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Review

Depletion of dopamine in Parkinson's disease and relevant therapeutic options: A review of the literature

  • Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects motor and cognition functions. The etiology of Parkinson's disease remains largely unknown, but genetic and environmental factors are believed to play a role. The neurotransmitter dopamine is implicated in regulating movement, motivation, memory, and other physiological processes. In individuals with Parkinson's disease, the loss of dopaminergic neurons leads to a reduction in dopamine levels, which causes motor impairment and may also contribute to the cognitive deficits observed in some patients. Therefore, it is important to understand the pathophysiology that leads to the loss of dopaminergic neurons, along with reliable biomarkers that may help distinguish PD from other conditions, monitor its progression, or indicate a positive response to a therapeutic intervention. Important advances in the treatment, etiology, and pathogenesis of Parkinson's disease have been made in the past 50 years. Therefore, this review tries to explain the different possible mechanisms behind the depletion of dopamine in PD patients such as alpha-synuclein abnormalities, mitochondrial dysfunction, and 3,4-dihydroxyphenylacetaldehyde (DOPAL) toxicity, along with the current therapies we have and the ones that are in development. The clinical aspect of Parkinson's disease such as the manifestation of both motor and non-motor symptoms, and the differential diagnosis with similar neurodegenerative disease are also discussed.

    Citation: Sairam Ramesh, Arosh S. Perera Molligoda Arachchige. Depletion of dopamine in Parkinson's disease and relevant therapeutic options: A review of the literature[J]. AIMS Neuroscience, 2023, 10(3): 200-231. doi: 10.3934/Neuroscience.2023017

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  • Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects motor and cognition functions. The etiology of Parkinson's disease remains largely unknown, but genetic and environmental factors are believed to play a role. The neurotransmitter dopamine is implicated in regulating movement, motivation, memory, and other physiological processes. In individuals with Parkinson's disease, the loss of dopaminergic neurons leads to a reduction in dopamine levels, which causes motor impairment and may also contribute to the cognitive deficits observed in some patients. Therefore, it is important to understand the pathophysiology that leads to the loss of dopaminergic neurons, along with reliable biomarkers that may help distinguish PD from other conditions, monitor its progression, or indicate a positive response to a therapeutic intervention. Important advances in the treatment, etiology, and pathogenesis of Parkinson's disease have been made in the past 50 years. Therefore, this review tries to explain the different possible mechanisms behind the depletion of dopamine in PD patients such as alpha-synuclein abnormalities, mitochondrial dysfunction, and 3,4-dihydroxyphenylacetaldehyde (DOPAL) toxicity, along with the current therapies we have and the ones that are in development. The clinical aspect of Parkinson's disease such as the manifestation of both motor and non-motor symptoms, and the differential diagnosis with similar neurodegenerative disease are also discussed.



    Due to the wide variety of applications in fields including associative memories, signal processing, parallel computation, pattern recognition, neural networks have drawn a lot of interest in recent years. In many real-world systems, artificial neural networks, neural networks, biological systems have inevitable applications. [1,2,3,4,5,6,7]. Due to the obvious transmission frequency of interconnected neurons and the finite switching speeds of the amplifiers, time delays are ineluctably given. Their presence might affect a system's stability by causing oscillation and instability features [8,9,10,11,12,13,14,15,16,17,18]. Delayed dynamical networks and many time-delay system kinds have been studied, and numerous important outcomes have been presented [19,20,21,22,23,24,25].

    Kosko was the first person to propose that BAM-NNs could be important to the NNs theory [26]. Furthermore, BAM neural frameworks involving delays attracted significant thought and underwent in-depth research. While there is no connection between neurons in the same layer, neurons in another layer are entirely interconnected to those in the first layer. It performs a two-way affiliated hunt to store bipolar vector combines by cycles of forward and backward propagation information streams between the two layers, and it sums up the single-layer auto-cooperative Hebbian connection to a two-layer design coordinated hetero acquainted circuits. As a result, it has numerous applications in the fields of artificial intelligence and pattern identification [27,28,29,30,31,32,33,34,35]. Appropriately, the BAM neural system has been generally concentrated on both in principle and applications. This makes focusing on the stability of the BAM neural framework, which has largely been studied, fascinating and fundamental [36,37,38,39,40,41,42,43,44].

    The synchronization control of chaotic networks plays vital role in applications such as image encryption, secure communication, DC-DC motor and etc. [45,46,47,48]. Satellites play a significant part in the advancement of space technology, civic, military, and scientific endeavors. In particular, feedback control, adaptive control, sampled-data control, and other strategies have been employed to synchronize and regulate the satellite systems [49,50]. Recently, the issue of memristive BAM networks synchronization with stochastic feedback gain variations in [50].

