Loading [MathJax]/jax/output/SVG/jax.js
Research article

Distinct neural mechanisms of alpha binaural beats and white noise for cognitive enhancement in young adults

  • Received: 06 February 2025 Revised: 29 April 2025 Accepted: 15 May 2025 Published: 20 May 2025
  • Young adulthood is a critical period marked by significant cognitive demands, requiring efficient brain function to manage academic, professional, and social challenges. Many young adults struggle with focus, stress management, and information processing. Emerging research suggests that auditory stimulation, specifically binaural beats and white noise, may enhance cognitive abilities and address these challenges. This exploratory study investigates the immediate effects of alpha binaural beats (ABB) and alpha binaural beats combined with white noise (AWN) on brain connectivity in young adults using functional magnetic resonance imaging (fMRI). Twenty-nine participants (n = 14 ABB, n = 15 AWN; mean age ≈ 22.14 years) were randomly assigned to receive either ABB or AWN during fMRI scans. Using dynamic independent component analysis (dyn-ICA), we examined the modulation of functional brain circuits during auditory stimulation. Preliminary findings revealed distinct and overlapping patterns of brain connectivity modulation of ABB and AWN. ABB primarily modulated connectivity within circuits involving frontoparietal, visual-motor, and multisensory regions, potentially enhancing cognitive flexibility, attentional control, and multisensory processing. Conversely, AWN primarily modulated connectivity in salience and default mode networks, with notable effects in limbic or reward regions, suggesting enhancements in focused attention and emotional processing. These preliminary results demonstrate that ABB and AWN differentially modulate brain networks on an immediate timescale. ABB may promote cognitive adaptability, while AWN enhances focused attention and emotional stability. Although behavioral effects were not assessed, these findings provide a neurobiological basis for understanding how these stimuli impact brain circuits. These preliminary findings may aid the development of personalized strategies for cognitive and emotional well-being. Given the exploratory nature, small sample size, and lack of concurrent behavioral data, these findings should be interpreted cautiously. Future research with rigorous designs, including control groups and behavioral measures, is needed to explore the long-term effects and applications of these interventions in various settings.

    Citation: Aini Ismafairus Abd Hamid, Nurfaten Hamzah, Siti Mariam Roslan, Nur Alia Amalin Suhardi, Muhammad Riddha Abdul Rahman, Faiz Mustafar, Hazim Omar, Asma Hayati Ahmad, Elza Azri Othman, Ahmad Nazlim Yusoff. Distinct neural mechanisms of alpha binaural beats and white noise for cognitive enhancement in young adults[J]. AIMS Neuroscience, 2025, 12(2): 147-179. doi: 10.3934/Neuroscience.2025010

    Related Papers:

    [1] Jiaxin Shen, Yuqing Xia . Flag-transitive 2-designs with block size 5 and alternating groups. AIMS Mathematics, 2025, 10(5): 10308-10323. doi: 10.3934/math.2025469
    [2] Cenap Özel, Habib Basbaydar, Yasar Sñzen, Erol Yilmaz, Jung Rye Lee, Choonkil Park . On Reidemeister torsion of flag manifolds of compact semisimple Lie groups. AIMS Mathematics, 2020, 5(6): 7562-7581. doi: 10.3934/math.2020484
    [3] Simone Fiori . Coordinate-free Lie-group-based modeling and simulation of a submersible vehicle. AIMS Mathematics, 2024, 9(4): 10157-10184. doi: 10.3934/math.2024497
    [4] Yunpeng Xiao, Wen Teng . Representations and cohomologies of modified λ-differential Hom-Lie algebras. AIMS Mathematics, 2024, 9(2): 4309-4325. doi: 10.3934/math.2024213
    [5] Yupei Zhang, Yongzhi Luan . Dimension formulas of the highest weight exceptional Lie algebra-modules. AIMS Mathematics, 2024, 9(4): 10010-10030. doi: 10.3934/math.2024490
    [6] Amjad Hussain, Muhammad Khubaib Zia, Kottakkaran Sooppy Nisar, Velusamy Vijayakumar, Ilyas Khan . Lie analysis, conserved vectors, nonlinear self-adjoint classification and exact solutions of generalized (N+1)-dimensional nonlinear Boussinesq equation. AIMS Mathematics, 2022, 7(7): 13139-13168. doi: 10.3934/math.2022725
    [7] Nouf Almutiben, Ryad Ghanam, G. Thompson, Edward L. Boone . Symmetry analysis of the canonical connection on Lie groups: six-dimensional case with abelian nilradical and one-dimensional center. AIMS Mathematics, 2024, 9(6): 14504-14524. doi: 10.3934/math.2024705
    [8] Muhammad Asad Iqbal, Abid Ali, Ibtesam Alshammari, Cenap Ozel . Construction of new Lie group and its geometric properties. AIMS Mathematics, 2024, 9(3): 6088-6108. doi: 10.3934/math.2024298
    [9] Wei Shi . Nonexistence results of nonnegative solutions of elliptic equations and systems on the Heisenberg group. AIMS Mathematics, 2025, 10(5): 12576-12597. doi: 10.3934/math.2025567
    [10] Lilan Dai, Yunnan Li . Primitive decompositions of idempotents of the group algebras of dihedral groups and generalized quaternion groups. AIMS Mathematics, 2024, 9(10): 28150-28169. doi: 10.3934/math.20241365
  • Young adulthood is a critical period marked by significant cognitive demands, requiring efficient brain function to manage academic, professional, and social challenges. Many young adults struggle with focus, stress management, and information processing. Emerging research suggests that auditory stimulation, specifically binaural beats and white noise, may enhance cognitive abilities and address these challenges. This exploratory study investigates the immediate effects of alpha binaural beats (ABB) and alpha binaural beats combined with white noise (AWN) on brain connectivity in young adults using functional magnetic resonance imaging (fMRI). Twenty-nine participants (n = 14 ABB, n = 15 AWN; mean age ≈ 22.14 years) were randomly assigned to receive either ABB or AWN during fMRI scans. Using dynamic independent component analysis (dyn-ICA), we examined the modulation of functional brain circuits during auditory stimulation. Preliminary findings revealed distinct and overlapping patterns of brain connectivity modulation of ABB and AWN. ABB primarily modulated connectivity within circuits involving frontoparietal, visual-motor, and multisensory regions, potentially enhancing cognitive flexibility, attentional control, and multisensory processing. Conversely, AWN primarily modulated connectivity in salience and default mode networks, with notable effects in limbic or reward regions, suggesting enhancements in focused attention and emotional processing. These preliminary results demonstrate that ABB and AWN differentially modulate brain networks on an immediate timescale. ABB may promote cognitive adaptability, while AWN enhances focused attention and emotional stability. Although behavioral effects were not assessed, these findings provide a neurobiological basis for understanding how these stimuli impact brain circuits. These preliminary findings may aid the development of personalized strategies for cognitive and emotional well-being. Given the exploratory nature, small sample size, and lack of concurrent behavioral data, these findings should be interpreted cautiously. Future research with rigorous designs, including control groups and behavioral measures, is needed to explore the long-term effects and applications of these interventions in various settings.


