Schizophrenia is characterized by significant cognitive impairments and affects up to 98% of patients. Neurofeedback (NF) offers a means to modulate neural network function through cognitive processes such as learning and memorization, with documented structural changes in the brain, most notably an increase in grey matter volume in targeted regions.
Methods
The present 2-week, open-label, preliminary study aims to evaluate the efficacy on cognition of an adjunctive short and intensive (8 daily sessions lasting 30 minutes) alpha/theta NF training in a sample of subjects affected by schizophrenia on stabilized treatment with atypical antipsychotic drugs. The efficacy was measured at baseline and at the end of the study by the Brief Neuropsychological Examination 2 (ENB 2), the Mini Mental State Examination (MMSE), and the Stroop color-word interference test; the clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).
Results
A final sample of nine patients completed the study. Regarding the cognitive performance, at the final assessment (week 2), the NF treatment significantly improved the performance in the “Story Recall Immediate” (p = 0.024), “Story Recall Delayed” (p = 0.007), “Interference Memory 30 s” (p = 0.024), “Clock Test” (p = 0.014) sub-tests, and the ENB2 Total Score (p = 0.007). Concerning the clinical symptoms, no significant changes were observed in the PANSS subscales and the PANSS Total score.
Conclusions
NF could represent an adjunctive treatment strategy in the therapeutic toolbox for schizophrenia cognitive symptoms.
Citation: Fabrizio Turiaco, Fiammetta Iannuzzo, Giovanni Genovese, Clara Lombardo, Maria Catena Silvestri, Laura Celebre, Maria Rosaria Anna Muscatello, Antonio Bruno. Cognitive effects of brief and intensive neurofeedback treatment in schizophrenia: a single center pilot study[J]. AIMS Neuroscience, 2024, 11(3): 341-351. doi: 10.3934/Neuroscience.2024021
Related Papers:
[1]
Abdulaziz S. Alghamdi, Muhammad Ahsan-ul-Haq, Ayesha Babar, Hassan M. Aljohani, Ahmed Z. Afify .
The discrete power-Ailamujia distribution: properties, inference, and applications. AIMS Mathematics, 2022, 7(5): 8344-8360.
doi: 10.3934/math.2022465
[2]
Monthira Duangsaphon, Sukit Sokampang, Kannat Na Bangchang .
Bayesian estimation for median discrete Weibull regression model. AIMS Mathematics, 2024, 9(1): 270-288.
doi: 10.3934/math.2024016
[3]
Saurabh L. Raikar, Dr. Rajesh S. Prabhu Gaonkar .
Jaya algorithm in estimation of P[X > Y] for two parameter Weibull distribution. AIMS Mathematics, 2022, 7(2): 2820-2839.
doi: 10.3934/math.2022156
[4]
Rasha Abd El-Wahab Attwa, Shimaa Wasfy Sadk, Hassan M. Aljohani .
Investigation the generalized extreme value under liner distribution parameters for progressive type-Ⅱ censoring by using optimization algorithms. AIMS Mathematics, 2024, 9(6): 15276-15302.
doi: 10.3934/math.2024742
[5]
Haiping Ren, Xue Hu .
Estimation for inverse Weibull distribution under progressive type-Ⅱ censoring scheme. AIMS Mathematics, 2023, 8(10): 22808-22829.
doi: 10.3934/math.20231162
[6]
Xue Hu, Haiping Ren .
Statistical inference of the stress-strength reliability for inverse Weibull distribution under an adaptive progressive type-Ⅱ censored sample. AIMS Mathematics, 2023, 8(12): 28465-28487.
doi: 10.3934/math.20231457
[7]
A. M. Abd El-Raheem, Ehab M. Almetwally, M. S. Mohamed, E. H. Hafez .
Accelerated life tests for modified Kies exponential lifetime distribution: binomial removal, transformers turn insulation application and numerical results. AIMS Mathematics, 2021, 6(5): 5222-5255.
doi: 10.3934/math.2021310
[8]
Heba S. Mohammed, Zubair Ahmad, Alanazi Talal Abdulrahman, Saima K. Khosa, E. H. Hafez, M. M. Abd El-Raouf, Marwa M. Mohie El-Din .
