> Physiology > Organism Function > Head Movements

← Growth

Head Movements

Q: What are the different types of head movements?

Head movements can be classified into several distinct types, including rotation (turning the head left or right), flexion (tilting the head forward), extension (tilting the head back), and lateral movement (tilting the head to the side). These various movements serve important visual, vestibular, and communication functions.

Q: What are some common challenges in studying head movements?

Studying head movements can present several challenges, such as accurately capturing and quantifying subtle movement patterns, accounting for individual variability, and ensuring the reproducibility of findings. Researchers may struggle to identify the most appropriate protocols and technologies for their specific research goals.

Head Movements: The motions of the head, including rotation, flexion, extension, and lateral movement.
These movements are important for visual and vestibular functions, as well as for communication.
Optimizing head movements can enhance research in areas such as neuroscience, rehabilitation, and human-computer interaction.
PubCompare.ai's AI-driven platform streamlines this process by helping researchers easily locate relevant protocols and make informed decisions about the best approaches for their needs.

Most cited protocols related to «Head Movements»

Functional MRI Preprocessing Workflow

Cited 1506 times

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2011

bis(tri-n-hexylsiloxy)(2,3-naphthalocyaninato)silicon Brain fMRI Head Movements Movement Radionuclide Imaging Rage

fMRI Data Preprocessing and Functional Connectivity

Cited 628 times

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2013

bis(tri-n-hexylsiloxy)(2,3-naphthalocyaninato)silicon fMRI Head Movements Human Body Muscle Rigidity Radionuclide Imaging Rage

Functional MRI Data Preprocessing

Cited 99 times

Functional images were first processed to reduce artifacts [Miezin et al., 2000 (link)]. These steps included: (i) correction of odd versus even slice intensity differences attributable to interleaved acquisition without gaps, (ii) correction for head movement within- and across-runs, and (iii) within-run intensity normalization to a whole-brain mode value (across TRs and voxels) of 1,000.
Atlas transformation of the functional data was computed for each individual via the MP-RAGE scan. For Cohorts 1 and 2, the transformation was done by using an atlas-representative target composed of a mutually coregistered independent sample of 12 healthy adults and 12 healthy 7- to 8-year-old children, which was made to conform to the Talairach atlas using a spatial normalization method [Lancaster et al., 1995 ]. For Cohort 3, an atlas based on 12 healthy adults was used. Each run was then resampled in atlas space on an isotropic 3-mm grid combining movement correction and atlas transformation in a single interpolation. Data were resampled into 3-mm isotropic voxels for Cohorts 1 and 3 and into 2-mm isotropic voxels for Cohort 2. This discrepancy in voxel sizes arose incidentally but serves to demonstrate the generalizability of results beyond a single voxel size. The atlas-transformed image for each participant was checked against a reference average to ensure appropriate registration.
RMS movement was calculated from realignment parameters (rotational estimates converted to translational at radius of 50 mm). As previously mentioned, subjects were excluded from each study on the basis of study-specific RMS movement thresholds. This study thus documents the improvements that can be seen within “acceptable” subject populations. Excluded subjects are not reported in this study or in Table I.

Siegel J.S., Power J.D., Dubis J.W., Vogel A.C., Church J.A., Schlaggar B.L, & Petersen S.E. (2013). Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high‐motion data points. Human Brain Mapping, 35(5), 1981-1996.

Publication 2013

Adult Brain Child Head Movements Movement Population Group Protein Biosynthesis Radionuclide Imaging Radius Rage Vision

Motion-Minimized fMRI Acquisition Protocol

Cited 70 times

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2012

Acoustics Blood Oxygen Levels Brain ECHO protocol Eye fMRI HAVCR2 protein, human Head Head Movements Pulse Rate TRIO protein, human

Predicting Pain Perception from fMRI Data

Cited 65 times

In study 1, we used a machine-learning–based regression technique, LASSO-PCR (least absolute shrinkage and selection operator-regularized principal components regression),^{10 (link)} to predict pain reports from the fMRI activity. We selected relevant brain areas a priori using the NeuroSynth meta-analytic databasei^{11 (link)} (see the Supplementary Appendix) and averaged the brain activity for each intensity level within each participant.^{12 (link)–14 (link)} We used the signal values from the voxels, each of which measured 3 mm³, in the a priori map to predict continuous pain ratings, using leave-one-participant-out cross-validation⁴ (see the Supplementary Appendix). The result was a spatial pattern of regression weights across brain regions, which was prospectively applied to fMRI activity maps obtained from new participants. Application of the signature to an activity map (e.g., a map obtained during thermal stimulation) yielded a scalar response value, which constituted the predicted pain for that condition.
We used permutation tests to obtain unbiased estimates of accuracy and bootstrap tests to determine which brain areas made reliable contributions to prediction (Fig. 1). Stimulation did not elicit head movement, and head-movement estimates did not predict pain (for a description of head-movement analyses, see the Supplementary Appendix).

