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Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation Brain Inf. Pub Date : 2024-03-12 Shihao Yang, Meng Jiao, Jing Xiang, Neel Fotedar, Hai Sun, Feng Liu
EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions
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Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier Brain Inf. Pub Date : 2024-03-05 Pragati Patel, Sivarenjani Balasubramanian, Ramesh Naidu Annavarapu
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
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An automatic method using MFCC features for sleep stage classification Brain Inf. Pub Date : 2024-02-10 Wei Pei, Yan Li, Peng Wen, Fuwen Yang, Xiaopeng Ji
Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs), electromyograms (EMGs), and electroencephalograms (EEGs), to classify sleep stages. Recently, various state-of-the-art approaches based on a deep learning model
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3D convolutional neural networks uncover modality-specific brain-imaging predictors for Alzheimer’s disease sub-scores Brain Inf. Pub Date : 2024-02-04 Kaida Ning, Pascale B. Cannon, Jiawei Yu, Srinesh Shenoi, Lu Wang, Joydeep Sarkar
Different aspects of cognitive functions are affected in patients with Alzheimer’s disease. To date, little is known about the associations between features from brain-imaging and individual Alzheimer’s disease (AD)-related cognitive functional changes. In addition, how these associations differ among different imaging modalities is unclear. Here, we trained and investigated 3D convolutional neural
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The onset of motor learning impairments in Parkinson’s disease: a computational investigation Brain Inf. Pub Date : 2024-01-29 Ilaria Gigi, Rosa Senatore, Angelo Marcelli
The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson’s disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the
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Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics Brain Inf. Pub Date : 2024-01-14 Muhammad Atta Othman Ahmed, Yasser Abdel Satar, Eed M. Darwish, Elnomery A. Zanaty
In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients’ overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from
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Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders Brain Inf. Pub Date : 2024-01-09 Sara Saponaro, Francesca Lizzi, Giacomo Serra, Francesca Mainas, Piernicola Oliva, Alessia Giuliano, Sara Calderoni, Alessandra Retico
The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD)
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Addiction-related brain networks identification via Graph Diffusion Reconstruction Network Brain Inf. Pub Date : 2024-01-08 Changhong Jing, Hongzhi Kuai, Hiroki Matsumoto, Tomoharu Yamaguchi, Iman Yi Liao, Shuqiang Wang
Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for exploring addiction-related brain connectivity. However, effectively extracting addiction-related brain connectivity from fMRI data remains challenging due to the intricate and non-linear nature of brain connections. Therefore, this paper proposed the Graph
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Behavioural relevance of redundant and synergistic stimulus information between functionally connected neurons in mouse auditory cortex Brain Inf. Pub Date : 2023-12-05 Loren Koçillari, Marco Celotto, Nikolas A. Francis, Shoutik Mukherjee, Behtash Babadi, Patrick O. Kanold, Stefano Panzeri
Measures of functional connectivity have played a central role in advancing our understanding of how information is transmitted and processed within the brain. Traditionally, these studies have focused on identifying redundant functional connectivity, which involves determining when activity is similar across different sites or neurons. However, recent research has highlighted the importance of also
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Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning Brain Inf. Pub Date : 2023-12-03 Puskar Bhattarai, Ahmed Taha, Bhavin Soni, Deepa S. Thakuri, Erin Ritter, Ganesh B. Chand
Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer’s disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD.