Statistical modelling for Bladder cancer disease using the NLT-W distribution. AIMS Mathematics, 2021, 6(9): 9262-9276.
doi: 10.3934/math.2021538
[9]
Qasim Ramzan, Muhammad Amin, Ahmed Elhassanein, Muhammad Ikram .
The extended generalized inverted Kumaraswamy Weibull distribution: Properties and applications. AIMS Mathematics, 2021, 6(9): 9955-9980.
doi: 10.3934/math.2021579
[10]
Nora Nader, Dina A. Ramadan, Hanan Haj Ahmad, M. A. El-Damcese, B. S. El-Desouky .
Optimizing analgesic pain relief time analysis through Bayesian and non-Bayesian approaches to new right truncated Fréchet-inverted Weibull distribution. AIMS Mathematics, 2023, 8(12): 31217-31245.
doi: 10.3934/math.20231598
Abstract
Background
Schizophrenia is characterized by significant cognitive impairments and affects up to 98% of patients. Neurofeedback (NF) offers a means to modulate neural network function through cognitive processes such as learning and memorization, with documented structural changes in the brain, most notably an increase in grey matter volume in targeted regions.
Methods
The present 2-week, open-label, preliminary study aims to evaluate the efficacy on cognition of an adjunctive short and intensive (8 daily sessions lasting 30 minutes) alpha/theta NF training in a sample of subjects affected by schizophrenia on stabilized treatment with atypical antipsychotic drugs. The efficacy was measured at baseline and at the end of the study by the Brief Neuropsychological Examination 2 (ENB 2), the Mini Mental State Examination (MMSE), and the Stroop color-word interference test; the clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS).
Results
A final sample of nine patients completed the study. Regarding the cognitive performance, at the final assessment (week 2), the NF treatment significantly improved the performance in the “Story Recall Immediate” (p = 0.024), “Story Recall Delayed” (p = 0.007), “Interference Memory 30 s” (p = 0.024), “Clock Test” (p = 0.014) sub-tests, and the ENB2 Total Score (p = 0.007). Concerning the clinical symptoms, no significant changes were observed in the PANSS subscales and the PANSS Total score.
Conclusions
NF could represent an adjunctive treatment strategy in the therapeutic toolbox for schizophrenia cognitive symptoms.
1.
Introduction
Discrete lifetime data—such as the number of appliance failures of a particular brand within a given time frame, the total number of machine operations prior to a failure, the number of bullets fired by a weapon before the first malfunction, and the anticipated lifespan of humans (in years)—are frequently handled in reliability lifetime studies. For more classic examples, see Szymkowiak and Iwinska [1]. Data scientists typically employ discrete models as analysis tools, such as the Poisson distribution, negative binomial distribution, and geometric distribution, in order to more correctly define, analyze, and model these data. But in many situations, these discrete distribution functions are not the best options. For instance, seasonal or periodic data cannot be handled by the Poisson distribution, while underdispersed data cannot be described by the negative binomial distribution. More suitable discrete lifetime distributions are required to explore many additional kinds of complex discrete lifespan data. Discretizing continuous random variables is a useful strategy that yields a discrete life model with characteristics that are comparable to the continuous model.
The essential concept of discretizing continuous random variables was first presented by Roy [2]. Specifically, let Y be a continuous random variable with a survival function denoted by S(y). Define the random variable Z=[Y] as the maximum integer less than or equal to Y. The probability mass function (PMF) P(Z=z) of Z can be expressed as
P(Z=z)=SY(z)−SY(z+1).
Many researchers have introduced various new models for discrete life distributions by the approach. For instance, the discrete normal distribution was first introduced by Roy [3]. Using the generic method of discretizing a continuous distribution, Krishna and Pundir [4] introduced the discrete Burr and Pareto distributions. In addition, Bracquemond and Gaudoin [5] provided an extensive overview of discrete distributions, such as the Weibull distribution, that are employed in reliability to describe discrete lifetimes of nonrepairable systems. It is well-known that the Weibull distribution has become the most commonly used distribution for analyzing continuous life data due to its ability to fit various types of data and relatively simple structure (Johnson et al. [6]). At least three cases exist for the corresponding discrete Weibull distribution: (a) the Type Ⅰ discrete Weibull distribution, which maintains the form of the continuous survival function (SF), as introduced by Nakagawa and Osaki [7]; (b) the Type Ⅱ discrete Weibull distribution, as suggested by Stein and Dattero [8]; and (c) the three-parameter discrete Weibull distribution, as introduced by Padgett and Spurrier [9]. The most popular of them is the Type Ⅰ discrete Weibull distribution, whose features have been extensively researched by numerous academics. Englehardt and Li [10] employed the discrete Weibull distribution to analyze pathogen counts in treated water over time. Barbiero [11,12] compared several parameter estimation methods of this distribution, and solved the minimum Chi-square and least squares estimation. Vila et al. [13] studied in detail the basic theoretical properties of the Type Ⅰ discrete Weibull and analyzed the censored data. Yoo [14] extended the application of the discrete Weibull regression model to accommodate missing data. In addition, El-Morshedy et al. [15] conducted a detailed study on a new bivariate exponential discrete Weibull distribution.
