Neural Electrical Signals Research Articles

Nanofluidic Thermoelectric Transducer with Ultrahigh and Tunable Sensitivity.

Thermosensitive transient receptor potential (thermoTRP) ion channels can transduce external thermal stimuli to neural electrical signals, allowing organisms to detect and respond to changes in environmental temperature. Reproducing such ionic machinery holds promise for advancing the design of highly efficient low-grade thermal energy harvesters and ultrasensitive thermal sensors. However, there still exist challenges for artificial nanofluidic architectures to achieve comparable thermoelectric performance. Here, we report nanofluidic thermoelectric transducers with ultrahigh and tunable sensitivities controlled by electrostatic gating in graphene nanochannels. The equivalent Seebeck coefficient can be significantly boosted and reaches 1 order of magnitude higher than the current state of the art, even beyond thermoTRP ion channels. The improvement is attributed to substantial slippage on the highly charged graphene surface, leading to enhanced electrokinetic ion transport inside the graphene channel, which is confirmed by a scaling theory for thermoelectric coupling as well as molecular dynamic simulations. The dependence of the nanofluidic thermoelectric on the concentration, channel size, and cation types is also investigated to further clarify the underlying mechanism.

Multi-scale coupling between LFP and EMG in mice by low- intensity pulsed ultrasound stimulation with different number of tone-burst.

Low-intensity pulsed ultrasound stimulation (LIPUS) as a non-invasive, high-spatial resolution and high penetration depth brain modulation technology has been used for modulating neuromuscular function. However, the modulation of neural electrical signal changes in the neuromuscular system by LIPUS remains to be explored. In this study, we stimulated the mouse brain motor cortex by LIPUS with different number of tone burst (NTB) and recorded the local field potential (LFP) signals of the target region and electromyography (EMG) of tail muscle. Multi-Scale Transfer Entropy (MSTE) analysis method was used to explore the multi-scale synchronization characteristics and functional cortico-muscular coupling (FCMC) strength changes of mice LFP-EMG before and after LIPUS under different NTBs. The results show that the MSTE of LFP-EMG before and after LIPUS stimulation was higher than that of EMG-LFP. After adding multi-scale, MSTE has a significant relationship with time scales. When NTB = 200, the scale of extremum is the largest. There was a fitting intersection between LFP-EMG and EMG-LFP scale 7-21 before and after stimulation. After scale averaging, the LFP-EMG after stimulation was lower than that before stimulation, and the EMG-LFP after stimulation was higher than that before stimulation.Conclusion: There is a significant correlation between NTB and time scale before and after LIPUS,as well as upward and downward. Consequently,This study used FCMC methods to study different NTBs and multi-scale relationships, provides new variables from LIPUS parameters and analysis, and provides new reference for clinical applications of LIPUS.

Analysis of the Structure and Working Principle of Intelligent Bionic Hand

Intelligent bionic prosthetic hand controlled by intelligent neural networks with perceptual feedback function is a hot topic of research for scientists around the world. This article analyzes the development status of electromyographic prosthetic hands both domestically and internationally over the years, and provides a detailed introduction to the structure of intelligent bionic hands. The intelligent bionic hand consists of three important components: 3D printed bionic hand bone structure, sensors, and intelligent neural network algorithm control system. Among them, the intelligent neural network algorithm control system is its core. The focus is on the working principle of an intelligent bionic hand, where sensors collect electromyography and neural electrical signals generated by human muscle movements. Through the intelligent neural network algorithm control system, the data information obtained is continuously optimized and adjusted to achieve motion control of the prosthetic hand. This article analyzes some problems of intelligent bionic hands and provides development ideas and suggestions. It is pointed out that the reconstruction of the sensing function of the intelligent bionic hand should focus on multimodal feedback, using diverse stimuli and more sensitive sensors for human-machine interaction, so that the intelligent bionic hand can move flexibly and freely, benefiting most disabled patients.

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect's input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development.

Remote Sensing System for Motor Nerve Impulse.

