Loading [MathJax]/jax/output/SVG/jax.js
Review

Cerebral Connectivity and High-grade Gliomas: Evolving Concepts of Eloquent Brain in Surgery for Glioma

  • Technological advances in imaging the human brain help us map and understand the intricacies of cerebral connectivity. Current techniques and specific imaging sequences, however, do come with limitations. Image resolution, variability of techniques and interpretation of images across institutions are just a few concerns. In the setting of high-grade gliomas, understanding how these pathways are affected during tumor growth, surgical resection, and in the brain plasticity presents an even greater challenge. Clinical symptoms, tumor growth, and intraoperative electrical stimulation are important peri-operative considerations to assist in determining neuronal re-wiring and establish a basis of anatomic and functional correlation. The application of functional mapping coupled with the understanding of the natural history of gliomas and implications of neural plasticity, is critical in achieving the goals of maximal tumor resection while minimizing post operative deficits and improving quality of life.

    Citation: Sanjay Konakondla, Steven A. Toms. Cerebral Connectivity and High-grade Gliomas: Evolving Concepts of Eloquent Brain in Surgery for Glioma[J]. AIMS Medical Science, 2017, 4(1): 52-70. doi: 10.3934/medsci.2017.1.52

    Related Papers:

    [1] Shahbaz Ali, Muhammad Khalid Mahmmod, Raúl M. Falcón . A paradigmatic approach to investigate restricted hyper totient graphs. AIMS Mathematics, 2021, 6(4): 3761-3771. doi: 10.3934/math.2021223
    [2] Moussa Benoumhani . Restricted partitions and convex topologies. AIMS Mathematics, 2025, 10(4): 10187-10203. doi: 10.3934/math.2025464
    [3] Victoria May P. Mendoza, Renier Mendoza, Youngsuk Ko, Jongmin Lee, Eunok Jung . Managing bed capacity and timing of interventions: a COVID-19 model considering behavior and underreporting. AIMS Mathematics, 2023, 8(1): 2201-2225. doi: 10.3934/math.2023114
    [4] Zhenhua Su, Zikai Tang . Minimum distance–unbalancedness of the merged graph of C3 and a tree. AIMS Mathematics, 2024, 9(7): 16863-16875. doi: 10.3934/math.2024818
    [5] Xingwei Ren . Statistical inference of the mixed linear model with incorrect stochastic linear restrictions. AIMS Mathematics, 2025, 10(5): 11349-11368. doi: 10.3934/math.2025516
    [6] Yinzhen Mei, Chengxiao Guo . The minimal degree Kirchhoff index of bicyclic graphs. AIMS Mathematics, 2024, 9(7): 19822-19842. doi: 10.3934/math.2024968
    [7] Nesrin Güler, Melek Eriş Büyükkaya . Statistical inference of a stochastically restricted linear mixed model. AIMS Mathematics, 2023, 8(10): 24401-24417. doi: 10.3934/math.20231244
    [8] JingJun Yu . Some congruences for -regular partitions with certain restrictions. AIMS Mathematics, 2024, 9(3): 6368-6378. doi: 10.3934/math.2024310
    [9] Nik Muhammad Farhan Hakim Nik Badrul Alam, Ku Muhammad Naim Ku Khalif, Nor Izzati Jaini . Synergic ranking of fuzzy Z-numbers based on vectorial distance and spread for application in decision-making. AIMS Mathematics, 2023, 8(5): 11057-11083. doi: 10.3934/math.2023560
    [10] Xiao Xu, Hong Liao, Xu Yang . An automatic density peaks clustering based on a density-distance clustering index. AIMS Mathematics, 2023, 8(12): 28926-28950. doi: 10.3934/math.20231482
  • Technological advances in imaging the human brain help us map and understand the intricacies of cerebral connectivity. Current techniques and specific imaging sequences, however, do come with limitations. Image resolution, variability of techniques and interpretation of images across institutions are just a few concerns. In the setting of high-grade gliomas, understanding how these pathways are affected during tumor growth, surgical resection, and in the brain plasticity presents an even greater challenge. Clinical symptoms, tumor growth, and intraoperative electrical stimulation are important peri-operative considerations to assist in determining neuronal re-wiring and establish a basis of anatomic and functional correlation. The application of functional mapping coupled with the understanding of the natural history of gliomas and implications of neural plasticity, is critical in achieving the goals of maximal tumor resection while minimizing post operative deficits and improving quality of life.



    Several branches of mathematics and engineering use Chebyshev polynomials (CPs), a famous class of orthogonal polynomials. In approximation theory, CPs are often used to represent and estimate functions precisely. They are ideal for giving highly accurate approximations because of their remarkable ability to minimize the greatest error. Filters with customized frequency responses are designed using CPs in signal processing. CPs also arise in other branches, such as mathematical physics, control theory, and mechanics; see [1,2]. In numerical analysis, the different kinds of CPs approximate solutions of different differential equations (DEs). Among the various kinds of CPs, four kinds are recognized as special Jacobi polynomials (see, for example, [3,4,5,6]). Various applications have introduced and utilized other types of CPs. Masjed-Jamei, in [7], introduced other CPs that differ from the first four kinds. This motivated him to call them the fifth and sixth kinds of CPs. In several applications, they were used; see, for example, [8,9,10]. Recently, the authors of [11] introduced other CPs and employed them to solve the nonlinear time-fractional generalized Kawahara equation.

    Spectral methods, which are numerical approaches, are frequently employed to address partial differential equations (PDEs). The core concept of these methods is based on choosing two groups of trial and test functions; see [12,13,14,15]. In the Galerkin method, the two sets coincide; see [16,17,18]. In tau and collocation methods, we have more freedom to choose basis functions, for example; see [19,20,21,22,23]. The collocation method is advantageous due to its easy implementation; see, for example, [24,25,26,27].

    Nonlinear equations are fundamental in many branches of mathematics, physics, and engineering because of the wide variety of phenomena they explain. The solutions to these equations are more difficult than the linear ones. Engineering encounters nonlinear equations in many areas, such as structural analysis, control systems, and electrical circuits. Since most nonlinear equations do not have known analytical solutions, numerical approaches are necessary. One of the most significant nonlinear equations is the FitzHugh–Nagumo (FH–N) equation, which predicts the propagation of nerve impulses. FitzHugh [28] and Nagumo [29] developed this equation. This equation has long piqued the attention of mathematicians and theoretical biologists; see [30]. Many numerical approaches have been followed to find analytical and numerical solutions to the FH–N problem. For instance, the authors of [31] used the cubic B-spline method with a finite one-step hybrid block approach to treat these equations. In addition, the authors of [32] also used a block method and a finite-difference scheme to treat them. The authors of [33] proposed analytical and numerical solutions to the FH–N equation. The authors of [34] found a few analytic solutions to the FH–N equation. The problem was solved using the Jacobi–Gauss–Lobatto collocation technique in [35]. In [36], a pseudospectral approach was used. In [37], a spline approach was followed. The finite difference method was used in [38]. A hybrid block method was used in [39].

