scholarly journals When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI

2021 ◽  
Vol 15 ◽  
Author(s):  
Catriona L. Scrivener
Keyword(s):  
Brain Function ◽  
Statistical Power ◽  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Relevant Literature ◽  
Simultaneous Recording ◽  
Functional Magnetic Resonance ◽  
Spatial And Temporal Scales ◽  
Non Invasive ◽  
Research Questions

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.

scholarly journals Ethical Aspects of BCI Technology: What Is the State of the Art?

Philosophies ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 31
Author(s):  
Allen Coin ◽  
Megan Mulder ◽  
Veljko Dubljević
Keyword(s):  
Ethical Issues ◽  
Brain Activity ◽  
Relevant Literature ◽  
Research Area ◽  
Policy Makers ◽  
Ethical Implications ◽  
Non Invasive ◽  
Areas Of Concern ◽  
Future Work ◽  
Monitoring Devices

Brain–Computer Interface (BCI) technology is a promising research area in many domains. Brain activity can be interpreted through both invasive and non-invasive monitoring devices, allowing for novel, therapeutic solutions for individuals with disabilities and for other non-medical applications. However, a number of ethical issues have been identified from the use of BCI technology. In this paper, we review the academic discussion of the ethical implications of BCI technology in the last five years. We conclude that some emerging applications of BCI technology—including commercial ventures that seek to meld human intelligence with AI—present new and unique ethical concerns. Further, we seek to understand how academic literature on the topic of BCIs addresses these novel concerns. Similar to prior work, we use a limited sample to identify trends and areas of concern or debate among researchers and ethicists. From our analysis, we identify two key areas of BCI ethics that warrant further research: the physical and psychological effects of BCI technology. Additionally, questions of BCI policy have not yet become a frequent point of discussion in the relevant literature on BCI ethics, and we argue this should be addressed in future work. We provide guiding questions that will help ethicists and policy makers grapple with the most important issues associated with BCI technology.

scholarly journals Recurrence plot structures reflect motor-related EEG pattern

2019 ◽  
pp. 282-286
Author(s):  
Elena Pitsik ◽  
Nikita Frolov
Keyword(s):  
Signal To Noise Ratio ◽  
Brain Activity ◽  
Recurrence Plot ◽  
Mathematical Tool ◽  
Recurrence Plots ◽  
Time Frequency ◽  
Non Invasive ◽  
Power Spectral ◽  
Nonstationary Data ◽  
Eeg Recordings

Detection and classification of motor-related brain patterns from non-invasive electroencephalograms (EEGs) is challenging due to their non-stationarity and low signal-to-noise ratio and requires using advanced mathematical approaches. Traditionally applied methods such as time-frequency analysis and spatial filtering allow to quantify the main attribute of the motor-related brain activity – contralateral desynchronization of mu-band oscillations (8-13 Hz) in sensorimotor cortex – by measuring EEG signal’s amplitude, power spectral density, location etc. However, these features suffer from strong inter- and intra-subject variability. So, special attention is paid to the finding of stable features. In present paper, we investigate application of the recurrence plots – robust mathematical tool for nonstationary data analysis – to explore properties of motor-related EEG samples. Our goal is to show that recurrence plots are sensitive to the changes in brain activity accessed from noninvasive EEG recordings and may provide us a new context for interpretation of motor-related pattern in EEG.

scholarly journals Interactions between sleep disturbances and Alzheimer’s disease on brain function: a preliminary study combining the static and dynamic functional MRI

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaicheng Li ◽  
Xiao Luo ◽  
Qingze Zeng ◽  
Yerfan Jiaerken ◽  
Shuyue Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Sleep Disturbance ◽  
Brain Function ◽  
Sleep Disturbances ◽  
Brain Activity ◽  
Vascular Risk Factor ◽  
Underlying Mechanism ◽  
Amyloid Pet

AbstractThough sleep disturbance constitutes the risk factor for Alzheimer’s disease (AD), the underlying mechanism is still unclear. This study aims to explore the interaction between sleep disturbances and AD on brain function. We included 192 normal controls, 111 mild cognitive impairment (MCI), and 30 AD patients, with either poor or normal sleep (PS, NS, respectively). To explore the strength and stability of brain activity, we used static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance. Further, we examined white matter hyperintensities (WMH) and amyloid PET deposition, representing the vascular risk factor and AD-related hallmark, respectively. We observed that sleep disturbance significantly interacted with disease severity, exposing distinct effects on sALFF and dALFF variance. Interestingly, PS groups showed the dALFF variance trajectory of initially increased, then decreased and finally increased along the AD spectrum, while showing the opposite trajectory of sALFF. Further correlation analysis showed that the WMH burden correlates with dALFF variance in PS groups. Conclusively, our study suggested that sleep disturbance interacts with AD severity, expressing as effects of compensatory in MCI and de-compensatory in AD, respectively. Further, vascular impairment might act as important pathogenesis underlying the interaction effect between sleep and AD.