    Abbreviations

    ABB:

    alpha binaural beats; 

    EEG:

    electroencephalogram; 

    WN:

    white noise; 

    AWN:

    alpha embedded binaural beats within WN; 

    fMRI:

    functional magnetic resonance imaging; 

    dyn-ICA:

    dynamic independent component analysis; 

    USM:

    Universiti Sains Malaysia; 

    WAIS-III:

    Wechsler Adult Intelligence Scale—Third Edition; 

    EPI:

    echo-planar imaging; 

    TR:

    repetition time; 

    TE:

    echo time; 

    TA:

    acquisition time; 

    SPM12:

    Statistical Parametric Mapping 12; 

    MNI:

    Montreal Neurological Institute; 

    BOLD:

    blood-oxygen-level-dependent; 

    ROIs:

    regions of interest; 

    FDR:

    false discovery rate; 

    MVPA:

    multivoxel pattern analysis

    A 2-(v,k,λ) design D is a pair (P,B), where P is a set of v points, and B is a set of k-subsets of P called blocks, such that any 2 points are contained in exactly λ blocks. A flag is a point-block pair (α,B) with αB. The Fisher's inequality in [8, 1.3.8] shows that the number of blocks is at least v. Design D is said to be non-symmetric if v<b and non-trivial if 2<k<v1. We always assume D to be non-trivial and non-symmetric in this paper. An automorphism of D is a permutation of P that leaves B invariant. All automorphisms of the design D form a group called the full automorphism group of D, denoted by Aut(D). Let GAut(D). The design D is called point (block, flag)-transitive if G acts transitively on the set of points (blocks, flags) and point-primitive if G acts primitively on P, that is, G does not preserve a partition of P in classes of size c with 1<c<v.

    For decades, works have been done on the classification of 2-designs admitting a transitive automorphism group. In 1988, Buekenhout, Delandtsheer, and Doyen first proved in [5] that the flag-transitive automorphism group of a 2-(v,k,1) design must be of affine or almost simple type. Then, the classification of flag-transitive 2-(v,k,1) designs was given in [6] by a six-person team, except for the case of the one-dimensional affine type. In recent years, some researchers have focused on into classifying 2-(v,k,λ) designs with general λ admitting flag-transitive automorphism group, such as [1,3,12,16,25,26,27]. Moreover, some of the works also considered classification of such designs admitting automorphism groups in a weaker condition, namely, block-transitive rather than flag-transitive [21,22,23,24].

    The current paper tackles the 2-(v,k,λ) designs where λ is a prime. In [25], Zhang and Chen reduced the flag-transitive, point-primitive automorphism groups of such 2-designs either to the affine type (with an elementary abelian p-group as socle) or to the almost simple type (with a nonabelian simple socle). Hence, it is possible to classify such 2-designs based on the classification of simple groups. The aim of this paper is to consider the case when the socle of the automorphism group G is an exceptional simple group of Lie type. Note that groups G2(2), 2G2(3), 2B2(2), and 2F4(2) are not simple, so they are not under consideration in this work. It is also worth noting that the symmetric 2-designs with exceptional simple socle have been studied in [1,2,20]. The main result of the current paper is the following:

    Theorem 1.1. Let D be a non-symmetric 2-(v,k,λ) design with λ prime and G a flag-transitive automorphism group of D. If the socle T of G is an exceptional Lie type simple group in characteristic p, then one of the following holds:

    (1) T is 2B2(q) with q=22n+1>2 and (v,k,λ)=(q2+1,q,q1), where q1 is a Mersenne prime;

    (2) T is G2(q), and (v,k,λ)=(q3(q31)2,q32,q+1) where q>2 is even and q+1 is a Fermat prime.

    Remark 1.1. For the parameters in Theorem 1.1(1), the design D is described in [26]. For the parameters in Theorem 1.1(2), the existence of such a design remains uncertain at this time.

    We begin with some well-established results about the parameters of 2-(v,k,λ) designs and the automorphism groups of them. For any point α, we denote by r the number of blocks that contain α, as it is a constant.

    Lemma 2.1. ([8]) For a 2-(v,k,λ) design D, it is well known that

    (1) bk=vr;

    (2) λ(v1)=r(k1);

    (3) λv<r2.

    Lemma 2.2. ([8,Section 1.2]) Assume that G is an automorphism group of D. Then the flag-transitivity of G is equivalent to one of the following:

    (1) G is point-transitive, and the point stabilizer Gα is transitive on all blocks that contain α;

    (2) G is block-transitive, and the block stabilizer GB is transitive on the k points in block B.

    Lemma 2.3. [7]) Assume that G is a flag-transitive automorphism group of D, and T is the socle of G. Then, we have

    (1) r|Gα|, where Gα is the point-stabilizer of G;

    (2) rλdi, where di is any nontrivial subdegree of G.

    Assume that λ is a prime. Then either (λ,r)=1 or λr. For the former case, by the results of [26], we immediately obtain the following Lemma:

    Lemma 3.1. Assume that G and D satisfy the hypothesis of Theorem 1.1. If (λ,r)=1, then T=2B2(q) with q=22n+18, and D is a 2-(q2+1,q,q1) design with q1 a Mersenne prime. In particular, 2n+1 is prime.

    Therefore, we always assume λr in the remaining content. Let r0=rλ. We get the following from Lemmas 2.1 and 2.3.

    Lemma 3.2. Assume that D is a 2-(v,k,λ) design where λ is a prime divisor of r, and G is a flag-transitive automorphism group of D. Then we have

    (1) v<λr20;

    (2) r0 divides the greatest common divisor of |Gα|, v1 and all nontrivial subdegrees of G.

    Since G is point-primitive, the point stabilizer Gα is a maximal subgroup of G. In this section, we first deal with the case when Gα is a maximal parabolic subgroup of G.

    Lemma 3.3. Assume that T=2B2(q) with q=22n+1>2. Then Gα cannot be the maximal parabolic subgroup of G.

    Proof. If Gα is a maximal parabolic subgroup of G, we know that |Gα|=fq2(q1) with f(2n+1) from [19], and hence v=q2+1. Then, according to (1) and (2) in Lemma 2.1 and the fact λr, we further get k1q2 and b=λq2(q2+1)k(k1). Since G is flag-transitive, Lemma 2.2 implies that |GB|=|G|b=fk(k1)(q1)λ. All maximal subgroups of G can be read off from [19], and let M be any one of them with GBM. The fact that |GB| divides |M| implies that M is the maximal parabolic subgroup of G, and k(k1) divides λq2. This forces k=λ, for otherwise k(k1)q2, which is a contradiction. It follows that GB is primitive on B, and so TB is transitive on B. Namely, |TB:Tγ,B|=k for any point γB. On the other hand, since M is parabolic, there exists a point α such that M=Gα. That is to say, TBTα and therefore Tγ,BTγ,α for γB. Since the stabilizer of any two points in 2B2(q) is a cyclic group of order q1 by [9, p.187], |Tγ,B| divides (q1). Also, |T:Tγ,α| divides bk by the flag-transitivity of G. It follows that (k1)λ, which holds only when λ=k=2, for it has been proved that k=λ above. This is impossible as D is nontrivial.