    This article aims to introduce special polynomials derived from the generalized Gegenbauer polynomials. We can categorize these polynomials as CPs because they connect to the existing forms of CPs through simple linear combinations. We present the essential characteristics and some significant formulas of these polynomials. Moreover, we solve the FH–N problem using the collocation method. We utilize the introduced CPs as basis functions. These polynomials are used for the first time, to the best of our knowledge. This motivated us to introduce and utilize them. The significance of the paper can be listed in the following items:

    ● Introducing Chebsyhev polynomials that are related to the well-known ones;

    ● Establishing some fundamental formulas of these polynomials, such as their power form and inversion formulas;

    ● Developing the operational matrices for the shifted Chebyshev polynomials;

    ● Designing a numerical algorithm based on the collocation method to solve the FH–N equation.

    This article is structured with the following components: Section 2 discusses the generalized ultraspherical polynomials. In Section 3, new CPs are introduced alongside novel formulas for these polynomials. Section 4 analyzes a collocation method used to solve the FH–N problem. Error analysis is investigated in Section 5. Various examples to demonstrate the proposed scheme can be found in Section 6. Finally, conclusions are presented in Section 7.

    In this section, an account of the generalized Gegenbauer polynomials is given. Some useful formulas for these polynomials will also be presented.

    The generalized Gegenbauer polynomials {G(ζ,θ)n(x)}n0 are orthogonal polynomials on [1,1] with respect to w(x)=(1x2)ζ12|x|2θ. These polynomials can be defined as [40,41]

    G(ζ,θ)n(x)={(ζ+θ)n2(θ+12)n2P(ζ12,θ12)n2(2x21),if n is even,(ζ+θ)n+12(θ+12)n+12xP(ζ12,θ+12)n12(2x21),if n is odd, (2.1)

    where (x)m=Γ(x+m)Γ(x) is the Pochhammer symbol and {P(ζ,θ)n(x)}n0 are the classical Jacobi polynomials. On [1,1], the polynomials G(ζ,θ)n(x) are orthogonal according to [41]. We have

    11w(x)G(ζ,θ)n(x)G(ζ,θ)m(x)dx={hζ,θn,nm,0,n=m, (2.2)

    where hζ,θn is given by

    hζ,θn=(Γ(θ+12))2(n+ζ+θ)(Γ(ζ+θ))2{Γ(ζ+n+12)Γ(n2+ζ+θ)(n2)!Γ(1+n2+θ),n even,Γ(n2+ζ)Γ(n2+ζ+θ+12)(n12)!Γ(n2+θ+1),n odd. (2.3)

    Remark 2.1. The standard Gegenbauer (ultraspherical) polynomials can be deduced from the generalized Gegenbauer polynomials. More definitely, we have

    C(ζ)n(x)=G(ζ,0)n(x),

    where {C(ζ)n(x)}n0 are the Gegenbauer polynomials.

    Remark 2.2. It is worth noting that many important classes of G(ζ,θ)n(x), were previously investigated. More definitely, the fifth, sixth, seventh, and eighth kinds of CPs, denoted, respectively, by Xn(x),Yn(x),Cn(x) and Zn(x), are given as

    Xn(x)=G(0,1)n(x),Yn(x)=G(1,1)n(x),Cn(x)=G(0,2)n(x),Zn(x)=G(1,2)n(x).

    Now we will introduce a certain kind of CPs, Nk(x), k0, which is considered a special case of G(ζ,θ)n(x) after putting ζ=2,θ=1. Therefore, from (2.1), these polynomials can be defined as:

    Nk(x)={(3)k2(32)k2P(32,12)k2(2x21),if k is even;x(3)k+12(32)k+12P(32,32)k12(2x21),if k is odd. (3.1)

    Remark 3.1. We comment on our choices for the parameters ζ=2,θ=1 for the following reasons:

    The generalized polynomials G(ζ,θ)n(x) depend on two parameters, requiring extensive research to derive theoretical formulas for their application in solving different differential equations. The specific choice of parameters helps simplify the computations for some formulas.

    Previous studies have considered specific values of the two parameters of G(ζ,θ)n(x). For instance, in [7], the author introduced the fifth and sixth kinds of CPs by selecting specific parameters. In this paper, we investigate the practical and theoretical implications of choosing ζ=2 and θ=1.

    To our knowledge, this specific choice has not been investigated, making it a new contribution to the literature.

    The choice of ζ=2 and θ=1, also enables us to investigate the analysis of the errors resulting from the approximation.

    We anticipate that additional theoretical formulas for these polynomials, such as their linearization formulas, will be helpful in other applications.

    The first few instances of Nk(x) are as follows:

    N0(x)=1,N1(x)=2x,N2(x)=8x23,N3(x)=16x38x.

    From (2.2), it is easy to see that the orthogonality relation of Nk(x) takes the following form:

    11x2(1x2)3/2Nn(x)Nm(x)dx=hn, (3.2)

    where

    hn=π128{(n+2)(n+4),if n=m,n even,(n+1)(n+5),if n=m,n odd,0,if nm. (3.3)

    Lemma 3.1. Let j be a positive integer. The polynomials Nj(x) can be expressed as

    Nj(x)=j2r=0(1)r(3)jrr!(j2r)!(32)r+1+j2xj2r. (3.4)

    Proof. Formula (3.4) can be split into the following two formulas:

    N2(x)=m=0(1)m(3)2m(m)!m!(32)mx22m, (3.5)
    N2+1(x)=m=0(1)m(3)2m+1(m)!m!(32)m+1x22m+1. (3.6)

    Thus, we show (3.5) and (3.6). If we note the following expression for the Jacobi polynomials [42]:

    P(μ,ν)m(y)=(1)m(ν+1)mm! 2F1(m,m+μ+ν+1ν+1|1+y2), (3.7)

    then, the form in (3.1) implies that N2(x) and N2+1(x) can be, respectively, expressed as

    N2(x)=(3)(32)P(32,12)(2x21), (3.8)
    N2+1(x)=(3)+1(32)+1xP(32,32)(2x21), (3.9)

    which yield the following two hypergeometric expressions:

    N2(x)=12(1)(+1)(+2)2F1(,+332|x2), (3.10)
    N2+1(x)=13(1)(+1)3x 2F1(,+452|x2). (3.11)

    By expanding the two hypergeometric functions 2F1(z) in (3.10) and (3.11) as two finite series and performing some computations, formulas (3.5), and (3.6) can be obtained.

    Lemma 3.2. Let j be a positive integer. The inversion formula of Nj(x) is given as follows:

    xj=j2r=0(j2r+3)j2!(32)j+12r!(3)jr+1Nj2r(x). (3.12)

    Proof. The inversion formula (3.12) can be split into

    x2j=j!(32)jjr=03+2j2rr!(3)2jr+1N2j2r(x), (3.13)
    x2j+1=2j!(32)j+1jr=02+jrr!(3)2jr+2N2j2r+1(x), (3.14)

    which can be proved following similar procedures to those given for the fifth kind of CPs in [43].