In this article, we present our research achievements regarding the development of a remote sensing system for motor pulse acquisition, as a first step towards a complete neuroprosthetic arm. We present the fabrication process of an implantable electrode for nerve impulse acquisition, together with an innovative wirelessly controlled system. In our study, these were combined into an implantable device for attachment to peripheral nerves. Mechanical and biocompatibility tests were performed, as well as in vivo testing on pigs using the developed system. This testing and the experimental results are presented in a comprehensive manner, demonstrating that the system is capable of accomplishing the requirements of its designed application. Most significantly, neural electrical signals were acquired and transmitted out of the body during animal experiments, which were conducted according to ethical regulations in the field.

Transcerebral information coordination in directional hippocampus-prefrontal cortex network during working memory based on bimodal neural electrical signals.

Working memory (WM) is a kind of advanced cognitive function, which requires the participation and cooperation of multiple brain regions. Hippocampus and prefrontal cortex are the main responsible brain regions for WM. Exploring information coordination between hippocampus and prefrontal cortex during WM is a frontier problem in cognitive neuroscience. In this paper, an advanced information theory analysis based on bimodal neural electrical signals (local field potentials, LFPs and spikes) was employed to characterize the transcerebral information coordination across the two brain regions. Firstly, LFPs and spikes were recorded simultaneously from rat hippocampus and prefrontal cortex during the WM task by using multi-channel in vivo recording technique. Then, from the perspective of information theory, directional hippocampus-prefrontal cortex networks were constructed by using transfer entropy algorithm based on spectral coherence between LFPs and spikes. Finally, transcerebral coordination of bimodal information at the brain-network level was investigated during acquisition and performance of the WM task. The results show that the transfer entropy in directional hippocampus-prefrontal cortex networks is related to the acquisition and performance of WM. During the acquisition of WM, the information flow, local information transmission ability and information transmission efficiency of the directional hippocampus-prefrontal networks increase over learning days. During the performance of WM, the transfer entropy from the hippocampus to prefrontal cortex plays a leading role for bimodal information coordination across brain regions and hippocampus has a driving effect on prefrontal cortex. Furthermore, bimodal information coordination in the hippocampus → prefrontal cortex network could predict WM during the successful performance of WM.

A self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature

A self-powered wearable brain-machine-interface system for real-time monitoring and regulating body temperature has been designed. This system can real-time monitor body temperature and transmit neural electrical stimulation signal into brain.

THE ALZHEIMER’S DISEASE IMPACT ON ARTIFICIAL NEURAL NETWORKS

The Alzheimer’s Disease main impact on the brain is the memory loss effect. Therefore, in the “neuron world” this makes a disorder of signal impulses and disconnects neurons that causes the neuron death and memory loss. The research main aim is to determine the average loss of signal and develop memory loss prediction models for artificial neuron network. The Izhikevich neural networking model is often used for constructing neuron neural electrical signal modeling. The neuron model signal rhythm and spikes are used as model neuron characteristics for understanding if the system is stable at certain moment and in time. In addition, the electrical signal parameters are used in similar way as they are used in a biological brain. During the research the neural network initial conditions are assumed to be randomly selected in specified the working neuron average sigma I parameters range.

Spike-sorting analysis of neural electrical signals evoked by acupuncture based on model.

Acupuncturing the Zusanli (ST 36) point with different types of manual acupuncture manipulations (MAs) and different frequencies can evoke a lot of neural response activities in spinal dorsal root neurons. The action potential is the basic unit of communication in the neural response process. With the rapid development of the electrode acquisition technology, we can simultaneously obtain neural electrical signals of multiple neurons in the target area. So it is crucial to extract spike trains of each neuron from raw recorded data. To solve the problem of variability of the spike waveform, this paper adopts a optimization algorithm based on model to improve the wave-cluster algorithm, which can provide higher accuracy and reliability. Further, through this optimization algorithm, we make a statistical analysis on spike events evoked by different MAs. Results suggest that numbers of response spikes under reinforcing manipulations are far more than reducing manipulations, which mainly embody in synchronous spike activities.