    It is inferred that in real-world scenarios, a nearby float framework as noted in practical model controller does not avoid the proceed out of coefficient uncertainty, leading to the murkiness in controller execution caused by the required word length in any updated structures or additional parameter turning in the final controller use. Along the same ideas, it is crucial to design a non-fragile controller such that it is insensitive to uncertainty. Non-fragile control has developed into a fascinating topic in both theory and practical application. There has been extensive research into the use of non-fragile controllers in recent years [51,52]. However, the problem of non-fragile control of a BAM delayed neural network and controller gain fluctuation has not been thoroughly examined.

    This study examines the non-fragile synchronization for a BAM delayed neural network and a randomly occurring controller gain fluctuation, which is motivated by the studies. New stability requirements for BAM neural networks with arbitrarily occurring controller gain fluctuation are derived in terms of LMI by building an appropriate Lyapunov-Krasovskii functional (LKF) and using conventional integral inequality techniques. Solving the suggested LMI condition yields the gain matrices for the proposed controller design. Finally, a numerical example is used to illustrate the proposed strategy.

    Notations: In this work, Rp represents Euclidean space of p dimension. Rp×q denotes the set of all p×q real matrices. The identity matrix is denoted by I. Here R>0(<0) represents R is a symmetric positive (negative) definite matrices. The elements below the main diagonal of a symmetric matrixl is given by '*'.

    In this study, we will investigate the synchronization of BAM delayed neural network with randomly occurring. The BAM NNs with time-varying delay components can be modeled as follows

    ˙θi()=aiθi()+nj=1w(1)ij˜fj(λj())+nj=1w(2)ij˜fj(λj(σ()))+Ii,˙λj()=bjλj()+ni=1v(1)ji˜gi(θi())+ni=1v(2)ji˜gi(θi(κ()))+Jj,} (2.1)

    where γi() and δj() are the state variables of the neuron at time respectively. The non linear functions ˜fj(), ˜gi() are neuron activation functions. The positive constants ai,bj denote the time scales of the respective layers of the neurons. w(1)ij, w(2)ij, v(1)ji, v(2)ji are connection weights of the network. Ii and Jj denotes the external inputs. σ(),κ() are time varying delays satisfying

    0σ()σ,  ˙σ()=μ1<1, 0κ()κ,  ˙κ()=μ2<2, (2.2)

    and, σ, κ, μ1, and μ2 are constants.

    Consequently, the corresponding compact matrix form can be used to describe the master system (2.1) as,

    M:{˙θ()=Aθ()+W1˜f(λ())+W2˜f(λ(σ())),˙λ()=Bλ()+V1˜g(θ())+V2˜g(θ(κ())), (2.3)

    where, B=diag[b1,b2,...,bn]>0,  A=diag[a1,a2,...,an]>0, Wk=(w(k)ij)(n×n), Vk=(v(k)ij)(n×n),k=1,2.

    Assumption (1). ˜gj(),˜fi() are neuron activation functions, which is bounded, then there exist constants Hi, H+i, Lj, L+j such that

    Lj˜gj(ϕ)˜gj(ϱ)ϕϱL+j,Hi˜fi(ϕ)˜fi(ϱ)ϕϱH+i, (2.4)

    where, j=1,..,n,i=1,...m, and ϱ,ϕR with ϕϱ. We define the subsequent matrices for ease of notation:

    H1=diag{H+1H1,H+2H2,...,H+mHm},H2=diag{H1+H+12,H+2+H22,...,H+m+Hm2},L1=diag{L+1L1,L+2L2,...,L+nLn},L2=diag{L+1+L12,L+2+L22,...,L+n+Ln2}.

    Master system and slave system state variables are represented by θ(),λ() and ˆθ() and ˆλ(), respectively. We take into account the master system's slave system in the follows.

