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Cognitive Neurodynamics logoLink to Cognitive Neurodynamics
. 2024 Mar 2;18(5):2243–2261. doi: 10.1007/s11571-024-10071-7

Multiple time delay induced Hopf bifurcation of a cortex - basal ganglia model for Parkinson’s Disease

Qiaohu Zhang 1,#, Quansheng Liu 1,, Yuanhong Bi 2,3,#
PMCID: PMC11564637  PMID: 39555276

Abstract

Exploring the origin of beta - band oscillation in the cortex - basal ganglia model plays an important role in understanding the mechanism of Parkinson’s disease. In this paper, we investigate the effect of three synaptic transmission time delays in the subthalamic nucleus(STN) - the globus pallidus external segment(GPe) loop, the excitatory neurons in the cortex(EXN) - the inhibitory neurons in the cortex(INN) loop and EXN - STN loop on critical conditions of occurrence of beta - band oscillation through Hopf bifurcation theory including linear stability analysis, center manifold theorem and normal form analysis. Our results reveal that suitable transmission time delay can induce beta - band oscillation through Hopf bifurcation, and the critical condition under which Hopf bifurcation occurs is more sensitive to the transmission time delay in EXN - STN loop T3, where if T3>0.00185, beta - band oscillation always occurs for any transmission time delay in STN - GPe, EXN - INN loops. Our theoretical analyses provide some ideas for the future research of Parkinson’s disease.

Keywords: Hopf Bifurcation, Parkinson’s disease, Time delay, Basal ganglia, Cortex

Introduction

Parkinson’s disease(PD) is the second most common neurodegenerative disorder with motor dysfunction including rest tremor, motor retardation and nonmotor impairment such as mood and sleep disorders, cognitive decline and incontinence, which seriously affect the patients’s normal life and put great burdens on families and society (Parkinson 2002; Aarsland et al. 2021). These symptoms of PD are related to the decrease in the dopamine level in striatum, which caused by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) in midbrain (Jankovic 2008). The lack of dopamine (DA) in the SNc induce the excessive beta - band (13 Hz - 35 Hz) oscillation activity in the cortex and basal ganglia of Parkinson’s patients with severe motor dysfunction symptoms (Leventhal et al. 2012; McGregor and Nelson 2019), which can be alleviated by suppressing these oscillations via drugs and surgical treatments (Holt and Netoff 2014). Therefore, analyzing the origin of beta - band oscillation in PD plays an important role in understanding PD pathogenesis and has attracted more attention of theoretical researchers, who carry out dynamical analyses on the mathematical models.

A number of mathematical models are established to explore the conditions for generating beta - band oscillation in PD (Hu et al. (2022); Muddapu and Chakravarthy (2020); Shouno et al. (2017); Muddapu et al. (2019)). The excitatory - inhibitory loop between the subthalamic nucleus(STN) and external segment of the globus pallidus(GPe) is a core one in these models, in which STN consisting of the glutamatergic neurons sends excitatory projection to GPe while GPe composing the gammaaminobutyric acid neurons exerts inhibitory projections to STN (Holgado et al. 2010; Pavlides et al. 2012). The mean - field models consisting of STN - GPe networks are developed to obtain the conditions of beta - band oscillation (Holgado et al. 2010). In addition to STN and GPe, the cortex neurons projecting to STN play an important role in regulating beta - band oscillation according to experiments results (Bevan et al. 2006). A cortex - basal ganglia model with excitatory and inhibitory neurons in the cortex is proposed by Pavlides et al. to verify the beta - band oscillation in the experimental observation in Ref. Tachibana et al. (2011). These models have been put forward to obtain the boundary conditions of the beta - band oscillation through analyzing the conditions under which Hopf bifurcation occurs in these models.

Analyzing the conditions under which Hopf bifurcation occurs in cortex - basal ganglia models play an important role in understanding the mechanism of beta - band oscillation in PD. Hopf bifurcation may make a stable steady state lose stability and induce the appearance of a stable limit cycle corresponding to oscillation state. In the aspect of numerical simulations, Ref. Nevado-Holgado et al. (2011) obtains the conditions for the onset of Hopf bifurcation when the connection weights in STN - GPe are linearly increased from the healthy to the parkinsonian parameters and the effect of the connection weights, synaptic transmission time delay and time constant on Hopf bifurcation are explored in GPe - Gpe model in Ref. Chen et al. (2023). On the theoretical side, the conditions for the occurrence of Hopf bifurcation are analyzed with identical synaptic transmission time delays in a cortex - basal ganglia network through normal form theory and central manifold theorem (Chen et al. 2020) . Besides, critical conditions of Hopf bifurcation induced by two heterogeneous synaptic time delays in EXN - INN and STN - GPe loops are derived in an improved cortex - basal ganglia network with additional self - feedback projections in the EXN and INN (Wang et al. 2022). Also, the critical stability boundaries separating stable and oscillatory neural firing are obtained in terms of connection weights and two synaptic transmission time delays in a STN - GP(globus pallidus) network simplifying from a STN - GP one with three different synaptic time delays through time - shift transformation (Rahman et al. 2018). The synaptic transmission is a biological process in which a neuron sends projection to a target neuron across synapse with different time delays between different neurons (Rahman et al. 2018). The effect of different time delays between different neuronal populations in cortex - basal ganglia on the mechanism of beta - band oscillation are worth further exploration through analyzing the conditions for generating Hopf bifurcation.

Motivated by the above idea, in this paper, we explore the effect of three synaptic transmission time delays in the connections between GPe and STN(T1), between EXN and INN(T2) and between EXN in the cortex and basal ganglia(T3) on Hopf bifurcation for four cases. In each case, the conditions for the beta - band oscillations in the model are obtained using stability theory through analyzing the characteristic equation of the linearized system. The direction of Hopf bifurcation is judged through the center manifold theorem and normal form method. These theoretical results are further confirmed by numerical simulations. Our results reveal the important role of multiple time delays on beta - band oscillation and enhance the understanding of beta-band oscillation dynamics. This research may help us provide some clue for the treatment of PD.

This paper is organized as follows. The cortex - basal ganglia model is given in Sect. “The model”. Sect.Hopf bifurcation induced by multiple time    delay through theoretical analyses” presents the sufficient conditions for the existence of Hopf bifurcation in four cases. Then numerical simulations verify these theoretical results in Sect. “Hopf bifurcation induced by multiple time delay through numerical simulations”. Finally, we conclude the paper in Sect. “Conclusion”.

The model

Figure 1 shows the schematic framework of the cortex - basal ganglia model in Ref. Pavlides et al. (2015); Wang et al. (2023) with EXN and INN in the cortex and STN and GPe in basal ganglia. The excitatory and inhibitory projections between these neurons are represented by the black lines ended with the arrow and the bar, respectively. EXN exerts excitatory projections to the INN and STN, while INN and GPe exert inhibitory projections to EXN and STN, respectively. STN projects a direct excitatory connection to GPe and an indirect inhibitory connection to EXN via thalamus (Wang et al. 2023). C denotes a constant excitatory input to the cortex and Str represents inhibitory projection from the striatum. ωEI, ωIE, ωCS, ωSC, ωGS, ωSG are connection weights between these neuron populations and TEI, TIE, TCS, TSC, TGS, TSG are synaptic transmission time delays.

Fig. 1.

Fig. 1

The improved cortex - basal ganglia model

The mathematical model describing the dynamics of the firing rate of STN, GPe, EXN, INN are given as follows,

τSdS(t)dt=FS(-ωGSG(t-TGS)+ωCSE(t-TCS))-S(t),τGdG(t)dt=FG(ωSGS(t-TSG)-Str)-G(t),τEdE(t)dt=FE(-ωSCS(t-TSC)-ωIEI(t-TIE)+C)-E(t),τIdI(t)dt=FI(ωEIE(t-TEI))-I(t). 1

where S(t), G(t), E(t), I(t) are the firing rates of STN, GPe, EXI, INN. τX(X=S,G,E,I) represent the membrane time constants for neuron population X. ωXY and TXY represent the connection weights and transmission time delays from neuron populations X to neuron populations Y, respectively. C represents the constant excitatory input to cortical excitatory neurons and Str denotes the constant inhibitory input to GPe from striatum. The activation function FX(·)(X=S,G,E,I) characterizing the effect of synaptic input on firing rate of the neuron populations X are given as follows,

FX(in)=MX1+(MX-BXBX)exp(-4inMX),(X=S,G,E,I) 2

Where MX represents the maximum firing rate of the neuron populations X and BX represents the firing rate of the neuron populations X without external input.

