A Swine Model of Changes in the Neuronal Electromagnetic Field After Traumatic Brain Injury: A Pilot Study

Background Traumatic brain injury (TBI) is a global cause of disability and mortality. Treatment depends on mitigation of secondary injury resulting in axonal injury, necrosis, brain dysfunction, and disruption of electrical and chemical signaling in neural circuits. To better understand TBI, translational models are required to study physiology, diagnostics, and treatments in homologous species, such as swine. Electromagnetic fields (EMFs) from altered neural circuits can be measured and historically have been reliant on expensive shielding and supercooling in magnetoencephalography. Using proprietary induction sensors, it has been found that a non-invasive, non-contact approach with an engineered Mu-metal and copper mesh-shielded helmet effectively measures EMFs. This has not yet been investigated in swine models. We wished to evaluate the efficacy of this technology to assess TBI-dependent EMF changes in swine to describe the efficacy of these sensors and this model using a gravity-dependent controlled cortical impact (CCI). Methods A Yucatan miniswine was evaluated using non-contact, non-invasive proprietary induction sensors with an engineered dual-layer Mu-metal and interlaced copper mesh helmet with sensors within EMF channels connected to a helmet. Swine EMF recordings were obtained prior to induced gravity-dependent CCI followed by post-TBI measurements. Behavioral changes and changes in EMF measurements were assessed. EMF measurements were evaluated with an artificial intelligence (AI) model. Results Differences between room "noise" EMF measurements and pre-TBI swine electromagnetic field measurements were identified. Morphological characteristics between pre-injury and post-injury measurements were noted. AI modeling differentiated pre-injury and post-injury patterns in the swine EMF. Frequently identified frequencies seen post-injury were peaks at 2.5 Hz and 6.5 Hz and a valley at 11 Hz. The AI model identified less changes in the slope and thus decreased variation of EMF measurements post-TBI between 4.5 Hz and 7 Hz. Conclusions For the first time, it was identified that cortical function in a swine can be appropriately measured using novel induction sensors and shielding isolated to a helmet and EMF channels. The swine model can be appropriately differentiated from the external noise signal with identifiably different pre-injury and post-injury EMFs. Patterns can be recognized within the post-injury EMF due to altered neural circuits that can be measured using these sensors continuously, non-invasively, and in real time.

A Novel Spectrum Contrast Mapping Method for Functional Magnetic Resonance Imaging Data Analysis

Many studies reported that spontaneous fluctuation of the blood oxygen level-dependent signal exists in multiple frequency components and changes over time. By assuming a reliable energy contrast between low- and high-frequency bands for each voxel, we developed a novel spectrum contrast mapping (SCM) method to decode brain activity at the voxel-wise level and further validated it in designed experiments. SCM consists of the following steps: first, the time course of each given voxel is subjected to fast Fourier transformation; the corresponding spectrum is divided into low- and high-frequency bands by given reference frequency points; then, the spectral energy ratio of the low- to high-frequency bands is calculated for each given voxel. Finally, the activity decoding map is formed by the aforementioned energy contrast values of each voxel. Our experimental results demonstrate that the SCM (1) was able to characterize the energy contrast of task-related brain regions; (2) could decode brain activity at rest, as validated by the eyes-closed and eyes-open resting-state experiments; (3) was verified with test-retest validation, indicating excellent reliability with most coefficients > 0.9 across the test sessions; and (4) could locate the aberrant energy contrast regions which might reveal the brain pathology of brain diseases, such as Parkinson’s disease. In summary, we demonstrated that the reliable energy contrast feature was a useful biomarker in characterizing brain states, and the corresponding SCM showed excellent brain activity-decoding performance at the individual and group levels, implying its potentially broad application in neuroscience, neuroimaging, and brain diseases.

Usefulness of postural sway spectral analysis in the diagnostic route and clinical integration of cervicogenic and vestibular sources of dizziness: A cross-sectional preliminary study

BACKGROUND

Posturography power spectra (PS) implementation has been proven to discriminate between sensory inputs detriment of vestibular and proprioceptive origin.

OBJECTIVE

To deepen the role of posturography testing in the diagnostic route of dizzy conditions, by comparing two groups of patients -93 affected by cervicogenic dizziness (CGD) and 72 by unilateral vestibular hypofunction (UVH) -with a group of 98 age- and gender-matched healthy subjects, serving as control group (CON).

METHODS

All participants underwent otoneurological testing including video head impulse test (vHIT) and posturography testing with PS analysis. They also filled in Dizziness Handicap Inventory (DHI), Tampa Scale for Kinesiophobia and Hospital Anxiety and Depression Scale questionnaires.

RESULTS

UVH and CGD patients were found to have significant increase in vestibular- and proprioceptive-related PS values when compared with CON. Receiver operating characteristic curves found PS values to reliably discriminate both groups from CON. Positive and negative correlations were respectively found between vestibular-/proprioceptive-related PS domain and DHI in both groups and between PS and vHIT scores in UVH patients.

