Hiroaki Mizuhara

Human cortical circuits for central executive function emerge by theta phase synchronization.

Dynamic networking of brain regions is suggested to be one of the key factors involved in various brain computations. Central executive function typically requires instantaneous coordination among the medial prefrontal regions and other distant regions, depending on the on-going task situation. In human scalp-recorded electroencephalography (EEG), the medial prefrontal area is estimated to be the current source of the theta rhythm, while there is no direct evidence that the theta rhythm is involved in the dynamic networking of central executive circuits. Here we hypothesize that the central executive circuit over the prefrontal and task-related cortices is dynamically linked by theta synchronization. By using simultaneous functional magnetic resonance imaging (fMRI) and EEG, we elucidated cortical circuits emerging with theta phase synchronization during free pacing repeated subtraction. Theta phase synchronization in the scalp EEG was found to emerge at two major clusters of electrode pairs, between the right frontal and left parietal sites and between the frontal and right parietal sites. The phase synchronization of two clusters is accompanied by fMRI responses in the cortical regions responsible for central executive function, working memory, visual imagery and cognitive action sequence. Here we report the first evidence that theta phase synchronization dynamically coordinates the central executive circuits, including the medial prefrontal cortex and relevant cortical regions.

Nonimmersive Virtual Reality Mirror Visual Feedback Therapy and Its Application for the Treatment of Complex Regional Pain Syndrome: An Open‐Label Pilot Study

OBJECTIVE Chronic pain conditions such as phantom limb pain and complex regional pain syndrome are difficult to treat, and traditional pharmacological treatment and invasive neural block are not always effective. Plasticity in the central nervous system occurs in these conditions and may be associated with pain. Mirror visual feedback therapy aims to restore normal cortical organization and is applied in the treatment of chronic pain conditions. However, not all patients benefit from this treatment. Virtual reality technology is increasingly attracting attention for medical application, including as an analgesic modality. An advanced mirror visual feedback system with virtual reality technology may have increased analgesic efficacy and benefit a wider patient population. In this preliminary work, we developed a virtual reality mirror visual feedback system and applied it to the treatment of complex regional pain syndrome. DESIGN A small open-label case series. Five patients with complex regional pain syndrome received virtual reality mirror visual feedback therapy once a week for five to eight sessions on an outpatient basis. Patients were monitored for continued medication use and pain intensity. RESULTS Four of the five patients showed >50% reduction in pain intensity. Two of these patients ended their visits to our pain clinic after five sessions. CONCLUSION Our results indicate that virtual reality mirror visual feedback therapy is a promising alternative treatment for complex regional pain syndrome. Further studies are necessary before concluding that analgesia provided from virtual reality mirror visual feedback therapy is the result of reversing maladaptive changes in pain perception.

Neuronal ensemble for visual working memory via interplay of slow and fast oscillations

The current focus of studies on neural entities for memory maintenance is on the interplay between fast neuronal oscillations in the gamma band and slow oscillations in the theta or delta band. The hierarchical coupling of slow and fast oscillations is crucial for the rehearsal of sensory inputs for short‐term storage, as well as for binding sensory inputs that are represented in spatially segregated cortical areas. However, no experimental evidence for the binding of spatially segregated information has yet been presented for memory maintenance in humans. In the present study, we actively manipulated memory maintenance performance with an attentional blink procedure during human scalp electroencephalography (EEG) recordings and identified that slow oscillations are enhanced when memory maintenance is successful. These slow oscillations accompanied fast oscillations in the gamma frequency range that appeared at spatially segregated scalp sites. The amplitude of the gamma oscillation at these scalp sites was simultaneously enhanced at an EEG phase of the slow oscillation. Successful memory maintenance appears to be achieved by a rehearsal of sensory inputs together with a coordination of distributed fast oscillations at a preferred timing of the slow oscillations.

Subsequent memory-dependent EEG θ correlates to parahippocampal blood oxygenation level-dependent response

The 4–12 Hz (&thgr; rhythm)-dependent neural dynamics play a fundamental role in the memory formation of the rat hippocampus. Although the power of human scalp electroencephalography &thgr; (EEG &thgr;) is known to be associated with a hippocampus-dependent memory encoding, it remains unclear whether the human hippocampus uses &thgr; rhythm. In this study, we aim to identify the scalp EEG &thgr;-related neural regions during memory encoding by using a simultaneous EEG–functional magnetic resonance imaging recording. We showed that the parahippocampal and the medial frontal and posterior regions were significantly correlated to subsequent memory-dependent EEG &thgr; power. This evidence suggests that the human parahippocampal region and associated structures use &thgr; rhythm during hippocampal memory encoding as in rodents.