The value of all parameters in this paper are shown in Table 1, which are adapted from Ref. Chen et al. (2020); Yan and Wang (2017); Wang et al. (2023, 2022). Here, τX(X=S,G,E,I) are the same for simplicity.

Table 1.

Parameter values used in the paper

Parameter Value Parameter Value
τS 10 ms MS 300 spk/s
τG 10 ms BS 17 spk/s
τE 10 ms MG 400 spk/s
τI 10 ms BG 75 spk/s
ωGS 3.22 ME 71.77 spk/s
ωCS 6.6 BE 3.62 spk/s
ωSG 2.56 MI 277.39 spk/s
ωEI 1.56 BI 9.87 spk/s
ωIE 1.56 Str 40.51 spk/s
ωSC 4 C 172.18 spk/s

Based on the fact that the time delays TEI, TIE, TCS, TSC, TGS, TSG play crucial roles in generating beta - band oscillations in the cortex - basal ganglia circuit, we will explore the effect of these transmission time delays on beta - band oscillations through Hopf bifurcation theory in the following section.

Hopf bifurcation induced by multiple time delay through theoretical analyses

In this section, the critical condition under which Hopf bifurcation occurs in the cortex - basal ganglia model (1) with multiple time delay are analyzed through quasilinearization method, center manifold and normal form (Wang et al. 2022; Eugeni et al. 2018). Here, the cortex - basal ganglia model includes three excitatory - inhibitory loop STN - EXN, STN - GPE and EXN - INN, transmission time delays between these neurons in each loop are assumed to be the same TGS=TSG=T1, TEI=TIE=T2, TCS=TSC=T3 for facilitating mathematical analysis. Then the model (1) becomes the following form,

τdS(t)dt=FS(-ωGSG(t-T1)+ωCSE(t-T3))-S(t),τdG(t)dt=FG(ωSGS(t-T1)-Str)-G(t),τdE(t)dt=FE(-ωSCS(t-T3)-ωIEI(t-T2)+C)-E(t),τdI(t)dt=FI(ωEIE(t-T2))-I(t). 3

Let u0=(S,G,E,I) be the equilibrium of the system (3) and expand the system (3) at the equilibrium u0 as follows,

du(t)dt=C1u(t)+C2u(t-T1)+C3u(t-T2)+C4u(t-T3)+f(u(t-T1),u(t-T2),u(t-T3)), 4

where u(t)=(S(t)-S,G(t)-G,E(t)-E,I(t)-I),

C1=-1τ0000-1τ0000-1τ0000-1τ,C2=0a1200a2100000000000,C3=00000000000a3400a430,C4=00a1300000a310000000, 5

with

a12=4ωGSS2τMS2-4ωGSSτMS,a13=-4ωCSS2τMS2+4ωCSSτMS,a21=-4ωSGG2τMG2+4ωSGGτMG,a31=4ωSCE2τME2-4ωSCEτME,a34=4ωIEE2τME2-4ωIEEτME,a43=-4ωEII2τMI2+4ωEIIτMI, 6

where f(u(t-T1),u(t-T2),u(t-T3)) is a higher order term.

The characteristic equation of the Eq. (4) is given as follows,

Δ(λ,T1,T2,T3)=λ+1τ-a12e-λT1-a13e-λT30-a21e-λT1λ+1τ00-a31e-λT30λ+1τ-a34e-λT200-a43e-λT2λ+1τ=(λ+1τ)4-(λ+1τ)2(a12a21e-2λT1+a34a43e-2λT2+a13a31e-2λT3)+a12a21a34a43e-2λT1e-2λT2=0. 7

Here, Lemma1 used in the following analyses is given as follows,

Lemma 1

Ruan and Wei (2003) For the follow exponential polynomial:

P(λ,e-λT1,...,e-λTm)=λn+k10λn-1+...+kn-10λ+kn0+[k11λn-1+...+kn-11λ+kn1]e-λT1+...+[k1mλn-1+...+kn-1mλ+knm]e-λTm

where Tj0(j=1,2,...,m) and kij(i=1,2,...,n;j=1,2,...,m) are constants. As (T1,T2,...,Tm) change, the sum of the orders of the zeros of P(λ,e-λT1,...,e-λTm) in the open right half plane can change only if a zero appears on or crosses the imaginary axis.

In order to explore the effect of transmission time delays T1, T2 and T3 on beta - band oscillations, we investigate the local stability of positive equilibrium u0=(S,G,E,I) of the system (3) and the existence of Hopf bifurcation for four case (1) T1=T2=T3=0 (2) T1=T2=0, T3>0 (3) T2=0, T1>0, T3>0 (4) T1>0, T3>0,T2>0.

Case 1 T1=T2=T3=0.

For T1=T2=T3=0, the characteristic equation (7) becomes

Δ(λ,0,0,0)=(λ+1τ)4-(λ+1τ)2(a12a21+a34a43+a13a31)+a12a21a34a43=λ4+b1λ3+b2λ2+b3λ+b4=0, 8

where

b1=4τ,b2=6τ2-(a12a21+a34a43+a13a31),b3=4τ3-2τ(a12a21+a34a43+a13a31),b4=1τ4-1τ2(a12a21+a34a43+a13a31)+a12a21a34a43. 9

According to Routh - Hurwitz Lemma (DeJesus and Kaufman (1987)), the conditions of local stability of u0=(S,G,E,I) are given in Theorem 1.

Theorem 1

All roots of Eq. (7) have negative real parts and u0=(S,G,E,I) is locally asymptotically stable if and only if the following conditions (H1) is satisfied. (H1):

bi>0(i=1,...,4),b1b2>b3,b1b2b3>b12b4+b32. 10

Case 2 T1=T2=0,T3>0

For T1=T2=0,T3>0, the characteristic equation (7) becomes

Δ(λ,0,0,T3)=(λ+1τ)4-(λ+1τ)2(a12a21+a34a43+a13a31e-2λT3)+a12a21a34a43=0. 11

Suppose that λ=iω(ω>0) is a root of Eq. (11), so

(iω+1τ)4-(iω+1τ)2(a12a21+a34a43+a13a31e-2iωT3)+a12a21a34a43=(1τ4+ω4-6ω2τ2)+i(4ωτ3-4ω3τ)-(1τ2-ω2+i2ωτ)[a12a21+a34a43+a13a31(cos(2ωT3)-isin(2ωT3))]+a12a21a34a43=0. 12

Separating the real part and the imaginary part of Eq. (12), we get

(1τ4+ω4-6ω2τ2)-(1τ2-ω2)(a12a21+a34a43+a13a31cos(2ωT3))-a13a312ωτsin(2ωT3)+a12a21a34a43=0,4ωτ3-4ω3τ-2ωτ(a12a21+a34a43+a13a31cos(2ωT3))+(1τ2-ω2)a13a31sin(2ωT3)=0. 13

Then, we obtain

sin(2ωT3)=c1ω5+c2ω3+c3ωc4ω4+c5ω2+c6,cos(2ωT3)=c7ω6+c8ω4+c9ω2+c10c4ω4+c5ω2+c6, 14

where

c1=2a13a31τ,c2=4a13a31τ3,c3=2a13a31τ5-2a12a21a13a31a34a43τ,c4=-a132a312,c5=-2a132a312τ2,c6=-a132a312τ4,c7=a13a31,c8=4a13a31τ2+a13a31(a12a21+a34a43),c9=2a13a31(a12a21+a34a43)τ2-a13a31τ4+a12a21a13a31a34a43,c10=a13a31(a12a21+a34a43)τ4-a13a31τ6-a12a21a13a31a34a43τ2. 15

According to Eq. (14) and sin2(2ωT3)+cos2(2ωT3)=1, we have

d1ω12+d2ω10+d3ω8+d4ω6+d5ω4+d6ω2+d7=0, 16

where

d1=c72,d2=c12+2c7c8,d3=2c1c2+2c7c9+c82-c42,d4=2c1c3+c22+2c7c10+2c8c9-2c4c5,d5=2c2c3+2c8c9+c92-2c4c6-c52,d6=c32+2c9c10-2c5c6,d7=c102-c62. 17

Let z=ω2, then Eq. (16) becomes

d1z6+d2z5+d3z4+d4z3+d5z2+d6z+d7=0. 18

Here, we put forward the assumption

(H2): Eq. (18) has at least one positive root.