scholarly journals DOES BRAIN ACTIVATION DURING FUNCTIONAL MOVEMENT TASKS DIFFERENTIATE BETWEEN GOOD AND BAD MOVERS? AN INTEGRATED NEUROIMAGING ASSESSMENT OF MOTOR CONTROL IN YOUNG ATHLETES

2021 ◽  
Vol 9 (7_suppl3) ◽  
pp. 2325967121S0013
Author(s):  
Manish Anand ◽  
Jed A. Diekfuss ◽  
Dustin R. Grooms ◽  
Alexis B. Slutsky-Ganesh ◽  
Scott Bonnette ◽  
...  
Keyword(s):  
Motor Control ◽  
Range Of Motion ◽  
Brain Function ◽  
Sagittal Plane ◽  
Brain Activity ◽  
Frontal Plane ◽  
Acl Injury ◽  
Timing Error ◽  
Lingual Gyrus ◽  
Increased Activity

Background: Aberrant frontal and sagittal plane knee motor control biomechanics contribute to increased anterior cruciate ligament (ACL) injury risk. Emergent data further indicates alterations in brain function may underlie ACL injury high risk biomechanics and primary injury. However, technical limitations have limited our ability to assess direct linkages between maladaptive biomechanics and brain function. Hypothesis/Purpose: (1) Increased frontal plane knee range of motion would associate with altered brain activity in regions important for sensorimotor control and (2) increased sagittal plane knee motor control timing error would associate with altered activity in sensorimotor control brain regions. Methods: Eighteen female high-school basketball and volleyball players (14.7 ± 1.4 years, 169.5 ± 7 cm, 65.8 ± 20.5 kg) underwent brain functional magnetic resonance imaging (fMRI) while performing a bilateral, combined hip, knee, and ankle flexion/extension movements against resistance (i.e., leg press) Figure 1(a). The participants completed this task to a reference beat of 1.2 Hz during four movement blocks of 30 seconds each interleaved in between 5 rest blocks of 30 seconds each. Concurrent frontal and sagittal plane range of motion (ROM) kinematics were measured using an MRI-compatible single camera motion capture system. Results: Increased frontal plane ROM was associated with increased brain activity in one cluster extending over the occipital fusiform gyrus and lingual gyrus ( p = .003, z > 3.1). Increased sagittal plane motor control timing error was associated with increased brain activity in multiple clusters extending over the occipital cortex (lingual gyrus), frontal cortex, and anterior cingulate cortex ( p < .001, z > 3.1); see Figure 1 (b). Conclusion: The associations of increased knee frontal plane ROM and sagittal plane timing error with increased activity in regions that integrate visuospatial information may be indicative of an increased propensity for knee injury biomechanics that are, in part, driven by reduced spatial awareness and an inability to adequately control knee abduction motion. Increased activation in these regions during movement tasks may underlie an impaired ability to control movements (i.e., less neural efficiency), leading to compromised knee positions during more complex sports scenarios. Increased activity in regions important for cognition/attention associating with motor control timing error further indicates a neurologically inefficient motor control strategy. [Figure: see text]

scholarly journals Preliminary Study of the Use of Root with Sedline® EEG Monitoring for Assessment of Anesthesia Depth in 6 Horses

2020 ◽  
Vol 10 (3) ◽  
pp. 1050
Author(s):  
Olga Drewnowska ◽  
Bernard Turek ◽  
Barbara Lisowska ◽  
Charles E. Short
Keyword(s):  
Brain Activity ◽  
Muscle Relaxation ◽  
Eeg Monitoring ◽  
Careful Monitoring ◽  
Spectral Edge Frequency ◽  
Non Invasive ◽  
Anesthesia Monitoring ◽  
Physiologic Parameters ◽  
Preliminary Study ◽  
Anesthesia Depth

Management of equine anesthesia monitoring is still a challenge. Careful monitoring to provide guidelines for anesthesia depth assessment currently relies upon eye signs, cardiopulmonary responses, and the level of muscle relaxation. Electroencephalography, as a non-invasive brain activity monitor, may be used to complement the routinely monitored physiologic parameters. Six horses, undergoing various surgical procedures and anesthesia protocols, were monitored with the use of a Root with Sedline EEG monitor and a routine monitor of life parameters. The life parameters were compared to the changes on the EEG density spectral array observed live during anesthesia. During all procedures the level of awareness was monitored using the EEG, with higher frequency and power of waves indicating a higher level of awareness. It was evident from this that there were variations according to the type of procedure and the anesthetic protocol. Cerebral activity was elevated during painful moments of the surgery and recovery, requiring adjustments in anesthetic concentrations. Evaluation of changes in the spectral edge frequency (SEF) could show the periods when the patient is stabilized. EEG monitoring has the potential to be used in clinical anesthesiology of horses. It was shown that this system may be used in horses under general anesthesia but is currently less effective in a standing horse for diagnostic or minor procedures.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

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