Citation: Lin Kooi Ong, Frederick Rohan Walker, Michael Nilsson. Is Stroke a Neurodegenerative Condition? A Critical Review of Secondary Neurodegeneration and Amyloid-beta Accumulation after Stroke[J]. AIMS Medical Science, 2017, 4(1): 1-16. doi: 10.3934/medsci.2017.1.1
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Several years ago, Bensoussan, Sethi, Vickson and Derzko [1] have been considered the case of a factory producing one type of economic goods and observed that it is necessary to solve the simple partial differential equation
{−σ22Δzαs+14|∇zαs|2+αzαs=|x|2forx∈RN,zαs=∞as|x|→∞, | (1.1) |
where σ∈(0,∞) denotes the diffusion coefficient, α∈[0,∞) represents psychological rate of time discount, x∈RN is the product vector, z:=zαs(x) denotes the value function and |x|2 is the loss function.
Regime switching refers to the situation when the characteristics of the state process are affected by several regimes (e.g., in finance bull and bear market with higher volatility in the bear market).
It is important to point out that, when dealing with regime switching, we can describe a wide variety of phenomena using partial differential equations. In [1], the authors Cadenillas, Lakner and Pinedo [2] adapted the model problem in [1] to study the optimal production management characterized by the two-state regime switching with limited/unlimited information and corresponding to the system
{−σ212Δus1+(a11+α1)us1−a11us2−ρσ212∑i≠j∂2us1∂xi∂xj−|x|2=−14|∇us1|2,x∈RN,−σ222Δus2+(a22+α2)us2−a22us1−ρσ222∑i≠j∂2us2∂xi∂xj−|x|2=−14|∇us2|2,x∈RN,us1(x)=us2(x)=∞as|x|→∞, | (1.2) |
where σ1,σ2∈(0,∞) denote the diffusion coefficients, α1,α2∈[0,∞) represent the psychological rates of time discount from what place the exponential discounting, x∈RN is the product vector, usr:=usr(x) (r=1,2) denotes the value functions, |x|2 is the loss function, ρ∈[−1,1] is the correlation coefficient and anm (n,m=1,2) are the elements of the Markov chain's rate matrix, denoted by G=[ϑnm]2×2 with
ϑnn=−ann≤0,ϑnm=anm≥0andϑ2nn+ϑ2nm≠0forn≠m, |
the diagonal elements ϑnn may be expressed as ϑnn=−Σm≠nϑnm.
Furthermore, in civil engineering, Dong, Malikopoulos, Djouadi and Kuruganti [3] applied the model described in [2] to the study of the optimal stochastic control problem for home energy systems with solar and energy storage devices; the two regimes switching are the peak and the peak energy demands.
After that, there have been numerous applications of regime switching in many important problems in economics and other fields, see the works of: Capponi and Figueroa-López [4], Elliott and Hamada [5], Gharbi and Kenne [6], Yao, Zhang and Zhou [7] and Wang, Chang and Fang [8] for more details. Other different research studies that explain the importance of regime switching in the real world are [9,10].
In this paper, we focus on the following parabolic partial differential equation and system, corresponding to (1.1)
{∂z∂t(x,t)−σ22Δz(x,t)+14|∇z(x,t)|2+αz(x,t)=|x|2,(x,t)∈RN×(0,∞),z(x,0)=c+zαs(x),forallx∈RNandfixedc∈(0,∞),z(x,t)=∞as|x|→∞,forallt∈[0,∞), | (1.3) |
and (1.2) respectively
{∂u1∂t−σ212Δu1+(a11+α1)u1−a11u2−ρσ212∑i≠j∂2u1∂xi∂xj−|x|2=−14|∇u1|2,(x,t)∈RN×(0,∞),∂u2∂t−σ222Δu2+(a22+α2)u2−a22u1−ρσ222∑i≠j∂2u2∂xi∂xj−|x|2=−14|∇u2|2,(x,t)∈RN×(0,∞),(u1(x,0),u2(x,0))=(c1+us1(x),c2+us2(x))forallx∈RNandforfixedc1,c2∈(0,∞),∂u1∂t(x,t)=∂u2∂t(x,t)=∞as|x|→∞forallt∈[0,∞), | (1.4) |
where zαs is the solution of (1.1) and (us1(x),us2(x)) is the solution of (1.2). The existence and the uniqueness for the case of (1.1) is proved by [10] and the existence for the system case of (1.2) by [11].
From the mathematical point of view the problem (1.3) has been extensively studied when the space RN is replaced by a bounded domain and when α=0. In particular, some great results can be found in the old papers of Barles, Porretta [12] and Tchamba [13]. More recently, but again for the case of a bounded domain, α=0 and in the absence of the gradient term, the problem (1.3) has been also discussed by Alves and Boudjeriou [14]. The interest of these authors [12,13,14] is to give an asymptotic stable solution at infinity for the considered equation, i.e., a solution which tends to the stationary Dirichlet problem associated with (1.3) when the time go to infinity.