Without loss of generality, we assume that Eq. (18) has six positive root zk(k=1,...,N,N6). Then the roots of Eq. (16) can be expressed as ωk=zk. According to Eq. (14), we have

T3kn=12ωkarccosc7ωk6+c8ωk4+c9ωk2+c10c4ωk4+c5ωk2+c6+nπωk,(k=1,...,N;n=0,1,...). 19

Let T30=mink=1,...,N{T3kn} be the critical transmission time delay corresponding to pure imaginary root ±iω0(3).

Next, we will prove the transversality condition with respect to time delay T3.

Differentiating the two sides of Eq. (11) about time delay T3, we have

(dλdT3)-1=-4(λ+1τ)3-2(λ+1τ)(a12a21+a34a43+a13a31e-2λT3)+2a13a31T3(λ+1τ)2e-2λT32a13a31λ(λ+1τ)2e-2λT3=-P1Q1. 20

Therefore, Re{(dλdT3)λ=iω0(3)-1}=-P1RQ1R+P1IQ1IQ1R2+Q1I2 , where P1R(Q1R) and P1I(Q1I) represent the real and imaginary parts of P1(Q1), respectively.

P1R=4(1τ3-3(ω0(3))2τ)-2[a12a21+a34a43+a13a31cos(2ω0(3)T3)τ+a13a31ω0(3)sin(2ω0(3)T3)]+2a13a31T3[(1τ2-(ω0(3))2)cos(2ω0(3)T3)+2ω0(3)τsin(2ω0(3)T3)],P1I=4(3ω0(3)τ2-(ω0(3))3)-2[ω0(3)(a12a21+a34a43+a13a31cos(2ω0(3)T3))-a13a31sin(2ω0(3)T3)τ]+2a13a31T3[2ω0(3)τcos(2ω0(3)T3)+((ω0(3))2-1τ2)sin(2ω0(3)T3)],Q1R=2a13a31ω0(3)[(1τ2-(ω0(3))2)sin(2ω0(3)T3)-2ω0(3)τcos(2ω0(3)T3)],Q1I=2a13a31ω0(3)[(1τ2-(ω0(3))2)cos(2ω0(3)T3)+22ω0(3)τsin(2ω0(3)T3)]. 21

If the condition (H3): P1RQ1R+P1IQ1I0 holds, Re{(dλdT3)λ=iω0(3)-1}0 , so the transversality condition of Hopf bifurcation is satisfied.

Based on the above analyses, we get the following Theorem.

Theorem 2

For T30 as the minimum value that makes the characteristic equation (11) has pure imaginary roots, if the conditions (H1) - (H3) are true, combining with Lemma 1, we can draw the following conclusions,

  1. For T1=T2=0, all roots of the characteristic equation (11) have negative real parts for any T3(0,T30) , the system (3) is asymptotically stable at the equilibrium u0 for any T3(0,T30) and is unstable for T3>T30.

  2. The system (3) experiences Hopf bifurcation at the equilibrium u0 when T3 crosses the critical value T30.

Case 3 T2=0,T3(0,T30),T1>0

For T2=0,T3(0,T30),T1>0, the characteristic equation (7) becomes

Δ(λ,T1,0,T3)=(λ+1τ)4-(λ+1τ)2(a12a21e-2λT1+a34a43+a13a31e-2λT3)+a12a21a34a43e-2λT1=0. 22

We suppose that λ=iω(ω>0) is a root of Eq. (22),

(iω+1τ)4-(iω+1τ)2(a12a21e-2iωT1+a34a43+a13a31e-2iωT3)+a12a21a34a43e-2iωT1=(iω+1τ)4+[a12a21a34a43-a12a21(iω+1τ)2]e-2iωT1-(iω+1τ)2(a34a43+a13a31e-2iωT3)=[(1τ4+ω4-6ω2τ2)+i(4ωτ3-4ω3τ)]+[(a12a21a34a43+a12a21(ω2-1τ2))-i2a12a21ωτ](cos(2ωT1)-isin(2ωT1))+[(ω2-1τ2)-i2ωτ][(a34a43+a13a31cos(2ωT3))-ia13a31sin(2ωT3]=0. 23

Separating the real part and the imaginary part of Eq.(23), we can get

(1τ4+ω4-6ω2τ2)+[a12a21a34a43+a12a21(ω2-1τ2)]cos(2ωT1)-2a12a21ωτsin(2ωT1)+(ω2-1τ2)(a34a43+a13a31cos(2ωT3))-2a13a31ωτsin(2ωT3)=0,4ωτ3-4ω3τ-[a12a21a34a43+a12a21(ω2-1τ2)]sin(2ωT1)-2a12a21ωτcos(2ωT1)+(1τ2-ω2)a13a31sin(2ωT3)-2ωτ(a34a43+a13a31cos(2ωT3))=0. 24

Then, we have

sin(2ωT1)=-e1ω5+e2ω4+e3ω3+e4ω2+e5ω+e6e7ω4+e8ω2+e9,cos(2ωT1)=e10ω6+e11ω4+e12ω2+e13ω+e14e7ω4+e8ω2+e9. 25

where

e1=2a12a21τ,e2=a12a21a13a31sin(2ωT3),e3=4a12a21τ(a34a43+1τ2),e4=2a12a21a13a31sin(2ωT3)τ2+a12a21a13a31a34a43sin(2ωT3),e5=2a12a21τ5-2a12a21a34a43τ(2τ2-a34a43-a13a31cos(2ωT3)),e6=(1τ2-a34a43)a12a21a13a31sin(2ωT3)τ2,e7=(a12a21)2,e8=2(a12a21)2(1τ2+a34a43),e9=(a12a21)2(a34a43-1τ2)2,e10=-a12a21,e11=a12a21(1τ2+2a34a43+a13a31cos(2ωT3)),e12=a12a21τ2(1τ2-2a34a43-2a13a31cos(2ωT3))-a12a21a34a43(a34a43+a13a31cos(2ωT3)-6τ2),e13=2a12a21a13a31a34a43sin(2ωT3)τ,e14=a12a21(1τ2-a34a43)[1τ4-1τ2(a34a43+a13a31cos(2ωT3))]. 26

According to Eq.(25) and sin2(2ωT1)+cos2(2ωT1)=1, we have

f1ω12+f2ω10+f3ω9+f4ω8+f5ω7+f6ω6+f7ω5+f8ω4+f9ω3+f10ω2+f11ω+f12=0, 27

where

f1=e102,f2=e12+2e10e11,f3=2e1e2,f4=2e1e3+e22+2e10e12+e112-e72,f5=2e1e4+2e2e3+2e10e13,f6=2e1e5+2e2e4+e32+2e10e14+2e11e12-2e7e8,f7=2e1e6+2e2e5+2e3e4+2e11e13,f8=2e2e6+2e3e5+e42+2e11e14+e122-2e7e9-e82,f9=2e3e6+2e4e5+2e12e13,f10=2e4e6+e52+2e12e14+e132-2e8e9,f11=2e5e6+2e13e14,f12=e62+e142-e92. 28

Firstly, we give the assumption

(H4): Eq. (27) has at most twelve positive roots ωk(k=1,...,N,N12).