    S:{˙ˆθ()=Aˆθ()+W1˜f(ˆλ())+W2˜f(ˆλ(σ()))+u(),˙ˆλ()=Bˆλ()+V1˜g(ˆθ())+V2˜g(ˆθ(κ()))+v() (2.5)

    We choose the following non-fragile controller:

    u()=(K1+ϕ()ΔK1())ζ(), v()=(K2+ϱ()ΔK2())ϖ(), (2.6)

    Here the controller gain matrices are given by K1,K2. The matrix ΔKi()(i=1,2) satisfy

    ΔKi()=HiΔ()Ei  (i=1,2) (2.7)

    and

    ΔT()Δ()I, (2.8)

    where Hi,Ei are known constant matrices. To desribe randomly accuring control gain fluctuation we introduced the stachastic variables ϕ(),ϱ()R. It is a sequence of white noise generated by Bernoulli distribution with values of zero or one

    Pr{ϱ()=0}=1ϱ,  Pr{ϱ()=1}=ϱ,Pr{ϕ()=0}=1ϕ,  Pr{ϕ()=1}=ϕ, (2.9)

    where 0ϱ,ϕ1 is constant.

    Set the synchronization error signals ϖ()=ˆλ()λ() and ζ()=ˆθ()θ(). Error dynamics between systems (2.3) and (2.5) can therefore be written as follows:

    ˙ζ()=(A+(K1+ϕ()ΔK1()))ζ()+W1f(ϖ())+W2f(ϖ(σ())),˙ϖ()=(B+(K2+ϱ()ΔK2()))ϖ()+V1g(ζ())+V2g(ζ(κ()))}. (2.10)

    where g(ζ())=˜g(ˆθ())˜g(θ()), and f(ϖ())=˜f(ˆλ())˜f(λ()).

    The following crucial conditions are used to obtain our main findings:

    Lemma 2.1. [53] (Schur complement) Let Z, V,O be given matrices such that Z>0, then

    [OVTvZ]<0iffO+VTZ1V<0.

    Lemma 2.2. [54] Given matrices Z=ZT,R,O and Q=QT>0 with appropriate dimensions

    Z+RL()O+OTLT()RT<0,

    for all L() satisfying LT()L()I if and only if there exists a scalar ε>0 such that

    Z+ε1RRT+εOTQO<0.

    Lemma 2.3. [55] For a given matrix ZS+n and a function ς:[c,d]Rn whose derivative ˙ςPC([c,d],Rn), the following inequalities hold: dc˙ςT(r)Z˙ς(r)dr1dc ˆφ¯Zˆφ, where ¯Z=diag{Z,3Z,5Z}, ˆφ=[φT1 φT2 φT3]T, φ1=ς(d)ς(c), φ2=ς(d)+ς(c)2dcdcς(r)dr, φ3=ς(d)ς(c)+6dcdcς(r)dr12(dc)2dcdcς(u)duds.

    The necessary requirements for guaranteeing the stability of system (2.10) are established in this part.

    Theorem 3.1. From Assumption (A), ϕ,ϱ, and ϵi are positive scalars, if there exist symmetric matrices Pi>0, Qi>0, Ri>0, Zi>0, any matrices Ji and Gi, diagonal matrices Si>0 (i=1,2) satisfying

    Ξ=[ΠΩΛ]<0, (3.1)

    where

    Π=[Π113Z1κS2L2024Z1κ260Z1κ3Π17Π220036Z1κ260Z1κ30Π330000Π44000192Z1κ3360Z1κ40720Z1κ50Π77],Ω=[00J1W1J1W20000000000VT1JT200000VT1JT2VT2JT200000VT2JT20000000000000000J1W1J1W2000],Λ=[Λ113Z2σS1H2024Z2σ260Z2σ3Λ17Λ220036Z2σ260Z2σ30Λ330000Λ44000192Z2σ3360Z2σ40720Z2σ50Λ77],
    Π11=R1S2L19Z1κJ1A+G1+ϕJ1ΔK1()+GT1ATJT1+ϕΔKT1()JT1,Π17=J1ϵ1ATJT1+ϵ1GT1+ϵ1ϕΔKT1()JT1+P1,Π22=R19Z1κ,Π33=Q2S2,Π44=(1μ2)Q2,Π77=ϵ1J1+κZ1ϵ1JT1,Λ11=R2S1H1J2B+G2+ϱJ2ΔK2()9Z2σBTJT+GT2+ϱΔKT2()JT2,Λ17=J2ϵ2BTJT2+ϵ2GT2+ϵ2ϱΔKT2()JT2+P2,Λ22=R29Z2σ,Λ33=Q1S1,Λ44=(1μ1)Q1,Λ77=σZ2ϵ2J2ϵ2JT2,

    When this happens, the system (2.10) is asymptotically stable and Ki=J1iGi. are control gain matrices.

