Brain Informatics最新文献

{"title":"Harnessing the potential of machine learning and artificial intelligence for dementia research.","authors":"Janice M Ranson, Magda Bucholc, Donald Lyall, Danielle Newby, Laura Winchester, Neil P Oxtoby, Michele Veldsman, Timothy Rittman, Sarah Marzi, Nathan Skene, Ahmad Al Khleifat, Isabelle F Foote, Vasiliki Orgeta, Andrey Kormilitzin, Ilianna Lourida, David J Llewellyn","doi":"10.1186/s40708-022-00183-3","DOIUrl":"10.1186/s40708-022-00183-3","url":null,"abstract":"<p><p>Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10848945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Four-way classification of Alzheimer's disease using deep Siamese convolutional neural network with triplet-loss function.","authors":"Faizal Hajamohideen, Noushath Shaffi, Mufti Mahmud, Karthikeyan Subramanian, Arwa Al Sariri, Viswan Vimbi, Abdelhamid Abdesselam","doi":"10.1186/s40708-023-00184-w","DOIUrl":"10.1186/s40708-023-00184-w","url":null,"abstract":"<p><p>Alzheimer's disease (AD) is a neurodegenerative disease that causes irreversible damage to several brain regions, including the hippocampus causing impairment in cognition, function, and behaviour. Early diagnosis of the disease will reduce the suffering of the patients and their family members. Towards this aim, in this paper, we propose a Siamese Convolutional Neural Network (SCNN) architecture that employs the triplet-loss function for the representation of input MRI images as k-dimensional embeddings. We used both pre-trained and non-pretrained CNNs to transform images into the embedding space. These embeddings are subsequently used for the 4-way classification of Alzheimer's disease. The model efficacy was tested using the ADNI and OASIS datasets which produced an accuracy of 91.83% and 93.85%, respectively. Furthermore, obtained results are compared with similar methods proposed in the literature.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937523/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10769562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards automatic text-based estimation of depression through symptom prediction.","authors":"Kirill Milintsevich,&nbsp;Kairit Sirts,&nbsp;Gaël Dias","doi":"10.1186/s40708-023-00185-9","DOIUrl":"https://doi.org/10.1186/s40708-023-00185-9","url":null,"abstract":"<p><p>Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person's day-to-day activity. In addition, MDD affects one's linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person's condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient-psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2023-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9925661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10734475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage.","authors":"Di Zang, Xiangyu Zhao, Yuanfang Qiao, Jiayu Huo, Xuehai Wu, Zhe Wang, Zeyu Xu, Ruizhe Zheng, Zengxin Qi, Ying Mao, Lichi Zhang","doi":"10.1186/s40708-022-00181-5","DOIUrl":"10.1186/s40708-022-00181-5","url":null,"abstract":"<p><p>Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient's consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852379/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10560039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addictive brain-network identification by spatial attention recurrent network with feature selection.","authors":"Changwei Gong,&nbsp;Xinyi Chen,&nbsp;Bushra Mughal,&nbsp;Shuqiang Wang","doi":"10.1186/s40708-022-00182-4","DOIUrl":"https://doi.org/10.1186/s40708-022-00182-4","url":null,"abstract":"<p><p>Addiction in the brain is associated with adaptive changes that reshape addiction-related brain regions and lead to functional abnormalities that cause a range of behavioral changes, and functional magnetic resonance imaging (fMRI) studies can reveal complex dynamic patterns of brain functional change. However, it is still a challenge to identify functional brain networks and discover region-level biomarkers between nicotine addiction (NA) and healthy control (HC) groups. To tackle it, we transform the fMRI of the rat brain into a network with biological attributes and propose a novel feature-selected framework to extract and select the features of addictive brain regions and identify these graph-level networks. In this framework, spatial attention recurrent network (SARN) is designed to capture the features with spatial and time-sequential information. And the Bayesian feature selection(BFS) strategy is adopted to optimize the model and improve classification tasks by restricting features. Our experiments on the addiction brain imaging dataset obtain superior identification performance and interpretable biomarkers associated with addiction-relevant brain regions.