The primary goal in this study is to enhance the current techniques for estimating the complex discrete probability distribution model. The probability distribution's score function typically lacks an explicit analytical solution, hence the Newton approach is usually used to estimate the numerical solution for parameter estimation. Nevertheless, the algorithm's low convergence and strong dependence on the initial value make it challenging to achieve the best estimation outcomes. Recently, Liu et al. [16] employed the majorize minimize (MM) algorithm to enhance the resolution of the maximum likelihood estimation for the simplex distribution. Li and Tian [17] introduced a novel root-finding method known as the upper-crossing/solution (US) algorithm. In contrast to conventional iterative algorithms (like Newton's algorithm), the US algorithm can lessen the influence of initial values and achieve a strong, stable convergence to the objective equation's real root at each iteration. The benefits of this technique have been illustrated through the use of a few classic models, such as the Weibull distribution, gamma distribution, zeta distribution, and generalized Poisson distribution. Cai [18] has improved the maximum likelihood estimation of generalized gamma distribution parameters by combining the US algorithm with the second-derivative lower-bound function (SeLF) algorithm.
The essence of the US algorithm is to identify a U-function U(θ|θ(t)), which simplifies the solution of the complicated nonlinear equation h(θ)=0 to the solution of the equation U(θ|θ(t))=0 with an explicit solution. Li and Tian [17] presented a variety of approaches to discover the U-function, among which the first-derivative lower bound (FLB) function method requires only by using the first derivative of the objective function h(θ), thereby diminishing algorithmic complexity. In previous research on the US algorithm, it was generally used to solve the roots of univariate nonlinear equations or the maximum likelihood estimation problem of a multi-parameter probability distribution with an explicit partial score function. Specifically, for a probability distribution with two parameters (α,β), while solving for maximum likelihood estimation, the estimator of the parameter α can be explicitly expressed by the other estimator of the parameter β(α). However, there is no further discussion provided in Li and Tian [17] about whether this approach can be applicable in more complex multi-parameter distributions, where an estimator of one parameter cannot be clearly represented by the other, and it is an issue deserving of more investigation.
The rest of the paper will proceed as follows. Section 2 provides a detailed introduction to the US algorithm and FLB function method. The application of the suggested approach to the Type Ⅰ discrete Weibull distribution's maximum likelihood parameter estimation is covered in Section 3. Numerical simulation experiments will be conducted in Section 4 in order to evaluate the performance of the employed methods and compare them with alternative estimation approaches. Section 5 will demonstrate the applicability of the US algorithm through the analysis of two real data sets. Conclusions and discussions will be provided in Section 6.
2.
The US algorithm
One of the most frequent issues in numerical computations is figuring out the zero point of a function or an equation's root. In classical statistics, the maximum likelihood estimate (MLE) of parameters and the calculation of maximum a posteriori probability in Bayesian statistics may typically be turned into the problem of solving the zero point of a nonlinear function h(θ). In summary, since h(θ) is a nonlinear function of a single variable θ, we must identify the unique root θ⋆ such that
h(θ)=0,θ∈Θ⊆R.
(2.1)
The US algorithm is the most recent method for discovering roots. It has a similar procedure to the commonly used EM (expectation maximum) and MM (maximize minimize) algorithms[17]. There are two primary steps in this process: the upper-crossing step (U-step) and the solution step (S-step). The two primary advantages of this algorithm are as follows:
(a) It converges strongly and stably to the root θ⋆ of the Eq (2.1) with each iteration, that is, for an iterative points set sequence {θ(t)}∞t=0, there is
θ(0)<θ(1)<⋯<θ(t)<⋯≤θ⋆orθ⋆≤⋯<θ(t)<⋯<θ(1)<θ(0).
