural encoding of opposing strategy values in anterior and posterior cingulate cortex

Humans, and animals, often encounter ambiguous situations that require a decision on whether to take an offense or a defense strategy. Behavioral studies suggest that a strategy decision is frequently made before concrete options are evaluated. It remains enigmatic, however, how a strategy is determined without exploration of options. Here we investigated neural correlates of quick offense-versus-defense strategy decision in a board game, shogi. We found that the rostral anterior cingulate cortex and the posterior cingulate cortex complementally encoded the defense and attack strategy values, respectively. The dorsolateral prefrontal cortex compared the two strategy values. Several brain regions were activated during decision of concrete moves under an instructed strategy, whereas none of them showed correlation with defense or attack strategy values in their activities during strategy decision. These findings suggest that values of alternative strategies represented in different parts of the cingulate cortex have essential roles in intuitive strategy decision-making.

Electromagnetic source imaging: Backus–Gilbert resolution spread function-constrained and functional MRI-guided spatial filtering

Electromagnetic source imaging techniques are usually limited by their low spatial resolution, even though these techniques have high temporal resolution. Our heuristic analysis shows that the spatial ambiguity of electromagnetic source localization arises from interference from other sources. In this paper, we suggest a new inverse solution based on the principle of spatial filtering to effectively suppress the interference from other sources, especially from the far sources. By means of this approach, functional MRI information can also be effectively integrated into the inverse solution to further improve spatial accuracy of source localization. Most importantly, the results of source localization by this approach are not significantly biased by incompatible fMRI information. Our simulations and experimental results using electroencephalography based on a realistic head model show that the Backus–Gilbert resolution spread function‐constrained and functional MRI‐guided spatial filtering suggested in this paper provide high spatial accuracy and resolution of source localization, even in the presence of multiple simultaneously active sources. Hum Brain Mapp, 2008.