Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-27T06:20:01.641Z Has data issue: false hasContentIssue false
  • English
  • Français

Neural Networks and Parkinson's Disease

Published online by Cambridge University Press:  18 September 2015

Donald S. Borrett ,
Tet H. Yeap  and
Hon C. Kwan
Donald S. Borrett*
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
Tet H. Yeap
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
Hon C. Kwan
Affiliation:
Division of Neurology, Department of Medicine, Toronto East General Hospital, Toronto (D.S.B.); Department of Electrical Engineering. University of Ottawa, Ottawa (T.H.Y.); and Department of Physiology, University of Toronto, Toronto (H.C.K.)
*
Department of Physiology, Medical Sciences Building, University of Toronto, Toronto, Ontario, Canada M5S 1A8
Rights & Permissions [Opens in a new window]

Abstract:

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A closed-loop or recurrent neural network was taught to generate output discharges to reproduce the prototypical activations in agonist and antagonist muscles which produce the displacement of a limb about a single joint. By introducing a generalized decrease in the excitability of the pre-output layer in the network, the network made the displacement more slowly and also showed an inability to maintain a repetitive movement. These concepts can be applied to the human nervous system in the understanding of the physical basis of movement and its disorders. It is suggested that a movement represents the output of a closed-loop network, such as the cortical-basal ganglia-thalamic-cortical motor loop, which iterates repetitively to its end point or attractor. The model provides an explanation of how the state of thalamic inhibition seen in Parkinson's disease physically may produce bradykinesia and the inability to maintain a repetitive movement.

Type
Research Article
Information
Canadian Journal of Neurological Sciences , Volume 20 , Issue 2 , May 1993 , pp. 107 - 113
Copyright
Copyright © Canadian Neurological Sciences Federation 1993