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Effect of data harmonization of multicentric dataset in ASD/TD classification Brain Inf. Pub Date : 2023-11-25 Giacomo Serra, Francesca Mainas, Bruno Golosio, Alessandra Retico, Piernicola Oliva
Machine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of appropriate size for training, which in neuroimaging are typically obtained collecting data from multiple acquisition centers. However, analyzing large multicentric
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Explainability of random survival forests in predicting conversion risk from mild cognitive impairment to Alzheimer’s disease Brain Inf. Pub Date : 2023-11-18 Alessia Sarica, Federica Aracri, Maria Giovanna Bianco, Fulvia Arcuri, Andrea Quattrone, Aldo Quattrone
Random Survival Forests (RSF) has recently showed better performance than statistical survival methods as Cox proportional hazard (CPH) in predicting conversion risk from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). However, RSF application in real-world clinical setting is still limited due to its black-box nature. For this reason, we aimed at providing a comprehensive study of RSF
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Semantic representation of neural circuit knowledge in Caenorhabditis elegans Brain Inf. Pub Date : 2023-11-10 Sharan J. Prakash, Kimberly M. Van Auken, David P. Hill, Paul W. Sternberg
In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate
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Predicting object properties based on movement kinematics Brain Inf. Pub Date : 2023-11-04 Lena Kopnarski, Laura Lippert, Julian Rudisch, Claudia Voelcker-Rehage
In order to grasp and transport an object, grip and load forces must be scaled according to the object’s properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot’s weight estimation less dependent on prior learning and, thereby
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Attribute Selection Hybrid Network Model for risk factors analysis of postpartum depression using Social media Brain Inf. Pub Date : 2023-10-31 Abinaya Gopalakrishnan, Raj Gururajan, Revathi Venkataraman, Xujuan Zhou, Ka Chan Ching, Arul Saravanan, Maitrayee Sen
Postpartum Depression (PPD) is a frequently ignored birth-related consequence. Social network analysis can be used to address this issue because social media network serves as a platform for their users to communicate with their friends and share their opinions, photos, and videos, which reflect their moods, feelings, and sentiments. In this work, the depression of delivered mothers is identified using
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Investigating the mental health of university students during the COVID-19 pandemic in a UK university: a machine learning approach using feature permutation importance Brain Inf. Pub Date : 2023-10-10 Tianhua Chen
Mental wellbeing of university students is a growing concern that has been worsening during the COVID-19 pandemic. Numerous studies have gathered empirical data to explore the mental health impact of the pandemic on university students and investigate factors associated with higher levels of distress. While the online questionnaire survey has been a prevalent means to collect data, regression analysis
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Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment Brain Inf. Pub Date : 2023-10-06 Andrea Bianconi, Luca Francesco Rossi, Marta Bonada, Pietro Zeppa, Elsa Nico, Raffaele De Marco, Paola Lacroce, Fabio Cofano, Francesco Bruno, Giovanni Morana, Antonio Melcarne, Roberta Ruda, Luca Mainardi, Pietro Fiaschi, Diego Garbossa, Lia Morra
Clinical and surgical decisions for glioblastoma patients depend on a tumor imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance imaging (MRI) assessment to support clinical practice, surgery planning and prognostic predictions. In a real-world context, the current obstacles for AI are low-quality imaging and postoperative reliability. The aim of this study is
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Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals Brain Inf. Pub Date : 2023-09-09 Neha Gour, Taimur Hassan, Muhammad Owais, Iyyakutti Iyappan Ganapathi, Pritee Khanna, Mohamed L. Seghier, Naoufel Werghi
Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify
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Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions Brain Inf. Pub Date : 2023-09-09 Nalinda D. Liyanagedera, Ali Abdul Hussain, Amardeep Singh, Sunil Lal, Heather Kempton, Hans W. Guesgen
While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial
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Automatic identification of scientific publications describing digital reconstructions of neural morphology Brain Inf. Pub Date : 2023-09-08 Patricia Maraver, Carolina Tecuatl, Giorgio A. Ascoli
