Brain Informatics

This review focuses on the recent advancements in neuroimaging enabled by deep learning techniques, specifically highlighting their applications in brain disorder detection and diagnosis. The integration of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has significantly improved feature extraction, pattern recognition, and predictive modeling, leading to enhanced accuracy, sensitivity, and specificity in diagnosing Alzheimer's, Parkinson's, schizophrenia, and brain tumors across MRI, fMRI, and PET scans. Despite these advancements, current challenges persist, including limitations in interpretability, data scarcity, and ethical concerns. To address these issues, future perspectives involve leveraging transfer learning, federated learning, and multimodal data sources. This review aims to provide a comprehensive overview of the current state of deep learning in neuroimaging, highlight the existing challenges, and discuss potential solutions for future innovation. By addressing the current limitations and exploring innovative techniques, deep learning can unlock new possibilities in neuroimaging, ultimately leading to improved diagnosis, treatment, and patient outcomes.

The development of sound clinical reasoning, while essential for optimal patient care, can be quite an elusive process. Researchers typically rely on a self-report or observational measures to study decision making, but clinicians' reasoning processes may not be apparent to themselves or outside observers. This study explored electroencephalography (EEG) to examine neurocognitive correlates of clinical decision making during a simulated American Board of Anesthesiology-style standardized oral exam. Eight novice anesthesiology residents and eight fellows who had recently passed their board exams were included in the study. Measures included EEG recordings from each participant, demographic information, self-reported cognitive load, and observed performance. To examine neurocognitive correlates of clinical decision making, power spectral density (PSD) and functional connectivity between pairs of EEG channels were analyzed. Although both groups reported similar cognitive load (p = 0.840), fellows outperformed novices based on performance scores (p < 0.001). PSD showed no significant differences between the groups. Several coherence features showed significant differences between fellows and residents, mostly related to the channels within the frontal, between the frontal and parietal, and between the frontal and temporal areas. The functional connectivity patterns found in this study could provide some clues for future hypothesis-driven studies in examining the underlying cognitive processes that lead to better clinical reasoning.

Background: While efficacy of Sudarshan Kriya Yoga (SKY) has been demonstrated in a number of prior studies, little is known about the effects of SKY taught as part of the Your Enlightened Side (YES+) workshop designed for college students and other young adults. Aims: This study aimed to assess the effects of YES+, a yogic breathing-based life skills workshop, on multiple measures of well-being and physiological stress response. Two nonrandomized open-trial pilot studies were conducted with a total of 74 young adults (age 25.4 ± 6.6 years; 55% female). Study 1 collected a variety of self-report questionnaires at baseline, postworkshop, and 1-month follow-up. Study 2 collected self-report questionnaires in addition to electrocardiography with a stationary cycling challenge at baseline and 1-month follow-up. Results: Study 1: Improvements in self-reported depression (P's ≤ 0.010), perceived stress (P's ≤ 0.002), life satisfaction (P's ≤ 0.002), social connectedness (P's ≤ 0.004), and gratitude (P's ≤ 0.090) were observed at postworkshop and 1-month after workshop relative to baseline. Study 2: Improvements in self-reported emotion regulation were observed at 1-month follow-up relative to baseline (P = 0.019). Positive and Negative Affect Schedule-Expanded Form positive affect increased (P = 0.021), while fatigue and sadness decreased (P's ≤ 0.005). During the stationary cycling challenge, rate to recovery of electrocardiography inter-beat interval also increased from baseline to 1-month follow-up (P = 0.077). Conclusions: These findings suggest that a life skills workshop integrating yogic breathing techniques may provide self-empowering tools for enhancing well-being in young adults. Future research is indicated to further explore these effects, particularly in regards to vagal tone and other aspects of stress physiology.

In this paper a computational model is presented for how a desire triggers responses and feelings. The model shows how these feelings can be biased, for example due to addicting experiences in the past. Both the strength of a response and of the associated feeling result from a converging dynamic pattern modelled by reciprocal causal interactions between the two. The model has been used to conduct a number of simulation experiments under varying circumstances. Moreover, it has been evaluated by formal analysis of emerging patterns entailed by the model. Furthermore, it has been pointed out how the computational model can be applied within an ambient agent system supporting a human in not being tempted. In a simple example scenario it is shown such an ambient agent system is able to predict and assess a human's desire state, and use this assessment to suggest alternatives to avoid falling for certain temptations.

Marketing stands at a critical crossroads: the imperative of speed inherently conflicts with the necessity of profound consumer insight, generating an "agility-insight gap" that diminishes strategic efficacy. Legacy agile approaches expedite campaign rollout but relinquish psychological depth in favor of superficial behavior that is poorly predictive of actual engagement. In contrast, traditional neuromarketing uncovers rich subconscious drivers but on a timescale too protracted for turbulent markets, with the effect that insight is often obsolete by the time of deployment. This manuscript presents Neuro-Agile Marketing (NAM) as the ultimate solution-a paradigm reconciling the iterative dynamism of agile execution with the precision of neuroscience through realtime biometrics (EEG, eye-tracking) and adaptive, reinforcement learning-based predictive control. NAM defines a closed-loop framework continually calibrating marketing stimuli to occult neural signatures-cognitive load, emotional valence, attentional focus-optimizing based on how consumers neurologically process content, not merely on what they say or do. This facilitates an unprecedented symbiosis with the subconscious topography of decision. By way of illustration, near-subliminal negative emotional reactions to packaging, detected in real-time via EEG during testing, can initiate rapid redesigns, preventing expensive failures-illustrating the revolutionary potential of NAM. Tapping this capability necessitates uncompromising ethical watchfulness: stringent frameworks enforcing algorithmic transparency, clear consumer opt-in, bias mitigation (with consideration for neurodiverse/crosscultural cohorts), and equitable benefit distribution are essential. NAM's full realization mandates an unprecedented convergence of marketing science, neuroscience, AI ethics, data engineering, and legal scholarship to pioneer standards, inclusive biometric baselines, explainable AI, and next-generation computational methodologies such as quantum ML. NAM embodies a fundamental revolution, closing the agility-insight gap to bring about marketing that is profoundly resonant, ethically centered, and authentically humanoriented by harnessing the real-time neurocognitive symphony that underlies choice.

This research investigates the application of Artificial Intelligence of Everything (AIoE) in neuromarketing to decode consumer behavior by analyzing neurophysiological and biometric signals. The study uses EEG, GSR, and eye-tracking data to examine the cognitive and emotional responses to digital marketing stimuli. MATLAB-based machine learning techniques, including multiple linear regression and K-means clustering, are employed to identify patterns and predict purchase intent. The findings indicate a significant relationship between neural activity, emotional arousal, visual engagement, and purchase decisions. EEG alpha and beta activity, GSR responses, and fixation duration demonstrate high predictive power, with the model achieving an R 2 of 0.803. The results underscore the potential of AIoE-powered neuromarketing in optimizing marketing strategies through real-time, data-driven insights. The study highlights the transformative role of neuromarketing technologies while addressing ethical considerations related to neurodata privacy.

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that consi...

Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects elderly individuals. The disease is dangerous because it causes damage to brain cells and tissues before the symptoms appear, and there is no medicinal or surgical treatment available yet for AD. AD causes loss of memory and functionality control in multiple degrees according to AD's progression level. However, early diagnosis of AD can hinder its progression. Brain imaging tools such as magnetic resonance imaging (MRI), computed tomography (CT) scans, positron emission tomography (PET), etc. can help in medical diagnosis of AD. Recently, computer-aided diagnosis (CAD) such as deep learning applied to brain images obtained with these tools, has been an established strategic methodology that is widely used for clinical assistance in prognosis of AD. In this study, we proposed an intelligent methodology for building a convolutional neural network (CNN) from scratch to detect AD stages from the brain MRI images dataset and to improve patient care. It is worth mentioning that training a deep-learning model requires a large amount of data to produce accurate results and prevent the model from overfitting problems. Therefore, for better understanding of classifiers and to overcome the model overfitting problem, we applied data augmentation to the minority classes in order to increase the number of MRI images in the dataset. All experiments were conducted using Alzheimer's MRI dataset consisting of brain MRI scanned images. The performance of the proposed model determines detection of the four stages of AD. Experimental results show high performance of the proposed model in that the model achieved a 99.38% accuracy rate, which is the highest so far. Moreover, the proposed model performance in terms of accuracy, precision, sensitivity, specificity, and f-measures is promising when compared to the very recent state-of-the-art domain-specific models existing in the literature.

The use of machine learning (ML) in the field of marketing has recently gained momentum in parallel with the development of technology. ML not only enables customers to predict their digital actions but also supports targeting the right customers with the best content at the right time. The study aims to predict the relationship between consumer buying behavior (CBB) and consumption metaphor (CM) through ML. In this context, the application of the study was built on two scenarios. In the first scenario, CBB is associated with the CM to confirm the ML estimation. In the second scenario, it is aimed that ML both predicts CBB and estimates and confirms the effect of CM on CBB. As a result, the k-nearest neighbors algorithm was able to predict consumers at the rate of 91.02% accuracy and predict consumers who do not intend to have tattoos at the rate of 90.98%. When the CM is considered, ML predicted consumers at the rate of 78.33% accuracy, and predicted consumers who do not tend to buy at the rate of 79%.

Rapid technological advancements are revolutionizing human augmentation, making cognitive and physical enhancements for military personnel not only feasible but also a priority for global superpowers such as the United States and China. As technology advances and global competition intensifies, military scholars explore ways to enhance U.S. Special Operations Forces (SOF) through emerging technologies such as brain-computer interfaces (BCIs). However, current debates on cognitive and physical enhancement tend to focus on mitigating perceived operator limitations, rather than exploring the full potential of both the human and the technologies.

Instead of merely compensating for weaknesses, enhancement technologies should be leveraged to amplify operator strengths. BCI technologies offer the potential to harness operators’ inherent resources, such as tacit knowledge, and unlock latent human capabilities while reinforcing the SOF ethos that humans are more important than hardware. Additionally, to fully realize the technological potential, it is important to distinguish between enhancing the operator and augmenting the mission—two closely interrelated yet distinct goals that demand tailored strategies. This dual focus is explored through a series of practical examples, illustrating the transformative and nuanced applications of emerging BCI technologies. Ultimately, these capabilities could enable more precise, adaptive, and deniable special operations within the evolving context of strategic competition and integrated deterrence.

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learningbased infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leaveone-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learningbased infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leaveone-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.

The LSB method in steganography usually only uses the last bit or the last few bits that are the same for all pixels. This is very easy to solve by using a bitwise shift left operation so that the last bit becomes the leading bit (MSB). Some techniques combine steganography and cryptography through two different processes. In this study, a new technique is proposed to perform steganography and cryptography together. The random sequence obtained from the logistic map is used to determine the number of bits in the LSB method. Furthermore, testing was carried out on several grayscale images. The result obtained is that the hidden images cannot be opened easily. The level of sensitivity is very small, reaching 10-15 .

In recent days, deep learning technologies have achieved tremendous success in computer vision-related tasks with the help of large-scale annotated dataset. Obtaining such dataset for medical image analysis is very challenging. Working with the limited dataset and small amount of annotated samples makes it difficult to develop a robust automated disease diagnosis model. We propose a novel approach to generate synthetic medical images using generative adversarial networks (GANs). Our proposed model can create brain PET images for three different stages of Alzheimer’s disease—normal control (NC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD).

Alzheimer&#39;s disease is an incurable, progressive neurological brain disorder. Earlier detection of Alzheimer&#39;s disease can help with proper treatment and prevent brain tissue damage. Several statistical and machine learning models have been exploited by researchers for Alzheimer&#39;s disease diagnosis. Analyzing magnetic resonance imaging (MRI) is a common practice for Alzheimer&#39;s disease diagnosis in clinical research. Detection of Alzheimer&#39;s disease is exacting due to the similarity in Alzheimer&#39;s disease MRI data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer&#39;s disease diagnosis using brain MRI data analysis. While most of the existing approaches perform binary classification, our model can identify different stages of Alzheimer&#39;s disease an...

Artificial Intelligence (AI) has impacted our lives in many meaningful ways. For our research, we focus on improving disease diagnosis systems by analyzing medical images using AI, specifically deep learning technologies. The recent advances in deep learning technologies are leading to enhanced performance for medical image analysis and computer-aided disease diagnosis. In this dissertation, we explore a major research area in medical image analysis Image classification. Image classification is the process to assign an image a label from a fixed set of categories. For our research, we focus on the problem of Alzheimer’s Disease (AD) diagnosis from 3D structural Magnetic Resonance Imaging (sMRI) and Positron Emission Tomography (PET) brain scans. Alzheimer’s Disease is a severe neurological disorder. In this dissertation, we address challenges related to Alzheimer’s Disease diagnosis and propose several models for improved diagnosis. We focus on analyzing the 3D Structural MRI (sMRI)...

Background: To assist doctors to diagnose mild cognitive impairment (MCI) and Alzheimer's disease (AD) early and accurately, convolutional neural networks based on structural magnetic resonance imaging (sMRI) images have been developed and shown excellent performance. However, they are still limited in their capacity in extracting discriminative features because of large sMRI image volumes yet small lesion regions and the small number of sMRI images. Methods: We proposed a task-driven hierarchical attention network (THAN) taking advantage of the merits of patch-based and attention-based convolutional neural networks for MCI and AD diagnosis. THAN consists of an information sub-network and a hierarchical attention sub-network. In the information subnetwork, an information map extractor, a patch-assistant module, and a mutual-boosting loss function are designed to generate a task-driven information map, which automatically highlights disease-related regions and their importance for final classification. In the hierarchical attention sub-network, a visual attention module and a semantic attention module are devised based on the information map to extract discriminative features for disease diagnosis. Results: Extensive experiments were conducted for four classification tasks: MCI versus (vs.) normal controls (NC), AD vs. NC, AD vs. MCI, and AD vs. MCI vs. NC. Results demonstrated that THAN attained the accuracy of 81.6% for MCI vs. NC, 93.5% for AD vs. NC, 80.8% for AD vs. MCI, and 62.9% for AD vs. MCI vs. NC. It outperformed advanced attention-based and patch-based methods. Moreover, information maps generated by the information sub-network could highlight the potential biomarkers of MCI and AD, such as the hippocampus and ventricles. Furthermore, when the visual and semantic attention modules were combined, the performance of the four tasks was highly improved. The information sub-network can automatically highlight the disease-related regions. The hierarchical attention sub-network can extract discriminative visual and semantic features. Through the two sub-networks, THAN fully exploits the visual and semantic features of disease-related regions and meanwhile considers global features of sMRI images, which finally facilitate the diagnosis of MCI and AD.

This systematic review aims to evaluate the effectiveness of various machine learning models in predicting PD progression using longitudinal data. Despite the increasing use of ML in PD research, gaps remain in understanding the impact of longitudinal data on prediction accuracy and model generalizability. This study aims to bridge this gap by examining how multimodal data sources, including clinical, genetic, and imaging datasets, contribute to improved predictive performance. The review focuses on the types of models used, data sources, performance metrics, and their potential to improve personalized treatment and clinical decision-making. A Systematic Review Article Adeniyi et al.; Asian J. The inclusion criteria focused on studies employing ML techniques for analyzing longitudinal PD data, yielding 14 eligible studies. Data were extracted on ML models used, dataset characteristics, performance metrics, and the integration of multimodal data sources such as clinical, genetic, and imaging data. The findings were synthesized to assess model performance and generalizability. Long Short-Term Memory (LSTM) and ensemble methods like Random Forest and Light Gradient Boosting Machine (LGBM) are effective in capturing disease progression with high accuracy and robust performance metrics. LSTM models achieved accuracies up to 90% and AUC scores of 93.79%, while LGBM models achieved 90.73% and AUC of 94.57%. The Matthews Correlation Coefficient (MCC) scores in longitudinal studies increased over time, and Mean Absolute Error (MAE) also improved. Integrating multimodal data, including clinical, genetic, and imaging information, further improved model reliability and generalizability. ML models, particularly those incorporating longitudinal and multimodal data, show promise in predicting PD progression. Future research should prioritize dataset diversity, enhance model interpretability, and leverage real-world wearable data for improved clinical applicability.

Efficiency and optimization in virtual reality (VR) technology is an urgent need, especially in the context of optimizing algorithms to recognize user emotions while using VR. Efficient VR technology can improve user experience and enable more immersive and responsive interactions. This study adopts the preferred reporting items for systematic reviews and metaanalyses (PRISMA) (2020) method to identify and analyze gaps in the existing literature, focusing on the optimization of electroencephalogram (EEG) signal classification algorithms to recognize VR users' emotions. The literature search was conducted through the Scopus database, with article selection based on the type of emotion classified, the classification method used, the limitations of the research, and the results obtained. Of the 1478 articles found, 74 articles passed the initial selection stage, and the final stage 13 articles were selected for further analysis. The selected articles provide important insights into the development of EEG classification algorithms for VR users, especially in multiuser settings. The findings identify potential and opportunities in the development of more efficient and accurate EEG signal classification algorithms for VR users. By focusing on emotion classification in a multiuser VR environment, this research contributes to improving the efficiency of VR technology and supporting a better and more responsive user experience.

Information processing in the brain or other decision making systems, such as in multimedia, involves fusion of information from multiple sensors, sources, and systems at the data, feature or decision level. Combinatorial Fusion Analysis (CFA), a recently developed information fusion paradigm, uses a combinatorial method to model the decision space and the Rank-Score Characteristic (RSC) function to measure cognitive diversity. In this paper, we first introduce CFA and its practice in a variety of application domains such as computer vision and target tracking, information retrieval and Internet search, and virtual screening and drug discovery. We then apply CFA to investigate gender variation in facial attractiveness judgment on three tasks: liking, beauty and mentalization using RSC function. It is demonstrated that the RSC function is useful in the differentiation of gender variation and task judgment, and hence can be used to complement the notion of correlation which is widely used in statistical decision making. In addition, it is shown that CFA is a viable approach to deal with various issues and problems in brain informatics.

Alzheimer's disease (AD) is among the neurological diseases (dementia) that afflict the elderly most frequently. We introduce a novel machine learning-based approach in this research to differentiate individuals with the early AD classification. Preprocessing, feature selection, training data, and classifiers all affect the outcomes of machine learning-based methods for classifying AD. A novel composite comprehensive MRI development of Alzheimer's disease is provided in this chapter (AD-DCP-MRI). The results were analyzed in terms of accuracy, precision, recall, and F1-score using the data package that included T1-weighted MRI clinical OASIS temporal data. Our recommendation model is effective for AD categorization, as evidenced by its increased accuracy. These methods can also be successfully applied in the medical field to help with the early identification and diagnosis of disease.

Parkinson's disease (PD) is a kind of neurodegenerative disorder characterized by the loss of dopamine-producing cells in the brain. The disruption of brain cells that create dopamine, a chemical that allows brain cells to connect with one another, causes Parkinson's disease. Control, adaptability, and rapidity of movement are all controlled by dopamine-producing cells in the brain. Researchers have been investigating for techniques to identify non-motor symptoms that show early in the disease as soon as possible, slowing the disease's progression. A machine learning-based detection of Parkinson's disease is proposed in this research. Feature selection and classification techniques are used in the proposed detection technique. Boruta, Recursive Feature Elimination (RFE) and Random Forest (RF) Classifier have been used for the feature selection process. Four classification algorithms are considered to detect Parkinson disease which are gradient boosting, extreme gradient boosting, bagging and Extra Tree Classifier. Bagging with recursive feature elimination was found to outperform the other methods. The lowest number of voice characteristics for the diagnosis in Parkinson attained 82.35% accuracy.

Music has various effects on the brain and body. Multiple studies have suggested a marked difference in information processing between musicians and non-musicians when listening to music. However, the occurrence of these changes within the brain is yet to be instantiated. The amount of data obtained to study information processing in the brain is huge and surplus features obtained may be redundant and there is thus an increased need to reduce amount of data. In this study information processing in the brain is determined by obtaining optimum features using data reduction processes. Features obtained are compared with brain activation in an attempt to predict behavioral data. Twenty healthy subjects were considered of which 10 were musicians and 10 were nonmusicians. All subjects were made to listen to a music stimulus that was played. Electroencephalography was used to record responses and behavioral data too was obtained. The Major predictors for musicians were frontal and temporal...

Research advancements in neuroscience entail the production of a substantial amount of data requiring interpretation, analysis, and integration. The complexity and diversity of neuroscience data necessitate the development of specialized databases and associated standards and protocols. NeuroMorpho.Org is an online repository of over one hundred thousand digitally reconstructed neurons and glia shared by hundreds of laboratories worldwide. Every entry of this public resource is associated with essential metadata describing animal species, anatomical region, cell type, experimental condition, and additional information relevant to contextualize the morphological content. Until recently, the lack of a user-friendly, structured metadata annotation system relying on standardized terminologies constituted a major hindrance in this effort, limiting the data release pace. Over the past 2 years, we have transitioned the original spreadsheet-based metadata annotation system of NeuroMorpho.Or...

Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and cognitive function, necessitating early detection for its effective management. This poses a significant challenge to global public health. The early and accurate detection of dementia is crucial for several reasons. First, timely detection facilitates early intervention and planning of treatment. Second, precise diagnostic methods are essential for distinguishing dementia from other cognitive disorders and medical conditions that may present with similar symptoms. Continuous analysis and improvements in detection methods have contributed to advancements in medical research. It helps to identify new biomarkers, refine existing diagnostic tools, and foster the development of innovative technologies, ultimately leading to more accurate and efficient diagnostic approaches for dementia. This paper presents a critical analysis of multimodal imaging datasets, learning algorithms, and optimisation techniques utilised in the context of Alzheimer's dementia detection. The focus is on understanding the advancements and challenges in employing diverse imaging modalities, such as MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and EEG (ElectroEncephaloGram). This study evaluated various machine learning algorithms, deep learning models, transfer learning techniques, and generative adversarial networks for the effective analysis of multi-modality imaging data for dementia detection. In addition, a critical examination of optimisation techniques encompassing optimisation algorithms and hyperparameter tuning strategies for processing and analysing images is presented in this study to discern their influence on model performance and generalisation. Thorough examination and enhancement of methods for dementia detection are fundamental for addressing the healthcare challenges posed by dementia, facilitating timely interventions, improving diagnostic accuracy, and advancing research in neurodegenerative diseases.

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many resear...

Human brain has always been considered as a black box and is the source of all emotions. Analyzing cultural and language role through human emotion by looking at the brain activity can thus help us understand depression and stress better. This paper focuses on understanding and analyzing undergraduate students' emotions with different background and culture after completing their semester final examination. Brain wave signals were captured using EEG device and analyzed through proposing an affective computation model. EEG signal was collected from 8 healthy subjects from different states of Malaysia with different dialects where each subject was emotionally induced with audio and video emotion stimuli using the International Affective Pictures and System (IAPS). Features were extracted from the captured EEG signals using Kernel Density Estimation (KDE), which was then categorized into four basic emotions of happy, calm, sad and fear using the Multi-layer Perceptron (MLP). Results of the study show potential of using such analysis in understanding stress, anxiety and depression.

In this paper the issue of relating a specification of the internal processes within an agent to a specification of the behaviour of the agent is addressed. A previously proposed approach for automated generation of behavioural specifications from an internal specification was limited to stratified specifications of internal processes. Therefore, it cannot be applied to mutually interacting cognitive and affective processes described by interacting loops. However, such processes are not rare in agent models addressing integration of cognitive and affective processes and agent learning. In this paper a novel approach is proposed which addresses this issue. The proposed technique for loop abstraction is based on identifying dependencies of equilibrium states for interacting loops. The technique is illustrated by an example of an internal agent model with interdependent processes of believing, feeling, and trusting.

Depressions impose a huge burden on both the patient suffering from a depression as well as society in general. In order to make interventions for a depressed patient during a therapy more personalized and effective, a supporting personal software agent can be useful. Such an agent should then have a good idea of the current state of the person. A computational model for human mood regulation and depression has been developed in previous work, but in order for the agent to give optimal support during an intervention, it should also have knowledge on the precise functioning of the intervention in relation with the mood regulation and depression. This paper therefore presents computational models for these interventions for different types of therapy. Simulation results are presented showing that the mood regulation and depression indeed follow the expected patterns when applying these therapies. The intervention models have been evaluated for a variety of patient types by simulation experiments and formal verification.

Readers experience various emotions while reading, which may affect their overall enjoyment and comprehension of the material. The current work presents a study on brainwaves or EEG signals and their association to emotions while a person is reading literary fiction. EEG data from 32 participants, aged 18 years old and above, were collected with the use of an EEG headset. We describe our methodology for data acquisition and processing, feature extraction and dataset building, as well as the classification experiments done.

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that consi...

The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signal processing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (δ , θ , α, β 1 , β 2 , γ) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01±0.71) using 5-fold cross-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.

Diagnosing Autism Spectrum Disorder (ASD) presents a multifaceted challenge, demanding accurate and efficient screening methods. Applying machine learning techniques offers a promising avenue for enhancing diagnostic accuracy and efficiency. This research investigates the efficiency of machine learning in distinguishing individuals with ASD from those without, utilizing a comprehensive dataset comprising screening questions, demographic factors, and ASD related diagnostic classifications. We applied chi-square feature selection technique and also tested Random Forest, Logistic Regression, Gradient Boosting Classifier, and Extra Trees Classifier. Each model showed optimal performance and exhibit high precision, recall, and F1-score for both ASD-positive and ASD-negative instances. Additionally, AUROC curves further validated the models' exceptional discriminatory abilities, with exceptional results. Our findings highlight the potential of machine learning algorithms for enhancing ASD diagnosis accuracy and efficiency in clinical settings. Further research and validation on larger datasets are required to understand the importance of machine learning methods in ASD diagnosis.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an everincreasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/ or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI-and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

Humans have the ability to easily separate a composed speech and to form perceptual representations of the constituent sources in an acoustic mixture thanks to their ears. Until recently, researchers attempt to build computer models of high-level functions of the auditory system. The problem of the composed speech segregation is still a very challenging problem for these researchers. In our case, we are interested in approaches that are addressed to the monaural speech segregation. For this purpose, we study in this paper the computational auditory scene analysis (CASA) to segregate speech from monaural mixtures. CASA is the reproduction of the source organization achieved by listeners. It is based on two main stages: segmentation and grouping. In this work, we have presented, and compared several studies that have used CASA for speech separation and recognition.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an everincreasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/ or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI-and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

Deception detection through facial and audio transcript features has gained traction due to its potential in enhancing security and communication integrity. This review aims to consolidate existing research on leveraging facial and audio features for identifying deceptive behavior. The motivation behind this study is the increasing demand for reliable deception detection mechanisms in various domains, including security and psychology. Despite advancements, limitations persist in achieving high accuracy across diverse contexts and individual differences. The objective of this review is to evaluate the effectiveness of different methods used in detecting deception from facial expressions and audio cues, identifying strengths and weaknesses of each approach, and suggesting future directions for improving accuracy through advanced techniques.

Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements. • Subject independent human emotion identification is studied using SEED data set. • Tsallis entropy is employed as feature and performance variation with Tsallis parameter (q = 2, 3, 4) is examined. • Performance of kNN classifier is examined with Tsallis entropy feature. • Emotion identification at various levels is studied, brain region, EEG rhythms, brain hemisphere. • Prospects of TsEn-based real-time emotion recognition framework is canvassed.

Brain–computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onward...

Background Abstracting cerebrovascular disease (CeVD) from inpatient electronic medical records (EMRs) through natural language processing (NLP) is pivotal for automated disease surveillance and improving patient outcomes. Existing methods rely on coders’ abstraction, which has time delays and under-coding issues. This study sought to develop an NLP-based method to detect CeVD using EMR clinical notes. Methods CeVD status was confirmed through a chart review on randomly selected hospitalized patients who were 18 years or older and discharged from 3 hospitals in Calgary, Alberta, Canada, between January 1 and June 30, 2015. These patients’ chart data were linked to administrative discharge abstract database (DAD) and Sunrise™ Clinical Manager (SCM) EMR database records by Personal Health Number (a unique lifetime identifier) and admission date. We trained multiple natural language processing (NLP) predictive models by combining two clinical concept extraction methods and two supervis...

Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer's Disease Neuroimaging Initiative.

Feature selection plays a key role in multivoxel pattern analysis because functional magnetic resonance imaging data are typically noisy, sparse, and highdimensional. Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights. In this study, we aim to investigate the stability of feature selection methods and test the stability-based feature selection scheme on two benchmark datasets. Top-k feature selection with a ranking score of mutual information and correlation, recursive feature elimination integrated with support vector machine, and L1 and L2-norm regularizations were adapted to a bootstrapped stability selection framework, and the selected algorithms were compared based on both accuracy and stability scores. The results indicate that regularizationbased methods are generally more stable in StarPlus dataset, but in Haxby dataset they failed to perform as well as others.

When two speech signals are mixed in a single channel the voiced parts of any of them remain mostly unaltered during the voicing interruptions of the other, i.e. pauses and voiceless consonants. The mixture is made of 3 types of multivoiced segments noted 0V (unvoiced), 1V (one voicing) and 2V (two voicings). A statistical study of readaloud texts reveals that total time spent in the 1V state is twice as long as the time spent in any of the other states. The HSC multipitch algorithm, based on a specific mechanism that eliminates the f 0 halving and doubling errors, is used to locate the 3 segments types in the signal. This feature is illustrated by the task of spotting a short utterance repeatedly mixed with a long text.

Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many resear...

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progression of a disease and/or how do patients evolve or recover when they are subjected to some treatment. In this study, we investigate the problem of patients' evolution on the basis of medical tests before and after treatment after brain trauma: we want to understand to what extend a patient can become similar to a healthy participant. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patients' subpopulations, i.e. prediction of label if they recovered or not. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls. Keywords Traumatic brain injury Á Label prediction Á Evolution modelling Á Clustering Mathematics Subject Classification MSC code1 Á MSC code2 Á more The dataset used in this work was made available through the cooperation ''Data Mining and Stream Mining for Epidemiological Studies on the Human Brain'' (StreaMED) between the Centre of Biomedical Technology (CTB) of the University Polytecnica de Madrid and the Otto-von-Guericke University Magdeburg.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an everincreasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/ or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI-and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

Telepathy, often considered a supernatural phenomenon, is the purported ability to transmit thoughts directly from one mind to another without using any known human sensory channels or physical interaction. With the advent of advanced neural technologies and the fifth generation of mobile networks (5G), telepathy-like communication is no longer confined to the realm of science fiction. This paper explores the concept of telepathy, the technological advancements enabling brain-machine interfaces (BMIs), and how these developments can be integrated into mobile devices to create new forms of interaction within the 5G framework. We discuss the potential applications, benefits, and challenges of this futuristic technology.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an everincreasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/ or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI-and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

Recent works point to the importance of emotions in special-numerical associations. There remains a notable gap in understanding the electrophysiological underpinnings of such associations. Exploring resting- state (rs) EEG, particularly in frontal regions, could elucidate emotional aspects, while other EEG measures might offer insights into the cognitive dimensions correlating with behavioral performance. The present work investigated the relationship between rs-EEG measures (emotional and cognitive traits) and performance in the mental number line (MNL). EEG activity in theta (3– 7 Hz), alpha (8–12 Hz, further subdivided into low-alpha and high-alpha), sensorimotor rhythm (SMR, 13–15 Hz), beta (16–25 Hz), and high-beta/gamma (28–40 Hz) bands was assessed. 76 university students participated in the study, undergoing EEG recordings at rest before engaging in a computerized number-to-position (CNP) task. Analysis revealed significant associations between frontal asymmetry, specific EEG frequencies, and MNL performance metrics (i.e., mean direction bias, mean absolute error, and mean reaction time). Notably, theta and beta asymmetries correlated with direction bias, while alpha peak frequency (APF) and beta activity related to absolute errors in numerical estimation. Moreover, the study identified significant correlations between relative amplitude indices (i.e., theta/beta ratio, theta/SMR ratio) and both absolute errors and reaction times (RTs). Our findings offer novel insights into the emotional and cognitive aspects of EEG patterns and their links to MNL performance.