    Theorem 3.1. The k-th derivative: dkNs(x)dxk may be expanded as

    dkNs(x)dxk=sk2L=0GkL,sNsk2L(x), sk, (3.15)

    where

    GkL,s=2k× {(1+sk)(3+s2Lk)(s+2)!(s+1)!L!(sLk+3)! 4F3(L,12s2,12s2+k2,3s+L+k2s,12s2,12s2+k2|1),s even,k even,(1s+k)(3s+2L+k)(s+1)!L!(sLk+3)! 4F3(L,1s2,12s2+k2,3s+L+k2s,s2,12s2+k2|1),s odd,k odd,(2s+k)(3s+2L+k)(s+1)!L!(sLk+3)! 4F3(L,1s2,s2+k2,3s+L+k2s,s2,1s2+k2|1),s odd,k even,(2+sk)(3+s2Lk)(s+2)!(1+s)L!(sLk+3)! 4F3(L,12s2,s2+k2,3s+L+k2s,12s2,1s2+k2|1),s even,k odd. (3.16)

    Proof. Formula (3.15) may be divided into the following four equations:

    D2kN2s(x)=4k(1+2s2k)(2s+2)!2s+1skL=03+2s2L2kL!(2sL2k+3)!× 4F3(L,12s,12s+k,32s+L+2k22s,12s,12s+k|1)N2s2L2k(x), (3.17)
    D2k+1N2s+1(x)=21+2k(2s+2)!skL=0(12s+2k)(32s+2L+2k)L!(2sL2k+3)!×4F3(L,32s,12s+k,32s+L+2k32s,12s,12s+k|1)N2s2L2k(x), (3.18)
    D2kN2s+1(x)=21+2k(32s+2k)(2s+2)!skL=02s+L+kL!(2sL2k+4)!×4F3(L,32s,12s+k,42s+L+2k32s,12s,32s+k|1)N2s2L2k+1(x), (3.19)
    D2k+1N2s(x)=41+k(12s+2k)(2s+2)!1+2sskL=01s+L+kL!(2sL2k+2)!×4F3(L,12s,12s+k,22s+L+2k22s,12s,12s+k|1)N2s2L2k1(x). (3.20)

    The above four formulae have analogous proofs. We prove (3.18). Using formula (3.6), we can write

    D2k+1N2s+1(x)=sr=0(1)r22(sr)(2sr+3)!(3+2s2r)r!(2s2k2r)!x2s2r2k.

    The utilization of (3.13) converts the last formula into

    D2k+1N2s+1(x)=sr=0(1)1+r4k(3+2sr)!(32s+2r)(2s2k2r)!r!×srkL=0(3+2s2L2k2r)(2s2k2r+1)!L!(3)1+2sL2k2rN2s2L2k2r(x), (3.21)

    which can be rewritten as

    D2k+1N2s+1(x)=4kskL=0(3+2s2L2k)×Lp=0(1)p+1(12s+2p+2k)(2sp+3)!(32+sp)p!(Lp)!(2sLp2k+3)!N2s2L2k(x). (3.22)

    That last formula can be written as in (3.18).

    Remark 3.2. It is now more beneficial for us to reformulate Theorem 3.1 as follows:

    Theorem 3.2. The k-th derivative: dkNs(x)dxk may be expanded as

    dkNs(x)dxk=sk2L=0ϑL,s,kBkL,sNL(x), sk, (3.23)

    where

    ϑL,s,k={1,(skL), even,0,otherwise,

    and

    BkL,s=GkskL2,s, (3.24)

    and GkL,s is as given in (3.16).

    Now we define the shifted CPs Nk(x) on [0,1], which are given by

    Nk(x)=Nk(2x1). (3.25)

    From (3.2), and (3.3), it can be shown that {Nk(x)}n0 are orthogonal on [0,1] such that

    10Nn(x)Nm(x)w(x)dx=116hn, (3.26)

    where w(x)=(12x)2(x(1x))3/2 and hn is given in (3.3).

    Theorem 3.3. The kth-derivative of Nj(x) may be written as

    dkNj(x)dxk=jk2L=0˜BkL,jNL(x),jk, (3.27)

    with

    ˜BkL,j=2kBkL,j,

    where BkL,j is given in (3.24).

    Proof. Substituting (2x1) for x in Theorem 3.2 results in (3.27).

    Within this section, we will examine the following FH–N equation [37]:

    zt=zxxz(az)(1z),0<a1, (4.1)

    which is constrained by the following initial and boundary conditions:

    z(x,0)=a0(x),0<x1,z(0,t)=a1(t),z(1,t)=a2(t),0<t1, (4.2)

    where a0(x),a1(t) and a2(t) are given functions. Define

    (Ω)=span{Ni(x)Nj(t):i,j=0,1,,M}, (4.3)

    where Ω=(0,1]2. Then, any function zM(x,t)(Ω) may be written as

    zM(x,t)=Mi=0Mj=0cijNi(x)Nj(t)=N(x)CNT(t), (4.4)

    where N(x)=[N0(x),N1(x),,NM(x)] and C=(cij)0i,jM is the matrix of unknowns with the order (M+1)2.

    Now we analyze a collocation algorithm for solving (4.1) governed by (4.2). This algorithm utilizes the operational matrices of the derivatives of Nk(x). These two formulas follow directly from Theorem 3.3.

    Corollary 4.1. Consider the vector N(x)=[N0(x),N1(x),,NM(x)]. Theorem 3.3 enables us to write the first and second derivatives of Ns(x) in matrix form as

    dN(x)dx=QN(x)T,d2N(x)dx2=MN(x)T, (4.5)

    where Q=(˜B1L,s) and M=(˜B2L,s) are the operational matrices of derivatives given by

    Q=(0000˜B11,0000˜B12,0˜B12,100˜B1M,0˜B1M,1˜B1M,M10),
    M=(0000000000B22,00000˜B23,0˜B23,1000˜B2M,0˜B2M,1˜B2M,M200).

    Now, by using Eq (4.4), we can represent the residual R(x,t) of Eq (4.1) as

    R(x,t)=zMt(x,t)zMxx(x,t)+azM(x,t)+(zM(x,t))3(a+1)(zM(x,t))2. (4.6)

    Applying Corollary 4.1 enables us to represent the residual R(x,t) in (4.6) in the following matrix form:

    R(x,t)=N(x)C(QNT(t))TMNT(x)CNT(t)+aN(x)CNT(t)+(N(x)CNT(t))3(a+1)(N(x)CNT(t))2. (4.7)

    Remark 4.1. Nr(0) and Nr(1) can be deduced to give the following relations:

    Nr(0)={18(1)r/2(r+2)(r+4),ifriseven,0,ifrisodd,Nr(1)=124{(r+2)(r+3)(r+4),ifriseven,(r+1)(r+3)(r+5),ifrisodd. (4.8)

    By using the spectral collocation approach and causing the residual R(x,t) to be zero at certain collocation points (xi,tj), we may get the expansion coefficients cij.

    R(xr,ts)=0,r=1,2,3,,M1,s=1,2,3,,M. (4.9)

    Moreover, the conditions in (4.2) lead to

    N(xr)CNT(0)=a0(xr),r=1,2,3,,M+1,N(0)CNT(ts)=a1(ts),s=1,2,3,,M,N(1)CNT(ts)=a2(ts),s=1,2,3,,M, (4.10)

    where {(xr,ts):r,s=1,2,3,,M+1} are the first distinct roots of Nr(x), and Ns(t), respectively. Hence, Newton's iterative approach may be used to solve the (M+1)2 nonlinear system in (4.9)-(4.10).

    Here, we comprehensively examine the suggested polynomial expansion's error analysis corresponding to the one-dimensional (1-D), and two-dimensional (2-D) CP-weighted Sobolev spaces. Four theorems will be formulated and shown.