The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a
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Cerebrovascular disease case identification in inpatient electronic medical record data using natural language processing Brain Inf. Pub Date : 2023-09-02 Jie Pan, Zilong Zhang, Steven Ray Peters, Shabnam Vatanpour, Robin L. Walker, Seungwon Lee, Elliot A. Martin, Hude Quan
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. CeVD status
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Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour Brain Inf. Pub Date : 2023-08-05 Siaw-Hong Liew, Yun-Huoy Choo, Yin Fen Low, Fadilla ‘Atyka Nor Rashid
This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance
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Brain–computer interface: trend, challenges, and threats Brain Inf. Pub Date : 2023-08-04 Baraka Maiseli, Abdi T. Abdalla, Libe V. Massawe, Mercy Mbise, Khadija Mkocha, Nassor Ally Nassor, Moses Ismail, James Michael, Samwel Kimambo
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
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An evaluation of transfer learning models in EEG-based authentication Brain Inf. Pub Date : 2023-08-03 Hui Yen Yap, Yun-Huoy Choo, Zeratul Izzah Mohd Yusoh, Wee How Khoh
Electroencephalogram(EEG)-based authentication has received increasing attention from researchers as they believe it could serve as an alternative to more conventional personal authentication methods. Unfortunately, EEG signals are non-stationary and could be easily contaminated by noise and artifacts. Therefore, further processing of data analysis is needed to retrieve useful information. Various
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Machine learning for cognitive behavioral analysis: datasets, methods, paradigms, and research directions Brain Inf. Pub Date : 2023-07-31 Priya Bhatt, Amanrose Sethi, Vaibhav Tasgaonkar, Jugal Shroff, Isha Pendharkar, Aditya Desai, Pratyush Sinha, Aditya Deshpande, Gargi Joshi, Anil Rahate, Priyanka Jain, Rahee Walambe, Ketan Kotecha, N. K. Jain
Human behaviour reflects cognitive abilities. Human cognition is fundamentally linked to the different experiences or characteristics of consciousness/emotions, such as joy, grief, anger, etc., which assists in effective communication with others. Detection and differentiation between thoughts, feelings, and behaviours are paramount in learning to control our emotions and respond more effectively in
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A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease Brain Inf. Pub Date : 2023-07-14 Akhilesh Deep Arya, Sourabh Singh Verma, Prasun Chakarabarti, Tulika Chakrabarti, Ahmed A. Elngar, Ali-Mohammad Kamali, Mohammad Nami
Alzheimer’s disease (AD) is a brain-related disease in which the condition of the patient gets worse with time. AD is not a curable disease by any medication. It is impossible to halt the death of brain cells, but with the help of medication, the effects of AD can be delayed. As not all MCI patients will suffer from AD, it is required to accurately diagnose whether a mild cognitive impaired (MCI) patient
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Assessing consciousness in patients with disorders of consciousness using soft-clustering Brain Inf. Pub Date : 2023-07-14 Sophie Adama, Martin Bogdan
Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person’s consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand
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Prediction and detection of virtual reality induced cybersickness: a spiking neural network approach using spatiotemporal EEG brain data and heart rate variability Brain Inf. Pub Date : 2023-07-12 Alexander Hui Xiang Yang, Nikola Kirilov Kasabov, Yusuf Ozgur Cakmak
Virtual Reality (VR) allows users to interact with 3D immersive environments and has the potential to be a key technology across many domain applications, including access to a future metaverse. Yet, consumer adoption of VR technology is limited by cybersickness (CS)—a debilitating sensation accompanied by a cluster of symptoms, including nausea, oculomotor issues and dizziness. A leading problem is
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Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning Brain Inf. Pub Date : 2023-06-21 Muhammad Arifur Rahman, David J. Brown, Mufti Mahmud, Matthew Harris, Nicholas Shopland, Nadja Heym, Alexander Sumich, Zakia Batool Turabee, Bradley Standen, David Downes, Yangang Xing, Carolyn Thomas, Sean Haddick, Preethi Premkumar, Simona Nastase, Andrew Burton, James Lewis