    Lemma 3.4. Assume that T=2G2(q) with q=32n+1>3. Then Gα cannot be the maximal parabolic subgroup of G.

    Proof. If Gα is the maximal parabolic subgroup of G, then we know that |Gα|=fq3(q1) with f(2n+1) from [11], and so v=q3+1. Similar as to Lemma 3.3, we have

    b=λv(v1)k(k1)=λq3(q3+1)k(k1)

    and k1q3. Let f1 be a divisor of f such that |GB:TB|=f1. Then by the flag-transitivity of G, we get

    |TB|=f(q1)k(k1)f1λ.

    Here, we also consider the maximal subgroups M of 2G2(q) such that TBM. From [11], either M is parabolic, or MZ2×PSL2(q).

    If M is a parabolic subgroup, then k(k1)λq3. Since k1q3, we have kλ and therefore λ=k. It follows that λ1q3 and λ=3n1+1, which forces λ=k=2, for λ is prime. However, now we get b=q3(q3+1)>(v2), which is obviously impossible. Hence, in the remaining part of the proof, we assume that TBZ2×PSL2(q).

    According to the list of the maximal subgroups of PSL2(q) in [4, Tables 8.1 and 8.2], TB is isomorphic to a subgroup of Z2×A4, Z2×Dq±1, Z2×([q]:Zq12) or Z2×PSL2(q0) with q0=q=32n+1. Obviously, the former two cases are impossible as k1q3. Then, if TBZ2×([q]:Zq12), we also have λ=k, a contradiction again. For the last case, the condition that |TB| divides |Z2×PSL2(q0)| forces q0=q, which implies that TB is isomorphic to Z2×PSL2(q) or PSL2(q). Then, by |T:TB|b, we have k(k1)q(q+1)λ. This, together with k1q2, implies that k1q when λ3, and k13q when λ=3. Furthermore, the facts that q+1 is the smallest degree of non-trivial action of PSL2(q) since q is an odd power of 3 and that |TB:Tα,B| divides k imply k=q+1. Hence, |TB|=fk(k1)(q1)f1λ=q(q21)a, with a=1 or 2 when TB is Z2×PSL2(q) or PSL2(q), respectively. It follows that λf when TB is Z2×PSL2(q), or λ=2 when TB is PSL2(q).

    Let R be the Ree unital of order q (which is a 2-(q3+1,q+1,1) design). For the former case, let σ be the central involution of Z2×PSL2(q). It can be deduced from [15] that σ fixes a block of R pointwise and preserves a point-partition Sσ of R into q2q blocks, each of them invariant by σ. Now, Z2×PSL2(q) induces PSL2(q) on Sσ{}, and PSL2(q) preserves acting on this one in its natural 2-transitive action of degree q+1. Further, PSL2(q) partitions Sσ into two orbits each of length q2q2. Thus, is the unique Z2×PSL2(q)-orbit of points of R of length q+1. Note that k=q+1, which means B=. This means that |BG|=|G|=q2(q2q+1) by [6], and so λ=1, which contradicts with λ being prime. For the latter case, the block stabilizer T for the Ree unital is Z2×PSL2(q), and Z2 fixed all points in . However, since αTBαT and |αTB|=|αT|=q+1, we have αTB=αT. This means that Z2 fixed all points in B, and so Z2TB, an obvious contradiction.

    For the remaining possibility of T in T, where

    T={2F4(q),3D4(q),G2(q),F4(q),Eϵ6(q),E7(q),E8(q)},

    we use the following Lemma from [14] to prove that Gα cannot be the maximal parabolic subgroup. Note that in the following we denote by np the p-part of n and np the p-part of n, i.e., np=pt where ptn but pt+1n, and np=n/np.

    Lemma 3.5. ([14]) Assume that T is a simple group of Lie type in characteristic p and acts on the set of cosets of a maximal parabolic subgroup. Then T has a unique subdegree which is a power of p except when T is Ld(q), Ω+2m(q) (m is odd) or E6(q).

    Lemma 3.6. If TT, then Gα cannot be a parabolic subgroup of G.

    Proof. By Lemma 3.5, for all cases where TT{E6(q)}, there is a unique subdegree which is a power of p. Then, Lemma 3.2 implies that r0 divides |v1|p. Since we also have λ divides |Gα|, we can easily check that r0 is too small to satisfy the condition v<λr20. Therefore, we assume that T=E6(q). If G contains a graph automorphism, or GαT is P2 or P4, then there is also a unique subdegree that is a power of p. However, r0 is too small again. If GαT is P3 with type A1A4, we have λq51q1 by λ|Gα| and

    v=(q3+1)(q4+1)(q91)(q6+1)(q4+q2+1)(q1).

    Moreover, from [1, Proposition 6.3], we know that there exist two nontrivial subdegrees: q13q51q1 and q(q51)(q41)(q1)2. Lemma 3.2 then implies that r divides λqq51q1. However, the condition v<λr20 cannot be satisfied again. If GαT is P1 with type D5, then

    v=(q8+q4+1)(q91)q1,

    and there exist two nontrivial subdegrees (see [13]): q(q3+1)(q81)(q1) and q8(q4+1)(q51)(q1). It follows that rλq(q4+1). This, together with λ|Gα|, implies that r2<λ2q2(q4+1)2<λv, which is contradictive with Lemma 2.1.

    In this section, we assume that Gα is a non-parabolic maximal subgroup of G.

    Lemma 3.7. Assume that G and D satisfy the hypothesis of Theorem 1.1. Then, |G|<|Gα|3.

    Proof. From Lemma 2.3, we know that r divides every nontrivial subdegree of G, and so r divides |Gα|. Since v<r2 by (3) of Lemma 2.1, it follows that |G|<|Gα|3.

    Lemma 3.7 implies that Gα is a large maximal non-parabolic subgroup of G. The type of Gα can be read from [1, Table 2]. Note that Theorem 1.1(2) just corresponds to the non-parabolic case here, with T=G2(q) and the type of Gα being SLϵ3(q).2.

    Lemma 3.8. Assume that G and D satisfy the hypothesis of Theorem 1.1. If T=G2(q) and the type of Gα is SLϵ3(q).2 with ϵ=±, then ϵ=, T is flag-transitive on D, and the parameters of D are (v,b,r,k,λ)=(q3(q31)2,(q+1)(q61),(q+1)(q3+1),q32,q+1), where q is even, and λ=q+1 is a Fermat prime.

    Proof. It is obvious that |Tα|=2q3(q21)(q3ϵ1), and hence v=12q3(q3+ϵ1). We first deal with the case when q is even. Since G2(2) is not simple (G2(2)PSU3(3):2), we assume that q>2. From [17, Section 3, Case 8], we know that r divides λ(q3ϵ1). Then, the equality λ(v1)=r(k1) from Lemma 2.1 implies that there exists an odd integer t dividing (q3ϵ1) such that

    k=t(q3+ϵ2)2+1andr=λ(q3ϵ1)t.