Next, we propose to find a similar result as of [12,13,14], for the case of equation (1.3) and system (1.4) that model some real phenomena. More that, our first interest is to provide a closed form solution for (1.3) and (1.4). Our second objective is inspired by the paper of [14,15], and it is to solve the parabolic partial differential equation
{∂z∂t(x,t)−σ22Δz(x,t)+14|∇z(x,t)|2=|x|2,inBR×[0,T),z(x,T)=0,for|x|=R, | (1.5) |
where T<∞ and BR is a ball of radius R>0 with origin at the center of RN.
Let us finish our introduction and start with the main results.
We use the change of variable
u(x,t)=e−z(x,t)2σ2, | (2.1) |
in
∂z∂t(x,t)−σ22Δz(x,t)+14|∇z(x,t)|2+αz(x,t)=|x|2 |
to rewrite (1.3) and (1.5) in an equivalent form
{∂u∂t(x,t)−σ22Δu(x,t)+αu(x,t)lnu(x,t)+12σ2|x|2u(x,t)=0,if(x,t)∈Ω×(0,T)u(x,T)=u1,0,on∂Ω,u(x,0)=e−c+zαs(x)2σ2,forx∈Ω=RN,c∈(0,∞) | (2.2) |
where
u1,0={1ifΩ=BR,i.e.,|x|=R,T<∞,0ifΩ=RN,i.e.,|x|→∞,T=∞. |
Our first result is the following.
Theorem 2.1. Assume Ω=BR, N≥3, T<∞ and α=0.There exists a unique radially symmetric positive solution
u(x,t)∈C2(BR×[0,T))∩C(¯BR×[0,T]), |
of (2.2) increasing in the time variable and such that
limt→Tu(x,t)=us(x), | (2.3) |
where us∈C2(BR)∩C(¯BR) is the unique positive radially symmetric solution of theDirichlet problem
{σ22Δus=(12σ2|x|2+1)us,inBR,us=1,on∂BR, | (2.4) |
which will be proved. In addition,
z(x,t)=−2σ2(t−T)−2σ2lnus(|x|),(x,t)∈¯BR×[0,T], |
is the unique radially symmetric solution of the problem (1.5).
Instead of the existence results discussed in the papers of [12,13,14], in our proof of the Theorem 2.1 we give the numerical approximation of solution u(x,t).
The next results refer to the entire Euclidean space RN and present closed-form solutions.
Theorem 2.2. Assume Ω=RN, N≥1, T=∞, α>0 and c∈(0,∞) is fixed. There exists aunique radially symmetric solution
u(x,t)∈C2(RN×[0,∞)), |
of (2.2), increasing in the time variable and such that
u(x,t)→uαs(x)ast→∞forallx∈RN, | (2.5) |
where uαs∈C2(RN) is the uniqueradially symmetric solution of the stationary Dirichlet problem associatedwith (2.2)
{σ22Δuαs=αuαslnuαs+12σ2|x|2uαs,inRN,uαs(x)→0,as|x|→∞. | (2.6) |
Moreover, the closed-form radially symmetric solution of the problem (1.3) is
z(x,t)=ce−αt+B|x|2+D,(x,t)∈RN×[0,∞),c∈(0,∞), | (2.7) |
where
B=1Nσ2(12Nσ2√α2+4−12Nασ2),D=12α(Nσ2√α2+4−Nασ2). | (2.8) |
The following theorem is our main result regarding the system (1.4).
Theorem 2.3. Suppose that N≥1, α1,α2∈(0,∞) and\ a11,a22∈[0,∞) with a211+a222≠0. Then, the system (1.4) has a uniqueradially symmetric convex solution
(u1(x,t),u2(x,t))∈C2(RN×[0,∞))×C2(RN×[0,∞)), |
of quadratic form in the x variable and such that
(u1(x,t),u2(x,t))→(us1(x),us2(x))ast→∞uniformlyforallx∈RN, | (2.9) |
where
(us1(x),us2(x))∈C2(RN)×C2(RN) |
is the radially symmetric convex solution of quadratic form in the xvariable of the stationary system (1.2) which exists from the resultof [11].
Our results complete the following four main works: Bensoussan, Sethi, Vickson and Derzko [1], Cadenillas, Lakner and Pinedo [2], Canepa, Covei and Pirvu [15] and Covei [10], which deal with a stochastic control model problem with the corresponding impact for the parabolic case (see [13,16] for details).
To prove our Theorem 2.1, we use a lower and upper solution method and the comparison principle that can be found in [17].
Lemma 2.1. If, there exist ¯u(x), u_(x)∈C2(BR)∩C(¯BR) two positive functions satisfying
{−σ22Δ¯u(x)+(12σ2|x|2+1)¯u(x)≥0≥−σ22Δu_(x)+(12σ2|x|2+1)u_(x)inBR,¯u(x)=1=u_(x)on∂BR, |
then
¯u(x)−u_(x)≥0forallx∈¯BR, |
and there exists
u(x)∈C2(BR)∩C(¯BR), |
a solution of (2.4) such that
u_(x)≤u(x)≤¯u(x),x∈¯BR, |
where u_(x) and ¯u(x) arerespectively, called a lower solution and an upper solution of (2.4).