Virtual reality exposure therapy (VRET) is a novel intervention technique that allows individuals to experience anxiety-evoking stimuli in a safe environment, recognise specific triggers and gradually increase their exposure to perceived threats. Public-speaking anxiety (PSA) is a prevalent form of social anxiety, characterised by stressful arousal and anxiety generated when presenting to an audience
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Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference Brain Inf. Pub Date : 2023-06-07 Francisco Salto, Carmen Requena, Paula Alvarez-Merino, Víctor Rodríguez, Jesús Poza, Roberto Hornero
Logically valid deductive arguments are clear examples of abstract recursive computational procedures on propositions or on probabilities. However, it is not known if the cortical time-consuming inferential processes in which logical arguments are eventually realized in the brain are in fact physically different from other kinds of inferential processes. In order to determine whether an electrical
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BOARD-FTD-PACC: a graphical user interface for the synaptic and cross-frequency analysis derived from neural signals Brain Inf. Pub Date : 2023-05-08 Cécile Gauthier-Umaña, Mario Valderrama, Alejandro Múnera, Mauricio O. Nava-Mesa
In order to understand the link between brain functional states and behavioral/cognitive processes, the information carried in neural oscillations can be retrieved using different analytic techniques. Processing these different bio-signals is a complex, time-consuming, and often non-automatized process that requires customization, due to the type of signal acquired, acquisition method implemented,
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Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan Brain Inf. Pub Date : 2023-05-08 Christian Goelz, Eva-Maria Reuter, Stephanie Fröhlich, Julian Rudisch, Ben Godde, Solveig Vieluf, Claudia Voelcker-Rehage
The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial
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Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment Brain Inf. Pub Date : 2023-04-24 Matthew Squires, Xiaohui Tao, Soman Elangovan, Raj Gururajan, Xujuan Zhou, U Rajendra Acharya, Yuefeng Li
Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis
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Quantifying numerical and spatial reliability of hippocampal and amygdala subdivisions in FreeSurfer Brain Inf. Pub Date : 2023-04-07 Isabella Kahhale, Nicholas J. Buser, Christopher R. Madan, Jamie L. Hanson
On-going, large-scale neuroimaging initiatives can aid in uncovering neurobiological causes and correlates of poor mental health, disease pathology, and many other important conditions. As projects grow in scale with hundreds, even thousands, of individual participants and scans collected, quantification of brain structures by automated algorithms is becoming the only truly tractable approach. Here
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Measuring cognitive load of digital interface combining event-related potential and BubbleView Brain Inf. Pub Date : 2023-03-03 Shaoyu Wei, Ruiling Zheng, Rui Li, Minghui Shi, Junsong Zhang
Helmet mounted display systems (HMDs) are high-performance display devices for modern aircraft. We propose a novel method combining event-related potentials (ERPs) and BubbleView to measure cognitive load under different HMD interfaces. The distribution of the subjects’ attention resources is reflected by analyzing the BubbleView, and the input of the subjects’ attention resources on the interface
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Machine learning determination of applied behavioral analysis treatment plan type Brain Inf. Pub Date : 2023-03-02 Jenish Maharjan, Anurag Garikipati, Frank A. Dinenno, Madalina Ciobanu, Gina Barnes, Ella Browning, Jenna DeCurzio, Qingqing Mao, Ritankar Das
Applied behavioral analysis (ABA) is regarded as the gold standard treatment for autism spectrum disorder (ASD) and has the potential to improve outcomes for patients with ASD. It can be delivered at different intensities, which are classified as comprehensive or focused treatment approaches. Comprehensive ABA targets multiple developmental domains and involves 20–40 h/week of treatment. Focused ABA
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Harnessing the potential of machine learning and artificial intelligence for dementia research Brain Inf. Pub Date : 2023-02-24 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
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
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Four-way classification of Alzheimer’s disease using deep Siamese convolutional neural network with triplet-loss function Brain Inf. Pub Date : 2023-02-17 Faizal Hajamohideen, Noushath Shaffi, Mufti Mahmud, Karthikeyan Subramanian, Arwa Al Sariri, Viswan Vimbi, Abdelhamid Abdesselam