    Obviously, the fact that k<r implies t<λ. Moreover, by Lemma 2.1 we have

    b=λv(v1)k(k1)=λq3(q61)(q3+ϵ2)4k(k1)=λq3(q61)2kt. (3.1)

    Note also that (2k,q3ϵ1)3t+ϵ2, (2k,q3+1)t+ϵ2, (k,q32)t+ϵ1, and therefore (2k,q21)(t+ϵ2)(3t+ϵ2). Since b is an integer, it follows from (3.1) that kλq32(q3ϵ1)(q3+ϵ1). Hence, we have

    t(q3+ϵ2)2+1λ(t+ϵ2)(t+ϵ1)(3t+ϵ2). (3.2)

    Since 3t+ϵ25t, it follows that q3+ϵ2<10λ(t+ϵ2)(t+ϵ1) except when t=1 and ϵ=. When t1, the above together with t<λ further implies that λ cannot be a prime divisor of |Out(T)|, and hence λ divides |SLϵ3(q).2|.

    In the following, we prove that t=1. Obviously, t2, for t is odd. When t3, we have rλ13(q3ϵ1) and 3t2<λ by t(q3+ϵ2)2<krλ(q3ϵ1)3. Now, assume that λk. Then λ divides (2q3(q21)(q3ϵ1),2k), and it follows that λ4(t+ϵ1)(t+ϵ2)(3t+ϵ2)2. Since 32t<λ, we have λ=3t+ϵ2, or ϵ=+ and λ=3t+22. If λ=3t+2ϵ, then k<r forces (t,λ,ϵ)=(5,17,+), (5,13,), (3,11,+), (3,7,), or (1,5,+). Note that kλ(t+ϵ2)(t+ϵ1)(3t+ϵ2), and we check each case and know that it is impossible. If λ=3t+22, then we get (t,λ)=(4,7), which can be ruled out similarly. Hence, λk, and it follows (3.2) that t>q. On the other hand, since |T:TB|b, there exists an integer f1 dividing f such that f1|T:TB|=b and

    |TB|=2f1q3(q21)kλt.

    Since λk and λ>t>q2, λ is a divisor of f1, (q1), q+1, or q, and so λq+1. Since q<t<23λ, we get a contradiction. Therefore, t=1 as we claim.

    Let t=1. Then, rλ=(q3ϵ1), and k=(q3+2ϵ)2+1 with q even. If ϵ=+, then r=λ(q31), and k=q3+42. Since b is an integer, we get that q3+4 divides λq3(q61). It follows that q3+460λ, and so λ divides q3+4, which is impossible as λ is a prime divisor of 2q3(q21)(q31). We now assume that ϵ=. Then, k=q32 and b=λ(q61), and r=λ(q3+1) for q4. Moreover, in this case |TB|=f1q6(q21)λ and we further find that TB is contained in a maximal parabolic subgroup M=q5:GL2(q) of G2(q). Since G is flag-transitive, Lemma 2.2 implies that |SU3(q).2:Tα,B| divides λ(q3+1). Using the maximal subgroup list for SU3(q) provided in [4, Tables 8.5 and 8.6], we get that Tα,B is isomorphic to a subgroup of M1=q3:Cq21.2. If Tα,B=M1 or λq21, then Tα,B contains a cyclic group of order q21, which contradicts Tα,BTBq5:GL2(q). Hence, |M1:Tα,B|=λ divides q21. This also implies that T is flag-transitive, and so |T:M||M:TB|=λ(q61). It follows that |M:TB|=|GL2(q):TBGL2(q)|=λ(q1), which gives |TBGL2(q)|=q(q21)λ. Then, using the list of maximal subgroups of SL(2,q) provided in [4, Tables 8.1 and 8.2], we get that λq1, and so λq+1, which further implies that λ=q+1. This is to say, if such design exists, then the design parameters tuple is (v,b,r,k,λ)=(q3(q31)2,(q+1)(q61),(q+1)(q3+1),q32,q+1), where λ=q+1 is a Fermat prime.

    Now, we assume that q is odd. Then, we conclude that r divides λ(q3ϵ1)2 from [17, Section 4, Case 1, i=1]. Let rt=λ(q3ϵ1)2. Similar as in the even case, we also have t=1. That is to say, k=q3+ϵ2+1 and r=λ(q3ϵ1)2. When ϵ=+, the fact of k dividing λq3(q61) q3+3 implies that q3+3 divides 24λ, and so λ divides q3+3, which is impossible as λ is a prime divisor of 2q3(q21)(q31). If ϵ=, we have k=q31, and so b=λq3(q3+1)4. We consider a maximal subgroup M containing TB. It is proven later that MTBSL3(q).2 and hence that is unique. The fact that |T:M|b implies that M is SL3(q).2 by [4,Tables 8.41 and 8.42] and that |T:M|=q3(q3+1)2. It follows that 2|M:TB|λ, which forces λ=2 and M=TBSL3(q).2. Since TαSU3(q).2 and r=q3+1, we have Tα,Bq3.Cq21.2 or q3.Cq21. According to the maximal subgroups of SL3(q) in [4, Tables 8.3 and 8.4], we know that Tα,B is isomorphic to a subgroup of q2.GL2(q).2, which is impossible.

    All other types of Gα in [1, Table 2], except two cases which we will discuss in Lemma 3.10, can be ruled out using the method stated below. First, for each possibility of Gα, the order of Gα and the value of v can be determined. We can hence get an upper bound of λ according to λ|Gα|. Then, to get an upper bound of r0, we consider the divisors of |Gα| in two parts: i1i=1Φi for which Φi divides v, and i2j=1Ψj=|Gα|/i1i=1Φi. Obviously, all Φi are coprime with v1. For each Ψj, we calculate the remainder ˉΨj of Ψj divided by v1. This implies that (|Gα|,v1) divides |Out(T)|i2j=1ˉΨj, which implies that r0|Out(T)|i2j=1ˉΨj. Finally, one can check that the values of r0 for all these cases are too small to satisfy the condition that v<λr20. That is, no new designs arise in these cases. To be more explicit, we take T=E8(q) as an example.

    Lemma 3.9. Assume that G and D satisfy the hypothesis of Theorem 1.1. If T=E8(q) with q=pe, then Gα cannot be a non-parabolic maximal subgroup of G.

    Proof. Let T=E8(q). Then, it follows from [1, Table 2] that the type of Gα is one of the following:

    {A1(q)E7(q),D8(q),E8(q12),E8(q13),Aϵ2(q)Eϵ6(q)}.

    For the case that Gα is of type A1(q)E7(q), we have λ<q8 since λ|Gα| and v=q56(q6+1)(q10+1)(q12+1)q301q21 by v=|G:Gα|. Obviously, q(q6+1)v and q301q21v, which also implies q61q21v and q101q21v. This means (|Gα|,v1) divides |Out(T)|(q21)5(q81)(q141)(q181). Since r0(|Gα|,v1), we have r0<q51. However, Lemma 3.2 shows q112<v<λr20<q110, a contradiction.

    For the case that Gα is of type D8(q), we have λ<q7 and

    v=q64(q12+q6+1)(q16+q8+1)(q10+1)(q301)q41.