Dynamic parieto-premotor network for mental image transformation revealed by simultaneous eeg and fmri measurement

Previous studies have suggested that the posterior parietal cortices and premotor areas are involved in mental image transformation. However, it remains unknown whether these regions really cooperate to realize mental image transformation. In this study, simultaneous EEG and fMRI were performed to clarify the spatio-temporal properties of neural networks engaged in mental image transformation. We adopted a modified version of the mental clock task used by Sack et al. [Sack, A. T., Camprodon, J. A., Pascual-Leone, A., & Goebel, R. The dynamics of interhemispheric compensatory processes in mental imagery. Science, 308, 702–704, 2005; Sack, A. T., Sperling, J. M., Prvulovic, D., Formisano, E., Goebel, R., Di Salle, F., et al. Tracking the minds image in the brain II: Transcranial magnetic stimulation reveals parietal asymmetry in visuospatial imagery. Neuron, 35, 195–204, 2002]. In the modified mental clock task, participants mentally rotated clock hands from the position initially presented at a learned speed for various durations. Subsequently, they matched the position to the visually presented clock hands. During mental rotation of the clock hands, we observed significant beta EEG suppression with respect to the amount of mental rotation at the right parietal electrode. The beta EEG suppression accompanied activity in the bilateral parietal cortices and left premotor cortex, representing a dynamic cortical network for mental image transformation. These results suggest that motor signals from the premotor area were utilized for mental image transformation in the parietal areas and for updating the imagined clock hands represented in the right posterior parietal cortex.

Cortical networks dynamically emerge with the interplay of slow and fast oscillations for memory of a natural scene.

Neural oscillations are crucial for revealing dynamic cortical networks and for serving as a possible mechanism of inter-cortical communication, especially in association with mnemonic function. The interplay of the slow and fast oscillations might dynamically coordinate the mnemonic cortical circuits to rehearse stored items during working memory retention. We recorded simultaneous EEG-fMRI during a working memory task involving a natural scene to verify whether the cortical networks emerge with the neural oscillations for memory of the natural scene. The slow EEG power was enhanced in association with the better accuracy of working memory retention, and accompanied cortical activities in the mnemonic circuits for the natural scene. Fast oscillation showed a phase-amplitude coupling to the slow oscillation, and its power was tightly coupled with the cortical activities for representing the visual images of natural scenes. The mnemonic cortical circuit with the slow neural oscillations would rehearse the distributed natural scene representations with the fast oscillation for working memory retention. The coincidence of the natural scene representations could be obtained by the slow oscillation phase to create a coherent whole of the natural scene in the working memory.

Top-down and bottom-up attention cause the ventriloquism effect with distinct electroencephalography modulations

The ventriloquism effect is a critical phenomenon for understanding the underlying mechanisms of multisensory integration. Cross-modal spatial attention causes a distortion of sound localization, although the neural basis of the effect remains an unanswered question. We hypothesized that top-down and bottom-up visual-spatial attention causes the ventriloquism effect with different modulations of ongoing neural oscillation. To test this hypothesis, human scalp electroencephalography (EEG) was measured during a sound localization task. Top-down attention suppressed the EEG amplitude in the alpha frequency (10 Hz) over the contralateral temporal electrode sites to visual cue hemifields. Bottom-up attention shifted the EEG phase to the theta frequency (7 Hz), rather than suppressing the amplitude. Two different neural mechanisms of ongoing neural oscillation contributed toward the ventriloquism effect, with different spatial attention.

The Interaction Between the Parietal and Motor Areas in Dynamic Imagery Manipulation: An fMRI Study

Mental imagery is a cognitive function that includes sub-functions such as generation, transformation, and matching. However, the neural substrates for each sub-function are not yet clear. In the present study, we used event-related functional MRI during a modified version of a mental clock task to investigate these neural substrates. While participants were mentally transforming the clock hands, we found activations in the left inferior parietal lobule, left motor related regions (premotor area and supplementary motor area), and left insula, which were contra-lateral to the right hand used to manipulate a 3-D mouse in the learning phase. These results suggest that motor imagery was utilized for transformation of mental imagery.

Is Mu Rhythm an Index of the Human Mirror Neuron System? A Study of Simultaneous fMRI and EEG

EEG mu rhythm is one of the prominent oscillatory patterns for investigating the dynamics of the human mirror system. However, because of the blurring effect caused by the volume conduction in EEG measures, the origin of this rhythm remains an open question. Here we propose a novel method to identify the EEG distribution on the scalp by using the simultaneous fMRI and EEG recording. The results indicate that the mu rhythm appeared on the lateral central sites indexes as activities in the sensorimotor cortex, while the contamination from activities in other cortices also appears as the mu rhythm.

Ongoing slow oscillatory phase modulates speech intelligibility in cooperation with motor cortical activity

Neural oscillation is attracting attention as an underlying mechanism for speech recognition. Speech intelligibility is enhanced by the synchronization of speech rhythms and slow neural oscillation, which is typically observed as human scalp electroencephalography (EEG). In addition to the effect of neural oscillation, it has been proposed that speech recognition is enhanced by the identification of a speaker’s motor signals, which are used for speech production. To verify the relationship between the effect of neural oscillation and motor cortical activity, we measured scalp EEG, and simultaneous EEG and functional magnetic resonance imaging (fMRI) during a speech recognition task in which participants were required to recognize spoken words embedded in noise sound. We proposed an index to quantitatively evaluate the EEG phase effect on behavioral performance. The results showed that the delta and theta EEG phase before speech inputs modulated the participant’s response time when conducting speech recognition tasks. The simultaneous EEG-fMRI experiment showed that slow EEG activity was correlated with motor cortical activity. These results suggested that the effect of the slow oscillatory phase was associated with the activity of the motor cortex during speech recognition.