    Proof. Take into account the LKF candidate below:

    V(ζ,ϖ,)=4i=1Vi(ζ,ϖ,), (3.2)

    where

    V1(ζ,ϖ,)=ζT()P1ζ()+ϖT()P2ϖ(),V2(ζ,ϖ,)=σ()fT(ϖ(s))Q1f(ϖ(s))ds+κ()gT(ζ(s))Q2g(ζ(s))ds,V3(ζ,ϖ,)=κζT(s)R1ζ(s)ds+σϖT(s)R2ϖ(s)ds,V4(ζ,ϖ,)=0κ+θ˙ζT(s)Z1˙ζ(s)dsdθ+0σ+θ˙ϖT(s)Z2˙ϖ(s)dsdθ.

    The infinitesimal operator L ofV(ζ,ϖ,) is:

    LV(ζ,ϖ,)=limΔ01Δ{E{V(ζ+Δ,y+Δ,)|(ζ,ϖ,)}V(ζ,ϖ,)},E{LV(ζ,ϖ,)}=4i=1E{LVi(ζ,ϖ,)}. (3.3)

    Using the stochastic derivative of V(ζ,ϖ,), we can determine:

    E{LV1(ζ,ϖ,)}=2ζT()P1˙ζ()+2ϖT()P2˙ϖ(), (3.4)
    E{LV2(ζ,ϖ,)}fT(ϖ())Q1f(ϖ())(1μ1)fT(ϖ(σ()))Q1f(ϖ(σ()))+gT(ζ())Q2g(ζ())(1μ2)gT(ζ(κ()))Q2g(ζ(κ())), (3.5)
    E{LV3(ζ,ϖ,)}=ζT()R1ζ()ζT(κ)R1ζ(κ)+ϖT()R2ϖ()ϖT(σ)R2ϖ(σ), (3.6)
    E{LV4(ζ,ϖ,)}=κ˙ζT()Z1˙ζ()κ˙ζT(s)Z1˙ζ(s)ds+σ˙ϖT()Z2˙ϖ()σ˙ϖT(s)Z2˙ϖ(s)ds, (3.7)

    By applying Lemma 2.3 in (2.17), we can obtain

    κ˙ζT(s)Z1˙ζ(s)ds1κηT1()[Z10003Z10005Z1]η1(), (3.8)
    σ˙ϖT(s)Z2˙ϖ(s)ds1σηT2()[Z20003Z20005Z2]η2(), (3.9)

    where

    η1()=[ζT()ζT(κ)ζT()+ζT(κ)2κκζT(s)dsζT()ζT(κ)+6κκζT(s)ds12κ2κtsζT(u)duds]T,η2()=[ϖT()ϖT(σ)ϖT()+ϖT(σ)2σσϖT(s)dsϖT()ϖT(σ)+6σσϖT(s)ds12σ2κtsϖT(u)duds]T.

    From the assumption (A), there exists a diagonal matrices S1>0, S2>0 and Hi, Li(i=1,2), the following inequalities hold:

    0[ϖ()f(ϖ())]T[S1H1S1H2S1][ϖ()f(ϖ())], (3.10)
    0[ζ()g(ζ())]T[S2L1S2L2S2][ζ()g(ζ())]. (3.11)

    The following equations satisfy for any matrices J1,J2 and scalar ϵ1,ϵ2:

    0=2[ζT()+ϵ1˙ζ()]J1[˙ζ()+(A+(K1+ϕ()ΔK1()))ζ()+W1f(ϖ())+W2f(ϖ(σ()))], (3.12)
    0=2[ϖT()+ϵ2˙ϖ()]J2[˙ϖ()+(B+(K2+ϱ()ΔK2()))ϖ()+V1g(ζ())+V2g(ζ(κ()))], (3.13)

    substituting (3.4)(3.6), and (3.7)(3.23) in (3.3), we get

    E{LV(ζ,ϖ,)}ξT() Ξ ξ(), (3.14)

    where ξ()=[ζT() ζT(κ) gT(ζ()) gT(ζ(κ())) κζT(s)ds κsζT(s)duds ˙ζT() ϖT()ϖT(σ) fT(ϖ()) fT(ϖ(κ())) σϖT(s)ds σsϖT(s)duds ˙ϖT()]T.

    It gives that Ξ <0. This demonstrates that the system (2.10) is asymptotically stable using the Lyapunov stability theory. The proof is now complete.