(b) The Newton algorithm's sensitivity to the initial value is decreased.
Two new symbols, sgn(α)≤ and sgn(α)≥, are introduced regarding the changing direction (CD) inequalities in order to simplify the explanation of the US algorithm. The specific definition is presented as follows: for two functions f1(x) and f2(x) on the same domain Q,
It is typically challenging to locate the root θ⋆ of the nonlinear equation h(θ)=0 directly. The US algorithm aims to create an alternative function U(θ|θ(T)) to replace h(θ), transforming the challenge of solving complex nonlinear equations into solving the equation U(θ|θ(T)) with explicit solutions. First, we assume that
h(θ)<0,∀θ>θ⋆andh(θ)>0,∀θ<θ⋆.
(2.2)
If h(θ)<0 when θ<θ⋆, then both sides of the equation h(θ)=0 can be multiplied by -1, which can also obtain the same root θ⋆ satisfying the assumption (2.2). Let θ(t) represent the solution after the (t-1)-th iteration, and the function U(θ|θ(t)) satisfying the following criteria is designated as the U-function of h(θ) at θ=θ(t):
According to the definition of the CD inequalities symbol, the above condition may be represented as
h(θ)sgn(θ−θ(t))≥U(θ|θ(t)),∀θ,θ(t)∈Θ.
(2.4)
2.2. The U-equation
As described above, the US algorithm is an iterative approach for solving nonlinear equations, with each iteration including a U-step and an S-step. The purpose of the U-step is to find a U-function that satisfies the condition (2.4), whereas the S-step involves solving the simplified U-equation: U(θ|θ(t))=0 to obtain its root θ(t+1),
θ(t+1)=sol{U(θ|θ(t))=0,∀θ,θ(t)∈Θ}.
(2.5)
In typical scenarios, θ(t+1) can be explicitly expressed, even as a linear equation. Through the iterative execution of these two steps, {θ(t)}∞t=0 can gradually converge to the real root θ⋆ of the U-equation.
2.3. The first-derivative lower bound function method
There are numerous U-functions for a given objective function h(θ); as Eq (2.4) illustrates, distinct U-functions correlate to distinct US algorithms. We may express the U-function using the lower-order derivatives of the goal function h(θ). This can be accomplished by a variety of techniques, such as the first-derivative lower bound (FLB), second-derivative lower-upper bound (SLUB) constants method, and third-derivative lower bound (TLB) constant method [17]. These three methodologies enhance efficient solutions when the objective function is complex and the solution is not closed, each with a distinct convergence speed. In terms of maximizing the objective function, the US algorithm based on the FLB approach shares qualities with the EM algorithm and the MM algorithm, both of which exhibit linear convergence. The FLB function technique, which is dependent on the target function's first-order derivative, is mostly used in this article to generate the required U-function. First for parameter space Θ, we suppose that there exists a certain first-derivative lower bound function b(θ) for the first derivative of h(θ), i.e.,
h′(θ)≥b(θ),∀θ∈Θ.
(2.6)
The U-function of h(θ) at θ=θ(t) can be formally defined as follows
where g(θ(t))=g(θ⋆)+(θ(t)−θ⋆)h′(θ⋆)+0.5(θ(t)−θ⋆)2h′′(ˆθ) is the first-order Taylor expansion around θ⋆, and θ⋆ is a point between θ(t) and θ⋆.
Although Li and Tian [17] proposed the idea of the US algorithm, in practical applications, only the distribution of univariate and binary parameters were studied. In the case of binary parameter distribution, when discussing the solution of the scoring equation, only one parameter can be explicitly expressed with another parameter. However, when one parameter cannot be explicitly expressed by the other parameter for this more general and complex situation, whether the US algorithm can be effectively applied is not further discussed, which is the issue to be carried out in this article. For the parameters of interest, the new FLB functions are constructed in this article, then, starting with initial values, the iterative values are updated using the corresponding S-step until the convergence criteria are met.
3.
The US algorithm for the MLE of the Type Ⅰ discrete Weibull distribution
The discrete Weibull distribution's score function has a complex double exponential form, which makes it impossible to depict its solution and, thus, prevents its two parameters from being mutually expressed. For investigating the US algorithm's applicability in complicated models, we combine the US algorithm with the FLB method in this section to optimize the maximum likelihood estimation.