Wager T.D., Atlas L.Y., Lindquist M.A., Roy M., Woo C.W, & Kross E. (2013). An fMRI-Based Neurologic Signature of Physical Pain. The New England journal of medicine, 368(15), 1388-1397.

Publication 2013

Brain fMRI Head Movements Microtubule-Associated Proteins Pain

Most recents protocols related to «Head Movements»

Detection of Respiratory Effort and Cerebral Activation from Mandibular Movements

Not available on PMC !

Example 15

In a 15^thexample, reference is made to FIGS. 12 and 13. FIG. 12 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject suffers a temporary disappearance of all control of cerebral origin, which is characteristic of central hypopnoea. This disappearance is characterized by the mouth opening passively because it is no longer held up by the muscles. It is therefore seen in the streams F1 and F2 that between the peaks the signal does not indicate any activity. On the other hand at the moment of the peak there is observed a high amplitude of the movement of the mandible. Toward the end of the peaks there is seen a movement that corresponds to a non-respiratory frequency, which is the consequence of cerebral activation that will then result in a micro-arousal. The digit 1 indicates the period of hypopnoea where a reduction of the flow is clearly visible on the stream F5th from the thermistor. The digits 2 and 3 indicate the disappearance of mandibular movement in the streams F1 and F2 during the period of central hypopnoea. FIG. 13 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject experiences a prolonged respiratory effort that will terminate in cerebral activation. It is seen that the signal from the accelerometer F1 indicates at the location indicated by H a large movement of the head and of the mandible. Thereafter the stream F2 remains virtually constant whereas in that F1 from the accelerometer the level drops, which shows that there is in any event a movement of the mandible, which is slowly lowered. There then follows a high peak I that is a consequence of a change in the position of the head during the activation that terminates the period of effort. The digit 1 indicates this long period of effort marked by snoring. It is seen, as indicated by the digit 2, that the effort is increasing with time. This effort terminates, as indicated by the digit 3, in cerebral activation that results in movements of the head and the mandible, indicated by the letter I.

The analysis unit holds in its memory models of these various signals that are the result of processing employing artificial intelligence as described hereinbefore. The analysis unit will process these streams using those results to produce a report on the analysis of those results.

It was found that the accelerometer is particularly suitable for measuring movements of the head whereas the gyroscope, which measures rotation movements, was found to be particularly suitable for measuring rotation movements of the mandible. Thus cerebral activation that leads to rotation of the mandible without the head changing position can be detected by the gyroscope. On the other hand, an IMM type movement will be detected by the accelerometer, in particular if the head moves on this occasion. An RMM type movement will be detected by the gyroscope, which is highly sensitive thereto.

US11864910B2. System comprising a sensing unit and a device for processing data relating to disturbances that may occur during the sleep of a subject (2024-01-09). Sunrise SA [BE]. Inventors: Pierre Martinot [BE].

Patent 2024

ARID1A protein, human Arousal Exhaling Fingers Gene Expression Regulation Head Head Movements Mandible Medical Devices Memory Movement Muscle Tissue Oral Cavity Respiratory Rate Sleep Thumb Vision

Central Apnoea Respiratory Monitoring

Not available on PMC !

Example 14

In a 14^thexample, reference is made to FIG. 11. FIG. 11 shows an example of the first measurement signal stream F1 and of the second measurement signal stream F2 in the situation where the subject suffers a central apnoea. The peaks F show a movement of the head and of the mandible on resumption of respiration. It is also seen that between the peaks F there is so to speak no movement of the mandible. The digit 1 indicates an absence of respiratory flow that goes hand in hand with an absence of effort, indicated by the digit 2, and activation and resumption of the effort, indicated by the digit 3.