    Since v12(modq4+1), (v1,q4+1)=2 or 1. This, together with qv and q301q21v, implies that (|Gα|,v1) divides 4|Out(T)|(q21)3(q41)3(q121)(q141). It follows that r04|Out(T)|q44<4q45, and q128<v<λr20<4q97, which is a contradiction.

    Assume that Gα is of type E8(q12). Then, λq15 and v=q60(q+1)(q4+1)(q6+1)(q7+1)(q9+1)(q10+1)(q12+1)(q15+1). Since q, q3+1, q4+1, q5+1, and q6+1 are divisors of v, we get that (|Gα|,v1) divides |Out(T)|(q1)2(q31)2(q51)(q71)(q91)(q151). It follows that r0<q45, and so q124<v<λr20<q105, a contradiction again.

    Assume that Gα is of type Aϵ2(q)Eϵ6(q) or E8(q13). Then, since Gα is non-parabolic, the Tits lemma in [18, 1.6] implies that p divides v=|G:Gα|, and so (|Gα|,v1) is coprime with p. It follows that r0|Gα|p as r0 divides (|Gα|,v1). This implies that v<λ|Out(T)|2|Tα|2p by Lemma 3.2, which cannot be satisfied when Gα is of type Aϵ2(q)Eϵ6(q) or E8(q13).

    Lemma 3.10. Assume that G and D satisfy the hypothesis of Theorem 1.1. Then the type of Gα cannot be either (qϵ1)Dϵ5(q) when T=Eϵ6(q) or (qϵ1)Eϵ6(q) when T=E7(q).

    Proof. Assume that T is Eϵ6(q) and Gα is of type (qϵ1)Dϵ5(q). Then, λ<2q4 as λ divides |Gα| and v=q16(q9ϵ1)(q121)(3,q1)(qϵ1)(q41). In addition, we know from [1, Theorem 4.1] that there exist two subdegrees: q8(q5ϵ)(q4+1) and q10(q3+ϵ)(q81). Since r0 divides the greatest common divisors of every non-trivial subdegree and v1 (Lemma 2.3), we have (r0,p)=1, and so r02(qϵ1)(q4+1), which implies that r0 is too small to satisfy v<λr20 again.

    If T is E7(q) and Gα is of type (qϵ1)Eϵ6(q), we have λ2q6 and v=q27(q5+ϵ1)(q9+ϵ1)(q141)qϵ1. [1, Theorem 4.1] shows that there exist two subdegrees, which divide q12(q5ϵ)(q9ϵ) and(4,qm1ϵ)q16(q5ϵ)(q121q41), respectively. However, by Lemma 2.3 we know that r0 is too small again.

    Proof of Theorem 1.1. It follows immediately from Lemmas 3.1–3.10.

    In this paper, we figure out all possible parameters of 2-(v,k,λ) designs D (with λ prime) that admit flag-transitive point-primitive automorphism groups with an exceptional Lie type socle. Our work contributes to the classification of flag-transitive 2-(v,k,λ) designs. In addition, the cases that the automorphism groups of such designs with classical socle will be the main focus in our future work.

    Y. Zhang: Data curation, writing-review and editing; J. Shen: Writing-original draft. All authors have read and agreed to the published version of the manuscript.

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

    The authors would like to thank the anonymous referees for corrections and valuable comments that led to the improvement of this paper.

    This work is financially supported by the National Natural Science Foundation of China (Grant number: 12301020 and 12201469) and the Science and Technology Projects in Guangzhou (Grant number: 2023A04J0027).

    The authors declare no conflict of interest.


    Acknowledgments



    This work was supported by Universiti Sains Malaysia, Short-Term Grant with Project No.: 304/PPSP/6315647. Additionally, we appreciate the support provided by the Bench Fee Program Sarjana Neurosains Kognitif 401/PPSP/E3170003. We extend our gratitude to the MRI technologists, Wan Nazyrah Abdul Halim, Che Munirah Che Abdullah, and Siti Afidah Mamat, for their invaluable assistance with data acquisition.

    Funding



    Universiti Sains Malaysia, Short-Term Grant with Project No.: 304/PPSP/6315647 and Bench Fee Program Sarjana Neurosains Kognitif 401/PPSP/E3170003.

    Authors' contributions



    All authors made direct and significant contributions to the manuscript. Aini Ismafairus Abd Hamid: Secured funding, conceptualized the study design, managed the project, wrote the original draft, and reviewed and edited the manuscript. Nurfaten Hamzah: Conducted data analysis and reviewed and edited the manuscript. Siti Mariam Roslan: Contributed to the study design, managed data collection, and conducted data analysis. Nur Alia Amalin Suhardi: Conducted data analysis and contributed to the literature review. Muhammad Riddha Abdul Rahman: Contributed to the literature review and reviewed the manuscript. Faiz Mustafar: Contributed to the study design and reviewed the manuscript. Hazim Omar: Contributed to the study design and reviewed the manuscript. Asma Hayati Ahmad, Elza Azri Othman, and Ahmad Nazlim Yusoff: Reviewed the manuscript. All authors approved the final version.

    Conflict of interest



    The authors declared no potential conflicts of interest.