Apo-Opsin and Its Dark Constitutive Activity across Retinal Cone Subtypes

Retinal rod and cone photoreceptors mediate vision in dim and bright light, respectively, by transducing absorbed photons into neural electrical signals. Their phototransduction mechanisms are essentially identical. However, one difference is that, whereas a rod visual pigment remains stable in darkness, a cone pigment has some tendency to dissociate spontaneously into apo-opsin and retinal (the chromophore) without isomerization. This cone-pigment property is long known but has mostly been overlooked. Importantly, because apo-opsin has weak constitutive activity, it triggers transduction to produce electrical noise even in darkness. Currently, the precise dark apo-opsin contents across cone subtypes are mostly unknown, as are their dark activities. We report here a study of goldfish red (L), green (M), and blue (S) cones, finding with microspectrophotometry widely different apo-opsin percentages in darkness, being ∼30% in L cones, ∼3% in M cones, and negligible in S cones. L and M cones also had higher dark apo-opsin noise than holo-pigment thermal isomerization activity. As such, given the most likely low signal amplification at the pigment-to-transducin/phosphodiesterase phototransduction step, especially in L cones, apo-opsin noise may not be easily distinguishable from light responses and thus may affect cone vision near threshold.

Characterizing the brain's dynamical response from scalp-level neural electrical signals: a review of methodology development.

The brain displays dynamical system behaviors at various levels that are functionally and cognitively relevant. Ample researches have examined how the dynamical properties of brain activity reflect the neural cognitive working mechanisms. A prevalent approach in this field is to extract the trial-averaged brain electrophysiological signals as a representation of the dynamical response of the complex neural system to external stimuli. However, the responses are intrinsically variable in latency from trial to trial. The variability compromises the accuracy of the detected dynamical response pattern based on trial-averaged approach, which may mislead subsequent modelling works. More accurate characterization of the brain's dynamical response incorporating single trial variability information is of profound significance in deepening our understanding of neural cognitive dynamics and brain's working principles. Various methods have been attempted to address the trial-to-trial asynchrony issue in order to achieve an improved representation of the dynamical response. We review the latest development of methodology in this area and the contribution of latency variability-based decomposition and reconstruction of dynamical response to neural cognitive researches.

Contribution of sensorimotor beta oscillations during value-based action selection

Choosing a course of daily-life actions requires an accurate assessment of the associated risks and potential rewards. We investigated the neural dynamics of this decision process by analyzing the neural electrical signals acquired from electroencephalography (EEG) during a value-based action-selection task. In particular, we determined whether sensorimotor beta oscillations, traditionally studied in the context of motor control, are also involved in value-based decision making for actions. Additionally, we examined the involvement of this beta signal relative to other neural signals such as the ERP components P2 and P3, which have been previously identified in reward processing and value computations. Our results from healthy young adults (N = 31), showed a significant decrease in sensorimotor beta power during a decision phase without any motor response, in addition to an action phase when a response was made. The decision-phase beta signal was preceded by the P2/P3b components, and all of these neural signals reliably dissociated the different reward and risk levels, suggesting the encoding of decision variables. Importantly, while the beta signal during both the action and decision phase predicted behavioral performance (i.e., response time) in the action phase, the preceding P2/P3b had no such predictive association with the behavior. Collectively, these results demonstrate a unique contribution of the motor system in value-based decision making for actions, via the translation of motivational information into a motor signal across time.

Neural Dynamics during Resting State: A Functional Magnetic Resonance Imaging Exploration with Reduction and Visualization

The brain is a complex high-order system. Body movements or mental activities are both dependent on the transmission of information among billions of neurons. However, potential patterns are hardly discoverable due to the high dimensionality in neural signals. Previous studies have identified rotary trajectories in rhythm and nonrhythm movements when projecting the neural electrical signals into a two-dimensional space. However, it is unclear how well this analogy holds at the resting state. Given the low-frequency fluctuations noted during spontaneous neural activities using functional magnetic resonance imaging (fMRI), it is natural to hypothesize that the neural response at resting state also shows a periodic trajectory. In this study, we explored the potential patterns in resting state fMRI data at four frequency bands(slow 2–slow 5)on two cohorts, one of which consisted of young and elderly adults and the other of patients with Alzheimer’s disease and normal controls (NC). The jPCA algorithm was applied to reduce the high-dimensional BOLD signal into a two-dimensional space for visualization of the trajectory. The results indicated that the “resting state” is a basic state showing an inherent dynamic pattern with a low frequency and long period during normal aging, with changes appearing in the rotary period at theslow 4frequency band (0.027–0.073 Hz) during the pathological process of Alzheimer’s disease (AD). These findings expand the original understanding that neural signals can rotate themselves and that motor executive signals consist of neural signals. Meanwhile, the rotary period at bandslow 4may be a physiological marker for AD, and studies of this frequency band may be useful for understanding the potential pathophysiology of AD and ultimately facilitate characterization and auxiliary diagnosis of AD.