References

1.Mackey, MC and Glass, L.Oscillations and chaos in physiological control systems. Science 1977; 177: 287–289.CrossRefGoogle Scholar
2.Mackey, MC and Milton, JG.Dynamical diseases. Ann NY Acad Sci 1987; 504: 16–32.CrossRefGoogle ScholarPubMed
3.Milton, JG, Longtiw, A, Beuter, A, Mackey, MC and Glass, L.Complex dynamics and bifurcations in neurology. J Theor Biol 1989; 138: 129–147.CrossRefGoogle ScholarPubMed
4.Allen, Gl and Tsukahara, N.Cerebrocerebellar communication systems. Physiol Rev 1974; 54: 957.CrossRefGoogle ScholarPubMed
5.Marsden, CD.The mysterious motor function of the basal ganglia: The Robert Wartenberg Lecture. Neurology 1982; 32: S14–S39.CrossRefGoogle ScholarPubMed
6.Evans, NW, Illner, R and Kwan, HC.On information processing abilities of chaotic dynamical systems. J Statistical Physics 1992; 86: 549–561.CrossRefGoogle Scholar
7.Devaney, RL.An Introduction to Chaotic Dynamical Systems. Menlo Park, CA: Benjamin Cummings. 1986.Google Scholar
8.Hallett, M, Shahani, BT and Young, RR.EMG analysis of stereo-typed voluntary movement in man. J Neurol Neurosurg Psychiatry 1975; 38: 1154–1162.CrossRefGoogle Scholar
9.Hallett, M and Marsden, CD.Ballistic flexion movements of the human thumb. J Physiol London 1979; 294: 33–50.CrossRefGoogle ScholarPubMed
10.Hallett, M and Khoshbin, S.A physiological mechanism of bradykinesia. Brain 1980; 103: 301–314.CrossRefGoogle ScholarPubMed
11.Kwan, HC, Yeap, TH, Jiang, BC and Borrett, D.Neural network control of simple limb movements. Can J Physiol Pharmacol 1990; 68: 126–130.CrossRefGoogle ScholarPubMed
12.Borrett, DS, Yeap, TH and Kwan, HC.The nonlinear dynamics of connectionist networks: the basis of motor control. Behav Brain Sci 1992; 15: 712–714.Google Scholar
13.Bergman, H, Wichmann, T and DeLong, MR.Reversal of experimental parkinsonism by lesions of the subthalamic nucleus. Science 1990; 249: 1436–1438.CrossRefGoogle ScholarPubMed
14.Hopfield, JJ and Tank, DW. Computing with neural circuits: a model. Science 1986; 233: 625–633.CrossRefGoogle ScholarPubMed
15.Rumelhart, DE, Hinton, GE and Williams, RJ. Learning Internal Representations by Error Propagation. In: Rumelhart, DE and McClelland, JL. eds. Parallel Distributed Processing Volume I. Cambridge, Mass: The MIT Press 1986; 318–362.CrossRefGoogle Scholar
16.Hoehn, MM and Yahr, MD. Parkinsonism: onset, progression and mortality. Neurology 1967; 17:427–442.CrossRefGoogle ScholarPubMed
17.Murphy, JT. Wong, YC and Kwan, HC. Sequential activation of neurons in primate motor cortex during unrestrained forelimb movement. J Neurophysiol 1985: 53: 435–445.CrossRefGoogle ScholarPubMed
18.Lenz, FA, Kwan, HC. Dostrovsky, JO. et al.Single unit analysis of the human ventral thalamic nuclear group. Activity correlated with movement. Brain 1990; 113: 1795–1821.CrossRefGoogle ScholarPubMed
19.Reggia, JA and Sutton, GG. Self-processing networks and their biomedical implications. Proc IEEE 1988: 76: 680–692.CrossRefGoogle Scholar
20.Gordon, B.Confrontation naming: computational model and disconnection simulation. In: Arbib, M, Caplan, D and Marshall, J. eds. Neural Models of Language Process. New York. NY: Academic Press 1982; 511–530.CrossRefGoogle Scholar
21.Wong, R, Traub, R and Miles, R. Cellular basis of neuronal synchrony. Adv Neurol 1986; 44: S83–S92.Google ScholarPubMed
22.Lorenz, EN. Deterministic non-periodic flow. J Atmosph Sci 1963: 20: 130–141.2.0.CO;2>CrossRefGoogle Scholar
23.Jensen, RV.Classical chaos. American Scientist 1987: 75: 168–181.Google Scholar
24.Rapp, PE. Oscillations and chaos in cellular metabolism and physiological systems. In: Holden, AV. Chaos. Princeton: Princeton University Press 1986; 179–208.CrossRefGoogle Scholar
25.Goldman-Rakic, PS and Seleman, LD. New frontiers in basal ganglia research. TINS 1990: 13:241–244.Google ScholarPubMed
26.Alexander, GE and Crutcher, MD. Functional architecture of basal ganglia circuits: neural substrates of parallel processing. TINS 1990; 13: 266–271.Google ScholarPubMed
27.Alexander, GE, DeLong, MR and Crutcher, MD. Do cortical and basal ganglia motor areas use ‘motor programs’ to control movement? Behav Brain Sci 1992: 15: 656–665.Google Scholar
28.Gerfen, CR. The neostriatal mosaic: multiple levels of compartmental organization in the basal ganglia. Ann Rev Neurosci 1992: 15: 285–320.CrossRefGoogle ScholarPubMed
29.Mink, JW and Thach, WT. Basal ganglia motor control. II. Late pallidal timing relative to movement onset and inconsistent pallidal coding of movement parameters. J Neurophysiol 1991; 65: 301–329.Google ScholarPubMed
30.DeLong, MR and Georgopoulos, AP.Motor functions of the basal ganglia. In: Handbook of Physiology, Section 1. The Nervous System, Vol II. Bethesda. American Physiological Society 1981: 1017–1061.Google Scholar
31.DeLong, MR. Primate models of movement disorders of basal ganglia origin. TINS 1990; 13: 281–285.Google ScholarPubMed
32.Dick, JPR, Rothwell, JC, Day, BL.et al.The Bereitschlaflspotential is abnormal in Parkinson's disease. Brain 1989; 112: 233–244.CrossRefGoogle ScholarPubMed
33.Adler, LE.Pecevich, M and Nagamoto, H.Bereitschaftspotential in tardive dyskinesia. Movement Disorders 1989: 4: 105–112.CrossRefGoogle ScholarPubMed