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9832209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10523840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single classifier vs. ensemble machine learning approaches for mental health prediction.","authors":"Jetli Chung, Jason Teo","doi":"10.1186/s40708-022-00180-6","DOIUrl":"10.1186/s40708-022-00180-6","url":null,"abstract":"<p><p>Early prediction of mental health issues among individuals is paramount for early diagnosis and treatment by mental health professionals. One of the promising approaches to achieving fully automated computer-based approaches for predicting mental health problems is via machine learning. As such, this study aims to empirically evaluate several popular machine learning algorithms in classifying and predicting mental health problems based on a given data set, both from a single classifier approach as well as an ensemble machine learning approach. The data set contains responses to a survey questionnaire that was conducted by Open Sourcing Mental Illness (OSMI). Machine learning algorithms investigated in this study include Logistic Regression, Gradient Boosting, Neural Networks, K-Nearest Neighbours, and Support Vector Machine, as well as an ensemble approach using these algorithms. Comparisons were also made against more recent machine learning approaches, namely Extreme Gradient Boosting and Deep Neural Networks. Overall, Gradient Boosting achieved the highest overall accuracy of 88.80% followed by Neural Networks with 88.00%. This was followed by Extreme Gradient Boosting and Deep Neural Networks at 87.20% and 86.40%, respectively. The ensemble classifier achieved 85.60% while the remaining classifiers achieved between 82.40 and 84.00%. The findings indicate that Gradient Boosting provided the highest classification accuracy for this particular mental health bi-classification prediction task. In general, it was also demonstrated that the prediction results produced by all of the machine learning approaches studied here were able to achieve more than 80% accuracy, thereby indicating a highly promising approach for mental health professionals toward automated clinical diagnosis.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"10 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9810771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10855483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Age-dependent vestibular cingulate-cerebral network underlying gravitational perception: a cross-sectional multimodal study.","authors":"Tritan J Plute,&nbsp;Dennis D Spencer,&nbsp;Rafeed Alkawadri","doi":"10.1186/s40708-022-00176-2","DOIUrl":"https://doi.org/10.1186/s40708-022-00176-2","url":null,"abstract":"<p><strong>Background and objectives: </strong>The cingulate gyrus (CG) is a frequently studied yet not wholly understood area of the human cerebrum. Previous studies have implicated CG in different adaptive cognitive-emotional functions and fascinating or debilitating symptoms. We describe an unusual loss of gravity perception/floating sensation in consecutive persons with drug-resistant epilepsy undergoing electrical cortical stimulation (ECS), network analysis, and network robustness mapping.</p><p><strong>Methods: </strong>Using Intracranial-EEG, Granger causality analysis, cortico-cortical evoked potentials, and fMRI, we explicate the functional networks arising from this phenomenon's anterior, middle, and posterior cingulate cortex.</p><p><strong>Results: </strong>Fifty-four icEEG cases from 2013 to 2019 were screened. In 40.7% of cases, CG was sampled and in 22.2% the sampling was bilateral. ECS mapping was carried out in 18.5% of the entire cohort and 45.4% of the cingulate sampled cases. Five of the ten CG cases experienced symptoms during stimulation. A total of 1942 electrodes were implanted with a median number of 182 electrode contacts per patient (range: 106-274). The electrode contacts sampled all major cortex regions. Sixty-three contacts were within CG. Of those, 26 were electrically stimulated; 53.8% of the stimulated contacts produced positive responses, whereas 46.2% produced no observable responses. Our study reports a unique perceptive phenomenon of a subjective sense of weightlessness/floating sensation triggered by anterior and posterior CG stimulation, in 30% of cases and 21.42% of electrode stimulation sites. Notable findings include functional connections between the insula, the posterior and anterior cingulate cortex, and networks between the middle cingulate and the frontal and temporal lobes and the cerebellum. We also postulate a vestibular-cerebral-cingulate network responsible for the perception of gravity while suggesting that cingulate functional connectivity follows a long-term developmental trajectory as indicated by a robust, positive correlation with age and the extent of Granger connectivity (r = 0.82, p = 0.0035).</p><p><strong>Discussion: </strong>We propose, in conjunction with ECS techniques, that a better understanding of the underlying gravity perception networks can lead to promising neuromodulatory clinical applications.