The corresponding result of Lemma 2.1 for the parabolic equations can be found in the work of Pao [18] and Amann [19]. To achieve our goal, complementary to the works [12,13,14,15] it can be used the well known books of Gilbarg and Trudinger [20], Sattinger [17], Pao [18] and a paper of Amann [19]. Further on, we can proceed to prove Theorem 2.1.
By a direct calculation, if there exists and is unique, us∈C2(BR)∩C(¯BR), a positive solution of the stationary Dirichlet problem (2.4) then
u(x,t)=et−Tus(x),(x,t)∈¯BR×[0,T], |
is the solution of the problem (2.2) and
z(x,t)=−2σ2(t−T)−2σ2lnus(x),(x,t)∈¯BR×[0,T], |
is the solution of the problem (1.5) belonging to
C2(BR×[0,T))∩C(¯BR×[0,T]). |
We prove that (2.4) has a unique radially symmetric solution. The existence of solution for (2.4) is obtained by a standard monotone iteration and the lower and the upper solution method, Lemma 2.1. Hence, starting from the initial iteration
u0s(x)=e−R2−|x|22σ2, |
we construct a sequence {uks(x)}k≥1 successively by
{σ22Δuks(x)=(12σ2|x|2+1)uk−1s(x),inBR,uks(x)=1,on∂BR, | (3.1) |
and this sequence will be pointwise convergent to a solution us(x) of (2.4).
Indeed, since for each k the right-hand side of (3.1) is known, the existence theory for linear elliptic boundary-value problems implies that {uks(x)}k≥1 is well defined, see [20].
Let us prove that {uks(x)}k≥1 is a pointwise convergent sequence to a solution of (2.4) in ¯BR. To do this, first we prove that {uks(x)}k≥1 is monotone nondecreasing of k. We apply the mathematical induction by verifying the first step, k=1.
{σ22Δu1s(x)≤σ22Δu0s(x),inBR,u1s(x)=1=u0s(x),on∂BR. |
Now, by the standard comparison principle, Lemma 2.1, we have
u0s(x)≤u1s(x)in¯BR. |
Moreover, the induction argument yields the following
u0s(x)=e−R2−|x|22σ2≤...≤uks(x)≤uk+1s(x)≤...in¯BR, | (3.2) |
i.e., {uks(x)}k≥1 is a monotone nondecreasing sequence.
Next, using again Lemma 2.1, we find
u_s(x):=u0s(x)=e−R2−|x|22σ2≤...≤uks(x)≤uk+1s(x)≤...≤¯us(x):=1in¯BR, | (3.3) |
where we have used
σ22Δu_s(x)=u_s(x)σ22(|x|2+σ2σ4+N−1σ2)≥u_s(x)(12σ2|x|2+1)σ22Δ¯us(x)=σ22Δ1=0≤¯us(x)(12σ2|x|2+1) |
i.e., Lemma 2.1 confirm.Thus, in view of the monotone and bounded property in (3.3) the sequence {uks(x)}k≥1 converges. We may pass to the limit in (3.3) to get the existence of a solution
us(x):=limk→∞uks(x)in¯BR, |
associated to (2.4), which satisfies
u_s(x)≤us(x)≤¯us(x)in¯BR. |
Furthermore, the convergence of {uks(x)} is uniformly to us(x) in ¯BR and us(x) has a radial symmetry, see [15] for arguments of the proof. The regularity of solution us(x) is a consequence of classical results from the theory of elliptic equations, see Gilbarg and Trudinger [20]. The uniqueness of us(x) follows from a standard argument with the use of Lemma 2.1 and we omit the details.
Clearly, u(x,t) is increasing in the time variable. The regularity of u(x,t) follows from the regularity of us(x). Letting t→T we see that (2.3) holds. The solution of the initial problem (1.5) is saved from (2.1).