CONCLUSIONS

PS analysis demonstrated to be useful in differentiating CGD and UVH patients each other and when compared to CON, to objectively represent perceived symptoms filled along the DHI scale and to corroborate the rate of vestibular deficit in UVH patients.

Diagnostic route of cervicogenic dizziness: usefulness of posturography, objective and subjective testing implementation and their correlation

PURPOSE

To evaluate posturography measurements, and their association with other clinical tests used for cervicogenic dizziness diagnosis, in a cohort of subjects suffering from cervicogenic dizziness, compared with healthy subjects.

MATERIALS AND METHODS

Ninety-three cervicogenic dizziness patients and 98 age- and gender-matched healthy subjects underwent video-Head impulse test, posturography testing, evaluation of cervical spine movements by means of cervical range of motion goniometer and self-report and performance measures, including Dizziness Handicap Inventory, Neck Disability Index, Neck Pain Intensity, Tampa Scale for Kinesiophobia and Hospital Anxiety and Depression Scale.

RESULTS

Cervicogenic dizziness patients demonstrated significant increases in classical posturography parameters (i.e., surface and length) and in power spectra values within middle and high-frequency interval depicting balance control alterations especially due to proprioceptive integration changes. Furthermore, decreases in degrees of cervical range of motion and increases of self-report and performance measures - highlighting significant complaints of subjective feeling of dizziness - were found in these patients when compared with healthy subjects. Multiple correlations were found between posturography testing and cervical range of motion and Dizziness Handicap Inventory as well as between different self-report and performance measures in cervicogenic dizziness patients.

CONCLUSIONS

The implementation of posturography - including power spectra analysis - coupled with appropriate exclusion of other disorders, may represent a useful tool in improving cervicogenic dizziness assessment in terms of cost, time consumption and correlation with other measurements.Implications for rehabilitationUncertainties in diagnostic assessment of cervicogenic dizziness reflect doubts regarding the causes underpinning the symptoms of imbalance, unsteadiness, and disorientation.Posturography measurement is a low-cost, time-sparing technique and its implementations have proven to be useful in detecting significant alterations in body sway during perturbating situations.Implementation of power spectra analysis on posturography testing may represent a useful tool in improving cervicogenic dizziness assessment also in terms of rehabilitative treatment follow-up.

Analysis of Agile Canine Gait Characteristics Using Accelerometry

The high rate of severe injuries associated with racing greyhounds poses a significant problem for both animal welfare and the racing industry. Using accelerometry to develop a better understanding of the complex gait of these agile canines may help to eliminate injury contributing factors. This study used a single Inertial Measurement Unit (IMU) equipped with a tri-axial accelerometer to characterise the galloping of thirty-one greyhounds on five different race tracks. The dorsal-ventral and anterior-posterior accelerations were analysed in both the time and frequency domains. The fast Fourier transform (FFT) and Morlet wavelet transform were applied to signals. The time-domain signals were synced with the corresponding high frame rate videos of the race. It was observed that the acceleration peaks in the dorsal-ventral accelerations correspond to the hind-leg strikes which were noted to be fifteen times the greyhound's weight. The FFT analysis showed that the stride frequencies in all tracks were around 3.5 Hz. The Morlet wavelet analysis also showed a reduction in both the frequency and magnitude of signals, which suggests a speed reduction throughout the race. Also, by detecting abrupt changes along the track, the wavelet analysis highlighted potentially hazardous locations on the track. In conclusion, the methods applied in this research contribute to animal safety and welfare by eliminating the factors leading to injuries through optimising the track design and surface type.

Eliminating Non-linear Raman Shift Displacement Between Spectrometers via Moving Window Fast Fourier Transform Cross-Correlation

Obtaining consistent spectra by using different spectrometers is of critical importance to the fields that rely heavily on Raman spectroscopy. The quality of both qualitative and quantitative analysis depends on the stability of specific Raman peak shifts across instruments. Non-linear drifts in the Raman shifts can, however, introduce additional complexity in model building, potentially even rendering a model impractical. Fortunately, various types of shift correction methods can be applied in data preprocessing in order to address this problem. In this work, a moving window fast Fourier transform cross-correlation is developed to correct non-linear shifts for synchronization of spectra obtained from different Raman instruments. The performance of this method is demonstrated by using a series of Raman spectra of pharmaceuticals as well as comparing with data obtained by using an existing standard Raman shift scattering procedure. The results show that after the removal of shift displacements, the spectral consistency improves significantly, i.e., the spectral correlation coefficient of the two Raman instruments increased from 0.87 to 0.95. The developed standardization method has, to a certain extent, reduced instrumental systematic errors caused by measurement, while enhancing spectral compatibility and consistency through a simple and flexible moving window procedure.

Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle

The analysis of electroencephalographic signals in frequency is usually not performed by transforms that can extract the instantaneous characteristics of the signal. However, the non-steady state nature of these low voltage electrical signals makes them suitable for this kind of analysis. In this paper a novel tool based on Stockwell transform is tested, and compared with techniques such as Hilbert-Huang transform and Fast Fourier Transform, for several healthy individuals and patients that suffer from lower limb disability. Methods are compared with the Weighted Discriminator, a recently developed comparison index. The tool developed can improve the rehabilitation process associated with lower limb exoskeletons with the help of a Brain-Machine Interface.

Exploiting image registration for automated resonance assignment in NMR

Analysis of protein NMR data involves the assignment of resonance peaks in a number of multidimensional data sets. To establish resonance assignment a three-dimensional search is used to match a pair of common variables, such as chemical shifts of the same spin system, in different NMR spectra. We show that by displaying the variables to be compared in two-dimensional plots the process can be simplified. Moreover, by utilizing a fast Fourier transform cross-correlation algorithm, more common to the field of image registration or pattern matching, we can automate this process. Here, we use sequential NMR backbone assignment as an example to show that the combination of correlation plots and segmented pattern matching establishes fast backbone assignment in fifteen proteins of varying sizes. For example, the 265-residue RalBP1 protein was 95.4% correctly assigned in 10 s. The same concept can be applied to any multidimensional NMR data set where analysis comprises the comparison of two variables. This modular and robust approach offers high efficiency with excellent computational scalability and could be easily incorporated into existing assignment software.

Effect Transformation of the Micro Electrode Recording (MER) Data to Fast Fourier Transform (FFT) for the Main Target Nucleus Determination for STN-DBS

INTRODUCTION

Advanced PD stimulation of the STN reduces tremor, rigidity, and bradykinesia. Due to hemorrhagic complications the use of micro electrode recordings during DBS operation was still questioning for some of surgeons. But use micro electrodes were still the best choice for the positioning during surgery of DBS.

AIM

The aim of the current study was to investigate the effect transformation of the micro electrode recording data to fast fourier transform for the main target nucleus determination. This process needs a multidisciplinary approach from neurosurgery, neurology and specialists on electrophysiology such as biophysics.

CASE REPORT

We present the case of a 63 year-old male with medically intractable PD is focused on behalf of the surgical treatment. Patient had a 4-year history of progressively severe hand tremor on right side. The patient was successfully treated unilaterally with the STN DBS.

siGnum: graphical user interface for EMG signal analysis

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

Longitudinal sleep EEG trajectories indicate complex patterns of adolescent brain maturation

New longitudinal sleep data spanning ages 6-10 yr are presented and combined with previous data to analyze maturational trajectories of delta and theta EEG across ages 6-18 yr in non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. NREM delta power (DP) increased from age 6 to age 8 yr and then declined. Its highest rate of decline occurred between ages 12 and 16.5 yr. We attribute the delta EEG trajectories to changes in synaptic density. Whatever their neuronal underpinnings, these age curves can guide research into the molecular-genetic mechanisms that underlie adolescent brain development. The DP trajectories in NREM and REM sleep differed strikingly. DP in REM did not initially increase but declined steadily from age 6 to age 16 yr. We hypothesize that the DP decline in REM reflects maturation of the same brain arousal systems that eliminate delta waves in waking EEG. Whereas the DP age curves differed in NREM and REM sleep, theta age curves were similar in both, roughly paralleling the age trajectory of REM DP. The different maturational curves for NREM delta and theta indicate that they serve different brain functions despite having similar within-sleep dynamics and responses to sleep loss. Period-amplitude analysis of NREM and REM delta waveforms revealed that the age trends in DP were driven more by changes in wave amplitude rather than incidence. These data further document the powerful and complex link between sleep and brain maturation. Understanding this relationship would shed light on both brain development and the function of sleep.

Calculation of Computational Complexity for Radix-2 (p) Fast Fourier Transform Algorithms for Medical Signals

Owing to its simplicity radix-2 is a popular algorithm to implement fast fourier transform. Radix-2(p) algorithms have the same order of computational complexity as higher radices algorithms, but still retain the simplicity of radix-2. By defining a new concept, twiddle factor template, in this paper, we propose a method for exact calculation of multiplicative complexity for radix-2(p) algorithms. The methodology is described for radix-2, radix-2 (2) and radix-2 (3) algorithms. Results show that radix-2 (2) and radix-2 (3) have significantly less computational complexity compared with radix-2. Another interesting result is that while the number of complex multiplications in radix-2 (3) algorithm is slightly more than radix-2 (2), the number of real multiplications for radix-2 (3) is less than radix-2 (2). This is because of the twiddle factors in the form of which need less number of real multiplications and are more frequent in radix-2 (3) algorithm.