According to Eq.(25), we have

T1kn=12ωkarccose10ωk6+e11ωk4+e12ωk2+e13ωk+e14e7ωk4+e8ωk2+e9+nπωk,(k=1,...,N;n=0,1,...) 29

Let T10=mink=1,...,N{T1kn} is the critical value of T1 corresponding to pure imaginary root of Eq.(22) ±iω0(1).

Next, we prove the transversal condition about time delay T1. Differentiating the two sides of Eq.(22) about time delay T1, we have

dλdT1-1=P2Q2, 30

where

P2=2(λ+1τ)3-(λ+1τ)(a12a21e-2λT1+a34a43+a13a31e-2λT3)+(λ+1τ)2(a12a21T1e-2λT1+a13a31T3e-2λT3),Q2=a12a21a34a43λe-2λT1-a12a21λ(λ+1τ)2e-2λT1. 31

Therefore, Re{(dλdT1)λ=iω0(1)-1}=P2RQ2R+P2IQ2IQ2R2+Q2I2,

where P2R(Q2R) and P2I(Q2I) represent the real and imaginary parts of P2(Q2), respectively.

P2R=2(1τ3-3(ω0(1))2τ)-[a12a21cos(2ω0(1)T1)+a13a31cos(2ω0(1)T3)+a34a43τ+(a12a21sin(2ω0(1)T1)+a13a31sin(2ω0(1)T3))ω0(1)]+(1τ2-(ω0(1))2)(a12a21T1cos(2ω0(1)T1)+a13a31T3cos(2ω0(1)T3))+2ω0(1)τ(a12a21T1sin(2ω0(1)T1)+a13a31T3sin(2ω0(1)T3)),P2I=2(3ω0(1)τ2-(ω0(1))3)-[(a12a21cos(2ω0(1)T1)+a13a31cos(2ω0(1)T3)+a34a43)ω0(1)-a12a21sin(2ω0(1)T1)+a13a31sin(2ω0(1)T3)τ]+2ω0(1)τ(a12a21T1cos(2ω0(1)T1)+a13a31T3cos(2ω0(1)T3))-(1τ2-(ω0(1))2)(a12a21T1sin(2ω0(1)T1)+a13a31T3sin(2ω0(1)T3)),Q2R=a12a21a34a43ω0(1)sin(2ω0(1)T1)-a12a21ω0(1)[(1τ2-(ω0(1))2)sin(2ω0(1)T1)-2ω0(1)τcos(2ω0(1)T1)],Q2I=a12a21a34a43ω0(1)cos(2ω0(1)T1)-a12a21ω0(1)[(1τ2-(ω0(1))2)cos(2ω0(1)T1)+2ω0(1)τsin(2ω0(1)T1)]. 32

Then, we give the hypothesis

(H5): P2RQ2R+P2IQ2I0.

Therefore, if the condition (H5) holds, Re{(dλdT1)λ=iω0(1)-1}0 is true, then the transversal condition of Hopf bifurcation is satisfied.

From the above analyses, we get the following Theorem.

Theorem 3

For T10 as the minimum value that makes the characteristic equation (22) has pure imaginary roots, if the conditions H1,H4-H5 are true, combining with Lemma 1, we can draw the following conclusions,

  1. For T2=0,T3(0,T30), all the roots of the characteristic equation (22) have negative real parts for T1(0,T10), the system (3) is asymptotically stable at the equilibrium point u0 for T1(0,T10) and is unstable for T1>T10.

  2. The system (3) experiences Hopf bifurcation at the equilibrium u0 when T1 crosses the critical value T10.

Case 4 T1(0,T10),T3(0,T30),T2>0.

For T1(0,T10),T3(0,T30),T2>0, the characteristic equation (7) becomes

Δ(λ,T1,T2,T3)=(λ+1τ)4-(λ+1τ)2(a12a21e-2λT1+a34a43e-2λT2+a13a31e-2λT3)+a12a21a34a43e-2λT1e-2λT2=0. 33

We suppose that λ=iω(ω>0) is a root of Eq. (33), then

(iω+1τ)4-(iω+1τ)2(a12a21e-2iωT1+a34a43e-2iωT2+a13a31e-2iωT3)+a12a21a34a43e-2iωT1e-2iωT2=(iω+1τ)4+[a12a21a34a43e-2iωT1-a34a43(iω+1τ)2]e-2iωT2-(iω+1τ)2(a12a21e-2iωT1+a13a31e-2iωT3)=[(1τ4+ω4-6ω2τ2)+i(4ωτ3-4ω3τ)]+[a12a21a34a43cos(2ωT1)+a34a43(ω2-1τ2))-i(a12a21a34a43sin(2ωT1)+2a34a43ωτ)](cos(2ωT2)-isin(2ωT2))-(1τ2-ω2+i2ωτ)[(a12a21cos(2ωT1)+a13a31cos(2ωT3)-i(a12a21sin(2ωT1)+a13a31sin(2ωT3))]=0. 34

Separating the real part and the imaginary part of Eq.(34), we can get

(1τ4+ω4-6ω2τ2)+[a12a21a34a43cos(2ωT1)+a34a43(ω2-1τ2)]cos(2ωT2)+(ω2-1τ2)[a12a21cos(2ωT1)-a12a21a34a43sin(2ωT1)+2a34a43ωτsin(2ωT2)+a13a31cos(2ωT3)]-2ωτ[a12a21sin(2ωT1)+a13a31sin(2ωT3)]=0,4ωτ3-4ω3τ-[a12a21a34a43cos(2ωT1)+a34a43(ω2-1τ2)]sin(2ωT2)-[a12a21a34a43sin(2ωT1)+2a34a43τ]cos(2ωT2)+(1τ2-ω2)[a12a21sin(2ωT1)+a13a31sin(2ωT3)]-2ωτ[a12a21cos(2ωT1)+a13a31cos(2ωT3)]=0. 35

Simplifying Eq.(35), we get

(k1ω+k2)sin(2ωT2)+(k3ω2+k4)cos(2ωT2)=ω4+k5ω2+k6ω+k7,(k8ω2+k9)sin(2ωT2)+(k10ω2+k11)cos(2ωT2)=k12ω3+k13ω2+k14ω+k15, 36

where

k1=2a34a43τ,k2=a12a21a34a43sin(2ωT1),k3=-a34a43,k4=a34a43τ2-a12a21a34a43cos(2ωT1),k5=a12a21cos(2ωT1)+a13a31cos(2ωT3)-6τ2,k6=-2τ(a12a21sin(2ωT1)+a13a31sin(2ωT3)),k7=1τ4-1τ2(a12a21cos(2ωT1)+a13a31cos(2ωT3)),k8=a34a43,k9=a12a21a34a43cos(2ωT1)-a34a43τ2,k10=2a34a43τ,k11=a12a21a34a43sin(2ωT1),k12=-4τ,k13=-(a12a21sin(2ωT1)+a13a31sin(2ωT3)),k14=2τ(2τ2-a12a21cos(2ωT1)-a13a31cos(2ωT3)),k15=1τ2(a12a21sin(2ωT1)+a13a31sin(2ωT3)), 37

Then, we have

sin(2ωT2)=g1ω5+g2ω4+g3ω3+g4ω2+g5ω+g6g7ω4+g8ω2+g9ω+g10,cos(2ωT2)=g11ω6+g12ω4+g13ω3+g14ω2+g15ω+g16g7ω4+g8ω2+g9ω+g10, 38

where

g1=k10-k3k12,g2=k11-k3k13,g3=k5k10-k3k14-k4k12,g4=k6k10+k5k11-k3k15-k4k13,g5=k7k10+k6k11-k4k14,g6=k2k11-k4k9,g7=-k3k8,g8=k1k10-k3k9-k4k8,g9=k1k11+k2k10,g10=k2k11-k4k9,g11=-k8,g12=k1k12-k5k8-k9,g13=k1k13+k2k12-k6k8,g14=k1k14+k2k13-k7k8-k5k9,g15=k1k15+k2k14-k6k9,g16=k2k15-k7k9, 39

According to Eq.(38) and sin2(2ωT2)+cos2(2ωT2)=1, we have

h1ω12+h2ω10+h3ω9+h4ω8+h5ω7+h6ω6+h7ω5+h8ω4+h9ω3+h10ω2+h11ω+h12=0, 40

where

h1=g112,h2=g12+2g11g12,h3=2g1g2+2g11g13,h4=2g1g3+g22+2g11g14+g122-g72,h5=2g1g4+2g2g3+2g11g15+2g12g13,h6=2g1g5+2g2g4+g32+2g11g16+2g12g14+g132,h7=2g1g6+2g2g5+2g3g4+2g12g15+2g13g14-2g7g9,h8=2g2g6+2g3g5+g42+2g12g16+2g13g15+g142-2g7g10-g82,h9=2g3g6+2g4g5+2g13g16+2g14g15-2g8g9,h10=2g4g6+g52+2g14g16+g152-2g8g9-g92,h11=2g5g6+2g15g16-2g9g10,h12=g62+g162-g102. 41

Here, we put forward the assumption

(H6): Eq. (40) has at most twelve positive roots.

