Cognitive training interventions for substance use disorders: what they really offer?

Cognitive training (CT) has emerged as a potential therapeutic approach for substance use disorders (SUD), aiming to restore cognitive impairments and potentially improve treatment outcomes. However, despite promising findings, the effectiveness of CT in real-life applications and its impact on SUD symptoms has remained unclear. This perspective article critically examines the existing evidence on CT for SUD and explores the challenges and gaps in implementing CT interventions. It emphasizes the need for clarity in expectations and decision-making from a public health standpoint, advocating for comprehensive studies that consider a broader range of SUD consequences and utilize measures that reflect patients' actual experiences.

Reversing valproic acid-induced autism-like behaviors through a combination of low-frequency repeated transcranial magnetic stimulation and superparamagnetic iron oxide nanoparticles.

Transcranial magnetic stimulation (TMS) is a neurostimulation device used to modulate brain cortex activity. Our objective was to enhance the therapeutic effectiveness of low-frequency repeated TMS (LF-rTMS) in a rat model of autism spectrum disorder (ASD) induced by prenatal valproic acid (VPA) exposure through the injection of superparamagnetic iron oxide nanoparticles (SPIONs). For the induction of ASD, we administered prenatal VPA (600 mg/kg, I.P.) on the 12.5th day of pregnancy. At postnatal day 30, SPIONs were injected directly into the lateral ventricle of the brain. Subsequently, LF-rTMS treatment was applied for 14 consecutive days. Following the treatment period, behavioral analyses were conducted. At postnatal day 60, brain tissue was extracted, and both biochemical and histological analyses were performed. Our data revealed that prenatal VPA exposure led to behavioral alterations, including changes in social interactions, increased anxiety, and repetitive behavior, along with dysfunction in stress coping strategies. Additionally, we observed reduced levels of SYN, MAP2, and BDNF. These changes were accompanied by a decrease in dendritic spine density in the hippocampal CA1 area. However, LF-rTMS treatment combined with SPIONs successfully reversed these dysfunctions at the behavioral, biochemical, and histological levels, introducing a successful approach for the treatment of ASD.

Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback.

Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.

A fuzzy-oscillatory model of medial prefrontal cortex control function in spatial memory retrieval in human navigation function.

Navigation can be broadly defined as the process of moving from an origin to a destination through path-planning. Previous research has shown that navigation is mainly related to the function of the medial temporal lobe (MTL), including the hippocampus (HPC), and medial prefrontal cortex (mPFC), which controls retrieval of the spatial memories from this region. In this study, we suggested a cognitive and computational model of human navigation with a focus on mutual interactions between the hippocampus (HPC) and the mPFC using the concept of synchrony. The Van-der-pol oscillator was used to model the synchronous process of receiving and processing "what stream" information. A fuzzy lookup table system was applied for modeling the controlling function of the mPFC in retrieving spatial information from the HPC. The effect of attention level was also included and simulated. The performance of the model was evaluated using information reported in previous experimental research. Due to the inherent stability of the proposed fuzzy-oscillatory model, it is less sensitive to the exact values of the initial conditions, and therefore, it is shown that it is consistent with the actual human performance in real environments. Analyzing the proposed cognitive and fuzzy-oscillatory computational model demonstrates that the model is able to reproduce certain cognitive and functional disturbances in navigation in related diseases such as Alzheimer's disease (AD). We have shown that an increase in the bifurcation parameter of the Van-der-pol equation represents an increase in the low-frequency spectral power density and a decrease in the high-frequency spectral power as occurs in AD due to an increase in the amyloid plaques in the brain. These changes in the frequency characteristics of neuronal activity, in turn, lead to impaired recall and retrieval of landmarks information and learned routes upon encountering them. As a result, and because of the wrong frequency code being transmitted, the relevant set of rules in the mPFC is not activated, or another unrelated set will be activated, which leads to forgetfulness and erroneous decisions in routing and eventually losing the route in Alzheimer's patients.

Socioeconomic Status and Childhood Executive Function: Differing Conceptualizations, Diverse Assessments, and Decontextualized Investigations.