Patent 2024

Cell Respiration Fingers Head Movements Mandible Medical Devices Movement Respiratory Rate Sleep Sleep Apnea, Central Thumb Vision

Multimodal Brain Imaging Acquisition

MEG data were recorded using a 151-channel CTF system (CTF MEG International Services LP, Coquitlam, Canada) in a magnetically shielded room, while participants lay in the supine position. Data were collected at a sampling rate of 600 Hz with an online antialiasing filter (0–150 Hz) and a third-order spatial gradient to attenuate background noise. Participants were fitted with fiducial coils placed at the nasion and left and right pre-auricular areas to continuously track head movement throughout the recording. Following MEG recording, the fiducial coils were substituted with radio-opaque markers for MRI co-registration. Individual T1-weighted MR images were collected in all participants using a Siemens 3 T MAGNETOM Trio with a 12-channel head coil (TR/TE = 2300/2.96 ms, FA = 9°, FOV = 240x256mm, # slices = 192, resolution = 1.0 mm isotropic) scanner for MRI co-registration with the MEG data.

Sato J., Safar K., Vogan V.M, & Taylor M.J. (2023). Functional connectivity changes during working memory in autism spectrum disorder: A two-year longitudinal MEG study. NeuroImage : Clinical, 37, 103364.

Publication 2023

Head Head Movements Radio-Opaque acrylic resin TRIO protein, human

Functional Brain Network Analysis Pipeline

Cited 1 time

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023

Birth Brain Cloning Vectors Elbow Head Movements Human Body Muscle Rigidity Radionuclide Imaging

fMRI Data Preprocessing Protocol

For fMRI data, the pre-processing was performed using SPM12 (Wellcome Department of Imaging Neurosciences, University College London, UK, http://www.fil.ion.ucl.ac.uk/spm), and the statistical analyses of imaging data were performed using GRETNA (GRETNA v2.0) in Matlab R2021b. First, the first 10-time point-scanned images were removed owing to the instability of the magnetic field at the beginning of the scan. Second, all functional images were realigned to the first image to correct head movement. All participants met the criteria of < 2 mm translation and < 2° rotation in any direction. Otherwise, their data were excluded. Third, the functional images were normalized to the MNI space using DARTEL and resampled to a 3 × 3 × 3 mm³ voxel size^{62 (link)}. Fourth, we used an anisotropic 6-mm full-width half-maximum Gaussian kernel⁶³ for spatial smoothing of the obtained images. Fifth, we detrended and removed linear trends. Sixth, we removed covariates, excluding white matter, grey matter, and cerebrospinal fluid influences. Seventh, 0.01‒0.08 Hz bandpass filtering was used to remove high and low-frequency signals. Eighth, we removed the FD_Threshold > 0.5 mm time points by “scrubbing” 1-time point before and 2-time points after. In summary, the pre-processing procedures included slice timing correction, realignment, normalization, smoothing, detrending, filtering, and scrubbing.

Wu J., Fang Y., Tan X., Kang S., Yue X., Rao Y., Huang H., Liu M., Qiu S, & Yap P.T. (2023). Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity. Scientific Reports, 13, 3940.

Publication 2023

Anisotropy Cerebrospinal Fluid fMRI Gray Matter Head Movements Magnetic Fields Radionuclide Imaging White Matter

Top products related to «Head Movements»

Matlab by MathWorks

Sourced in United States, United Kingdom, Germany, Canada, Japan, Sweden, Austria, Morocco, Switzerland, Australia, Belgium, Italy, Netherlands, China, France, Denmark, Norway, Hungary, Malaysia, Israel, Finland, Spain

MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.

Eyelink 1000 by SR Research

Sourced in Canada

The EyeLink 1000 is a high-performance eye tracker that provides precise and accurate eye movement data. It is capable of recording monocular or binocular eye position at sampling rates up to 2000 Hz. The system uses infrared illumination and video-based eye tracking technology to capture eye movements. The EyeLink 1000 is designed for use in a variety of research applications, including cognitive science, psychology, and human-computer interaction studies.

Discovery mr750 by GE Healthcare

Sourced in United States, Germany, United Kingdom, Netherlands

The Discovery MR750 is a magnetic resonance imaging (MRI) system developed by GE Healthcare. It is designed to provide high-quality imaging for a variety of clinical applications.

Tim trio by Siemens

Sourced in Germany, United States, United Kingdom, China

The Tim Trio is a versatile laboratory equipment that serves as a temperature-controlled magnetic stirrer. It features three independent stirring positions, allowing simultaneous operation of multiple samples. The device maintains a consistent temperature range to ensure accurate and reliable results during experimental procedures.

