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2015Learning Computational Models of Video Memorability from fMRI Brain ImagingJunwei Han, Changyuan Chen, Ling Shao et al. · IEEE Transactions on Cybernetics2010Bridging low-level features and high-level semantics via fMRI brain imaging for video classificationXintao Hu, Fan Deng, Kaiming Li et al. · ACM MultimediaThere are meaningful couplings between brain's fMRI responses and video stimuli, suggesting the validity of linking semantics and low-level features via fMRI; and the computationally learned low- level feature sets from fMRI-derived semantic features can significantly improve the classification of video categories in comparison with that based on original low-levels features.2008Crossed cerebral lateralization for verbal and visuo‐spatial function in a pair of handedness discordant monozygotic twins: MRI and fMRI brain imagingS. Lux, S. Keller, C. Mackay et al. · Journal of AnatomyThe twins were discordant for MRI anatomical asymmetries of the pars triangularis and planum temporale, whose asymmetry was consistent with verbal laterality on fMRI, and the right‐handed twin had right lateralized verbal with left lateralized visuo‐spatial activation; these data lend further support for to the conclusions of Sommer et al.2002A fMRI brain imaging study of presentiment.D. Bierman, Huibert Steven Scholte2014Decoding Auditory Saliency from FMRI Brain ImagingShijie Zhao, Xi Jiang, Junwei Han et al. · ACM MultimediaA computational framework to decode biologically-plausible auditory saliency using high-level features derived from functional magnetic resonance imaging (fMRI) which monitors the human brain's response under the natural stimulus of audio listening is proposed.2011fMRI Brain Imaging and the Experience of SoundMorana Alač2024Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of ThingsAdnan Ashraf, Qingjie Zhao, W. Bangyal et al. · IEEE transactions on consumer electronicsThis study has tried to classify and represent learning tasks of the most powerful deep learning network such as Convolution Neural network (CNN) and Transfer Learning algorithm for a combination of data from Autism Brain Imaging Data Exchange (ABIDE I and ABIDE II) datasets.2016DPABI: Data Processing & Analysis for (Resting-State) Brain ImagingChaogan Yan, Xindi Wang, X. Zuo et al. · NeuroinformaticsThe newly developed toolbox, DPABI, which was evolved from REST and DPARSF is introduced, designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies.2022fMRI Brain Decoding and Its Applications in Brain–Computer Interface: A SurveyBing Du, Xiaomu Cheng, Yiping Duan et al. · Brain ScienceCurrent brain activity decoding models with high attention are reviewed: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN).2020Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRIE. Lake, Xinxin Ge, X. Shen et al. · Nature MethodsIn a transgenic murine model, it is shown that calcium predicts the BOLD signal, using a model that optimizes a gamma-variant transfer function and finds consistent predictions across the cortex, which are best at low frequency.2015Brain Imaging in Communication Research: A Practical Guide to Understanding and Evaluating fMRI StudiesR. Weber, J. M. Mangus, Richard HuskeyA pragmatic introduction to fMRI data collection and analysis for social-science-oriented communication scholars is provided and practical guidelines and a checklist for reporting and evaluating fMRI studies are included.2017Implications of neurovascular uncoupling in functional magnetic resonance imaging (fMRI) of brain tumorsRebecca W. Pak, Darian H. Hadjiabadi, J. Senarathna et al. · Journal of Cerebral Blood Flow and MetabolismThe physiology of the neurovascular unit is reviewed, how it is remodeled, and functionally altered by brain cancer cells, and how the suppression of anomalous tumor blood vessel formation with antiangiogenic therapies can “normalize” the brain tumor vasculature, and potentially restore neurov vascular coupling.2010Multiband Multislice GE-EPI at 7 Tesla, With 16-Fold Acceleration Using Partial Parallel Imaging With Application to High Spatial and Temporal Whole-Brain FMRIS. Moeller, E. Yacoub, C. Olman et al. · Magnetic Resonance in MedicineParallel imaging in the form of multiband radiofrequency excitation, together with reduced k‐space coverage in the phase‐encode direction, was applied to human gradient echo functional MRI at 7 T for increased volumetric coverage and concurrent high spatial and temporal resolution.2022FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network GenerationXuan Kan, Hejie Cui, J. Lukemire