Finally, we prove the uniqueness for (2.2). Let
u(x,t),v(x,t)∈C2(BR×[0,T))∩C(¯BR×[0,T]), |
be two solutions of the problem (2.2), i.e., its hold
{∂u∂t(x,t)−σ22Δu(x,t)+12σ2|x|2u(x,t)=0,if(x,t)∈BR×[0,T),u(x,T)=1,on∂BR, |
and
{∂v∂t(x,t)−σ22Δv(x,t)+12σ2|x|2v(x,t)=0,if(x,t)∈BR×[0,T),v(x,T)=1,on∂BR. |
Setting
w(x,t)=u(x,t)−v(x,t),inBR×[0,T], |
and subtracting the two equations corresponding to u and v we find
{∂w∂t(x,t)=σ22Δw(x,t)−12σ2|x|2w(x,t),if(x,t)∈BR×[0,T),w(x,T)=0,on∂BR. |
Let us prove that u(x,t)−v(x,t)≤0 in ¯BR×[0,T]. If the conclusion were false, then the maximum of
w(x,t),inBR×[0,T), |
is positive. Assume that the maximum of w in ¯BR×[0,T] is achieved at (x0,t0). Then, at the point (x0,t0)∈BR×[0,T), where the maximum is attained, we have
∂w∂t(x0,t0)≥0,Δw(x0,t0)≤0,∇w(x0,t0)=0, |
and
0≤∂w∂t(x0,t0)=σ22Δw(x0,t0)−12σ2|x|2w(x0,t0)<0 |
which is a contradiction. Reversing the role of u and v we obtain that u(x,t)−v(x,t)≥0 in ¯BR×[0,T]. Hence u(x,t)=v(x,t) in ¯BR×[0,T]. The proof of Theorem 2.1 is completed.
Finally, our main result, Theorem 2.2 will be obtained by a direct computation.
In view of the arguments used in the proof of Theorem 2.1 and the real world phenomena, we use a purely intuitive strategy in order to prove Theorem 2.2.
Indeed, for the verification result in the production planning problem, we need z(x,t) to be almost quadratic with respect to the variable x.
More exactly, we observe that there exists and is unique
u(x,t)=e−h(t)+B|x|2+D2σ2,(x,t)∈RN×[0,∞),withB,D∈(0,∞), |
that solve (2.2), where
h(0)=c, | (4.1) |
and B, D are given in (2.8). The condition (4.1) is used to obtain the asymptotic behaviour of solution to the stationary Dirichlet problem associated with (2.2). Then our strategy is reduced to find B,D∈(0,∞) and the function h which depends of time and c∈(0,∞) such that
−12h′(t)σ2−σ22[−Bσ4(σ2−B|x|2)−(N−1)Bσ2]+α(−h(t)+B|x|2+D2σ2)+12σ2|x|2=0, |
or, after rearranging the terms
|x|2(1−αB−B2)+Nσ2B−αD−h′(t)−αh(t)=0, |
where (4.1) holds. Now, by a direct calculation we see that the system of equations
{1−αB−B2=0Nσ2B−αD=0−h′(t)−αh(t)=0h(0)=c |
has a unique solution that satisfies our expectations, namely,
u(x,t)=e−ce−αt+B|x|2+D2σ2,(x,t)∈RN×[0,∞), | (4.2) |
where B and D are given in (2.8), is a radially symmetric solution of the problem (2.2). The uniqueness of the solution is followed by the arguments in [10] combined with the uniqueness proof in Theorem 2.1. The justification of the asymptotic behavior and regularity of the solution can be proved directly, once we have a closed-form solution. Finally, the closed-form solution in (2.7) is due to (2.1)–(4.2) and the proof of Theorem 2.2 is completed.
One way of solving this system of partial differential equation of parabolic type (1.4) is to show that the system (1.4) is solvable by
(u1(x,t),u2(x,t))=(h1(t)+β1|x|2+η1,h2(t)+β2|x|2+η2), | (5.1) |
for some unique β1,β2,η1,η2∈(0,∞) and h1(t), h2(t) are suitable chosen such that
h1(0)=c1andh2(0)=c2. | (5.2) |
The main task for the proof of existence of (5.1) is performed by proving that there exist
β1,β2,η1,η2,h1,h2, |
such that
{h′1(t)−2β1Nσ212+(a11+α1)[h1(t)+β1|x|2+η1]−a11[h2(t)+β2|x|2+η2]−|x|2=−14(2β1|x|)2,h′2(t)−2β2Nσ222+(a22+α2)[h2(t)+β2|x|2+η2]−a22[h1(t)+β1|x|2+η1]−|x|2=−14(2β2|x|)2, |
or equivalently, after grouping the terms
{|x|2[−a11β2+(a11+α1)β1+β21−1]−β1Nσ21−a11η2+(a11+α1)η1+h′1(t)+(a11+α1)h1(t)−a11h2(t)=0,|x|2[−a22β1+(a22+α2)β2+β22−1]−β2Nσ22−a22η1+(a22+α2)η2+h′2(t)+(a22+α2)h2(t)−a22h1(t)=0, |
where h1(t), h2(t) must satisfy (5.2). Now, we consider the system of equations
{−a11β2+(a11+α1)β1+β21−1=0−a22β1+(a22+α2)β2+β22−1=0−β1Nσ21−a11η2+(a11+α1)η1=0−β2Nσ22−a22η1+(a22+α2)η2=0h′1(t)+(a11+α1)h1(t)−a11h2(t)=0h′2(t)+(a22+α2)h2(t)−a22h1(t)=0. | (5.3) |
To solve (5.3), we can rearrange those equations 1, 2 in the following way
{−a11β2+(a11+α1)β1+β21−1=0−a22β1+(a22+α2)β2+β22−1=0. | (5.4) |
We distinguish three cases:
1.in the case a22=0 we have an exact solution for (5.4) of the form
β1=−12α1−12a11+12√α21+a211−4a11(12α2−12√α22+4)+2α1a11+4β2=−12α2+12√α22+4 |
2.in the case a11=0 we have an exact solution for (5.4) of the form
β1=−12α1+12√α21+4β2=−12α2−12a22+12√α22+a222−4a22(12α1−12√α21+4)+2α2a22+4 |
3.in the case a11≠0 and a22≠0, to prove the existence and uniqueness of solution for (5.4) we will proceed as follows. We retain from the first equation of (5.4)
β1=12√α21+2α1a11+a211+4β2a11+4−12a11−12α1. |
and from the second equation
β2=12√α22+2α2a22+a222+4β1a22+4−12a22−12α2. |
The existence of β1, β2∈(0,∞) for (5.4) can be easily proved by observing that the continuous functions f1,f2:[0,∞)→R defined by
f1(β1)=−a11(12√α22+2α2a22+a222+4β1a22+4−12a22−12α2)+(a11+α1)β1+β21−1,f2(β2)=−a22(12√α21+2α1a11+a211+4β2a11+4−12a11−12α1)+(a22+α2)β2+β22−1, |
have the following properties
f1(∞)=∞andf2(∞)=∞, | (5.5) |
respectively
f1(0)=−a11(12√α22+2α2a22+a222+4−12a22−12α2)−1<0,f2(0)=−a22(12√α21+2α1a11+a211+4−12a11−12α1)−1<0. | (5.6) |
The observations (5.5) and (5.6) imply
{f1(β1)=0f2(β2)=0 |
has at least one solution (β1,β2)∈(0,∞)×(0,∞) and furthermore it is unique (see also, the references [21,22] for the existence and the uniqueness of solutions).
The discussion from cases 1–3 show that the system (5.4) has a unique positive solution. Next, letting
(β1,β2)∈(0,∞)×(0,∞), |
be the unique positive solution of (5.4), we observe that the equations 3, 4 of (5.3) can be written equivalently as a system of linear equations that is solvable and with a unique solution
(a11+α1−a11−a22a22+α2)(η1η2)=(β1Nσ21β2Nσ22). | (5.7) |
By defining
Ga,α:=(a11+α1−a11−a22a22+α2), |
we observe that
G−1a,α=(α2+a22α1α2+α2a11+α1a22a11α1α2+α2a11+α1a22a22α1α2+α2a11+α1a22α1+a11α1α2+α2a11+α1a22). |
Using the fact that G−1a,α has all ellements positive and rewriting (5.7) in the following way
(η1η2)=G−1a,α(β1Nσ21β2Nσ22), |
we can see that there exist and are unique η1, η2∈(0,∞) that solve (5.7). Finally, the equations 5, 6, 7 of (5.3) with initial condition (5.2) can be written equivalently as a solvable Cauchy problem for a first order system of differential equations
{(h′1(t)h′2(t))+Ga,α(h1(t)h2(t))=(00),h1(0)=c1andh2(0)=c2, | (5.8) |
with a unique solution and then (5.1) solve (1.4). The rest of the conclusions are easily verified.
Next, we present an application.
Application 1. Suppose there is one machine producing two products (see [23,24], for details). We consider a continuous time Markov chain generator
(−121212−12), |
and the time-dependent production planning problem with diffusion σ1=σ2=1√2 and let α1=α2=12 the discount factor. Under these assumptions, we can write the system (5.4) with our data
{β21+β1−12β2−1=0β22−12β1+β2−1=0 |
which has a unique positive solution
β1=14(√17−1),β2=14(√17−1). |
On the other hand, the system (5.7) becomes
(1−12−121)(η1η2)=(β1β2), |
which has a unique positive solution
η1=43β1+23β2=12(√17−1),η2=23β1+43β2=12(√17−1). |
Finally, the system in (5.8) becomes
{(h′1(t)h′2(t))+(1−12−121)(h1(t)h2(t))=(00),h1(0)=c1andh2(0)=c2, |
which has the solution
h1(t)=s1e−12t−s2e−32t,h2(t)=s1e−12t+s2e−32t,withs1,s2∈R. |
Next, from
h1(0)=c1andh2(0)=c2, |
we have
{s1−s2=c1s1+s2=c2⟹s1=12c1+12c2,s2=12c2−12c1, |
and finally
{h1(t)=12(c1+c2)e−12t−12(c2−c1)e−32t,h2(t)=12(c1+c2)e−12t+12(c2−c1)e−32t, |
from where we can write the unique solution of the system (1.4) in the form (5.1).
Let us point that in Theorem 2.3 we have proved the existence and the uniqueness of a solution of quadratic form in the x variable and then the existence of other different types of solutions remain an open problem.
Some closed-form solutions for equations and systems of parabolic type are presented. The form of the solutions is unique and tends to the solutions of the corresponding elliptic type problems that were considered.