Magnetom tim trio by Siemens

Sourced in Germany, United States, United Kingdom

The Magnetom Tim Trio is a magnetic resonance imaging (MRI) system developed by Siemens. It is designed to provide high-quality imaging for medical applications. The Magnetom Tim Trio utilizes advanced technology to capture detailed images of the human body.

Optomotry by Cerebral Mechanics

Sourced in Canada, United States

The OptoMotry is a laboratory equipment designed for visual function assessment. It utilizes an automated virtual-reality system to measure various aspects of visual perception in laboratory animals.

12 channel head coil by Siemens

Sourced in Germany, United States, Canada, China, Australia

The 12-channel head coil is a medical imaging device designed for use with MRI (Magnetic Resonance Imaging) systems. It is an essential component that enables the acquisition of high-quality images of the human head. The coil is constructed with 12 individual receiver channels, allowing for efficient signal detection and enhanced image quality.

Magnetom prisma by Siemens

Sourced in Germany, United States, Switzerland

The MAGNETOM Prisma is a magnetic resonance imaging (MRI) system designed and manufactured by Siemens. It is a powerful diagnostic imaging device that uses strong magnetic fields and radio waves to generate detailed images of the human body. The MAGNETOM Prisma is capable of providing high-quality, high-resolution imaging data to support clinical decision-making and patient care.

32 channel head coil by Siemens

Sourced in Germany, United States, Australia, United Kingdom, Canada

The 32-channel head coil is a key component in magnetic resonance imaging (MRI) systems. It is designed to acquire high-quality images of the human head, enabling detailed visualization and analysis of brain structure and function.

Trio 3t scanner by Siemens

Sourced in Germany, United States, Canada

The Trio 3T scanner is a magnetic resonance imaging (MRI) system developed by Siemens. It operates at a field strength of 3 Tesla, providing high-resolution images for various diagnostic and research applications. The core function of the Trio 3T scanner is to capture detailed anatomical and functional information about the human body.

What are the different types of head movements?

How can head movements be optimized for research and applications?

Optimizing head movements is crucial for research in areas like neuroscience, rehabilitation, and human-computer interaction. By understanding the nuances of different head movement types and their effects, researchers can design more effective protocols and interventions. This can lead to breakthroughs in fields ranging from neurological assessments to the development of advanced assistive technologies.

What are some common challenges in studying head movements?

How can PubCompare.ai assist with head movements research?

PubCompare.ai's AI-driven platform can greatly streamline the process of conducting head movements research. The platform allows researchers to efficiently screen protocol literature, leveraging AI to pinpoint critical insights. By comparing the effectiveness of different protocols related to head movements, PubCompare.ai can help researchers identify the most appropriate approaches for their specific needs, enhancing reproducibility and accuracy. This can save time and resources, while also leading to more informed decisions about the best protocols and products to use.

What are some key applications of head movements research?

Head movements research has a wide range of applications, including the assessment of neurological conditions, the development of advanced human-computer interfaces, the optimization of rehabilitation therapies, and the study of human communication and social interactions. By understanding the nuances of head movements, researchers can develop more effective diagnostic tools, design more intuitive and ergonomic technologies, and create more personalized and effective interventions for individuals with movement disorders or other related conditions.

More about "Head Movements"

Head movements, also known as cephalic motions, refer to the various rotational, flexion, extension, and lateral movements of the head.
These head movements are essential for visual and vestibular functions, as well as for nonverbal communication.
Optimizing head movement research can have significant implications in fields like neuroscience, rehabilitation, and human-computer interaction.
Researchers can leverage advanced technologies like MATLAB, EyeLink 1000, Discovery MR750, Tim Trio, Magnetom Tim Trio, OptoMotry, 12-channel head coils, MAGNETOM Prisma, 32-channel head coils, and Trio 3T scanners to capture and analyze head movements.
These tools can provide valuable insights into the underlying neurological and physiological mechanisms that govern head movements, as well as their role in various cognitive and behavioral processes.
By understanding the nuances of head movements, researchers can develop more effective rehabilitation protocols for conditions affecting head and neck mobility, such as neck injuries, neurological disorders, or vestibular impairments.
Additionally, this knowledge can inform the design of more natural and intuitive human-computer interfaces, where head movements can be utilized as a means of control or interaction.
The PubCompare.ai platform offers a streamlined and AI-driven approach to navigating the wealth of literature, preprints, and patents related to head movements.
This tool can help researchers quickly identify relevant protocols and make informed decisions about the best approaches for their specific research needs, ultimately advancing the field of head movement studies.