Sex, puberty, and the timing of sleep EEG measured adolescent brain maturation

The steep adolescent decline in the slow wave (delta, 1-4 Hz) electroencephalogram (EEG) of nonrapid eye movement (NREM) sleep is a dramatic maturational change in brain electrophysiology thought to be driven by cortical synaptic pruning. A perennial question is whether this change in brain electrophysiology is related to sexual maturation. Applying Gompertz growth models to longitudinal data spanning ages 9-18 y, we found that the timing of the delta decline was significantly (P < 0.0001) linked to timing of pubertal maturation. This timing relation remained significant when sex differences in the timing of the delta decline were statistically controlled. Sex differences and the relation to the timing of puberty jointly explained 67% of the between-subject variance in the timing of the delta decline. These data provide a demonstration of a temporal relation between puberty and an electrophysiological marker of adolescent brain development. They can guide research into whether the neuroendocrine events of puberty are mechanistically linked to cortical maturation or whether, instead, the two maturational processes are parallel but independent programs of human ontogenesis.

The Fast Multipole Method and Fourier Convolution for the Solution of Acoustic Scattering on Regular Volumetric Grids

The fast multipole method (FMM) is applied to the solution of large-scale, three-dimensional acoustic scattering problems involving inhomogeneous objects defined on a regular grid. The grid arrangement is especially well suited to applications in which the scattering geometry is not known a priori and is reconstructed on a regular grid using iterative inverse scattering algorithms or other imaging techniques. The regular structure of unknown scattering elements facilitates a dramatic reduction in the amount of storage and computation required for the FMM, both of which scale linearly with the number of scattering elements. In particular, the use of fast Fourier transforms to compute Green's function convolutions required for neighboring interactions lowers the often-significant cost of finest-level FMM computations and helps mitigate the dependence of FMM cost on finest-level box size. Numerical results demonstrate the efficiency of the composite method as the number of scattering elements in each finest-level box is increased.

A new FFT-based algorithm to compute Born radii in the generalized Born theory of biomolecule solvation

In this paper, a new method for calculating effective atomic radii within the generalized Born (GB) model of implicit solvation is proposed, for use in computer simulations of bio-molecules. First, a new formulation for the GB radii is developed, in which smooth kernels are used to eliminate the divergence in volume integrals intrinsic in the model. Next, the Fast Fourier Transform (FFT) algorithm is applied to integrate smoothed functions, taking advantage of the rapid spectral decay provided by the smoothing. The total cost of the proposed algorithm scales as O(N(3)logN + M) where M is the number of atoms comprised in a molecule, and N is the number of FFT grid points in one dimension, which depends only on the geometry of the molecule and the spectral decay of the smooth kernel but not on M. To validate our algorithm, numerical tests are performed for three solute models: one spherical object for which exact solutions exist and two protein molecules of differing size. The tests show that our algorithm is able to reach the accuracy of other existing GB implementations, while offering much lower computational cost.

AUTOMATIC GENERATION OF FFT FOR TRANSLATIONS OF MULTIPOLE EXPANSIONS IN SPHERICAL HARMONICS

The fast multipole method (FMM) is an efficient algorithm for calculating electrostatic interactions in molecular simulations and a promising alternative to Ewald summation methods. Translation of multipole expansion in spherical harmonics is the most important operation of the fast multipole method and the fast Fourier transform (FFT) acceleration of this operation is among the fastest methods of improving its performance. The technique relies on highly optimized implementation of fast Fourier transform routines for the desired expansion sizes, which need to incorporate the knowledge of symmetries and zero elements in the input arrays. Here a method is presented for automatic generation of such, highly optimized, routines.

Varieties of attention-deficit/hyperactivity disorder-related intra-individual variability

Intra-individual variability in behavior and functioning is ubiquitous among children with attention-deficit/hyperactivity disorder (ADHD), but it has not been systematically examined or integrated within causal models. This article seeks to provide a conceptual, methodologic, and analytic framework as a foundation for future research. We first identify five key research questions and methodologic issues. For illustration, we examine the periodic structure of Eriksen Flanker task reaction time (RT) data obtained from 24 boys with ADHD and 18 age-matched comparison boys. Reaction time variability in ADHD differed quantitatively from control subjects, particularly at a modal frequency around .05 Hz (cycle length approximately 20 sec). These oscillations in RT were unaffected by double-blind placebo and were suppressed by double-blind methylphenidate. Together with converging lines of basic and clinical evidence, these secondary data analyses support the speculative hypothesis that the increased power of multisecond oscillations in ADHD RT data, and by inference, in attentional performance, represents a catecholaminergic deficit in the ability to appropriately modulate such oscillations in neuronal activity. These results highlight the importance of retaining time-series data and quantitatively examining intra-subject measures of variability as a putative endophenotype for ADHD.