Developing the ability to regulate actions, thoughts, and emotions is necessary for successfully engaging in goal-directed activities, which form the essence of success in many life situations. In this regard, executive function (EF), as an aspect of these top-down control processes, has been the subject of inquiry in many developmental studies aiming to identify its link with various other aspects of a child's life, including family socioeconomic status (SES). This article presents a critical look at the association between family SES and childhood EF by reviewing studies that provide contrasting perspectives compared to the generally reported positive correlation between these two constructs. We first address the various aspects of assessing SES and childhood EF, emphasizing their multifaceted nature. Next, we discuss the interplays between different components of these two constructs, especially as it unfolds in various cultural contexts. Finally, we conclude by discussing mainly neglected lines of research that could further improve our understanding of the extent of socioeconomic impacts on child development, specifically regarding executive function. Considering these lines of research is a necessary step to a more accurate analysis of the link between socioeconomic factors and childhood executive function.

Conflict of interest as a cognitive bias.

For many years, conflict of interest has been a topic of debate in professional ethics, whereby the performance of a professional obligation can be potentially affected by a financial or non-financial interest. However, studies on conflict of interest often do not include cognitive perspectives. In this study, it was hypothesized that conflict of interest might shape the perception of the situation in a subconscious manner. To test the hypothesis, we have designed an experiment using one of the well-known empirical paradigms of consciousness studies - the masking paradigm. In three experimental conditions (i.e., conflict of interest, neutral, and alignment of interest), participants were exposed to several subliminal visual stimuli in a series of trials. Results show that interest can have a direct impact on subconscious processes and subsequently perception in such a way that alignment of interest is accompanied by increasing the probability of correct perception, and conflict of interest would lead to more wrong perception. Finally, some of the impacts of this cognitive bias on scientific observation, data management, and underdetermination resolution are investigated.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

"Choice-supportive bias" in science: Explanation and mitigation.

Mistakes in scientific research, their origins, and the possible solutions have always been noteworthy. On the other hand, researchers in some fields have focused on cognitive biases and their interventions in thinking and information processing. In this article, by bridging between these two issues, i.e., scientific research mistakes and cognitive bias, it will be first shown that scientific research requires scientists' choices in various stages such as subject selection, data treatment, theory determination, and technological design. Then, we show the involvement of choice-supportive bias in scientific inquiry as one of the potential cognitive limitations of science. Finally, three ways are discussed to mitigate the effects of this bias in science: more scores to self-criticism publications, recording of the shortcomings of the research by the researcher before publishing, and emphasizing control processes before researcher choices in scientific inquiry.

Nonlinear Analysis of Electroencephalogram Signals while Listening to the Holy Quran.

BACKGROUND: Electrical activity of the brain, resulting from electrochemical signaling between neurons, is recorded by electroencephalogram (EEG). The neural network has complex behavior at different levels that strongly confirms the nonlinear nature of interactions in the human brain. This study has been designed and implemented with the aim of determining the effects of religious beliefs and the effect of listening to Holy Quran on electrical activity of the brain of the Iranian Persian-speaking Muslim volunteers. METHODS: The brain signals of 47 Persian-speaking Muslim volunteers while listening to the Holy Quran consciously, and while listening to the Holy Quran and the Arabic text unconsciously were used. Therefore, due to the nonlinear nature of EEG signals, these signals are studied using approximate entropy, sample entropy, Hurst exponent, and Detrended Fluctuation Analysis. RESULTS: Statistical analysis of the results has shown that listening to the Holy Quran consciously increases approximate entropy and sample entropy, and decreases Hurst Exponent and Detrended Fluctuation Analysis compared to other cases. CONCLUSION: Consciously listening to the Holy Quran decreases self-similarity and correlation of brain signal and instead increases complexity and dynamicity in the brain.

Is It Possible to Determine the Level of Spiritual Well-Being by Measuring Heart Rate Variability During the Reading of Heavenly Books?

A previous study stated that reading holy books can make a meaningful change in heart rate variability (HRV). The purpose of this study was to test the effect of reading the Quran, the heavenly religious book of the Muslim people in the Arabic language, on the Farsi (Persian)-speaking Muslims with various levels of spiritual well-being (SWB). In addition, novel to this study was the assessment of whether or not it is possible to use HRV features to distinguish individuals with high SWB from those with medium SWB. First, a questionnaire was completed by 31 volunteers to measure their SWB. Baseline ECG measurements were recorded during the resting stage. The volunteers were then asked to read the Quran for 5 min while ECG was recorded again. HRV indexes were calculated and four features were extracted and analyzed based on their correlation with the different levels of SWB. Independent t-tests were conducted and the results established a significant difference in these four features between high SWB and medium SWB groups, during the reading stage. Subsequently, with the use of these four HRV features, an artificial neural network and a decision tree were designed to classify the levels of SWB in volunteers. The outcome of this study demonstrated that it is possible to evaluate the level of SWB in individuals while they are reading the Quran.

Presenting a Neuroid model of wind-up based on dynamic synapse.

The treatment of chronic pain depends mainly on our understanding of the mechanisms such as central sensitization which is involved in it. Wind-up of spinal cord is one of the most important phenomena in the study of central sensitization which has received considerable attention in recent years. Wind-up is a form of short-term synaptic plasticity (STP) that can lead to central sensitivity. Although several models have been proposed for wind-up, none of them are based on the experimental evidence. In this study, a new network model is introduced according to the gate control theory of pain. Neuroids are used as neuron models in which their parameters are captured from available experimental data. Adjusting the weights of the network is based on the short-term synaptic plasticity. The results of the time and frequency domain show that the model can well simulate wind-up behavior. This model can be used for analyzing, predicting and controlling chronic pain in the future.

Coexistence of Stochastic Oscillations and Self-Organized Criticality in a Neuronal Network: Sandpile Model Application.

Self-organized criticality (SOC) and stochastic oscillations (SOs) are two theoretically contradictory phenomena that are suggested to coexist in the brain. Recently it has been shown that an accumulation-release process like sandpile dynamics can generate SOC and SOs simultaneously. We considered the effect of the network structure on this coexistence and showed that the sandpile dynamics on a small-world network can produce two power law regimes along with two groups of SOs-two peaks in the power spectrum of the generated signal simultaneously. We also showed that external stimuli in the sandpile dynamics do not affect the coexistence of SOC and SOs but increase the frequency of SOs, which is consistent with our knowledge of the brain.

Bifurcation Theory Approach to Neuro-Developmental Language Impairment in Autistic Children.

Autism Spectrum Disorders (ASD) is defined by a spectrum of deficits in social interactions and limited or stereotyped patterns of behaviours. Consequently, language as the result and the tool of communication is impaired in individuals affected by ASD. Thus, a better knowledge of language impairment leads to a better solution of the communicating problems. "Dynamical systems theory" could help us understand the development of language and the related impairments. Based on this theory, language development can be considered as a dynamical system with the trajectory of variation. In the present study, we hypothesised that a kind of bifurcation happens in language development trajectory of autistic children when they reach a higher level of language like pragmatics.

Scale-freeness of dominant and piecemeal perceptions during binocular rivalry.

When two eyes are simultaneously stimulated by two inconsistent images, the observer's perception switches between the two images every few seconds such that only one image is perceived at a time. This phenomenon is named binocular rivalry (BR). However, sometimes the perceived image is a piecemeal mixed of two stimuli known as piecemeal perceptions. In this study, a BR task was designed in which orthogonal gratings are presented to the two eyes. The subjects were trained to report 3 states: dominant perceptions (two state matching to perceived grating orientation) and the piecemeal perceptions (third state). We explored the scale-freeness of the BR percept durations considering the two dominant monocular states as well as the piecemeal transition state using detrended fluctuation analysis. Our results reproduced the previous finding of memory in the perceptual switches between the monocular perception states. Moreover, we showed that such memory also exists in the transitory periods of dichoptic piecemeal perceptions. These results support our hypothesis that the pool of unstable piecemeal perceptions could be regarded as separate multiple low-depth basin in the perceptual state landscape. Likewise, the transitions from these piecemeal state basins and stable monocular state basins are faced with resistance. Therefore there is inertia and memory (i.e. positive serial correlation) for the piecemeal dichoptic perception states as well as the monocular states.

Effect of Social Stimuli on Postural Responses in Individuals with Autism Spectrum Disorder.

This study was aimed to investigate the effects of social versus non-social stimuli on postural responses in 21 boys with autism spectrum disorder (ASD) (mean age of 11.6 ± 1.5) compared with 30 typically developing (TD) boys (mean age of 11.7 ± 1.8). Postural control of children was examined while they were standing on a force plate and viewing images of an object, male face, or female face in sequence. Each image was shown in two trials and each trial lasted for 20 s. Results indicated a significant interaction between group and task (p < 0.05), meaning that children with ASD but not TD children showed an increased postural sway during face tasks than during object task. Furthermore children with higher autism severity compared to those with lower severity showed an increased change in response to social stimuli (p < 0.01). It seems that the postural control of children with ASD was more affected by the social stimuli than TD children.

The role of driver nodes in managing epileptic seizures: Application of Kuramoto model.

Synchronization is an important global phenomenon which could be found in a wide range of complex systems such as brain or electronic devices. However, in some circumstances the synchronized states are not desirable for the system and should be suppressed. For example, excessively synchronized activities in the brain network could be the root of neuronal disorders like epileptic seizures. According to the controllability theory of the complex networks, a minimum set of driver nodes has the ability to control the entire system. In this study, we examine the role of driver nodes in suppressing the excessive synchronization in a generalized Kuramoto model, which consists of two types of oscillators: contrarian and regular ones. We used two different structural topologies: Barabási-Albert scale-free (BASF) network and Caenorhabditis elegans (C.elegans) neuronal network. Our results show that contrarian driver nodes have the sufficient ability to break the synchronized level of the systems. In this case, the system coherency level is not fully suppressed that is avoiding dysfunctions of normal brain functions which require the neuronal synchronized activities. Moreover, in this case, the oscillators grouped in two distinct synchronized clusters that could be an indication of chaotic behavior of the system known as resting-state activity of the brain.

Mesoscopic model of neuronal system deficits in Multiple Sclerosis.

Multiple Sclerosis (MS) is a devastating autoimmune disease which deteriorates the connections in central nervous system (CNS) through the attacks to oligodendrocytes. Studying its origin and progression, in addition to clinical developments such as MRI brain images, cerebrospinal fluid (CSF) variation and quantitative measures of disability (EDSS), which sought to early diagnosis and efficient therapy, there is an increasing interest in developing computational models using the experimental data obtained from MS patients. From the perspective of mathematical modelling, although the origin of systemic symptoms might be attributed to cellular phenomena in microscopic level such as axonal demyelination, symptoms mainly are observed in macroscopic levels. How to fill the gap between these two levels of system modelling, however, remains as a challenge in systems biology studies. Trying to provide a conceptual framework to bridge between these two levels of modelling in systems biology, we have suggested a mesoscopic model composed of interacting neuronal population, which successfully replicates the changes in neuronal population synchrony due to MS progression.

The role of time in conflict-triggered control: Extending the theory of response-conflict monitoring.

Flexible goal-directed behavior requires monitor-control networks to detect the need for behavioral adjustments and to implement the required regulations. Among event-related brain potentials related to the function of such networks is the feedback-related negativity (FRN), which is detected in trial-and-error learning tasks. Conflict monitoring theory (CMT) as one of the influential theories of such networks cannot describe the FRN. Recently, we have proposed a cost-conflict monitoring system that extends the CMT. The cost-conflict monitoring holds that the monitoring system can detect conflict signal, but the conflict is over the costs of alternative outcomes of the selected action rather than the response conflict as proposed by the CMT. In the cost-conflict monitoring, cost functions are computed based on waiting times from the response to feedback delivery and from these quantities a conflict signal is derived. Here, we present a computational realization of such cost-conflict monitor-controller network. We utilize this computational model to simulate existing human performance and ERP data of a trial-and-error learning task. The model successfully simulated the behavioral data and FRN signals under different conditions in this task.

Classification of Asthma Based on Nonlinear Analysis of Breathing Pattern.

Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty age-matched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing pattern was quantified by calculating detrended fluctuation analysis, largest Lyapunov exponents, sample entropy, and cross-sample entropy. The IBI as well as LV fluctuations showed decreased long-range correlation, increased regularity and reduced sensitivity to initial conditions in patients with asthma, particularly in uncontrolled state. Our results also showed a strong synchronization between the IBI and LV in patients with uncontrolled asthma. Receiver operating characteristic (ROC) curve analysis showed that nonlinear analysis of breathing pattern has a diagnostic value in asthma and can be used in differentiating uncontrolled from controlled and non-atopic from atopic asthma. We suggest that complexity analysis of breathing dynamics may represent a novel physiologic marker to facilitate diagnosis and management of patients with asthma. However, future studies are needed to increase the validity of the study and to improve these novel methods for better patient management.