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
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Towards automatic text-based estimation of depression through symptom prediction Brain Inf. Pub Date : 2023-02-13 Kirill Milintsevich, Kairit Sirts, Gaël Dias
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
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Enhanced brain parcellation via abnormality inpainting for neuroimage-based consciousness evaluation of hydrocephalus patients by lumbar drainage Brain Inf. Pub Date : 2023-01-19 Di Zang, Xiangyu Zhao, Yuanfang Qiao, Jiayu Huo, Xuehai Wu, Zhe Wang, Zeyu Xu, Ruizhe Zheng, Zengxin Qi, Ying Mao, Lichi Zhang
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
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Addictive brain-network identification by spatial attention recurrent network with feature selection Brain Inf. Pub Date : 2023-01-10 Changwei Gong, Xinyi Chen, Bushra Mughal, Shuqiang Wang
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
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Single classifier vs. ensemble machine learning approaches for mental health prediction Brain Inf. Pub Date : 2023-01-03 Jetli Chung, Jason Teo
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
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Age-dependent vestibular cingulate–cerebral network underlying gravitational perception: a cross-sectional multimodal study Brain Inf. Pub Date : 2022-12-21 Tritan J. Plute, Dennis D. Spencer, Rafeed Alkawadri
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)
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Error-related brain state analysis using electroencephalography in conjunction with functional near-infrared spectroscopy during a complex surgical motor task Brain Inf. Pub Date : 2022-12-09 Pushpinder Walia, Yaoyu Fu, Jack Norfleet, Steven D. Schwaitzberg, Xavier Intes, Suvranu De, Lora Cavuoto, Anirban Dutta
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
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Inferring the temporal evolution of synaptic weights from dynamic functional connectivity Brain Inf. Pub Date : 2022-12-08 Marco Celotto, Stefan Lemke, Stefano Panzeri
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
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A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research Brain Inf. Pub Date : 2022-11-14 Adam Byrne, Emma Bonfiglio, Colin Rigby, Nicky Edelstyn
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
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Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.Org Brain Inf. Pub Date : 2022-11-07 Kayvan Bijari, Yasmeen Zoubi, Giorgio A. Ascoli
The amount of unstructured text produced daily in scholarly journals is enormous. Systematically identifying, sorting, and structuring information from such a volume of data is increasingly challenging for researchers even in delimited domains. Named entity recognition is a fundamental natural language processing tool that can be trained to annotate, structure, and extract information from scientific
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Hemodynamic functional connectivity optimization of frequency EEG microstates enables attention LSTM framework to classify distinct temporal cortical communications of different cognitive tasks Brain Inf. Pub Date : 2022-10-11 Swati Agrawal, Vijayakumar Chinnadurai, Rinku Sharma
Temporal analysis of global cortical communication of cognitive tasks in coarse EEG information is still challenging due to the underlying complex neural mechanisms. This study proposes an attention-based time-series deep learning framework that processes fMRI functional connectivity optimized quasi-stable frequency microstates for classifying distinct temporal cortical communications of the cognitive
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Machine learning methods for the study of cybersickness: a systematic review Brain Inf. Pub Date : 2022-10-09 Alexander Hui Xiang Yang, Nikola Kasabov, Yusuf Ozgur Cakmak
This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness
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Early detection of Alzheimer’s disease using neuropsychological tests: a predict–diagnose approach using neural networks Brain Inf. Pub Date : 2022-09-27 Devarshi Mukherji, Manibrata Mukherji, Nivedita Mukherji
Alzheimer’s disease (AD) is a slowly progressing disease for which there is no known therapeutic cure at present. Ongoing research around the world is actively engaged in the quest for identifying markers that can help predict the future cognitive state of individuals so that measures can be taken to prevent the onset or arrest the progression of the disease. Researchers are interested in both biological