    ● For the truncation error variable t, the first theorem provides error estimation for the kth-derivative in 1-D CP-weighted Sobolev space.

    ● For the truncation error variable x, the second theorem provides error estimation for the pth-derivative in two-dimensional (2-D) CP-weighted Sobolev space.

    ● For the truncation error variable t, the third theorem provides error estimation for the qth- derivative in 2-D CP-weighted Sobolev space.

    ● The last theorem shows that RM(x,t)L2ω(x,t), for a sufficiently large M, will be small enough.

    Assume that the following CPs-weighted Sobolev space:

    Hmw(t)(I)={u:DktuL2w(t)(I),0km}, (5.1)

    where I=(0,1] with the inner product, norm, and semi-norm, which are, respectively,

    (u,v)Hmw(t)=mk=0(Dktu,Dktv)L2w(t),||u||2Hmw(t)=(u,u)Hmw(t),|u|Hmw(t)=||Dmtu||L2w(t), (5.2)

    where mN.

    Lemma 5.1. As in [44], for n1, n+r>1, and n+s>1, where r,s, are any constants, we have

    Γ(n+r)Γ(n+s)or,snnrs, (5.3)

    where

    or,sn=exp(rs2(n+s1)+112(n+r1)+(rs)2n). (5.4)

    Theorem 5.1. Let ˉχ(t)=Mj=0ˆχjNj(t) be the approximate solution of χ(t)Hα,mw(t)(I). Then for 0kmM+1, we get

    ||Dkt(χ(t)ˆχ(t))||L2w(t)M14(mk)|χ(t)|2Hmw(t), (5.5)

    where AB indicates the existence of a constant ν such that AνB.

    Proof. The definitions of χ(t) and ˆχ(t) allow us to have

    ||Dkt(χ(t)ˆχ(t))||2L2w(t)=n=M+1|ˆχn|2||DktNn(t)||2L2w(t)=n=M+1|ˆχn|2||DktNn(t)||2L2w(t)||DmtNn(t)||2L2w(t)||DmtNn(t)||2L2w(t)||DktNM+1(t)||2L2w(t)||DmtNM+1(t)||2L2w(t)|χ(t)|2Hmw(t). (5.6)

    To estimate the factor ||DktNM+1(t)||2L2w(t)||DmtNM+1(t)||2L2w(t), we first find ||DktNM+1(t)||2L2w(t) as follows:

    ||DktNM+1(t)||2L2w(t)=10DktNM+1(t)DktNM+1(t)w(t)dt. (5.7)

    If we make use of Eq (3.4), and the relation: (2t1)j=jk=0(1)jk(jk)(2t)k, followed by expanding, rearranging, and collecting the similar terms, then the following formula can be obtained:

    Nj(t)=jr=0λr,jtr, (5.8)

    where

    λr,j=jm=02r(1)2r3m+j2aj+m(mr)(3)j+m2jm2!(32)mj2+j+12Γ(12(j+m+2)+j2), (5.9)

    and

    aj={1,if  j  is even,0,otherwise. (5.10)

    By applying Dkt to Eq (5.8), one gets

    DktNM+1(t)=M+1r=kλr,M+1r!(rk)!trk. (5.11)

    Accordingly, we have

    ||DktNM+1(t)||2L2w(t)=M+1r=kλ2r,M+1(r!)2((rk)!)210(2t1)2t32+2r2k(1t)32dt=M+1r=kλ2r,M+1(r!)2((rk)!)210(t2k+2r+324t2k+2r+52+4t2k+2r+72)(1t)32dt=M+1r=k3πλ2r,M+14(Γ(r+1)Γ(rk+1))2(Γ(2k+2r+52)Γ(2k+2r+5)4Γ(2k+2r+72)Γ(2k+2r+6)+4Γ(2k+2r+92)Γ(2k+2r+7)). (5.12)

    The application of the Stirling formula enables one to obtain the following inequalities:

    Γ(r+1)Γ(rk+1)rk,Γ(2k+2r+52)Γ(2k+2r+5)(rk)52,4Γ(2k+2r+72)Γ(2k+2r+6)(rk)52,4Γ(2k+2r+92)Γ(2k+2r+7)(rk)52. (5.13)

    By virtue of the Stirling formula and Lemma 5.1, ||DktNM+1(t)||2L2w(t) can be written as

    ||DktNM+1(t)||2L2w(t)λ(M+1)2k(Mk+1)52M+1r=k1=λ(Γ(M+2)Γ(M+1))2k(Mk+1)52(Mk+2)λM2k(Γ(Mk+2)Γ(Mk+1))52(Γ(Mk+3)Γ(Mk+2))M2k(Mk)32, (5.14)

    where λ=max0rM+1{3πλ2r,M+14}.

    Similarly, we can get

    ||DmtNM+1(t)||2L2w(t)M2m(Mm)32. (5.15)

    Accordingly, we have

    ||DktNM+1(t)||2L2w(t)||DmtNM+1(t)||2L2w(t)M2(km)(MkMm)32<M2(km)(Γ(Mm+1)Γ(Mk+1))32M12(mk). (5.16)

    By inserting Eq (5.16) into Eq (5.6), one has

    ||Dkt(χ(t)ˆχ(t))||2L2ws(t)M12(mk)|χ(t)|2Hmw(t). (5.17)

    Therefore, we get the desired result. Now, assume the following 2-D CP-weighted Sobolev space:

    Hr,sω(x,t)(I×I)={u:p+quxptqL2ω(x,t)(I×I),rp0,sq0}, (5.18)

    equipped with the following norm and semi-norm:

    ||u||Hr,sω(x,t)=(rp=0sq=0||p+quxptq||2L2ω(x,t))12,|u|Hr,sω(x,t)=||r+suxrts||L2ω(x,t), (5.19)

    where ω(x,t)=w(x)w(t) and r,sN.

    Theorem 5.2. Given the assumptions 0prM+1, the approximation to z(x,t)Hr,sω(x,t)(I×I) is zM(x,t). As a result, the estimation that follows is applicable:

    ||pxp(z(x,t)zM(x,t))||L2ω(x,t)M14(rp)|z(x,t)|Hr,0ω(x,t). (5.20)

    Proof. According to the definitions of z(x,t) and zM(x,t), one has

    z(x,t)zM(x,t)=Mi=0j=M+1cijNi(x)Nj(t)+i=M+1j=0cijNi(x)Nj(t)Mi=0j=0cijNi(x)Nj(t)+i=M+1j=0cijNi(x)Nj(t). (5.21)

    Now, by applying the same procedures as in Theorem 5.1, we obtain

    ||pxp(z(x,t)zM(x,t))||L2ω(x,t)M14(rp)|z(x,t)|Hr,0ω(x,t). (5.22)

    Theorem 5.3. Given the assumptions 0qsM+1, the approximation to z(x,t)Hr,sω(x,t)(I×I) is zM(x,t). As a result, the estimation that follows is applicable:

    ||qtq(z(x,t)zM(x,t))||L2ω(x,t)M14(sq)|z(x,t)|H0,sω(x,t). (5.23)

    Proof. The proof of this theorem is similar to the proof of Theorems 5.1 and 5.2.

    Theorem 5.4. Let RM(x,t) be the residual of Eq (4.1). In that case, RM(x,t)L2ω(x,t) will be sufficiently small for sufficiently large values of M.