    Theorem 3.2. Under Assumption (A), ϕ,ϱ and ϵi are positive scalars, then there exist symmetric matrices Pi>0, Qi>0, Ri>0, Zi>0, diagonal matrices Si>0, for any matrices Ji, Gi, and scalars ρi>0 (i=1,2) such that

    ˜Ξ=[ˆΞΥ1ρ1Υ2Ψ1Ψ2ρ1I000ρ1I00ρ2I0ρ2I]<0, (3.15)

    where

    ˆΞ=[ˆΠΩˆΛ],ˆΠ=[ˆΠ113Z1κS2L2024Z1κ260Z1κ3ˆΠ17Π220036Z1κ260Z1κ30Π330000Π44000192Z1κ3360Z1κ40720Z1κ50Π77],ˆΛ=[ˆΛ113Z2σS1H2024Z2σ260Z2σ3ˆΛ17Λ220036Z2σ260Z2σ30Λ330000Λ44000192Z2σ3360Z2σ40720Z2σ50Λ77],
    ˆΠ11=R1S2L19Z1κJ1A+G1+GT1ATJT1,ˆΠ17=J1ϵ1ATJT1+ϵ1GT1+P1,ˆΛ11=R2S1H1J2B+G29Z2σBTJT+GT2,ˆΛ17=J2ϵ2BTJT2+ϵ2GT2+P2,Υ1=[ϕHT1JT1 0,...,05elements ϕϵ1HT1JT1 0,...,07elements]T,Υ2=[E1 0,...,013elements]T,Ψ1=[0,...,07elements ϱHT2JT2 0,...,05elements ϱϵ2HT2JT2]T,Ψ2=[0,...,07elements ρ2E2 0,...,06elements]T

    The controller gain matrices are also provided by in equation (2.6) are given by Ki=J1iGi.

    Proof. By using Schur complement,

    [ˆΞ]+Υ1ΔK1()ΥT2+Υ2ΔKT1()ΥT1+Ψ1ΔK2()ΨT2+Ψ2ΔKT2()ΨT1<0.

    From Lemma 2, we get

    ˜Ξ=[ˆΞΥ1ρ1Υ2Ψ1Ψ2ρ1I000ρ1I00ρ2I0ρ2I]<0

    It is clear that the disparities in (3.15) still exist. The evidence is now complete.

    Two examples are provided in this part to show the applicability of our findings.

    Example 4.1. The following BAM neural network with time varying delays:

    ˙ζ()=(A+(K1+ϕ()ΔK1()))ζ()+W1f(ϖ())+W2f(ϖ(σ())),˙ϖ()=(B+(K2+ϱ()ΔK2()))ϖ()+V1g(ζ())+V2g(ζ(κ())),} (4.1)

    with the following parameters:

    A=[1001], W1=[20.1153.2],W2=[1.60.80.182.5], H1=[0.45000.45],E1=[0.35000.35], B=[3002], V1=[11.1121.2],V2=[0.61.80.680.5],H2=[0.25000.25], E2=[0.15000.15], H1=L1=0, H2=L2=I.

    Additionally, we take κ=3.2, σ=3.2, μ1=0.9, μ2=0.9, ϕ=0.9, ϱ=0.9, ϵ1=0.5, and ϵ2=0.5. By solving the LMIs in Theorem 3.2, we get

    P1=[48.38230.57070.570749.5117], P2=[46.62800.25890.258950.0702],Q1=[19.28354.76024.760228.1068], Q2=[19.03795.74755.747525.7761],R1=[35.03181.41081.410831.6352],R2=[35.27981.52881.528833.4075],Z1=[0.01670.00090.00090.0116], Z2=[0.03320.01960.01960.0971],G1=[96.51481.09481.094899.3356],G2=[91.47451.38211.382196.5435],J1=[0.15710.03320.03320.1827], J2=[0.39030.18070.18070.9874]ρ1=42.3781, ρ2=44.9762.

    As well as the following non-fragile controller gains matrices:

    K1=[639.9952126.6829122.2775566.6860], K2=[256.750653.331248.3949107.5330].

    In this scenario is proved the given system is asymptotically stable. In Figure 1, state response are depicted.

    Figure 1.  The dynamical behavior of the system in Example 4.1.

    The issue of BAM delayed neural networks with non-fragile control and controller gain fluctuation has been examined in this article. To ensure the stability of the aforementioned systems, delay-dependent conditions are established. In terms of linear matrix inequalities (LMIs), necessary conditions are obtained by building an L-K functional and utilizing the traditional integral inequality technique. This technique ensures asymptotically stability of addressed to the concerned neural networks. Lastly, a numerical example is provided to demonstrate the viability of the findings in this study. Stochastic differential equations and complex networks both respond well to this approach in future research.



    Conflicts of interest



    The authors have no conflicts of interest to declare.

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