According to Eq. (38), we get

T2kn=12ωkarccosg11ωk6+g12ωk4+g13ωk3+g14ωk2+g15ωk+g16g7ωk4+g8ωk2+g9ωk+g10+nπωk(k=1,...,N;n=0,1,...). 42

Let T20=mink=1,...,N{T2kn} be the minimum critical value of T2 corresponding to pure imaginary roots ±iω0(2).

Next, we prove the transversal condition about time delay T2. Differentiating the two sides of Eq. (33) about time delay T2, we have

(dλdT2)-1=P3Q3, 43

where

P3=2(λ+1τ)3-(λ+1τ)(a12a21e-2λT1+a34a43e-2λT2+a13a31e-2λT3)+(λ+1τ)2(a12a21T1e-2λT1+a34a43T2e-2λT2+a13a31T3e-2λT3)-a12a21a34a43(T1+T2)e-2λ(T1+T2),Q3=a12a21a34a43λe-2λ(T1+T2)-a34a43λ(λ+1τ)2e-2λT2. 44

Therefore, Re{(dλdT2)λ=iω0(2)-1}=P3RQ3R+P3IQ3IQ3R2+Q3I2, where P3R(Q3R) and P3I(Q3I) represent the real and imaginary parts of P3(Q3), respectively.

where

P3R=2(1τ3-3(ω0(2))2τ)-1τ(a12a21cos(2ω0(2)T1)+a34a43cos(2ω0(2)T2)+a13a31cos(2ω0(2)T3))-ω0(2)(a12a21sin(2ω0(2)T1)+a34a43sin(2ω0(2)T2)+a13a31sin(2ω0(2)T3))+(1τ2-(ω0(2))2)(a12a21T1cos(2ω0(2)T1)+a34a43T2cos(2ω0(2)T2)+a13a31T3cos(2ω0(2)T3))+2ω0(2)τ(a12a21T1sin(2ω0(2)T1)+a34a43T2sin(2ω0(2)T2)+a13a31T3sin(2ω0(2)T3))-a12a21a34a43(T1+T2)cos(2ω0(2)(T1+T2)),P3I=2(3ω0(2)τ2-(ω0(2))3)+1τ(a12a21sin(2ω0(2)T1)+a34a43sin(2ω0(2)T2)+a13a31sin(2ω0(2)T3))-ω0(2)(a12a21cos(2ω0(2)T1)+a34a43cos(2ω0(2)T2)+a13a31cos(2ω0(2)T3))+2ω0(2)τ(a12a21T1cos(2ω0(2)T1)+a34a43T2cos(2ω0(2)T2)+a13a31T3cos(2ω0(2)T3))-(1τ2-(ω0(2))2)(a12a21T1sin(2ω0(2)T1)+a34a43T2sin(2ω0(2)T2)+a13a31T3sin(2ω0(2)T3))+a12a21a34a43(T1+T2)sin(2ω0(2)(T1+T2)),Q3R=a12a21a34a43ω0(2)sin(2ω0(2)(T1+T2))+a34a43ω0(2)[2ω0(2)τcos(2ω0(2)T2)-(1τ2-(ω0(2))2)sin(2ω0(2)T2)],Q3I=a12a21a34a43ω0(2)cos(2ω0(2)(T1+T2))-a34a43ω0(2)[(1τ2-(ω0(2))2)cos(2ω0(2)T2)+2ω0(2)τsin(2ω0(2)T2)]. 45

We make the assumption (H7): P3RQ3R+P3IQ3I0.

If the condition (H7) holds, Re{(dλdT2)λ=iω0(2)-1}0 is true, the transversal condition of Hopf bifurcation is satisfied.

From the above analysis, we get the following Theorem.

Theorem 4

For T20 as the minimum value that makes the characteristic equation (33) has pure imaginary roots, if the conditions H1,H6-H7 are true, combining with Lemma 1, we can draw the following conclusions,

  1. For T1(0,T10),T3(0,T30), all the roots of Eq. (33) have negative real parts for T2(0,T20), the system (3) is asymptotically stable at the equilibrium u0 for T2(0,T20) and is unstable for T2>T20 .

  2. The system (3) experiences Hopf bifurcation at the equilibrium u0 when T2 crosses the critical value T20 for T1(0,T10),T3(0,T30).

In summary, the conditions under which the system (3) undergoes Hopf bifurcation are given in Theorems 1 - 4 for four case (1) T1=T2=T3=0 (2) T1=T2=0, T3>0 (3) T2=0, T1>0, T3>0 (4) T1>0, T3>0,T2>0, respectively.

The direction and stability of Hopf bifurcation

In this section, for T1(0,T10),T3(0,T30), we use central manifold theory and normal theory to judge the direction and stability of Hopf bifurcation at the critical value T20.

First of all, let v(t)=u(Tt), T=T20+μ, μR, and then the system (4) becomes the following form,

v˙(t)=Lμ(vt)+F(μ,vt), 46

where vt(θ)=v(t+θ).

The Lμ:CR4,F:R×CR4 are given by

Lμ(Φ)=(T20+μ)C1Φ(0)+(T20+μ)C2Φ(-T1T20)+(T20+μ)C4Φ(-T3T20)+(T20+μ)C3Φ(-1), 47

where

Φ(t)=(Φ1(t),Φ2(t),Φ3(t),Φ4(t))T, 48

and

F(μ,vt)=(T20+μ)F2S+F3SF2G+F3GF2E+F3EF2I+F3I, 49

with

F2SF2GF2EF2I=rS[ωGS2Φ22(t-T1T20)+ωCS2Φ32(t-T3T20)-ωGSωCSΦ2(t-T1T20)Φ3(t-T3T20)]rG[ωSG2Φ12(t-T1T20)]rE[ωSC2Φ12(t-T3T20)+ωCC2Φ42(t-1)+ωSCωCCΦ1(t-T3T20)Φ4(t-1)]rI[ωCC2Φ32(t-1)],F3SF3GF3EF3I=l1l2l3l4, 50

where

l1=eS[ωCS3Φ33(t-T3T20)-ωGS3Φ23(t-T1T20)+ωGS2ωCSΦ22(t-T1T20)Φ3(t-T3T20)-ωGSωCS2Φ2(t-T1T20)Φ32(t-T3T20)],l2=eG[ωSG3Φ13(t-T1T20)],l3=eE[-ωSC3Φ13(t-T3T20)-ωCC3Φ43(t-1)-ωSC2ωCCΦ12(t-T3T20)Φ4(t-1)-ωSCωCC2Φ1(t-T3T20)Φ42(t-1)],l4=eI[ωCC3Φ33(t-1)], 51

and

rX=-8X(MX-X)(2X-MX)τMX4,eX=32X(MX-X)(MX2+6X2-6MXX)3τMX6(X=S,G,E,I). 52

Based on the Riesz representation theorem, there is a 4×4 matrix function η(θ,μ) in θ[-1,0] such that

