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Coherence Analysis in the Brain Network of ASD Children using Connectivity Model and Graph Theory


Affiliations
1 Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
 

Autism Spectrum Disorder (ASD) belongs with the category of neuro-developmental disorders, which can be majorly categorized under decreased social relationships, communication and thought processes. Various studies in the field of biological networks prove that one of the defining features of ASD is altered brain connectivity. Hence, the understanding of the brain networks can pave the way to delve deeper into the underlying behaviour of the Autistic brains. Moreover, many studies also reveal that human brains exhibit small-world characteristics which are usually seen in simple model neural networks that emerge spontaneously upon adaptive rewiring according to the dynamical functional connectivity. Graph theory-based approaches are finding their way into the understanding of the altered connectivity in various neurological disorders. For that matter, the study focuses on implementing a graph theory-based approach to investigate on the small-world network of Autistic as well as typically developing brains and understand the behavioural changes for an Audio and Video Stimuli. The graphically generated data is then measured for functional connectivity using a symmetrical parameter known as the coherence measure.

Keywords

Autism spectrum disorder, Biological network, Coherence measure, Functional connectivity
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  • Bal V H, Kim S‐H, Fok M & LordC, Autism spectrum disorder symptoms from ages 2 to 19 years: Implications for diagnosing adolescents and young adults, Autism Res, 12(1) (2019) 88–99.

  • Schneider D, Glaser M & Senju, Autism Spectrum Disorder, in Encyclopedia of Personality and Individual Differences, edited by V Zeigler-Hill, T Shackelford (Springer, Cham), (2017) 1–5.

  • Bal V H, Kim S‐H, Fok M & Lord C, Autism spectrum disorder symptoms from ages 2 to 19 years: implications for diagnosing adolescents and young adults, Autism Res, 12(1) (2019) 88–99.

  • Bock S J, Borders C M, Probst K & Freeman-Green S, Special education for young learners with autism spectrum disorder advances in special education (Emerald Publishing Limited) 2019, 103–118.

  • Hollocks M, Lerh J, Magiati I, Meiser-Stedman R & Brugha T, Anxiety and depression in adults with autism spectrum disorder: A systematic review and meta-analysis., Psychological Med, (2019) 559–572.

  • Bal V H, Kim S‐H, Fok M & Lord C, Autism spectrum disorder symptoms from ages 2 to 19 years: Implications for diagnosing adolescents and young adults, Autism Res, 12(1) (2019) 88–99.

  • Chauhan A, Sahu J K, Jaiswal N, Kumar K, Agarwal A, Kaur J, Singh S & Singh M, Prevalence of autism spectrum disorder in Indian children: A systematic review and meta-analysis, Neurol India, 67(1) (2019) 100–104.

  • Sunita & Bilszta J L C, Early identification of autism: A comparison of the checklist for autism in toddlers and the modified checklist for autism in toddlers, J Paediatr Child Health, 49(6) (2013) 438–444. DOI: 10.1111/j.1440- 1754.2012.02558.x

  • Havdahl K A, Von T S, Huerta M, Lord C & Bishop S L, Utility of the child behavior checklist as a screener for autism spectrum disorder, Autism Res, 9(1) (2016) 33–42.

  • Rescorla L A, Winder-Patel B M, Paterson S J, Pandey J, Wolff J J, Schultz R T & Piven J, Autism spectrum disorder screening with the CBCL/1½-5: Findings for young children at high risk for autism spectrum disorder, Autism, 23(1) (2019) 29–38.

  • Cociu B A, Das S, Billeci L, Jamal W, Maharatna K, Calderoni S, Narzisi A & Muratori F, Multimodal functional and structural brain connectivity analysis in autism: A preliminary integrated approach with EEG, fMRI and DTI, IEEE Trans Cogn Dev Syst, 10(2) (2017).

  • Bakhshayesh H, Grummett T S, Janani A S, Fitzgibbon S P & Pope K J, Towards detecting connectivity in EEG: A comparative study of parameters of effective connectivity measures on simulated data, IEEE-EMBS Conf on Biomedical Engineering and Sciences (IECBES) 2018, 297–301.

  • Light G A, Williams L E, Minow F, Sprock J, Rissling A, Sharp R, Swerdlow N R & Braff D L, Electroencephalography (EEG) and event-related potentials (ERPs) with human participants, Curr Protocols Neurosci, 52(1) (2010) 1–24.

  • Koudelková Z, Strmiska M & Jašek R, Analysis of brain waves according to their frequency, Int J Biol Biomed Eng, 12 (2018) 202–207.

  • Strzelecka J, Electroencephalographic studies in children with autism spectrum disorders, Res Autism Spectr Disord, 8(3) (2014) 317–323.

  • Nicotera A G, Hagerman R J, Catania M V, Buono S, Di N S, Liprino E M, Stracuzzi E, Giusto S, Di V G & Musumeci S A, EEG abnormalities as a neurophysiological biomarker of severity in autism spectrum disorder: A pilot cohort study, J Autism Dev Disord, 49(6) (2019) 2337–2347.

  • Swatzyna R J, Tarnow J D, Turner R P, Roark A J, MacInerney E K & Kozlowski G P, Integration of EEG into psychiatric practice: A step toward precision medicine for autism spectrum disorder, J Clin Neurophysiol, 34(3) (2017) 230–235.

  • Sinha T, Munot M V & Sreemathy R, An efficient approach for detection of autism spectrum disorder using electroencephalography signal, IETE J Res, 68(2) (2019) 824–832.

  • Bosl W, Tierney A, Tager-Flusberg, Tager-Flusberg H and Nelson C, EEG complexity as a biomarker for autism spectrum disorder risk, BMC Med, 9(11) (2011) 1–16.

  • Boersma M, Kemner C, de Reus M A, Collin G, Snijders T M, Hofman D, Buitelaar J K, Stam C J & van den Heuvel M P, Disrupted functional brain networks in autistic toddlers, Brain Connect, 3(1) (2013) 41–49.

  • Supekar K, Musen M & Menon V, Development of largescale functional brain networks in children, PLoS Biol, 7(7) (2009) e1000157.

  • Smit D J A, Stam C J, Posthuma D, Boomsma D I & De Geus E J C, Heritability of ‘Small-world’ networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity, Behav Genet, 37 (2007) 794–795.

  • Bassett D S & Bullmore E T, Small-world brain networks revisited, The Neuroscientist, 23(5) (2017) 499–516.

  • Chang W, Wang H, Hua C, Wang Q & Yuan Y, Comparison of different functional connectives based on EEG during concealed information test, Biomed Signal Processing and Control, 49 (2019) 149–159.

  • Talebi N, Nasrabadi A M, Mohammad-Rezazadeh I & Coben R, nCREANN: Nonlinear causal relationship estimation by artificial neural network; applied for autism connectivity study, IEEE Trans Med Imaging, 38(12) (2019) 2883–2890.

  • Hellrigel S, Jarman N & Van L C, Adaptive rewiring in weighted networks, Cognitive Syst Res, 55(2019) 205–218.

  • Tomasi D & Volkow N D, Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in autism spectrum disorder, Cerebral Cortex, 29(2) (2019) 573–585.

  • Rabany L, Brocke S, Calhoun V D, Pittman B, Corbera S, Wexler B E, Bell M D, Pelphrey K, Pearlson G D & Assaf M, Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification, NeuroImage Clin, 24 (2019) 101966.

  • Iandolo R, Samogin J, Barban F, Buccelli S, Taberna G, Semprini M, Mantini D & Chiappalone M, A pipeline integrating high-density EEG analysis and graph theory: a feasibility study on resting state functional connectivity, 9th Int IEEE/EMBS Conf Neural Eng (NER), (2019) 271–274.

  • Stam C J, Jones B F, Nolte G, Breakspear M & Scheltens P, Small-world networks and functional connectivity in Alzheimer’s disease, Cerebral Cortex, 17(1) (2007) 92-99.

  • Peters J M, Taquet M, Vega C, Jeste S S, Fernández I S, Tan J, Nelson C A, Sahin M & Warfield S K, Brain functional networks in syndromic and non-syndromic autism: a graph theoretical study of EEG connectivity, BMC Med, 11(1) (2013) 1–16.

  • Courchesne E, Pierce K, Schumann C M, Redcay E, Buckwalter J A, Kennedy D P & Morgan J, Mapping early brain development in autism, Neuron, 56(2) (2007) 399–413.

  • TsiarasV, Simos P G, Rezaie R, Sheth B R, Garyfallidis E, Castillo E M & Papanicolaou A C, Extracting biomarkers of autism from MEG resting-state functional connectivity networks, Comput in Biol and Med, 41(12) (2011) 1166–1177.

  • Saunders A, Kirk I J & Waldie K E, Hemispheric coherence in ASD with and without comorbid ADHD and anxiety, BioMed Res Int, (2016).

  • Collura T F, Guan J, Tarrant J, Bailey J & Starr F, EEG biofeedback case studies using live z-score training and a normative database, J of Neurother, 14(1) (2019) 22–46.

  • Lang E W, Tomé A M, Keck I R, Górriz-Sáez J M & Puntonet C G, Brain connectivity analysis: A short survey, Comput Intelligence and Neurosci, (2012).

  • Bullmore E T & Bassett D S, Brain Graphs: Graphical models of the human brain connectome, Annu Review of Clinical Psychology, 7 (2011) 113–140.

  • Murias M, Webb S J, Greenson J & Dawson G, Restingstate cortical connectivity reflected in EEG coherence in individuals with autism, Biol Psychiatry, 62(3) (2007) 270–273.

  • Decker S, Fillmore P T & Roberts A M, Coherence: The Measurement and Application of Brain Connectivity, NeuroRegulation, 4(1) (2017) 3–3.

  • Kasabov N K, NeuCube: A Spiking neural network architecture for mapping, learning, and understanding of spatio-temporal brain data, Neural Netw, 52 (2014) 62–76.

  • Nuntalid N, Evolving probabilistic spiking neural networks for modelling and pattern recognition of spatiotemporal data on the case study of Electroencephelography (EEG), Brain Data, (2012).

  • Kasabov N, Scott N M, Tu E, Marks S, Sengupta N, Capecci E, Othman M, Doborjeh M G, Murli N, Hartono R & Espinosa-Ramos J I, Evolvingspatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications, Neural Netw, 78 (2016) 1–14.


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  • Coherence Analysis in the Brain Network of ASD Children using Connectivity Model and Graph Theory

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Authors

Menaka R
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
Karthik R
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
Aaditya Parthasarathy
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
Manideep P
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India
Varsha V
Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600 127, India

Abstract


Autism Spectrum Disorder (ASD) belongs with the category of neuro-developmental disorders, which can be majorly categorized under decreased social relationships, communication and thought processes. Various studies in the field of biological networks prove that one of the defining features of ASD is altered brain connectivity. Hence, the understanding of the brain networks can pave the way to delve deeper into the underlying behaviour of the Autistic brains. Moreover, many studies also reveal that human brains exhibit small-world characteristics which are usually seen in simple model neural networks that emerge spontaneously upon adaptive rewiring according to the dynamical functional connectivity. Graph theory-based approaches are finding their way into the understanding of the altered connectivity in various neurological disorders. For that matter, the study focuses on implementing a graph theory-based approach to investigate on the small-world network of Autistic as well as typically developing brains and understand the behavioural changes for an Audio and Video Stimuli. The graphically generated data is then measured for functional connectivity using a symmetrical parameter known as the coherence measure.

Keywords


Autism spectrum disorder, Biological network, Coherence measure, Functional connectivity

References