Studying brain activity in sports performance: Contributions and issues.

Understanding the interactions between brain activity and behavior comprehensively in achieving optimal exercise performance in sports is still lacking. The existent research in this area has been limited by the constraints of sports environments and the robustness of the most suitable non-invasive functional neuroimaging methods (electroencephalography, EEG and functional near-infrared spectroscopy, fNIRS) to motion artifacts and noise. However, recent advances in brain mapping technology should improve the capabilities of the future brain imaging devices to assess and monitor the level of adaptive cognitive-motor performance during exercise in sports environments. The purpose of this position manuscript is to discuss the contributions and issues in behavioral neuroscience related to brain activity measured during exercise and in various sports. A first part aims to give an overview of EEG and fNIRS neuroimaging methods assessing electrophysiological activity and hemodynamic responses of the acute and chronic relation of physical exercise on the human brain. Then, methodological issues, such as the reliability of brain data during physical exertion, key limitations and possible prospects of fNIRS and EEG methods are provided. While the use of such methods in sports environments remains scarce and limited to controlled cycling task, new generation of wearable, whole-scalp EEG and fNIRS technologies could open up a range of new applications in sports sciences for providing neuroimaging-based biomarkers (hemodynamic and/or neural electrical signals) to various types of exercise and innovative training.

A new algorithm for automatically detecting epileptiform spikes and its application in epilepsy models

Epilepsy is characterized by abnormally synchronized firing of neuronal populations, which is presented as epileptiform spikes in neural electrical signal recordings. In order to investigate the epileptiform spikes quantitatively, we designed a new window-based algorithm to automatically detect population spikes (PS) in acute epilepsy models in rat hippocampus CA1 region, and to calculate characteristic parameters of PS. Results show that the algorithm could recognize PS waveforms directly in wideband recording signals in epilepsy models induced by 4-aminopyridine (4-AP), a potassium channel blocker, or by picrotoxin (PTX), an antagonist of γ-aminobutyric acid A-type receptor. The PS detection ratios of the two epilepsy models were 94.2%±1.6% ( n=11) and 95.9%±1.9% ( n=12), respectively. The false positive ratios were 3.5%±2.3% ( n=11) and 4.8%±2.3% ( n=12), which were significantly lower than those of the conventional threshold method. Comparisons of the PS patterns between the 4-AP model and the PTX model showed that the PS of the 4-AP model had wider waveforms and fired more dispersedly with intervals mainly in the range of 100-700 ms. The PS of the PTX model fired as Burst with a higher firing rate and with intervals mainly in the range of 2-20 ms, resulting in a larger sum of spike amplitudes per second than the 4-AP model. Thus, the synchronous firing of neuronal populations in the PTX model was more intense than that in the 4-AP model. In conclusion, the new algorithm of PS detection can correctly detect and analyze epileptiform population spikes. It provides a useful tool of data analysis for investigating the underlying mechanism of seizure generation and for evaluating new therapeutics of epilepsy.

Cilia- and Flagella-Associated Protein 69 Regulates Olfactory Transduction Kinetics in Mice.