</p><p><strong>Classification of evidence: </strong>This study provides Class II evidence for CG's involvement in the higher order processing of gravity perception and related actions.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"9 1","pages":"30"},"PeriodicalIF":0.0,"publicationDate":"2022-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9772366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9170273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task.","authors":"Pushpinder Walia,&nbsp;Yaoyu Fu,&nbsp;Jack Norfleet,&nbsp;Steven D Schwaitzberg,&nbsp;Xavier Intes,&nbsp;Suvranu De,&nbsp;Lora Cavuoto,&nbsp;Anirban Dutta","doi":"10.1186/s40708-022-00179-z","DOIUrl":"https://doi.org/10.1186/s40708-022-00179-z","url":null,"abstract":"<p><p>Error-based learning is one of the basic skill acquisition mechanisms that can be modeled as a perception-action system and investigated based on brain-behavior analysis during skill training. Here, the error-related chain of mental processes is postulated to depend on the skill level leading to a difference in the contextual switching of the brain states on error commission. Therefore, the objective of this paper was to compare error-related brain states, measured with multi-modal portable brain imaging, between experts and novices during the Fundamentals of Laparoscopic Surgery (FLS) \"suturing and intracorporeal knot-tying\" task (FLS complex task)-the most difficult among the five psychomotor FLS tasks. The multi-modal portable brain imaging combined functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for brain-behavior analysis in thirteen right-handed novice medical students and nine expert surgeons. The brain state changes were defined by quasi-stable EEG scalp topography (called microstates) changes using 32-channel EEG data acquired at 250 Hz. Six microstate prototypes were identified from the combined EEG data from experts and novices during the FLS complex task that explained 77.14% of the global variance. Analysis of variance (ANOVA) found that the proportion of the total time spent in different microstates during the 10-s error epoch was significantly affected by the skill level (p < 0.01), the microstate type (p < 0.01), and the interaction between the skill level and the microstate type (p < 0.01). Brain activation based on the slower oxyhemoglobin (HbO) changes corresponding to the EEG band power (1-40 Hz) changes were found using the regularized temporally embedded Canonical Correlation Analysis of the simultaneously acquired fNIRS-EEG signals. The HbO signal from the overlying the left inferior frontal gyrus-opercular part, left superior frontal gyrus-medial orbital, left postcentral gyrus, left superior temporal gyrus, right superior frontal gyrus-medial orbital cortical areas showed significant (p < 0.05) difference between experts and novices in the 10-s error epoch. We conclude that the difference in the error-related chain of mental processes was the activation of cognitive top-down attention-related brain areas, including left dorsolateral prefrontal/frontal eye field and left frontopolar brain regions, along with a 'focusing' effect of global suppression of hemodynamic activation in the experts, while the novices had a widespread stimulus(error)-driven hemodynamic activation without the 'focusing' effect.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"9 1","pages":"29"},"PeriodicalIF":0.0,"publicationDate":"2022-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10383576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inferring the temporal evolution of synaptic weights from dynamic functional connectivity.","authors":"Marco Celotto, Stefan Lemke, Stefano Panzeri","doi":"10.1186/s40708-022-00178-0","DOIUrl":"10.1186/s40708-022-00178-0","url":null,"abstract":"<p><p>How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"9 1","pages":"28"},"PeriodicalIF":0.0,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9732068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10377111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research.","authors":"Adam Byrne,&nbsp;Emma Bonfiglio,&nbsp;Colin Rigby,&nbsp;Nicky Edelstyn","doi":"10.1186/s40708-022-00175-3","DOIUrl":"https://doi.org/10.1186/s40708-022-00175-3","url":null,"abstract":"<p><strong>Introduction: </strong>The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.</p><p><strong>Methods: </strong>Search terms 'neuromarketing' and 'consumer neuroscience' identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.</p><p><strong>Results: </strong>Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.</p><p><strong>Conclusions and implications: </strong>FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"27"},"PeriodicalIF":0.0,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9663791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40702683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}