    Proof. RM(x,t) of Eq (4.1) can be written as

    RM(x,t)=zMt2zMx2+zM(azM)(1zM). (5.24)

    Subtracting Eq (5.24) from Eq (4.1) leads to the following equation:

    RM(x,t)=t(z(x,t)zM(x,t))2x2(z(x,t)zM(x,t))+a(z(x,t)zM(x,t))+(z(x,t)zM(x,t))3(a+1)(z(x,t)zM(x,t))2. (5.25)

    If we consider L2-norm, then, with the aid of Theorems 5.2 and 5.3, we get

    RM(x,t)L2ω(x,t)Ms4|z(x,t)|H0,sω(x,t)+M14(r2)|z(x,t)|Hr,0ω(x,t)+aMr4|z(x,t)|Hr,0ω(x,t)+(Mr4|z(x,t)|Hr,0ω(x,t))3+(a+1)(Mr4|z(x,t)|Hr,0ω(x,t))2. (5.26)

    It is clear from Eq (5.26) that for sufficiently high values of M, RM(x,t)L2ω(x,t) will be small enough. Thus, the proof of this theorem is finished.

    In this section, we present test examples to show the applicability of our proposed method and compare it with other methods.

    Example 6.1. The authors of [37] considered the FH–N equation of the form

    zt=zxxz(az)(1z),0<a1, (6.1)

    governed by the following initial and boundary conditions:

    z(x,0)=aeax2+ex2eax2+ex2+1,0<x1,z(0,t)=aea2t2+et/2ea2t2+eat+et/2,0<t1,z(1,t)=ae12a(at+2)+e12(t+2)eat+e12a(at+2)+e12(t+2),0<t1, (6.2)

    where the exact solution to this problem is given by

    z(x,t)=ae(a21)at+ax2+e(12a)t+x2e(12a)t+x2+e(a21)at+ax2+1.

    Table 1 gives a comparison of the maximum absolute error (MAE) between our method and the method in [37] at t=1. Figure 1 shows the exact and the approximate solutions at a=0.5 and M=12. Figure 2 shows the MAE at a=1 and M=12. Table 2 presents the absolute error (AE) at a=0.9 and M=12. Figure 3 shows the AE (left) and the approximate solution (right) at a=0.1 and M=12.

    Table 1.  Comparison of the MAE of Example 6.1 at t=1.
    Our method at M=12
    x a=0.2 a=0.5 a=0.7 Method in [37]
    0.1 4.38538×1015 5.10703×1015 3.66374×1015 1.41366×106
    0.2 1.66533×1015 1.88738×1015 2.9976×1015 1.39287×106
    0.3 4.77396×1015 1.22125×1015 1.88738×1015 1.18067×106
    0.4 4.996×1015 2.9976×1015 1.11022×1015 1.16488×106
    0.5 6.99441×1015 5.88418×1015 5.77316×1015 8.85464×107
    0.6 1.49887×1014 7.77156×1016 7.54952×1015 9.60502×107
    0.7 1.82077×1014 6.77236×1015 1.39888×1014 5.91894×107
    0.8 4.4964×1014 4.36318×1014 4.77396×1015 7.75841×107
    0.9 8.03801×1014 1.42886×1013 8.55982×1014 3.12309×107

     | Show Table
    DownLoad: CSV
    Figure 1.  The exact and the approximate solutions for Example 6.1 at a=0.5, and M=12.
    Figure 2.  The MAE of Example 6.1 at a=1.
    Table 2.  The AE of Example 6.1 at a=0.9.
    x t=0.3 t=0.5 t=0.7 t=0.9
    0.1 1.22125×1015 2.10942×1015 2.10942×1015 1.88738×1015
    0.2 2.77556×1015 4.21885×1015 4.21885×1015 3.66374×1015
    0.3 3.88578×1015 6.10623×1015 6.21725×1015 5.55112×1015
    0.4 5.55112×1015 8.21565×1015 8.88178×1015 6.99441×1015
    0.5 7.21645×1015 1.04361×1014 1.09912×1014 7.54952×1015
    0.6 9.21485×1015 1.26565×1014 1.28786×1014 6.55032×1015
    0.7 1.08802×1014 1.54321×1014 1.5099×1014 5.77316×1015
    0.8 1.34337×1014 1.77636×1014 1.82077×1014 8.21565×1015
    0.9 1.59872×1014 2.13163×1014 2.17604×1014 1.67644×1014

     | Show Table
    DownLoad: CSV
    Figure 3.  The AE (left) and the approximate solution (right) for Example 6.1 at a=0.1, and M=12.

    Example 6.2. The authors of [45] considered the FH–N equation of the form

    zt=zxxz(az)(1z),0<a1, (6.3)

    governed by the following initial and boundary conditions:

    z(x,0)=2ex2+2,0<x1,z(0,t)=2et2+2,0<t1,z(1,t)=2et212+2,0<t1, (6.4)

    where the exact solution of this problem when a=1, is

    z(x,t)=22+et2x2.

    Table 3 gives a comparison of AE at t=0.001 between our method and the method in [45]. Figure 4 shows the absolute error (AE) (left) and the approximate solution (right) at M=12. Table 4 presents the MAE and the CPU time (in seconds) used at different values of M.

    Table 3.  Comparison of the AE of Example 6.2 at t=0.001.
    x Method in [45] Our method at M=12
    0.00 9.260091360374645×109 4.44089×1016
    0.001 9.266945988350983×109 1.11022×1016
    0.002 9.273449896873842×109 1.11022×1016
    0.003 9.279814694451716×109 0
    0.004 9.286474367264930×109 2.22045×1016
    0.005 9.292864144860857×109 2.22045×1016
    0.006 9.299528924699985×109 1.11022×1016

     | Show Table
    DownLoad: CSV
    Figure 4.  The AE (left) and the approximate solution (right) for Example 6.2 at M=12.
    Table 4.  the MAE for Example 6.2.
    M 2 4 6 8 10 12
    Error 1.73803×103 4.14311×105 9.11356×107 9.69633×109 4.76566×1011 6.78901×1014
    CPU time 1.157 1.813 3.344 6.11 11.374 23.248

     | Show Table
    DownLoad: CSV

    Example 6.3. The authors of [35] considered the FH–N equation of the form

    zt=zxxz(az)(1z),0<a1, (6.5)

    subject to the following initial and boundary conditions:

    z(x,0)=12tanh(x22)+12,0<x1,z(0,t)=1212tanh(14(2a1)t),0<t1,z(1,t)=12tanh(1(2a1)t222)+12,0<t1, (6.6)

    where z(x,t)=12tanh(x(2a1)t222)+12 is the exact solution of this problem.

    Table 5 presents the MAE and the CPU time used (in seconds) when a=0.1 and a=0.5 at different values of M. Figure 5 shows the AE (left) and the approximate solution (right) at a=1 and M=12. Table 6 shows the AE at different values of a when M=12.