3.1. The Type Ⅰ discrete Weibull distribution
Assuming a random variable following the Weibull distribution W(λ,β), where λ>0 and β>0, the cumulative distribution function (CDF) of the Weibull distribution is defined as H(t,λ,β)=1−e−λtβ, where t>0. Define α=e−λ, and then 0<α<1. If the probability mass function (PMF) for a random variable X can be represented as
P(X=x;α,β)=α([x]−1)β−α[x]β,(x≥1),
then we say that X follows the Type Ⅰ discrete Weibull distribution, denoted as X∼DW(θ). Here, [x] represents the maximum integer less than or equal to x. When β = 1, the discrete Weibull distribution degenerates to the geometric distribution Geo(q) with q=1−α.
Naturally, the cumulative distribution function of X takes the following form:
F(x;α,β)=1−α[x]β.
3.2. The US algorithm for the MLE of α and β
This section will go into detail on the application of the US algorithm for maximum likelihood estimation of the Type Ⅰ discrete Weibull distribution. Assume X is a random variable with the Type Ⅰ discrete Weibull distribution DW(θ), where the parameter vector θ=(α,β)T is in the parameter space Θ⊂R2. Let x=(x1,...,xn) denote the observed values of the random sample (X1,...,Xn). Then, the log-likelihood function of the parameter vector θ is given by
ℓ(θ|x)=n∑i=1log(α(xi−1)β−α(xi)β).
First, the first-order partial derivative of ℓ(θ|x) with respect to α can be calculated as
where C1=h1(α(t)), C2=∑ni=1[4x2β(t)i+2xβ(t)i], and C3=3C2[1α(t)−11−α(t)]. Similarly, we can obtain the first-order partial derivative of ℓ(θ|x) with respect to β,
In order to construct the FLB function and derive the US algorithm without explicit solutions for the two parameters, we need the following two lemmas.
The algorithm process for estimating two parameters can be described as follows. In the first stage, we determine the FLB functions for parameters α and β using Eqs (3.2) and (3.5), respectively. Subsequently, we set two initial values α(t) and β(t), calculate Eq (3.3) to get α(t+1), and then compute β(t+1) via Eq (3.6) using α(t+1) and β(t). If both of the estimates for the parameters satisfy the convergence criteria, then their corresponding values will be returned. Otherwise, we resume to update the iteration value and repeat the preceding steps until the two estimated parameters converge.
Algorithm :Calculating the MLEs of α and β via the US algorithm.
Input: The initial value α(0) and β(0); The observed data Xobs={xi}ni=0;
Output:ˆα,ˆβ.
1 Select FLB function for parameters α and β, respectively;
2 Set initial values α(t),β(t), t = 0;
3 repeat
4 Using α(t) and β(t), calculate α(t+1) based on (3.3);
5 Using α(t+1) and β(t), calculate β(t+1) based on (3.6), update t = t + 1;
6 until convergence.
4.
Simulation study
In this section, we conduct simulation studies to confirm the applicability to complex nonlinear equations and compare its performance to that of the classic Newton algorithm. First, we provide the calculation steps for parameter estimation using the Newton algorithm as follows:
The sample size of the studies is set as n = (50,100,200), and the parameters are set as α=(0.2,0.4,0.6,0.8) and β=(0.5,1.0,1.5,2.0), respectively. We independently generated X(k)1,…,X(k)niid∼DW(α,β), where k=1,…,K(K = 1000). The MLE of the parameters under the US algorithm were computed via Eqs (3.2) and (3.4). For every combination of parameters, we ran 1000 iterations of the experiments and evaluated the two methods' fitting performance using the convergence percentage and the mean squared error (MSE) of parameter estimation.
Tables 1–4 display the outcomes of the two algorithms' simulations for each scenario. The MSE of the parameters under both algorithms progressively drops as the sample size rises, according to the statistics in the table, suggesting that both techniques are asymptotically unbiased. When the value of β is fixed, as the value of α increases, the MSE of β will steadily decrease. Overall, the MSE of both parameters under the US algorithm is smaller than that of the Newton algorithm, suggesting that the US algorithm performs better when it comes to estimation. Furthermore, it is evident from the table's convergence percentages that the US algorithm is more stable.
Table 1.
The MSE and percentage from simulated data for β=0.5.
The trend of the predicted values of α and β using the US algorithm and Newton technique as the number of iterations grows is shown in Figure 1. From a stability standpoint, the Newton method shows significant instability and often requires multiple twists to preserve the correct trend, while the US algorithm approaches the true values of parameters monotonically. From a convergence speed perspective, the FLB method exhibits linear convergence, while the Newton algorithm demonstrates quadratic convergence. Consequently, the FLB method has a comparatively slower convergence rate.
This section describes the analysis of two different real data sets to illustrate the applicability of the US algorithm. The first data set in Table 5 contains the remission times in weeks of 20 leukemia patients with treatment studied by Hassan et al. [19]. Another data set is from the National Highway Traffic Safety Administration (www-fars.nhtsa.dot.gov) of the United States, which reports the number of fatalities due to motor vehicle accidents among children under the age of 5 in 32 states during the year 2022.
Table 5.
The leukaemia patients data and the vehicle fatalities data.
We model the two data sets with the Type Ⅰ discrete Weibull distribution and the geometric distribution. The fitting results for the leukemia patients data by two distributions are provided in Table 6. The results of the Cramer-von Mises test, Anderson-Darling test, and Kolmogorov-Smirnov test show that the two distributions can successfully fit the data set. The p-values from the three tests of the Type Ⅰ discrete Weibull distribution employing the US algorithm demonstrate the best fitting effect. Moreover, the values of Akaike information criterion (AIC) [20] and Bayesian information criterion (BIC) [21] also show that the DW distribution based on the US algorithm has better estimation effect.
Table 6.
Fitting for the leukaemia patients data by the DW distribution and the geometric distribution.
Table 7 shows the results of fitting the vehicle fatality data with the Type Ⅰ discrete Weibull distribution and the geometric distribution. Comparing the p-values of the three tests at the significance level α=0.05 reveals that the Geo distribution and the Type Ⅰ discrete Weibull distribution estimated by the Newton algorithm are considered to be insufficient. The fitting effectiveness of the DW distribution using the US algorithm is significant, as indicated by the values of AIC and BIC. The histogram for two different data sets evaluated by the DW distribution and the Geo distribution is shown in Figure 2. Figures 3 and 4 present QQ plots for these two distributions. It is also evident that the US algorithm performs better.
Table 7.
Fitting for the vehicle fatalities data by the DW distribution and the geometric distribution.
Figure 2.
Histogram of leukaemia patients data (left panel) and vehicle fatalities data (right panel), and the correlation density curve fitted by the DW and Geo distributions.
The US algorithm is a novel iterative method with high stability and convergence. The existing research only involves simple models such as univariate nonlinear equations or univariate functions. This paper extends the US algorithm to more complex cases of two parameter discrete distribution functions, where one parameter cannot be explicitly represented by the other parameter estimate. In order to successfully estimate the parameters, this paper combines the FLB method to perform optimization estimation of a distribution function. The simulation results for the Type Ⅰ discrete Weibull distribution demonstrate that the US algorithm has good accuracy and stability. Simultaneously, for the purpose of demonstrating the applicability of the algorithm in complex situations, this paper conducted empirical research on two real data sets that follow the Type Ⅰ discrete Weibull distribution, namely, the data from patients with leukemia and children who die from motor vehicle accidents. After comparing and analyzing the US method with the conventional Newton algorithm, the results show that the recommended strategy has an excellent fitting effect.
Author contributions
Yuanhang Ouyang: Formal analysis, Writing original draft, Software, Investigation, Methodology, Data curation; Ruyun Yan: Validation, Software, Formal analysis; Jianhua Shi: Validation, Resources, Writing-review and editing, Methodology. All authors have read and approved the final version of the manuscript for publication.
Use of AI tools declaration
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
Acknowledgments
This research was conducted under a project titled "The National Social Science Fund of China" (20XTJ003).
Conflict of interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Acknowledgments
The authors thank Massimo Cacciola - Psychiatry Unit, Polyclinic Hospital University of Messina, Messina, Italy – and Federica Rapisarda - University of Messina, Italy - for assistance with recruitment and data collection.
Conflict of interest
None.
Funding source
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Heinrichs RW, Zakzanis KK (1998) Neurocognitive Deficit in Schizophrenia: A Quantitative Review of the Evidence. Neuropsychology 12: 426-445. https://doi.org/10.1037//0894-4105.12.3.426
[3]
Green MF, Kern RS, Braff DL, et al. (2000) Neurocognitive Deficits and Functional Outcome in Schizophrenia: Are We Measuring the ‘Right Stuff’?. Schizophr Bull 26: 119-136. https://doi.org/10.1093/oxfordjournals.schbul.a033430
[4]
Keefe RSE, Bilder RM, Davis SM, et al. (2007) Neurocognitive Effects of Antipsychotic Medications in Patients With Chronic Schizophrenia in the CATIE Trial. Arch Gen Psychiatry 64: 633-647. https://doi.org/10.1001/archpsyc.64.6.633
[5]
Bon L, Franck N (2018) The impact of cognitive remediation on cerebral activity in schizophrenia: Systematic review of the literature. Brain Behav 8: e00908. https://doi.org/10.1002/brb3.908
[6]
Marzbani H, Marateb HR, Mansourian M (2016) Methodological note: Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci 7: 143-158. https://doi.org/10.15412/J.BCN.03070208
[7]
Markiewicz R (2017) The use of EEG biofeedback/neurofeedback in psychiatric rehabilitation. Psychiatr Pol 51: 1095-1106. https://doi.org/10.12740/PP/68919
[8]
Schabus M, Griessenberger H, Gnjezda MT, et al. (2017) Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia. Brain 140: 1041-1052. https://doi.org/10.1093/brain/awx011
[9]
Coben R, Evans RJ (2010) Neurofeedback and Neuromodulation Techniques and Applications. Academic Press.
[10]
Angelakis E, Stathopoulou S, Frymiare JL, et al. (2007) EEG neurofeedback: A brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. Clin Neuropsychol 21: 110-129. https://doi.org/10.1080/13854040600744839
[11]
Lecomte G, Juhel J (2011) The Effects of Neurofeedback Training on Memory Performance in Elderly Subjects. Psychology 02: 846-852. https://doi.org/10.4236/psych.2011.28129
[12]
Gruzelier JH (2014) EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants. Neurosci Biobehav Rev 44: 124-141. https://doi.org/10.1016/j.neubiorev.2013.09.015
[13]
Reis J, Portugal AM, Fernandes L, et al. (2016) An alpha and theta intensive and short neurofeedback protocol for healthy aging working-memory training. Front Aging Neurosci 8: 157. https://doi.org/10.3389/fnagi.2016.00157
Surmeli T, Ertem A, Eralp E, et al. (2012) Schizophrenia and the efficacy of qEEG-guided neurofeedback treatment: A clinical case series. Clin EEG Neurosci 43: 133-144. https://doi.org/10.1177/1550059411429531
[16]
Gruzelier J, Hardman E, Wild J, et al. (1999) Learned control of slow potential interhemispheric asymmetry in schizophrenia. Int J Psychophysiol 34: 341-348. https://doi.org/10.1016/s0167-8760(99)00091-4
Cordes JS, Mathiak KA, Dyck M, et al. (2015) Cognitive and neural strategies during control of the anterior cingulate cortex by fMRI neurofeedback in patients with schizophrenia. Front Behav Neurosci 9: 169. https://doi.org/10.3389/fnbeh.2015.00169
[19]
Rieger K, Rarra MH, Diaz Hernandez L, et al. (2018) Neurofeedback-Based Enhancement of Single-Trial Auditory Evoked Potentials: Treatment of Auditory Verbal Hallucinations in Schizophrenia. Clin EEG Neurosci 49: 79-92. https://doi.org/10.1177/1550059417708935
[20]
Singh F, Shu IW, Hsu SH, et al. (2020) Modulation of frontal gamma oscillations improves working memory in schizophrenia. Neuroimage Clin 27: 102339. https://doi.org/10.1016/j.nicl.2020.102339
[21]
Markiewicz R, Markiewicz-Gospodarek A, Dobrowolska B, et al. (2021) Improving Clinical, Cognitive, and Psychosocial Dysfunctions in Patients with Schizophrenia: A Neurofeedback Randomized Control Trial. Neural Plast 2021: 4488664. https://doi.org/10.1155/2021/4488664
[22]
Zweerings J, Hummel B, Keller M, et al. (2019) Neurofeedback of core language network nodes modulates connectivity with the default-mode network: A double-blind fMRI neurofeedback study on auditory verbal hallucinations. Neuroimage 189: 533-542. https://doi.org/10.1016/j.neuroimage.2019.01.058
[23]
Orlov ND, Giampietro V, O'Daly O, et al. (2018) Real-time fMRI neurofeedback to down-regulate superior temporal gyrus activity in patients with schizophrenia and auditory hallucinations: A proof-of-concept study. Transl Psychiatry 8: 46. https://doi.org/10.1038/s41398-017-0067-5
[24]
Balconi M, Frezza A, Vanutelli ME (2018) Emotion Regulation in Schizophrenia: A Pilot Clinical Intervention as Assessed by EEG and Optical Imaging (Functional Near-Infrared Spectroscopy). Front Hum Neurosci 12: 395. https://doi.org/10.3389/fnhum.2018.00395
[25]
Schneider F, Rockstroh B, Heimann H, et al. (1992) Self-Regulation of Slow Cortical Potentials in Psychiatric Patients: Schizophrenia. Biofeedback Self-regulation 17: 277-292. https://doi.org/10.1007/BF01000051
[26]
Lin Y, Shu IW, Hsu SH, et al. (2022) Novel EEG-Based Neurofeedback System Targeting Frontal Gamma Activity of Schizophrenia Patients to Improve Working Memory. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS . Institute of Electrical and Electronics Engineers Inc. 4031-4035. https://doi.org/10.1109/EMBC48229.2022.9870878
[27]
Mondini S, Montemurro S, Pucci V, et al. (2022) Global Examination of Mental State: An open tool for the brief evaluation of cognition. Brain Behav 12: e2710. https://doi.org/10.1002/brb3.2710
[28]
Baek MJ, Kim K, Park YH, et al. (2016) The validity and reliability of the mini-mental state examination-2 for detecting mild cognitive impairment and Alzheimer's disease in a Korean population. PLoS One 11: e0163792. https://doi.org/10.1371/journal.pone.0163792
[29]
Periáñez JA, Lubrini G, García-Gutiérrez A, et al. (2021) Construct validity of the stroop color-word test: Influence of speed of visual search, verbal fluency, working memory, cognitive flexibility, and conflict monitoring. Arch Clin Neuropsych 36: 99-111. https://doi.org/10.1093/arclin/acaa034
[30]
Edgar CJ, Blaettler T, Bugarski-Kirola D, et al. (2014) Reliability, validity and ability to detect change of the PANSS negative symptom factor score in outpatients with schizophrenia on select antipsychotics and with prominent negative or disorganized thought symptoms. Psychiatry Res 218: 219-224. https://doi.org/10.1016/j.psychres.2014.04.009
[31]
Pazooki K, Leibetseder M, Renner W, et al. (2019) Neurofeedback Treatment of Negative Symptoms in Schizophrenia: Two Case Reports. Appl Psychophys Biof 44: 31-39. https://doi.org/10.1007/s10484-018-9417-1
[32]
Nan W, Wan F, Chang L, et al. (2017) An Exploratory Study of Intensive Neurofeedback Training for Schizophrenia. Behav Neurol 2017: 6914216. https://doi.org/10.1155/2017/6914216
[33]
Gomes JS, Ducos DV, Gadelha A, et al. (2018) Hemoencephalography self-regulation training and its impact on cognition: A study with schizophrenia and healthy participants. Schizophr Res 195: 591-593. https://doi.org/10.1016/j.schres.2017.08.044
Fabrizio Turiaco, Fiammetta Iannuzzo, Giovanni Genovese, Clara Lombardo, Maria Catena Silvestri, Laura Celebre, Maria Rosaria Anna Muscatello, Antonio Bruno. Cognitive effects of brief and intensive neurofeedback treatment in schizophrenia: a single center pilot study[J]. AIMS Neuroscience, 2024, 11(3): 341-351. doi: 10.3934/Neuroscience.2024021
Fabrizio Turiaco, Fiammetta Iannuzzo, Giovanni Genovese, Clara Lombardo, Maria Catena Silvestri, Laura Celebre, Maria Rosaria Anna Muscatello, Antonio Bruno. Cognitive effects of brief and intensive neurofeedback treatment in schizophrenia: a single center pilot study[J]. AIMS Neuroscience, 2024, 11(3): 341-351. doi: 10.3934/Neuroscience.2024021