    [1] Baddeley A (2003) Working memory: Looking back and looking forward. Nat Rev Neurosci 4: 829-839. https://doi.org/10.1038/nrn1201
    [2] Baddeley A, Hitch G, Allen R (2020) A Multicomponent Model of Working Memory. Working Memory: State of the Science : 10-43. https://doi.org/10.1093/oso/9780198842286.003.0002
    [3] Chai WJ, Abd Hamid AI, Abdullah JM (2018) Working memory from the psychological and neurosciences perspectives: A review. Front Psychol 9: 327922. https://doi.org/10.3389/fpsyg.2018.00401
    [4] Cowan N (2014) Working Memory Underpins Cognitive Development, Learning, and Education. Educ Psychol Rev 26: 197. https://doi.org/10.1007/s10648-013-9246-y
    [5] D'Esposito M, Postle BR (2015) The Cognitive Neuroscience of Working Memory. Annu Rev Psychol 66: 115. https://doi.org/10.1146/annurev-psych-010814-015031
    [6] Izmalkova A, Barmin A, Velichkovsky BB, et al. (2022) Cognitive Resources in Working Memory: Domain-Specific or General?. Behav Sci 12: 459. https://doi.org/10.3390/bs12110459
    [7] Davidow JY, Insel C, Somerville LH (2018) Adolescent Development of Value-Guided Goal Pursuit. Trends Cogn Sci 22: 725-736. https://doi.org/10.1016/j.tics.2018.05.003
    [8] Dwyer DB, Harrison BJ, Yücel M, et al. (2016) Adolescent Cognitive Control: Brain Network Dynamics. Stress: Concepts, Cognition, Emotion, and Behavior . Academic Press 177-185. https://doi.org/10.1016/B978-0-12-800951-2.00021-2
    [9] Larsen B, Luna B (2018) Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci Biobehav Rev 94: 179-195. https://doi.org/10.1016/j.neubiorev.2018.09.005
    [10] Marakushyn DI, Bulynina OD, Isaieva IM, et al. (2024) Impact of stress on emotional health and cognitive function. Med Sci Ukraine 20: 136-142. https://doi.org/10.32345/2664-4738.2.2024.16
    [11] Scullin MK, Bliwise DL (2015) Sleep, cognition, and normal aging: integrating a half century of multidisciplinary research. Perspect Psychol Sci 10: 97-137. https://doi.org/10.1177/1745691614556680
    [12] Zeek ML, Savoie MJ, Song M, et al. (2015) Sleep Duration and Academic Performance Among Student Pharmacists. Am J Pharm Educ 79: 63. https://doi.org/10.5688/ajpe79563
    [13] Rangesh HD, Ramanathan R, Mani V, et al. (2024) Study on stress perceived and the impact of stress on sleep among medical college students. Natl J Physiol Pharm Pharmacol 14: 348-352. https://doi.org/10.5688/ajpe79563
    [14] Pace-Schott EF, Spencer RMC (2011) Age-related changes in the cognitive function of sleep. Prog Brain Res 191: 75-89. https://doi.org/10.1016/B978-0-444-53752-2.00012-6
    [15] Belleville S, Cuesta M, Bier N, et al. (2024) Five-year effects of cognitive training in individuals with mild cognitive impairment. Alzheimers Dement (Amst) 16: e12626. https://doi.org/10.1002/dad2.12626
    [16] Mowszowski L, Batchelor J, Naismith SL (2010) Early intervention for cognitive decline: can cognitive training be used as a selective prevention technique?. Int Psychogeriatr 22: 537-548. https://doi.org/10.1017/S1041610209991748
    [17] Naismith SL, Glozier N, Burke D, et al. (2009) Early intervention for cognitive decline: is there a role for multiple medical or behavioural interventions?. Early Interv Psychiatry 3: 19-27. https://doi.org/10.1111/j.1751-7893.2008.00102.x
    [18] Leistiko NM, Madanat L, Yeung WKA, et al. (2023) Effects of gamma frequency binaural beats on attention and anxiety. Curr Psychol 1: 1. https://doi.org/10.1007/s12144-023-04681-3
    [19] Kim YJ, Kim KB, Kim JS, et al. (2023) Effects of inaudible binaural beats on visuospatial memory. Neuroreport 34: 501-505. https://doi.org/10.1097/WNR.0000000000001916
    [20] Aloysius NMF, Hamid AIA, Mustafar F (2023) Alpha and Low Gamma Embedded With White Noise Binaural Beats Modulating Working Memory among Malaysian Young Adult: A Preliminary fMRI Study. Malays J Medicine Health Sci 19: 113-124. https://doi.org/10.47836/mjmhs.19.1.17
    [21] Beauchene C, Abaid N, Moran R, et al. (2017) The effect of binaural beats on verbal working memory and cortical connectivity. J Neural Eng 14: 026014. https://doi.org/10.1088/1741-2552/aa5d67
    [22] Kim HW, Happe J, Lee YS (2023) Beta and gamma binaural beats enhance auditory sentence comprehension. Psychol Res 87: 2218-2227. https://doi.org/10.1007/s00426-023-01808-w
    [23] Baseanu ICC, Roman NA, Minzatanu D, et al. (2024) The Efficiency of Binaural Beats on Anxiety and Depression—A Systematic Review. Appl Sci 14: 5675. https://doi.org/10.3390/app14135675
    [24] Orozco Perez HD, Dumas G, Lehmann A (2020) Binaural Beats through the Auditory Pathway: From Brainstem to Connectivity Patterns. eNeuro 7: ENEURO.0232-19.2020. https://doi.org/10.1101/623231
    [25] Ingendoh RM, Posny ES, Heine A (2023) Binaural beats to entrain the brain? A systematic review of the effects of binaural beat stimulation on brain oscillatory activity, and the implications for psychological research and intervention. PLoS One 18: e0286023. https://doi.org/10.1371/journal.pone.0286023
    [26] Jurczyk J (2022) Influence of binaural beats on the state of relaxation and mood. Pharmac Psychiatry Neurol 37: 221-233. https://doi.org/10.5114/fpn.2021.115644
    [27] Rakhshan V, Hassani-Abharian P, Joghataei M, et al. (2022) Effects of the Alpha, Beta, and Gamma Binaural Beat Brain Stimulation and Short-Term Training on Simultaneously Assessed Visuospatial and Verbal Working Memories, Signal Detection Measures, Response Times, and Intrasubject Response Time Variabilities: A Within-Subject Randomized Placebo-Controlled Clinical Trial. Biomed Res Int 2022: 8588272. https://doi.org/10.1155/2022/8588272
    [28] Kraus J, Porubanová M (2015) The effect of binaural beats on working memory capacity. Stud Psychol (Bratisl) 57: 135-145. https://doi.org/10.21909/sp.2015.02.689
    [29] Klichowski M, Wicher A, Kruszwicka A, et al. (2023) Reverse effect of home-use binaural beats brain stimulation. Sci Rep 13: 11079. https://doi.org/10.1038/s41598-023-38313-4
    [30] López-Caballero F, Escera C (2017) Binaural beat: A failure to enhance EEG power and emotional arousal. Front Hum Neurosci 11: 244473. https://doi.org/10.3389/fnhum.2017.00557
    [31] Herweg NA, Bunzeck N (2015) Differential effects of white noise in cognitive and perceptual tasks. Front Psychol 6: 162351. https://doi.org/10.3389/fpsyg.2015.01639
    [32] Chen IC, Chan HY, Lin KC, et al. (2022) Listening to White Noise Improved Verbal Working Memory in Children with Attention-Deficit/Hyperactivity Disorder: A Pilot Study. Int J Environ Res Public Health 19: 7283. https://doi.org/10.3390/ijerph19127283
    [33] Helps SK, Bamford S, Sonuga-Barke EJS, et al. (2014) Different Effects of Adding White Noise on Cognitive Performance of Sub-, Normal and Super-Attentive School Children. PLoS One 9: e112768. https://doi.org/10.1371/journal.pone.0112768