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Epilepsy seizure prediction with few-shot learning method Brain Inf. Pub Date : 2022-09-16 Jamal Nazari, Ali Motie Nasrabadi, Mohammad Bagher Menhaj, Somayeh Raiesdana
Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them. In the previous works, generalized methods were inevitably used
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RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing Brain Inf. Pub Date : 2022-09-16 Kostas Georgiadis, Fotis P. Kalaganis, Vangelis P. Oikonomou, Spiros Nikolopoulos, Nikos A. Laskaris, Ioannis Kompatsiaris
Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported
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A multi-expert ensemble system for predicting Alzheimer transition using clinical features Brain Inf. Pub Date : 2022-09-03 Mario Merone, Sebastian Luca D’Addario, Pierandrea Mirino, Francesca Bertino, Cecilia Guariglia, Rossella Ventura, Adriano Capirchio, Gianluca Baldassarre, Massimo Silvetti, Daniele Caligiore
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
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ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals Brain Inf. Pub Date : 2022-09-01 Marcos Fabietti, Mufti Mahmud, Ahmad Lotfi, M. Shamim Kaiser
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making
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SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging Brain Inf. Pub Date : 2022-08-04 Monica Moroni, Marco Brondi, Tommaso Fellin, Stefano Panzeri
Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally
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A robust framework to investigate the reliability and stability of explainable artificial intelligence markers of Mild Cognitive Impairment and Alzheimer’s Disease Brain Inf. Pub Date : 2022-07-26 Angela Lombardi, Domenico Diacono, Nicola Amoroso, Przemysław Biecek, Alfonso Monaco, Loredana Bellantuono, Ester Pantaleo, Giancarlo Logroscino, Roberto De Blasi, Sabina Tangaro, Roberto Bellotti
In clinical practice, several standardized neuropsychological tests have been designed to assess and monitor the neurocognitive status of patients with neurodegenerative diseases such as Alzheimer’s disease. Important research efforts have been devoted so far to the development of multivariate machine learning models that combine the different test indexes to predict the diagnosis and prognosis of
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Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study Brain Inf. Pub Date : 2022-07-25 Manu Kohli, Arpan Kumar Kar, Anjali Bangalore, Prathosh AP
Autism spectrum is a brain development condition that impairs an individual’s capacity to communicate socially and manifests through strict routines and obsessive–compulsive behavior. Applied behavior analysis (ABA) is the gold-standard treatment for autism spectrum disorder (ASD). However, as the number of ASD cases increases, there is a substantial shortage of licensed ABA practitioners, limiting
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Classifying oscillatory brain activity associated with Indian Rasas using network metrics Brain Inf. Pub Date : 2022-07-15 Pankaj Pandey, Richa Tripathi, Krishna Prasad Miyapuram
Neural signatures for the western classification of emotions have been widely discussed in the literature. The ancient Indian treatise on performing arts known as Natyashastra categorizes emotions into nine classes, known as Rasas. Rasa—as opposed to a pure emotion—is defined as a superposition of certain transitory, dominant, and temperamental emotional states. Although Rasas have been widely discussed
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Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata Brain Inf. Pub Date : 2022-06-27 Alisha Menon, Anirudh Natarajan, Reva Agashe, Daniel Sun, Melvin Aristio, Harrison Liew, Yakun Sophia Shao, Jan M. Rabaey
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition provides valuable information for human–computer interactions; however, the large number of input channels (> 200) and modalities (> 3 ) involved in emotion recognition are significantly expensive from a memory perspective
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Stroke recovery phenotyping through network trajectory approaches and graph neural networks Brain Inf. Pub Date : 2022-06-19 Sanjukta Krishnagopal, Keith Lohse, Robynne Braun
Stroke is a leading cause of neurological injury characterized by impairments in multiple neurological domains including cognition, language, sensory and motor functions. Clinical recovery in these domains is tracked using a wide range of measures that may be continuous, ordinal, interval or categorical in nature, which can present challenges for multivariate regression approaches. This has hindered