Lμ(Φ)=-10dη(θ,μ)Φ(θ),ΦC([-1,0],R4). 53

Now we choose

η(θ,μ)=(T20+μ)(C1+C2+C4+C3)θ=0,(T20+μ)(C2+C4+C3)θ[-T1T20,0),(T20+μ)(C4+C3)θ[-T3T20,-T1T20),(T20+μ)C3θ(-1,-T3T20),0θ=-1. 54

For ΦC1([-1,0],R4), we define

A(μ)Φ=dΦ(θ)dθθ[-1,0),-10dξη(ξ,μ)Φ(ξ)θ=0. 55

and

R(μ)Φ=0θ[-1,0),F(μ,Φ)θ=0. 56

Then, Eq. (46) can be converted into the following abstract ODE form,

v˙(t)=A(μ)v(t)+R(μ)v(t). 57

For ΨC1([0,1],R4), R4 is the 4 dimensional complex row vector space. The adjoint operator A of A can be represented as follows,

AΨ(s)=-dΨ(s)dss(0,1],-10dsηT(s,μ)Ψ(-s)s=0. 58

In which, ΦC1([-1,0],R4) and ΨC1([0,1],R4) are suited to double linear inner product of complex vector,

<Ψ,Φ>=Ψ¯(0)Φ(0)--10ξ=0θΨ¯(ξ-θ)dη(θ)Φ(ξ)dξ, 59

which satisfies

<Ψ,AΦ>=<AΨ,Φ>. 60

According to the above analysis, we know that ±iω0(2)T20 are the eigenvalues of A(0) and A(0), let q(θ)=(1,α,β,γ)-1eiω0(2)T20θ and q(s)=D(1,α,β,γ)-1eiω0(2)T20s are the eigenvectors of A(0) and A(0) corresponding to iω0(2)T20 and -iω0(2)T20, respectively. With a simple calculation, we can obtain

α=a21e-iω0(2)T1iω0(2)+1τ,β=(iω0(2)+1τ)2-a12a21e-2iω0(2)T1(iω0(2)+1τ)a13e-iω0(2)T3,γ=(iω0(2)+1τ)2-a12a21e-2iω0(2)T1a13a34e-iω0(2)T3e-iω0(2)T20-a13a34e-iω0(2)T3e-iω0(2)T20,α=a12e-iω0(2)T11τ-iω0(2),β=(1τ-iω0(2))2-a12a21e-2iω0(2)T1(1τ-iω0(2))a31e-iω0(2)T3,γ=a34e-iω0(2)T201τ-iω0(2)β. 61

By bilinear inner product, we know

<q(s),q(θ)>=q¯T(0)q(0)--10ξ=0θD¯(1,α¯,β¯,γ¯)e-i(ξ-θ)ω0(2)T20dη(θ,0)(1,α,β,γ)Teiξω0(2)T20dξ=q¯T(0)q(0)-q¯T(0)-10θeiθω0(2)T20dη(θ,0)q(0)=q¯T(0)q(0)+q¯T(0)(T1C2e-iω0(2)T1+T20C3e-iω0(2)T20+T3C4e-iω0(2)T3)q(0)=D¯[1+αα¯+ββ¯+γγ¯+T1e-iω0(2)T1(a21α¯+a12α)+T3e-iω0(2)T3(a31β¯+a13β)+T20e-iω0(2)T20(a43βγ¯a34γβ¯)]=1. 62

Therefore, we have,

D¯=1m, 63

and

D=1n, 64

where

m=1+αα¯+ββ¯+γγ¯+T1e-iω0(2)T1(a21α¯+a12α)+T3e-iω0(2)T3(a31β¯+a13β)+T20e-iω0(2)T20(a43βγ¯+a34γβ¯),n=1+α¯α+β¯β+γ¯γ+T1eiω0(2)T1(a21α+a12α¯)+T3eiω0(2)T3(a31β+a13β¯)+T20eiω0(2)T20(a43β¯γ+a34γ¯β). 65

We compute the coordinates to describe the center manifold C0 at μ=0. Let vt be the solution of Eq. (46) at μ=0.

Define

z(t)=<q(s),vt>, 66

and

W(t,θ)=vt(θ)-z(t)q(θ)-z¯(t)q¯(θ)=vt(θ)-2Re{z(t)q(θ)}. 67

On the center manifold C0, W(t,θ)=W(z,z¯,θ), in which W(z,z¯,θ) can be expressed as,

W(z,z¯,θ)=W20(θ)z22+W11(θ)zz¯+W02(θ)z¯22+..., 68

where z and z¯ are local coordinates for the C0 in the direction of q and q¯ , respectively. The flow of Eq. (46) on the central manifold is determined by the following equation,

z˙(t)=iω0(2)T20z(t)+q¯(0)F0(z,z¯), 69

where

F0(z,z¯)=F(0,W(z,z¯,θ)+z(t)q(0)+z¯(t)q¯(0)). 70

Let

F0(z,z¯)=Fz2z22+Fzz¯zz¯+Fz¯2z¯22+Fz2z¯z2z¯2+... 71

Eq. (69) can be rewritten as,

z˙(t)=iω0(2)T20z(t)+g(z,z¯)(t), 72

where

g(z,z¯)(t)=g20z22+g11zz¯+g02z¯22+g21z2z¯2+... 73

and

g20=q¯(0)Fz2,g11=q¯(0)Fzz¯,g02=q¯(0)Fz¯2,g21=q¯(0)Fz2z¯. 74

According to Eqs. (66) - (68), we have

vt=W(t,θ)+2Re{z(t)q(θ)}=W20(θ)z22+W11(θ)zz¯+W02(θ)z¯22+z(t)q(θ)+z¯(t)q¯(θ). 75

From Eq.(49), we obtain

g(z,z¯)(t)=q¯(0)F0(z,z¯)=q¯(0)F(0,vt). 76

Inserting Eq. (75) into Eq.(76) and comparing the coefficients with Eq. (73), we get,

g20=2g(0,0)z2=T20D¯(2F2Sz2+α¯2F2Gz2+β¯2F2Ez2+γ¯2F2Iz2),g02=2g(0,0)z¯2=T20D¯(2F2Sz¯2+α¯2F2Gz¯2+β¯2F2Ez¯2+γ¯2F2Iz¯2),g11=2g(0,0)zz¯=T20D¯(2F2Szz¯+α¯2F2Gzz¯+β¯2F2Ezz¯+γ¯2F2Izz¯). 77

where

2F2Sz2=2rS[ωGS2α2e-2iω0(2)T1+ωCS2β2e-2iω0(2)T3-2ωGSωCSαβe-iω0(2)(T1+T3)],2F2Gz2=2rG[ωSG2e-2iω0(2)T1],2F2Ez2=2rE[ωSC2e-2iω0(2)T3+ωCC2γ2e-2iω0(2)T20+2ωSCωCCe-iω0(2)(T3+T20)],2F2Iz2=2rI[ωCC2β2e-2iω0(2)T20],2F2Xz¯2=conj(2F2Xz2),(X=S,G,E,I),2F2Szz¯=2rS[ωGS2αα¯+ωCS2ββ¯-ωGSωCS(αβ¯e-iω0(2)(T1-T3)+α¯βeiω0(2)(T1-T3))],2F2Gzz¯=2rGωSG2,2F2Ezz¯=2rE[ωSC2+ωCC2γγ¯+ωSCωCC(γ¯e-iω0(2)(T3-T20)+γeiω0(2)(T3-T20))],2F2Izz¯=2rI[ωCC2ββ¯]. 78