    Table 5.  the MAE for Example 6.3.
    M 2 4 6 8 10 12
    a=0.1 6.13615×103 9.12786×105 1.14583×106 9.32286×109 4.61591×1011 1.27232×1013
    CPU time 1.376 2.264 3.594 7.204 14.812 32.282
    a=0.5 5.21245×103 7.19718×105 9.21914×107 7.23042×109 3.28784×1011 5.61773×1014
    CPU time 1.173 1.797 3.406 7.156 14.721 32.236

     | Show Table
    DownLoad: CSV
    Figure 5.  The AE (left) and the approximate solution (right) for Example 6.3 at a=1, and M=12.
    Table 6.  AE of Example 6.3 at M=12.
    (x,t) a=0.3 a=0.6 a=0.8
    (0.1, 0.1) 3.33067×1016 3.33067×1016 0
    (0.2, 0.2) 5.55112×1016 1.88738×1015 2.44249×1015
    (0.3, 0.3) 4.44089×1016 3.9968×1015 6.88338×1015
    (0.4, 0.4) 2.66454×1015 6.66134×1015 1.34337×1014
    (0.5, 0.5) 6.55032×1015 1.04361×1014 2.16493×1014
    (0.6, 0.6) 1.17684×1014 1.48771×1014 3.14193×1014
    (0.7, 0.7) 1.77636×1014 2.00951×1014 4.22995×1014
    (0.8, 0.8) 2.53131×1014 2.50912×1014 5.34017×1014
    (0.9, 0.9) 3.27516×1014 3.24185×1014 6.45041×1014

     | Show Table
    DownLoad: CSV

    Example 6.4. Consider the FH–N equation of the form

    zt=zxxz(az)(1z),0<a1, (6.7)

    subject to the following initial and boundary conditions:

    z(x,0)=0,0<x1,z(0,t)=z(1,t)=0,0<t1. (6.8)

    Since the exact solution is not available, we define the following absolute residual error norm:

    RE=max(x,t)(0,1]2|zMt(x,t)zMxx(x,t)+azM(x,t)+(zM(x,t))3(a+1)(zM(x,t))2|, (6.9)

    and apply the presented method at M=10 to get Figure 6, which illustrates the RE at different values of a.

    Figure 6.  The RE of Example 6.4.

    This article has provided numerical solutions for the FH–N equation. The celebrated collocation method was applied to obtain the proposed approximate solutions. New basis functions were considered. These basis functions are special cases of the generalized Gegenbauer orthogonal polynomials. They are related to CPs. New derivative expressions for these polynomials were developed. Hence, new operational matrices of the derivatives were introduced. These matrices and the application of the collocation algorithm helped transform the FH–N equation into nonlinear systems of equations that can be treated using a suitable solver. To our knowledge, these polynomials have not been used before in approximating solutions to differential equations. We hope to use them to approximate other differential equations. All codes were written and debugged by Mathematica 11 on an HP Z420 workstation, (processor: Intel(R) Xeon(R) CPU E5-1620 v2 at 3.70 GHz, 16 GB RAM DDR3, and 512 GB storage).

    Waleed Mohamed Abd-Elhameed: Conceptualization, methodology, validation, formal analysis, investigation, supervision, project administration, writing—original draft, writing—review & editing; Omar Mazen Alqubori: Methodology, validation, investigation, writing—review & editing, funding acquisition; Ahmed Gamal Atta: Conceptualization, methodology, software, validation, formal analysis, investigation, writing—original draft, writing—review & editing, visualization. All authors have read and approved the final version of the manuscript for publication.

    The authors declare that they have not used artificial intelligence tools in the creation of this article.

    The authors declare that they have no competing interests.