The author is grateful to the anonymous referees for their useful suggestions which improved the contents of this article.
The authors declare there is no conflict of interest.
[1] |
Baron JC, Yamauchi H, Fujioka M, et al. (2014) Selective neuronal loss in ischemic stroke and cerebrovascular disease. J Cereb Blood Flow Metab 34: 2-18. doi: 10.1038/jcbfm.2013.188
![]() |
[2] |
Zhang J, Zhang Y, Xing S, et al. (2012) Secondary neurodegeneration in remote regions after focal cerebral infarction: a new target for stroke management? Stroke 43: 1700-1705. doi: 10.1161/STROKEAHA.111.632448
![]() |
[3] |
Levine DA, Galecki AT, Langa KM, et al. (2015) Trajectory of Cognitive Decline After Incident Stroke. JAMA 314: 41-51. doi: 10.1001/jama.2015.6968
![]() |
[4] |
Yang J, Wong A, Wang Z, et al. (2015) Risk factors for incident dementia after stroke and transient ischemic attack. Alzheimers Dement 11: 16-23. doi: 10.1016/j.jalz.2014.01.003
![]() |
[5] |
Querfurth HW, LaFerla FM (2010) Alzheimer's disease. N Engl J Med 362: 329-344. doi: 10.1056/NEJMra0909142
![]() |
[6] |
Caughey B, Lansbury PT (2003) Protofibrils, pores, fibrils, and neurodegeneration: separating the responsible protein aggregates from the innocent bystanders. Annu Rev Neurosci 26: 267-298. doi: 10.1146/annurev.neuro.26.010302.081142
![]() |
[7] | Haass C, Selkoe DJ (2007) Soluble protein oligomers in neurodegeneration: lessons from the Alzheimer's amyloid beta-peptide. Nat Rev Mol Cell Biol 8: 101-112. |
[8] |
Roychaudhuri R, Yang M, Hoshi MM, et al. (2009) Amyloid beta-protein assembly and Alzheimer disease. J Biol Chem 284: 4749-4753. doi: 10.1074/jbc.R800036200
![]() |
[9] | Giuffrida ML, Caraci F, De Bona P, et al. (2010) The monomer state of beta-amyloid: where the Alzheimer's disease protein meets physiology. Rev Neurosci 21: 83-93. |
[10] |
Gilbert BJ (2013) The role of amyloid beta in the pathogenesis of Alzheimer's disease. J Clin Pathol 66: 362-366. doi: 10.1136/jclinpath-2013-201515
![]() |
[11] | Carrillo-Mora P, Luna R, Colin-Barenque L (2014) Amyloid beta: multiple mechanisms of toxicity and only some protective effects? Oxid Med Cell Longev 2014: 795375. |
[12] |
Viola KL, Klein WL (2015) Amyloid beta oligomers in Alzheimer's disease pathogenesis, treatment, and diagnosis. Acta Neuropathol 129: 183-206. doi: 10.1007/s00401-015-1386-3
![]() |
[13] | Bruggink KA, Muller M, Kuiperij HB, et al. (2012) Methods for analysis of amyloid-beta aggregates. J Alzheimers Dis 28: 735-758. |
[14] |
Pryor NE, Moss MA, Hestekin CN (2012) Unraveling the early events of amyloid-beta protein (Abeta) aggregation: techniques for the determination of Abeta aggregate size. Int J Mol Sci 13: 3038-3072. doi: 10.3390/ijms13033038
![]() |
[15] |
van Groen T, Puurunen K, Maki HM, et al. (2005) Transformation of diffuse beta-amyloid precursor protein and beta-amyloid deposits to plaques in the thalamus after transient occlusion of the middle cerebral artery in rats. Stroke 36: 1551-1556. doi: 10.1161/01.STR.0000169933.88903.cf
![]() |
[16] |
Aho L, Jolkkonen J, Alafuzoff I (2006) Beta-amyloid aggregation in human brains with cerebrovascular lesions. Stroke 37: 2940-2945. doi: 10.1161/01.STR.0000248777.44128.93
![]() |
[17] |
Makinen S, van Groen T, Clarke J, et al. (2008) Coaccumulation of calcium and beta-amyloid in the thalamus after transient middle cerebral artery occlusion in rats. J Cereb Blood Flow Metab 28: 263-268. doi: 10.1038/sj.jcbfm.9600529
![]() |
[18] |
Ly JV, Rowe CC, Villemagne VL, et al. (2012) Subacute ischemic stroke is associated with focal 11C PiB positron emission tomography retention but not with global neocortical Abeta deposition. Stroke 43: 1341-1346. doi: 10.1161/STROKEAHA.111.636266
![]() |
[19] |
Lipsanen A, Kalesnykas G, Pro-Sistiaga P, et al. (2013) Lack of secondary pathology in the thalamus after focal cerebral ischemia in nonhuman primates. Exp Neurol 248: 224-227. doi: 10.1016/j.expneurol.2013.06.016
![]() |
[20] |
Sahathevan R, Linden T, Villemagne VL, et al. (2016) Positron Emission Tomographic Imaging in Stroke: Cross-Sectional and Follow-Up Assessment of Amyloid in Ischemic Stroke. Stroke 47: 113-119. doi: 10.1161/STROKEAHA.115.010528
![]() |
[21] | Ong LK, Zhao Z, Kluge M, et al. (2016) Chronic stress exposure following photothrombotic stroke is associated with increased levels of Amyloid beta accumulation and altered oligomerisation at sites of thalamic secondary neurodegeneration in mice. J Cereb Blood Flow Metab. |
[22] |
Lesne S, Koh MT, Kotilinek L, et al. (2006) A specific amyloid-beta protein assembly in the brain impairs memory. Nature 440: 352-357. doi: 10.1038/nature04533
![]() |
[23] |
Selkoe DJ, Hardy J (2016) The amyloid hypothesis of Alzheimer's disease at 25 years. EMBO Mol Med 8: 595-608. doi: 10.15252/emmm.201606210
![]() |
[24] | Sheikh S, Safia, Haque E, et al. (2013) Neurodegenerative Diseases: Multifactorial Conformational Diseases and Their Therapeutic Interventions. J Neurodegener Dis 2013: 563481. |
[25] |
Walsh DM, Klyubin I, Fadeeva JV, et al. (2002) Naturally secreted oligomers of amyloid beta protein potently inhibit hippocampal long-term potentiation in vivo. Nature 416: 535-539. doi: 10.1038/416535a
![]() |
[26] |
Buell AK, Dobson CM, Knowles TP, et al. (2010) Interactions between amyloidophilic dyes and their relevance to studies of amyloid inhibitors. Biophys J 99: 3492-3497. doi: 10.1016/j.bpj.2010.08.074
![]() |
[27] |
Nesterov EE, Skoch J, Hyman BT, et al. (2005) In vivo optical imaging of amyloid aggregates in brain: design of fluorescent markers. Angew Chem Int Ed Engl 44: 5452-5456. doi: 10.1002/anie.200500845
![]() |
[28] |
Klunk WE, Engler H, Nordberg A, et al. (2004) Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 55: 306-319. doi: 10.1002/ana.20009
![]() |
[29] |
Cohen AD, Rabinovici GD, Mathis CA, et al. (2012) Using Pittsburgh Compound B for in vivo PET imaging of fibrillar amyloid-beta. Adv Pharmacol 64: 27-81. doi: 10.1016/B978-0-12-394816-8.00002-7
![]() |
[30] |
Liu W, Wong A, Au L, et al. (2015) Influence of Amyloid-beta on Cognitive Decline After Stroke/Transient Ischemic Attack: Three-Year Longitudinal Study. Stroke 46: 3074-3080. doi: 10.1161/STROKEAHA.115.010449
![]() |
[31] | Rowe CC, Villemagne VL (2011) Brain amyloid imaging. J Nucl Med 52: 1733-1740. |
[32] |
Glass CK, Saijo K, Winner B, et al. (2010) Mechanisms underlying inflammation in neurodegeneration. Cell 140: 918-934. doi: 10.1016/j.cell.2010.02.016
![]() |
[33] |
Martin JB (1999) Molecular basis of the neurodegenerative disorders. N Engl J Med 340: 1970-1980. doi: 10.1056/NEJM199906243402507
![]() |
[34] | Kovacs GG (2016) Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine. Int J Mol Sci 17. |
[35] |
Tamura A, Tahira Y, Nagashima H, et al. (1991) Thalamic atrophy following cerebral infarction in the territory of the middle cerebral artery. Stroke 22: 615-618. doi: 10.1161/01.STR.22.5.615
![]() |
[36] |
Ogawa T, Yoshida Y, Okudera T, et al. (1997) Secondary thalamic degeneration after cerebral infarction in the middle cerebral artery distribution: evaluation with MR imaging. Radiology 204: 255-262. doi: 10.1148/radiology.204.1.9205256
![]() |
[37] |
Nakane M, Tamura A, Sasaki Y, et al. (2002) MRI of secondary changes in the thalamus following a cerebral infarct. Neuroradiology 44: 915-920. doi: 10.1007/s00234-002-0846-3
![]() |
[38] |
Li C, Ling X, Liu S, et al. (2011) Early detection of secondary damage in ipsilateral thalamus after acute infarction at unilateral corona radiata by diffusion tensor imaging and magnetic resonance spectroscopy. BMC Neurol 11: 49. doi: 10.1186/1471-2377-11-49
![]() |
[39] |
Buffon F, Molko N, Herve D, et al. (2005) Longitudinal diffusion changes in cerebral hemispheres after MCA infarcts. J Cereb Blood Flow Metab 25: 641-650. doi: 10.1038/sj.jcbfm.9600054
![]() |
[40] |
Herve D, Molko N, Pappata S, et al. (2005) Longitudinal thalamic diffusion changes after middle cerebral artery infarcts. J Neurol Neurosurg Psychiatry 76: 200-205. doi: 10.1136/jnnp.2004.041012
