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Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience
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Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 5453

Special Issue Editors

Prof. Dr. Peter Walla

E-Mail Website
Guest Editor
Faculty of Psychology, Sigmund Freud Private University, Freudplatz 1, 1020 Vienna, Austria
Interests: non-conscious affective and cognitive brain processes; memory; perception; self-referential processing; olfaction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alexander N. Pisarchik

E-Mail Website
Guest Editor
Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
Interests: complex systems; bioinformatics; mathematical and computational biology; optics and photonics; biological physics; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Affective and cognitive neuroscience are relevant to essentially all other disciplines. Following the idea that human behavior, including prior decision-making, is a consequence of neural activity mainly occurring in the brain, one can understand why several academic marriages between neuroscience and other fields already took place. To name just a few, neuroeconomics, neurophilosophy, neuromarketing and neurofinance have become their own new disciplines. Their creation is strongly driven by the fact that non-conscious brain processes dominantly feed into decision-making and ultimately the production of human behavior. Hence, there is increased interest in the completion of questionnaire-based investigations with methods that can watch the brain at work. The sole collection of consciously given responses can be misleading, similar to perceived optical illusions. In contrast to that, the brain itself always tells the truth, but it cannot speak. This is where affective and cognitive neuroscience comes into play. Due to growing interest in its capacities by other disciplines, this Special Issue is meant to collect original articles (empirical research), reviews, and theoretical papers around the topic of applied affective and cognitive neuroscience.

Prof. Dr. Peter Walla
Prof. Dr. Alexander N. Pisarchik
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • affection
  • cognition
  • neuroscience
  • brain imaging
  • non-conscious brain processes
  • startle reflex modulation
  • electroencephalography
  • EEG

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Published Papers (6 papers)

