Immunogenetics: Open Access
Open Access

Electroencephalography Associated with Immunogenetics Studies in Animal Diseases

Roberta Carvalho Basile^{* *}Correspondence: Roberta Carvalho Basile, Department of Clinics and Veterinary Surgery, University of Jaguariuna, São Paulo, Brazil, Email:

Author info »

Description

The Electroencephalic dysfunctions are permeated among several etiologies, such as neoplasms, endocrine and metabolic alterations, intoxications, morphological alterations, among others. To fully understand them, it is necessary to access not only the structural alterations of the brain, but also its functional disorders. To this end, only the electroencephalogram can provide information about the neuroelectrical activity [1].

The Electro Encephalo Gram (EEG) records spontaneous cortical electrical function and can identify waves with frequencies between 0.5 and 90 Hz and amplitude between 1 and 500 microvolts. The electrical signal originates mainly from three sources: the synaptic activity of cortical neurons, changes of action potentials at the surface of glial cells, and generalized electrical activity generated by large populations of neurons, mainly from deeper neuronal layers [2].

Although electroencephalography is a diagnostic method available for many years in veterinary medicine, having been employed in cattle since the work of Golikov in 1966; dogs [3], horses [4] and cats [5], its routine use in veterinary clinical neurology has received little attention [6]. Still, recent years have shown much promise for the experimental use of this technique, as it is being recognized as essential for the verification of brain function in association with imaging examinations, which only illustrate the structural portion [7]. The International Veterinary Epilepsy Task Force (IVETF) itself strongly recommends that EEG be used as a diagnostic method for idiopathic level III epilepsies [8]. In addition, EEG has evolved greatly due to the development of new technologies for data acquisition [9] and signal processing [10].

Research has shown that electroencephalography can be used beyond the diagnosis of idiopathic or structural epilepsy. EEG has been used to monitor the sleep quality of dogs and correlate with ease of learning [11], indicate which foals are best managed as a function of sleep depth during the day and night [12]. In the behavioral field, it has been used to monitor the degree of stress (animal welfare) in horses [13], check neurological responses associated with behavioral changes in dogs [14] and horses [15].

In humans it has been used as a method for classifying depression [16] and monitoring neuronal degeneration states [17].

Another extremely promising area of EEG use in animals is the quantitative aid in pain diagnosis. From verifying the pain associated with the quality of the slaughter blade in cattle [18], to proving the presence of pain in castrated calves without anesthetic procedures [19], the electroencephalogram has been very useful in proving and understanding pain mechanisms in animals. It is important to emphasize that pain is different from nociception because it requires brain processing of nociceptive impulses for it to occur. Thus, the EEG, as a method for evaluating brain activity, becomes an interesting quantitative tool for direct assessment of pain and not only nociception [20].

Immunogenetic studies have been associated with electroencephalography exams in order to gather information about brain function and gene expression in certain cerebral areas. Thus, relevant information has been obtained for understanding mechanisms such as narcolepsy [21], epilepsy [22], hereditary neurodegenerations [23], senile neurodegenerations [24], and adverse reactions to medications [25].

The most recent method for real-time EEG analysis is computer analysis of EEG signals in the form of power spectrum analysis [26]. Today it is a standard practice in human medicine and already starting in veterinary medicine, used in the assessment of brain activity. Based on the mathematical analysis of FFT (Fast- Fourier Transformation), the waves generated in the time domain are transformed into a graph the frequency domain, reflecting the intensity of the colors in relation to the intensity of the waves at a given frequency, using generally accepted limits of: delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13-30 Hz). The spectrogram (graph) changes dynamically and allows instant visual interpretation, providing data for various applications such as anesthetic monitoring, sleep disorders, epileptic disorders, level of consciousness, pain, brain injuries, cognition disorders, behavioral assessment, degenerative injuries, emotion recognition, machine learning, and others [27]. It is created during measurements (in real time) and does not require further analysis. To date, some veterinary colleges in Europe are conducting research using this system.

Conclusion

EEG is a brain electrical evaluation technique that allows checking intracranial neuronal function in animals and humans. It has methods studied for the main domestic species and its applicability goes beyond the diagnosis of epileptic patients. Signal post-processing methods associated with immunogenetics studies allow the evaluation of sleep, neurodegenerations syndromes and drugs toxicity, expanding the universe of evaluation and verification of neurological scenarios in domestic animals.