    [1] Duffau H (2015) A two-level model of interindividual anatomo-functional variability of the brain and its implications for neurosurgery. Cortex 1–11.
    [2] Vassal F, Schneider F, Sontheimer A, et al. (2013) Intraoperative visualisation of language fascicles by diffusion tensor imaging-based tractography in glioma surgery. Acta Neurochir (Wien) 155: 437–448. doi: 10.1007/s00701-012-1580-1
    [3] Roux FE, Boulanouar K, Lotterie JA, et al. (2003) Language functional magnetic resonance imaging in preoperative assessment of language areas: Correlation with direct cortical stimulation. Neurosurgery 52: 1335–1347. doi: 10.1227/01.NEU.0000064803.05077.40
    [4] Guillevin R, Herpe G, Verdier M, et al. (2014) Low-grade gliomas: The challenges of imaging. Diagn Interv Imaging 95: 957–963. doi: 10.1016/j.diii.2014.07.005
    [5] Nimsky C, Ganslandt O, Hastreiter P, et al. (2005) Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 56: 130–137. doi: 10.1227/01.NEU.0000144842.18771.30
    [6] Altman DA, Atkinson DS, Brat DJ (2007) Best cases from the AFIP: glioblastoma multiforme. Radiographics 27: 883–888. doi: 10.1148/rg.273065138
    [7] Upadhyay N, Waldman AD (2011) Conventional MRI evaluation of gliomas. Br J Radiol 84: 107–111. doi: 10.1259/bjr/65711810
    [8] Ostrom QT, Gittleman H, Chen Y, et al. (2015) CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2008–2012. Neuro Oncol 17: iv1–iv62. doi: 10.1093/neuonc/nov189
    [9] Glasser MF, Coalson TS, Robinson EC, et al. (2016) A multi-modal parcellation of human cerebral cortex. Nature 536: 171–178. doi: 10.1038/nature18933
    [10] Fernández Coello A, Moritz-Gasser S, Martino J, et al. (2013) Selection of intraoperative tasks for awake mapping based on relationships between tumor location and functional networks. J Neurosurg 119: 1380–1394. doi: 10.3171/2013.6.JNS122470
    [11] Duffau H (2014) The huge plastic potential of adult brain and the role of connectomics: New insights provided by serial mappings in glioma surgery. Cortex 58: 325–337. doi: 10.1016/j.cortex.2013.08.005
    [12] Duffau H, Moritz-Gasser S, Mandonnet E (2014) A re-examination of neural basis of language processing: Proposal of a dynamic hodotopical model from data provided by brain stimulation mapping during picture naming. Brain Lang 131: 1–10. doi: 10.1016/j.bandl.2013.05.011
    [13] Duffau H (2005) Lessons from brain mapping in surgery for low-grade glioma: Insights into associations between tumour and brain plasticity. Lancet Neurol 4: 476–486. doi: 10.1016/S1474-4422(05)70140-X
    [14] Duffau H (2013) A plea to pay more attention on anatomo-functional connectivity in surgical management of brain cavernomas. World Neurosurg 80: e221–e223. doi: 10.1016/j.wneu.2012.10.025
    [15] Sawaya R, Hammoud M, Schoppa D, et al. (1998) Neurosurgical Outcomes in a Modern Series of 400 Craniotomies for Treatment of Parenchymal Tumors. Neurosurgery 42.
    [16] Lacroix M, Abi-Said D, Fourney DR, et al. (2001) A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95: 190–198. doi: 10.3171/jns.2001.95.2.0190
    [17] Marko NF, Weil RJ, Schroeder JL, et al. (2014) Extent of resection of glioblastoma revisited: Personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery. J Clin Oncol 32: 774–782. doi: 10.1200/JCO.2013.51.8886
    [18] Stupp R, Taillibert S, Kanner AA, et al. (2015) Maintenance Therapy With Tumor-Treating Fields Plus Temozolomide vs Temozolomide Alone for Glioblastoma: A Randomized Clinical Trial. JAMA 314: 2535–2543. doi: 10.1001/jama.2015.16669
    [19] Liau LM, Prins RM, Kiertscher SM, et al. (2005) Dendritic cell vaccination in glioblastoma patients induces systemic and intracranial T-cell responses modulated by the local central nervous system tumor microenvironment. Clin Cancer Res 11: 5515–5525. doi: 10.1158/1078-0432.CCR-05-0464
    [20] Berger A (2002) Magnetic resonance imaging. BMJ Br Med J 324: 35. doi: 10.1136/bmj.324.7328.35
    [21] Gore JCJC (2003) Principles and practice of functional MRI of the human brain. J Clin Invest 112: 4–9. doi: 10.1172/JCI200319010
    [22] Stadlbauer A, Nimsky C, Buslei R, et al. (2007) Diffusion tensor imaging and optimized fiber tracking in glioma patients: Histopathologic evaluation of tumor-invaded white matter structures. Neuroimage 34: 949–956. doi: 10.1016/j.neuroimage.2006.08.051
    [23] Kuhnt D, Bauer MHA, Sommer J, et al. (2013) Optic Radiation Fiber Tractography in Glioma Patients Based on High Angular Resolution Diffusion Imaging with Compressed Sensing Compared with Diffusion Tensor Imaging-Initial Experience. Plos One 8: e70973. doi: 10.1371/journal.pone.0070973
    [24] Price CJ (2012) NeuroImage A review and synthesis of the fi rst 20 years of PET and fMRI studies of heard speech , spoken language and reading. Neuroimage 62: 816–847. doi: 10.1016/j.neuroimage.2012.04.062
    [25] Ueno T, Lambon Ralph MA (2013) The roles of the 'ventral' semantic and 'dorsal' pathways in conduite d'approche: a neuroanatomically-constrained computational modeling investigation. Front Hum Neurosci 7: 422.
    [26] Saur D, Kreher BW, Schnell S, et al. (2008) Ventral and dorsal pathways for language. Proc Natl Acad Sci U S A 105: 18035–18040. doi: 10.1073/pnas.0805234105
    [27] Huth AG, Heer WA De, Griffiths TL, et al. (2016) Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532: 453–458. doi: 10.1038/nature17637
    [28] Tzourio-Mazoyer N, Joliot M, Marie D, et al. (2016) Variation in homotopic areas' activity and inter-hemispheric intrinsic connectivity with type of language lateralization: an FMRI study of covert sentence generation in 297 healthy volunteers. Brain Struct Funct 221: 2735–2753. doi: 10.1007/s00429-015-1068-x
    [29] Rech F, Herbet G, Moritz-Gasser S, et al. (2015) Somatotopic organization of the white matter tracts underpinning motor control in humans: an electrical stimulation study. Brain Struct Funct 221: 3743–3753.
    [30] Bello L, Gambini A, Castellano A, et al. (2008) Motor and language DTI Fiber Tracking combined with intraoperative subcortical mapping for surgical removal of gliomas. Neuroimage 39: 369–382. doi: 10.1016/j.neuroimage.2007.08.031
    [31] Kalani MYS, Kalani M a, Gwinn R, et al. (2009) Embryological development of the human insula and its implications for the spread and resection of insular gliomas. Neurosurg Focus 27: E2.
    [32] Michaud K, Duffau H (2016) Surgery of insular and paralimbic diffuse low-grade gliomas: technical considerations. J Neurooncol 1–10.
    [33] Oppenlander ME, Wolf AB, Snyder LA, et al. (2014) An extent of resection threshold for recurrent glioblastoma and its risk for neurological morbidity. J Neurosurg 120: 846–853. doi: 10.3171/2013.12.JNS13184
    [34] Stupp R, Mason WP, van den Bent MJ, et al. (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352: 987–996. doi: 10.1056/NEJMoa043330
    [35] Lacroix M, Toms SA (2014) Maximum safe resection of glioblastoma multiforme. J Clin Oncol 32: 727–728. doi: 10.1200/JCO.2013.53.2788
    [36] Brown PD, Maurer MJ, Rummans TA, et al. (2005) A prospective study of quality of life in adults with newly diagnosed high-grade gliomas: The impact of the extent of resection on quality of life and survival. Neurosurgery 57: 495–503. doi: 10.1227/01.NEU.0000170562.25335.C7
    [37] Jakola AS, Unsgård G, Solheim O (2011) Quality of life in patients with intracranial gliomas: the impact of modern image-guided surgery. J Neurosurg 114: 1622–1630. doi: 10.3171/2011.1.JNS101657
    [38] Le Mercier M, Hastir D, Moles Lopez X, et al. (2012) A Simplified Approach for the Molecular Classification of Glioblastomas. PLoS One 7: e45475. doi: 10.1371/journal.pone.0045475
    [39] Duffau H, Taillandier L (2015) New concepts in the management of diffuse low-grade glioma: Proposal of a multistage and individualized therapeutic approach. Neuro Oncol 17: 332–342. doi: 10.1093/neuonc/nov204.68
    [40] Duffau H (2013) A new philosophy in surgery for diffuse low-grade glioma (DLGG): Oncological and functional outcomes. Neurochirurgie 59: 2–8. doi: 10.1016/j.neuchi.2012.11.001