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Research

15 pages, 11010 KiB  
Article
Functional Connectivity Differences in the Perception of Abstract and Figurative Paintings
by Iffah Syafiqah Suhaili, Zoltan Nagy and Zoltan Juhasz
Appl. Sci. 2024, 14(20), 9284; https://doi.org/10.3390/app14209284 (registering DOI) - 12 Oct 2024
Viewed by 368
Abstract
The goal of neuroaesthetic research is to understand the neural mechanisms underpinning the perception and appreciation of art. The human brain has the remarkable ability to rapidly recognize different artistic styles. Using functional connectivity, this study investigates whether there are differences in connectivity [...] Read more.
The goal of neuroaesthetic research is to understand the neural mechanisms underpinning the perception and appreciation of art. The human brain has the remarkable ability to rapidly recognize different artistic styles. Using functional connectivity, this study investigates whether there are differences in connectivity networks formed during the processing of abstract and figurative paintings. Eighty paintings (forty abstract and forty figurative) were presented in a random order for eight seconds to each of the 29 participants. High-density EEG recordings were taken, from which functional connectivity networks were extracted at several time points (−300, 100, 300 and 500 ms). The debiased weighted phase lag index (dwPLI) was used to extract the connectivity networks for the abstract and figurative conditions across multiple frequency bands. Significant connectivity differences were detected for both conditions at each time point and in each frequency band: delta (p < 0.0273), theta (p < 0.0292), alpha (p < 0.0299), beta (p < 0.0275) and gamma (p < 0.0266). The topology of the connectivity networks also varied over time and frequency, indicating the multi-scale dynamics of art style perception. The method used in this study has the ability to identify not only brain regions but their interaction (communication) patterns and their dynamics at distinct time points, in contrast to average ERP waveforms and potential distributions. Our findings suggest that the early perception stage of visual art involves complex, distributed networks that vary with the style of the artwork. The difference between the abstract and figurative connectivity network patterns indicates the difference between the underlying style-related perceptual and cognitive processes. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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12 pages, 743 KiB  
Article
Can Virtual Reality Cognitive Remediation in Bipolar Disorder Enhance Specific Skills in Young Adults through Mirror Neuron Activity?—A Secondary Analysis of a Randomized Controlled Trial
by Diego Primavera, Gian Mario Migliaccio, Alessandra Perra, Goce Kalcev, Elisa Cantone, Giulia Cossu, Antonio Egidio Nardi, Dario Fortin and Mauro Giovanni Carta
Appl. Sci. 2024, 14(18), 8142; https://doi.org/10.3390/app14188142 - 10 Sep 2024
Viewed by 967
Abstract
Introduction: Impairments in social cognition and cognitive deficits in bipolar disorder (BD) offer insights into the disorder’s progression. Understanding how interventions impact both cognitive and emotional aspects of social cognition is essential. This study examines the effects of virtual reality (VR) cognitive remediation [...] Read more.
Introduction: Impairments in social cognition and cognitive deficits in bipolar disorder (BD) offer insights into the disorder’s progression. Understanding how interventions impact both cognitive and emotional aspects of social cognition is essential. This study examines the effects of virtual reality (VR) cognitive remediation on cognitive skills, stratified by age, in the early stages of the disorder. Methods: A secondary analysis of a randomized controlled trial (RCT) compared the efficacy of VR cognitive remediation on cognitive skills between young adults (≤58 years) and older adults (≥59 years) in the experimental group with BD. Results: The experimental group included 39 participants: 24 ≤ 58 years and 15 ≥ 59 years. Young adults showed greater improvement in the Digit Span Backward (0.37 ± 0.35 vs. 0.07 ± 0.26, F = 9.882, p = 0.020) and Digit Symbol tests (3.84 ± 3.05 vs. 1.16 ± 3.8, F = 5.895, p = 0.020). Older adults improved more in the Frontal Assessment Battery (1.00 ± 0.95 vs. 0.54 ± 0.21, F = 5.295, p = 0.027), Matrix test (0.58 ± 0.35 vs. 0.37 ± 0.26, F = 4.606, p = 0.038), and Test of Tale (0.81 ± 0.36 vs. 0.42 ± 0.38, F = 10.115, p = 0.003). Conclusions: Young adults improved more in complex cognitive tasks, while older adults showed better results in simpler tasks. The effectiveness of VR may be due to hyperstimulation of mirror neurons. Further studies are needed to confirm these findings. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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12 pages, 442 KiB  
Article
Exploring Autism Spectrum Disorder: A Comparative Study of Traditional Classifiers and Deep Learning Classifiers to Analyze Functional Connectivity Measures from a Multicenter Dataset
by Francesca Mainas, Bruno Golosio, Alessandra Retico and Piernicola Oliva
Appl. Sci. 2024, 14(17), 7632; https://doi.org/10.3390/app14177632 - 29 Aug 2024
Viewed by 543
Abstract
The investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social [...] Read more.
The investigation of functional magnetic resonance imaging (fMRI) data with traditional machine learning (ML) and deep learning (DL) classifiers has been widely used to study autism spectrum disorders (ASDs). This condition is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for the development of effective treatments. This study compares traditional ML and DL classifiers in the analysis of tabular data, in particular, functional connectivity measures obtained from the time series of a public multicenter dataset, and evaluates whether the features that contribute most to the classification task vary depending on the classifier used. Specifically, Support Vector Machine (SVM) classifiers, with both linear and radial basis function (RBF) kernels, and Extreme Gradient Boosting (XGBoost) classifiers are compared against the TabNet classifier (a DL architecture customized for tabular data analysis) and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. Among the latter, SVMs outperform the other classifiers with an AUC of around 75%, whereas the best performances of TabNet and MLP reach 65% and 71% at most, respectively. Furthermore, the analysis of the feature importance showed that the brain regions that contribute the most to the classification task are those primarily responsible for sensory and spatial perception, as well as attention modulation, which is known to be altered in ASDs. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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21 pages, 1578 KiB  
Article
Computerized Cognitive Training in the Older Workforce: Effects on Cognition, Life Satisfaction, and Productivity
by Zdenka Milič Kavčič, Voyko Kavcic, Bruno Giordani and Uros Marusic
Appl. Sci. 2024, 14(15), 6470; https://doi.org/10.3390/app14156470 - 24 Jul 2024
Viewed by 699
Abstract
Background: The accelerated aging of the world’s population will lead to an increase in the number of older people in the workforce. Computerized Cognitive Training (CCT) is effective in improving cognitive outcomes, but its benefits for older workers remain controversial. We investigate the [...] Read more.
Background: The accelerated aging of the world’s population will lead to an increase in the number of older people in the workforce. Computerized Cognitive Training (CCT) is effective in improving cognitive outcomes, but its benefits for older workers remain controversial. We investigate the real-world efficacy of CCT in the workplace, focusing on employees aged 50+ years from a public sector agency. Methods: Case managers (n = 82) were randomized to either an intervention group (24 40 min CCT sessions two times per week) or a waiting list passive control group. Cognitive ability, well-being, job satisfaction, and productivity outcome measures were collected and assessed before and after CCT or the comparable control wait time. Results: Participants undergoing CCT improved on a task of executive functioning (p = 0.04). There was a trend toward a change in work productivity after CCT (p = 0.09), with the control group showing a significant decrease (p = 0.02), while the intervention group remained stable. Conclusions: CCT during office hours has a positive effect on cognition and well-being without affecting productivity among white-collar office workers. CCT could be considered as an intervention to support the older workforce in managing the cognitive and behavioral challenges of changing workplace demands. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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12 pages, 1545 KiB  
Article
Evaluating the Efficacy of Transcranial Magnetic Stimulation in Symptom Relief and Cognitive Function in Obsessive–Compulsive Disorder, Substance Use Disorder, and Depression: An Insight from a Naturalistic Observational Study
by Andrea Stefano Moro, Daniele Saccenti, Alessandra Vergallito, Regina Gregori Grgič, Silvia Grazioli, Novella Pretti, Sofia Crespi, Antonio Malgaroli, Simona Scaini, Giovanni Maria Ruggiero, Sandra Sassaroli, Mattia Ferro and Jacopo Lamanna
Appl. Sci. 2024, 14(14), 6178; https://doi.org/10.3390/app14146178 - 16 Jul 2024
Cited by 2 | Viewed by 1153
Abstract
The utilization of non-invasive neurostimulation techniques, such as transcranial magnetic stimulation (TMS), is increasingly prevalent in psychiatry due to their efficacy and safety. Although the precise therapeutic mechanisms remain partially unclear, repetitive TMS, particularly high-frequency stimulation, may enhance cognitive functions, contributing to therapeutic [...] Read more.
The utilization of non-invasive neurostimulation techniques, such as transcranial magnetic stimulation (TMS), is increasingly prevalent in psychiatry due to their efficacy and safety. Although the precise therapeutic mechanisms remain partially unclear, repetitive TMS, particularly high-frequency stimulation, may enhance cognitive functions, contributing to therapeutic benefits. This within-subjects study examined the impact of TMS on cognitive and symptomatic outcomes in patients with obsessive–compulsive disorder (OCD), substance use disorder (SUD), and major depressive disorder (MDD). A total of 44 patients underwent cognitive tests and symptom assessments before and after an intensive four-week TMS treatment phase, followed by a four-week maintenance phase. Cognitive assessments included Raven’s matrices, verbal fluency, and digit span tests, while symptom severity was measured using the Italian version of the SCL-90-R. Decision-making performance was also evaluated by administering a delay discounting (DD) test. Principal component analysis was used to generate a dimensional characterization of subjects along cognitive and symptom-related axes before and after treatment. The results indicated that TMS significantly improved symptom scores, but no significant cognitive enhancement was observed. Statistical analysis based on linear mixed-effects models confirmed these findings, showing a significant fixed effect of TMS treatment on symptoms but not on cognitive performance. DD metrics remained unchanged. These findings suggest that while TMS effectively alleviates clinical symptoms, it does not produce consistent or appreciable enhancement of cognitive functions in these protocols. This study highlights the need for more personalized and combined therapeutic approaches to maximize the benefits of TMS, potentially incorporating cognitive enhancement strategies. Future studies will be useful to explore whether the results we obtained are valid for other pathologies, cognitive tests, and stimulation protocols. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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16 pages, 1492 KiB  
Article
Non-Conscious Affective Processing in Asset Managers during Financial Decisions: A Neurobiological Perspective
by Peter Walla and Maximilian Patschka
Appl. Sci. 2024, 14(9), 3633; https://doi.org/10.3390/app14093633 - 25 Apr 2024
Viewed by 787
Abstract
In the world of finance, considerable attention is given to improving machine learning techniques to predict the future of stock markets. However, for obvious reasons, this turns out to be an unsolvable mission, most likely because the real world is not driven by [...] Read more.
In the world of finance, considerable attention is given to improving machine learning techniques to predict the future of stock markets. However, for obvious reasons, this turns out to be an unsolvable mission, most likely because the real world is not driven by algorithms but by human beings. In response to this, the present study has its focus on raw affective responses in actual asset managers during their decision making regarding controlled financial scenarios. Nineteen asset managers were invited and asked to make sell/buy decisions related to visual presentations of three different price developments of different assets. The three scenarios were “crash”, “stable” and “gain”. Parallel to their decision making, startle reflex modulation (SRM) was used to measure non-conscious affective responses without demanding any respective explicit responses (no conscious language processing involved). Interestingly, two further factors were introduced. First, all participants had to make their decisions once while being informed that 0% prior investments (low exposure) have been made into the presented assets, and once being informed that a large investment consisting of 25% of ones’ overall portfolio has been made prior to making the decision (high exposure). Second, the factor experience was included dividing all participants into two groups, one with low experience and the other with high experience. First, across both these extra factors, it was found that “crash” scenarios resulted in the most negative affective responses. The most positive affective responses were found for “gain” scenarios, while the “stable” condition was in between. Interestingly, the factor of prior investment (i.e., exposure) had an effect. Non-conscious affective responses during decision making related to the “stable” condition varied as a function of “exposure”. In the low exposure condition, affective responses to decision making during the “stable” scenario were most negative, even more negative than in “crash” scenarios. The factor experience also had an effect, but due to the small sample size, no significant interaction occurred. However, t-tests revealed the same significant effects in the experienced group as found in the 0% prior investment condition. To our knowledge, this is the first empirical investigation measuring non-conscious affective responses during decision making in the context of asset management. Thus, this study might form an interesting basis for new strategies to explore non-conscious human brain functions instead of inventing new algorithms to make asset management more successful. Full article
(This article belongs to the Special Issue Editorial Board Members' Collection Series: Applied Affective and Cognitive Neuroscience)
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