    [34] Taitelbaum-Swead R, Fostick L (2016) The Effect of Age and Type of Noise on Speech Perception under Conditions of Changing Context and Noise Levels. Folia Phoniatr Logop 68: 16-21. https://doi.org/10.1159/000444749
    [35] Othman E, Yusoff AN, Mohamad M, et al. (2019) Low intensity white noise improves performance in auditory working memory task: An fMRI study. Heliyon 5: e02444. https://doi.org/10.1016/j.heliyon.2019.e02444
    [36] Yi JH, Kim KB, Kim YJ, et al. (2022) A Comparison of the Effects of Binaural Beats of Audible and Inaudible Frequencies on Brainwaves. Appl Sci 12: 13004. https://doi.org/10.3390/app122413004
    [37] Choi MH, Jung JJ, Kim KB, et al. (2022) Effect of binaural beat in the inaudible band on EEG (STROBE). Medicine 101: e29819. https://doi.org/10.1097/MD.0000000000029819
    [38] Whitehead AL, Julious SA, Cooper CL, et al. (2015) Estimating the sample size for a pilot randomised trial to minimise the overall trial sample size for the external pilot and main trial for a continuous outcome variable. Stat Methods Med Res 25: 1057. https://doi.org/10.1177/0962280215588241
    [39] Ryan JJ, Lopez SJ (2001) Wechsler Adult Intelligence Scale-III. Understanding Psychological Assessment, Perspectives on Individual Differences . Boston, MA: Springer. https://doi.org/10.1007/978-1-4615-1185-4_2
    [40] Becker M, Repantis D, Dresler M, et al. (2022) Cognitive enhancement: Effects of methylphenidate, modafinil, and caffeine on latent memory and resting state functional connectivity in healthy adults. Hum Brain Mapp 43: 4225-4238. https://doi.org/10.1002/hbm.25949
    [41] Dutta A, Khan S, Akhtar Z (2023) Use of mobile phone based hearing app Hear WHO for self detection of hearing loss. J ENT Surg Res 1: 1-4.
    [42] World Health OrganizationhearWHO, 2019 (2019). Available from: https://www.who.int/teams/noncommunicable-diseases/sensory-functions-disability-and-rehabilitation/hearwho
    [43] Zaidil NN, Ying JH, Begum T, et al. (2019) Syntactic language processing among women - An EEG/ERP study of visual pictorial stimuli. 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES) . Sarawak, Malaysia: 520-522. https://doi.org/10.1109/IECBES.2018.8626694
    [44] Nieto-Castanon A (2020) Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN . https://doi.org/10.56441/hilbertpress.2207.6598
    [45] Andersson JLR, Hutton C, Ashburner J, et al. (2001) Modeling geometric deformations in EPI time series. Neuroimage 13: 903-919. https://doi.org/10.1006/nimg.2001.0746
    [46] Friston KJ, Ashburner J, Frith CD, et al. (1995) Spatial registration and normalization of images. Hum Brain Mapp 3: 165-189. https://doi.org/10.1002/hbm.460030303
    [47] Henson R, Büchel C, Josephs O, et al. (1999) The Slice-Timing Problem in Event-related fMRI. Neuroimage 9: 125.
    [48] Sladky R, Friston KJ, Tröstl J, et al. (2011) Slice-timing effects and their correction in functional MRI. Neuroimage 58: 588-594. https://doi.org/10.1016/j.neuroimage.2011.06.078
    [49] Nieto-Castanon A (2022) Preparing fMRI Data for Statistical Analysis. arXiv : 2210.13564.
    [50] Power JD, Mitra A, Laumann TO, et al. (2014) Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84: 320-341. https://doi.org/10.1016/j.neuroimage.2013.08.048
    [51] Calhoun VD, Wager TD, Krishnan A, et al. (2017) The impact of T1 versus EPI spatial normalization templates for fMRI data analyses. Hum Brain Mapp 38: 5331-5342. https://doi.org/10.1002/hbm.23737
    [52] Friston KJ, Williams S, Howard R, et al. (1996) Movement-related effects in fMRI time-series. Magn Reson Med 35: 346-355. https://doi.org/10.1002/mrm.1910350312
    [53] Hallquist MN, Hwang K, Luna B (2013) The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82: 208-225. https://doi.org/10.1016/j.neuroimage.2013.05.116
    [54] Desikan RS, Ségonne F, Fischl B, et al. (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31: 968-980. https://doi.org/10.1016/j.neuroimage.2006.01.021
    [55] Nieto-Castanon A, Whitfield-Gabrieli S CONN functional connectivity toolbox: RRID SCR_009550, release 22 (2022). https://doi.org/10.56441/hilbertpress.2246.5840
    [56] McLaren DG, Ries ML, Xu G, et al. (2012) A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage 61: 1277-1286. https://doi.org/10.1016/j.neuroimage.2012.03.068
    [57] Friston KJ, Buechel C, Fink GR, et al. (1997) Psychophysiological and modulatory interactions in neuroimaging. Neuroimage 6: 218-229. https://doi.org/10.1006/nimg.1997.0291
    [58] Vinette SA, Bray S (2015) Variation in functional connectivity along anterior-to-posterior intraparietal sulcus, and relationship with age across late childhood and adolescence. Dev Cogn Neurosci 13: 32-42. https://doi.org/10.1016/j.dcn.2015.04.004
    [59] Osher DE, Brissenden JA, Somers DC (2019) Predicting an individual's dorsal attention network activity from functional connectivity fingerprints. J Neurophysiol 122: 232-240. https://doi.org/10.1152/jn.00174.2019
    [60] Van Ettinger-Veenstra HM, Huijbers W, Gutteling TP, et al. (2009) fMRI-guided TMS on cortical eye fields: the frontal but not intraparietal eye fields regulate the coupling between visuospatial attention and eye movements. J Neurophysiol 102: 3469-3480. https://doi.org/10.1152/jn.00350.2009
    [61] Petit L, Orssaud C, Tzourio N, et al. (1993) PET study of voluntary saccadic eye movements in humans: basal ganglia-thalamocortical system and cingulate cortex involvement. J Neurophysiol 69: 1009-1017. https://doi.org/101152/jn19936941009
    [62] Paré S, Bleau M, Dricot L, et al. (2023) Brain structural changes in blindness: a systematic review and an anatomical likelihood estimation (ALE) meta-analysis. Neurosci Biobehav Rev 150: 105165. https://doi.org/10.1016/j.neubiorev.2023.105165
    [63] Rilk AJ, Soekadar SR, Sauseng P, et al. (2011) Alpha coherence predicts accuracy during a visuomotor tracking task. Neuropsychologia 49: 3704-3709. https://doi.org/10.1016/j.neuropsychologia.2011.09.026
    [64] Lin CL, Shaw FZ, Young KY, et al. (2012) EEG correlates of haptic feedback in a visuomotor tracking task. Neuroimage 60: 2258-2273. https://doi.org/10.1016/j.neuroimage.2012.02.008
    [65] Park J, Kwon H, Kang S, et al. (2018) The effect of binaural beat-based audiovisual stimulation on brain waves and concentration. 2018 International Conference on Information and Communication Technology Convergence (ICTC) . IEEE 420-423. https://doi.org/10.1109/ICTC.2018.8539512
    [66] Shekar L, Suryavanshi CA, Nayak KR (2018) Effect of alpha and gamma binaural beats on reaction time and short-term memory. Natl J Physiol Pharm Pharmacol 8: 829-829. https://doi.org/10.5455/njppp.2018.8.1246506022018
