Top

Tip

Swipe om te navigeren naar een ander artikel

Gepubliceerd in:

18-11-2020 | Brief Report

Brief Report: Classification of Autistic Traits According to Brain Activity Recoded by fNIRS Using ε-Complexity Coefficients

Auteurs: Anat Dahan, Yuri A. Dubnov, Alexey Y. Popkov, Itai Gutman, Hila Gvirts Probolovski

Gepubliceerd in: Journal of Autism and Developmental Disorders | Uitgave 9/2021

Abstract

Individuals with ASD have been shown to have different pattern of functional connectivity. In this study, brain activity of participants with many and few autistic traits, was recorded using an fNIRS device, as participants preformed an interpersonal synchronization task. This type of task involves synchronization and functional connectivity of different brain regions. A novel method for assessing signal complexity, using ε-complexity coefficients, applied for the first i.e. on fNIRS recording, was used to classify brain recording of participants with many/few autistic traits. Successful classification was achieved implying that this method may be useful for classification of fNIRS recordings and that there is a difference in brain activity between participants with low and high autistic traits as they perform an interpersonal synchronization task.

vorige artikel Brief Report: Using Cognitive Screeners in Autistic Adults

Met onderstaand(e) abonnement(en) heeft u direct toegang:

BSL Psychologie Totaal

Met BSL Psychologie Totaal blijf je als professional steeds op de hoogte van de nieuwste ontwikkelingen binnen jouw vak. Met het online abonnement heb je toegang tot een groot aantal boeken, protocollen, vaktijdschriften en e-learnings op het gebied van psychologie en psychiatrie. Zo kun je op je gemak en wanneer het jou het beste uitkomt verdiepen in jouw vakgebied.

Meer informatie

Alleen toegankelijk voor geautoriseerde gebruikers

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub.

Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The Autism-Spectrum Quotient (AQ): Evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31, 5–17. CrossRef

Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 9. https://doi.org/10.1186/1741-7015-9-18.

Bronkhorst, M., Mukisa, R., Colier, W. N., Stothers, L., & Macnab, A. J. (2019). Functional near infrared spectroscopy (fNIRS) in pigmented subjects: a maneuver to confirm sufficient transcutaneous photon transmission for measurement of hemodynamic change in the anterior cortex. In Optical Sensors 2019 (Vol. 11028, p. 1102813). International Society for Optics and Photonics.

Cacioppo, S., Zhou, H., Monteleone, G., Majka, E. A., Quinn, K. A., Ball, A. B., et al. (2014). You are in sync with me: Neural correlates of interpersonal synchrony with a partner. Neuroscience, 277, 842–858. https://doi.org/10.1016/j.neuroscience.2014.07.051. CrossRefPubMed

Camchong, J., Stenger, A., & Fein, G. (2013). Resting-state synchrony during early alcohol abstinence can predict subsequent relapse. Cerebral Cortex, 23(9), 2086–2099. https://doi.org/10.1093/cercor/bhs190. CrossRefPubMed

Cevallos, C., Zarka, D., Hoellinger, T., Leroy, A., Dan, B., & Cheron, G. (2015). Oscillations in the human brain during walking execution, imagination and observation. Neuropsychologia, 79, 223–232. https://doi.org/10.1016/j.neuropsychologia.2015.06.039. CrossRefPubMed

Coben, R., Mohammad-Rezazadeh, I., & Cannon, R. L. (2014). Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: A theory of mixed over- and under-connectivity. Frontiers in Human Neuroscience, 8(1 FEB), 45. https://doi.org/10.3389/fnhum.2014.00045. CrossRefPubMedPubMedCentral

Cui, X., Bray, S., Neuroimage, A. R., & 2010, undefined. (n.d.). Functional near infrared spectroscopy (NIRS) signal improvement based on negative correlation between oxygenated and deoxygenated hemoglobin. Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S105381190901235X?casa_token=ZDnr_o_1qfUAAAAA:OUvKutiMrRyJWXYuu06mL3xaHplnYTDaYAOZaH6-TpQmBHRXmCpE1Xc0CWFOXE32f8-k6S8NCA

Curioni, A., Minio-Paluello, I., Sacheli, L. M., Candidi, M., & Aglioti, S. M. (2017). Autistic traits affect interpersonal motor coordination by modulating strategic use of role-based behavior. Molecular Autism, 8(1). https://doi.org/10.1186/s13229-017-0141-0.

Darkhovsky, B., Piryatinska, A., & Kaplan, A. (2016). Binary classification of multi-channel EEG records based on the ϵ-complexity of continuous vector functions Nonlinear Time Series View project Binary classification of multichannel-EEG records based on the-complexity of continuous vector function. Computer Methods and Programs in Biomedicine, 152, 131–139. https://doi.org/10.1016/j.cmpb.2017.09.001. CrossRef

Engel, A. K., & Fries, P. (2010). Beta-band oscillations – Signalling the status quo? (pp. 156–165). https://doi.org/10.1016/j.conb.2010.02.015. CrossRef

Ewen, J. B., Lakshmanan, B. M., Hallett, M., Mostofsky, S. H., Crone, N. E., & Korzeniewska, A. (2015). Dynamics of functional and effective connectivity within human cortical motor control networks. Clinical Neurophysiology, 126(5), 987–996. https://doi.org/10.1016/j.clinph.2014.09.006. CrossRefPubMed

Fitzpatrick, P., Frazier, J. A., Cochran, D. M., Mitchell, T., Coleman, C., & Schmidt, R. C. (2016). Impairments of social motor synchrony evident in autism spectrum disorder. Frontiers in Psychology, 7(AUG). https://doi.org/10.3389/fpsyg.2016.01323.

Fujisawa, S., Amarasingham, A., Harrison, M. T., & Buzsáki, G. (2008). Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex. Nature Neuroscience, 11(7), 823–833. https://doi.org/10.1038/nn.2134. CrossRefPubMedPubMedCentral

Gu, Y., Miao, S., Han, J., Zeng, K., Ouyang, G., Yang, J., & Li, X. (2017). Complexity analysis of fNIRS signals in ADHD children during working memory task. Scientific Reports, 7(1), 1–10. https://doi.org/10.1038/s41598-017-00965-4. CrossRef

Gvirts, H. Z., & Perlmutter, R. (2019). What guides us to neurally and behaviorally align with anyone specific? A neurobiological model based on fNIRS hyperscanning studies. The Neuroscientist, 107385841986191. https://doi.org/10.1177/1073858419861912.

Harris, A. Z., & Gordon, J. A. (2015). Long-range neural synchrony in behavior. Annual Review of Neuroscience, 38, 171–194. https://doi.org/10.1146/annurev-neuro-071714-034111. CrossRefPubMedPubMedCentral

Hove, M. J., & Risen, J. L. (2009). It’s all in the timing: Interpersonal synchrony increases affiliation. Social Cognition, 27(6), 949–960. https://doi.org/10.1521/soco.2009.27.6.949. CrossRef

Hutchison, R. M., Womelsdorf, T., Allen, E. A., Bandettini, P. A., Calhoun, V. D., Corbetta, M., et al. (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079. CrossRefPubMed

Kleinspehn, A. (n.d.). Goal-directed interpersonal action synchronization across the lifespan: A dyadic drumming study. In 2008. Retrieved from https://refubium.fu-berlin.de/handle/fub188/865

Li, X., Zhu, Z., Zhao, W., Sun, Y., Wen, D., Xie, Y., et al. (2018). Decreased resting-state brain signal complexity in patients with mild cognitive impairment and Alzheimer’s disease: A multi-scale entropy analysis. Biomedical Optics Express, 9(4), 1916. https://doi.org/10.1364/boe.9.001916. CrossRefPubMedPubMedCentral

Lisman, J. E., & Jensen, O. (2013, March 20). The theta-gamma neural code. Neuron, 77, 1002–1016. https://doi.org/10.1016/j.neuron.2013.03.007. CrossRefPubMedPubMedCentral

Liu, N., Cui, X., Bryant, D. M., Glover, G. H., & Reiss, A. L. (2015). Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy. Biomedical Optics Express, 6(3), 1074. https://doi.org/10.1364/boe.6.001074. CrossRefPubMedPubMedCentral

Marsh, K. L., Isenhower, R. W., Richardson, M. J., Helt, M., Verbalis, A. D., Schmidt, R. C., & Fein, D. (2013). Autism and social disconnection in interpersonal rocking. Frontiers in Integrative Neuroscience, 7(JAN), 4. https://doi.org/10.3389/fnint.2013.00004. CrossRefPubMedPubMedCentral

Maximo, J. O., Cadena, E. J., & Kana, R. K. (2014, March). The implications of brain connectivity in the neuropsychology of autism. Neuropsychology Review, 24, 16–31. https://doi.org/10.1007/s11065-014-9250-0. CrossRefPubMedPubMedCentral

Noonan, S. K., Haist, F., & Müller, R. A. (2009). Aberrant functional connectivity in autism: Evidence from low-frequency BOLD signal fluctuations. Brain Research, 1262, 48–63. https://doi.org/10.1016/j.brainres.2008.12.076. CrossRefPubMedPubMedCentral

Perpetuini, D., Bucco, R., Zito, M., & Merla, A. (2017). Study of memory deficit in Alzheimer’s disease by means of complexity analysis of fNIRS signal. Neurophotonics, 5(01), 1. https://doi.org/10.1117/1.nph.5.1.011010. CrossRef

Sporns, O., Tononi, G., & Edelman, G. M. (2000, November 1). Connectivity and complexity: The relationship between neuroanatomy and brain dynamics. Neural Networks, 13, 909–922. https://doi.org/10.1016/S0893-6080(00)00053-8. CrossRefPubMed

Stam, C. J. (2005). Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clinical Neurophysiology, 116, 2266–2301. https://doi.org/10.1016/j.clinph.2005.06.011. CrossRefPubMed

Sylos-Labini, F., d’Avella, A., Lacquaniti, F., & Ivanenko, Y. (2018). Human-human interaction forces and interlimb coordination during side-by-side walking with hand contact. Frontiers in Physiology, 9, 179. https://doi.org/10.3389/fphys.2018.00179. CrossRefPubMedPubMedCentral

Tachtsidis, I., & Scholkmann, F. (2016). False positives and false negatives in functional near-infrared spectroscopy: Issues, challenges, and the way forward. Neurophotonics, 3(3), 031405. https://doi.org/10.1117/1.nph.3.3.031405. CrossRefPubMedPubMedCentral

Tomassini, A., Spinelli, D., Jacono, M., Sandini, G., & Morrone, M. C. (2015). Rhythmic oscillations of visual contrast sensitivity synchronized with action. Journal of Neuroscience, 35(18), 7019–7029. https://doi.org/10.1523/JNEUROSCI.4568-14.2015. CrossRefPubMed

Van Drongelen, W. (2007). Signal processing for neuroscientists. Signal Processing for Neuroscientists. https://doi.org/10.1016/B978-0-12-370867-0.X5000-1.

Varlet, M., Marin, L., Lagarde, J., & Bardy, B. G. (2011). Social postural coordination. Journal of Experimental Psychology: Human Perception and Performance, 37(2), 473–483. https://doi.org/10.1037/a0020552. CrossRefPubMed

Whitehouse, A. J. O., Hickey, M., & Ronald, A. (2011). Are autistic traits in the general population stable across development? PLoS One, 6(8). https://doi.org/10.1371/journal.pone.0023029.

Widiger, T. A., & Trull, T. J. (2007). Plate tectonics in the classification of personality disorder: Shifting to a dimensional model. American Psychologist, 62(2), 71–83. https://doi.org/10.1037/0003-066X.62.2.71. CrossRef

Wilcox, T., Bortfeld, H., Woods, R., Wruck, E., & Boas, D. A. (2005). Using near-infrared spectroscopy to assess neural activation during object processing in infants. Journal of Biomedical Optics, 10(1), 011010. https://doi.org/10.1117/1.1852551. CrossRef

Zappasodi, F., Marzetti, L., Olejarczyk, E., Tecchio, F., & Pizzella, V. (2015). Age-related changes in electroencephalographic signal complexity. PLoS One, 10(11). https://doi.org/10.1371/journal.pone.0141995.

Zhang, Y., Brooks, D. H., Franceschini, M. A., & Boas, D. A. (2005). Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. Journal of Biomedical Optics, 10(1), 011014. https://doi.org/10.1117/1.1852552. CrossRef

Zhang, X., Noah, J. A., & Hirsch, J. (2016). Separation of the global and local components in functional near-infrared spectroscopy signals using principal component spatial filtering. Neurophotonics, 3(1), 015004. https://doi.org/10.1117/1.nph.3.1.015004. CrossRefPubMedPubMedCentral

Zivotofsky, A. Z., & Hausdorff, J. M. (2007). The sensory feedback mechanisms enabling couples to walk synchronously: An initial investigation. Journal of Neuroengineering and Rehabilitation, 4. https://doi.org/10.1186/1743-0003-4-28.

Titel: Brief Report: Classification of Autistic Traits According to Brain Activity Recoded by fNIRS Using ε-Complexity Coefficients
Auteurs: Anat Dahan
Yuri A. Dubnov
Alexey Y. Popkov
Itai Gutman
Hila Gvirts Probolovski
Publicatiedatum: 18-11-2020
Uitgeverij: Springer US
Gepubliceerd in: Journal of Autism and Developmental Disorders / Uitgave 9/2021
Print ISSN: 0162-3257
Elektronisch ISSN: 1573-3432
DOI: https://doi.org/10.1007/s10803-020-04793-w

Bohn Stafleu van Loghum

Tip

Swipe om te navigeren naar een ander artikel

Deel dit onderdeel of sectie (kopieer de link)

Abstract

Log in om toegang te krijgen

Met onderstaand(e) abonnement(en) heeft u direct toegang:

BSL Psychologie Totaal

Andere artikelen Uitgave 9/2021

Lifelong Tone Language Experience does not Eliminate Deficits in Neural Encoding of Pitch in Autism Spectrum Disorder

Family-Centered Care: How Close Do We Get When Talking to Parents of Children Undergoing Diagnosis for Autism Spectrum Disorders?

A Meta-analysis of Outcome Studies of Autistic Adults: Quantifying Effect Size, Quality, and Meta-regression

Risk for Premature Mortality and Intentional Self-harm in Autism Spectrum Disorders

Digital Interventions for Autism Spectrum Disorder: A Meta-analysis

Early Intervention Delivery Methods for New Zealand Children with Autism: Current Practices Versus Parental Preferences