References

1. Wrzosek M. Electroencephalography as a diagnostic technique for canine neurological diseases. J Vet Res. 2016;60: 181-187.
2. Fisch BJ. Basic principles of digital ana analog eeg. Elsevier. 1999;126:185-195.
3. Beaver BV, Klemm WR. Electroencephalograms of normal anesthetized cats. Am J Vet Res. 1973;34(11): 1441-1447.
4. Grabow JD, Anslow RO, Spalatin J. Electroencephalographic recordings with multicontact depth probes in a horse. Am J Vet Res. 1969;30(7): 1239-1243.
5. Fernando CPellegrinoaRoberto E.PSicab.Canine electroencephalographic recording technique: findings in normal and epileptic dogs. Clin Neurophysiol.2004; 115(2): 477-487.
6. Murugappan M, Zheng BS, Khairunizam W. Recurrent quantification analysis-based emotion classification in stroke using electroencephalogram signals. Arab J Sci Eng. 2021.
7. Berendt MT, Farqhuar R, Mandigers PJJ, Pakozdy A, Bhatti SFM. International veterinary epilepsy task force consensus report on epilepsy definition, classification, and terminology in companion animals. BMC Vet Res. 2015;11: 182.
8. Parmentier T, Monteith G. Effect of prior general anesthesia or sedation and antiseizure drugs on the diagnostic utility of wireless video electroencephalography in dogs. J Vet Intern Med. 2020;34: 1967-1974.
9. Lu JQ, Steve TA. Immune cell infiltrates in hippocampal sclerosis: correlation with neuronal loss. J Neuropathol Exp Neurol. 2017;76(3): 206-215.
10. Steingrimsson S, Bilonic G, Ekelund AC. Electroencephalography-based neurofeedback as treatment for post-traumatic stress disorder: a systematic review and meta-analysis. Eur Psychiatry. 2020;63(1):1-12.
11. Iotchev IB, Reicher V, Kovacs E, Kovacs T, Kis A, Gacsi M, et al. Averaging sleep spindle occurrence in dogs predicts learning performance better than single measurements. Nature Scientific Reports. 2020;10:22-461.
12. Zanker A, Wohr AC, Reese M. Qualitative and quantitative analyses of polysomnographic measurements in foals. Nature Scientific Reports. 2021;11: 162-188.
13. Stomp M, Dindeo S, Henry S. Eeg individual power profiles correlate with tension along spine in horses. Plos one. 2020;89: 1-15.
14. Wrzosek M, Plonek M, Nicpon CS. Retrospective multicenter evaluation of the “fly-catching syndrome” in 24 dogs: eeg, baer, mri, csf findings and response to antiepileptic and antidepressant treatment. Epilepsy Behavior. 2015;53:184-189.
15. Pickles KJ, Gibson TJ. Preliminary investigation of somatosensory evoked potentials in equine headshaking. Vet Rec. 2011;168:511-517.
16. Jiang C, Li Y, Tang Y, Guan C. Enhancing eeg-based classification of depression patients using spatial information. Ieee Trans Neural Syst Rehabil Eng. 2021;29: 566-575.
17. Nobukawa S, Yamanishi T, Nishimura H. Classification methods based on complexity and synchronization of electroencephalography signals in alzheimer’s disease. Front Psychiatry. 2020;11:255.
18. Bergamasco L, Edwards C, Bello NM, Mijares S, Cull CA, Mosher RA, et al. Unmitigated surgical castration in calves of different ages: electroencephalographic and neurohormonal findings. Animals. 2021;(11): 17-91.
19. Imlan JC, Kaka U, Goh YM, Idrus Z, Awad EA. Effects of slaughter positions on catecholamine, blood biochemical and electroencephalogram changes in cattle restrained using a modified mark iv box. Animals. 2021;11: 19-79.
20. Alonso VA, Candelaria GB, Vales I. Primary immunodeficiencies: a challenge for immuno-genetics. Revista Cubana De Reumatologia. 2020;22(2):1-28.
21. Szabo S, Thorpy MJ, Mayer G. Neurobiological and immunogenetic aspects of narcolepsy: implications for pharmacotherapy. Sleep Med Rev. 2019;43: 23-36.
22. Golikov AN, Liubimov EI. Encephalography in cattle. Veterinariia. 1966;43(6): 81-83.
23. Klem WR. Subjective and quantitative analyses of the electroencephalogram of anesthetized normal dogs: control data for clinical diagnosis. Am J Vet Res. 1968;29(6):1267-1277.
24. Leal B, Chaves J, Carvalho C. Immunogenetic predisposing factors for mesial temporal lobe epilepsy with hippocampal sclerosis. Int J Neurosci. 2018;128: 4-9.
25. Tensini TS, Von Glehn CQC, Berttinotti MP. Cutaneous adverse reactions associated with antiseizure medication: clinical characteristics and implications in epilepsy treatment. Epileptic Disord. 2021;23(3): 466-475.
26. Bickford RG, Fleming N, Billinger T. Compression of eeg data. Trans Am Neurol Assoc. 1971;96: 118-122.
27. Sharma P, Veer K. Recent approaches on classification and feature extraction of eeg signal: a review. Robotica. 2021.