Animals detect odorous chemicals through specialized olfactory sensory neurons (OSNs) that transduce odorants into neural electrical signals. We identified a novel and evolutionarily conserved protein, cilia- and flagella-associated protein 69 (CFAP69), in mice that regulates olfactory transduction kinetics. In the olfactory epithelium, CFAP69 is enriched in OSN cilia, where olfactory transduction occurs. Bioinformatic analysis suggests that a large portion of CFAP69 can form Armadillo-type α-helical repeats, which may mediate protein-protein interactions. OSNs lacking CFAP69, remarkably, displayed faster kinetics in both the on and off phases of electrophysiological responses at both the neuronal ensemble level as observed by electroolfactogram and the single-cell level as observed by single-cell suction pipette recordings. In single-cell analysis, OSNs lacking CFAP69 showed faster response integration and were able to fire APs more faithfully to repeated odor stimuli. Furthermore, both male and female mutant mice that specifically lack CFAP69 in OSNs exhibited attenuated performance in a buried food pellet test when a background of the same odor to the food pellet was present even though they should have better temporal resolution of coding olfactory stimulation at the peripheral. Therefore, the role of CFAP69 in the olfactory system seems to be to allow the olfactory transduction machinery to work at a precisely regulated range of response kinetics for robust olfactory behavior.SIGNIFICANCE STATEMENT Sensory receptor cells are generally thought to evolve to respond to sensory cues as fast as they can. This idea is consistent with mutational analyses in various sensory systems, where mutations of sensory receptor cells often resulted in reduced response size and slowed response kinetics. Contrary to this idea, we have found that there is a kinetic "damper" present in the olfactory transduction cascade of the mouse that slows down the response kinetics and, by doing so, it reduces the peripheral temporal resolution in coding odor stimuli and allows for robust olfactory behavior. This study should trigger a rethinking of the significance of the intrinsic speed of sensory transduction and the pattern of the peripheral coding of sensory stimuli.

Multiplexed neural recording along a single optical fiber via optical reflectometry.

.We introduce the design and theoretical analysis of a fiber-optic architecture for neural recording without contrast agents, which transduces neural electrical signals into a multiplexed optical readout. Our sensor design is inspired by electro-optic modulators, which modulate the refractive index of a waveguide by applying a voltage across an electro-optic core material. We estimate that this design would allow recording of the activities of individual neurons located at points along a 10-cm length of optical fiber with axial resolution and sensitivity down to using commercially available optical reflectometers as readout devices. Neural recording sites detect a potential difference against a reference and apply this potential to a capacitor. The waveguide serves as one of the plates of the capacitor, so charge accumulation across the capacitor results in an optical effect. A key concept of the design is that the sensitivity can be improved by increasing the capacitance. To maximize the capacitance, we utilize a microscopic layer of material with high relative permittivity. If suitable materials can be found—possessing high capacitance per unit area as well as favorable properties with respect to toxicity, optical attenuation, ohmic junctions, and surface capacitance—then such sensing fibers could, in principle, be scaled down to few-micron cross-sections for minimally invasive neural interfacing. We study these material requirements and propose potential material choices. Custom-designed multimaterial optical fibers, probed using a reflectometric readout, may, therefore, provide a powerful platform for neural sensing.

Impact of substance P on the correlation of spike train evoked by electro acupuncture

Substance P (SP) participates in the neural signal transmission evoked by electro-acupuncture (EA). This paper investigates the impact of SP on the correlation of spike train in the median nerve evoked by EA at 'Neiguan' acupoint (PC6). It shows that the spiking rate and interspike interval (ISI) distribution change obviously after inhibiting SP. This variation of spiking activity indicates that SP affects the temporal structure of spike train through modulating the action potential on median nerve filaments. Furtherly, the correlation coefficient and scaling exponent are considered to measure the correlation of spike train. Scaled Windowed Variance (SWV) method is applied to calculate scaling exponent which quantifies the long-range correlation of the neural electrical signals. It is found that the correlation coefficients of ISI increase after inhibiting SP released. In addition, the scaling exponents of neuronal spike train have significant differences between before and after inhibiting SP. These findings demonstrate that SP has an influence on the long-range correlation of spike train. Our results indicate that SP may play an important role in EA-induced neural spiking and encoding.

Implantable Graphene-based Neural Electrode Interfaces for Electrophysiology and Neurochemistry in In Vivo Hyperacute Stroke Model