et al. · International Conference on Medical Imaging with Deep LearningFBNETGEN is developed, a task-aware and interpretable fMRI analysis framework via deep brain network generation which learns to transform raw time-series features into task-oriented brain networks and provides unique interpretations by highlighting prediction-related brain regions.2020Rumination and the default mode network: Meta-analysis of brain imaging studies and implications for depressionHui Zhou, Xiao Chen, Yang-Qian Shen et al. · NeuroImageA meta-analysis consisting of experimental tasks that investigate rumination by using Signed Differential Mapping of 14 fMRI studies comprising 286 healthy participants confirms the suspected association between rumination and DMN activation and suggests a hypothesis of how DMN regions support rumination.2017Image processing and Quality Control for the first 10,000 brain imaging datasets from UK BiobankF. Alfaro-Almagro, M. Jenkinson, N. Bangerter et al. · bioRxivThe pipeline is described in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol and several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.2013The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in AutismA. di Martino, Chao-Gan Yan, Qingyang Li et al. · Molecular PsychiatryW Whole-brain analyses reconciled seemingly disparate themes of both hypo- and hyperconnectivity in the ASD literature; both were detected, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity.2010Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion ImagingD. Feinberg, S. Moeller, Stephen M. Smith et al. · PLoS ONEThe novel M-EPI pulse sequence resulted in a significantly increased temporal resolution for whole brain fMRI, and as such, this new methodology can be used for studying non-stationarity in networks and generally for expanding and enriching the functional information.2021Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study.S. Jamadar, Phillip G. D. Ward, E. Liang et al. · Cerebral CortexHigh-temporal resolution constant infusion functional positron emission tomography (fPET) was applied to measure subject-level metabolic connectivity simultaneously with fMRI connectivity to highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity.2021Resting fMRI guided TMS results in subcortical and brain network modulation indexed by interleaved TMS/fMRID. Oathes, Jared P. Zimmerman, R. Duprat et al. · Experimental Brain ResearchInterleaved functional MRI recordings with non-invasive transcranial magnetic stimulation to map causal communication between the frontal cortex and subcortical target structures including the subgenual anterior cingulate cortex (sgACC) and the amygdala found that the sgACC distributed brain network was modulated in response to fMRI-guided TMS.2020Full activation pattern mapping by simultaneous deep brain stimulation and fMRI with graphene fiber electrodesSiyuan Zhao, Gen Li, Chuanjun Tong et al. · Nature CommunicationsDBS-fMRI with GF electrodes at the subthalamic nucleus (STN) in Parkinsonian rats reveal robust blood-oxygenation-level-dependent responses along the basal ganglia-thalamocortical network in a frequency-dependent manner, with responses from some regions not previously detectable.2020Deep learning for brain disorder diagnosis based on fMRI imagesWutao Yin, Longhai Li, Fang-Xiang Wu · NeurocomputingA high-level overview of brain disorder diagnosis with fMRI images from the perspective of deep learning applications is provided to provide a high- level overview of feature engineering requirements and hence reduce domain knowledge requirements to some extent.2019Behavioural relevance of spontaneous, transient brain network interactions in fMRID. Vidaurre, A. Llera, Stephen M. Smith et al. · bioRxivMethods to predict behavioural traits of individuals from either time-varying functional connectivity, time-averagedfunctional connectivity, or structural brain data are developed to show that the time-Varying nature of functional brain networks in fMRI can be reliably measured and can explain aspects of behaviour not captured by structural data or time-aversaged functional connectivity.2012Characterizing Acupuncture Stimuli Using Brain Imaging with fMRI - A Systematic Review and Meta-Analysis of the LiteratureWen-jing Huang, Daniel Pach, V. Napadow et al. · Deutsche Zeitschrift für AkupunkturBrain response to acupuncture stimuli encompasses a broad network of regions consistent with not just somatosensory, but also affective and cognitive processing, and the evidence based on meta-analyses confirmed some of these results.2012Brain imaging: fMRI 2.0Kerri M. Smith · Nature