About g21, we have,

g21=3g(0,0)z2z¯=T20D¯(3F2Sz2z¯+α¯3F2Gz2z¯+β¯3F2Ez2z¯+γ¯3F2Iz2z¯)+T20D¯(3F3Sz2z¯+α¯3F3Gz2z¯+β¯3F3Ez2z¯+γ¯3F3Iz2z¯), 79

in which

3F2Sz2z¯=2rS[ωGS2(2αW11(2)(-T1T20)e-iω0(2)T1+α¯W20(2)(-T1T20)eiω0(2)T1)+ωCS2(2βW11(3)(-T3T20)e-iω0(2)T3+β¯W20(3)(-T3T20)eiω0(2)T3)-2ωGSωCS(βW11(2)(-T1T20)e-iω0(2)T3+αW11(3)(-T3T20)e-iω0(2)T1)-ωGSωCS(β¯W20(2)(-T1T20)eiω0(2)T3+α¯W20(3)(-T3T20)eiω0(2)T1)],3F2Gz2z¯=2rG[ωSG2(2W11(1)(-T1T20)e-iω0(2)T1+W20(1)(-T1T20)eiω0(2)T1)],3F2Ez2z¯=2rE[ωSC2(2W11(1)(-T3T20)e-iω0(2)T3+W20(1)(-T3T20)eiω0(2)T3)+ωCC2(2γW11(4)(-1)e-iω0(2)T20+γ¯W20(4)(-1)eiω0(2)T20)+2ωSCωCC(γW11(1)(-T3T20)e-iω0(2)T20+W11(4)(-1)e-iω0(2)T3)+ωSCωCC(γ¯W20(1)(-T3T20)eiω0(2)T20+W20(4)(-1)eiω0(2)T3))],3F2Iz2z¯=2rI[ωCC2(2βW11(3)(-1)e-iω0(2)T20+β¯W20(3)(-1)eiω0(2)T20)],3F3Sz2z¯=6eS[ωCS3β2β¯e-iω0(2)T3-ωGS3α2α¯e-iω0(2)T1+ωGS2ωCS(α2β¯e-iω0(2)(2T1-T3)+2αα¯βe-iω0(2)T3)-ωGSωCS2(β2α¯e-iω0(2)(2T3-T1)+2ββ¯αe-iω0(2)T1)],3F3Gz2z¯=6eG[ωSG3e-2iω0(2)T1],3F3Ez2z¯=6eE[-ωSC3e-iω0(2)T3-ωCC3γ2γ¯e-iω0(2)T20-ωSC2ωCC(γ¯e-iω0(2)(2T3-T20)+2γe-iω0(2)T20)-ωSCωCC2(γ2e-iω0(2)(2T20-T3)+2γγ¯e-iω0(2)T3)],3F3Iz2z¯=6eI[ωCC3β2β¯e-iω0(2)T20]. 80

Since W11(θ) and W20(θ) are unknown, we further calculate them in the following. From Eq. (67), we have W˙=v˙t-z˙q-z¯˙q¯, with Eqs. (57), (66) and (68), and catch

W˙=AW-gq(θ)-g¯q¯(θ),θ[-1,0),AW-gq(0)-g¯q¯(0)+F0θ=0. 81

On the other hand, when C0 is near to the origin, from Eq. (68)

W˙=Wzz˙+Wz¯z¯˙=[W20(θ)z+W11(θ)z¯]z˙+[W11(θ)z+W02(θ)z¯]z¯˙=[W20(θ)z+W11(θ)z¯](iω0z+g(z,z¯))+[W11(θ)z+W02(θ)z¯](-iω0z¯+g¯(z,z¯))+...=2iω0W20z22-iω0W02z¯2+... 82

Substitute Eqs. (72) and (73) into Eq. (81), comparing coefficient with Eq. (82) about z22 and zz¯, then

(2iω0(2)T20I-A)W20(θ)=-g20q(θ)-g¯02q¯(θ)θ[-1,0),-g20q(0)-g¯02q¯(0)+Fz2θ=0. 83

and

-AW11(θ)=-g11q(θ)-g¯11q¯(θ)θ[-1,0),-g11q(0)-g¯11q¯(0)+Fzz¯θ=0. 84

From Eq. (55) and Eq. (83), when θ[-1,0)

W20(θ)=2iω0(2)T20W20(θ)+g20q(θ)+g¯02q¯(θ). 85

We get

W20(θ)=-ig20ω0(2)T20q(0)eiω0(2)T20θ+ig¯02ω0(2)T20q¯(0)e-iω0(2)T20θ+E1e2iω0(2)T20θ. 86

For θ=0

-10dθη(0,θ)W20(θ)=2iω0(2)T20W20(θ)+g20q(0)+g¯02q¯(0)-Fz2. 87

Substituting Eq. (86) into Eq. (87), and (3iω0(2)T20I--10eiω0(2)T20θdθη(0,θ))q(0)=0, we have

E1=[2iω0(2)T20I--10e2iω0(2)T20θdη(0,θ)]-1Fz2, 88

where

-10e2iω0(2)T20θdθη(0,θ)=T20C1+T20C2e-2iω0(2)T1+T20C4e-2iω0(2)T3+T20C3e-2iω0(2)T20=T20-1τa12e-2iω0(2)T1a13e-2iω0(2)T30a21e-2iω0(2)T1-1τ00a31e-2iω0(2)T30-1τa34e-2iω0(2)T2000a43e-2iω0(2)T20-1τ, 89

and

Fz2=T202rS[ωGS2α2e-2iω0(2)T1+ωCS2β2e-2iω0(2)T3-2ωGSωCSαβe-iω0(2)(T1+T3)]2rG[ωSG2e-2iω0(2)T1]2rE[ωSC2e-2iω0(2)T3+ωCC2γ2e-2iω0(2)T20+2ωSCωCCe-iω0(2)(T3+T20)]2rI[ωCC2β2e-2iω0(2)T20]. 90

So, we can obtain E1 as follows

E1=2iω0(2)T20+1τ-a12e-2iω0(2)T1-a13e-2iω0(2)T30-a21e-2iω0(2)T12iω0(2)T20+1τ00-a31e-2iω0(2)T302iω0(2)T20+1τ-a34e-2iω0(2)T2000-a43e-2iω0(2)T202iω0(2)T20+1τ-12rS[ωGS2α2e-2iω0(2)T1+ωCS2β2e-2iω0(2)T3-2ωGSωCSαβe-iω0(2)(T1+T3)]2rG[ωSG2e-2iω0(2)T1]2rE[ωSC2e-2iω0(2)T3+ωCC2γ2e-2iω0(2)T20+2ωSCωCCe-iω0(2)(T3+T20)]2rI[ωCC2β2e-2iω0(2)T20]. 91

Substituting E1 into Eq. (86), we can calculate the value of W20(θ). Similarly, by Eq. (55) and Eq. (84) for θ[-1,0) we will get

W11(θ)=g11q(θ)+g¯11q¯(θ). 92

We have,

W11(θ)=-ig11ω0(2)T20q(0)eiω0(2)T20θ+ig¯11ω0(2)T20q¯(0)e-iω0(2)T20θ+E2. 93

For θ=0,

-10dη(0,θ)W11(θ)=g11q(0)+g¯11q¯(0)-Fzz¯, 94

Substituting Eq. (93) into Eq. (94), one can obtain

E2=-[-10dθη(0,θ)]-1Fzz¯, 95

where

-10dθη(0,θ)=T20C1+T20C2+T20C4+T20C3=T20-1τa12a130a21-1τ00a310-1τa3400a43-1τ, 96

and

Fzz¯=T202rS[ωGS2αα¯+ωCS2ββ¯-ωGSωCS(αβ¯e-iω0(2)(T1-T3)+α¯βeiω0(2)(T1-T3))]2rGωSG22rE[ωSC2+ωCC2γγ¯+ωSCωCC(γ¯e-iω0(2)(T3-T20)+γeiω0(2)(T3-T20))]2rI(ωCC2ββ¯). 97

Therefore, we can infer the following equation,

E2=--1τa12a130a21-1τ00a310-1τa3400a43-1τ-12rS[ωGS2αα¯+ωCS2ββ¯-ωGSωCS(αβ¯e-iω0(2)(T1-T3)+α¯βeiω0(2)(T1-T3))]2rGωSG22rE[ωSC2+ωCC2γγ¯+ωSCωCC(γ¯e-iω0(2)(T3-T20)+γeiω0(2)(T3-T20))]2rI(ωCC2ββ¯). 98

Finally, we should compute the following equation,

C1(0)=i2ω0(2)(g11g20-2|g11|2-|g02|23)+g212,μ2=-ReC1(0)Re(λ(T20)),β2=2ReC1(0),τ2=-ImC1(0)+μ2Im(λ(T20))ω0(2). 99

Therefore, we have the following results,

  1. The sign of μ2 determines the direction of the Hopf bifurcation, if μ2>0(μ2<0), the Hopf bifurcation is supercritical (subcritical);

  2. The sign of τ2 determines the period of the bifurcating periodic solutions: if τ2>0(τ2<0), the period increase (decrease);

  3. The sign of β2 determines the stability of the bifurcating periodic solutions: if β2<0(β2>0), the bifurcating periodic solutions are stable (unstable).

Hopf bifurcation induced by multiple time delay through numerical simulations

In this section, some numerical simulations are given to support the theoretical results in Sect. “Hopf bifurcation induced by multiple time  delay through theoretical analyses” through time series of firing rate of STN, GPE, EXI and INN in Fig. 2, in Figs. 3, 4, 5b–d, and one parameter bifurcation diagrams of firing rate of STN with respect to time delay in Fig. 3a, 4a, 5a. These figures are drawn by MATLAB software with all parameters in Table 1.

Fig. 2.

Fig. 2

Time series of firing rate of STN, GPe, EXN and INN at T1=T2=T3=0

Fig. 3.

Fig. 3

(a) Bifurcation diagram of firing rate of STN with respect to T3 at T1=T2=0. HB is Hopf bifurcation point with T3=0.00185. (b) - (d) Time series of firing rate of STN at T3=0.0014 (b) T3=0.00185 (c) T3=0.0022 (d)

Fig. 4.

Fig. 4

(a) Bifurcation diagram of firing rate of STN with respect to T1 at T2=0 and T3=0.00136 . HB is Hopf bifurcation point with T1=0.0021. (b) - (d) Time series of firing rate of STN at T1=0.0015 (b) T1=0.0021 (c) T1=0.0026 (d)

Fig. 5.

Fig. 5

(a) Bifurcation diagram of firing rate of STN with respect to T2 at T1=0.0011 and T3=0.00136. HB is Hopf bifurcation point with T2=0.00095. (b) - (d) Time series of firing rate of STN at T2=0.0005 (b) T2=0.00095 (c) T2=0.0013 (d)

First, for T1=0, T2=0 and T3=0 in Case 1, the system (3) has a positive equilibrium u=(17.71,77.98,38.54,22.39) corresponding to a stable steady state on the time series of firing rate of STN, GPe, EXN and INN in Fig. 2. Then the conditions in Theorem 1 are satisfied with b1=400>0, b2=1.3562×105>0, b3=1.9125×107>0, b4=8.2277×108>0, b1b2-b3=3.5125×107>0, b1b2b3-b12b4-b32=5.401×1014>0 in Eq. (10).

Then, in Case 2 - Case 4, for the conditions in Theorem 2 - Theorem 4, the critical time delays are obtained and verified through one parameter bifurcation diagrams of the firing rate of STN with respect to time delay in Fig. 3(a), Fig. 4(a) and Fig. 5(a), where the black solid lines are stable steady state and green dots are the maximum and minimum of oscillation.

In Case 2, for T1=T2=0 and the conditions in Theorem 2, we get the critical value of T3, T30=0.00185 labeled by HB in Fig. 3(a). Furthermore, the firing rate of STN reaches a stable steady state for T3=0.0014<T30 in Fig. 3(b) and shows oscillation for T3=0.0022>T30 in Fig. 3(d), and undergoes damped oscillation for T30=0.00185 in Fig. 3(c).

Similarly, for T2=0,T3=0.00136(0,0.00184) in Case 3, the critical value of T1, T10=0.0021, is obtained for the conditions in Theorem 3. Fig. 4(a) shows the firing rate of STN undergoes Hopf bifurcation at T10=0.0021. Also, the firing rate of STN switches from a stable steady state at T1=0.0015<T10 in Fig. 4(b) to oscillation at T1=0.0026>T10 in Fig. 4(d) through damped oscillation at T10=0.0021 in Fig. 4(c).

Lastly, for T1=0.0011(0,T10),T3=0.00136(0,T30) in Case 4, the critical value of T20=0.00095 is consistent with Hopf bifurcation point HB in Fig. 5(a). The firing rate of STN show damped oscillation at T20=0.00095 in Fig.5(c), and converges to a stable steady state at T2=0.0005<T20 in Fig.5(b) while show oscillation at T2=0.0013>T20 in Fig.5(d).

Furthermore, in order to gain a clearer and more comprehensive understanding of the impact of these time delays on beta - band oscillations in the cortex - basal ganglia loop, the critical surface between the stable steady state and the oscillating state are given in Fig. 6, where the surface is composed of the critical time delays, and SSS and OS denote the stable steady state and oscillating state. As shown in Fig. 6, the beta - band oscillation of the cortex - basal ganglia loop is sensitive to T3, and the system (3) show oscillation for T3>0.00185 regardless of the values of T1 and T2.

Fig. 6.

Fig. 6

(a)The boundary plane of Hopf bifurcation with respect to T1,T2,T3. SSS represents stable steady state and OS denotes oscillating state. (b) - (d) Time series of firing rate of STN at T3=0.0012 (b) T3=0.00127 (c) T3=0.00136 (d) with T1=T2=0.0015

To sum up, these numerical simulations confirm to the theoretical results in Sect. “Hopf bifurcation induced by multiple time delay  through theoretical analyses”.

Conclusion

The synaptic transmission time delay plays an important role in inducing beta - band oscillation in the cortex - basal ganglia loop for PD. In this paper, based on a cortex - basal ganglia model with three excitatory - inhibitory loops, STN - GPe, EXN - INN and STN - EXN, we investigate the effect of three transmission time delays T1,T2,T3 in STN - GPe, EXN - INN, STN - EXN loops on the critical condition for pathological beta - band oscillation through stability theory, manifold theorem and normal form analysis in four cases (1) T1=T2=T3=0 (2) T1=T2=0, T3>0 (3) T2=0, T1>0, T3>0 (4) T1>0, T3>0,T2>0. In each case, we provide the conditions for Hopf bifurcation induced by time delay in theorems 1 - 4, which are verified through time series of firing rate of STN, GPe, EXN, INN and bifurcation diagram of STN with respect to time delays. Our results show that suitable time delay T1, T2 and T3 may induce Hopf bifurcation, which is sensitive to synaptic transmission time delay T3 in STN - EXN loop. When T3<0.00097, firing rate of STN reach a stable steady state for any T1 and T2 and show oscillating state for T3>0.00185, regardless of T1 and T2. For T3 between 0.00097 and 0.00185, the critical transition points from stable steady state to oscillating state decreases with the increase of T1. Our investigation has demonstrated that reducing the synaptic transmission time delays between cortex and basal ganglia and between STN and GPe will suppress beta - band oscillation of Parkinson’s disease. These results may provide a clue to clinical physician in treating Parkinson’s disease.

In this study, we focus on beta - band oscillation in a cortex - basal ganglia model with three transmission time delays. Of course, a more complete network composed of cortex, striatum, basal ganglia and thalamus is certainly necessary to explore beta - band oscillation in response to different transmission time delays and connect weights between neuron populations (Yi et al. 2022; Yu et al. 2021; Xu et al. 2023). In addition, the effect of both environmental fluctuations and transmission time delay on beta - band oscillation in fractional-order neural network are worthy to be explored in future (Huang et al. 2023; Zhao et al. 2022; Bönsel et al. 2022).

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 12062017 and 12262025), Natural Science Foundation of Inner Mongolia Autonomous Region of China (Grants 2021ZD01), and Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region of China (No. NMGIRT2208). The authors acknowledge the reviewers for their valuable reviews and kind suggestions.

Declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Qiaohu Zhang and Yuanhong Bi have contributed equally to this work.

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