    [41] Pollard SM, Conti L, Sun Y, et al. (2006) Adherent neural stem (NS) cells from fetal and adult forebrain. Cereb Cortex 16: i112–i120. doi: 10.1093/cercor/bhj167
    [42] Aboody K, Capela A, Niazi N, et al. (2011) Translating Stem Cell Studies to the Clinic for CNS Repair: Current State of the Art and the Need for a Rosetta Stone. Neuron 70: 597–613. doi: 10.1016/j.neuron.2011.05.007
    [43] Ihrie RA, Álvarez-Buylla A (2011) Lake-Front Property: A Unique Germinal Niche by the Lateral Ventricles of the Adult Brain. Neuron 70: 674–686. doi: 10.1016/j.neuron.2011.05.004
    [44] Achanta P, Sedora Roman NI, et al. (2010) Gliomagenesis and the use of neural stem cells in brain tumor treatment. Anticancer Agents Med Chem 10: 121–130. doi: 10.2174/187152010790909290
    [45] Reya T, Morrison SJ, Clarke MF, et al. (2001) Stem cells, cancer, and cancer stem cells. Nature 414: 105–111. doi: 10.1038/35102167
    [46] Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system-A technical review. NMR Biomed 15: 435–455. doi: 10.1002/nbm.782
    [47] Ojemann JG, Neil JM, MacLeod a M, et al. (1998) Increased functional vascular response in the region of a glioma. J Cereb Blood Flow Metab 18: 148–153.
    [48] Ojemann JG, Miller JW, Silbergeld DL (1996) Preserved Function in Brain Invaded by Tumor. Neurosurgery 39: 253–259. doi: 10.1097/00006123-199608000-00003
    [49] Bello L, Castellano A, Fava E, et al. (2010) Intraoperative use of diffusion tensor imaging fiber tractography and subcortical mapping for resection of gliomas: technical considerations. Neurosurg Focus 28: E6.
    [50] Morita K, Matsuzawa H, Fujii Y, et al. (2005) Diffusion tensor analysis of peritumoral edema using lambda chart analysis indicative of the heterogeneity of the microstructure within edema. J Neurosurg 102: 336–341. doi: 10.3171/jns.2005.102.2.0336
    [51] Kinoshita M, Yamada K, Hashimoto N, et al. (2005) Fiber-tracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 25: 424–429. doi: 10.1016/j.neuroimage.2004.07.076
    [52] Giese A, Loo MA, Rief MD, et al. (1995) Substrates for astrocytoma invasion. Neurosurgery 37: 294–301. doi: 10.1227/00006123-199508000-00015
    [53] Giese A, Loo MA, Tran N, et al. (1996) Dichotomy of astrocytoma migration and proliferation. Int J cancer 67: 275–282.
    [54] Giese A, Westphal M (1996) Glioma invasion in the central nervous system. Neurosurgery 39: 235–252. doi: 10.1097/00006123-199608000-00001
    [55] Oliveira R, Christov C, Guillamo JS, et al. (2005) Contribution of gap junctional communication between tumor cells and astroglia to the invasion of the brain parenchyma by human glioblastomas. BMC Cell Biol 6: 7. doi: 10.1186/1471-2121-6-7
    [56] Demuth T, Berens ME (2004) Molecular mechanisms of glioma cell migration and invasion. J Neurooncol 70: 217–228. doi: 10.1007/s11060-004-2751-6
    [57] Knott JC, Mahesparan R, Garcia-Cabrera I, et al. (1998) Stimulation of extracellular matrix components in the normal brain by invading glioma cells. Int J cancer 75: 864–872.
    [58] Giese A, Kluwe L, Laube B, et al. (1996) Migration of human glioma cells on myelin. Neurosurgery 38: 755–764. doi: 10.1227/00006123-199604000-00026
    [59] Soroceanu L, Manning TJ, Sontheimer H (1999) Modulation of glioma cell migration and invasion using Cl- and K+ ion channel blockers. J Neurosci 19: 5942–5954.
    [60] Merzak A, Pilkington GJ (1997) Molecular and cellular pathology of intrinsic brain tumours. Cancer Metastasis Rev 16: 155–177. doi: 10.1023/A:1005760726850
    [61] Gaspar LE, Fisher BJ, Macdonald DR, et al. (1992) Supratentorial malignant glioma: patterns of recurrence and implications for external beam local treatment. Int J Radiat Oncol Biol Phys 24: 55–57. doi: 10.1016/0360-3016(92)91021-E
    [62] Puchner MJ, Herrmann HD, Berger J, et al. (2000) Surgery, tamoxifen, carboplatin, and radiotherapy in the treatment of newly diagnosed glioblastoma patients. J Neurooncol 49: 147–155. doi: 10.1023/A:1026533016912
    [63] Giese A, Bjerkvig R, Berens ME, et al. (2003) Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol 21: 1624–1636. doi: 10.1200/JCO.2003.05.063
    [64] Sanai N, Polley M-Y, McDermott MW, et al. (2011) An extent of resection threshold for newly diagnosed glioblastomas. J Neurosurg 115: 3–8. doi: 10.3171/2011.2.JNS10998
    [65] Duffau H (2014) Diffuse low-grade gliomas and neuroplasticity. Diagn Interv Imaging 95: 945–955. doi: 10.1016/j.diii.2014.08.001
    [66] Duffau H (2015) Resecting diffuse low-grade gliomas to the boundaries of brain functions: a new concept in surgical neuro-oncology. J Neurosurg Sci 59: 361–371.
    [67] McGirt MJ, Mukherjee D, Chaichana KL, et al. (2009) Association of surgically acquired motor and language deficits on overall survival after resection of glioblastoma multiforme. Neurosurgery 65: 463–469. doi: 10.1227/01.NEU.0000349763.42238.E9
    [68] Hauser SB, Kockro RA, Actor B, et al. (2016) Combining 5-aminolevulinic acid fluorescence and intraoperative magnetic resonance imaging in glioblastoma surgery: A histology-based evaluation. Neurosurgery 78: 475–483. doi: 10.1227/NEU.0000000000001035
    [69] Jaber M, Wölfer J, Ewelt C, et al. (2015) The Value of 5-ALA in Low-grade Gliomas and High-grade Gliomas Lacking Glioblastoma Imaging Features. Neurosurgery 78: 401–411.
    [70] Stummer W, Pichlmeier U, Meinel T, et al. (2006) Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. Lancet Oncol 7: 392–401. doi: 10.1016/S1470-2045(06)70665-9
    [71] Sanai N, Berger MS (2008) G Lioma E Xtent of R Esection and and Methods. Neurosurgery 62: 753–766. doi: 10.1227/01.neu.0000318159.21731.cf
    [72] De Benedictis A, Moritz-Gasser S, Duffau H (2010) Awake mapping optimizes the extent of resection for low-grade gliomas in eloquent areas. Neurosurgery 66: 1074–1084. doi: 10.1227/01.NEU.0000369514.74284.78
    [73] Desmurget M, Bonnetblanc F, Duffau H (2007) Contrasting acute and slow-growing lesions: a new door to brain plasticity. Brain 130: 898–914.
    [74] Pallud J, Audureau E, Blonski M, et al. (2014) Epileptic seizures in diffuse low-grade gliomas in adults. Brain 137: 449–462. doi: 10.1093/brain/awt345
    [75] De Witt Hamer PC, Robles SG, Zwinderman AH, et al. (2012) Impact of intraoperative stimulation brain mapping on glioma surgery outcome: a meta-analysis. J Clin Oncol 30: 2559–2565. doi: 10.1200/JCO.2011.38.4818
    [76] Chang EF, Clark A, Smith JS, et al. (2011) Functional mapping-guided resection of low-grade gliomas in eloquent areas of the brain: improvement of long-term survival. Clinical article. J Neurosurg 114: 566–573. doi: 10.3171/2010.6.JNS091246
    [77] Sanai N, Polley M-Y, Berger MS (2010) Insular glioma resection: assessment of patient morbidity, survival, and tumor progression. J Neurosurg 112: 1–9. doi: 10.3171/2009.6.JNS0952
    [78] Lee WA, Bonin V, Reed M, et al. (2016) Anatomy and function of an excitatory network in the visual cortex. Nature 532: 1–18. doi: 10.1038/532S1a
  • This article has been cited by:

    1. Waleed Mohamed Abd-Elhameed, Omar Mazen Alqubori, Naher Mohammed A. Alsafri, Amr Kamel Amin, Ahmed Gamal Atta, A Matrix Approach by Convolved Fermat Polynomials for Solving the Fractional Burgers’ Equation, 2025, 13, 2227-7390, 1135, 10.3390/math13071135
    2. Waleed Mohamed Abd-Elhameed, Omar Mazen Alqubori, Amr Kamel Amin, Ahmed Gamal Atta, Numerical Solutions for Nonlinear Ordinary and Fractional Duffing Equations Using Combined Fibonacci–Lucas Polynomials, 2025, 14, 2075-1680, 314, 10.3390/axioms14040314
    3. H. M. Ahmed, A New Shifted Chebyshev Galerkin Operational Matrix of Derivatives: Highly Accurate Method for a Nonlinear Singularly Perturbed Problem with an Integral Boundary Condition, 2025, 32, 1776-0852, 10.1007/s44198-025-00295-4
  • Reader Comments
  • © 2017 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(6677) PDF downloads(1250) Cited by(2)

Figures and Tables

Figures(2)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog