Simple Cyclic Movements as a Distinct Autism Feature: Computational Approach

1. Department of Informatics, Nicolaus Copernicus University, Toruń Institute of Computer Science, Maria Curie-Skłodowska University, Lublin Simple Cyclic Movementsas a Distinct Autism Feature: Computational Approach Krzysztof Dobosz DariuszMikołajewski Grzegorz M. Wójcik WłodzisławDuch The 12th Cracow Grid Workshop, 22-24 October 2012

2. Simple Cyclic Movements as a Distinct Autism Feature Autism – Introduction Leo Kanner: „extreme aloneness fromthe beginning of life and anxiously obsessivedesire for the preservation of sameness” Leo Kanner: „Autistic disturbances of affective contact”,Nervous Child 1943, 2: 217–250 • Incidence of ASD: estimated to 6 per 1,000 • Depends on sex: 4 times more boys suffer from autism than girls • Different forms of autism: „typical” autism, regressive autism, Asperger syndrome, Rett syndrome, Pervasive Developmental Disorder – Not Otherwise Specified (PDD–NOS), etc. • Autism epidemics?

3. Simple Cyclic Movements as a Distinct Autism Feature Autism – Symptoms

4. Simple Cyclic Movements as a Distinct Autism Feature Autism – Diagnostic Features • There are at least several common diagnostic features typical for ASD: • preference to be alone and difficulty in mixing with other children • insistence on sameness, resists changes in routine • difficulty in expressing needs, gestures/pointing instead of words, not responsive to verbal cues (acts as deaf) • inappropriate attachment to objects, spins objects, sustained odd play • inappropriate laughing and giggling, may not want cuddling or act cuddly • noticeable physical overactivity or extremeunderactivity • tantrums – extreme distress for no apparent reason • apparent insensitivity to pain • little or no eye contact • echolalia

5. Simple Cyclic Movements as a Distinct Autism Feature Research Problem • Diverse etiology: genetic mutations, immunologic/metabolic system disorders, etc. • Many different theories: Minicolumnopathy, Mirror Neuron System, Underconnectivity Theory, Function Connectivity Theory, Empathizing-Systemizing Theory, etc. • We are „data rich and theory poor” – lack of complete theory with satisfactory predictive power • Lack of good computational models joining different levels of analysis:

6. Simple Cyclic Movements as a Distinct Autism Feature Emergent Simulator • developed since 1995 (previous version known as PDP++) • open source, modular, object-oriented, based on C++ • cross-platform: MS Windows, Mac OS X, Unix/Linux (GPL) • some built-in visualization methods (cluster plots, PCA, SVD, MDS) • point neurons based on Hodgkin-Huxley model • 3 types of ion channels: K+, Na+, Cl- • accommodation mechanism controlling neural fatigue (K+) • different types of noise: synaptic, membrane, etc. • dedicated LEABRA algorithm (Local, Error-driven and Associative, Biologically Realistic Algorithm) – combining Hebbian learning and error-driven learning

7. Simple Cyclic Movements as a Distinct Autism Feature GENESIS / NESSIE Enviroment • GENESIS – GEneralNEuralSImulation System • developed since 1988 at California Institute of Technology (J. M. Bower) • compartmental neurons and Hodgkin-Huxley model • NESSIE – NEuroinformatic System for Science, Industry and Education • developed at Institute of Computer Science, Maria Curie-Skłodowska University, Lublin • provides easy-access to simulations in GENESIS software

8. Simple Cyclic Movements as a Distinct Autism Feature Model of Simple Cyclic Movements • Input layer – reflecting movement planning processes • Output layer – reflecting processes within motor cortex areas: activation of groups of neurons in this layer depends on patterns presented in the Input layer • Decussating of pyramids was built into the model • Adjacent patterns in the Output layer are overlapping

9. Simple Cyclic Movements as a Distinct Autism Feature Input & Output Patterns Different body parts are represented by partially overlapping areas in the primary motor cortex

10. Simple Cyclic Movements as a Distinct Autism Feature Emergent Model Results • For each input pattern several sequences of neural activations occurring cyclically in the Output layer (motor cortex) • Similar behavior like repetitive and stereotyped movements (RSM) typical for patients with ASD (especially young children) • Lack of neural noise leads to high repeatability of simulations • Appropriate level of noise resulted in changes in neurodynamics of the motor cortex layer preventing sequences of activation to follow cyclically and activating different areas of the layer • This corresponds to normal brain dynamics

11. Simple Cyclic Movements as a Distinct Autism Feature GENESIS Implementation • Moving from point neurons to full compartmental Hodgkin-Huxley cells will let observe the influence of other substantial parameters on the dynamics of the network • Parameters such as the capacitance of soma, its time constant and the value of potentials responsible for the ionic currents must also play an important role in signal transmission • Good simulation of Hodgkin-Huxley neurons requires high computational power – simulation of 1 ms of biological activity of a HH cell using first order Euler’s method with constant time interval of 1 ms requires 1200 floating point operations • Such simulation most effectively can be conducted in cluster-based environment (for large networks grid architecture may be necessary)

12. Simple Cyclic Movements as a Distinct Autism Feature Cluster Architecture • Consists of eight double-processor Xenon Quad Core machines • Detailed cluster configuration: • 4 Intel Xeon Quad-Core E5320 1,86 GHz processors • 12 Intel Xeon Quad-Core E5405 2 GHz processors • each node of the cluster offers 8 GB of RAM • 7 TB of hard disk in total • Simulation of 1 second of biological activity of the system takes several seconds of the production run of the cluster (still far from real-time simulations)

13. Simple Cyclic Movements as a Distinct Autism Feature Future Work Investigate the influence of different parameters on the model behavior including cell dimensions, ion channels conductance, neural fatigue mechanism, strength of connections, number of units, etc. More detailed analysis of attractor dynamics using visualization methods such as Recurrence Plots, Fuzzy Symbolic Dynamics, etc. Joining results of computational modeling on functional segregation and integration with genetic data to provide insight into possible genetic basis of autism Moving the model to the parallel GENESIS environment and with the increased number of neurons and connections prepare it to production runs in grid-based structures

14. Contact: kdobosz@mat.umk.pl Thank you for your attention