![]() |
[41] |
Pappata S, Levasseur M, Gunn RN, et al. (2000) Thalamic microglial activation in ischemic stroke detected in vivo by PET and [11C]PK1195. Neurology 55: 1052-1054. doi: 10.1212/WNL.55.7.1052
![]() |
[42] |
Gerhard A, Schwarz J, Myers R, et al. (2005) Evolution of microglial activation in patients after ischemic stroke: a [11C](R)-PK11195 PET study. Neuroimage 24: 591-595. doi: 10.1016/j.neuroimage.2004.09.034
![]() |
[43] |
Thiel A, Heiss WD (2011) Imaging of microglia activation in stroke. Stroke 42: 507-512. doi: 10.1161/STROKEAHA.110.598821
![]() |
[44] |
Fujie W, Kirino T, Tomukai N, et al. (1990) Progressive shrinkage of the thalamus following middle cerebral artery occlusion in rats. Stroke 21: 1485-1488. doi: 10.1161/01.STR.21.10.1485
![]() |
[45] |
Iizuka H, Sakatani K, Young W (1990) Neural damage in the rat thalamus after cortical infarcts. Stroke 21: 790-794. doi: 10.1161/01.STR.21.5.790
![]() |
[46] |
Dihne M, Grommes C, Lutzenburg M, et al. (2002) Different mechanisms of secondary neuronal damage in thalamic nuclei after focal cerebral ischemia in rats. Stroke 33: 3006-3011. doi: 10.1161/01.STR.0000039406.64644.CB
![]() |
[47] |
Justicia C, Ramos-Cabrer P, Hoehn M (2008) MRI detection of secondary damage after stroke: chronic iron accumulation in the thalamus of the rat brain. Stroke 39: 1541-1547. doi: 10.1161/STROKEAHA.107.503565
![]() |
[48] |
Bihel E, Pro-Sistiaga P, Letourneur A, et al. (2010) Permanent or transient chronic ischemic stroke in the non-human primate: behavioral, neuroimaging, histological, and immunohistochemical investigations. J Cereb Blood Flow Metab 30: 273-285. doi: 10.1038/jcbfm.2009.209
![]() |
[49] |
Ross DT, Ebner FF (1990) Thalamic retrograde degeneration following cortical injury: an excitotoxic process? Neuroscience 35: 525-550. doi: 10.1016/0306-4522(90)90327-Z
![]() |
[50] |
Jones KA, Zouikr I, Patience M, et al. (2015) Chronic stress exacerbates neuronal loss associated with secondary neurodegeneration and suppresses microglial-like cells following focal motor cortex ischemia in the mouse. Brain Behav Immun 48: 57-67. doi: 10.1016/j.bbi.2015.02.014
![]() |
[51] |
Patience MJ, Zouikr I, Jones K, et al. (2015) Photothrombotic stroke induces persistent ipsilateral and contralateral astrogliosis in key cognitive control nuclei. Neurochem Res 40: 362-371. doi: 10.1007/s11064-014-1487-8
![]() |
[52] |
Hiltunen M, Makinen P, Peraniemi S, et al. (2009) Focal cerebral ischemia in rats alters APP processing and expression of Abeta peptide degrading enzymes in the thalamus. Neurobiol Dis 35: 103-113. doi: 10.1016/j.nbd.2009.04.009
![]() |
[53] |
Zhang Y, Xing S, Zhang J, et al. (2011) Reduction of beta-amyloid deposits by gamma-secretase inhibitor is associated with the attenuation of secondary damage in the ipsilateral thalamus and sensory functional improvement after focal cortical infarction in hypertensive rats. J Cereb Blood Flow Metab 31: 572-579. doi: 10.1038/jcbfm.2010.127
![]() |
[54] |
Sarajarvi T, Lipsanen A, Makinen P, et al. (2012) Bepridil decreases Abeta and calcium levels in the thalamus after middle cerebral artery occlusion in rats. J Cell Mol Med 16: 2754-2767. doi: 10.1111/j.1582-4934.2012.01599.x
![]() |
[55] |
Zhang J, Zhang Y, Li J, et al. (2012) Autophagosomes accumulation is associated with beta-amyloid deposits and secondary damage in the thalamus after focal cortical infarction in hypertensive rats. J Neurochem 120: 564-573. doi: 10.1111/j.1471-4159.2011.07496.x
![]() |
[56] |
Mitkari B, Kerkela E, Nystedt J, et al. (2015) Unexpected complication in a rat stroke model: exacerbation of secondary pathology in the thalamus by subacute intraarterial administration of human bone marrow-derived mesenchymal stem cells. J Cereb Blood Flow Metab 35: 363-366. doi: 10.1038/jcbfm.2014.235
![]() |
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