    [67] Pehrs C, Zaki J, Schlochtermeier LH, et al. (2017) The Temporal Pole Top-Down Modulates the Ventral Visual Stream During Social Cognition. Cerebral Cortex 27: 777-792.
    [68] Hwang K, Shine JM, D'Esposito M (2017) Fronto-Parietal Interactions with Task-Evoked Functional Connectivity During Cognitive Control. BioRxi 2017: 133611. https://doi.org/10.1101/133611
    [69] Herlin B, Navarro V, Dupont S (2021) The temporal pole: From anatomy to function—A literature appraisal. J Chem Neuroanat 113: 101925. https://doi.org/10.1016/j.jchemneu.2021.101925
    [70] Nee DE (2021) Integrative frontal-parietal dynamics supporting cognitive control. Elife 10: e57244. https://doi.org/10.7554/eLife.57244
    [71] Braun U, Schäfer A, Walter H, et al. (2015) Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proc Natl Acad Sci U S A 112: 11678-11683. https://doi.org/10.1073/pnas.1422487112
    [72] Burgos PI, Mariman JJ, Makeig S, et al. (2018) Visuomotor coordination and cortical connectivity of modular motor learning. Hum Brain Mapp 39: 3836-3853. https://doi.org/10.1002/hbm.24215
    [73] Ebrahiminia F, Hosseinzadeh G (2014) Modulation of effective connectivity during finger movement task with visual stimulus. 22nd Iranian Conference on Electrical Engineering . ICEE 1928-1932. https://doi.org/10.1109/IranianCEE.2014.6999857
    [74] Vine SJ, Moore LJ, Wilson MR (2016) An Integrative Framework of Stress, Attention, and Visuomotor Performance. Front Psychol 7. https://doi.org/10.3389/fpsyg.2016.01671
    [75] Sulik MJ, Haft SL, Obradović J (2018) Visual-Motor Integration, Executive Functions, and Academic Achievement: Concurrent and Longitudinal Relations in Late Elementary School. Early Educ Dev 29: 956-970. https://doi.org/10.1080/10409289.2018.1442097
    [76] Lin HY (2022) The Effects of White Noise on Attentional Performance and On-Task Behaviors in Preschoolers with ADHD. Int J Environ Res Public Health 19: 15391. https://doi.org/10.3390/ijerph192215391
    [77] Baijot S, Slama H, Söderlund G, et al. (2016) Neuropsychological and neurophysiological benefits from white noise in children with and without ADHD. Behav Brain Funct 12: 11. https://doi.org/10.1186/s12993-016-0095-y
    [78] Rausch VH, Bauch EM, Bunzeck N (2014) White Noise Improves Learning by Modulating Activity in Dopaminergic Midbrain Regions and Right Superior Temporal Sulcus. J Cogn Neurosci 26: 1469-1480. https://doi.org/10.1162/jocn_a_00537
    [79] Niemeier M, Goltz HC, Kuchinad A, et al. (2005) A Contralateral Preference in the Lateral Occipital Area: Sensory and Attentional Mechanisms. Cerebral Cortex 15: 325-331. https://doi.org/10.1093/cercor/bhh134
    [80] Keefe JM, Störmer VS (2021) Lateralized alpha activity and slow potential shifts over visual cortex track the time course of both endogenous and exogenous orienting of attention. Neuroimage 225: 117495. https://doi.org/10.1016/j.neuroimage.2020.117495
    [81] Guggenmos M, Thoma V, Haynes JD, et al. (2015) Spatial attention enhances object coding in local and distributed representations of the lateral occipital complex. Neuroimage 116: 149-157. https://doi.org/10.1016/j.neuroimage.2015.04.004
    [82] Wen X, Liu Y, Yao L, et al. (2013) Top-down regulation of default mode activity in spatial visual attention. J Neurosci 33: 6444-6453. https://doi.org/10.1523/JNEUROSCI.4939-12.2013
    [83] Nathan Spreng R, Schacter DL (2011) Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults. Cereb Cortex 22: 2610. https://doi.org/10.1093/cercor/bhr339
    [84] Nenert R, Allendorfer JB, Szaflarski JP (2014) A Model for Visual Memory Encoding. PLoS One 9: e107761. https://doi.org/10.1371/journal.pone.0107761
    [85] Kragel JE, Polyn SM (2015) Functional interactions between large-scale networks during memory search. Cereb Cortex 25: 667-679. https://doi.org/10.1093/cercor/bht258
    [86] DeViva JC, Zayfert C, Pigeon WR, et al. (2005) Treatment of residual insomnia after CBT for PTSD: Case studies. J Trauma Stress 18: 155-159. https://doi.org/10.1002/jts.20015
    [87] Wang R, Ge S, Zommara NM, et al. (2019) Auditory White Noise Affects Left/Right Visual Working Memory in an Opposite Pattern. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) . Berlin, Germany: 2719-2722. https://doi.org/10.1109/EMBC.2019.8856688
    [88] Cole MW, Reynolds JR, Power JD, et al. (2013) Multi-task connectivity reveals flexible hubs for adaptive task control. Nat Neurosci 16: 1348-1355. https://doi.org/10.1038/nn.3470
    [89] Cocuzza CV, Ito T, Schultz D, et al. (2020) Flexible coordinator and switcher hubs for adaptive task control. BioRxiv 822213. https://doi.org/10.1101/822213
    [90] Xia M, Xu P, Yang Y, et al. (2021) Frontoparietal Connectivity Neurofeedback Training for Promotion of Working Memory: An fNIRS Study in Healthy Male Participants. IEEE Access 9: 62316-62331. https://doi.org/10.1109/ACCESS.2021.3074220
    [91] Kwon S, Watanabe M, Fischer E, et al. (2017) Attention reorganizes connectivity across networks in a frequency specific manner. Neuroimage 144: 217-226. https://doi.org/10.1016/j.neuroimage.2016.10.014
    [92] Küchenhoff S, Sorg C, Schneider SC, et al. (2021) Visual processing speed is linked to functional connectivity between right frontoparietal and visual networks. Eur J Neurosci 53: 3362-3377. https://doi.org/10.1111/ejn.15206
    [93] Chaieb L, Wilpert EC, Reber TP, et al. (2015) Auditory beat stimulation and its effects on cognition and mood states. Front Psychiatry 6: 136819. https://doi.org/10.3389/fpsyt.2015.00070
    [94] Garcia-Argibay M, Santed MA, Reales JM (2019) Efficacy of binaural auditory beats in cognition, anxiety, and pain perception: a meta-analysis. Psychol Res 83: 357-372. https://doi.org/10.1007/s00426-018-1066-8
    [95] Baars BJ, Geld N, Kozma R (2021) Global Workspace Theory (GWT) and Prefrontal Cortex: Recent Developments. Front Psychol 12. https://doi.org/10.3389/fpsyg.2021.749868
    [96] Dehaene S, Naccache L (2001) Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79: 1-37. https://doi.org/10.1016/S0010-0277(00)00123-2
    [97] Friston K, FitzGerald T, Rigoli F, et al. (2017) Active Inference: A Process Theory. Neural Comput 29: 1-49. https://doi.org/10.1162/NECO_a_00912
    [98] Friston K (2010) The free-energy principle: a unified brain theory?. Nat Rev Neurosci 11: 127-138. https://doi.org/10.1038/nrn2787
    [99] Jiang LP, Rao RPN (2022) Predictive Coding Theories of Cortical Function. arXiv : 2112.10048. https://doi.org/10.1093/acrefore/9780190264086.013.328
  • neurosci-12-02-010-s001.pdf
  • Reader Comments
  • © 2025 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(237) PDF downloads(22) Cited by(0)

Figures and Tables

Figures(13)  /  Tables(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog