Frontiers in Neurology, Год журнала: 2023, Номер 14
Опубликована: Дек. 18, 2023
Язык: Английский
Diagnostics, Год журнала: 2025, Номер 15(4), С. 456 - 456
Опубликована: Фев. 13, 2025
Background/Objectives: The following systematic review integrates neuroimaging techniques with deep learning approaches concerning emotion detection. It, therefore, aims to merge cognitive neuroscience insights advanced algorithmic methods in pursuit of an enhanced understanding and applications recognition. Methods: study was conducted PRISMA guidelines, involving a rigorous selection process that resulted the inclusion 64 empirical studies explore modalities such as fMRI, EEG, MEG, discussing their capabilities limitations It further evaluates architectures, including neural networks, CNNs, GANs, terms roles classifying emotions from various domains: human-computer interaction, mental health, marketing, more. Ethical practical challenges implementing these systems are also analyzed. Results: identifies fMRI powerful but resource-intensive modality, while EEG MEG more accessible high temporal resolution limited by spatial accuracy. Deep models, especially CNNs have performed well emotions, though they do not always require large diverse datasets. Combining data behavioral features improves classification performance. However, ethical challenges, privacy bias, remain significant concerns. Conclusions: has emphasized efficiencies detection, technical were highlighted. Future research should integrate advances, establish innovative enhance system reliability applicability.
Язык: Английский
Процитировано
6
Journal Of Big Data, Год журнала: 2023, Номер 10(1)
Опубликована: Июль 10, 2023
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started transform fields Neuroscience Neurology. Scientists clinicians collaborating global alliances, combining diverse datasets a massive scale, solving complex computational problems that demand utilization increasingly powerful resources. This revolution is opening new avenues for developing innovative treatments neurological diseases. Our paper surveys Data's impact patient care, as exemplified through work done comprehensive selection areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Pain, Addiction (e.g., Opioid Use Disorder). We present an overview research methodologies utilizing each area, well their current limitations technical challenges. Despite potential benefits, full these currently remains unrealized. close with recommendations future aimed at optimizing use Neurology improved outcomes.The online version contains supplementary material available 10.1186/s40537-023-00751-2.
Язык: Английский
Процитировано
24
Brain Sciences, Год журнала: 2024, Номер 14(1), С. 85 - 85
Опубликована: Янв. 16, 2024
Migraine is a burdensome neurological disorder that still lacks clear and easily accessible diagnostic biomarkers. Furthermore, straightforward pathway hard to find for migraineurs’ management, so the search response predictors has become urgent. Nowadays, artificial intelligence (AI) pervaded almost every aspect of our lives, medicine not been missed. Its applications are nearly limitless, ability use machine learning approaches given researchers chance give huge amounts data new insights. When it comes migraine, AI may play fundamental role, helping clinicians patients in many ways. For example, AI-based models can increase accuracy, especially non-headache specialists, help correctly classifying different groups patients. Moreover, analysing brain imaging studies reveal promising results identifying disease Regarding migraine showed value outcome measures, best treatment choices, therapy prediction. In present review, authors introduce various most recent clinical regarding migraine.
Язык: Английский
Процитировано
12
Diagnostics, Год журнала: 2023, Номер 13(23), С. 3552 - 3552
Опубликована: Ноя. 28, 2023
The role of functional magnetic resonance imaging (fMRI) is assuming an increasingly central in autism diagnosis. integration Artificial Intelligence (AI) into the realm applications further contributes to its development. This study’s objective analyze emerging themes this domain through umbrella review, encompassing systematic reviews. research methodology was based on a structured process for conducting literature narrative using review PubMed and Scopus. Rigorous criteria, standard checklist, qualification were meticulously applied. findings include 20 reviews that underscore key research, particularly emphasizing significance technological integration, including pivotal roles fMRI AI. study also highlights enigmatic oxytocin. While acknowledging immense potential field, outcome does not evade significant challenges limitations. Intriguingly, there growing emphasis innovation AI, whereas aspects related healthcare processes, such as regulation, acceptance, informed consent, data security, receive comparatively less attention. Additionally, these Personalized Medicine (PM) represents promising yet relatively unexplored area within research. concludes by encouraging scholars focus critical health vital routine implementation applications.
Язык: Английский
Процитировано
15
Reviews in the Neurosciences, Год журнала: 2024, Номер 35(4), С. 421 - 449
Опубликована: Фев. 3, 2024
Abstract Functional near-infrared spectroscopy (fNIRS) and its interaction with machine learning (ML) is a popular research topic for the diagnostic classification of clinical disorders due to lack robust objective biomarkers. This review provides an overview on psychiatric diseases by using fNIRS ML. Article search was carried out 45 studies were evaluated considering their sample sizes, used features, ML methodology, reported accuracy. To our best knowledge, this first that reports applications fNIRS. We found there has been increasing trend perform fNIRS-based biomarker since 2010. The most studied populations are schizophrenia ( n = 12), attention deficit hyperactivity disorder 7), autism spectrum 6) populations. There significant negative correlation between size (>21) accuracy values. Support vector (SVM) deep (DL) approaches classifier (SVM 20) (DL 10). Eight these recruited number participants more than 100 classification. Concentration changes in oxy-hemoglobin (ΔHbO) based features concentration deoxy-hemoglobin (ΔHb) ones ΔHbO-based mean ΔHbO 11) functional connections 11). Using data might be promising approach reveal specific biomarkers
Язык: Английский
Процитировано
6
Brain and Behavior, Год журнала: 2025, Номер 15(1)
Опубликована: Янв. 1, 2025
ABSTRACT Purpose : Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain‐specific assessment neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous corresponds heterogeneous cortical burden, suggesting customized, high‐resolution pathology as a candidate biomarker for assessment. Method Herein, we investigate potential recently Mosaic Approach (MAP), normative framework quantifying individual burden with respect population‐representative cohort, in predicting progression. Using MRI and data 135 diagnosed PD patients Progression Markers Initiative, first defined an extremity‐specific motor score. We then identified regions corresponding “extremity functions” restricted MAP, respectively, contrasted explanatory power MAP unrestricted MAP. As control conditions, domain‐related but less specific general function nondomain‐specific cognitive scores were considered. also tested predictive progression over 1 3 years using support vector machines. The restricted, yielded higher at baseline opposed unrestricted, whole‐brain On contrary, function, power. Finding No associations found function. predicted above chance level. allows prediction customized progression, which can inform machine learning, thereby contributing care.
Язык: Английский
Процитировано
0
AIP conference proceedings, Год журнала: 2025, Номер 3262, С. 020021 - 020021
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
Indian Journal of Neurotrauma, Год журнала: 2025, Номер unknown
Опубликована: Фев. 10, 2025
Язык: Английский
Процитировано
0
Translational Psychiatry, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 14, 2025
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0