Implantable microelectrode arrays have attracted considerable interest due to their high temporal and spatial resolution recording of neuronal activity in tissues. We herein presented an implantable multichannel neural probe with multiple real-time monitoring of neural-chemical and neural-electrical signals by a nonenzymatic neural-chemical interface, which was designed by creating the newly developed reduced graphene oxide-gold oxide (rGO/Au2O3) nanocomposite electrode. The modified electrode on the neural probe was prepared by a facile one-step cyclic voltammetry (CV) electrochemical method with simultaneous occurrence of gold oxidation and GOs reduction to induce the intimate attachment by electrostatic interaction using chloride ions (Cl(-)). The rGO/Au2O3-modified electrode at a low deposition scan rate of 10 mVs(-1) displayed significantly improved electrocatalytic activity due to large active areas and well-dispersive attached rGO sheets. The in vitro amperometric response to H2O2 demonstrated a fast response of less than 5 s and a very low detection limit of 0.63 μM. In in vivo hyperacute stroke model, the concentration of H2O2 was measured as 100.48 ± 4.52 μM for rGO/Au2O3 electrode within 1 h photothrombotic stroke, which was much higher than that (71.92 μM ± 2.52 μM) for noncoated electrode via in vitro calibration. Simultaneously, the somatosensory-evoked potentials (SSEPs) test provided reliable and precise validation for detecting functional changes of neuronal activities. This newly developed implantable probe with localized rGO/Au2O3 nanocomposite electrode can serve as a rapid and reliable sensing platform for practical H2O2 detection in the brain or for other neural-chemical molecules in vivo.

Differences in auditory imagery self-report predict neural and behavioral outcomes.

Mental imagery has been a topic of psychological inquiry for a number of years, with most researchers focusing on visual imagery. However, auditory imagery has recently been studied as a topic in its own right (see Hubbard, 2010 for a review). Auditory imagery has been implicated in a variety of mental processes, such as reality monitoring (Johnson & Raye, 1981), working memory and rehearsal (Rudner, Ronnberg & Hugdahl, 2005; Tinti, Cornoldi, & Marschark, 1997), and hallucinations (Vitrovic & Biller, 2013), as well as musical processing (Crowder, 1989; Cupchik, Phillips, & Hill, 2001; Halpern, 1988a, 1988b). The of hearing something in one's head is a phenomenologically strong one, particularly for music (Bailes, 2007) and is accompanied by identifiable changes in cerebral blood flow (Halpern & Zatorre, 1999; Zvyagintsev et al., 2013) as well as neural electrical signal (Schaefer, Vlek, & Desain, 2011).One aspect common to many of these studies is that performance is usually averaged over all the participants in a study. Sometimes group differences, particularly with regard to musical training, are examined. For instance, Aleman, Nieuwenstein, Bocker, and de Haan (2000b) found that musicians were superior to nonmusicians in both musical and nonmusical auditory imagery tasks, but not in a visual imagery task. However, individual differences in auditory imagery at a finer level have not typically been addressed.In contrast, individual differences in visual imagery have been studied extensively, most commonly by self-report measures such as the Vividness of Visual Imagery Questionnaire (VVIQ; Marks, 1973). This questionnaire is designed to elicit self-report measures of visual imagery vividness, and has been used in hundreds of studies either probing differences in reported imagery ability for different participant groups or assessing the predictive value of self-reported imagery for more objective tasks presumed to require imagery. McKelvie (1995) reviewed both reliability and validity of the VVIQ in a large meta-analysis. Reliability as measured by internal consistency was quite good. Predictive validity, on the other hand, seems to vary by type of task. As examples, VVIQ scores predict reasonably well performance on tasks such as memory for visual detail, but not performance on mental rotation tasks. Kozhevnikov, Kozhevnikov, Yu, and Blazhenkova (2013) link vividness to one aspect of visual imagery skill called object visualization; this predicts artistic creativity-in contrast to spatial visualization, which predicts scientific creativity.Similar questions about individual variability are of potential interest to auditory imagery researchers. For instance, Aleman, Nieuwenstein, Bocker, and de Haan (2000a) compared the imagery abilities of nonpsychotic individuals who report occasional verbal hallucinations to those of nonhallucinators. Hallucinators reported more vivid visual imagery on the VVIQ than nonhallucinators. Barrett (1993) found a similar result using the visual vividness subscale of Betts' (1909) Questionnaire Upon Mental Imagery or QMI (see more below). On the other hand, both studies reported that the two groups did not differ on self-reported vividness of auditory imagery using the auditory subscale of the QMI.Despite this interest in documenting individual differences in auditory imagery, scales for capturing this ability have been slow to develop. An early attempt to index individual differences was Betts' (1909) QMI, which assesses imagery in seven sensory modalities, including audition. The QMI presents people with written descriptions, using visual, auditory, or other sensory detail, and asks them to rate the vividness of their mental images of certain aspects of the descriptions. Participants use a 7-point rating scale for vividness and clarity, ranging from perfectly clear and vivid as the actual experience to no image present at all. …